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Subspace learning and matrix factorization problems have a great many applications in science and engineering, and efficient algorithms are critical as dataset sizes continue to grow. Many relevant problem formulations are non-convex, and in a variety of contexts it has been observed that solving the non-convex problem directly is not only efficient but reliably accurate. We discuss convergence theory for a particular method: first order incremental gradient descent constrained to the Grassmannian. The output of the algorithm is an orthonormal basis for a $d$-dimensional subspace spanned by an input streaming data matrix. We study two sampling cases: where each data vector of the streaming matrix is fully sampled, or where it is undersampled by a sampling matrix $A_t\in \R^{m\times n}$ with $m\ll n$. We propose an adaptive stepsize scheme that depends only on the sampled data and algorithm outputs. We prove that with fully sampled data, the stepsize scheme maximizes the improvement of our convergence metric at each iteration, and this method converges from any random initialization to the true subspace, despite the non-convex formulation and orthogonality constraints. For the case of undersampled data, we establish monotonic improvement on the defined convergence metric for each iteration with high probability.

['Dejiao Zhang', 'Laura Balzano']

Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation From Undersampled Data

Semantic inferencing and querying across large-scale RDF triple stores is notoriously slow. Our objective is to expedite this process by employing Google's MapReduce framework to implement scale-out distributed querying and reasoning. This approach requires RDF graphs to be decomposed into smaller units that are distributed across computational nodes. RDF Molecules appear to offer an ideal approach - providing an intermediate level of granularity between RDF graphs and triples. However, the original RDF molecule definition has inherent limitations that will adversely affect performance. In this paper, we propose a number of extensions to RDF molecules (hierarchy and ordering) to overcome these limitations. We then present some implementation details for our MapReduce-based RDF molecule store. Finally we evaluate the benefits of our approach in the context of the Bio-MANTA project - an application that requires integration and querying across large-scale protein-protein interaction datasets.

['Andrew Newman', 'Yuan-Fang Li', 'Jane Hunter']

Scalable Semantics The Silver Lining of Cloud Computing

In this letter, we show that many existing short-time spectral amplitude (STSA) Bayesian estimators for speech enhancement all have a similarly structured cost function. On this basis, we propose a new cost function that generalizes those of existent Bayesian STSA estimators and then obtain the corresponding closed-form solution for the optimal clean speech STSA. The resulting family of estimators, which we will term the generalized weighted family of STSA estimators (GWSA), features a new parameter that acts only on the estimated clean speech STSA. It is found that this new parameter yields an added flexibility in terms of achievable gain curves when compared to those of existing estimators. Moreover, we show that the new estimator family tends to a Wiener filter for high instantaneous signal-to-noise ratios.

['Eric Plourde', 'Benoit Champagne']

Generalized Bayesian Estimators of the Spectral Amplitude for Speech Enhancement

We present a set of metrics describing classification performance for individual contexts of a spoken dialog system as well as for the entire system. We show how these metrics can be used to train and tune system components and how they are related to Caller Experience, a subjective measure describing how well a caller was treated by the dialog system.

['David Suendermann', 'Jackson Liscombe', 'Krishna Dayanidhi', 'Roberto Pieraccini']

A Handsome Set of Metrics to Measure Utterance Classification Performance in Spoken Dialog Systems

Keywords can be used to query XML data without schema information. In this paper, a novel kind of query is proposed, top-k keyword search over XML streams. According to the set of keywords and the number of results, such query can retrieve the top-k XML data fragments most related to the keyword set. A novel ranking strategy for search result is proposed to represent the relativity of XML segments and the query. In order to efficiently and effectively process the top-k keyword query on XML streams, based on this ranking strategy, a stack-based algorithm is proposed to dynamically obtain the top-k results with the highest ranks at any time, with a filtering method to delete redundant elements. Extensive experiments are performed to verify the effectiveness and efficiency of the algorithms presented in this paper.

['Lingli Li', 'Hongzhi Wang', 'Jianzhong Li', 'Jizhou Luo']

Efficient Top-k Keyword Search on XML Streams

Efficient Identification of State in Reinforcement Learning. Efficient Identification of State in Reinforcement Learning. The Journal of Economic Behavior & Organization, October 1996 Issue Theorem 1: Information in Neural Networks: The Role of Attention Functions in Reinforcement Learning. The Journal of Economic Behavior & Organization, October 1996 Issue Efficient Identification of State in Reinforcement Learning. The Journal of Economic Behavior & Organization, October 1996 Issue Efficient Identification of State in Reinforcement Learning. the Journal of Economic Behavior & Organization, October 1996 Issue

['Stephan Timmer', 'Martin A. Riedmiller']

Efficient Identification of State in Reinforcement Learning.

Emulab is a large-scale, remotely-accessible network and distributed systems testbed used by over a thousand researchers around the world. In Emulab, users create "experiments" composed of arbitrarily interconnected groups of dedicated machines that are automatically configured according to user specifications. In the last year alone, users have run over 18,000 such experiments, expecting consistent and correct behavior in the face of the ever-evolving 500,000 line code base and 3,000 discrete hardware components that comprise Emulab. We have found normal testing to be insufficient to meet these expectations and have therefore provided continuous, automatic validation. This paper describes Linktest, an integral part of our validation framework that is responsible for end-to-end validation during the configuration of every experiment. Developing and deploying such a validation approach faces numerous challenges, including the need for a code path entirely independent of the rest of the Emulab software. We describe our system's motivation, its design and implementation and our experience.

['David Anderson', 'Mike Hibler', 'Leigh Stoller', 'Tim Stack', 'Jay Lepreau']

Automatic Online Validation of Network Configuration in the Emulab Network Testbed

Background#R##N#Adult children caregivers (ACCs) are increasingly caring for elderly parents, which can cause health declines Peer support—offered online and in person—can enhance caregiver well-being. To date, studies have largely focused on evaluating online interventions and therefore caregivers' personal experiences with web-based support are under-represented in the literature. Further, online and in-person support have been investigated independent of one another, limiting our understanding of how caregivers engage in and experience peer support across modalities.#R##N#Research questions#R##N#1) How do ACCs use online and in-person modalities to obtain support? 2) What type of support is exchanged within each modality?#R##N#Methods#R##N#Qualitative descriptive design. We conducted in-depth interviews with 15 ACCs. Data was thematically analyzed.#R##N#Findings#R##N#ACCs mobilized existing network members for support. ACCs pragmatically used a blend of modalities for peer support based on their complex needs. The nature of peer support that ACCs receive transcended the interaction modality.#R##N#Conclusion#R##N#Dichotomizing support as either ‘online’ or ‘in-person’ may detract from our ability to understand how ACCs use multiple modalities to achieve their support goals. ACCs' approach to peer support was complex. This highlights the need for future interventions to emulate their naturally pragmatic and flexible support-seeking style.

['Marina Bastawrous Wasilewski', 'Fiona Webster', 'Jennifer Stinson', 'Jill I. Cameron']

Adult children caregivers' experiences with online and in-person peer support

The RPPM model of access control uses relationships, paths and principal-matching in order to make access control decisions for general computing systems. Recently Stoller introduced a variant of an early RPPM model supporting administrative actions. Stoller's RPPM^2 model is able to make authorization decisions in respect of actions which affect the system graph and some policy elements. We also see utility in the RPPM model and believe that providing effective administration of the access control model is key to increasing the model's usefulness to real-world implementations. However, whilst we find inspiration in some aspects of Stoller's work, we believe that an alternative approach making use of the latest RPPM model as its basis will offer a wider range of operational and administrative capabilities. We motivate this work with specific requirements for an administrative model and then propose a decentralised discretionary access control approach to administration, whereby users are able to manage model components in the system graph through the addition and deletion of edges. The resulting Administrative RPPM (ARPPM) model supports administration of all of the model's components: the system model, the system graph, the authorization policies and all of their elements.

['Jason Crampton', 'James Sellwood']

ARPPM: Administration in the RPPM Model

Modern complex games and simulations pose many challenges for an intelligent agent, including partial observability, continuous time and effects, hostile opponents, and exogenous events. We present ARTUE (Autonomous Response to Unexpected Events), a domain-independent autonomous agent that dynamically reasons about what goals to pursue in response to unexpected circumstances in these types of environments. ARTUE integrates AI research in planning, environment monitoring, explanation, goal generation, and goal management. To explain our conceptualization of the problem ARTUE addresses, we present a new conceptual framework, goal-driven autonomy, for agents that reason about their goals. We evaluate ARTUE on scenarios in the TAO Sandbox, a Navy training simulation, and demonstrate its novel architecture, which includes components for Hierarchical Task Network planning, explanation, and goal management. Our evaluation shows that ARTUE can perform well in a complex environment and that each component is necessary and contributes to the performance of the integrated system.

['Matthew Molineaux', 'Matthew Klenk', 'David W. Aha']

Goal-driven autonomy in a navy strategy simulation

We define the complete joint weight enumerator in genus g for codes over /spl Zopf//sub 2k/ and use it to study self-dual codes and their shadows. These weight enumerators are related to the theta series of the associated lattices and Siegel and Jacobi forms are formed from these series.

['YoungJu Choie', 'Steven T. Dougherty', 'Haesuk Kim']

Complete joint weight enumerators and self-dual codes

Design of novel screening environments for Mild Cognitive Impairment: giving priority to elicited speech and language abilities Design of novel screening environments for Mild Cognitive Impairment: giving priority to elicited speech and language abilities [1]. Annals of the New York Academy of Sciences, 99, 1541–1545 (2006). Retrieved June 10, 2016, from: http://dx.doi.org/10.1002/a10205916 49. Wang, J. P. & Y. Liu, D. The role of the verbal and nonverbal components in perception of

['Sofia Segkouli', 'Ioannis Paliokas', 'Dimitrios Tzovaras', 'Dimitrios Giakoumis', 'Charalampos Karagiannidis']

Design of novel screening environments for Mild Cognitive Impairment: giving priority to elicited speech and language abilities

We introduce the Hausdorff measure for definable sets in an o-minimal structure, and prove the Cauchy-Crofton and co-area formulae for the o-minimal Hausdorff measure. We also prove that every definable set can be partitioned into "basic rectifiable sets", and that the Whitney arc property holds for basic rectifiable sets.

