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2101.00146

Leibo Liu

Leibo Liu, Oscar Perez-Concha, Anthony Nguyen, Vicki Bennett, Louisa Jorm

De-identifying Australian Hospital Discharge Summaries: An End-to-End Framework using Ensemble of Deep Learning Models

Journal of Biomedical Informatics 135 (2022) 104215

10.1016/j.jbi.2022.104215

cs.CL cs.IR

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Electronic Medical Records (EMRs) contain clinical narrative text that is of great potential value to medical researchers. However, this information is mixed with Personally Identifiable Information (PII) that presents risks to patient and clinician confidentiality. This paper presents an end-to-end deidentification framework to automatically remove PII from Australian hospital discharge summaries. Our corpus included 600 hospital discharge summaries which were extracted from the EMRs of two principal referral hospitals in Sydney, Australia. Our end-to-end de-identification framework consists of three components: 1) Annotation: labelling of PII in the 600 hospital discharge summaries using five pre-defined categories: person, address, date of birth, individual identification number, phone/fax number; 2) Modelling: training six named entity recognition (NER) deep learning base-models on balanced and imbalanced datasets; and evaluating ensembles that combine all six base-models, the three base-models with the best F1 scores and the three base-models with the best recall scores respectively, using token-level majority voting and stacking methods; and 3) De-identification: removing PII from the hospital discharge summaries. Our results showed that the ensemble model combined using the stacking Support Vector Machine (SVM) method on the three base-models with the best F1 scores achieved excellent results with a F1 score of 99.16% on the test set of our corpus. We also evaluated the robustness of our modelling component on the 2014 i2b2 de-identification dataset. Our ensemble model, which uses the token-level majority voting method on all six basemodels, achieved the highest F1 score of 96.24% at strict entity matching and the highest F1 score of 98.64% at binary token-level matching compared to two state-of-the-art methods.

2022-10-05

2101.00151

Hung Le

Hung Le and Chinnadhurai Sankar and Seungwhan Moon and Ahmad Beirami and Alborz Geramifard and Satwik Kottur

DVD: A Diagnostic Dataset for Multi-step Reasoning in Video Grounded Dialogue

20 pages, 14 figures, 8 tables

Association for Computational Linguistics (2021)

cs.AI cs.CL cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

A video-grounded dialogue system is required to understand both dialogue, which contains semantic dependencies from turn to turn, and video, which contains visual cues of spatial and temporal scene variations. Building such dialogue systems is a challenging problem, involving various reasoning types on both visual and language inputs. Existing benchmarks do not have enough annotations to thoroughly analyze dialogue systems and understand their capabilities and limitations in isolation. These benchmarks are also not explicitly designed to minimise biases that models can exploit without actual reasoning. To address these limitations, in this paper, we present DVD, a Diagnostic Dataset for Video-grounded Dialogues. The dataset is designed to contain minimal biases and has detailed annotations for the different types of reasoning over the spatio-temporal space of video. Dialogues are synthesized over multiple question turns, each of which is injected with a set of cross-turn semantic relationships. We use DVD to analyze existing approaches, providing interesting insights into their abilities and limitations. In total, DVD is built from $11k$ CATER synthetic videos and contains $10$ instances of $10$-round dialogues for each video, resulting in more than $100k$ dialogues and $1M$ question-answer pairs. Our code and dataset are publicly available at https://github.com/facebookresearch/DVDialogues.

2021-06-15

2101.00153

Bin Liu

Liu Bin, Yin Guosheng

Graphmax for Text Generation

Journal of Artificial Intelligence Research, vol. 78, pp.823-848, Nov. 2023

10.1613/jair.1.15280

cs.CL

http://creativecommons.org/licenses/by-nc-sa/4.0/

In text generation, a large language model (LM) makes a choice of each new word based only on the former selection of its context using the softmax function. Nevertheless, the link statistics information of concurrent words based on a scene-specific corpus is valuable in choosing the next word, which can help to ensure the topic of the generated text to be aligned with the current task. To fully explore the co-occurrence information,we propose a graphmax function for task-specific text generation. Using the graph-based regularization, graphmax enables the final word choice to be determined by both the global knowledge from the LM and the local knowledge from the scene-specific corpus. The traditional softmax function is regularized with a graph total variation (GTV) term, which incorporates the local knowledge into the LM and encourages the model to consider the statistical relationships between words in a scene-specific corpus. The proposed graphmax is versatile and can be readily plugged into any large pre-trained LM for text generation and machine translation. Through extensive experiments, we demonstrate that the new GTV-based regularization can improve performances in various natural language processing tasks in comparison with existing methods. Moreover, through human experiments, we observe that participants can easily distinguish the text generated by graphmax or softmax.

2023-12-20

2101.00173

Kai Yi

Mohamed Elhoseiny, Kai Yi, Mohamed Elfeki

CIZSL++: Creativity Inspired Generative Zero-Shot Learning

This paper is an extended version of a paper published on the International Conference on Computer Vision (ICCV), held in Seoul, Republic of Korea, October 27-Nov 2nd, 2019 CIZSL-v2 code is available here https://github.com/Vision-CAIR/CIZSLv2. arXiv admin note: substantial text overlap with arXiv:1904.01109

https://openaccess.thecvf.com/content_ICCV_2019/papers/Elhoseiny_Creativity_Inspired_Zero-Shot_Learning_ICCV_2019_paper.pdf

cs.CV cs.AI cs.CL

http://creativecommons.org/licenses/by/4.0/

Zero-shot learning (ZSL) aims at understanding unseen categories with no training examples from class-level descriptions. To improve the discriminative power of ZSL, we model the visual learning process of unseen categories with inspiration from the psychology of human creativity for producing novel art. First, we propose CIZSL-v1 as a creativity inspired model for generative ZSL. We relate ZSL to human creativity by observing that ZSL is about recognizing the unseen, and creativity is about creating a likable unseen. We introduce a learning signal inspired by creativity literature that explores the unseen space with hallucinated class-descriptions and encourages careful deviation of their visual feature generations from seen classes while allowing knowledge transfer from seen to unseen classes. Second, CIZSL-v2 is proposed as an improved version of CIZSL-v1 for generative zero-shot learning. CIZSL-v2 consists of an investigation of additional inductive losses for unseen classes along with a semantic guided discriminator. Empirically, we show consistently that CIZSL losses can improve generative ZSL models on the challenging task of generalized ZSL from a noisy text on CUB and NABirds datasets. We also show the advantage of our approach to Attribute-based ZSL on AwA2, aPY, and SUN datasets. We also show that CIZSL-v2 has improved performance compared to CIZSL-v1.

2021-02-18

2101.00336

Hanxun Huang

Hanxun Huang, Xingjun Ma, Sarah M. Erfani, James Bailey

Neural Architecture Search via Combinatorial Multi-Armed Bandit

10 pages, 7 figures

International Joint Conference on Neural Networks (IJCNN) 2021

cs.LG cs.CV stat.ML

http://creativecommons.org/licenses/by-nc-nd/4.0/

Neural Architecture Search (NAS) has gained significant popularity as an effective tool for designing high performance deep neural networks (DNNs). NAS can be performed via policy gradient, evolutionary algorithms, differentiable architecture search or tree-search methods. While significant progress has been made for both policy gradient and differentiable architecture search, tree-search methods have so far failed to achieve comparable accuracy or search efficiency. In this paper, we formulate NAS as a Combinatorial Multi-Armed Bandit (CMAB) problem (CMAB-NAS). This allows the decomposition of a large search space into smaller blocks where tree-search methods can be applied more effectively and efficiently. We further leverage a tree-based method called Nested Monte-Carlo Search to tackle the CMAB-NAS problem. On CIFAR-10, our approach discovers a cell structure that achieves a low error rate that is comparable to the state-of-the-art, using only 0.58 GPU days, which is 20 times faster than current tree-search methods. Moreover, the discovered structure transfers well to large-scale datasets such as ImageNet.

2021-04-27

2101.00360

Pingyi Fan Prof.

Pingyi Fan

New-Type Hoeffding's Inequalities and Application in Tail Bounds

8 pages, 1 figure

Open Journal of Mathematical Sciences Vol.5 No.1 pp.248 -261, 2021

10.30538/oms2021.0161

ISSN: 2523-0212 (Online) 2616-4906 (Print)

math.ST cs.AI cs.IT math.IT math.PR stat.TH

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

It is well known that Hoeffding's inequality has a lot of applications in the signal and information processing fields. How to improve Hoeffding's inequality and find the refinements of its applications have always attracted much attentions. An improvement of Hoeffding inequality was recently given by Hertz \cite{r1}. Eventhough such an improvement is not so big, it still can be used to update many known results with original Hoeffding's inequality, especially for Hoeffding-Azuma inequality for martingales. However, the results in original Hoeffding's inequality and its refinement one by Hertz only considered the first order moment of random variables. In this paper, we present a new type of Hoeffding's inequalities, where the high order moments of random variables are taken into account. It can get some considerable improvements in the tail bounds evaluation compared with the known results. It is expected that the developed new type Hoeffding's inequalities could get more interesting applications in some related fields that use Hoeffding's results.

2021-06-22

2101.00388

Houjin Yu

Houjin Yu, Xian-Ling Mao, Zewen Chi, Wei Wei and Heyan Huang

A Robust and Domain-Adaptive Approach for Low-Resource Named Entity Recognition

Best Student Paper of 2020 IEEE International Conference on Knowledge Graph (ICKG)

2020 IEEE International Conference on Knowledge Graph (ICKG) (pp. 297-304)-

10.1109/ICBK50248.2020.00050

cs.CL cs.AI cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Recently, it has attracted much attention to build reliable named entity recognition (NER) systems using limited annotated data. Nearly all existing works heavily rely on domain-specific resources, such as external lexicons and knowledge bases. However, such domain-specific resources are often not available, meanwhile it's difficult and expensive to construct the resources, which has become a key obstacle to wider adoption. To tackle the problem, in this work, we propose a novel robust and domain-adaptive approach RDANER for low-resource NER, which only uses cheap and easily obtainable resources. Extensive experiments on three benchmark datasets demonstrate that our approach achieves the best performance when only using cheap and easily obtainable resources, and delivers competitive results against state-of-the-art methods which use difficultly obtainable domainspecific resources. All our code and corpora can be found on https://github.com/houking-can/RDANER.

2021-01-05

2101.00395

Masahiro Toyoura

Siqiang Chen, Masahiro Toyoura, Takamasa Terada, Xiaoyang Mao, Gang Xu

Image-based Textile Decoding

Integrated Computer-Aided Engineering, Pre-press, pp. 1-14, 2020

10.3233/ICA-200647

cs.CV

http://creativecommons.org/licenses/by/4.0/

A textile fabric consists of countless parallel vertical yarns (warps) and horizontal yarns (wefts). While common looms can weave repetitive patterns, Jacquard looms can weave the patterns without repetition restrictions. A pattern in which the warps and wefts cross on a grid is defined in a binary matrix. The binary matrix can define which warp and weft is on top at each grid point of the Jacquard fabric. The process can be regarded as encoding from pattern to textile. In this work, we propose a decoding method that generates a binary pattern from a textile fabric that has been already woven. We could not use a deep neural network to learn the process based solely on the training set of patterns and observed fabric images. The crossing points in the observed image were not completely located on the grid points, so it was difficult to take a direct correspondence between the fabric images and the pattern represented by the matrix in the framework of deep learning. Therefore, we propose a method that can apply the framework of deep learning via the intermediate representation of patterns and images. We show how to convert a pattern into an intermediate representation and how to reconvert the output into a pattern and confirm its effectiveness. In this experiment, we confirmed that 93% of correct pattern was obtained by decoding the pattern from the actual fabric images and weaving them again.

2021-01-05

2101.00407

Liyuan Wang

Liyuan Wang, Kuo Yang, Chongxuan Li, Lanqing Hong, Zhenguo Li, Jun Zhu

ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning

CVPR 2021

cs.LG cs.AI stat.ML

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Continual learning usually assumes the incoming data are fully labeled, which might not be applicable in real applications. In this work, we consider semi-supervised continual learning (SSCL) that incrementally learns from partially labeled data. Observing that existing continual learning methods lack the ability to continually exploit the unlabeled data, we propose deep Online Replay with Discriminator Consistency (ORDisCo) to interdependently learn a classifier with a conditional generative adversarial network (GAN), which continually passes the learned data distribution to the classifier. In particular, ORDisCo replays data sampled from the conditional generator to the classifier in an online manner, exploiting unlabeled data in a time- and storage-efficient way. Further, to explicitly overcome the catastrophic forgetting of unlabeled data, we selectively stabilize parameters of the discriminator that are important for discriminating the pairs of old unlabeled data and their pseudo-labels predicted by the classifier. We extensively evaluate ORDisCo on various semi-supervised learning benchmark datasets for SSCL, and show that ORDisCo achieves significant performance improvement on SVHN, CIFAR10 and Tiny-ImageNet, compared to strong baselines.

2022-02-15

2101.00433

Michael Saxon

Michael Saxon, Sharon Levy, Xinyi Wang, Alon Albalak, William Yang Wang

Modeling Disclosive Transparency in NLP Application Descriptions

To appear at EMNLP 2021. 15 pages, 10 figures, 7 tables

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp 2023-2037

10.18653/v1/2021.emnlp-main.153

cs.CL cs.AI cs.HC

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Broader disclosive transparency$-$truth and clarity in communication regarding the function of AI systems$-$is widely considered desirable. Unfortunately, it is a nebulous concept, difficult to both define and quantify. This is problematic, as previous work has demonstrated possible trade-offs and negative consequences to disclosive transparency, such as a confusion effect, where "too much information" clouds a reader's understanding of what a system description means. Disclosive transparency's subjective nature has rendered deep study into these problems and their remedies difficult. To improve this state of affairs, We introduce neural language model-based probabilistic metrics to directly model disclosive transparency, and demonstrate that they correlate with user and expert opinions of system transparency, making them a valid objective proxy. Finally, we demonstrate the use of these metrics in a pilot study quantifying the relationships between transparency, confusion, and user perceptions in a corpus of real NLP system descriptions.

2022-05-26

2101.00441

Jakub Marecek

Sam D. Allen and Edmund K.Burke and Jakub Marecek

A space-indexed formulation of packing boxes into a larger box

arXiv admin note: substantial text overlap with arXiv:1412.2526

Operations Research Letters, Volume 40, Issue 1, January 2012, Pages 20-24

10.1016/j.orl.2011.10.008

math.OC cs.AI cs.DM

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Current integer programming solvers fail to decide whether 12 unit cubes can be packed into a 1x1x11 box within an hour using the natural relaxation of Chen/Padberg. We present an alternative relaxation of the problem of packing boxes into a larger box, which makes it possible to solve much larger instances.

2021-01-05

2101.00443

Sourav Garg

Sourav Garg, Niko S\"underhauf, Feras Dayoub, Douglas Morrison, Akansel Cosgun, Gustavo Carneiro, Qi Wu, Tat-Jun Chin, Ian Reid, Stephen Gould, Peter Corke, Michael Milford

Semantics for Robotic Mapping, Perception and Interaction: A Survey

81 pages, 1 figure, published in Foundations and Trends in Robotics, 2020

Foundations and Trends in Robotics: Vol. 8: No. 1-2, pp 1-224 (2020)

10.1561/2300000059

cs.RO cs.CV cs.HC cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

For robots to navigate and interact more richly with the world around them, they will likely require a deeper understanding of the world in which they operate. In robotics and related research fields, the study of understanding is often referred to as semantics, which dictates what does the world "mean" to a robot, and is strongly tied to the question of how to represent that meaning. With humans and robots increasingly operating in the same world, the prospects of human-robot interaction also bring semantics and ontology of natural language into the picture. Driven by need, as well as by enablers like increasing availability of training data and computational resources, semantics is a rapidly growing research area in robotics. The field has received significant attention in the research literature to date, but most reviews and surveys have focused on particular aspects of the topic: the technical research issues regarding its use in specific robotic topics like mapping or segmentation, or its relevance to one particular application domain like autonomous driving. A new treatment is therefore required, and is also timely because so much relevant research has occurred since many of the key surveys were published. This survey therefore provides an overarching snapshot of where semantics in robotics stands today. We establish a taxonomy for semantics research in or relevant to robotics, split into four broad categories of activity, in which semantics are extracted, used, or both. Within these broad categories we survey dozens of major topics including fundamentals from the computer vision field and key robotics research areas utilizing semantics, including mapping, navigation and interaction with the world. The survey also covers key practical considerations, including enablers like increased data availability and improved computational hardware, and major application areas where...

