26522967
Maximum entropy model-based baseball highlight detection and classification
In this paper, we propose a novel system that is able to automatically detect and classify baseball highlights by seamlessly integrating image, audio, and speech clues using a unique framework based on maximum entropy model (MEM). What distinguishes our system is that we emphasize on the integration of multimedia features and the acquisition of domain knowledge through automatic machine learning processes. Our MEM-based framework provides a simple platform capable of integrating multimedia features as well as their contextual information in a uniform fashion. Unlike the Hidden Markov Model and the Bayes Network-based approaches, this framework does not need to explicitly segment and classify the input video into states during its data modeling process, and hence remarkably simplifies the training data creation and the highlight detection/classification tasks. Experimental evaluations demonstrate the superiority of our proposed baseball highlight detection system in its capability of detecting more major baseball highlights, and in its overall accuracy of detecting these major highlights.
7153454
Temporal difference learning to detect unsafe system states
This paper proposes a general framework to detect unsafe states of a system whose basic realtime parameters are captured by multi-sensors. Our approach is to learn a danger level function which can be used to alert the users in advance of dangerous situations. The main challenge to this learning problem is the labelling issue, i.e., it is difficult to assign an objective danger level at each time step to the training data, except at the collapse points where a penalty can be assigned and at the successful ends where a certain reward can be assigned. In this paper, we treat the danger level as expected future reward (penalty is regarded as negative reward) and use temporal difference (TD) learning [2] to learn a function to approximate the expected future reward. The TD learning obtains the approximation by propagating the penalty/reward observable at collapse points or successful ends to the entire feature space following some constraints. Our approach is applied to, but not limited to, the application of monitoring of driving safety and the experimental results demonstrate the effectiveness of the approach.
15069022
An integrated baseball digest system using maximum entropy method
In this paper, we propose a novel system that is able to automatically detect and classify highlights from baseball game videos in TV broadcast. The digest system gives complete indexes of a baseball game which cover all of the status changes in a game. We achieve this by seamlessly integrating image, audio and speech clues using a maximum entropy based method. What distinguishes our system from previous ones is that we emphasize on the integration of multimedia features and the acquisition of domain knowledge through machine learning process. Integration of multimedia features is important because with the current state-of-the-art image and audio analysis techniques, most image and audio features we can extract from videos are very low level, and detecting/classifying sports game highlights based on features from single medium are doomed to yield poor performances. Acquiring domain knowledge through learning process is preferred over heuristic rules because machine learning process is more powerful for discovering and expressing domain knowledge. We perform extensive experiments on game videos including various stadiums, teams and broadcasted by different TV stations.
2237682
Document clustering based on non-negative matrix factorization
In this paper, we propose a novel document clustering method based on the non-negative factorization of the term-document matrix of the given document corpus. In the latent semantic space derived by the non-negative matrix factorization (NMF), each axis captures the base topic of a particular document cluster, and each document is represented as an additive combination of the base topics. The cluster membership of each document can be easily determined by finding the base topic (the axis) with which the document has the largest projection value. Our experimental evaluations show that the proposed document clustering method surpasses the latent semantic indexing and the spectral clustering methods not only in the easy and reliable derivation of document clustering results, but also in document clustering accuracies.
2171158
Detecting video events based on action recognition in complex scenes using spatio-temporal descriptor
Event detection plays an essential role in video content analysis and remains a challenging open problem. In particular, the study on detecting human-related video events in complex scenes with both a crowd of people and dynamic motion is still limited. In this paper, we investigate detecting video events that involve elementary human actions, e.g. making cellphone call, putting an object down, and pointing to something, in complex scenes using a novel spatio-temporal descriptor based approach. A new spatio-temporal descriptor, which temporally integrates the statistics of a set of response maps of low-level features, e.g. image gradients and optical flows, in a space-time cube, is proposed to capture the characteristics of actions in terms of their appearance and motion patterns. Based on this kind of descriptors, the bag-of-words method is utilized to describe a human figure as a concise feature vector. Then, these features are employed to train SVM classifiers at multiple spatial pyramid levels to distinguish different actions. Finally, a Gaussian kernel based temporal filtering is conducted to segment the sequences of events from a video stream taking account of the temporal consistency of actions. The proposed approach is capable of tolerating spatial layout variations and local deformations of human actions due to diverse view angles and rough human figure alignment in complex scenes. Extensive experiments on the 50-hour video dataset of TRECVid 2008 event detection task demonstrate that our approach outperforms the well-known SIFT descriptor based methods and effectively detects video events in challenging real-world conditions.