['Antongiulio Fornasiero', 'E. Vasquez Rifo']

Hausdorff measure on o-minimal structures

Path Integral Methods for Light Transport Simulation: Theory and Practice Path Integral Methods for Light Transport Simulation: Theory and Practice The following tables summarize all the methods used in light transfer modeling applications. Methods For Distance-Based Transport (FCT) Degenotaxis Method for FCT Theorem for Distance-Based Distance-Based Time-Transition Affected Effectiveness Methods for Distance-Based (L3) Simulation Tectonics of Light (THX) Method for Thox-Transport Transport

['Jaroslav Krivanek', 'Iliyan Georgiev', 'Anton S. Kaplanyan', 'Juan Cañada']

Path Integral Methods for Light Transport Simulation: Theory and Practice

Nowadays, the utilization of multiple sensors on every robotic platform is a reality due to their low cost, small size and light weight. Multi Sensor Fusion (MSF) algorithms are required to take advance of all the given measurements in a robust and complete manner. These high demanding developed algorithms need to be tested under real and challenging situations and environments. To the knowledge of the authors, none of the available datasets fulfills are fully suitable to be used for MSF applied to Robotics, due to the lack of multiple sensor measurements provided by light weight, small size and low cost sensors; due to their restricted motions (typically planar movements); or to their limited environmental conditions. The contributions of the paper are twofold. First, a low-cost portable and versatile testbed has been developed for MSF research with various types of sensors. Second, a group of datasets for MSF research for Robotics have been made public as a common framework for algorithm testing after a comparison with the existing databases in the state of the art, highlighting the differences and advantages of the one presented in this paper, that are: low-cost sensors for general use on Robotics, fully 3 dimensional movements (six degrees of freedom), as well as challenging indoor and outdoor small and large environments.

['Jose Luis Sanchez-Lopez', 'Changhong Fu', 'Pascual Campoy']

FuSeOn: A Low-Cost Portable Multi Sensor Fusion Research Testbed for Robotics

In this paper, knowledge creation is considered as a path-dependent evolutionary process that involves recombining knowledge spread over time. The findings of the paper suggest that a balance in combining current knowledge with the knowledge available across large time spans is an important factor that explains the impact of new knowledge. These ideas are empirically tested using patent data from the pharmaceutical industry. Results from the analysis offer support for the hypotheses developed in the paper.

['Atul Nerkar']

Old Is Gold? The Value of Temporal Exploration in the Creation of New Knowledge

This work deals with a problem, arising in the context of printed circuit testing, that will be formulated as a path covering problem on trees. For this problem, which consists in minimizing the number of continuity testing, an exact algorithm of linear complexity is here proposed for the first time. A general classification of path covering problems is also presented and a brief review of the literature is given.

['Giovanni Andreatta', 'Francesco Mason']

Path covering problems and testing of printed circuits

This study examines the factors that affect the selection of the best answer in a Community-driven Question Answering service (Yahoo! Answers). Factors identified were classified into three categories namely, social, textual and content-appraisal features. Social features refer to the community aspects of the users involved and are extracted from the explicit user interaction and feedback. Textual features refer to the surface aspects of the text such as answer length, number of unique words etc. The content-appraisal features emphasis on the quality of the content and the relevance judgment used by the asker to select the best answer. The framework built comprises 12 features from the three categories. Based on a randomly selected dataset of 800 question-answer pairs from Yahoo!Answers, social, textual and content-appraisal features were collected. The results of logistic regression showed the significance of content-appraisal features over social and textual features. The implications of these findings for system development and for future research are discussed.

['Mohan John Blooma', 'Alton Yeow-Kuan Chua', 'Dion Hoe-Lian Goh']

Selection of the Best Answer in CQA Services

In centralized transportation surveillance systems, video is captured and compressed at low processing power remote nodes and transmitted to a central location for processing. Such compression can reduce the accuracy of centrally run automated object tracking algorithms. In typical systems, the majority of communications bandwidth is spent on encoding temporal pixel variations such as acquisition noise or local changes to lighting. We propose a tracking-aware, H.264-compliant compression algorithm that removes temporal components of low tracking interest and optimizes the quantization of frequency coefficients, particularly those that most influence trackers, significantly reducing bitrate while maintaining comparable tracking accuracy. We utilize tracking accuracy as our compression criterion in lieu of mean squared error metrics. Our proposed system is designed with low processing power and memory requirements in mind, and as such can be deployed on remote nodes. Using H.264/AVC video coding and a commonly used state-of-the-art tracker we show that our algorithm allows for over 90% bitrate savings while maintaining comparable tracking accuracy.

['Eren Soyak', 'Sotirios A. Tsaftaris', 'Aggelos K. Katsaggelos']

Low-Complexity Tracking-Aware H.264 Video Compression for Transportation Surveillance

Based on the requirements of beyond 3G mobile communication systems, the access network architecture and media access control technique for B3G systems are studied. The proposed novel access network architecture, which can reduce network complexity and improve system performance, is introduced. Centralized mini-slot packet reservation multiple access (CMPRMA), based on OFDMA, is proposed, which not only can inherit the advantages of MPRMA and support real-time traffic well, but can also supply the resource reservation scheme for data traffic and support transmission for data traffic efficiently

['Youjun Gao', 'Hui Tian', 'Lei Sun', 'H. Xu']

Research on the access network and MAC technique for beyond 3G systems

We present a technique to infer a worm's infection sequence from traffic traces collected at a network telescope . We analyze the fidelity of the infection evolution as inferred by our technique, and explore its effectiveness under varying constraints including the scanning rate of the worm, the size of the vulnerable population, and the size of the telescope itself. Moreover, we provide guidance regarding the point at which our method's accuracy diminishes beyond practical value. As we show empirically, this point is reached well after a few hundred initial infected hosts (possibly including "patient zero'') has been reliably identified with more than 80% accuracy. We generalize our mechanism by exploiting the change in the pattern of inter-arrival times exhibited during the early stages of such an outbreak to detect the presence and approximate size of the hit-list. Our mechanism is resilient to varying parameters like the worm scanning rate and the size of the vulnerable population, and can provide significant insights into the characteristics of the hit-list even under spreading dynamics that exceed that of currently known worms. Lastly, to illustrate the practicality of our solution, we apply our approach to real-world traces of the Witty worm and provide a refined estimate on the previously suspected hit-list size.

['Moheeb Abu Rajab', 'Fabian Monrose', 'Andreas Terzis']

Worm evolution tracking via timing analysis

Gridlock is a microscopic traffic simulation platform and is now extended with electric vehicle support. Simulations for coordination mechanisms concerning on-line charging of electric vehicles on highway networks are performed using this simulation platform. The platform offers a means for gathering and processing simulation data and the real- time visualisation of simulation state aspects such as traffic density and charging station occupation. Electric vehicles (EVs) are gaining in popularity in an effort to reduce the car- bon footprint of vehicular transportation by creating environmentally friendly alternatives in the form of Plug-in Hybrid EVs and fully battery-powered EVs but the limited driving range is still considered as one of the main causes for the limited adoption of EVs, together with the long time needed to recharge the battery. Electric charging infrastructure will need to be built. These networks will, however, be subject to capacity constraints. If there is too much traffic on the network, congestion becomes unavoidable. Increasing the load on the charging infrastructure by increasing the number of vehicles that need to charge, causes the waiting times and queueing to increase rapidly. Prolonged peak loads on these infrastructure elements can also aversely affect the lifespan of the transformers in the electricity grid, driving up the financial cost for the grid operators. More efficiently using the available infrastructure by use of ICT-based coor- dination strategies offers a solution to mitigate the symptoms of these capacity problems. The primary goal of the work in (1) is finding coordination strategies capable of guiding the charging behaviour of individual agents to globally mini- mize waiting and queuing times and to avoid excessive peak loading of charging stations in the network. 2M ain Purpose This paper demonstrates the simulation platform used for evaluating coordi- nation mechanisms for on-line allocation of electric vehicles to fast charging

['Kristof Coninx', 'Tom Holvoet']

A microscopic traffic simulation platform for coordinated charging of electric vehicles

By focusing on the case of the Italian political movement “Five Stars”, founded in 2009 by former comedian Beppe Grillo, this article will analyze the resurfacing of a particular kind of power – charismatic authority – through a platform such as Web 2.0 that was expected to promote more rational consensus strategies. Although the political action of the Five Stars movement pretends to be inspired by a participative culture, it is in fact directly ruled by the founder via his blog, with a little space allowed for discussions. In this sense, the rise of Grillo as a political leader seems to both retrieve and renew an old form of authority grounded in a very traditional legitimacy – the charismatic and undisputed leadership of the boss – while at the same time being able to spread through the network. This article will offer an overview of events and also provide a theoretical interpretation.

['Andrea Miconi']

Italy’s “Five Stars” movement and the role of a leader: Or, how charismatic power can resurface through the web

In the RoboCup Standard Platform League (SPL), the robot platform is the same humanoid NAO robot for all the competing teams. The NAO humanoids are fully autonomous with two onboard directional cameras, computation, multi-joint body, and wireless communication among them. One of the main opportunities of having a team of robots is to have robots share information and coordinate. We address the problem of each humanoid building a model of the world in real-time, given a combination of its own limited sensing, known models of actuation, and the communicated information from its teammates. Such multi-humanoid world modeling is challenging due to the biped motion, the limited perception, and the tight coupling between behaviors, sensing, localization, and communication. We describe the real-world opportunities, constraints and limitations imposed by the NAO humanoid robots. We contribute a modeling approach that differentiates among the motion model of different objects, in terms of their dynamics, namely the static landmarks (e.g., goal posts, lines, corners), the passive moving ball, and the controlled moving robots, both teammates and adversaries. We present experimental results with the NAO humanoid robots to illustrate the impact of our multi-humanoid world modeling approach. The challenges and approaches we present are relevant to the general problem of assessing and sharing information among multiple humanoid robots acting in a world with multiple types of objects.