2021-01-05

2101.00529

Pengchuan Zhang

Pengchuan Zhang, Xiujun Li, Xiaowei Hu, Jianwei Yang, Lei Zhang, Lijuan Wang, Yejin Choi, Jianfeng Gao

VinVL: Revisiting Visual Representations in Vision-Language Models

CVPR 2021

cs.CV cs.AI cs.CL cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

This paper presents a detailed study of improving visual representations for vision language (VL) tasks and develops an improved object detection model to provide object-centric representations of images. Compared to the most widely used \emph{bottom-up and top-down} model \cite{anderson2018bottom}, the new model is bigger, better-designed for VL tasks, and pre-trained on much larger training corpora that combine multiple public annotated object detection datasets. Therefore, it can generate representations of a richer collection of visual objects and concepts. While previous VL research focuses mainly on improving the vision-language fusion model and leaves the object detection model improvement untouched, we show that visual features matter significantly in VL models. In our experiments we feed the visual features generated by the new object detection model into a Transformer-based VL fusion model \oscar \cite{li2020oscar}, and utilize an improved approach \short\ to pre-train the VL model and fine-tune it on a wide range of downstream VL tasks. Our results show that the new visual features significantly improve the performance across all VL tasks, creating new state-of-the-art results on seven public benchmarks. We will release the new object detection model to public.

2021-03-11

2101.00561

Tianxiao Zhang

Tianxiao Zhang, Wenchi Ma, Guanghui Wang

Six-channel Image Representation for Cross-domain Object Detection

2021 11th International Conference on Image and Graphics (ICIG)

10.1007/978-3-030-87355-4_15

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Most deep learning models are data-driven and the excellent performance is highly dependent on the abundant and diverse datasets. However, it is very hard to obtain and label the datasets of some specific scenes or applications. If we train the detector using the data from one domain, it cannot perform well on the data from another domain due to domain shift, which is one of the big challenges of most object detection models. To address this issue, some image-to-image translation techniques have been employed to generate some fake data of some specific scenes to train the models. With the advent of Generative Adversarial Networks (GANs), we could realize unsupervised image-to-image translation in both directions from a source to a target domain and from the target to the source domain. In this study, we report a new approach to making use of the generated images. We propose to concatenate the original 3-channel images and their corresponding GAN-generated fake images to form 6-channel representations of the dataset, hoping to address the domain shift problem while exploiting the success of available detection models. The idea of augmented data representation may inspire further study on object detection and other applications.

2022-03-09

2101.00603

Haotian Li

Zhuqing Jiang, Haotian Li, Liangjie Liu, Aidong Men, Haiying Wang

A Switched View of Retinex: Deep Self-Regularized Low-Light Image Enhancement

Neurocomputing 454 (2021): 361-372

10.1016/j.neucom.2021.05.025

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Self-regularized low-light image enhancement does not require any normal-light image in training, thereby freeing from the chains on paired or unpaired low-/normal-images. However, existing methods suffer color deviation and fail to generalize to various lighting conditions. This paper presents a novel self-regularized method based on Retinex, which, inspired by HSV, preserves all colors (Hue, Saturation) and only integrates Retinex theory into brightness (Value). We build a reflectance estimation network by restricting the consistency of reflectances embedded in both the original and a novel random disturbed form of the brightness of the same scene. The generated reflectance, which is assumed to be irrelevant of illumination by Retinex, is treated as enhanced brightness. Our method is efficient as a low-light image is decoupled into two subspaces, color and brightness, for better preservation and enhancement. Extensive experiments demonstrate that our method outperforms multiple state-of-the-art algorithms qualitatively and quantitatively and adapts to more lighting conditions.

2021-07-20

2101.00667

Idoia Ruiz

Idoia Ruiz, Lorenzo Porzi, Samuel Rota Bul\`o, Peter Kontschieder, Joan Serrat

Weakly Supervised Multi-Object Tracking and Segmentation

Accepted at Autonomous Vehicle Vision WACV 2021 Workshop

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2021

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We introduce the problem of weakly supervised Multi-Object Tracking and Segmentation, i.e. joint weakly supervised instance segmentation and multi-object tracking, in which we do not provide any kind of mask annotation. To address it, we design a novel synergistic training strategy by taking advantage of multi-task learning, i.e. classification and tracking tasks guide the training of the unsupervised instance segmentation. For that purpose, we extract weak foreground localization information, provided by Grad-CAM heatmaps, to generate a partial ground truth to learn from. Additionally, RGB image level information is employed to refine the mask prediction at the edges of the objects. We evaluate our method on KITTI MOTS, the most representative benchmark for this task, reducing the performance gap on the MOTSP metric between the fully supervised and weakly supervised approach to just 12% and 12.7% for cars and pedestrians, respectively.

2021-01-05

2101.00703

Samit Chakraborty

Samit Chakraborty, Marguerite Moore, Lisa Parrillo-Chapman

Automatic Defect Detection of Print Fabric Using Convolutional Neural Network

8 pages, 4 figures, Conference

Digital Fashion Innovation e-Symposium, 2020

cs.CV cs.LG

http://creativecommons.org/licenses/by-nc-nd/4.0/

Automatic defect detection is a challenging task because of the variability in texture and type of fabric defects. An effective defect detection system enables manufacturers to improve the quality of processes and products. Automation across the textile manufacturing systems would reduce fabric wastage and increase profitability by saving cost and resources. There are different contemporary research on automatic defect detection systems using image processing and machine learning techniques. These techniques differ from each other based on the manufacturing processes and defect types. Researchers have also been able to establish real-time defect detection system during weaving. Although, there has been research on patterned fabric defect detection, these defects are related to weaving faults such as holes, and warp and weft defects. But, there has not been any research that is designed to detect defects that arise during such as spot and print mismatch. This research has fulfilled this gap by developing a print fabric database and implementing deep convolutional neural network (CNN).

2021-01-19

2101.00784

Zekun Wang

Zekun Wang, Pengwei Wang, Peter C. Louis, Lee E. Wheless, Yuankai Huo

WearMask: Fast In-browser Face Mask Detection with Serverless Edge Computing for COVID-19

Electronic Imaging, 2023, pp 229-1 - 229-6

10.2352/EI.2023.35.11.HPCI-229

cs.CV eess.IV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The COVID-19 epidemic has been a significant healthcare challenge in the United States. According to the Centers for Disease Control and Prevention (CDC), COVID-19 infection is transmitted predominately by respiratory droplets generated when people breathe, talk, cough, or sneeze. Wearing a mask is the primary, effective, and convenient method of blocking 80% of all respiratory infections. Therefore, many face mask detection and monitoring systems have been developed to provide effective supervision for hospitals, airports, publication transportation, sports venues, and retail locations. However, the current commercial face mask detection systems are typically bundled with specific software or hardware, impeding public accessibility. In this paper, we propose an in-browser serverless edge-computing based face mask detection solution, called Web-based efficient AI recognition of masks (WearMask), which can be deployed on any common devices (e.g., cell phones, tablets, computers) that have internet connections using web browsers, without installing any software. The serverless edge-computing design minimizes the extra hardware costs (e.g., specific devices or cloud computing servers). The contribution of the proposed method is to provide a holistic edge-computing framework of integrating (1) deep learning models (YOLO), (2) high-performance neural network inference computing framework (NCNN), and (3) a stack-based virtual machine (WebAssembly). For end-users, our web-based solution has advantages of (1) serverless edge-computing design with minimal device limitation and privacy risk, (2) installation free deployment, (3) low computing requirements, and (4) high detection speed. Our WearMask application has been launched with public access at facemask-detection.com.

2023-04-03

2101.00843

Dennis Soemers

Cameron Browne and Dennis J. N. J. Soemers and Eric Piette

Strategic Features for General Games

Paper exactly as it appeared at KEG Workshop held at AAAI 2019

Proceedings of the 2nd Workshop on Knowledge Extraction from Games co-located with 33rd AAAI Conference on Artificial Intelligence (AAAI 2019)

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

This short paper describes an ongoing research project that requires the automated self-play learning and evaluation of a large number of board games in digital form. We describe the approach we are taking to determine relevant features, for biasing MCTS playouts for arbitrary games played on arbitrary geometries. Benefits of our approach include efficient implementation, the potential to transfer learnt knowledge to new contexts, and the potential to explain strategic knowledge embedded in features in human-comprehensible terms.

2021-01-05

2101.00910

Shang-Hua Gao

Shang-Hua Gao, Qi Han, Zhong-Yu Li, Pai Peng, Liang Wang, Ming-Ming Cheng

Global2Local: Efficient Structure Search for Video Action Segmentation

Accepted by CVPR 2021. Source code: https://github.com/ShangHua-Gao/G2L-search

CVPR 2021

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Temporal receptive fields of models play an important role in action segmentation. Large receptive fields facilitate the long-term relations among video clips while small receptive fields help capture the local details. Existing methods construct models with hand-designed receptive fields in layers. Can we effectively search for receptive field combinations to replace hand-designed patterns? To answer this question, we propose to find better receptive field combinations through a global-to-local search scheme. Our search scheme exploits both global search to find the coarse combinations and local search to get the refined receptive field combination patterns further. The global search finds possible coarse combinations other than human-designed patterns. On top of the global search, we propose an expectation guided iterative local search scheme to refine combinations effectively. Our global-to-local search can be plugged into existing action segmentation methods to achieve state-of-the-art performance.

2021-05-03

2101.01039

Suzan Verberne

Ken Voskuil and Suzan Verberne

Improving reference mining in patents with BERT

10 pages, 3 figures

Published in the 11th International Workshop on Bibliometric-enhanced Information Retrieval (BIR 2021)

cs.IR cs.CL

http://creativecommons.org/licenses/by/4.0/

In this paper we address the challenge of extracting scientific references from patents. We approach the problem as a sequence labelling task and investigate the merits of BERT models to the extraction of these long sequences. References in patents to scientific literature are relevant to study the connection between science and industry. Most prior work only uses the front-page citations for this analysis, which are provided in the metadata of patent archives. In this paper we build on prior work using Conditional Random Fields (CRF) and Flair for reference extraction. We improve the quality of the training data and train three BERT-based models on the labelled data (BERT, bioBERT, sciBERT). We find that the improved training data leads to a large improvement in the quality of the trained models. In addition, the BERT models beat CRF and Flair, with recall scores around 97% obtained with cross validation. With the best model we label a large collection of 33 thousand patents, extract the citations, and match them to publications in the Web of Science database. We extract 50% more references than with the old training data and methods: 735 thousand references in total. With these patent-publication links, follow-up research will further analyze which types of scientific work lead to inventions.

2021-03-11

2101.01213

Ana Sofia Medeiros Oliveira

Sofia Oliveira and Daniel Loureiro and Al\'ipio Jorge

Improving Portuguese Semantic Role Labeling with Transformers and Transfer Learning

30 pages, 3 figures; Fixed broken links in References

2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), 2021, pp. 1-9

10.1109/DSAA53316.2021.9564238

cs.CL

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The Natural Language Processing task of determining "Who did what to whom" is called Semantic Role Labeling. For English, recent methods based on Transformer models have allowed for major improvements in this task over the previous state of the art. However, for low resource languages, like Portuguese, currently available semantic role labeling models are hindered by scarce training data. In this paper, we explore a model architecture with only a pre-trained Transformer-based model, a linear layer, softmax and Viterbi decoding. We substantially improve the state-of-the-art performance in Portuguese by over 15 F1. Additionally, we improve semantic role labeling results in Portuguese corpora by exploiting cross-lingual transfer learning using multilingual pre-trained models, and transfer learning from dependency parsing in Portuguese, evaluating the various proposed approaches empirically.

2021-11-02

2101.01214

Eric Guzman

Eric Guzman and Joel Meyers

Reconstructing Patchy Reionization with Deep Learning

14 pages, 9 figures. Updated to match published version. Code available from https://github.com/EEmGuzman/resunet-cmb

Phys. Rev. D 104, 043529 (2021)

10.1103/PhysRevD.104.043529

astro-ph.CO cs.CV stat.ML

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The precision anticipated from next-generation cosmic microwave background (CMB) surveys will create opportunities for characteristically new insights into cosmology. Secondary anisotropies of the CMB will have an increased importance in forthcoming surveys, due both to the cosmological information they encode and the role they play in obscuring our view of the primary fluctuations. Quadratic estimators have become the standard tools for reconstructing the fields that distort the primary CMB and produce secondary anisotropies. While successful for lensing reconstruction with current data, quadratic estimators will be sub-optimal for the reconstruction of lensing and other effects at the expected sensitivity of the upcoming CMB surveys. In this paper we describe a convolutional neural network, ResUNet-CMB, that is capable of the simultaneous reconstruction of two sources of secondary CMB anisotropies, gravitational lensing and patchy reionization. We show that the ResUNet-CMB network significantly outperforms the quadratic estimator at low noise levels and is not subject to the lensing-induced bias on the patchy reionization reconstruction that would be present with a straightforward application of the quadratic estimator.

2021-08-25

2101.01228

Nicholas Botzer

Nicholas Botzer, Yifan Ding, Tim Weninger

Reddit Entity Linking Dataset

20 pages and 4 figures

Information Processing and Management Volume 58, Issue 3 (May 2021) 1-20

10.1016/j.ipm.2020.102479

cs.CL

http://creativecommons.org/licenses/by/4.0/

We introduce and make publicly available an entity linking dataset from Reddit that contains 17,316 linked entities, each annotated by three human annotators and then grouped into Gold, Silver, and Bronze to indicate inter-annotator agreement. We analyze the different errors and disagreements made by annotators and suggest three types of corrections to the raw data. Finally, we tested existing entity linking models that are trained and tuned on text from non-social media datasets. We find that, although these existing entity linking models perform very well on their original datasets, they perform poorly on this social media dataset. We also show that the majority of these errors can be attributed to poor performance on the mention detection subtask. These results indicate the need for better entity linking models that can be applied to the enormous amount of social media text.

2021-02-26

2101.01321

Sehoon Kim

Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer

I-BERT: Integer-only BERT Quantization

ICML 2021 (Oral)

cs.CL

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Transformer based models, like BERT and RoBERTa, have achieved state-of-the-art results in many Natural Language Processing tasks. However, their memory footprint, inference latency, and power consumption are prohibitive efficient inference at the edge, and even at the data center. While quantization can be a viable solution for this, previous work on quantizing Transformer based models use floating-point arithmetic during inference, which cannot efficiently utilize integer-only logical units such as the recent Turing Tensor Cores, or traditional integer-only ARM processors. In this work, we propose I-BERT, a novel quantization scheme for Transformer based models that quantizes the entire inference with integer-only arithmetic. Based on lightweight integer-only approximation methods for nonlinear operations, e.g., GELU, Softmax, and Layer Normalization, I-BERT performs an end-to-end integer-only BERT inference without any floating point calculation. We evaluate our approach on GLUE downstream tasks using RoBERTa-Base/Large. We show that for both cases, I-BERT achieves similar (and slightly higher) accuracy as compared to the full-precision baseline. Furthermore, our preliminary implementation of I-BERT shows a speedup of 2.4-4.0x for INT8 inference on a T4 GPU system as compared to FP32 inference. The framework has been developed in PyTorch and has been open-sourced.

2022-05-02

2101.01597

Nantheera Anantrasirichai

N. Anantrasirichai and David Bull

Contextual colorization and denoising for low-light ultra high resolution sequences

5 pages

2021 IEEE International Conference on Image Processing (ICIP)

10.1109/ICIP42928.2021.9506694

eess.IV cs.CV

http://creativecommons.org/licenses/by/4.0/

Low-light image sequences generally suffer from spatio-temporal incoherent noise, flicker and blurring of moving objects. These artefacts significantly reduce visual quality and, in most cases, post-processing is needed in order to generate acceptable quality. Most state-of-the-art enhancement methods based on machine learning require ground truth data but this is not usually available for naturally captured low light sequences. We tackle these problems with an unpaired-learning method that offers simultaneous colorization and denoising. Our approach is an adaptation of the CycleGAN structure. To overcome the excessive memory limitations associated with ultra high resolution content, we propose a multiscale patch-based framework, capturing both local and contextual features. Additionally, an adaptive temporal smoothing technique is employed to remove flickering artefacts. Experimental results show that our method outperforms existing approaches in terms of subjective quality and that it is robust to variations in brightness levels and noise.

