8038239
A novel approach for privacy-preserving video sharing
To support privacy-preserving video sharing, we have proposed a novel framework that is able to protect the video content privacy at the individual video clip level and prevent statistical inferences from video collections. To protect the video content privacy at the individual video clip level, we have developed an effective algorithm to automatically detect privacy-sensitive video objects and video events. To prevent the statistical inferences from video collections, we have developed a distributed framework for privacy-preserving classifier training, which is able to significantly reduce the costs of data transmission and reliably limit the privacy breaches by determining the optimal size of blurred test samples for classifier validation. Our experiments on a specific domain of patient training and counseling videos show convincing results.
10379627
Medical video mining for efficient database indexing, management and access
To achieve more efficient video indexing and access, we introduce a video database management framework and strategies for video content structure and events mining. The video shot segmentation and representative frame selection strategy are first utilized to parse the continuous video stream into physical units. Video shot grouping, group merging, and scene clustering schemes are then proposed to organize the video shots into a hierarchical structure using clustered scenes, scenes, groups, and shots, in increasing granularity from top to bottom. Then, audio and video processing techniques are integrated to mine event information, such as dialog, presentation and clinical operation, from the detected scenes. Finally, the acquired video content structure and events are integrated to construct a scalable video skimming tool which can be used to visualize the video content hierarchy and event information for efficient access. Experimental results are also presented to evaluate the performance of the proposed framework and algorithms.
10472812
Building concept ontology for medical video annotation
Most existing systems for content-based video retrieval (CBVR) are now amenable to support automatic low-level video content analysis and feature extraction, but they have limited effectiveness from a user's perspective. To support semantic video retrieval via keywords, we have proposed a novel framework by incorporating the concept ontology to enable more effective modeling and representation of semantic video concepts. Specifically, this novel framework includes: (a) Using the salient objects to achieve a middle-level understanding of the semantics of video contents; (b) Building a domain dependent concept ontology to enable multi-level modeling and representation of semantic video concepts; (c) Developing a multi-task boosting technique to achieve hierarchical video classifier training for automatic multi-level video annotation. The experimental results in a certain domain of medical education videos are also provided.
147992
A hierarchical access control model for video database systems
Content-based video database access control is becoming very important, but it depends on the progresses of the following related research issues: (a) efficient video analysis for supporting semantic visual concept representation; (b) effective video database indexing structure; (c) the development of suitable video database models; and (d) the development of access control models tailored to the characteristics of video data. In this paper, we propose a novel approach to support multilevel access control in video databases. Our access control technique combines a video database indexing mechanism with a hierarchical organization of visual concepts (i.e., video database indexing units), so that different classes of users can access different video elements or even the same video element with different quality levels according to their permissions. These video elements, which, in our access control mechanism, are used for specifying the authorization objects, can be a semantic cluster, a subcluster, a video scene, a video shot, a video frame, or even a salient object (i.e., region of interest). In the paper, we first introduce our techniques for obtaining these multilevel video access units. We also propose a hierarchical video database indexing technique to support our multilevel video access control mechanism. Then, we present an innovative access control model which is able to support flexible multilevel access control to video elements. Moreover, the application of our multilevel video database modeling, representation, and indexing for MPEG-7 is discussed.
800122
Concept-oriented video skimming via semantic video classification
Effective video skimming requires a good understanding of the semantics of video contents. However, more existing systems for content-based video retrieval (CBVR) can only support low-level video analysis, but they have limited effectiveness on achieving semantic-sensitive video understanding. In this paper, we have developed a novel framework to achieve concept-oriented video skimming and it consists of three parts: (a) using salient objects for semantic-sensitive video content representation; (b) using finite mixture models for semantic video concept modeling and classification; (c) enabling concept-oriented video skimming via semantic video classification.