['Brian Coltin', 'Somchaya Liemhetcharat', 'Çetin Meriçli', 'Junyun Tay', 'Manuela M. Veloso']

Multi-humanoid world modeling in Standard Platform robot soccer

A review of the basic ideas of fuzzy systems modeling is provided. We introduce a hierarchical-type fuzzy systems model called a hierarchical prioritized structure (HPS) and review its structure, operation, and the interlevel aggregation algorithm. We then turn to the issue of constructing the HPS. Consideration is first given to the case in which rules are provided by an expert. Detailed consideration is given to the problem of completing incomplete priorities by use of the principle of maximal buoyancy. A mathematical programming method is introduced for the implementation of this approach. The issue of tuning hierarchical models is addressed. We next introduce a dynamic approach to the formulation of an HPS directly from data that enables us to continually update our model as more observations become available. This approach allows a system's builder to start with a default model and include exceptions.

['Ronald R. Yager']

On the construction of hierarchical fuzzy systems models

The conjunctiva is a thin membrane that covers the surface of the eye and reveals the small blood vessels of the microcirculation for detailed non-invasive study. The morphology of these vessels is of interest because structural changes in the vascular bed have been shown to occur coincident with certain diseases. Detecting these changes could be used as an early indication, leading to more timely treatment. Our efforts have been to find a reliable method to automate the currently manual methods of analyzing images of the conjunctiva. Our approach has been to first model the illumination/reflection processes that contribute to a scene. We have then used the products of this model to develop some algorithms based on fuzzy logic to reliably detect blood vessel pixels in actual conjunctiva images. Work is progressing in the use of fuzzy logic concepts to track vessels from these detected points so as to provide complete vessel paths and other morphological information. >

['Carl E. Wick', 'Murray H. Loew', 'Joseph Kurantsin-Mills']

Using modeling and fuzzy logic to detect and track microvessels in conjunctiva images

Traditional games have recently started to become online social games. Once accessible only through face to face encounters or slow mail exchanges, games such as bridge, chess, and go are now played online by millions of gamers. Other online social games, such as FarmVille and Cafe World, already exploit the characteristics of the social network formed by gamers to improve and grow the online communities. For example, FarmVille routinely gives high-level (expert) players new items and broadcasts gameplay achievements through the social links. User behavior, social network, and play style analysis are not new research topics [1]–[4], but the study of online social gaming communities provides a new domain of application with the potential to influence millions of lives. In this work we analyze and compare two communities of bridge players, Locomotiva and BBO Fans.

['Mihaela Balint', 'Vlad Posea', 'Alexandru Dimitriu', 'Alexandru Iosup']

User behavior, social networking, and playing style in online and face to face bridge communities

In this paper, we study the effectiveness of features from Convolutional Neural Networks (CNN) for predicting the ambient temperature as well as the time of the year in an outdoor scene. We follow the benchmark provided by Glasner et al. [3] one of whose findings was that simple handcrafted features are better than the deep features (from fully connected layers) for temperature prediction. As in their work, we use the VGG-16 architecture for our CNNs, pretrained for classification on ImageNet. Our main findings on the temperature prediction task are as follows. (i) The pooling layers provide better features than the fully connected layers. (ii) The quality of the features improves little with fine-tuning of the CNN on training data. (iii) Our best setup significantly improves over the results from Glasner et al. showing that the deep features are successful in turning a camera into a crude temperature sensor. Moreover, we validate our findings also for time prediction and achieve accurate season, month, week, time of the day, and hour prediction.

['Anna Volokitin', 'Radu Timofte', 'Luc Van Gool']

Deep Features or Not: Temperature and Time Prediction in Outdoor Scenes

Bayesian Exponential Inverse Document Frequency and Region-of-Interest Effect for Enhancing Instance Search Accuracy Bayesian Exponential Inverse Document Frequency and Region-of-Interest Effect for Enhancing Instance Search Accuracy by Predicting the Extendability of Multiple Species in a Small Network (2010) DOI: 10.1007/s00787-009-0145-5 Gillespie-Gardner, K. F., Sollivadze, F. S., and Bower, R. (2011). Estimating the likelihood of learning to use a word processor in

['Masaya Murata', 'Hidehisa Nagano', 'Kaoru Hiramatsu', 'Kunio Kashino', "Shin'ichi Satoh"]

Bayesian Exponential Inverse Document Frequency and Region-of-Interest Effect for Enhancing Instance Search Accuracy

Recently, deep neural network has shown promising performance in face image recognition. The inputs of most networks are face images, and there is hardly any work reported in literature on network with face videos as input. To sufficiently discover the useful information contained in face videos, we present a novel network architecture called input aggregated network which is able to learn fixed-length representations for variable-length face videos. To accomplish this goal, an aggregation unit is designed to model a face video with various frames as a point on a Riemannian manifold, and the mapping unit aims at mapping the point into high-dimensional space where face videos belonging to the same subject are close-by and others are distant. These two units together with the frame representation unit build an end-to-end learning system which can learn representations of face videos for the specific tasks. Experiments on two public face video datasets demonstrate the effectiveness of the proposed network.

['Zhen Dong', 'Su Jia', 'Chi Zhang', 'Mingtao Pei']

Input Aggregated Network for Face Video Representation

In online service systems, the delay experienced by users from service request to service completion is one of the most critical performance metrics. To improve user delay experience, recent industrial practices suggest a modern system design mechanism: proactive serving , where the service system predicts future user requests and allocates its capacity to serve these upcoming requests proactively. This approach complements the conventional mechanism of capability boosting. In this paper, we propose queuing models for online service systems with proactive serving capability and characterize the user delay reduction by proactive serving. In particular, we show that proactive serving decreases average delay exponentially (as a function of the prediction window size) in the cases where service time follows light-tailed distributions. Furthermore, the exponential decrease in user delay is robust against prediction errors (in terms of miss detection and false alarm) and user demand fluctuation. Compared with the conventional mechanism of capability boosting, proactive serving is more effective in decreasing delay when the system is in the light-load regime. Our trace-driven evaluations demonstrate the practical power of proactive serving: for example, for the data trace of light-tailed YouTube videos, the average user delay decreases by 50% when the system predicts 60 s ahead. Our results provide, from a queuing-theoretical perspective, justifications for the practical application of proactive serving in online service systems.

['Shaoquan Zhang', 'Longbo Huang', 'Minghua Chen', 'Xin Liu']

Proactive Serving Decreases User Delay Exponentially: The Light-Tailed Service Time Case

The objective of this paper is to give a fast square root com- putation method. First the Frobenius mapping is adopted. Then a lot of calculations over an extension field are reduced to that over a proper subfield by the norm computation. In addition a inverse square root algo- rithm and an addition chain are adopted to save the computation cost. All of the above-mentioned steps have been proven to make the pro- posed algorithm much faster than the conventional algorithm. From the table which compares the computation between the conventional and the proposed algorithm, it is clearly shown that the proposed algorithm ac- celerates the square root computation 10 times and 20 times faster than the conventional algorithm in Fp11 and Fp22 respectively. At the same time, the proposed algorithm reduces the computation cost 10 times and 20 times less than the conventional algorithm.

['Wang Feng', 'Yasuyuki Nogami', 'Yoshitaka Morikawa']

A Fast Square Root Computation Using the Frobenius Mapping

Abstract#R##N##R##N#Twenty-first century students are expected to utilise emerging technologies such as lecture podcasts as learning tools. This research explored the uptake of podcasts by undergraduate students enrolled in two very different cognitively challenging subjects in the second year of the nursing programme and in the first year of a business programme. Regardless of the semester, the different content being studied and the statistically significant demographic differences between the nursing and business cohorts, striking behavioural similarities emerged. Students from both cohorts in each semester under investigation spent similar amounts of time studying regardless of gender, age, Internet access and time spent on paid work. The patterns of podcast usage by responding nursing and business students were not significantly different. Non-listeners in both cohorts did not differ significantly from podcast users (listeners) either demographically or with regard to personal access to computers, the Internet and MP3/4 players. Non-listeners utilised lecture notes, text resources and the learning management system in a similar way to listeners. The only significant difference was the longer hours spent in paid work by non-listeners.#R##N##R##N##R##N##R##N#These findings reinforce the emerging concept that podcasts are not embraced by everyone. Despite the flexibility and mobile learning opportunities afforded by podcasts, significant numbers of students prefer to learn in face-to-face environments and by reading and/or listening in set study environments.

['Alanah Kazlauskas', 'Kathy Robinson']

Podcasts are not for everyone

We address the problem of control of uncertain systems with time delays. Using the fuzzy logic control and artificial neural network methodologies, we present a self-learning fuzzy neural control scheme for general uncertain processes. In this scheme, a neural network compensator is designed instead of the classical Smith predictor for attenuating the adverse effects of time delays of the uncertain systems. The scheme has been used in control of welding pool dynamics of the arc welding process, and the experiment results show the control scheme available.

['Shanben Chen', 'L. H. Wu', 'Wang Q']

Self-learning fuzzy neural networks for control of uncertain systems with time delays

With the emergence of data-intensive applications, recent years have seen a fast-growing volume of I/O traffic propagated through the local I/O interconnect bus. This raises up a question for storage servers on how to resolve such a potential bottleneck. In this paper, we present a hierarchical data cache architecture called DCA to effectively slash local interconnect traffic and thus boost the storage server performance. A popular iSCSI storage server architecture is chosen as an example. DCA is composed of a read cache in NIC called NIC cache and a read/write unified cache in host memory called helper cache. The NIC cache services most portions of read requests without fetching data via the PCI bus, while the helper cache (1) supplies some portions of read requests per partial NIC cache hit, (2) directs cache placement for NIC cache, and (3) absorbs most transient writes locally. We develop a novel state-locality-aware cache placement algorithm called SLAP to improve the NIC cache hit ratio for mixed read and write workloads. To demonstrate the effectiveness of DCA, we develop a DCA prototype system and evaluate it with an open source iSCSI implementation under representative storage server workloads. Experimental results showed that DCA can boost iSCSI storage server throughput by up to 121 percent and reduce the PCI traffic by up to 74 percent compared with an iSCSI target without DCA.

['Jun Wang', 'Xiaoyu Yao', 'Christopher Mitchell', 'Peng Gu']

A New Hierarchical Data Cache Architecture for iSCSI Storage Server

This paper presents a robust method for handwritten text line extraction. We use morphological dilation with a dynamic adaptive mask for line extraction. Line separation occurs because of the repulsion and attraction between connected components. The characteristics of the Arabic script are considered to ensure a high performance of the algorithm. Our method is evaluated on the CENPARMI Arabic handwritten documents database which contains multi-skewed and touching lines. With a matching score of 0.95, our method achieved precision and recall rates of 96:3% and 96:7% respectively, which demonstrate the effectiveness of our approach.