2022-03-04

2101.01665

Rana Mostafa AbdElMohsen AbdElMolla

Reem Abdel-Salam, Rana Mostafa and Mayada Hadhood

Human Activity Recognition using Wearable Sensors: Review, Challenges, Evaluation Benchmark

Accepted at 2ND International Workshop on Deep Learning for Human Activity Recognition, Held in conjunction with IJCAI-PRICAI 2020, January 2021, Japan and published at Springer Communications in Computer and Information Science (CCIS) proceedings

CCIS. 1370(2021) 1-15

10.1007/978-981-16-0575-8_1

cs.CV

http://creativecommons.org/licenses/by/4.0/

Recognizing human activity plays a significant role in the advancements of human-interaction applications in healthcare, personal fitness, and smart devices. Many papers presented various techniques for human activity representation that resulted in distinguishable progress. In this study, we conduct an extensive literature review on recent, top-performing techniques in human activity recognition based on wearable sensors. Due to the lack of standardized evaluation and to assess and ensure a fair comparison between the state-of-the-art techniques, we applied a standardized evaluation benchmark on the state-of-the-art techniques using six publicly available data-sets: MHealth, USCHAD, UTD-MHAD, WISDM, WHARF, and OPPORTUNITY. Also, we propose an experimental, improved approach that is a hybrid of enhanced handcrafted features and a neural network architecture which outperformed top-performing techniques with the same standardized evaluation benchmark applied concerning MHealth, USCHAD, UTD-MHAD data-sets.

2023-11-22

2101.01710

Prune Truong

Prune Truong and Martin Danelljan and Luc Van Gool and Radu Timofte

Learning Accurate Dense Correspondences and When to Trust Them

CVPR 2021 ORAL Code: https://github.com/PruneTruong/PDCNet Website:https://prunetruong.com/research/pdcnet

IEEE/CVF Conference on Computer Vision and Pattern Recognition 2021, CVPR 2021

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Establishing dense correspondences between a pair of images is an important and general problem. However, dense flow estimation is often inaccurate in the case of large displacements or homogeneous regions. For most applications and down-stream tasks, such as pose estimation, image manipulation, or 3D reconstruction, it is crucial to know when and where to trust the estimated matches. In this work, we aim to estimate a dense flow field relating two images, coupled with a robust pixel-wise confidence map indicating the reliability and accuracy of the prediction. We develop a flexible probabilistic approach that jointly learns the flow prediction and its uncertainty. In particular, we parametrize the predictive distribution as a constrained mixture model, ensuring better modelling of both accurate flow predictions and outliers. Moreover, we develop an architecture and training strategy tailored for robust and generalizable uncertainty prediction in the context of self-supervised training. Our approach obtains state-of-the-art results on multiple challenging geometric matching and optical flow datasets. We further validate the usefulness of our probabilistic confidence estimation for the task of pose estimation. Code and models are available at https://github.com/PruneTruong/PDCNet.

2021-04-02

2101.01844

Qiaojun Feng

Qiaojun Feng, Nikolay Atanasov

Mesh Reconstruction from Aerial Images for Outdoor Terrain Mapping Using Joint 2D-3D Learning

7 pages, 7 figures. Accepted at ICRA 2021

2021 IEEE International Conference on Robotics and Automation (ICRA), Xi'an, China, pp. 5208-5214

10.1109/ICRA48506.2021.9561337

cs.CV cs.LG cs.RO

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

This paper addresses outdoor terrain mapping using overhead images obtained from an unmanned aerial vehicle. Dense depth estimation from aerial images during flight is challenging. While feature-based localization and mapping techniques can deliver real-time odometry and sparse points reconstruction, a dense environment model is generally recovered offline with significant computation and storage. This paper develops a joint 2D-3D learning approach to reconstruct local meshes at each camera keyframe, which can be assembled into a global environment model. Each local mesh is initialized from sparse depth measurements. We associate image features with the mesh vertices through camera projection and apply graph convolution to refine the mesh vertices based on joint 2-D reprojected depth and 3-D mesh supervision. Quantitative and qualitative evaluations using real aerial images show the potential of our method to support environmental monitoring and surveillance applications.

2022-04-27

2101.02032

Lu Cheng

Lu Cheng, Kush R. Varshney, Huan Liu

Socially Responsible AI Algorithms: Issues, Purposes, and Challenges

45 pages, 8 figures

Journal of Artificial Intelligence Research 71 (2021) 1137-1181

cs.CY cs.AI

http://creativecommons.org/licenses/by/4.0/

In the current era, people and society have grown increasingly reliant on artificial intelligence (AI) technologies. AI has the potential to drive us towards a future in which all of humanity flourishes. It also comes with substantial risks for oppression and calamity. Discussions about whether we should (re)trust AI have repeatedly emerged in recent years and in many quarters, including industry, academia, healthcare, services, and so on. Technologists and AI researchers have a responsibility to develop trustworthy AI systems. They have responded with great effort to design more responsible AI algorithms. However, existing technical solutions are narrow in scope and have been primarily directed towards algorithms for scoring or classification tasks, with an emphasis on fairness and unwanted bias. To build long-lasting trust between AI and human beings, we argue that the key is to think beyond algorithmic fairness and connect major aspects of AI that potentially cause AI's indifferent behavior. In this survey, we provide a systematic framework of Socially Responsible AI Algorithms that aims to examine the subjects of AI indifference and the need for socially responsible AI algorithms, define the objectives, and introduce the means by which we may achieve these objectives. We further discuss how to leverage this framework to improve societal well-being through protection, information, and prevention/mitigation.

2021-08-24

2101.02115

Ruben Ohana

Alessandro Cappelli, Ruben Ohana, Julien Launay, Laurent Meunier, Iacopo Poli, Florent Krzakala

Adversarial Robustness by Design through Analog Computing and Synthetic Gradients

ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing,

10.1109/ICASSP43922.2022.9746671

cs.CV cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We propose a new defense mechanism against adversarial attacks inspired by an optical co-processor, providing robustness without compromising natural accuracy in both white-box and black-box settings. This hardware co-processor performs a nonlinear fixed random transformation, where the parameters are unknown and impossible to retrieve with sufficient precision for large enough dimensions. In the white-box setting, our defense works by obfuscating the parameters of the random projection. Unlike other defenses relying on obfuscated gradients, we find we are unable to build a reliable backward differentiable approximation for obfuscated parameters. Moreover, while our model reaches a good natural accuracy with a hybrid backpropagation - synthetic gradient method, the same approach is suboptimal if employed to generate adversarial examples. We find the combination of a random projection and binarization in the optical system also improves robustness against various types of black-box attacks. Finally, our hybrid training method builds robust features against transfer attacks. We demonstrate our approach on a VGG-like architecture, placing the defense on top of the convolutional features, on CIFAR-10 and CIFAR-100. Code is available at https://github.com/lightonai/adversarial-robustness-by-design.

2022-10-03

2101.02136

Vicky Kalogeiton

Manuel J. Marin-Jimenez, Vicky Kalogeiton, Pablo Medina-Suarez, and Andrew Zisserman

LAEO-Net++: revisiting people Looking At Each Other in videos

16 pages, 16 Figures. arXiv admin note: substantial text overlap with arXiv:1906.05261

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020

10.1109/TPAMI.2020.3048482

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Capturing the 'mutual gaze' of people is essential for understanding and interpreting the social interactions between them. To this end, this paper addresses the problem of detecting people Looking At Each Other (LAEO) in video sequences. For this purpose, we propose LAEO-Net++, a new deep CNN for determining LAEO in videos. In contrast to previous works, LAEO-Net++ takes spatio-temporal tracks as input and reasons about the whole track. It consists of three branches, one for each character's tracked head and one for their relative position. Moreover, we introduce two new LAEO datasets: UCO-LAEO and AVA-LAEO. A thorough experimental evaluation demonstrates the ability of LAEO-Net++ to successfully determine if two people are LAEO and the temporal window where it happens. Our model achieves state-of-the-art results on the existing TVHID-LAEO video dataset, significantly outperforming previous approaches. Finally, we apply LAEO-Net++ to a social network, where we automatically infer the social relationship between pairs of people based on the frequency and duration that they LAEO, and show that LAEO can be a useful tool for guided search of human interactions in videos. The code is available at https://github.com/AVAuco/laeonetplus.

2021-01-07

2101.02185

Seyed Sajjadi

Volkan Ustun, Rajay Kumar, Adam Reilly, Seyed Sajjadi, Andrew Miller

Adaptive Synthetic Characters for Military Training

2020 Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC)

cs.AI cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Behaviors of the synthetic characters in current military simulations are limited since they are generally generated by rule-based and reactive computational models with minimal intelligence. Such computational models cannot adapt to reflect the experience of the characters, resulting in brittle intelligence for even the most effective behavior models devised via costly and labor-intensive processes. Observation-based behavior model adaptation that leverages machine learning and the experience of synthetic entities in combination with appropriate prior knowledge can address the issues in the existing computational behavior models to create a better training experience in military training simulations. In this paper, we introduce a framework that aims to create autonomous synthetic characters that can perform coherent sequences of believable behavior while being aware of human trainees and their needs within a training simulation. This framework brings together three mutually complementary components. The first component is a Unity-based simulation environment - Rapid Integration and Development Environment (RIDE) - supporting One World Terrain (OWT) models and capable of running and supporting machine learning experiments. The second is Shiva, a novel multi-agent reinforcement and imitation learning framework that can interface with a variety of simulation environments, and that can additionally utilize a variety of learning algorithms. The final component is the Sigma Cognitive Architecture that will augment the behavior models with symbolic and probabilistic reasoning capabilities. We have successfully created proof-of-concept behavior models leveraging this framework on realistic terrain as an essential step towards bringing machine learning into military simulations.

2021-01-07

2101.02231

Seyed Sajjadi

Volkan Ustun, Paul S. Rosenbloom, Seyed Sajjadi, Jeremy Nuttal

Controlling Synthetic Characters in Simulations: A Case for Cognitive Architectures and Sigma

Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC) 2018

cs.AI cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Simulations, along with other similar applications like virtual worlds and video games, require computational models of intelligence that generate realistic and credible behavior for the participating synthetic characters. Cognitive architectures, which are models of the fixed structure underlying intelligent behavior in both natural and artificial systems, provide a conceptually valid common basis, as evidenced by the current efforts towards a standard model of the mind, to generate human-like intelligent behavior for these synthetic characters. Sigma is a cognitive architecture and system that strives to combine what has been learned from four decades of independent work on symbolic cognitive architectures, probabilistic graphical models, and more recently neural models, under its graphical architecture hypothesis. Sigma leverages an extended form of factor graphs towards a uniform grand unification of not only traditional cognitive capabilities but also key non-cognitive aspects, creating unique opportunities for the construction of new kinds of cognitive models that possess a Theory-of-Mind and that are perceptual, autonomous, interactive, affective, and adaptive. In this paper, we will introduce Sigma along with its diverse capabilities and then use three distinct proof-of-concept Sigma models to highlight combinations of these capabilities: (1) Distributional reinforcement learning models in; (2) A pair of adaptive and interactive agent models that demonstrate rule-based, probabilistic, and social reasoning; and (3) A knowledge-free exploration model in which an agent leverages only architectural appraisal variables, namely attention and curiosity, to locate an item while building up a map in a Unity environment.

2021-01-08

2101.02323

Vishwesh Nath

Vishwesh Nath, Dong Yang, Bennett A. Landman, Daguang Xu, Holger R. Roth

Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation

19 pages, 13 figures, Transactions of Medical Imaging

IEEE Transactions on Medical Imaging, 2020

10.1109/TMI.2020.3048055

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Active learning is a unique abstraction of machine learning techniques where the model/algorithm could guide users for annotation of a set of data points that would be beneficial to the model, unlike passive machine learning. The primary advantage being that active learning frameworks select data points that can accelerate the learning process of a model and can reduce the amount of data needed to achieve full accuracy as compared to a model trained on a randomly acquired data set. Multiple frameworks for active learning combined with deep learning have been proposed, and the majority of them are dedicated to classification tasks. Herein, we explore active learning for the task of segmentation of medical imaging data sets. We investigate our proposed framework using two datasets: 1.) MRI scans of the hippocampus, 2.) CT scans of pancreas and tumors. This work presents a query-by-committee approach for active learning where a joint optimizer is used for the committee. At the same time, we propose three new strategies for active learning: 1.) increasing frequency of uncertain data to bias the training data set; 2.) Using mutual information among the input images as a regularizer for acquisition to ensure diversity in the training dataset; 3.) adaptation of Dice log-likelihood for Stein variational gradient descent (SVGD). The results indicate an improvement in terms of data reduction by achieving full accuracy while only using 22.69 % and 48.85 % of the available data for each dataset, respectively.

2021-01-08

2101.02359

Xiangyang Li

Xiangyang Li, Yu Xia, Xiang Long, Zheng Li, Sujian Li

Exploring Text-transformers in AAAI 2021 Shared Task: COVID-19 Fake News Detection in English

3rd solution of 'Constraint@AAAI2021 - COVID19 Fake News Detection in English'

First International Workshop, CONSTRAINT 2021 co-located with AAAI 2021

cs.CL cs.AI cs.LG

http://creativecommons.org/licenses/by/4.0/

In this paper, we describe our system for the AAAI 2021 shared task of COVID-19 Fake News Detection in English, where we achieved the 3rd position with the weighted F1 score of 0.9859 on the test set. Specifically, we proposed an ensemble method of different pre-trained language models such as BERT, Roberta, Ernie, etc. with various training strategies including warm-up,learning rate schedule and k-fold cross-validation. We also conduct an extensive analysis of the samples that are not correctly classified. The code is available at:https://github.com/archersama/3rd-solution-COVID19-Fake-News-Detection-in-English.

2021-09-24

2101.02442

Clement Leroy

Cl\'ement Leroy (INTUIDOC), Eric Anquetil (INTUIDOC), Nathalie Girard (INTUIDOC)

Drift anticipation with forgetting to improve evolving fuzzy system

25th International Conference on Pattern Recognition (ICPR2020), Jan 2021, Milan, Italy

cs.AI cs.LG cs.NE

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Working with a non-stationary stream of data requires for the analysis system to evolve its model (the parameters as well as the structure) over time. In particular, concept drifts can occur, which makes it necessary to forget knowledge that has become obsolete. However, the forgetting is subjected to the stability-plasticity dilemma, that is, increasing forgetting improve reactivity of adapting to the new data while reducing the robustness of the system. Based on a set of inference rules, Evolving Fuzzy Systems-EFS-have proven to be effective in solving the data stream learning problem. However tackling the stability-plasticity dilemma is still an open question. This paper proposes a coherent method to integrate forgetting in Evolving Fuzzy System, based on the recently introduced notion of concept drift anticipation. The forgetting is applied with two methods: an exponential forgetting of the premise part and a deferred directional forgetting of the conclusion part of EFS to preserve the coherence between both parts. The originality of the approach consists in applying the forgetting only in the anticipation module and in keeping the EFS (called principal system) learned without any forgetting. Then, when a drift is detected in the stream, a selection mechanism is proposed to replace the obsolete parameters of the principal system with more suitable parameters of the anticipation module. An evaluation of the proposed methods is carried out on benchmark online datasets, with a comparison with state-of-the-art online classifiers (Learn++.NSE, PENsemble, pclass) as well as with the original system using different forgetting strategies.

2021-01-08

2101.02480

Tugdual Ceillier

Alex Goupilleau, Tugdual Ceillier, Marie-Caroline Corbineau

Active learning for object detection in high-resolution satellite images

Conference on Artificial Intelligence for Defense, Dec 2020, Rennes, France

cs.CV cs.LG cs.NE eess.IV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In machine learning, the term active learning regroups techniques that aim at selecting the most useful data to label from a large pool of unlabelled examples. While supervised deep learning techniques have shown to be increasingly efficient on many applications, they require a huge number of labelled examples to reach operational performances. Therefore, the labelling effort linked to the creation of the datasets required is also increasing. When working on defense-related remote sensing applications, labelling can be challenging due to the large areas covered and often requires military experts who are rare and whose time is primarily dedicated to operational needs. Limiting the labelling effort is thus of utmost importance. This study aims at reviewing the most relevant active learning techniques to be used for object detection on very high resolution imagery and shows an example of the value of such techniques on a relevant operational use case: aircraft detection.

