2062974
A Conditional Random Field Model for Video Super-resolution
In this paper, we propose a learning-based method for video super-resolution. There are two main contributions of the proposed method. First, information from cameras with different spatial-temporal resolutions is combined in our framework. This is achieved by constructing training dictionary using the high resolution images captured by still camera and the low resolution video is enhanced via searching in this customized database. Second, we enforce the spatio-temporal constraints using the conditional random field (CRF) and the problem of video super-resolution is posed as finding the high resolution video that maximizes the conditional probability. We apply the algorithm to video sequences taken from different scenes using cameras with different qualities and promising results are presented
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.
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.
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.
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.
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.
38063423
A series of explicit and exact travelling wave solutions of the B(m, n) equations
Abstract In this paper, explicit and exact travelling wave solutions of B ( m ,  n ) equations are investigated by using a generalized algebraic method. A series of explicit exact solutions, such as solitary wave, triangular periodic, rational, Jacobi and Weierstrass elliptic function periodic solutions are obtained. Many new results are presented.
8176507
Research and Application of Spatio-temporal Data Mining Based on Ontology
In this paper, the spatio-temporal data mining is studied by adopting the ontology theory. The method of spatio-temporal data mining based on ontology is presented and a new spatio-temporal forecasting model based on ontology is designed. The model uses two measures of spatio-temporal autocorrelation: spatio-temporal adjacency matrix and spatio-temporal contiguity measure. We use this model in the forecast about medium and long term coarse granularity in the railway passenger flow, and the results show that using the method of spatio-temporal forecasting based on ontology can obtain a good forecasting precision
11721203
Automatic Analysis of Fair Exchange Protocols in TLA
Fair exchange protocols have been studied intensively in recent years. But a lot of methods are still performed manually. In this paper an automatic method is proposed for analyzing fair exchange protocols. In this method we formalize security properties of fairness and non-repudiation in TLA (temporal logic of action) and define common predicates to make the analysis automatic. An "end" rule is introduced to solve the different type of channels. Protocol of fair Zhou-Gollmann has been analyzed in our own implemented tool and an attack trace can be found.
17469824
Extending OBDD graphs for composite event matching in content-based pub/sub systems
Content-based publish/subscribe offers a convenient abstraction for the information producers and consumers, supporting a large-scale system design and evolution by integrating several distributed independent application systems. Its core problem is how to match events by predicates on the content of events. In this paper, we propose three matching models, extend the simple matching to the multi-semantic matching and introduce the temporal constraint variable. The multi-semantic matching allows using different operations in accordance with different semantics; the temporal constraint variable supports processing the discrete events in the temporal sequence. Then, we extend OBDD graphs into hierarchy coloured OBDD graphs and prove the equivalence of the transformation. At last, based on OBDD graphs the composite matching algorithm is presented and analysed. By experiments, we show the proposed algorithm is efficient.
3222518
Control Considerations for Scalable Event Processing
The growth in the scale of systems and networks has created many challenges for their management, especially for event processing. Our premise is that scaling event processing requires parallelism. To this end, we observe that event processing can be divided into intra-event processing such as filtering and inter-event processing such as root cause analysis. Since intra-event processing is easily parallelized, we propose an architecture in which intra-event processing elements (IAPs) are replicated to scale to larger event input rates. We address two challenges in this architecture. First, the IAPs are subject to overloads that require effective flow control, a capability that was not present in the components we used to build IAPs. Second, we need to balance the loads on IAPs to avoid creating resource bottlenecks. These challenges are further complicated by the presence of disturbances such as CPU intensive administrative tasks that reduce event processing rates. We address these challenges using designs based on control theory, a technique for analyzing stability, accuracy, and settling times. We demonstrate the effectiveness of our approaches with testbed experiments that include a disturbance in the form of a CPU intensive application.