['Muna Khayyat', 'Louisa Lam', 'Ching Y. Suen', 'Fei Yin', 'Cheng-Lin Liu']

Arabic Handwritten Text Line Extraction by Applying an Adaptive Mask to Morphological Dilation

Epilepsy is a neurological disorder caused by intense electrical activity in the brain. The electrical activity, which can be modelled through the superposition of several electrical dipoles, can be determined in a non-invasive way by analysing the electro-encephalogram. This source localization requires the solution of an inverse problem. Locally convergent optimization algorithms may be trapped in local solutions and when using global optimization techniques, the computational effort can become expensive. Fast recovery of the electrical sources becomes difficult that way. Therefore, there is a need to solve the inverse problem in an accurate and fast way. This paper performs the localization of multiple dipoles using a global–local hybrid algorithm. Global convergence is guaranteed by using space mapping techniques and independent component analysis in a computationally efficient way. The accuracy is locally obtained by using the Recursively Applied and Projected-MUltiple Signal Classification (RAP-MUSIC) algorithm. When using this hybrid algorithm, a four times faster solution is obtained.

['Guillaume Crevecoeur', 'Hans Hallez', 'Peter Van Hese', "Yves D'asseler", 'Luc Dupré', 'Rik Van de Walle']

A hybrid algorithm for solving the EEG inverse problem from spatio-temporal EEG data

Evaluation of formant-like features for ASR Evaluation of formant-like features for ASR-100 The S1 was constructed of a single cell with a T-shaped surface layer that contained a tessellation of the pore. The cell had a single cell with the surface layer covered with 2-3 T and a single-cell tessellation of the layer with a T-shaped depth of 1 µm. Here the tessellation was determined as the thickness of the surface, to ensure

['Katrin Weber', 'Febe de Wet', 'Bert Cranen', 'Lou Boves', 'Samy Bengio', 'Hervé Bourlard']

Evaluation of formant-like features for ASR

Over the Internet today, computing and communications environments are more complex and chaotic than classical distributed systems, lacking any centralized organization or hierarchical control. Peer-to-Peer network overlays provide a good substrate for creating large-scale data sharing, content distribution and application-level multicast applications. We present DistHash, a P2P overlay network designed to share large sets of replicated distributed objects in the context of large-scale highly dynamic infrastructures. The system uses original solutions to achieve optimal message routing in hop-count and throughput, provide an adequate consistency among replicas, as well as provide a fault-tolerant substrate. In this we present result proving that the system is able to scale to a large number of nodes, and it includes the fault tolerance and system orchestration mechanisms, added in order to assess the reliability and availability of the distributed system in an autonomic manner.

['Ciprian Dobre', 'Florin Pop', 'Valentin Cristea']

A Fault-tolerant Approach to Storing Objects in Distributed Systems

Nowadays, with the development of micro-electro- mechanical technologies, sweep coverage are more and more popular in wireless sensor networks, which is also applied widely in other scenarios, such as message ferrying and data routing in the ad-hoc network. In order to reduce the sweep cycle and the number of required mobile sensors, we propose the \emph{Distance-Sensitive-Route-Scheduling} (DSRS) problem, which is the first to consider the effect of sensing range. We prove that DSRS is NP-complete, and consider two different scenarios: the single kissing-point case and the general case. The former case requires a mobile sensor to change its moving direction after visiting a target. Correspondingly, we propose an approximation ROSE to schedule the routes of mobile sensors efficiently. For the latter general case, we present another approximation G- ROSE based on ROSE. We further characterize the non- locality property and design a distributed sweep algorithm D-ROSE, cooperating sensors to guarantee the required sweep requirements with the best effort. Our algorithms is scalable to different sweep coverage problems involving route schedules. We compare our algorithms with several previous algorithms, and the simulation results show that our algorithms greatly outperform other works especially with large sensing range, which can be improved up to $45\%$.

['Zhiyin Chen', 'Xudong Zhu', 'Xiaofeng Gao', 'Fan Wu', 'Jian Gu', 'Guihai Chen']

Efficient Scheduling Strategies for Mobile Sensors in Sweep Coverage Problem

Methods developed for image annotation usually make use of region clustering algorithms. Visual codebooks generated for region clusters, using low level features are matched with words in various ways. In this work, we ensured that clustering is more meaningful by using words in associated text in addition to image data in clustering of image regions to generate a codebook. We first compute topic probabilities of text documents associated with each image in the training set. Next, we eliminate low probability topics and use highly probable ones in the supervision of region clustering algorithm. To implement this supervision, we force our region clustering algorithm to assign each region to one of the clusters reserved for high probability topics of the associated text. Consequently, regions in generated clusters not only become visually closer, but also the probability of them to belong to the same topic increases. Experiment results show that image annotation with semi-supervised clustering is more successful compared to existing methods. To implement the algorithm parallel computation methods have been used.

['Ahmet Sayar', 'Fatos T. Yarman-Vural']

Image annotation with semi-supervised clustering

The unexploded landmines remain dangerous to the human community, if not swept post war. It causes long-term fatal issues to civilians in addition to rendering the land impassable and unusable for decades. The need of the hour is a reliable and suitable technology for identifying the accurate location of these landmines so that they can be demined with safety. The aim of designing this prototype is to develop an autonomous robot, which exploits its ability to detect and mark the landmines accurately with minimal rate of false alarms using efficient demining strategies. An effective plan for the path is provided for the movement of robot, considering the coverage of all critical points to detect mines so as to increase the efficiency. The differential steer drive dead reckoning method is used for determining the position and location of the robot. A user friendly GUI is developed, enabling the operator to control the robot, to plot the position of landmines and to monitor the scanned/left over area remotely. The key advantages of the developed model are its cost effectiveness, high sensitivity and reliability.

['Booma Govindaram', 'A. Umamakeswari']

An autonomous approach for efficient landmine detection and marking using high sensitive robot

In this paper, we propose a method to enhance the color image of a low-light scene by using a single sensor that simultaneously captures red, green, blue (RGB) and near-infrared (NIR) information. Typical image enhancement methods require two cameras to simultaneously capture color and NIR images. In such cases, meticulous calibration is required to adjust the pixel positions of the two cameras. By contrast, our proposed system is calibration free, but achieves accurate color image restoration. We divide the captured multi-spectral data into RGB and NIR information based on the spectral sensitivity of our imaging system. Using the NIR information for guidance, we reconstruct the corresponding clear color image based on a joint demosaicking and denoising technique. Our experiments show the effectiveness of our method using raw data captured by our imaging system.

['Hiroki Yamashita', 'Daisuke Sugimura', 'Takayuki Hamamoto']

Enhancing low-light color images using an RGB-NIR single sensor

The move to cloud computing is the next stage of an unstoppable trend in the breakdown of the enterprise perimeter, both technically and organisationally. This new paradigm presents a number of security challenges that still need to be resolved but sufficient change in the IT environment has already happened - so that most organisations are working in a transitional state where security exploits are happening across the enterprise boundary. In this situation, the compartmentalisation introduced by migrating to cloud services could result in much improved security.

['Philippe Dorey', 'A. Leite']

Commentary: Cloud computing - A security problem or solution?

Personas in uniform: police officers as users of information technology. Personas in uniform: police officers as users of information technology. One aspect of this type of "intelligence" that is often mentioned is the fact that people have been "supervised" by an intermediary between the real identity and their personal identity on a larger scale. For example, there may be a social network that identifies as well as someone who is actually using that social media site of Facebook. An intermediary person is being supervised by any such intermediary group, whether the entity or its members are

['Erik Borglund', 'Urban Nulden']

Personas in uniform: police officers as users of information technology.

In this paper, we propose an indoor localization system employing ordered sequence of access points (APs) based on received signal strength (RSS). Unlike existing indoor localization systems, our approach does not require any time-consuming and laborious site survey phase to characterize the radio signals in the environment. To be precise, we construct the fingerprint map by cutting the layouts of the interested area into regions with only the knowledge of positions of APs. This can be done offline within a second and has a potential for practical use. The localization is then achieved by matching the ordered AP-sequence to the ones in the fingerprint map. Different from traditional fingerprinting that employing all APs information, we use only selected APs to perform localization, due to the fact that, without site survey, the possibility in obtaining the correct AP sequence is lower if it involves more APs. Experimental results show that, the proposed system achieves localization accuracy < 5m with an accumulative density function (CDF) of 50% to 60% depending on the density of APs. Furthermore, we observe that, using all APs for localization might not achieve the best localization accuracy, e.g. in our case, 4 APs out of total 7 APs achieves the best performance. In practice, the number of APs used to perform localization should be a design parameter based on the placement of APs.

['Ran Liu', 'Chau Yuen', 'Jun Zhao', 'Jindong Guo', 'Ronghong Mo', 'Vishesh N. Pamadi', 'Xiang Liu']

Selective AP-Sequence Based Indoor Localization without Site Survey

The development of next-generation portable MEMS (microelectrical-mechanical systems) call for PZT (lead zirconate titanate oxide) film sensors and actuators with thickness in the range of 1-10 mum. The aim of this project is to fabricate and characterize PZT films with the thickness between 1 and 10 mum by an improved sol-gel method. Two techniques were applied to improve the conventional sol-gel processing: precursor concentration modulation, rapid thermal annealing. Based on the inspection of XRD spectra and SEM, the films of one, two, three, and sixteen coatings from the same sol show the correct crystalline phase. Also the structure and morphology of the synthesized film were dense and crack-free with the thickness between 1 and 10 mum. The characterization methods of the PZT films for the dynamic performance and simulation were developed. The actuation tests demonstrated the silicon cantilever (4 cm times 7 mm times 0.56 mm) could be driven linearly by the PZT film (4 mm times 4 mm times 6.5 mum) at the 1st bending mode of the specimen, 346 Hz, with the amplitude of 240 nm. According to experiments, the frequency of the PZT sensing signal was the same as the driving frequency and the signal strength was proportional to the excitation voltage. This work successfully demonstrates the feasibility of PZT thick films fabricated by the improved sol-gel method

['Yi-Chu Hsu', 'Kuo-Ching Kuo', 'Ling-Sheng Jang']

Development of characterization of PZT thick films fabricated by an improved sol-gel method

We describe the design and implementation of graphical interaction widgets for use with a steerable projector-camera unit. The design of our widgets is adapted to provide the right visual cues when projected and they are controlled by the user's hand. The widgets' input regions are arranged in an ergonomic way and they use a simple but robust computer vision technique for interaction.