2021-01-08

2101.02486

Leonardo Maria Millefiori

Samuele Capobianco, Leonardo M. Millefiori, Nicola Forti, Paolo Braca, and Peter Willett

Deep Learning Methods for Vessel Trajectory Prediction based on Recurrent Neural Networks

Accepted for publications in IEEE Transactions on Aerospace and Electronic Systems, 17 pages, 9 figures

IEEE Transactions on Aerospace and Electronic Systems, vol. 57, no. 6, pp. 4329-4346, 2021

10.1109/TAES.2021.3096873

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Data-driven methods open up unprecedented possibilities for maritime surveillance using Automatic Identification System (AIS) data. In this work, we explore deep learning strategies using historical AIS observations to address the problem of predicting future vessel trajectories with a prediction horizon of several hours. We propose novel sequence-to-sequence vessel trajectory prediction models based on encoder-decoder recurrent neural networks (RNNs) that are trained on historical trajectory data to predict future trajectory samples given previous observations. The proposed architecture combines Long Short-Term Memory (LSTM) RNNs for sequence modeling to encode the observed data and generate future predictions with different intermediate aggregation layers to capture space-time dependencies in sequential data. Experimental results on vessel trajectories from an AIS dataset made freely available by the Danish Maritime Authority show the effectiveness of deep-learning methods for trajectory prediction based on sequence-to-sequence neural networks, which achieve better performance than baseline approaches based on linear regression or on the Multi-Layer Perceptron (MLP) architecture. The comparative evaluation of results shows: i) the superiority of attention pooling over static pooling for the specific application, and ii) the remarkable performance improvement that can be obtained with labeled trajectories, i.e., when predictions are conditioned on a low-level context representation encoded from the sequence of past observations, as well as on additional inputs (e.g., port of departure or arrival) about the vessel's high-level intention, which may be available from AIS.

2023-01-18

2101.02496

Manuel Lagunas

Manuel Lagunas, Ana Serrano, Diego Gutierrez, Belen Masia

The joint role of geometry and illumination on material recognition

15 pages, 16 figures, Accepted to the Journal of Vision, 2021

Journal of Vision February 2021, Vol.21, 2

10.1167/jov.21.2.2

cs.CV cs.AI cs.GR

http://creativecommons.org/licenses/by-nc-nd/4.0/

Observing and recognizing materials is a fundamental part of our daily life. Under typical viewing conditions, we are capable of effortlessly identifying the objects that surround us and recognizing the materials they are made of. Nevertheless, understanding the underlying perceptual processes that take place to accurately discern the visual properties of an object is a long-standing problem. In this work, we perform a comprehensive and systematic analysis of how the interplay of geometry, illumination, and their spatial frequencies affects human performance on material recognition tasks. We carry out large-scale behavioral experiments where participants are asked to recognize different reference materials among a pool of candidate samples. In the different experiments, we carefully sample the information in the frequency domain of the stimuli. From our analysis, we find significant first-order interactions between the geometry and the illumination, of both the reference and the candidates. In addition, we observe that simple image statistics and higher-order image histograms do not correlate with human performance. Therefore, we perform a high-level comparison of highly non-linear statistics by training a deep neural network on material recognition tasks. Our results show that such models can accurately classify materials, which suggests that they are capable of defining a meaningful representation of material appearance from labeled proximal image data. Last, we find preliminary evidence that these highly non-linear models and humans may use similar high-level factors for material recognition tasks.

2021-02-05

2101.02522

Vincent Aranega

Ronie Salgado, Marcus Denker (RMOD), St\'ephane Ducasse (RMOD), Anne Etien (RMOD), Vincent Aranega (RMOD)

Towards a Smart Data Processing and Storage Model

IWST20: International Workshop on Smalltalk Technologies, Sep 2020, Novi Sad, Serbia

cs.CL cs.PL cs.SE

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In several domains it is crucial to store and manipulate data whose origin needs to be completely traceable to guarantee the consistency, trustworthiness and reliability on the data itself typically for ethical and legal reasons. It is also important to guarantee that such properties are also carried further when such data is composed and processed into new data. In this article we present the main requirements and theorethical problems that arise by the design of a system supporting data with such capabilities. We present an architecture for implementing a system as well as a prototype developed in Pharo.

2021-01-08

2101.02559

Lois Orosa

Muhammad Shafique, Mahum Naseer, Theocharis Theocharides, Christos Kyrkou, Onur Mutlu, Lois Orosa, Jungwook Choi

Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead

Final version appears in https://ieeexplore.ieee.org/document/8979377

IEEE Design and Test (Volume: 37, Issue: 2, April 2020): 30-57

10.1109/MDAT.2020.2971217

cs.CR cs.AI cs.AR cs.LG cs.SY eess.SY

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Machine Learning (ML) techniques have been rapidly adopted by smart Cyber-Physical Systems (CPS) and Internet-of-Things (IoT) due to their powerful decision-making capabilities. However, they are vulnerable to various security and reliability threats, at both hardware and software levels, that compromise their accuracy. These threats get aggravated in emerging edge ML devices that have stringent constraints in terms of resources (e.g., compute, memory, power/energy), and that therefore cannot employ costly security and reliability measures. Security, reliability, and vulnerability mitigation techniques span from network security measures to hardware protection, with an increased interest towards formal verification of trained ML models. This paper summarizes the prominent vulnerabilities of modern ML systems, highlights successful defenses and mitigation techniques against these vulnerabilities, both at the cloud (i.e., during the ML training phase) and edge (i.e., during the ML inference stage), discusses the implications of a resource-constrained design on the reliability and security of the system, identifies verification methodologies to ensure correct system behavior, and describes open research challenges for building secure and reliable ML systems at both the edge and the cloud.

2021-01-08

2101.02647

Juana Valeria Hurtado

Juana Valeria Hurtado, Laura Londo\~no, and Abhinav Valada

From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation

Frontiers in Robotics and AI, 2021

10.3389/frobt.2021.650325

cs.RO cs.CV cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The exponentially increasing advances in robotics and machine learning are facilitating the transition of robots from being confined to controlled industrial spaces to performing novel everyday tasks in domestic and urban environments. In order to make the presence of robots safe as well as comfortable for humans, and to facilitate their acceptance in public environments, they are often equipped with social abilities for navigation and interaction. Socially compliant robot navigation is increasingly being learned from human observations or demonstrations. We argue that these techniques that typically aim to mimic human behavior do not guarantee fair behavior. As a consequence, social navigation models can replicate, promote, and amplify societal unfairness such as discrimination and segregation. In this work, we investigate a framework for diminishing bias in social robot navigation models so that robots are equipped with the capability to plan as well as adapt their paths based on both physical and social demands. Our proposed framework consists of two components: \textit{learning} which incorporates social context into the learning process to account for safety and comfort, and \textit{relearning} to detect and correct potentially harmful outcomes before the onset. We provide both technological and societal analysis using three diverse case studies in different social scenarios of interaction. Moreover, we present ethical implications of deploying robots in social environments and propose potential solutions. Through this study, we highlight the importance and advocate for fairness in human-robot interactions in order to promote more equitable social relationships, roles, and dynamics and consequently positively influence our society.

2021-03-04

2101.02767

Joris Gu\'erin

Joris Guerin, Stephane Thiery, Eric Nyiri, Olivier Gibaru, Byron Boots

Combining pretrained CNN feature extractors to enhance clustering of complex natural images

21 pages, 16 figures, 10 tables, preprint of our paper published in Neurocomputing

Guerin, J., Thiery, S., Nyiri, E., Gibaru, O., & Boots, B. (2021). Combining pretrained CNN feature extractors to enhance clustering of complex natural images. Neurocomputing, 423, 551-571

10.1016/j.neucom.2020.10.068

cs.CV cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Recently, a common starting point for solving complex unsupervised image classification tasks is to use generic features, extracted with deep Convolutional Neural Networks (CNN) pretrained on a large and versatile dataset (ImageNet). However, in most research, the CNN architecture for feature extraction is chosen arbitrarily, without justification. This paper aims at providing insight on the use of pretrained CNN features for image clustering (IC). First, extensive experiments are conducted and show that, for a given dataset, the choice of the CNN architecture for feature extraction has a huge impact on the final clustering. These experiments also demonstrate that proper extractor selection for a given IC task is difficult. To solve this issue, we propose to rephrase the IC problem as a multi-view clustering (MVC) problem that considers features extracted from different architectures as different "views" of the same data. This approach is based on the assumption that information contained in the different CNN may be complementary, even when pretrained on the same data. We then propose a multi-input neural network architecture that is trained end-to-end to solve the MVC problem effectively. This approach is tested on nine natural image datasets, and produces state-of-the-art results for IC.

2021-01-11

2101.02780

Tanujay Saha

Tanujay Saha, Najwa Aaraj, Neel Ajjarapu, Niraj K. Jha

SHARKS: Smart Hacking Approaches for RisK Scanning in Internet-of-Things and Cyber-Physical Systems based on Machine Learning

This article has been accepted in IEEE Transactions on Emerging Topics in Computing. 17 pages, 12 figures, IEEE copyright

IEEE Transactions on Emerging Topics in Computing, 2021

10.1109/TETC.2021.3050733

cs.CR cs.AI cs.LG

http://creativecommons.org/licenses/by-nc-nd/4.0/

Cyber-physical systems (CPS) and Internet-of-Things (IoT) devices are increasingly being deployed across multiple functionalities, ranging from healthcare devices and wearables to critical infrastructures, e.g., nuclear power plants, autonomous vehicles, smart cities, and smart homes. These devices are inherently not secure across their comprehensive software, hardware, and network stacks, thus presenting a large attack surface that can be exploited by hackers. In this article, we present an innovative technique for detecting unknown system vulnerabilities, managing these vulnerabilities, and improving incident response when such vulnerabilities are exploited. The novelty of this approach lies in extracting intelligence from known real-world CPS/IoT attacks, representing them in the form of regular expressions, and employing machine learning (ML) techniques on this ensemble of regular expressions to generate new attack vectors and security vulnerabilities. Our results show that 10 new attack vectors and 122 new vulnerability exploits can be successfully generated that have the potential to exploit a CPS or an IoT ecosystem. The ML methodology achieves an accuracy of 97.4% and enables us to predict these attacks efficiently with an 87.2% reduction in the search space. We demonstrate the application of our method to the hacking of the in-vehicle network of a connected car. To defend against the known attacks and possible novel exploits, we discuss a defense-in-depth mechanism for various classes of attacks and the classification of data targeted by such attacks. This defense mechanism optimizes the cost of security measures based on the sensitivity of the protected resource, thus incentivizing its adoption in real-world CPS/IoT by cybersecurity practitioners.

2022-10-21

2101.02797

Nisreen Ali

Nisreen AbdAllah and Serestina Viriri

Off-Line Arabic Handwritten Words Segmentation using Morphological Operators

16 pages,27 figures

Signal & Image Processing: An International Journal (SIPIJ) Vol.11, No.6, December 2020

10.5121/sipij.2020.11602

cs.CV cs.CL cs.LG

http://creativecommons.org/licenses/by/4.0/

The main aim of this study is the assessment and discussion of a model for hand-written Arabic through segmentation. The framework is proposed based on three steps: pre-processing, segmentation, and evaluation. In the pre-processing step, morphological operators are applied for Connecting Gaps (CGs) in written words. Gaps happen when pen lifting-off during writing, scanning documents, or while converting images to binary type. In the segmentation step, first removed the small diacritics then bounded a connected component to segment offline words. Huge data was utilized in the proposed model for applying a variety of handwriting styles so that to be more compatible with real-life applications. Consequently, on the automatic evaluation stage, selected randomly 1,131 images from the IESK-ArDB database, and then segmented into sub-words. After small gaps been connected, the model performance evaluation had been reached 88% against the standard ground truth of the database. The proposed model achieved the highest accuracy when compared with the related works.

2021-01-11

2101.02991

Pathan Faisal Khan

Faisal Khan and Debdeep Bose

Artificial Intelligence enabled Smart Learning

4

ETH Learning and Teaching Journal: ICED 2020 Proceedings (2020) 153-156

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Artificial Intelligence (AI) is a discipline of computer science that deals with machine intelligence. It is essential to bring AI into the context of learning because it helps in analysing the enormous amounts of data that is collected from individual students, teachers and academic staff. The major priorities of implementing AI in education are making innovative use of existing digital technologies for learning, and teaching practices that significantly improve traditional educational methods. The main problem with traditional learning is that it cannot be suited to every student in class. Some students may grasp the concepts well, while some may have difficulties in understanding them and some may be more auditory or visual learners. The World Bank report on education has indicated that the learning gap created by this problem causes many students to drop out (World Development Report, 2018). Personalised learning has been able to solve this grave problem.

2021-01-11

2101.03013

Iknoor Singh

Iknoor Singh, Carolina Scarton, Kalina Bontcheva

Multistage BiCross encoder for multilingual access to COVID-19 health information

PLOS ONE 2021

10.1371/journal.pone.0256874

cs.AI cs.IR cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The Coronavirus (COVID-19) pandemic has led to a rapidly growing 'infodemic' of health information online. This has motivated the need for accurate semantic search and retrieval of reliable COVID-19 information across millions of documents, in multiple languages. To address this challenge, this paper proposes a novel high precision and high recall neural Multistage BiCross encoder approach. It is a sequential three-stage ranking pipeline which uses the Okapi BM25 retrieval algorithm and transformer-based bi-encoder and cross-encoder to effectively rank the documents with respect to the given query. We present experimental results from our participation in the Multilingual Information Access (MLIA) shared task on COVID-19 multilingual semantic search. The independently evaluated MLIA results validate our approach and demonstrate that it outperforms other state-of-the-art approaches according to nearly all evaluation metrics in cases of both monolingual and bilingual runs.

2022-05-31

2101.03037

Bobak Kiani

Bobak Toussi Kiani, Giacomo De Palma, Milad Marvian, Zi-Wen Liu, Seth Lloyd

Learning quantum data with the quantum Earth Mover's distance

Quantum Science and Technology 7(4), 045002 (2022)

10.1088/2058-9565/ac79c9

quant-ph cs.AI cs.LG stat.ML

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Quantifying how far the output of a learning algorithm is from its target is an essential task in machine learning. However, in quantum settings, the loss landscapes of commonly used distance metrics often produce undesirable outcomes such as poor local minima and exponentially decaying gradients. To overcome these obstacles, we consider here the recently proposed quantum earth mover's (EM) or Wasserstein-1 distance as a quantum analog to the classical EM distance. We show that the quantum EM distance possesses unique properties, not found in other commonly used quantum distance metrics, that make quantum learning more stable and efficient. We propose a quantum Wasserstein generative adversarial network (qWGAN) which takes advantage of the quantum EM distance and provides an efficient means of performing learning on quantum data. We provide examples where our qWGAN is capable of learning a diverse set of quantum data with only resources polynomial in the number of qubits.

2022-07-07

2101.03154

Fanjie Kong

Fanjie Kong, Xiao-yang Liu, Ricardo Henao

Quantum Tensor Network in Machine Learning: An Application to Tiny Object Classification

8 pages, 7 figures

https://tensorworkshop.github.io/NeurIPS2020/CFP.html

cs.CV cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Tiny object classification problem exists in many machine learning applications like medical imaging or remote sensing, where the object of interest usually occupies a small region of the whole image. It is challenging to design an efficient machine learning model with respect to tiny object of interest. Current neural network structures are unable to deal with tiny object efficiently because they are mainly developed for images featured by large scale objects. However, in quantum physics, there is a great theoretical foundation guiding us to analyze the target function for image classification regarding to specific objects size ratio. In our work, we apply Tensor Networks to solve this arising tough machine learning problem. First, we summarize the previous work that connects quantum spin model to image classification and bring the theory into the scenario of tiny object classification. Second, we propose using 2D multi-scale entanglement renormalization ansatz (MERA) to classify tiny objects in image. In the end, our experimental results indicate that tensor network models are effective for tiny object classification problem and potentially will beat state-of-the-art. Our codes will be available online https://github.com/timqqt/MERA_Image_Classification.

2021-01-11

2101.03169

Wen Liu

Maohan Liang, Ryan Wen Liu, Shichen Li, Zhe Xiao, Xin Liu, Feng Lu

An Unsupervised Learning Method with Convolutional Auto-Encoder for Vessel Trajectory Similarity Computation

22 pages, 16 figures

Ocean Engineering, 2021

10.1016/j.oceaneng.2021.108803

cs.LG cs.AI cs.CV

http://creativecommons.org/licenses/by/4.0/

To achieve reliable mining results for massive vessel trajectories, one of the most important challenges is how to efficiently compute the similarities between different vessel trajectories. The computation of vessel trajectory similarity has recently attracted increasing attention in the maritime data mining research community. However, traditional shape- and warping-based methods often suffer from several drawbacks such as high computational cost and sensitivity to unwanted artifacts and non-uniform sampling rates, etc. To eliminate these drawbacks, we propose an unsupervised learning method which automatically extracts low-dimensional features through a convolutional auto-encoder (CAE). In particular, we first generate the informative trajectory images by remapping the raw vessel trajectories into two-dimensional matrices while maintaining the spatio-temporal properties. Based on the massive vessel trajectories collected, the CAE can learn the low-dimensional representations of informative trajectory images in an unsupervised manner. The trajectory similarity is finally equivalent to efficiently computing the similarities between the learned low-dimensional features, which strongly correlate with the raw vessel trajectories. Comprehensive experiments on realistic data sets have demonstrated that the proposed method largely outperforms traditional trajectory similarity computation methods in terms of efficiency and effectiveness. The high-quality trajectory clustering performance could also be guaranteed according to the CAE-based trajectory similarity computation results.

