H_(∞) state estimation for Markov jump neural networks with transition probabilities subject to the persistent dwell-time switching rule

We investigate the problem of H_(∞) state estimation for discrete-time Markov jump neural networks. The transition probabilities of the Markov chain are assumed to be piecewise time-varying, and the persistent dwell-time switching rule,as a more general switching rule, is adopted to describe this variation characteristic. Afterwards, based on the classical Lyapunov stability theory, a Lyapunov function is established, in which the information about the Markov jump feature of the system mode and the persistent dwell-time switching of the transition probabilities is considered simultaneously.Furthermore, via using the stochastic analysis method and some advanced matrix transformation techniques, some sufficient conditions are obtained such that the estimation error system is mean-square exponentially stable with an H_(∞) performance level, from which the specific form of the estimator can be obtained. Finally, the rationality and effectiveness of the obtained results are verified by a numerical example.

Reclaiming the Efficiency of Opportunistic Routing in Wireless Multi-Hop Networks

Opportunistic routing(OR) is an effective way to guarantee transmission reliability in wireless multi-hop networks.However,little research focuses on transmission efficiency.Thus,an analytical model based on open queuing network with Markov chains was proposed to evaluate the efficiency.By analyzing two typical ORs,we find duplicate transmission and collision avoidance overhead are the root reasons behind inefficiency.Therefore,a new scheme called dual priority cooperative opportunistic routing(DPCOR) was proposed.In DPCOR,forwarding candidates are configured with dual priority,which enables the network to classify forwarding candidates more effectively so as to reduce the back-off time and obtain more diversity gain.Theoretical analysis and simulation results show DPCOR achieves significant performance improvement with less time overhead compared with traditional routings and typical ORs.

Evolution Handoff Strategy for Real-Time Video Transmission over Practical Cognitive Radio Networks

The transmission delay of realtime video packet mainly depends on the sensing time delay(short-term factor) and the entire frame transmission delay(long-term factor).Therefore,the optimization problem in the spectrum handoff process should be formulated as the combination of microscopic optimization and macroscopic optimization.In this paper,we focus on the issue of combining these two optimization models,and propose a novel Evolution Spectrum Handoff(ESH)strategy to minimize the expected transmission delay of real-time video packet.In the microoptimized model,considering the tradeoff between Primary User's(PU's) allowable collision percentage of each channel and transmission delay of video packet,we propose a mixed integer non-linear programming scheme.The scheme is able to achieve the minimum sensing time which is termed as an optimal stopping time.In the macro-optimized model,using the optimal stopping time as reward function within the partially observable Markov decision process framework,the EHS strategy is designed to search an optimal target channel set and minimize the expected delay of packet in the long-term real-time video transmission.Meanwhile,the minimum expected transmission delay is obtained under practical cognitive radio networks' conditions,i.e.,secondary user's mobility,PU's random access,imperfect sensing information,etc..Theoretical analysis and simulation results show that the ESH strategy can effectively reduce the transmission delay of video packet in spectrum handoff process.

Generating Markov Logic Networks Rulebase Based on Probabilistic Latent Semantics Analysis

Human Activity Recognition(HAR)has become a subject of concern and plays an important role in daily life.HAR uses sensor devices to collect user behavior data,obtain human activity information and identify them.Markov Logic Networks(MLN)are widely used in HAR as an effective combination of knowledge and data.MLN can solve the problems of complexity and uncertainty,and has good knowledge expression ability.However,MLN structure learning is relatively weak and requires a lot of computing and storage resources.Essentially,the MLN structure is derived from sensor data in the current scene.Assuming that the sensor data can be effectively sliced and the sliced data can be converted into semantic rules,MLN structure can be obtained.To this end,we propose a rulebase building scheme based on probabilistic latent semantic analysis to provide a semantic rulebase for MLN learning.Such a rulebase can reduce the time required for MLN structure learning.We apply the rulebase building scheme to single-person indoor activity recognition and prove that the scheme can effectively reduce the MLN learning time.In addition,we evaluate the parameters of the rulebase building scheme to check its stability.

Research on Action Recognition and Content Analysis in Videos Based on DNN and MLN

In the current era of multimedia information,it is increasingly urgent to realize intelligent video action recognition and content analysis.In the past few years,video action recognition,as an important direction in computer vision,has attracted many researchers and made much progress.First,this paper reviews the latest video action recognition methods based on Deep Neural Network and Markov Logic Network.Second,we analyze the characteristics of each method and the performance from the experiment results.Then compare the emphases of these methods and discuss the application scenarios.Finally,we consider and prospect the development trend and direction of this field.