['D. Reiter', 'Andreas Butz']

Design and implementation of a widget set for steerable projector-camera units

A small community hospital (67 beds) in Central New York was undergoing a major technological change within the organization, as they move from the use of several legacy information systems to a hospital-wide information system. The focus of the present research is to explore the privacy and security information issues using a framework called Information Boundary Theory [Stanton, 2002]. IBT explains the motivational factors that lead to the revelation or disclosing of information.

['Nasriah Zakaria', 'Jeffrey M. Stanton', 'Kathryn R. Stam']

Exploring security and privacy issues in hospital information system: an Information Boundary Theory perspective.

This paper presents a method of path planning for skid-steer robots using an energy-based heuristic. A kinematic model of skid-steer motion utilizing the instantaneous centers of rotation (ICRs) between the tracks and the ground surface is used to predict vehicle motion. A model of skid-steer robot power usage, which also utilizes ICR estimates for slip velocity calculation, is implemented to generate estimates of energy usage. The kinematic and power use models are fused with a Sampling Based Model Predictive Optimization algorithm to plan energy efficient paths through operational areas with mixed surface types. The results of planning paths through both simulated and real-world environments are presented and show that small increases in distance can result in significant energy savings for skid-steer robots.

['Jesse Pentzer', 'Karl Reichard', 'Sean N. Brennan']

Energy-based path planning for skid-steer vehicles operating in areas with mixed surface types

Cloud computing provides a framework which can support many new possibilities for teaching and learning. Cloud-based services and applications are increasingly used by educational establishments to support many aspects of general and educational activity. There is also a related, growing emphasis on independent learning and open resources. The first part of this paper surveys current published work relating to education and the cloud. One noticeable aspect is that while there is a good deal written about infrastructure, technology and applications there is currently very little on pedagogy relating to the cloud. The second part of this paper considers one aspect of this relating to conceptual understanding and matching available resources to each individual's learning needs. We outline our work on a concept-based approach to user-assessment and to classification of learning materials, and consider how this can be used to personalise resource recommendation according to a concept model. We describe a prototype Moodle plug-in developed to support this work.

['Russell Boyatt', 'Jane Sinclair']

Meeting learners' needs inside the educational cloud

In the context of Internet of Things (IoT), multiple cooperative nodes in wireless sensor networks (WSNs) can be used to monitor an event, jointly generate a report and then send it to one or more Internet nodes for further processing. A primary security requirement in such applications is that every event data report be authenticated to intended Internet users and effectively filtered on its way to the Internet users to realize the security of data collection and transmission from the WSN. However, most present schemes developed for WSNs don&#x2019;t consider the Internet scenario while traditional mechanisms developed for the Internet are not suitable due to the resource constraint of sensor nodes. In this paper, we propose a scheme, which we refer to as Data Authentication and En-route Filtering (DAEF), for WSNs in the context of IoT. In DAEF, signature shares are generated and distributed based on verifiable secret sharing cryptography and an efficient ID-based signature algorithm. Our security analysis shows that DAEF can defend against node compromise attacks as well as denial of service (DoS) attacks in the form of report disruption and selective forwarding. We also analyze energy consumption to show the advantages of DAEF over some comparable schemes.

['Hong Yu', 'Jingsha He', 'Ruohong Liu', 'Dajie Ji']

On the Security of Data Collection and Transmission from Wireless Sensor Networks in the Context of Internet of Things

Most software quality research has focused on identifying faults (i.e., information is incorrectly recorded in an artifact). Because software still exhibits incorrect behavior, a different approach is needed. This paper presents a systematic literature review to develop taxonomy of errors (i.e., the sources of faults) that may occur during the requirements phase of software lifecycle. This taxonomy is designed to aid developers during the requirement inspection process and to improve overall software quality. The review identified 149 papers from the software engineering, psychology and human cognition literature that provide information about the sources of requirements faults. A major result of this paper is a categorization of the sources of faults into a formal taxonomy that provides a starting point for future research into error-based approaches to improving software quality.

['Gursimran S. Walia', 'Jeffrey C. Carver']

A systematic literature review to identify and classify software requirement errors

We present and explore in detail a pair of digital images with $c_u$-adjacencies that are homotopic but not pointed homotopic. For two digital loops $f,g: [0,m]_Z \rightarrow X$ with the same basepoint, we introduce the notion of {\em tight at the basepoint (TAB)} pointed homotopy, which is more restrictive than ordinary pointed homotopy and yields some different results. #R##N#We present a variant form of the digital fundamental group. Based on what we call {\em eventually constant} loops, this version of the fundamental group is equivalent to that of Boxer (1999), but offers the advantage that eventually constant maps are often easier to work with than the trivial extensions that are key to the development of the fundamental group in Boxer (1999) and many subsequent papers. #R##N#We show that homotopy equivalent digital images have isomorphic fundamental groups, even when the homotopy equivalence does not preserve the basepoint. This assertion appeared in Boxer (2005), but there was an error in the proof; here, we correct the error.

['Laurence A. Boxer', 'P. Christopher Staecker']

Remarks on pointed digital homotopy

This paper presents an algorithm for full 3D shape reconstruction of indoor and outdoor environments with mobile robots. Data is acquired with laser range finders installed on a mobile robot. Our approach combines efficient scan matching routines for robot pose estimation with an algorithm for approximating environments using flat surfaces. On top of that, our approach includes a mesh simplification technique to reduce the complexity of the resulting models. In extensive experiments, our method is shown to produce accurate models of indoor and outdoor environments that compare favorably to other methods.

['Dirk Hähnel', 'Wolfram Burgard', 'Sebastian Thrun']

Learning Compact 3D Models of Indoor and Outdoor Environments with a Mobile Robot

In this paper a unique landmark identification method is proposed for identifying large distinguishable landmarks for 3D visual simultaneous localization and mapping (SLAM) in unknown cluttered urban search and rescue (USAR) environments. The novelty of the method is the utilization of both 3D (i.e., depth images) and 2D images. By utilizing a scale invariant feature transform (SIFT)-based approach and incorporating 3D depth imagery, we can achieve more reliable and robust recognition and matching of landmarks from multiple images for 3D mapping of the environment. Preliminary experiments utilizing the proposed methodology verify: (i) its ability to identify clusters of SIFT keypoints in both 3D and 2D images for representation of potential landmarks in the scene, and (ii) the use of the identified landmarks in constructing a 3D map of unknown cluttered USAR environments.

['Zhe Zhang', 'Goldie Nejat']

Robot-assisted intelligent 3D mapping of unknown cluttered search and rescue environments

This paper reports on the results of an interview study that surveyed current practices regarding information security incident management in small and large distribution system operators (DSOs) in the Norwegian electric power industry. The findings indicate that current risk perception and preparedness are low, especially among small electricity distribution system operators. Further, small distribution system operators rely heavily on their suppliers should incidents occur. At the same time, small distribution system operators are confident that they can handle the worst-case scenarios. This paper documents current perceptions and discusses the extent to which they are likely to hold given the transition towards smart electric grids. Several recommendations are provided based on the findings and the accompanying discussion. In particular, small distribution system operators should strengthen the collaboration with their information technology (IT) suppliers and other small distribution system operators. Furthermore, distribution system operators in general should establish written documentation of procedures, perform preparedness exercises and improve detection capabilities in control systems.

['Maria B. Line', 'Inger Anne Tøndel', 'Martin Gilje Jaatun']

Current practices and challenges in industrial control organizations regarding information security incident management - Does size matter? Information security incident management in large and small industrial control organizations

Emotion Detection Using Feature Extraction Tools Emotion Detection Using Feature Extraction Tools) This approach combines natural visual stimuli such as face and body hair with the most intense imagery to allow the neural data to be captured in vivid detail. These can be used to track heart rate, eye movements, and breathing patterns. In general, the techniques are described as making an image in a consistent way that allows for meaningful data-researches. Here's an example: Imagine a small, brightly orange box with an electrical spike

['Jacek Grekow']

Emotion Detection Using Feature Extraction Tools

We consider the problem of distributed detection in a large wireless sensor network. An adaptive data fusion scheme, group-ordered sequential probability ratio test (GO-SPRT), is proposed. This scheme groups sensors according to the informativeness of their data. Fusion center collects sensor data sequentially, starting from the most informative data and terminates the process when the target performance is reached. To analyze the average sample number, we establish the asymptotic equivalence between GO-SPRT, a multinomial experiment, and a normal experiment. Closed-form approximates are obtained. Our analysis and simulations show that, compared with fixed sample size test and traditional sequential probability ratio test (SPRT), the proposed scheme achieves significant savings in the cost of data fusion.

['Yingwei Yao']

Group-ordered SPRT for distributed detection

We study the conformity of marginal unconditional and conditional models with a joint model under assumptions of epistemic irrelevance and independence, within Walley’s theory of coherent lower previsions. By doing so, we make a link with a number of prominent models within this theory: the marginal extension, the irrelevant natural extension, the independent natural extension and the strong product.

['Enrique Miranda', 'Marco Zaffalon']

Conformity and independence with coherent lower previsions

Based on the recently developed finite integration method for solving one-dimensional partial differential equation, we extend in this paper the method by using the technique of least squares to tackle higher-dimensional singular perturbation problems with multiple boundary layers. Theoretical convergence and numerical stability tests indicate that, even with the most simple numerical trapezoidal integration rule, the proposed method provides a stable, efficient, and highly accurate approximate solutions to the singular perturbation problems. An adaptive scheme on the refinement of integration points is also devised to better capture the stiff boundary layers. Illustrative examples are given in both 1D and 2D with comparison among some existing numerical methods.