14087397
Statistical Model Order Reduction for Interconnect Circuits Considering Spatial Correlations
In this paper, we propose a novel statistical model order reduction technique, called statistical spectrum model order reduction (SS-MOR) method, which considers both intra-die and inter-die process variations with spatial correlations. The SSMOR generates order-reduced variational models based on given variational circuits. The reduced model can be used for fast statistical performance analysis of interconnect circuits with variational input sources, such as power grid and clock networks. The SSMOR uses statistical spectrum method to compute the variational moments and Monte Carlo sampling method with the modified Krylov subspace reduction method to generate the variational reduced models. To consider spatial correlations, we apply orthogonal decomposition to map the correlated random variables into independent and uncorrelated variables. Experimental results show that the proposed method can deliver about 100x speedup over the pure Monte Carlo projection-based reduction method with about 2% of errors for both means and variances in statistical transient analysis.
1153368
Indirect anaphora resolution as semantic path search
Anaphora occur commonly in natural language text, and resolving them is essential for capturing the knowledge encoded in text. Indirect anaphora are especially challenging to resolve because the referring expression and the antecedent are related by unstated background knowledge. Such anaphora need to be resolved properly in order to automatically capture the knowledge expressed in natural language. Resolving indirect anaphora has been treated as a unique problem that requires special-purpose methods, and these methods have had limited success in precision and recall. In this study, we used a generic tool for finding semantic paths between two concepts to resolve these anaphora, and it achieved approximately twice the recall of the best previous system without loss of precision. A series of ablation study showed that the biggest increase in recall came from an abductive stopping criterion of the search.
37970940
Prefix-preserving IP address anonymization: measurement-based security evaluation and a new cryptography-based scheme
Real-world traffic traces are crucial for Internet research, but only a very small percentage of traces collected are made public. One major reason why traffic trace owners hesitate to make the traces publicly available is the concern that confidential and private information may be inferred from the trace. We focus on the problem of anonymizing IP addresses in a trace. More specifically, we are interested in prefix-preserving anonymization in which the prefix relationship among IP addresses is preserved in the anonymized trace, making such a trace usable in situations where prefix relationships are important. The goal of our work is two fold. First, we develop a cryptography-based, prefix-preserving anonymization technique that is provably as secure as the existing well-known TCPdpriv scheme, and unlike TCPdpriv, provides consistent prefix-preservation in large scale distributed setting. Second, we evaluate the security properties inherent in all prefix-preserving IP address anonymization schemes (including TCPdpriv). Through the analysis of Internet backbone traffic traces, we investigate the effect of some types of attacks on the security of any prefix-preserving anonymization algorithm. We also derive results for the optimum manner in which an attack should proceed, which provides a bound on the effectiveness of attacks in general.
5975278
The Knowledge Required to Interpret Noun Compounds
This paper presents a novel approach to the estimation of user’s affective states in Human-Computer Interaction. Most of the present approaches divide emotions strictly between positive or negative. However, recent discoveries in the field of Emotional Intelligence show that emotions should be rather perceived as context-sensitive engagements with the world. This leads to a need to specify whether the emotions conveyed in a conversation are appropriate for a situation they are expressed in. In the proposed method we use a system for affect analysis on textual input to recognize users emotions and a Web mining technique to verify the contextual appropriateness of those emotions. On this basis a conversational agent can choose to either sympathize with the user or help them manage their emotions. Finally, the results of evaluation of the proposed method with two different conversational agents are discussed, and perspectives for further development of the method are proposed.
228217
Localized on-chip power delivery network optimization via sequence of linear programming
In this paper, we propose an efficient algorithm to reduce the voltage noises for on-chip power/ground (P/G) networks of VLSI. The new method is based on the sequence of linear programming (SLP) method as the optimization engine and a localized scheme via partitioning for dealing with large circuits. We show that by directly optimizing the decap area as the objective function and using the time-domain adjoint method, SLP can deliver much better quality than existing methods based on the merged time-domain adjoint method. The partitioning strategy further improves the scalability of the proposed algorithm and makes it efficient for large circuits. The resulting algorithm is general enough for any P/G network. Experimental results demonstrate the advantage of the proposed method over existing state-of-the-art methods in terms of solution quality at a mild computation cost increase