9180515
Incremental Spectral Clustering With Application to Monitoring of Evolving Blog Communities
In recent years, spectral clustering method has gained attentions because of its superior performance compared to other traditional clustering algorithms such as K-means algorithm. The existing spectral clustering algorithms are all off-line algorithms, i.e., they can not incrementally update the clustering result given a small change of the data set. However, the capability of incrementally updating is essential to some applications such as real time monitoring of the evolving communities of websphere or blogsphere. Unlike traditional stream data, these applications require incremental algorithms to handle not only insertion/deletion of data points but also similarity changes between existing items. This paper extends the standard spectral clustering to such evolving data by introducing the incidence vector/matrix to represent two kinds of dynamics in the same framework and by incrementally updating the eigenvalue system. Our incremental algorithm, initialized by a standard spectral clustering, continuously and efficiently updates the eigenvalue system and generates instant cluster labels, as the data set is evolving. The algorithm is applied to a blog data set. Compared with recomputation of the solution by standard spectral clustering, it achieves similar accuracy but with much lower computational cost. Close inspection into the blog content shows that the incremental approach can discover not only the stable blog communities but also the evolution of the individual multi-topic blogs.
10278961
Lights and Camera: Intelligently Controlled Multi-channel Pose Estimation System
Guiding the spacecraft docking process requires the use of sensors that estimate the relative position of the two vessels. This task is complicated by the widely variable on-orbit illumination. To combat this, controllable docking cameras are augmented by computer-controlled illuminants. But how should these illumination and capture parameters be controlled and how should the images obtained under different conditions be combined in order to estimate the relative pose of the vessels? We address these issues in the "Lights and Camera" system. Images captured with the same camera and scene geometry but under different lighting conditions are merged, and the resulting edges are used to estimate the target’s pose. A high level controller monitors the imaging process and determines the set of images to capture and use for pose estimation. This paper describes the "Lights and Camera" system architecture and initial results of its operation on mockups of space hardware.
15658838
An Improved and Efficient Tool for Analyzing Industrial Security Protocols
Formal methods and automatic tools are always necessary for analyzing security protocols, and model checking has become a hot spot of research for its ability of verifying finite-state concurrent system automatically. In this paper, we propose a model of security protocols based on MSR (multi-set rewriting), coupled with PLTL describing security properties. Based on the model, a series of key automatic analysis algorithms close to the implementation are designed for our own tool. By using the tool, we illustrate the method’s effectiveness with some typical protocols and give the experimental results.
14985315
Spin-transfer torque magnetoresistive content addressable memory (CAM) cell structure design with enhanced search noise margin
This paper presents a new memory cell structure for content addressable memory (CAM) based on magnetic tunneling junction (MTJ). Each CAM cell uses a pair of differential MTJs as basic storage element and incorporates transistors to greatly improve the cell search noise margin at low sensing current. Using the same design principle, we further develop an area-efficient cell structure for ternary CAM (TCAM), which occupies about 25% less area compared with directly using two CAM cells to form one TCAM cell. The effectiveness of the proposed CAM and TCAM cell structures has been demonstrated by circuit simulation at 0.18 mum CMOS technology.
2239051
Detecting large-scale system problems by mining console logs
Surprisingly, console logs rarely help operators detect problems in large-scale datacenter services, for they often consist of the voluminous intermixing of messages from many software components written by independent developers. We propose a general methodology to mine this rich source of information to automatically detect system runtime problems. We first parse console logs by combining source code analysis with information retrieval to create composite features. We then analyze these features using machine learning to detect operational problems. We show that our method enables analyses that are impossible with previous methods because of its superior ability to create sophisticated features. We also show how to distill the results of our analysis to an operator-friendly one-page decision tree showing the critical messages associated with the detected problems. We validate our approach using the Darkstar online game server and the Hadoop File System, where we detect numerous real problems with high accuracy and few false positives. In the Hadoop case, we are able to analyze 24 million lines of console logs in 3 minutes. Our methodology works on textual console logs of any size and requires no changes to the service software, no human input, and no knowledge of the software's internals.
28314291
Effect of Gaussian White Noise on the Dynamical Behaviors of an Extended Duffing-van der Pol oscillator
The influence induced by random noise on dynamical behaviors is a classical yet challenging subject. This paper discusses the influence of Gaussian white noise on the dynamics of a self-excited triple well extended Duffing–Van der Pol oscillator already subjected to harmonic excitation. Firstly, the condition for the rise of hom/heteroclinic chaos is derived by random Melnikov's technique under its corresponding mean-square criterion and the result indicates that the threshold amplitude of harmonic excitation is lowered by the appearance of Gaussian white noise. Moreover, the threshold is decreased as the noise intensity increases. Since the Melnikov's criterion is only a necessary condition for the occurrence of chaotic motion, this prediction is tested against numerical simulations of the basins of attraction and the Lyapunov exponents. By vanishing the largest Lyapunov exponents, another criterion for the onset of chaos is obtained which is accorded with the theoretical one. Finally, how the noise effe...