['D.F. Yun', 'Zhi-Tao Wen', 'Y. C. Hon']

Adaptive least squares finite integration method for higher-dimensional singular perturbation problems with multiple boundary layers

A Play on Words: Using Cognitive Computing as a Basis for AI Solvers in Word Puzzles A Play on Words: Using Cognitive Computing as a Basis for AI Solvers in Word Puzzles (Paperback). Published by Cambridge: Cambridge University Press, 1998. Kris.G.L., et al., "The Cognitive Processing of Word Proposals," Proceedings of the American Association for the Advancement of Science 5, 477-486 (2012). Krispach.R. and Lipscomb.W., "Learning Complex Language: The Cognitive Brain,"

['Thomas Manzini', 'Simon Ellis', 'James A. Hendler']

A Play on Words: Using Cognitive Computing as a Basis for AI Solvers in Word Puzzles

This paper is a conceptual thought experiment that discusses the need for efficient, interactive and inter- operative, application- and learner-centered collaborative technologies that use cognitive apprenticeship, training, and other types of education and sociological techniques to recruit more non-traditional learners to become STEM professionals. The problem of poor to no interest in STEM courses and career paths by non-traditional (female and minority) science and engineering learners is well-understood. The authors recommend using holistic systems engineering design approaches to develop collaborative technology (CT) that can be used to change academic, private, and public engineering work cultures; to derive new ways of increasing the numbers of non-traditional STEM learners; and also to use CT to teach STEM to non-traditional learners according to their preference. The authors conclude that multi-disciplinary work teams that include educators, social science systems engineers, and history of science and technology systems engineers who have support from the highest levels of management, could be used to resolve the identified problems.

['Keith W. Jones', 'Daniel Kristof', 'L.C. Jenkins', 'Jeffry Ramsey', 'D. Patrick', 'S. Burnham', 'I.L. Turner']

Collaborative technologies: Cognitive apprenticeship, training, and education

Koordination der ungebundenen Flüchtlingshilfe durch soziale Medien Koordination der ungebundenen Flüchtlingshilfe durch soziale Medien, angebiet und einem Kritik in einem Kritik gevracht werden Änderige Gedichtzalungen angebiet von Verlichten werden abreiter aktivisch auszugeütigen, auch nicht bescheren korten und ver

['Thomas Ludwig', 'Christoph Kotthaus', 'Robin Stumpf']

Koordination der ungebundenen Flüchtlingshilfe durch soziale Medien

Two approaches to Spoken Language Understanding based on frames describing chunked knowledge are described. They are applied to the MEDIA corpus annotated in terms of concepts expressing chunks of spoken sentences. General rules of knowledge composition and inference appear to be adequate to effectively applying the application ontology for obtaining frame based representations of dialogue turns. The main difficulty appears to be the characterization of the syntactic knowledge expressing semantic links between knowledge chunks. This knowledge can be hand-crafted or automatically learned from examples. It is shown that the latter approach outperforms the former if applied to ASR error prone transcriptions.

['Frédéric Béchet', 'Christian Raymond', 'Frédéric Duvert', 'Renato De Mori']

Frame based interpretation of conversational speech

Overcomplete ICA is a method for solving blind source separation problems if the number of observed signals is less than that of source ones. In this paper, we propose an overcomplete ICA algorithm based on a simple contrast function which is defined as the sum of the covariances of the squares of signals over all the pairs. By applying non-orthogonal pair optimizations to the function, a simple ICA algorithm is derived. Theoretical analysis and numerical experiments suggest the validity of the proposed algorithm.

['Yoshitatsu Matsuda', 'Kazunori Yamaguchi']

A simple overcomplete ICA algorithm by non-orthogonal pair optimizations

Implicit methods for partial differential equations using unstructured meshes allow for an efficient solution strategy for many real-world problems (e.g., simulation-based virtual surgical planning). Scalable solvers employing these methods not only enable solution of extremely-large practical problems but also lead to dramatic compression in time-to-solution. We present a parallelization paradigm and associated procedures that enable our implicit, unstructured flow-solver to achieve strong scalability. We consider fluid-flow examples in two application areas to show the effectiveness of our procedures that yield near-perfect strong-scaling on various (including near-petascale) systems. The first area includes a double-throat nozzle (DTN) whereas the second considers a patient-specific abdominal aortic aneurysm (AAA) model. We present excellent strong-scaling on three cases ranging from relatively small to large; a DTN model with O(10 6 ) elements up to 8,192 cores (9 core-doublings), an AAA model with O(10 8 ) elements up to 32,768 cores (6 core-doublings) and O(10 9 ) elements up to 163,840 cores.

['Onkar Sahni', 'Min Zhou', 'Mark S. Shephard', 'Kenneth E. Jansen']

Scalable implicit finite element solver for massively parallel processing with demonstration to 160K cores

The main goal of the motif finding problem is to detect novel, over-represented unknown signals in a set of sequences (e.g. transcription factor binding sites in a genome). The most widely used algorithms for finding motifs obtain a generative probabilistic representation of these over-represented signals and try to discover profiles that maximize the information content score. Although these profiles form a very powerful representation of the signals, the major difficulty arises from the fact that the best motif corresponds to the global maximum of a non-convex continuous function. Popular algorithms like Expectation Maximization (EM) and Gibbs sampling tend to be very sensitive to the initial guesses and are known to converge to the nearest local maximum very quickly. In order to improve the quality of the results, EM is used with multiple random starts or any other powerful stochastic global methods that might yield promising initial guesses (like projection algorithms). Global methods do not necessarily give initial guesses in the convergence region of the best local maximum but rather suggest that a promising solution is in the neighborhood region. In this paper, we introduce a novel optimization framework that searches the neighborhood regions of the initial alignment in a systematic manner to explore the multiple local optimal solutions. This effective search is achieved by transforming the original optimization problem into its corresponding dynamical system and estimating the practical stability boundary of the local maximum. Our results show that the popularly used EM algorithm often converges to sub-optimal solutions which can be significantly improved by the proposed neighborhood profile search. Based on experiments using both synthetic and real datasets, our method demonstrates significant improvements in the information content scores of the probabilistic models. The proposed method also gives the flexibility in using different local solvers and global methods depending on their suitability for some specific datasets.

['Chandan K. Reddy', 'Y. Weng', 'Hsiao-Dong Chiang']

Refining motifs by improving information content scores using neighborhood profile search

Model-Based Testing as a Service for IoT Platforms Model-Based Testing as a Service for IoT Platforms, and other Projects This document is intended to describe the basics of how a developer can use the T-Mobile mobile data center in a T-Mobile application. If you have any questions about the T-Mobile mobile data center, please visit the T-Mobile Blog post. This talk will cover the following topics: How to Install or Configure your T-Mobile T-Mobile Mobile Data Centers How to

['Abbas Ahmad', 'Fabrice Bouquet', 'Elizabeta Fourneret', 'Franck Le Gall', 'Bruno Legeard']

Model-Based Testing as a Service for IoT Platforms

A systematic methodology is proposed for mathematically quantifying the effects of measurement inaccuracies due to instrument uncertainty in a human calorimetry project. Human thermal mechanisms are poorly understood at the systems level and this study investigates the importance of these mechanisms quantitatively. The proposed methodology uses sensitivity derivatives combined with sensor accuracies to quantify the effect of each heat transfer mechanism contributing to the errors in the system equations. The method is applicable to any differentiable model to be validated by experimentation. To illustrate the methodology, two example cases, a reclining nude resting subject and a reclining clothed working subject, are analyzed. The calculated expected errors clearly suggest specific modifications.

['Samuel Thornton', 'Satish S. Nair']

Parametric studies of human thermal mechanisms and measurements

We are developing an intelligent robot and attempting to teach it language. While there are many aspects of this research, for the purposes here the most important are the following ideas. Language is primarily based on semantics, not syntax, which is still the focus in speech recognition research these days. To truly learn meaning, a language engine cannot simply be a computer program running on a desktop computer analyzing speech. It must be part of a more general, embodied intelligent system, one capable of using associative learning to form concepts from the perception of experiences in the world, and further capable of manipulating those concepts symbolically. In this paper, we present a general cascade model for learning concepts, and explore the use of hidden Markov models (HMMs) as part of the cascade model. HMMs are capable of automatically learning and extracting the underlying structure of continuous-valued inputs and representing that structure in the states of the model. These states can then be treated as symbolic representations of the inputs. We show how a cascade of HMMs can be embedded in a small mobile robot and used to find correlations among sensory inputs to learn a set of symbolic concepts, which are used for decision making and could eventually be manipulated linguistically

['Kevin Squire', 'Stephen E. Levinson']

HMM-Based Concept Learning for a Mobile Robot

This paper proposes a cooperative jamming strategy for two-hop relay networks where the eavesdropper can wiretap the relay channels in both hops. The problems of jamming beamformer design and power allocation are investigated jointly for two scenarios where the eavesdropper has either a single or multiple antennas, with the assumption that the global channel state information (CSI) is available. Under a constraint that the jamming signal lies in a subspaces orthogonal to the channels to legitimate nodes, we derive closed-form solutions for the jamming beamformers. Based on these results, we find the optimal solution for power allocation via geometric programming.

['Jing Huang', 'A. Lee Swindlehurst']

Secure Communications via Cooperative Jamming in Two-Hop Relay Systems

This brief reports on an algorithm and corresponding processor architecture for the construction of high-performance processors targeted at linear time invariant (LTI) control. The overall approach involves reformulating the controller into a particular discrete state-space representation, which is optimized for numerical efficiency using the /spl delta/ operator, then programming this into a specially-designed control system processor (CSP) implemented using a "programmable ASIC" device. This architecture presents large cost and performance benefits for control applications over traditional architectures, particularly for large multiple-input-multiple-output (MIMO) controllers. Results of implementing control of the vertical modes of a Maglev vehicle are presented and compared with implementations using commercial processors.

