17071534
Document clustering with prior knowledge
Document clustering is an important tool for text analysis and is used in many different applications. We propose to incorporate prior knowledge of cluster membership for document cluster analysis and develop a novel semi-supervised document clustering model. The method models a set of documents with weighted graph in which each document is represented as a vertex, and each edge connecting a pair of vertices is weighted with the similarity value of the two corresponding documents. The prior knowledge indicates pairs of documents that known to belong to the same cluster. Then, the prior knowledge is transformed into a set of constraints. The document clustering task is accomplished by finding the best cuts of the graph under the constraints. We apply the model to the Normalized Cut method to demonstrate the idea and concept. Our experimental evaluations show that the proposed document clustering model reveals remarkable performance improvements with very limited training samples, and hence is a very effective semi-supervised classification tool.
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.
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.
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.
7170011
Accelerating regularized iterative ct reconstruction on commodity graphics hardware (GPU)
Iterative reconstruction algorithms augmented with regularization can produce high-quality reconstructions from few views and even in the presence of significant noise. In this paper we focus on the particularities associated with the GPU acceleration of these. First, we introduce the idea of using exhaustive benchmark tests to determine the optimal settings of various parameters in iterative algorithm, here OS-SIRT, which proofs decisive for obtaining optimal GPU performance. Then we introduce bilateral filtering as a viable and cost-effective means for regularization, and we show that GPU-acceleration reduces its overhead to very moderate levels.
1723065
An approach for realizing privacy-preserving web-based services
The past decade has witnessed a phenomenal growth in the number of users who routinely use the web to obtain information, conduct research, or carry out financial transactions. While the ability of the web to provide customized information and financial services has boosted personal and business productivity, it has raised significant concerns regarding an individual’s privacy. The privacy concerns are exacerbated further with the advent of more complex services that involve multiple service providers, as it raises the potential of personal information being shared across these providers in ways that weren’t intended by the owner of the information. To illustrate the increased privacy concerns raised by composite web-based services, consider a web-based service that allows an individual to research and order over-the-counter medication (or health supplements) at a low cost, while ensuring that there are no adverse interactions based on the individuals current prescription. Such a service will require access to the individual’s personal medical history, and will also require interaction with other entities such as web-based medical references, discount pharmacies and creditcard companies. Users may not want their personal health histories to be shared with discount pharmacies or credit card companies. Similarly, they may not want their credit card information to be shared with providers of free on-line information. Web-based service providers have long recognized the importance of addressing user privacy concerns, and taken steps to state their policies regarding the storage and use of personal user information. Unfortunately, since these policies are stated in English, they are imprecise and ambiguous, posing problems in accountability and enforceability, as well as in checking compatibility between consumer and provider policies. Indeed, the verbose nature of these policies discourage most users from even reading them! To address the above problem, we present a new approach where the consumers as well as providers can express their privacy concerns in a formal way. Specifically, consumers express their requirements in the form of policies, while providers specify their use of consumer data using models. Our approach automates compatibility checking between policies and models. If there is an incompatibility, the consumer is informed how she can refine her policies in order to use the service. If she does not want to change her policies in any way, the approach passes on additional privacy requirements to the provider. Service access can continue in case
18109532
Building Document Graphs for Multiple News Articles Summarization: An Event-Based Approach
Since most of news articles report several events and these events are referred in many related documents, we propose an event-based approach to visualize documents as graph on different conceptual granularities. With graph-based ranking algorithm, we illustrate the application of document graph to multi-document summarization. Experiments on DUC data indicate that our approach is competitive with state-of-the-art summarization techniques. This graphical representation which does not require training corpora can be potentially adapted to other languages.
9032052
Deriving Event Relevance from the Ontology Constructed with Formal Concept Analysis
In this paper, we present a novel approach to derive event relevance from event ontology constructed with Formal Concept Analysis (FCA), a mathematical approach to data analysis and knowledge representation. The ontology is built from a set of relevant documents and according to the named entities associated to the events. Various relevance measures are explored, from binary to scaled, and from symmetrical to asymmetrical associations. We then apply the derived event relevance to the task of multi-document summarization. The experiments on DUC 2004 data set show that the relevant-event-based approaches outperform the independent-event-based approach.
2663170
A Framework for Building Privacy-Conscious Composite Web Services
The rapid growth of Web applications has prompted increasing interest in the area of composite Web services that involve several service providers. The potential for such composite Web services can be realized only if consumer privacy concerns are satisfactorily addressed. In this paper, we propose a framework that addresses consumer privacy concerns in the context of highly customizable composite Web services. Our approach involves service producers exchanging their terms-of-use with consumers in the form of "models". Our framework provides automated techniques for checking these models at the consumer site for compliance of consumer privacy policies. In the event of a policy violation, our framework supports automatic generation of "obligations" that the consumer generates for the composite service. These obligations are automatically enforced through a dynamic program analysis approach on the Web service composition code. We illustrate our approach with the implementation of two example services