2021-06-11

2101.03198

Badri Narayanan

Badri Narayanan, Mohamed Saadeldin, Paul Albert, Kevin McGuinness, and Brian Mac Namee

Extracting Pasture Phenotype and Biomass Percentages using Weakly Supervised Multi-target Deep Learning on a Small Dataset

Irish Machine Vision and Image Processing Conference (2020) 21-28

cs.CV cs.LG

http://creativecommons.org/licenses/by/4.0/

The dairy industry uses clover and grass as fodder for cows. Accurate estimation of grass and clover biomass yield enables smart decisions in optimizing fertilization and seeding density, resulting in increased productivity and positive environmental impact. Grass and clover are usually planted together, since clover is a nitrogen-fixing plant that brings nutrients to the soil. Adjusting the right percentages of clover and grass in a field reduces the need for external fertilization. Existing approaches for estimating the grass-clover composition of a field are expensive and time consuming - random samples of the pasture are clipped and then the components are physically separated to weigh and calculate percentages of dry grass, clover and weeds in each sample. There is growing interest in developing novel deep learning based approaches to non-destructively extract pasture phenotype indicators and biomass yield predictions of different plant species from agricultural imagery collected from the field. Providing these indicators and predictions from images alone remains a significant challenge. Heavy occlusions in the dense mixture of grass, clover and weeds make it difficult to estimate each component accurately. Moreover, although supervised deep learning models perform well with large datasets, it is tedious to acquire large and diverse collections of field images with precise ground truth for different biomass yields. In this paper, we demonstrate that applying data augmentation and transfer learning is effective in predicting multi-target biomass percentages of different plant species, even with a small training dataset. The scheme proposed in this paper used a training set of only 261 images and provided predictions of biomass percentages of grass, clover, white clover, red clover, and weeds with mean absolute error of 6.77%, 6.92%, 6.21%, 6.89%, and 4.80% respectively.

2021-01-12

2101.03221

Stefano Martina

Stefano Martina, Stefano Gherardini, Filippo Caruso

Machine learning classification of non-Markovian noise disturbing quantum dynamics

19 pages, 3 figures, 3 tables; v3: Changed title and improved presentation of the results

Physica Scripta 98 (3), 035104 (2023)

10.1088/1402-4896/acb39b

quant-ph cond-mat.dis-nn cs.AI cs.LG cs.NE

http://creativecommons.org/licenses/by/4.0/

In this paper machine learning and artificial neural network models are proposed for the classification of external noise sources affecting a given quantum dynamics. For this purpose, we train and then validate support vector machine, multi-layer perceptron and recurrent neural network models with different complexity and accuracy, to solve supervised binary classification problems. As a result, we demonstrate the high efficacy of such tools in classifying noisy quantum dynamics using simulated data sets from different realizations of the quantum system dynamics. In addition, we show that for a successful classification one just needs to measure, in a sequence of discrete time instants, the probabilities that the analysed quantum system is in one of the allowed positions or energy configurations. Albeit the training of machine learning models is here performed on synthetic data, our approach is expected to find application in experimental schemes, as e.g. for the noise benchmarking of noisy intermediate-scale quantum devices.

2023-02-17

2101.03553

Sayar Ghosh Roy

Sayar Ghosh Roy, Nikhil Pinnaparaju, Risubh Jain, Manish Gupta, Vasudeva Varma

Summaformers @ LaySumm 20, LongSumm 20

Proceedings of the First Workshop on Scholarly Document Processing (SDP) at EMNLP 2020

In Proceedings of the First Workshop on Scholarly Document Processing, pages 336 - 343, 2020, Online. Association for Computational Linguistics

10.18653/v1/2020.sdp-1.39

IIIT/TR/2020/75

cs.CL cs.AI cs.IR cs.LG

http://creativecommons.org/licenses/by/4.0/

Automatic text summarization has been widely studied as an important task in natural language processing. Traditionally, various feature engineering and machine learning based systems have been proposed for extractive as well as abstractive text summarization. Recently, deep learning based, specifically Transformer-based systems have been immensely popular. Summarization is a cognitively challenging task - extracting summary worthy sentences is laborious, and expressing semantics in brief when doing abstractive summarization is complicated. In this paper, we specifically look at the problem of summarizing scientific research papers from multiple domains. We differentiate between two types of summaries, namely, (a) LaySumm: A very short summary that captures the essence of the research paper in layman terms restricting overtly specific technical jargon and (b) LongSumm: A much longer detailed summary aimed at providing specific insights into various ideas touched upon in the paper. While leveraging latest Transformer-based models, our systems are simple, intuitive and based on how specific paper sections contribute to human summaries of the two types described above. Evaluations against gold standard summaries using ROUGE metrics prove the effectiveness of our approach. On blind test corpora, our system ranks first and third for the LongSumm and LaySumm tasks respectively.

2021-01-12

2101.03678

Yan Qin

Xuewen Zhang, Yan Qin, Chau Yuen (Fellow IEEE), Lahiru Jayasinghe, and Xiang Liu

Time-Series Regeneration with Convolutional Recurrent Generative Adversarial Network for Remaining Useful Life Estimation

This paper has been accetped by IEEE Transactions on Industrial Informatics in Dec. 2020

cs.LG cs.AI

http://creativecommons.org/licenses/by-sa/4.0/

For health prognostic task, ever-increasing efforts have been focused on machine learning-based methods, which are capable of yielding accurate remaining useful life (RUL) estimation for industrial equipment or components without exploring the degradation mechanism. A prerequisite ensuring the success of these methods depends on a wealth of run-to-failure data, however, run-to-failure data may be insufficient in practice. That is, conducting a substantial amount of destructive experiments not only is high costs, but also may cause catastrophic consequences. Out of this consideration, an enhanced RUL framework focusing on data self-generation is put forward for both non-cyclic and cyclic degradation patterns for the first time. It is designed to enrich data from a data-driven way, generating realistic-like time-series to enhance current RUL methods. First, high-quality data generation is ensured through the proposed convolutional recurrent generative adversarial network (CR-GAN), which adopts a two-channel fusion convolutional recurrent neural network. Next, a hierarchical framework is proposed to combine generated data into current RUL estimation methods. Finally, the efficacy of the proposed method is verified through both non-cyclic and cyclic degradation systems. With the enhanced RUL framework, an aero-engine system following non-cyclic degradation has been tested using three typical RUL models. State-of-art RUL estimation results are achieved by enhancing capsule network with generated time-series. Specifically, estimation errors evaluated by the index score function have been reduced by 21.77%, and 32.67% for the two employed operating conditions, respectively. Besides, the estimation error is reduced to zero for the Lithium-ion battery system, which presents cyclic degradation.

2021-01-13

2101.03916

Sourav Ghosh

Sourav Ghosh, Sourabh Vasant Gothe, Chandramouli Sanchi, Barath Raj Kandur Raja

edATLAS: An Efficient Disambiguation Algorithm for Texting in Languages with Abugida Scripts

Published in 2021 IEEE 15th International Conference on Semantic Computing (ICSC)

2021 IEEE 15th International Conference on Semantic Computing (ICSC), Laguna Hills, CA, USA, 2021, pp. 325-332

10.1109/ICSC50631.2021.00061

cs.CL

http://creativecommons.org/licenses/by-nc-nd/4.0/

Abugida refers to a phonogram writing system where each syllable is represented using a single consonant or typographic ligature, along with a default vowel or optional diacritic(s) to denote other vowels. However, texting in these languages has some unique challenges in spite of the advent of devices with soft keyboard supporting custom key layouts. The number of characters in these languages is large enough to require characters to be spread over multiple views in the layout. Having to switch between views many times to type a single word hinders the natural thought process. This prevents popular usage of native keyboard layouts. On the other hand, supporting romanized scripts (native words transcribed using Latin characters) with language model based suggestions is also set back by the lack of uniform romanization rules. To this end, we propose a disambiguation algorithm and showcase its usefulness in two novel mutually non-exclusive input methods for languages natively using the abugida writing system: (a) disambiguation of ambiguous input for abugida scripts, and (b) disambiguation of word variants in romanized scripts. We benchmark these approaches using public datasets, and show an improvement in typing speed by 19.49%, 25.13%, and 14.89%, in Hindi, Bengali, and Thai, respectively, using Ambiguous Input, owing to the human ease of locating keys combined with the efficiency of our inference method. Our Word Variant Disambiguation (WDA) maps valid variants of romanized words, previously treated as Out-of-Vocab, to a vocabulary of 100k words with high accuracy, leading to an increase in Error Correction F1 score by 10.03% and Next Word Prediction (NWP) by 62.50% on average.

2021-03-31

2101.03929

Shaofei Huang

Shaofei Huang, Si Liu, Tianrui Hui, Jizhong Han, Bo Li, Jiashi Feng and Shuicheng Yan

ORDNet: Capturing Omni-Range Dependencies for Scene Parsing

Published at TIP

IEEE Transactions on Image Processing, 2020, 29: 8251-8263

10.1109/TIP.2020.3013142

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Learning to capture dependencies between spatial positions is essential to many visual tasks, especially the dense labeling problems like scene parsing. Existing methods can effectively capture long-range dependencies with self-attention mechanism while short ones by local convolution. However, there is still much gap between long-range and short-range dependencies, which largely reduces the models' flexibility in application to diverse spatial scales and relationships in complicated natural scene images. To fill such a gap, we develop a Middle-Range (MR) branch to capture middle-range dependencies by restricting self-attention into local patches. Also, we observe that the spatial regions which have large correlations with others can be emphasized to exploit long-range dependencies more accurately, and thus propose a Reweighed Long-Range (RLR) branch. Based on the proposed MR and RLR branches, we build an Omni-Range Dependencies Network (ORDNet) which can effectively capture short-, middle- and long-range dependencies. Our ORDNet is able to extract more comprehensive context information and well adapt to complex spatial variance in scene images. Extensive experiments show that our proposed ORDNet outperforms previous state-of-the-art methods on three scene parsing benchmarks including PASCAL Context, COCO Stuff and ADE20K, demonstrating the superiority of capturing omni-range dependencies in deep models for scene parsing task.

2021-01-12

2101.03963

Sourav Ghosh

Sourabh Vasant Gothe, Sourav Ghosh, Sharmila Mani, Guggilla Bhanodai, Ankur Agarwal, Chandramouli Sanchi

Language Detection Engine for Multilingual Texting on Mobile Devices

2020 IEEE 14th International Conference on Semantic Computing (ICSC). Accessible at https://ieeexplore.ieee.org/document/9031474

2020 IEEE 14th International Conference on Semantic Computing (ICSC), San Diego, CA, USA, 2020, pp. 279-286

10.1109/ICSC.2020.00057

cs.CL

http://creativecommons.org/licenses/by-nc-nd/4.0/

More than 2 billion mobile users worldwide type in multiple languages in the soft keyboard. On a monolingual keyboard, 38% of falsely auto-corrected words are valid in another language. This can be easily avoided by detecting the language of typed words and then validating it in its respective language. Language detection is a well-known problem in natural language processing. In this paper, we present a fast, light-weight and accurate Language Detection Engine (LDE) for multilingual typing that dynamically adapts to user intended language in real-time. We propose a novel approach where the fusion of character N-gram model and logistic regression based selector model is used to identify the language. Additionally, we present a unique method of reducing the inference time significantly by parameter reduction technique. We also discuss various optimizations fabricated across LDE to resolve ambiguity in input text among the languages with the same character pattern. Our method demonstrates an average accuracy of 94.5% for Indian languages in Latin script and that of 98% for European languages on the code-switched data. This model outperforms fastText by 60.39% and ML-Kit by 23.67% in F1 score for European languages. LDE is faster on mobile device with an average inference time of 25.91 microseconds.

2021-01-12

2101.03966

Anis Rahman

Maryam Qamar Butt and Anis Ur Rahman

Audiovisual Saliency Prediction in Uncategorized Video Sequences based on Audio-Video Correlation

9 pages, 2 figures, 4 tables

IEEE Access 11 (2023) 15460-15470

10.1109/ACCESS.2023.3244191

eess.IV cs.CV eess.SP

http://creativecommons.org/licenses/by-nc-sa/4.0/

Substantial research has been done in saliency modeling to develop intelligent machines that can perceive and interpret their surroundings. But existing models treat videos as merely image sequences excluding any audio information, unable to cope with inherently varying content. Based on the hypothesis that an audiovisual saliency model will be an improvement over traditional saliency models for natural uncategorized videos, this work aims to provide a generic audio/video saliency model augmenting a visual saliency map with an audio saliency map computed by synchronizing low-level audio and visual features. The proposed model was evaluated using different criteria against eye fixations data for a publicly available DIEM video dataset. The results show that the model outperformed two state-of-the-art visual saliency models.

2023-02-27

2101.03967

Sourav Ghosh

Sharmila Mani, Sourabh Vasant Gothe, Sourav Ghosh, Ajay Kumar Mishra, Prakhar Kulshreshtha, Bhargavi M, Muthu Kumaran

Real-Time Optimized N-gram For Mobile Devices

2019 IEEE 13th International Conference on Semantic Computing (ICSC). Accessible at https://ieeexplore.ieee.org/document/8665639

2019 IEEE 13th International Conference on Semantic Computing (ICSC), Newport Beach, CA, USA, 2019, pp. 87-92

10.1109/ICOSC.2019.8665639

cs.CL

http://creativecommons.org/licenses/by-nc-nd/4.0/

With the increasing number of mobile devices, there has been continuous research on generating optimized Language Models (LMs) for soft keyboard. In spite of advances in this domain, building a single LM for low-end feature phones as well as high-end smartphones is still a pressing need. Hence, we propose a novel technique, Optimized N-gram (Op-Ngram), an end-to-end N-gram pipeline that utilises mobile resources efficiently for faster Word Completion (WC) and Next Word Prediction (NWP). Op-Ngram applies Stupid Backoff and pruning strategies to generate a light-weight model. The LM loading time on mobile is linear with respect to model size. We observed that Op-Ngram gives 37% improvement in Language Model (LM)-ROM size, 76% in LM-RAM size, 88% in loading time and 89% in average suggestion time as compared to SORTED array variant of BerkeleyLM. Moreover, our method shows significant performance improvement over KenLM as well.

2021-01-12

2101.04017

Antonio Lieto

Antonio Lieto, Gian Luca Pozzato, Stefano Zoia, Viviana Patti, Rossana Damiano

A Commonsense Reasoning Framework for Explanatory Emotion Attribution, Generation and Re-classification

50 pages. This work has been partially funded from the European Research Council (ERC) under the European Union'sHorizon 2020 research and innovation programme, grant agreement n{\deg}870811

Knowledge-Based Systems, 2021

10.1016/j.knosys.2021.107166

cs.AI

http://creativecommons.org/licenses/by/4.0/

We present DEGARI (Dynamic Emotion Generator And ReclassIfier), an explainable system for emotion attribution and recommendation. This system relies on a recently introduced commonsense reasoning framework, the TCL logic, which is based on a human-like procedure for the automatic generation of novel concepts in a Description Logics knowledge base. Starting from an ontological formalization of emotions based on the Plutchik model, known as ArsEmotica, the system exploits the logic TCL to automatically generate novel commonsense semantic representations of compound emotions (e.g. Love as derived from the combination of Joy and Trust according to Plutchik). The generated emotions correspond to prototypes, i.e. commonsense representations of given concepts, and have been used to reclassify emotion-related contents in a variety of artistic domains, ranging from art datasets to the editorial contents available in RaiPlay, the online platform of RAI Radiotelevisione Italiana (the Italian public broadcasting company). We show how the reported results (evaluated in the light of the obtained reclassifications, the user ratings assigned to such reclassifications, and their explainability) are encouraging, and pave the way to many further research directions.

2021-06-03

2101.04086

An Nguyen

An Nguyen, Stefan Foerstel, Thomas Kittler, Andrey Kurzyukov, Leo Schwinn, Dario Zanca, Tobias Hipp, Da Jun Sun, Michael Schrapp, Eva Rothgang, Bjoern Eskofier

System Design for a Data-driven and Explainable Customer Sentiment Monitor

IEEE Access 9 (2021): 117140-117152

10.1109/ACCESS.2021.3106791

cs.LG cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The most important goal of customer services is to keep the customer satisfied. However, service resources are always limited and must be prioritized. Therefore, it is important to identify customers who potentially become unsatisfied and might lead to escalations. Today this prioritization of customers is often done manually. Data science on IoT data (esp. log data) for machine health monitoring, as well as analytics on enterprise data for customer relationship management (CRM) have mainly been researched and applied independently. In this paper, we present a framework for a data-driven decision support system which combines IoT and enterprise data to model customer sentiment. Such decision support systems can help to prioritize customers and service resources to effectively troubleshoot problems or even avoid them. The framework is applied in a real-world case study with a major medical device manufacturer. This includes a fully automated and interpretable machine learning pipeline designed to meet the requirements defined with domain experts and end users. The overall framework is currently deployed, learns and evaluates predictive models from terabytes of IoT and enterprise data to actively monitor the customer sentiment for a fleet of thousands of high-end medical devices. Furthermore, we provide an anonymized industrial benchmark dataset for the research community.