['René Cumplido', 'Simon Jones', 'Roger M. Goodall', 'Stephen Bateman']

A high-performance processor for embedded real-time control

A time-hopping multicarrier code-division multiple-access (TH/MC-CDMA) scheme is proposed and investigated. In the proposed TH/MC-CDMA, each information symbol is transmitted by a number of time-domain pulses with each time-domain pulse modulating a subcarrier. The transmitted information at the receiver is extracted from one of the, say, M possible time-slot positions, i.e., assuming that M-ary pulse-position modulation is employed. Specifically, in this paper, we concentrate on the scenarios such as system design, power spectral density (PSD) and single-user-based signal detection. The error performance of the TH/MC-CDMA system is investigated when each subcarrier signal experiences flat Nakagami-m fading in addition to additive white Gaussian noise. According to our analysis and results, it can be shown that the TH/MC-CDMA signal is capable of providing a near ideal PSD, which is flat over the system bandwidth available, while decreasing rapidly beyond that bandwidth. Explicitly, signals having this type of PSD are beneficial to both broadband and ultrawide-bandwidth communications. Furthermore, our results show that when optimum user address codes are employed, the single-user detector considered is near-far resistant, provided that the number of users supported by the system is lower than the number of subcarriers used for conveying an information symbol

['Lie-Liang Yang']

Time-Hopping Multicarrier Code-Division Multiple Access

Information systems engineering projects in e-government are confronted with high costs, lack of expertise and developing similar functionality over and over. A shared services centre might provide common services to local government organizations without affecting the autonomy of organizations and providing the flexibility to enhance and include additional functionality. As such a SSC promises tremendous economies of scale and scope. A promise is however not sufficient, research yields ambiguous results. A sound analysis of motives to use a shared services center and management issues determining success and failure is necessary. The goal of the research presented in this paper is to explore the concept of a shared services center by investigating the motives and management issues determining its successful implementation. We explore the concept by investigating a SSC at the Dutch judicial organization.

['Marijn Janssen', 'René W. Wagenaar']

An analysis of a shared services centre in e-government

Using Genetic Algorithm for Optimal Dispatching of Reactive Power in Power Systems Using Genetic Algorithm for Optimal Dispatching of Reactive Power in Power Systems at Low Altitudes to Determine Performance and Scalability. J. Appl. Phys. 85, 3285-3303 (2004). 36. Stromowitz D. M. Theoretical Models for Optimizing Neural Networks for Nonlinear Programming in Particle Physics. In H. M. Alperovitch (ed.), Concepts of Artificial Neural Networks. Springer, Berlin-Sheffield. pp.

['Robert Łukomski']

Using Genetic Algorithm for Optimal Dispatching of Reactive Power in Power Systems

We give the full automorphism groups as groups of semiaffine transformations, of the extended generalized quadratic residue codes. We also present a proof of the Gleason-Prange theorem for the extended generalized quadratic residue codes that relies only on their definition and elementary theory of linear characters. >

['W.C. Huffman']

The automorphism groups of the generalized quadratic residue codes

Full Disk Encryption: Bridging Theory and Practice. Full Disk Encryption: Bridging Theory and Practice.pdf http://tibiblio.org/papers/r3.pdf https://github.com/davidkulber/crackdown-brickwall-analysis-partner-pdf http://en.wikipedia.org/wiki/Crackdown_brickwall_Analysis In this presentation [2]: The two most popular and frequently cited crack tools of the past decade are

['Louiza Khati', 'Nicky Mouha', 'Damien Vergnaud']

Full Disk Encryption: Bridging Theory and Practice.

In order to improve throughput and perform load balancing, many routing algorithms in WMNs (Wireless Mesh Networks) have been applied to take full advantages of multi-radio, multi-channel and multi-path of WMNs. Even though a lot of proposals have already shown their merits like LQSR, MR-LQSR etc., up to now, few schemes could universally achieve all of these objectives. In this paper, we present MR-OLSR, an optimized link state routing algorithm in multi-radio/multi-channel WMNs. It was improved by OLSR (Optimized Link State Routing) protocol in MANET. It can distribute data traffic among diverse multiple paths to avoid congestion, and improve channel throughput substantially. It uses the novel metric named IWCETT (Improved Weighted Culminated Estimate Transfer Time) to evaluate path quality. Besides, the proposed channel allocation strategy and path scheduling algorithm offer it the ability of loading balance. MR-OLSR is experimented in OPNET simulation environment, and the results prove that our proposal not merely maintains the merits of robustness and scalability in OLSR scheme. What's more, the proposal enhances the stability and reliability in the situation of links failing, and keeps the promise of increasing network throughput apparently.

['Guangwu Hu', 'Chaoqin Zhang']

MR-OLSR: A link state routing algorithm in multi-radio/multi-channel Wireless Mesh Networks

The development of technology, people have a huge demand for smart homes. In recent years, a variety of smart home designs cannot be satisfied with satisfy people. The new technology used in construction, which was called smart homes. Smart home offers many architectural features which are more suitable for human life situations, such as the smart of life, family care, home security, and green building. Conventional smart home has focused on developing embedded devices. But it cannot meet the needs of today's human-machine interface of smarter home. Therefore, in this paper we propose an intelligent management model based on CBR-SDA approach. The smart-life recommendation system is proposed in this paper, which it is an example for choosing clothes and accessories. Finally, the smart home features by using the intelligent management model based on CBR-SDA approach in order to improve the users' experiences will be further various contributions.

['Hsing-Chung Chen', 'Qiu-Hua Ruan', 'Pei-Chi Yeh', 'Ze-Min Lin']

Intelligent Management Model Based on CBR-SDA Approach – An Example of Smart Life Recommendation System for Choosing Clothes and Accessories

In this paper, we present our audio fingerprinting system that detects a transformed copy of an audio from a large collection of audios in a database. The audio fingerprints in this system encode the positions of salient regions of binary images derived from a spectrogram matrix. The similarity between two fingerprints is defined as the intersection of their elements (i.e. positions of the salient regions). The search algorithm labels each reference fingerprint in the database with the closest query frame and then counts the number of matching frames when the query is overlaid over the reference. The best match is based on this count. The salient regions fingerprints together with this nearest-neighbor search give excellent copy detection results. However, for a large database, this search is time consuming. To reduce the search time, we accelerate this similarity search by using a graphics processing unit (GPU). To speed this search even further, we use a two-step search based on a clustering technique and a lookup table that reduces the number of comparisons between the query and the reference fingerprints. We also explore the tradeoff between the speed of search and the copy detection performance. The resulting system achieves excellent results on TRECVID 2009 and 2010 datasets and outperforms several state-of-the-art audio copy detection systems in detection performance, localization accuracy and run time. For a fast detection scenario with detection speed comparable to the Ellis' Shazam-based system, our system achieved the same min NDCR as the NN-based system, and significantly better detection accuracy than Ellis' Shazam-based system.

['Chahid Ouali', 'Pierre Dumouchel', 'Vishwa Gupta']

Fast audio fingerprinting system using GPU and a clustering-based technique

A smart home aims at building intelligence automation with a goal to provide its inhabitants with maximum possible comfort, minimize the resource consumption and thus overall cost of maintaining the home. 'Context awareness' is perhaps the most salient feature of such an intelligent environment. Clearly, an inhabitant's mobility and activities play a significant role in defining his contexts in and around the home. Although there exists an optimal algorithm for location and activity tracking of a single inhabitant, the correlation and dependence between multiple inhabitants' contexts within the same environment make the location and activity tracking more challenging. In this paper, we first prove that the optimal location prediction across multiple inhabitants in smart homes is an NP-hard problem. Next, to capture the correlation and interactions of different inhabitants' movements (and hence activities), we develop a novel framework based on a game theoretic, Nash H-learning approach that attempts to minimize the joint location uncertainty. The framework achieves a Nash equilibrium such that no inhabitant is given preference over others. This results in more accurate prediction of contexts and better adaptive control of automated devices, leading to a mobility-aware resource (say, energy) management scheme in multi-inhabitant smart homes. Experimental results demonstrate that the proposed framework is capable of adaptively controlling a smart environment, thus reducing energy consumption and enhancing the comfort of the inhabitants.

['Nirmalya Roy', 'Abhishek Roy', 'Sajal K. Das']

Context-aware resource management in multi-inhabitant smart homes a Nash H-learning based approach

In this paper, we prove the genericity of the differential observability for discrete-time systems with more outputs than inputs.

['Sabeur Ammar', 'Mohamed Mabrouk', 'Jean-Claude Vivalda']

On the genericity of the differential observability of controlled discrete-time systems

Unsupervised Selection of Robust Audio Feature Subsets. Unsupervised Selection of Robust Audio Feature Subsets. The project includes a number of new and useful features that combine with the previous examples and make them a useful base for other projects of our type to use. If you have questions, comments: contact us!

['Gerhard Sageder', 'Maia Zaharieva', 'Matthias Zeppelzauer']

Unsupervised Selection of Robust Audio Feature Subsets.

This research examines the impact of various factors on the use of IT in clinical practice, prescriptions, and patient information. This was done using a national sample of 3425 physicians who worked in a solo or group practice in the United States. Besides the extent of use of electronic medical records by physicians and number of physicians in practice, none of the other factors consistently impacted the use of IT in clinical practice, prescriptions, and patient information, respectively. The results of this study highlight the need to develop specific strategies to increase the use of information technology in healthcare.

['Jim P. DeMello', 'Satish P. Deshpande']

Factors Impacting Use of Information Technology by Physicians in Private Practice

Properly determining the driving range is critical for accurately predicting the sales and social benefits of battery electric vehicles BEVs. This study proposes a framework for optimizing the driving range by minimizing the sum of battery price, electricity cost, and range limitation cost-referred to as the "range-related cost"-as a measurement of range anxiety. The objective function is linked to policy-relevant parameters, including battery cost and price markup, battery utilization, charging infrastructure availability, vehicle efficiency, electricity and gasoline prices, household vehicle ownership, daily driving patterns, discount rate, and perceived vehicle lifetime. Qualitative discussion of the framework and its empirical application to a sample N = 36,664 representing new car drivers in the United States is included. The quantitative results strongly suggest that ranges of less than 100 miles are likely to be more popular in the BEV market for a long period of time. The average optimal range among U.S. drivers is found to be largely inelastic. Still, battery cost reduction significantly drives BEV demand toward longer ranges, whereas improvement in the charging infrastructure is found to significantly drive BEV demand toward shorter ranges. The bias of a single-range assumption and the effects of range optimization and diversification in reducing such biases are both found to be significant.

['Zhenhong Lin']

Optimizing and Diversifying Electric Vehicle Driving Range for U.S. Drivers

So far different studies have tackled the sentiment analysis in several domains such as restaurant and movie reviews. But, this problem has not been studied in scholarly book reviews which is different in terms of review style and size. In this paper, we propose to combine different features in order to be presented to a supervised classifiers which extract the opinion target expressions and detect their polarities in scholarly book reviews. We construct a labeled corpus for training and evaluating our methods in French book reviews. We also evaluate them on English restaurant reviews in order to measure their robustness across the do- mains and languages. The evaluation shows that our methods are enough robust for English restaurant reviews and French book reviews.

