9167227
A hierarchical naive Bayes mixture model for name disambiguation in author citations
Because of name variations, an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, web search, database integration, and may cause improper attribution to authors. This paper presents a hierarchical naive Bayes mixture model, an unsupervised learning approach, for name disambiguation in author citations. This method partitions a collection of citations1 into clusters, with each cluster containing only citations authored by the same author, thus disambiguating authorship in citations to induce author name identities. Three types of citation features are used: co-author names, paper title words, and journal or proceeding title words. The approach is illustrated with 16 name datasets that are constructed based on the publication lists collected from author homepages and DBLP computer science bibliography.
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.
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.
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.
38041019
Towards an Efficient Self-organizing Reconfiguration Method for Self-reconfigurable Robots
This paper deals with the essential technique for self-organizing metamorphosis of self-reconfigurable robots. A general model describing self-reconfigurable robots is proposed; the model is capable of expressing the topological structure of typical 2-D and 3-D self-reconfigurable robots. Afterwards, a self-organizing metamorphic strategy of self-reconfigurable robots, based on full-discrete local intelligence, is proposed. The modules concurrently process the local interactive information. A reasonable motion sequence for modules can be obtained by means of determining and evolving the rules of modules motion. Consequently, the global self-organizing behavior will be obtained, and the complexity of the algorithm is reduced. Simulation samples are provided to illustrate this approach and demonstrate the effectiveness of the proposed strategy.
8986777
Bifurcation method and traveling wave solution to Whitham-Broer-Kaup equation
By using bifurcation method to Whitham-Broer-Kaup shallow water equations, bifurcation parameter sets are shown. Numbers of solitary waves, kink waves and periodic waves are given. Under various parameter conditions, all explicit formulas of solitary wave solutions, kink wave solutions and periodic wave solutions are listed.
18534223
The Distance Between Randomly Constructed Genomes
In this paper, we study the exact probability distribution of the number of cycles c in the breakpoint graph of two random genomes with n genes or markers and 1 and 2 linear chromosomes, respectively. The genomic distance d between the two genomes is d = n c. In the limit we find that the expectation of d is n 2 1 2 2 1+2 2 1 1 2 ln
16810261
A Shared-Path-Protection Based Heuristic Algorithm for the Capacity Placement Problem in Mesh Networks
The total transmission capacity required by a transport network to satisfy demand and protect it from failures contributes significantly to its cost. Unlike SCP (spare capacity placement) scheme, JOCP (jointly optimized capacity placement) scheme could reach more optimal results since it tries to jointly optimize both working and spare capacity. However, the later is more complex and time- consuming according to previous research based on IP solution. This paper presents a heuristic algorithm MSC-JOCP, which is based on shared-path protection, to address this problem. The key idea of the algorithm is to dynamically modulate the metrics of the network's spans, so as to choose primary and backup routes given attention to load balancing as well as maximally share the spare capacity. Simulation results show that the developed scheme effectively reduces the required total capacity and enhances the network resource utilization.
46077798
A Component-Based Analytical Performance Model of IP-Based SAN
In this paper we propose a component-based analytical performance model for IP-based SAN. This model evaluates and analyzes the behavior of IP network, cache, RAID and disks under multiple synchronous I/O workloads by using closed queueing networks. The theoretical results are compared with the results from a real IP-based SAN using a variety of synthetic workloads. These experiments prove that the component-based method is an appropriate way to build performance model for a complex storage system. Using the model, we can find out that the bottleneck of SAN performance is network or storage node under different workloads or applications. We also identify areas for future research in the performance analysis of real IP-based SAN.