2022-01-11

2101.04255

Dominic Widdows

Dominic Widdows and Kirsty Kitto and Trevor Cohen

Quantum Mathematics in Artificial Intelligence

Adding journal reference, recommended by JAIR editors upon publication

Journal of Artificial Intelligence Research 72 (2021) 1307-1341

10.1613/jair.1.12702

cs.AI cs.CL cs.IR

http://creativecommons.org/licenses/by-sa/4.0/

In the decade since 2010, successes in artificial intelligence have been at the forefront of computer science and technology, and vector space models have solidified a position at the forefront of artificial intelligence. At the same time, quantum computers have become much more powerful, and announcements of major advances are frequently in the news. The mathematical techniques underlying both these areas have more in common than is sometimes realized. Vector spaces took a position at the axiomatic heart of quantum mechanics in the 1930s, and this adoption was a key motivation for the derivation of logic and probability from the linear geometry of vector spaces. Quantum interactions between particles are modelled using the tensor product, which is also used to express objects and operations in artificial neural networks. This paper describes some of these common mathematical areas, including examples of how they are used in artificial intelligence (AI), particularly in automated reasoning and natural language processing (NLP). Techniques discussed include vector spaces, scalar products, subspaces and implication, orthogonal projection and negation, dual vectors, density matrices, positive operators, and tensor products. Application areas include information retrieval, categorization and implication, modelling word-senses and disambiguation, inference in knowledge bases, and semantic composition. Some of these approaches can potentially be implemented on quantum hardware. Many of the practical steps in this implementation are in early stages, and some are already realized. Explaining some of the common mathematical tools can help researchers in both AI and quantum computing further exploit these overlaps, recognizing and exploring new directions along the way.

2021-12-17

2101.04262

Praveen Abbaraju

Upinder Kaur, Praveen Abbaraju, Harrison McCarty, and Richard M. Voyles

Clutter Slices Approach for Identification-on-the-fly of Indoor Spaces

First two authors share equal contribution. Presented at ICPR2020 The 25th International Conference on Pattern Recognition, PRAConBE Workshop

2020 Springer Lecture Notes in Computer Science

cs.RO cs.CV

http://creativecommons.org/licenses/by/4.0/

Construction spaces are constantly evolving, dynamic environments in need of continuous surveying, inspection, and assessment. Traditional manual inspection of such spaces proves to be an arduous and time-consuming activity. Automation using robotic agents can be an effective solution. Robots, with perception capabilities can autonomously classify and survey indoor construction spaces. In this paper, we present a novel identification-on-the-fly approach for coarse classification of indoor spaces using the unique signature of clutter. Using the context granted by clutter, we recognize common indoor spaces such as corridors, staircases, shared spaces, and restrooms. The proposed clutter slices pipeline achieves a maximum accuracy of 93.6% on the presented clutter slices dataset. This sensor independent approach can be generalized to various domains to equip intelligent autonomous agents in better perceiving their environment.

2021-01-13

2101.04355

Ilias Chalkidis

Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis, Ion Androutsopoulos

Neural Contract Element Extraction Revisited: Letters from Sesame Street

6 pages

updated version of the paper presented at Document Intelligence Workshop (NeurIPS 2019 Workshop)

cs.CL

http://creativecommons.org/licenses/by-sa/4.0/

We investigate contract element extraction. We show that LSTM-based encoders perform better than dilated CNNs, Transformers, and BERT in this task. We also find that domain-specific WORD2VEC embeddings outperform generic pre-trained GLOVE embeddings. Morpho-syntactic features in the form of POS tag and token shape embeddings, as well as context-aware ELMO embeddings do not improve performance. Several of these observations contradict choices or findings of previous work on contract element extraction and generic sequence labeling tasks, indicating that contract element extraction requires careful task-specific choices. Analyzing the results of (i) plain TRANSFORMER-based and (ii) BERT-based models, we find that in the examined task, where the entities are highly context-sensitive, the lack of recurrency in TRANSFORMERs greatly affects their performance.

2021-02-23

2101.04377

Jiajia Guo

Jiajia Guo, Chao-Kai Wen, Shi Jin

CAnet: Uplink-aided Downlink Channel Acquisition in FDD Massive MIMO using Deep Learning

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

IEEE Transactions on Communications 2021

10.1109/TCOMM.2021.3120294

cs.IT cs.AI math.IT

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In frequency-division duplexing systems, the downlink channel state information (CSI) acquisition scheme leads to high training and feedback overheads. In this paper, we propose an uplink-aided downlink channel acquisition framework using deep learning to reduce these overheads. Unlike most existing works that focus only on channel estimation or feedback modules, to the best of our knowledge, this is the first study that considers the entire downlink CSI acquisition process, including downlink pilot design, channel estimation, and feedback. First, we propose an adaptive pilot design module by exploiting the correlation in magnitude among bidirectional channels in the angular domain to improve channel estimation. Next, to avoid the bit allocation problem during the feedback module, we concatenate the complex channel and embed the uplink channel magnitude to the channel reconstruction at the base station. Lastly, we combine the above two modules and compare two popular downlink channel acquisition frameworks. The former framework estimates and feeds back the channel at the user equipment subsequently. The user equipment in the latter one directly feeds back the received pilot signals to the base station. Our results reveal that, with the help of uplink, directly feeding back the pilot signals can save approximately 20% of feedback bits, which provides a guideline for future research.

2021-11-23

2101.04378

Laurent Najman

Jord{\~a}o Bragantini (IC), Alexandre X Falc{\~a}o (IC), Laurent Najman (LIGM)

Rethinking Interactive Image Segmentation: Feature Space Annotation

Pattern Recognition, Elsevier, In press

10.1016/j.patcog.2022.108882

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Despite the progress of interactive image segmentation methods, high-quality pixel-level annotation is still time-consuming and laborious - a bottleneck for several deep learning applications. We take a step back to propose interactive and simultaneous segment annotation from multiple images guided by feature space projection. This strategy is in stark contrast to existing interactive segmentation methodologies, which perform annotation in the image domain. We show that feature space annotation achieves competitive results with state-of-the-art methods in foreground segmentation datasets: iCoSeg, DAVIS, and Rooftop. Moreover, in the semantic segmentation context, it achieves 91.5% accuracy in the Cityscapes dataset, being 74.75 times faster than the original annotation procedure. Further, our contribution sheds light on a novel direction for interactive image annotation that can be integrated with existing methodologies. The supplementary material presents video demonstrations. Code available at https://github.com/LIDS-UNICAMP/rethinking-interactive-image-segmentation.

2022-07-12

2101.04431

Jorge Beltr\'an

Jorge Beltr\'an, Carlos Guindel, Arturo de la Escalera, Fernando Garc\'ia

Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups

Published on IEEE Transactions on Intelligent Transportation Systems

IEEE Transactions on Intelligent Transportation Systems, 2022

10.1109/TITS.2022.3155228

cs.RO cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Most sensor setups for onboard autonomous perception are composed of LiDARs and vision systems, as they provide complementary information that improves the reliability of the different algorithms necessary to obtain a robust scene understanding. However, the effective use of information from different sources requires an accurate calibration between the sensors involved, which usually implies a tedious and burdensome process. We present a method to calibrate the extrinsic parameters of any pair of sensors involving LiDARs, monocular or stereo cameras, of the same or different modalities. The procedure is composed of two stages: first, reference points belonging to a custom calibration target are extracted from the data provided by the sensors to be calibrated, and second, the optimal rigid transformation is found through the registration of both point sets. The proposed approach can handle devices with very different resolutions and poses, as usually found in vehicle setups. In order to assess the performance of the proposed method, a novel evaluation suite built on top of a popular simulation framework is introduced. Experiments on the synthetic environment show that our calibration algorithm significantly outperforms existing methods, whereas real data tests corroborate the results obtained in the evaluation suite. Open-source code is available at https://github.com/beltransen/velo2cam_calibration

2022-03-16

2101.04493

Kseniya Cherenkova

Kseniya Cherenkova, Djamila Aouada, Gleb Gusev

PvDeConv: Point-Voxel Deconvolution for Autoencoding CAD Construction in 3D

2020 IEEE International Conference on Image Processing (ICIP)

2020 IEEE International Conference on Image Processing (ICIP), 2020, pp. 2741-2745

10.1109/ICIP40778.2020.9191095

cs.CV cs.LG

http://creativecommons.org/licenses/by-nc-nd/4.0/

We propose a Point-Voxel DeConvolution (PVDeConv) module for 3D data autoencoder. To demonstrate its efficiency we learn to synthesize high-resolution point clouds of 10k points that densely describe the underlying geometry of Computer Aided Design (CAD) models. Scanning artifacts, such as protrusions, missing parts, smoothed edges and holes, inevitably appear in real 3D scans of fabricated CAD objects. Learning the original CAD model construction from a 3D scan requires a ground truth to be available together with the corresponding 3D scan of an object. To solve the gap, we introduce a new dedicated dataset, the CC3D, containing 50k+ pairs of CAD models and their corresponding 3D meshes. This dataset is used to learn a convolutional autoencoder for point clouds sampled from the pairs of 3D scans - CAD models. The challenges of this new dataset are demonstrated in comparison with other generative point cloud sampling models trained on ShapeNet. The CC3D autoencoder is efficient with respect to memory consumption and training time as compared to stateof-the-art models for 3D data generation.

2021-01-13

2101.04520

Morteza Haghir Chehreghani

Victor Eberstein, Jonas Sj\"oblom, Nikolce Murgovski, Morteza Haghir Chehreghani

A Unified Framework for Online Trip Destination Prediction

This work is published by Springer, Machine Learning

Machine Learning, 111, 3839-3865, 2022

10.1007/s10994-022-06175-y

cs.LG cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Trip destination prediction is an area of increasing importance in many applications such as trip planning, autonomous driving and electric vehicles. Even though this problem could be naturally addressed in an online learning paradigm where data is arriving in a sequential fashion, the majority of research has rather considered the offline setting. In this paper, we present a unified framework for trip destination prediction in an online setting, which is suitable for both online training and online prediction. For this purpose, we develop two clustering algorithms and integrate them within two online prediction models for this problem. We investigate the different configurations of clustering algorithms and prediction models on a real-world dataset. We demonstrate that both the clustering and the entire framework yield consistent results compared to the offline setting. Finally, we propose a novel regret metric for evaluating the entire online framework in comparison to its offline counterpart. This metric makes it possible to relate the source of erroneous predictions to either the clustering or the prediction model. Using this metric, we show that the proposed methods converge to a probability distribution resembling the true underlying distribution with a lower regret than all of the baselines.

2023-01-02

2101.04640

Filip Ilievski

Filip Ilievski, Alessandro Oltramari, Kaixin Ma, Bin Zhang, Deborah L. McGuinness, Pedro Szekely

Dimensions of Commonsense Knowledge

Knowledge-Based Systems 2021

cs.AI

http://creativecommons.org/publicdomain/zero/1.0/

Commonsense knowledge is essential for many AI applications, including those in natural language processing, visual processing, and planning. Consequently, many sources that include commonsense knowledge have been designed and constructed over the past decades. Recently, the focus has been on large text-based sources, which facilitate easier integration with neural (language) models and application to textual tasks, typically at the expense of the semantics of the sources and their harmonization. Efforts to consolidate commonsense knowledge have yielded partial success, with no clear path towards a comprehensive solution. We aim to organize these sources around a common set of dimensions of commonsense knowledge. We survey a wide range of popular commonsense sources with a special focus on their relations. We consolidate these relations into 13 knowledge dimensions. This consolidation allows us to unify the separate sources and to compute indications of their coverage, overlap, and gaps with respect to the knowledge dimensions. Moreover, we analyze the impact of each dimension on downstream reasoning tasks that require commonsense knowledge, observing that the temporal and desire/goal dimensions are very beneficial for reasoning on current downstream tasks, while distinctness and lexical knowledge have little impact. These results reveal preferences for some dimensions in current evaluation, and potential neglect of others.

2021-07-30

2101.04727

Hossein Rajaby Faghihi

Hossein Rajaby Faghihi, Roshanak Mirzaee, Sudarshan Paliwal, and Parisa Kordjamshidi

Latent Alignment of Procedural Concepts in Multimodal Recipes

Published in ALVR 2020, a workshop in ACL 2020

Proceedings of the First Workshop on Advances in Language and Vision Research 2020 (26-31)

10.18653/v1/2020.alvr-1.5

cs.CL cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We propose a novel alignment mechanism to deal with procedural reasoning on a newly released multimodal QA dataset, named RecipeQA. Our model is solving the textual cloze task which is a reading comprehension on a recipe containing images and instructions. We exploit the power of attention networks, cross-modal representations, and a latent alignment space between instructions and candidate answers to solve the problem. We introduce constrained max-pooling which refines the max-pooling operation on the alignment matrix to impose disjoint constraints among the outputs of the model. Our evaluation result indicates a 19\% improvement over the baselines.

2021-01-14

2101.04792

Nikolay Mikhaylovskiy

Roman Vygon, Nikolay Mikhaylovskiy

Learning Efficient Representations for Keyword Spotting with Triplet Loss

Submitted to SPECOM 2021

In: Karpov A., Potapova R. (eds) Speech and Computer. SPECOM 2021. Lecture Notes in Computer Science, vol 12997. Springer, Cham

10.1007/978-3-030-87802-3_69

eess.AS cs.AI cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In the past few years, triplet loss-based metric embeddings have become a de-facto standard for several important computer vision problems, most no-tably, person reidentification. On the other hand, in the area of speech recognition the metric embeddings generated by the triplet loss are rarely used even for classification problems. We fill this gap showing that a combination of two representation learning techniques: a triplet loss-based embedding and a variant of kNN for classification instead of cross-entropy loss significantly (by 26% to 38%) improves the classification accuracy for convolutional networks on a LibriSpeech-derived LibriWords datasets. To do so, we propose a novel phonetic similarity based triplet mining approach. We also improve the current best published SOTA for Google Speech Commands dataset V1 10+2 -class classification by about 34%, achieving 98.55% accuracy, V2 10+2-class classification by about 20%, achieving 98.37% accuracy, and V2 35-class classification by over 50%, achieving 97.0% accuracy.

2022-02-08

2101.04804

Beatriz Asfora

Beatriz Arruda Asfora

Embedded Computer Vision System Applied to a Four-Legged Line Follower Robot

23rd ABCM International Congress of Mechanical Engineering,December 6-11, 2015, Rio de Janeiro, RJ, Brazil

10.20906/CPS/COB-2015-1649

cs.RO cs.CV cs.SY eess.IV eess.SY

http://creativecommons.org/licenses/by-nc-nd/4.0/

Robotics can be defined as the connection of perception to action. Taking this further, this project aims to drive a robot using an automated computer vision embedded system, connecting the robot's vision to its behavior. In order to implement a color recognition system on the robot, open source tools are chosen, such as Processing language, Android system, Arduino platform and Pixy camera. The constraints are clear: simplicity, replicability and financial viability. In order to integrate Robotics, Computer Vision and Image Processing, the robot is applied on a typical mobile robot's issue: line following. The problem of distinguishing the path from the background is analyzed through different approaches: the popular Otsu's Method, thresholding based on color combinations through experimentation and color tracking via hue and saturation. Decision making of where to move next is based on the line center of the path and is fully automated. Using a four-legged robot as platform and a camera as its only sensor, the robot is capable of successfully follow a line. From capturing the image to moving the robot, it's evident how integrative Robotics can be. The issue of this paper alone involves knowledge of Mechanical Engineering, Electronics, Control Systems and Programming. Everything related to this work was documented and made available on an open source online page, so it can be useful in learning and experimenting with robotics.