['Hussam Hamdan', 'Patrice Bellot', 'Frédéric Béchet']

Sentiment Analysis in Scholarly Book Reviews

In this paper we describe a supervisory fuzzy controller. This system can be used to advise, or train, people who are in control of a complex plant. The advantage of the supervisory fuzzy controller is that it is designed to interact with the human operators through accepting verbal evaluations and producing verbal suggestions. In order to find the verbal suggestions we propose the use of a trained neural network for the task of inverse linguistic approximation.

['Thomas Feuring', 'James J. Buckley', 'Yoichi Hayashi']

Verbal controlling

Embedding pico/femto base-stations and relay nodes in a macro-cellular network is a promising method for achieving substantial gains in coverage and capacity compared to macro-only networks. These new types of base-stations can operate on the same wireless channel as the macro-cellular network, providing higher spatial reuse via cell splitting. However, these base-stations are deployed in an unplanned manner, can have very different transmit powers, and may not have traffic aggregation among many users. This could potentially result in much higher interference magnitude and variability. Hence, such deployments require the use of innovative cell association and inter-cell interference coordination techniques in order to realize the promised capacity and coverage gains. In this paper, we describe new paradigms for design and operation of such heterogeneous cellular networks. Specifically, we focus on cell splitting, range expansion, semi-static resource negotiation on third-party backhaul connections, and fast dynamic interference management for QoS via over-the-air signaling. Notably, our methodologies and algorithms are simple, lightweight, and incur extremely low overhead. Numerical studies show that they provide large gains over currently used methods for cellular networks.

['Ritesh Madan', 'Jaber Mohammad Borran', 'Ashwin Sampath', 'Naga Bhushan', 'Aamod Khandekar', 'Tingfang Ji']

Cell Association and Interference Coordination in Heterogeneous LTE-A Cellular Networks

This article presents a proposal to extent the XQuery query language called XQuery-Pref supporting conditional preferences. Aiming to become transparent to the user executing queries written in XQuery-Pref language, we use a system called XQPref, which is responsible for elicitation of dynamic preferences and the processing of these custom queries. We restrict the scope of this paper on Government Open Data that, given the information overload, has driven the demand for sensitive techniques to solve problems associated with querying XML documents. The government open data consist of the publication and dissemination of data and public information on the Web in an open format to facilitate analysis and reuse. However, information overload has attracted the concern of customize the query results according to the needs of each user.

['Angélica F. Medeiros', 'Valéria Gonçalves Soares', 'Eudisley Gomes dos Anjos']

A proposal for customizing queries on XML documents based on conditional preferences

Nowadays, Western countries have to face the growing population of seniors. New technologies can help people stay at home by providing a secure environment and improving their quality of life. The use of computer vision systems offers a new promising solution to analyze people behavior and detect some unusual events. In this paper, we propose a new method to detect falls, which are one of the greatest risk for seniors living alone. Our approach is based on a combination of motion history and human shape variation. Our algorithm provides promising results on video sequences of daily activities and simulated falls.

['Caroline Rougier', 'Jean Meunier', 'Alain St-Arnaud', 'Jacqueline Rousseau']

Fall Detection from Human Shape and Motion History Using Video Surveillance

The broad abundance of time series data, which is in sharp contrast to limited knowledge of the underlying network dynamic processes that produce such observations, calls for a rigorous and efficient method of causal network inference. Here we develop mathematical theory of causation entropy, an information-theoretic statistic designed for model-free causality inference. For stationary Markov processes, we prove that for a given node in the network, its causal parents form the minimal set of nodes that maximizes causation entropy, a result we refer to as the optimal causation entropy principle. Furthermore, this principle guides us in developing computational and data efficient algorithms for causal network inference based on a two-step discovery and removal algorithm for time series data for a network-coupled dynamical system. Validation in terms of analytical and numerical results for Gaussian processes on large random networks highlights that inference by our algorithm outperforms previous leading meth...

['Jie Sun', 'Dane Taylor', 'Erik M. Bollt']

Causal Network Inference by Optimal Causation Entropy

The problem of image enhancement arises in many applications such as scanners, copiers and digital cameras. Enhancement often includes a denoising and a deblurring or sharpening step. Similar to image compression, state-of-the-art denoising techniques use wavelet bases instead of Fourier bases since wavelet domain processing provides local adaptation in smooth and non-smooth parts due to the theoretical link between wavelets and smoothness spaces. In this paper the same smoothness spaces are used to propose a way of performing sharpening and smoothing of signals with wavelets (WSS) in Besov spaces. As an application the completely wavelet-based enhancement of a scanned document is discussed.

['Kathrin Berkner', 'Michael J. Gormish', 'Edward L. Schwartz', 'Martin Boliek']

A new wavelet-based approach to sharpening and smoothing of images in Besov spaces with applications to deblurring

We take a new look at parameter estimation for Gaussian Mixture Model (GMMs). Specifically, we advance Riemannian manifold optimization (on the manifold of positive definite matrices) as a potential replacement for Expectation Maximization (EM), which has been the de facto standard for decades. An out-of-the-box invocation of Riemannian optimization, however, fails spectacularly: it obtains the same solution as EM, but vastly slower. Building on intuition from geometric convexity, we propose a simple reformulation that has remarkable consequences: it makes Riemannian optimization not only match EM (a nontrivial result on its own, given the poor record nonlinear programming has had against EM), but also outperforms it in many settings. To bring our ideas to fruition, we develop a well-tuned Riemannian LBFGS method that proves superior to known competing methods (e.g., Riemannian conjugate gradient). We hope that our results encourage a wider consideration of manifold optimization in machine learning and statistics.

['Reshad Hosseini', 'Suvrit Sra']

Matrix manifold optimization for Gaussian mixtures

In this article, a novel mixed index strategy for real-time update is presented. The strategy combines the inverted file with a structure index and implements the retrieval of both context and structure in an XML collection. It can be effectively used to optimize the query of path expression, decrease the number of update operations, accelerate the process of real-time update and shorten the response time. At last, the experiment validates our strategy.

['Sheng-Qun Chen', 'Qin Hong', 'Zhongjian Teng', 'Yang Lin']

A Novel Mixed Index Strategy for Real-Time Update

In the orthognathic surgery, dental splints are important and necessary to help the surgeon reposition the maxilla or mandible. However, the traditional methods of manual design of dental splints are difficult and time-consuming. The research on computer-aided design software for dental splints is rarely reported. Our purpose is to develop a novel special software named EasySplint to design the dental splints conveniently and efficiently. The design can be divided into two steps, which are the generation of initial splint base and the Boolean operation between it and the maxilla-mandibular model. The initial splint base is formed by ruled surfaces reconstructed using the manually picked points. Then, a method to accomplish Boolean operation based on the distance filed of two meshes is proposed. The interference elimination can be conducted on the basis of marching cubes algorithm and Boolean operation. The accuracy of the dental splint can be guaranteed since the original mesh is utilized to form the result surface. Using EasySplint, the dental splints can be designed in about 10 minutes and saved as a stereo lithography (STL) file for 3D printing in clinical applications. Three phantom experiments were conducted and the efficiency of our method was demonstrated.

['Xiaojun Chen', 'Xing Li', 'Lu Xu', 'Yi Sun', 'Constantinus Politis', 'Jan Egger']

Development of a computer-aided design software for dental splint in orthognathic surgery.

Identifying biomarkers with predictive value for disease risk stratification is an important task in epidemiology. This paper describes an application of Bayesian linear survival regression to model cardiovascular event risk in diabetic individuals with measurements available on 55 candidate biomarkers. We extend the survival model to include data from a larger set of non-diabetic individuals in an effort to increase the predictive performance for the diabetic subpopulation. We compare the Gaussian, Laplace and horseshoe shrinkage priors, and find that the last has the best predictive performance and shrinks strong predictors less than the others. We implement the projection predictive covariate selection approach of Dupuis and Robert (2003) to further search for small sets of predictive biomarkers that could provide cost-efficient prediction without significant loss in performance. In passing, we present a derivation of the projective covariate selection in Bayesian decision theoretic framework.

['Tomi Peltola', 'Aki S. Havulinna', 'Veikko Salomaa', 'Aki Vehtari']

Hierarchical Bayesian survival analysis and projective covariate selection in cardiovascular event risk prediction

Automated Improvement of Software Architecture Models for Performance and Other Quality Attributes Automated Improvement of Software Architecture Models for Performance and Other Quality Attributes, 2018. [8] In this edition. "Performance and Application Software Analysis – The Future of Software Architecture". PDF PDF. [9] See, among other things, "The Emerging Software Architecture" in "App Development: The World of Automated Performance and Management". PDF PDF.

['Anne Koziolek']

Automated Improvement of Software Architecture Models for Performance and Other Quality Attributes

Power quality instrumentation requires accurate fundamental frequency estimation and signal synchronization, even in the presence of both stationary and transient disturbances. In this paper, the authors present a synchronization technique for power quality instruments based on a single-phase software phase-locked loop (PLL), which is able to perform the synchronization, even in the presence of such disturbances. Moreover, PLL is able to detect the occurrence of a transient disturbance. To evaluate if and how the synchronization technique is adversely affected by the application of stationary and transient disturbing influences, appropriate testing conditions have been developed, taking into account the requirements of the in-force standards and the presence of the voltage transducer.

['Antonio Cataliotti', 'Valentina Cosentino', 'Salvatore Nuccio']

A Phase-Locked Loop for the Synchronization of Power Quality Instruments in the Presence of Stationary and Transient Disturbances

A single valuable object must be allocated to at most one of n agents. Monetary transfers are possible and preferences are quasilinear. We offer an explicit description of the individually rational mechanisms which are Pareto-optimal in the class of feasible, strategy-proof, anonymous and envy-free mechanisms. These mechanisms form a one-parameter infinite family; the Vickrey mechanism is the only Groves mechanism in that family.

['Yves Sprumont']

Constrained-optimal strategy-proof assignment: Beyond the Groves mechanisms