An Introduction to the Chinese Speech Recognition Front-End of the NICT/ATR Multi-Lingual Speech Translation System

This paper introduces several important features of the Chinese large vocabulary continuous speech recognition system in the NICT/ATR multi-lingual speech-to-speech translation system. The features include： （1） a flexible way to derive an information rich phoneme set based on mutual information between a text corpus and its phoneme set; （2） a hidden Markov network acoustic model and a successive state splitting algorithm to generate its model topology based on a minimum description length criterion; and （3） advanced language modeling using multi-class composite N-grams. These features allow a recognition performance of 90% character accuracy in tourism related dialogue with a real time response speed.

Sequential stratified sampling belief propagation for multiple targets tracking

Rather than the difficulties of highly non-linear and non-Gaussian observation process and the state distribution in single target tracking, the presence of a large, varying number of targets and their interactions place more challenge on visual tracking. To overcome these difficulties, we formulate multiple targets tracking problem in a dynamic Markov network which consists of three coupled Markov random fields that model the following： a field for joint state of multi-target, one binary process for existence of individual target, and another binary process for occlusion of dual adjacent targets. By introducing two robust functions, we eliminate the two binary processes, and then apply a novel version of belief propagation called sequential stratified sampling belief propagation algorithm to obtain the maximum a posteriori （MAP） estimation in the dynamic Markov network, By using stratified sampler, we incorporate bottom-up information provided by a learned detector （e.g. SVM classifier） and belief information for the messages updating. Other low-level visual cues （e.g. color and shape） can be easily incorporated in our multi-target tracking model to obtain better tracking results. Experimental results suggest that our method is comparable to the state-of-the-art multiple targets tracking methods in several test cases.

Associative categorization of frequent patterns based on the probabilistic graphical model

Discovering the hierarchical structures of differ- ent classes of object behaviors can satisfy the requirements of various degrees of abstraction in association analysis, be- havior modeling, data preprocessing, pattern recognition and decision making, etc. In this paper, we call this process as associative categorization, which is different from classical clustering, associative classification and associative cluster- ing. Focusing on representing the associations of behaviors and the corresponding uncertainties, we propose the method for constructing a Markov network （MN） from the results of frequent pattern mining, called item-associative Markov net- work （IAMN）, where nodes and edges represent the frequent patterns and their associations respectively. We further dis- cuss the properties of a probabilistic graphical model to guar- antee the IAMN＇s correctness theoretically. Then, we adopt the concept of chordal to reflect the closeness of nodes in the IAMN. Adopting the algorithm for constructing join trees from an MN, we give the algorithm for IAMN-based associa- tive categorization by hierarchical bottom-up aggregations of nodes. Experimental results show the effectiveness, efficiency and correctness of our methods.

KeEL: knowledge enhanced entity linking in automatic biography construction

Biography is a direct and extensive way to know the representation of well known peoples, however, for common people, there is poor knowledge for them to be recognized. In recent years, information extraction （IE） technologies have been used to automatically generate biography for any people with online information. One of the key challenges is the entity linking （EL） which can link biography sentence to corresponding entities. Currently the used general EL systems usually generate errors originated from entity name variation and ambiguity. Compared with general text, biography sentences possess unique yet rarely studied relational knowledge （RK） and temporal knowledge （TK）, which could sufficiently distinguish entities. This article proposed a new statistical framework called the knowledge enhanced EL （KeEL） system for automated biography construction. It utilizes commonsense knowledge like PK and TK to enhance Entity Linking. The performance of KeEL on Wikipedia data was evaluated. It is shown that, compared with state-of-the-art method, KeEL significantly improves the precision and recall of Entity Linking.

DLP Learning from Uncertain Data

Description logic programs （DLP） are an expressive but tractable subset of OWL. This paper ana-lyzes the important under-researched problem of learning DLP from uncertain data. Current studies have rarely explored the plentiful uncertain data populating the semantic web. This algorithm handles uncertain data in an inductive logic programming framework by modifying the performance evaluation criteria. A pseudo-log-likelihood based measure is used to evaluate the performance of different literals under uncer-tainties. Experiments on two datasets demonstrate that the approach is able to automatically learn a rule-set from uncertain data with acceptable accuracy.