2021-01-14

2101.04869

Shengli Jiang

Shengli Jiang and Victor M. Zavala

Convolutional Neural Nets in Chemical Engineering: Foundations, Computations, and Applications

AIChE J. 2021; e17282

10.1002/aic.17282

cs.LG cs.AI

http://creativecommons.org/licenses/by/4.0/

In this paper we review the mathematical foundations of convolutional neural nets (CNNs) with the goals of: i) highlighting connections with techniques from statistics, signal processing, linear algebra, differential equations, and optimization, ii) demystifying underlying computations, and iii) identifying new types of applications. CNNs are powerful machine learning models that highlight features from grid data to make predictions (regression and classification). The grid data object can be represented as vectors (in 1D), matrices (in 2D), or tensors (in 3D or higher dimensions) and can incorporate multiple channels (thus providing high flexibility in the input data representation). CNNs highlight features from the grid data by performing convolution operations with different types of operators. The operators highlight different types of features (e.g., patterns, gradients, geometrical features) and are learned by using optimization techniques. In other words, CNNs seek to identify optimal operators that best map the input data to the output data. A common misconception is that CNNs are only capable of processing image or video data but their application scope is much wider; specifically, datasets encountered in diverse applications can be expressed as grid data. Here, we show how to apply CNNs to new types of applications such as optimal control, flow cytometry, multivariate process monitoring, and molecular simulations.

2021-07-08

2101.04904

Ali Ayub

Ali Ayub, Alan R. Wagner

EEC: Learning to Encode and Regenerate Images for Continual Learning

Added link to the code in the paper. A preliminary version of this work was presented at ICML 2020 Workshop on Lifelong Machine Learning: arXiv:2007.06637

International Conference on Learning Representations (ICLR) 2021

cs.CV cs.AI cs.LG

http://creativecommons.org/licenses/by/4.0/

The two main impediments to continual learning are catastrophic forgetting and memory limitations on the storage of data. To cope with these challenges, we propose a novel, cognitively-inspired approach which trains autoencoders with Neural Style Transfer to encode and store images. During training on a new task, reconstructed images from encoded episodes are replayed in order to avoid catastrophic forgetting. The loss function for the reconstructed images is weighted to reduce its effect during classifier training to cope with image degradation. When the system runs out of memory the encoded episodes are converted into centroids and covariance matrices, which are used to generate pseudo-images during classifier training, keeping classifier performance stable while using less memory. Our approach increases classification accuracy by 13-17% over state-of-the-art methods on benchmark datasets, while requiring 78% less storage space.

2021-05-04

2101.04924

Yu Wu

Yu Wu, Linchao Zhu, Xiaohan Wang, Yi Yang, Fei Wu

Learning to Anticipate Egocentric Actions by Imagination

Accepted to IEEE Transactions on Image Processing (TIP)

IEEE Transactions on Image Processing, vol. 30, pp. 1143-1152, 2021

10.1109/TIP.2020.3040521

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Anticipating actions before they are executed is crucial for a wide range of practical applications, including autonomous driving and robotics. In this paper, we study the egocentric action anticipation task, which predicts future action seconds before it is performed for egocentric videos. Previous approaches focus on summarizing the observed content and directly predicting future action based on past observations. We believe it would benefit the action anticipation if we could mine some cues to compensate for the missing information of the unobserved frames. We then propose to decompose the action anticipation into a series of future feature predictions. We imagine how the visual feature changes in the near future and then predicts future action labels based on these imagined representations. Differently, our ImagineRNN is optimized in a contrastive learning way instead of feature regression. We utilize a proxy task to train the ImagineRNN, i.e., selecting the correct future states from distractors. We further improve ImagineRNN by residual anticipation, i.e., changing its target to predicting the feature difference of adjacent frames instead of the frame content. This promotes the network to focus on our target, i.e., the future action, as the difference between adjacent frame features is more important for forecasting the future. Extensive experiments on two large-scale egocentric action datasets validate the effectiveness of our method. Our method significantly outperforms previous methods on both the seen test set and the unseen test set of the EPIC Kitchens Action Anticipation Challenge.

2021-01-20

2101.04954

Dazhen Deng

Dazhen Deng, Jiang Wu, Jiachen Wang, Yihong Wu, Xiao Xie, Zheng Zhou, Hui Zhang, Xiaolong Zhang, Yingcai Wu

EventAnchor: Reducing Human Interactions in Event Annotation of Racket Sports Videos

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

10.1145/3411764.3445431

cs.HC cs.CV

http://creativecommons.org/licenses/by/4.0/

The popularity of racket sports (e.g., tennis and table tennis) leads to high demands for data analysis, such as notational analysis, on player performance. While sports videos offer many benefits for such analysis, retrieving accurate information from sports videos could be challenging. In this paper, we propose EventAnchor, a data analysis framework to facilitate interactive annotation of racket sports video with the support of computer vision algorithms. Our approach uses machine learning models in computer vision to help users acquire essential events from videos (e.g., serve, the ball bouncing on the court) and offers users a set of interactive tools for data annotation. An evaluation study on a table tennis annotation system built on this framework shows significant improvement of user performances in simple annotation tasks on objects of interest and complex annotation tasks requiring domain knowledge.

2021-05-21

2101.05018

Xinggang Wang

Mengting Chen and Xinggang Wang and Heng Luo and Yifeng Geng and Wenyu Liu

Learning to Focus: Cascaded Feature Matching Network for Few-shot Image Recognition

14 pages

SCIENCE CHINA Information Sciences, 2021

10.1007/s11432-020-2973-7

cs.CV

http://creativecommons.org/licenses/by/4.0/

Deep networks can learn to accurately recognize objects of a category by training on a large number of annotated images. However, a meta-learning challenge known as a low-shot image recognition task comes when only a few images with annotations are available for learning a recognition model for one category. The objects in testing/query and training/support images are likely to be different in size, location, style, and so on. Our method, called Cascaded Feature Matching Network (CFMN), is proposed to solve this problem. We train the meta-learner to learn a more fine-grained and adaptive deep distance metric by focusing more on the features that have high correlations between compared images by the feature matching block which can align associated features together and naturally ignore those non-discriminative features. By applying the proposed feature matching block in different layers of the few-shot recognition network, multi-scale information among the compared images can be incorporated into the final cascaded matching feature, which boosts the recognition performance further and generalizes better by learning on relationships. The experiments for few-shot learning on two standard datasets, \emph{mini}ImageNet and Omniglot, have confirmed the effectiveness of our method. Besides, the multi-label few-shot task is first studied on a new data split of COCO which further shows the superiority of the proposed feature matching network when performing few-shot learning in complex images. The code will be made publicly available.

2021-01-14

2101.05050

Stassa Patsantzis

Stassa Patsantzis, Stephen H. Muggleton

Top Program Construction and Reduction for polynomial time Meta-Interpretive Learning

25 pages, 3 figures, to be published in Machine Learning Journal Special Issue on Learning and Reasoning

Mach.Learn. 100, 755-778 (2021)

10.1007/s10994-020-05945-w

cs.AI

http://creativecommons.org/licenses/by-nc-sa/4.0/

Meta-Interpretive Learners, like most ILP systems, learn by searching for a correct hypothesis in the hypothesis space, the powerset of all constructible clauses. We show how this exponentially-growing search can be replaced by the construction of a Top program: the set of clauses in all correct hypotheses that is itself a correct hypothesis. We give an algorithm for Top program construction and show that it constructs a correct Top program in polynomial time and from a finite number of examples. We implement our algorithm in Prolog as the basis of a new MIL system, Louise, that constructs a Top program and then reduces it by removing redundant clauses. We compare Louise to the state-of-the-art search-based MIL system Metagol in experiments on grid world navigation, graph connectedness and grammar learning datasets and find that Louise improves on Metagol's predictive accuracy when the hypothesis space and the target theory are both large, or when the hypothesis space does not include a correct hypothesis because of "classification noise" in the form of mislabelled examples. When the hypothesis space or the target theory are small, Louise and Metagol perform equally well.

2021-09-14

2101.05107

Benjamin Congram

Benjamin Congram and Timothy D. Barfoot

Relatively Lazy: Indoor-Outdoor Navigation Using Vision and GNSS

Presented at CRV2021

In Proceedings of the 18th Conference on Robots and Vision (CRV), pages 25-32. Burnaby, British Columbia, 26-28 May 2021

10.1109/CRV52889.2021.00015

cs.RO cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Visual Teach and Repeat has shown relative navigation is a robust and efficient solution for autonomous vision-based path following in difficult environments. Adding additional absolute sensors such as Global Navigation Satellite Systems (GNSS) has the potential to expand the domain of Visual Teach and Repeat to environments where the ability to visually localize is not guaranteed. Our method of lazy mapping and delaying estimation until a path-tracking error is needed avoids the need to estimate absolute states. As a result, map optimization is not required and paths can be driven immediately after being taught. We validate our approach on a real robot through an experiment in a joint indoor-outdoor environment comprising 3.5km of autonomous route repeating across a variety of lighting conditions. We achieve smooth error signals throughout the runs despite large sections of dropout for each sensor.

2021-07-20

2101.05108

Thea Aarrestad

Thea Aarrestad, Vladimir Loncar, Nicol\`o Ghielmetti, Maurizio Pierini, Sioni Summers, Jennifer Ngadiuba, Christoffer Petersson, Hampus Linander, Yutaro Iiyama, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Dylan Rankin, Sergo Jindariani, Kevin Pedro, Nhan Tran, Mia Liu, Edward Kreinar, Zhenbin Wu, and Duc Hoang

Fast convolutional neural networks on FPGAs with hls4ml

18 pages, 18 figures, 4 tables

Mach. Learn.: Sci. Technol. 2 045015 (2021)

10.1088/2632-2153/ac0ea1

cs.LG cs.CV hep-ex physics.ins-det stat.ML

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We introduce an automated tool for deploying ultra low-latency, low-power deep neural networks with convolutional layers on FPGAs. By extending the hls4ml library, we demonstrate an inference latency of $5\,\mu$s using convolutional architectures, targeting microsecond latency applications like those at the CERN Large Hadron Collider. Considering benchmark models trained on the Street View House Numbers Dataset, we demonstrate various methods for model compression in order to fit the computational constraints of a typical FPGA device used in trigger and data acquisition systems of particle detectors. In particular, we discuss pruning and quantization-aware training, and demonstrate how resource utilization can be significantly reduced with little to no loss in model accuracy. We show that the FPGA critical resource consumption can be reduced by 97% with zero loss in model accuracy, and by 99% when tolerating a 6% accuracy degradation.

2021-07-19

2101.05181

Lina Mezghani

Lina Mezghani, Sainbayar Sukhbaatar, Thibaut Lavril, Oleksandr Maksymets, Dhruv Batra, Piotr Bojanowski, Karteek Alahari

Memory-Augmented Reinforcement Learning for Image-Goal Navigation

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2022

cs.CV cs.AI cs.RO

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In this work, we present a memory-augmented approach for image-goal navigation. Earlier attempts, including RL-based and SLAM-based approaches have either shown poor generalization performance, or are heavily-reliant on pose/depth sensors. Our method is based on an attention-based end-to-end model that leverages an episodic memory to learn to navigate. First, we train a state-embedding network in a self-supervised fashion, and then use it to embed previously-visited states into the agent's memory. Our navigation policy takes advantage of this information through an attention mechanism. We validate our approach with extensive evaluations, and show that our model establishes a new state of the art on the challenging Gibson dataset. Furthermore, we achieve this impressive performance from RGB input alone, without access to additional information such as position or depth, in stark contrast to related work.

2023-01-06

2101.05231

Keaton Hamm

HanQin Cai, Keaton Hamm, Longxiu Huang, Deanna Needell

Robust CUR Decomposition: Theory and Imaging Applications

SIAM Journal on Imaging Sciences 14.4 (2021): 1472-1503

10.1137/20M1388322

cs.CV cs.LG eess.IV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

This paper considers the use of Robust PCA in a CUR decomposition framework and applications thereof. Our main algorithms produce a robust version of column-row factorizations of matrices $\mathbf{D}=\mathbf{L}+\mathbf{S}$ where $\mathbf{L}$ is low-rank and $\mathbf{S}$ contains sparse outliers. These methods yield interpretable factorizations at low computational cost, and provide new CUR decompositions that are robust to sparse outliers, in contrast to previous methods. We consider two key imaging applications of Robust PCA: video foreground-background separation and face modeling. This paper examines the qualitative behavior of our Robust CUR decompositions on the benchmark videos and face datasets, and find that our method works as well as standard Robust PCA while being significantly faster. Additionally, we consider hybrid randomized and deterministic sampling methods which produce a compact CUR decomposition of a given matrix, and apply this to video sequences to produce canonical frames thereof.

2023-02-28

2101.05339

Tian Xie

Tian Xie, Arthur France-Lanord, Yanming Wang, Jeffrey Lopez, Michael Austin Stolberg, Megan Hill, Graham Michael Leverick, Rafael Gomez-Bombarelli, Jeremiah A. Johnson, Yang Shao-Horn, Jeffrey C. Grossman

Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties

29 pages, 6 figures + supplementary information

Nature communications 13.1 (2022): 1-10

10.1038/s41467-022-30994-1

cond-mat.mtrl-sci cs.AI cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Polymer electrolytes are promising candidates for the next generation lithium-ion battery technology. Large scale screening of polymer electrolytes is hindered by the significant cost of molecular dynamics (MD) simulation in amorphous systems: the amorphous structure of polymers requires multiple, repeated sampling to reduce noise and the slow relaxation requires long simulation time for convergence. Here, we accelerate the screening with a multi-task graph neural network that learns from a large amount of noisy, unconverged, short MD data and a small number of converged, long MD data. We achieve accurate predictions of 4 different converged properties and screen a space of 6247 polymers that is orders of magnitude larger than previous computational studies. Further, we extract several design principles for polymer electrolytes and provide an open dataset for the community. Our approach could be applicable to a broad class of material discovery problems that involve the simulation of complex, amorphous materials.

2022-07-05

2101.05404

Justyna P. Zwolak

Shangjie Guo, Amilson R. Fritsch, Craig Greenberg, I. B. Spielman, Justyna P. Zwolak

Machine-learning enhanced dark soliton detection in Bose-Einstein condensates

17 pages, 5 figures

Mach. Learn.: Sci. Technol. 2: 035020 (2021)

10.1088/2632-2153/abed1e

cond-mat.quant-gas cs.CV cs.LG quant-ph

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Most data in cold-atom experiments comes from images, the analysis of which is limited by our preconceptions of the patterns that could be present in the data. We focus on the well-defined case of detecting dark solitons -- appearing as local density depletions in a Bose-Einstein condensate (BEC) -- using a methodology that is extensible to the general task of pattern recognition in images of cold atoms. Studying soliton dynamics over a wide range of parameters requires the analysis of large datasets, making the existing human-inspection-based methodology a significant bottleneck. Here we describe an automated classification and positioning system for identifying localized excitations in atomic BECs utilizing deep convolutional neural networks to eliminate the need for human image examination. Furthermore, we openly publish our labeled dataset of dark solitons, the first of its kind, for further machine learning research.

2021-06-18

2101.05418

EPTCS

Luc Jaulin (Robex, Lab-STICC), Beno\^it Desrochers (DGA-TN)

Enclosing the Sliding Surfaces of a Controlled Swing

In Proceedings SNR 2020, arXiv:2101.05256

EPTCS 331, 2021, pp. 43-55

10.4204/EPTCS.331.4

cs.RO cs.AI cs.LO

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

When implementing a non-continuous controller for a cyber-physical system, it may happen that the evolution of the closed-loop system is not anymore piecewise differentiable along the trajectory, mainly due to conditional statements inside the controller. This may lead to some unwanted chattering effects than may damage the system. This behavior is difficult to observe even in simulation. In this paper, we propose an interval approach to characterize the sliding surface which corresponds to the set of all states such that the state trajectory may jump indefinitely between two distinct behaviors. We show that the recent notion of thick sets will allows us to compute efficiently an outer approximation of the sliding surface of a given class of hybrid system taking into account all set-membership uncertainties. An application to the verification of the controller of a child swing is considered to illustrate the principle of the approach.

2021-01-15

2101.05439

Xiaofeng Liu

Xiaofeng Liu, Fangxu Xing, Jerry L. Prince, Aaron Carass, Maureen Stone, Georges El Fakhri, Jonghye Woo

Dual-cycle Constrained Bijective VAE-GAN For Tagged-to-Cine Magnetic Resonance Image Synthesis

Accepted to IEEE International Symposium on Biomedical Imaging (ISBI) 2021

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)

10.1109/ISBI48211.2021.9433852

eess.IV cs.CV

http://creativecommons.org/licenses/by/4.0/

Tagged magnetic resonance imaging (MRI) is a widely used imaging technique for measuring tissue deformation in moving organs. Due to tagged MRI's intrinsic low anatomical resolution, another matching set of cine MRI with higher resolution is sometimes acquired in the same scanning session to facilitate tissue segmentation, thus adding extra time and cost. To mitigate this, in this work, we propose a novel dual-cycle constrained bijective VAE-GAN approach to carry out tagged-to-cine MR image synthesis. Our method is based on a variational autoencoder backbone with cycle reconstruction constrained adversarial training to yield accurate and realistic cine MR images given tagged MR images. Our framework has been trained, validated, and tested using 1,768, 416, and 1,560 subject-independent paired slices of tagged and cine MRI from twenty healthy subjects, respectively, demonstrating superior performance over the comparison methods. Our method can potentially be used to reduce the extra acquisition time and cost, while maintaining the same workflow for further motion analyses.

2021-06-08

2101.05570

Aythami Morales

Alejandro Acien and Aythami Morales and John V. Monaco and Ruben Vera-Rodriguez and Julian Fierrez

TypeNet: Deep Learning Keystroke Biometrics

arXiv admin note: substantial text overlap with arXiv:2004.03627

IEEE Transactions on Biometrics, Behavior, and Identity Science, 2021

cs.CV

http://creativecommons.org/licenses/by-nc-nd/4.0/

We study the performance of Long Short-Term Memory networks for keystroke biometric authentication at large scale in free-text scenarios. For this we explore the performance of Long Short-Term Memory (LSTMs) networks trained with a moderate number of keystrokes per identity and evaluated under different scenarios including: i) three learning approaches depending on the loss function (softmax, contrastive, and triplet loss); ii) different number of training samples and lengths of keystroke sequences; iii) four databases based on two device types (physical vs touchscreen keyboard); and iv) comparison with existing approaches based on both traditional statistical methods and deep learning architectures. Our approach called TypeNet achieves state-of-the-art keystroke biometric authentication performance with an Equal Error Rate of 2.2% and 9.2% for physical and touchscreen keyboards, respectively, significantly outperforming previous approaches. Our experiments demonstrate a moderate increase in error with up to 100,000 subjects, demonstrating the potential of TypeNet to operate at an Internet scale. To the best of our knowledge, the databases used in this work are the largest existing free-text keystroke databases available for research with more than 136 million keystrokes from 168,000 subjects in physical keyboards, and 60,000 subjects with more than 63 million keystrokes acquired on mobile touchscreens.

2021-09-14

2101.05593

Renato Stoffalette Joao

Renato Stoffalette Joao

On the Temporality of Priors in Entity Linking

2020 European Conference on Information Retrieval

cs.CL cs.LG

http://creativecommons.org/licenses/by/4.0/

Entity linking is a fundamental task in natural language processing which deals with the lexical ambiguity in texts. An important component in entity linking approaches is the mention-to-entity prior probability. Even though there is a large number of works in entity linking, the existing approaches do not explicitly consider the time aspect, specifically the temporality of an entity's prior probability. We posit that this prior probability is temporal in nature and affects the performance of entity linking systems. In this paper we systematically study the effect of the prior on the entity linking performance over the temporal validity of both texts and KBs.

2021-01-15

2101.05779

Giovanni Paolini

Giovanni Paolini, Ben Athiwaratkun, Jason Krone, Jie Ma, Alessandro Achille, Rishita Anubhai, Cicero Nogueira dos Santos, Bing Xiang, Stefano Soatto

Structured Prediction as Translation between Augmented Natural Languages

International Conference on Learning Representations (ICLR) 2021

cs.LG cs.CL

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We propose a new framework, Translation between Augmented Natural Languages (TANL), to solve many structured prediction language tasks including joint entity and relation extraction, nested named entity recognition, relation classification, semantic role labeling, event extraction, coreference resolution, and dialogue state tracking. Instead of tackling the problem by training task-specific discriminative classifiers, we frame it as a translation task between augmented natural languages, from which the task-relevant information can be easily extracted. Our approach can match or outperform task-specific models on all tasks, and in particular, achieves new state-of-the-art results on joint entity and relation extraction (CoNLL04, ADE, NYT, and ACE2005 datasets), relation classification (FewRel and TACRED), and semantic role labeling (CoNLL-2005 and CoNLL-2012). We accomplish this while using the same architecture and hyperparameters for all tasks and even when training a single model to solve all tasks at the same time (multi-task learning). Finally, we show that our framework can also significantly improve the performance in a low-resource regime, thanks to better use of label semantics.

2021-12-06

2101.05880

Shenghui Li

Shenghui Li, Edith Ngai, Fanghua Ye, and Thiemo Voigt

Auto-weighted Robust Federated Learning with Corrupted Data Sources

ACM Transactions on Intelligent Systems and Technology (TIST) 13,5 (2022), 1-20

10.1145/3517821

cs.LG cs.AI

http://creativecommons.org/licenses/by/4.0/

Federated learning provides a communication-efficient and privacy-preserving training process by enabling learning statistical models with massive participants while keeping their data in local clients. However, standard federated learning techniques that naively minimize an average loss function are vulnerable to data corruptions from outliers, systematic mislabeling, or even adversaries. In addition, it is often prohibited for service providers to verify the quality of data samples due to the increasing concern of user data privacy. In this paper, we address this challenge by proposing Auto-weighted Robust Federated Learning (arfl), a novel approach that jointly learns the global model and the weights of local updates to provide robustness against corrupted data sources. We prove a learning bound on the expected risk with respect to the predictor and the weights of clients, which guides the definition of the objective for robust federated learning. The weights are allocated by comparing the empirical loss of a client with the average loss of the best p clients (p-average), thus we can downweight the clients with significantly high losses, thereby lower their contributions to the global model. We show that this approach achieves robustness when the data of corrupted clients is distributed differently from benign ones. To optimize the objective function, we propose a communication-efficient algorithm based on the blockwise minimization paradigm. We conduct experiments on multiple benchmark datasets, including CIFAR-10, FEMNIST and Shakespeare, considering different deep neural network models. The results show that our solution is robust against different scenarios including label shuffling, label flipping and noisy features, and outperforms the state-of-the-art methods in most scenarios.

2022-07-15

2101.05954

Devshree Patel

Devshree Patel, Ratnam Parikh, and Yesha Shastri

Recent Advances in Video Question Answering: A Review of Datasets and Methods

18 pages, 5 tables, Video and Image Question Answering Workshop, 25th International Conference on Pattern Recognition

Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science, vol 12662. Springer

10.1007/978-3-030-68790-8_27

cs.CV

http://creativecommons.org/licenses/by/4.0/

Video Question Answering (VQA) is a recent emerging challenging task in the field of Computer Vision. Several visual information retrieval techniques like Video Captioning/Description and Video-guided Machine Translation have preceded the task of VQA. VQA helps to retrieve temporal and spatial information from the video scenes and interpret it. In this survey, we review a number of methods and datasets for the task of VQA. To the best of our knowledge, no previous survey has been conducted for the VQA task.

2021-03-19

2101.06021

Pei Wang

Pei Wang, Wei Sun, Qingsen Yan, Axi Niu, Rui Li, Yu Zhu, Jinqiu Sun, Yanning Zhang

Non-uniform Motion Deblurring with Blurry Component Divided Guidance

Pattern Recognition,Volume 120, December 2021, 108082

10.1016/j.patcog.2021.108082

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Blind image deblurring is a fundamental and challenging computer vision problem, which aims to recover both the blur kernel and the latent sharp image from only a blurry observation. Despite the superiority of deep learning methods in image deblurring have displayed, there still exists major challenge with various non-uniform motion blur. Previous methods simply take all the image features as the input to the decoder, which handles different degrees (e.g. large blur, small blur) simultaneously, leading to challenges for sharp image generation. To tackle the above problems, we present a deep two-branch network to deal with blurry images via a component divided module, which divides an image into two components based on the representation of blurry degree. Specifically, two component attentive blocks are employed to learn attention maps to exploit useful deblurring feature representations on both large and small blurry regions. Then, the blur-aware features are fed into two-branch reconstruction decoders respectively. In addition, a new feature fusion mechanism, orientation-based feature fusion, is proposed to merge sharp features of the two branches. Both qualitative and quantitative experimental results show that our method performs favorably against the state-of-the-art approaches.

2021-10-22

2101.06040

Evangelos Mazomenos

Patrick Brandao, Odysseas Zisimopoulos, Evangelos Mazomenos, Gastone Ciuti, Jorge Bernal, Marco Visentini-Scarzanella, Arianna Menciassi, Paolo Dario, Anastasios Koulaouzidis, Alberto Arezzo, David J Hawkes, Danail Stoyanov

Towards a Computed-Aided Diagnosis System in Colonoscopy: Automatic Polyp Segmentation Using Convolution Neural Networks

10 pages, 6 figures

Journal of Medical Robotics Research, Volume 03, No. 02, 1840002 (2018) G

10.1142/S2424905X18400020

cs.CV cs.LG

http://creativecommons.org/licenses/by-nc-nd/4.0/

Early diagnosis is essential for the successful treatment of bowel cancers including colorectal cancer (CRC) and capsule endoscopic imaging with robotic actuation can be a valuable diagnostic tool when combined with automated image analysis. We present a deep learning rooted detection and segmentation framework for recognizing lesions in colonoscopy and capsule endoscopy images. We restructure established convolution architectures, such as VGG and ResNets, by converting them into fully-connected convolution networks (FCNs), fine-tune them and study their capabilities for polyp segmentation and detection. We additionally use Shape from-Shading (SfS) to recover depth and provide a richer representation of the tissue's structure in colonoscopy images. Depth is incorporated into our network models as an additional input channel to the RGB information and we demonstrate that the resulting network yields improved performance. Our networks are tested on publicly available datasets and the most accurate segmentation model achieved a mean segmentation IU of 47.78% and 56.95% on the ETIS-Larib and CVC-Colon datasets, respectively. For polyp detection, the top performing models we propose surpass the current state of the art with detection recalls superior to 90% for all datasets tested. To our knowledge, we present the first work to use FCNs for polyp segmentation in addition to proposing a novel combination of SfS and RGB that boosts performance

2021-01-18

2101.06159

David Fernandez Llorca

David Fern\'andez Llorca, Antonio Hern\'andez Mart\'inez, Iv\'an Garc\'ia Daza

Vision-based Vehicle Speed Estimation: A Survey

Manuscript published in the IET Intelligent Transport Systems journal

IET Intelligent Transport Systems 2021

10.1049/itr2.12079

cs.CV cs.AI cs.RO

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The need to accurately estimate the speed of road vehicles is becoming increasingly important for at least two main reasons. First, the number of speed cameras installed worldwide has been growing in recent years, as the introduction and enforcement of appropriate speed limits is considered one of the most effective means to increase the road safety. Second, traffic monitoring and forecasting in road networks plays a fundamental role to enhance traffic, emissions and energy consumption in smart cities, being the speed of the vehicles one of the most relevant parameters of the traffic state. Among the technologies available for the accurate detection of vehicle speed, the use of vision-based systems brings great challenges to be solved, but also great potential advantages, such as the drastic reduction of costs due to the absence of expensive range sensors, and the possibility of identifying vehicles accurately. This paper provides a review of vision-based vehicle speed estimation. We describe the terminology, the application domains, and propose a complete taxonomy of a large selection of works that categorizes all stages involved. An overview of performance evaluation metrics and available datasets is provided. Finally, we discuss current limitations and future directions.

2021-05-27

2101.06177

Miquel Junyent

Miquel Junyent, Vicen\c{c} G\'omez, Anders Jonsson

Hierarchical Width-Based Planning and Learning

Proceedings of the Thirty-First International Conference on Automated Planning and Scheduling (ICAPS 2021)

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Width-based search methods have demonstrated state-of-the-art performance in a wide range of testbeds, from classical planning problems to image-based simulators such as Atari games. These methods scale independently of the size of the state-space, but exponentially in the problem width. In practice, running the algorithm with a width larger than 1 is computationally intractable, prohibiting IW from solving higher width problems. In this paper, we present a hierarchical algorithm that plans at two levels of abstraction. A high-level planner uses abstract features that are incrementally discovered from low-level pruning decisions. We illustrate this algorithm in classical planning PDDL domains as well as in pixel-based simulator domains. In classical planning, we show how IW(1) at two levels of abstraction can solve problems of width 2. For pixel-based domains, we show how in combination with a learned policy and a learned value function, the proposed hierarchical IW can outperform current flat IW-based planners in Atari games with sparse rewards.

2022-04-29

2101.06310

Daniel Osaku

D. Osaku, C. F. Cuba, Celso T.N. Suzuki, J.F. Gomes, A.X. Falc\~ao

Automated Diagnosis of Intestinal Parasites: A new hybrid approach and its benefits

18 pages, 11 figures

Computers in Biology and Medicine, Volume 123, August 2020, 103917

10.1016/j.compbiomed.2020.103917

cs.CV cs.LG eess.IV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Intestinal parasites are responsible for several diseases in human beings. In order to eliminate the error-prone visual analysis of optical microscopy slides, we have investigated automated, fast, and low-cost systems for the diagnosis of human intestinal parasites. In this work, we present a hybrid approach that combines the opinion of two decision-making systems with complementary properties: ($DS_1$) a simpler system based on very fast handcrafted image feature extraction and support vector machine classification and ($DS_2$) a more complex system based on a deep neural network, Vgg-16, for image feature extraction and classification. $DS_1$ is much faster than $DS_2$, but it is less accurate than $DS_2$. Fortunately, the errors of $DS_1$ are not the same of $DS_2$. During training, we use a validation set to learn the probabilities of misclassification by $DS_1$ on each class based on its confidence values. When $DS_1$ quickly classifies all images from a microscopy slide, the method selects a number of images with higher chances of misclassification for characterization and reclassification by $DS_2$. Our hybrid system can improve the overall effectiveness without compromising efficiency, being suitable for the clinical routine -- a strategy that might be suitable for other real applications. As demonstrated on large datasets, the proposed system can achieve, on average, 94.9%, 87.8%, and 92.5% of Cohen's Kappa on helminth eggs, helminth larvae, and protozoa cysts, respectively.

2021-01-19

2101.06353

Shoffan Saifullah

Shoffan Saifullah, Yuli Fauziah, Agus Sasmito Aribowo

Comparison of Machine Learning for Sentiment Analysis in Detecting Anxiety Based on Social Media Data

Jurnal Informatika,15(1), 2021, 45-55

10.26555/jifo.v15i1.a20111

cs.CL cs.LG

http://creativecommons.org/licenses/by/4.0/

All groups of people felt the impact of the COVID-19 pandemic. This situation triggers anxiety, which is bad for everyone. The government's role is very influential in solving these problems with its work program. It also has many pros and cons that cause public anxiety. For that, it is necessary to detect anxiety to improve government programs that can increase public expectations. This study applies machine learning to detecting anxiety based on social media comments regarding government programs to deal with this pandemic. This concept will adopt a sentiment analysis in detecting anxiety based on positive and negative comments from netizens. The machine learning methods implemented include K-NN, Bernoulli, Decision Tree Classifier, Support Vector Classifier, Random Forest, and XG-boost. The data sample used is the result of crawling YouTube comments. The data used amounted to 4862 comments consisting of negative and positive data with 3211 and 1651. Negative data identify anxiety, while positive data identifies hope (not anxious). Machine learning is processed based on feature extraction of count-vectorization and TF-IDF. The results showed that the sentiment data amounted to 3889 and 973 in testing, and training with the greatest accuracy was the random forest with feature extraction of vectorization count and TF-IDF of 84.99% and 82.63%, respectively. The best precision test is K-NN, while the best recall is XG-Boost. Thus, Random Forest is the best accurate to detect someone's anxiety based-on data from social media.

2021-03-02

2101.06368

Ian Stewart

Ian Stewart, Diyi Yang, Jacob Eisenstein

Tuiteamos o pongamos un tuit? Investigating the Social Constraints of Loanword Integration in Spanish Social Media

Society for Computation in Linguistics, 2021

cs.CL

http://creativecommons.org/licenses/by/4.0/

Speakers of non-English languages often adopt loanwords from English to express new or unusual concepts. While these loanwords may be borrowed unchanged, speakers may also integrate the words to fit the constraints of their native language, e.g. creating Spanish "tuitear" from English "tweet." Linguists have often considered the process of loanword integration to be more dependent on language-internal constraints, but sociolinguistic constraints such as speaker background remain only qualitatively understood. We investigate the role of social context and speaker background in Spanish speakers' use of integrated loanwords on social media. We find first that newspaper authors use the integrated forms of loanwords and native words more often than social media authors, showing that integration is associated with formal domains. In social media, we find that speaker background and expectations of formality explain loanword and native word integration, such that authors who use more Spanish and who write to a wider audience tend to use integrated verb forms more often. This study shows that loanword integration reflects not only language-internal constraints but also social expectations that vary by conversation and speaker.

2021-01-19