Zhonghai Wu

A skeleton pruning algorithm based on information fusion

Skeleton pruning is an essential part of the processing and analysis of skeletons. It is still quite a challenging problem because of the lack of standard measurements for the importance or significance of a branch. The relative significance of the same branches will be different if we see them from different perspectives with different objectives. Different objective measurements have their advantages and limitations. To integrate the advantages of different objective measurements, we consider skeleton pruning as a multi-objective decision-making problem and propose a skeleton pruning algorithm based on information fusion. During the pruning process, we use combinatorial fusion analysis and the concept of cognitive diversity to fuse various measurements of branch significance including region reconstruction, contour reconstruction and visual contribution. Experimental results show that: (1) the proposed method is stable across a wide range of shapes and robust to boundary noise, and (2) it can effectively generate multi-scale skeletons according with visual judgment.

Sensor Feature Selection and Combination for Stress Identification Using Combinatorial Fusion

The identification of stressfulness under certain driving condition is an important issue for safety, security and health. Sensors and systems have been placed or implemented as wearable devices for drivers. Features are extracted from the data collected and combined to predict symptoms. The challenge is to select the feature set most relevant for stress. In this paper, we propose a feature selection method based on the performance and the diversity between two features. The feature sets selected are then combined using a combinatorial fusion. We also compare our results with other combination methods such as naïve Bayes, support vector machine, C4.5, linear discriminant function (LDF), and k-nearest neighbour (kNN). Our experimental results demonstrate that combinatorial fusion is an efficient approach for feature selection and feature combination. It can also improve the stress recognition rate.

Evaluating feature selection for stress identification

In modern society, more and more people are suffering from stress. The accumulation of stress will result in poor health condition to people. Effectively detecting the stress of human being in time provides a helpful way for people to better manage their stress. Much work has been done on recognizing the stress level of people by extracting features from the bio-signals acquired by physiological sensors. However, little work has been focused on the feature selection. In this paper, we propose a feature selection method based on Principal Component Analysis (PCA). After the features are selected, their effectiveness in terms of correct rate and computational time are evaluated using five classification algorithms, Linear Discriminant Function, C4.5 induction tree, Support Vector Machine (SVM), Naïve Bayes and K Nearest Neighbor (KNN). We use the driver stress database contributed by MIT Media lab for our experiments. Leaving one out as well as 10-fold data preparation approach is implemented as the cross validation method for our evaluation. Paired t-test is then performed to analyze and compare the experimental results, as well as for their statistical significance. Our study demonstrates the importance of feature selection and the effectiveness of the methods used in accurately classifying stress levels.

Securing data services: a security architecture design for private storage cloud based on HDFS

With the growth of business, an enterprise would like to make its PSC private storage cloud approach an infrastructure service in a partner/public cloud. In such PSCs, there are some new data security issues, First, how to keep the data rest in the PSC isolated from internal and external attackers; second, how to make secure intra-cloud data migration within the enterprise; third, how to secure inter-cloud data migrating between the PSC and the partner/public cloud. In this paper, we propose an architecture design for enforcing data security services on the layer of HDFS in the PSC, including secure data isolation service, secure intra-cloud data migration service, and secure inter-cloud data migration service. Finally, it gives the prototype implemented as pluggable security modules in accord with our custom security policies through AOP Aspect-Oriented Programming method. The time cost is given and evaluated efficiently.

SAPSC: Security Architecture of Private Storage Cloud Based on HDFS

With the growth of business, an enterprise would like to make its PSC(private storage cloud) approach an infrastructure service in a Partner/Public Cloud. In such PSCs, there are some new security issues, First, how to isolate the data stored in the PSC from internal and external attackers, Second, how to make secure intra-cloud data migration within an enterprise, Third, how to secure inter-cloud data migration between the PSC and the Partner/Public Cloud. In this paper, we propose an architecture of enforcing security services on the layer of HDFS, including Data Isolation Service, Secure Intra-Cloud Data Migration Service, and Secure Inter-Cloud Data Migration Service. Finally, a prototype has been implemented based on HDFS by our three custom security policies, and the time cost is given and evaluated.

COMBINING MULTIPLE SENSOR FEATURES FOR STRESS DETECTION USING COMBINATORIAL FUSION

Physiological sensors have been used to detect different stress levels in order to improve human health and well-being. When analyzing these sensor data, sensor features are generated in the experiment and a subset of the features are selected and then combined using a host of informatics techniques (machine learning, data mining, or information fusion). Our previous work studied feature selection using correlation and diversity as well as feature combination using five methods C4.5, Naive Bayes, Linear Discriminant Function, Support Vector Machine, and k-Nearest Neighbors. In this paper, we use combinatorial fusion, based on performance criterion (CF-P) and cognitive diversity (CF-CD), to combine those multiple sensor features. Our results showed that: (a) sensor feature combination method is distinctly much better than CF-CD and other algorithms, and (b) CF-CD is as good as other five feature combination methods, and is better in most of the cases.

SecDM: Securing Data Migration between Cloud Storage Systems

with the development of cloud computing, cloud security issues have recently gained traction in the research community. Although much of the efforts are focused on securing the operation system and virtual machine, or securing data storage inside a cloud system, this paper takes an alternative perspective to cloud security-the security of data migration between different clouds. First, we describe some threats when we are doing data migration. Second, we propose a security mechanism to deal with the security issues on data migration from one cloud to another. Third, we design a prototype to give the mechanism a brief implementation based on HDFS(Hadoop Distributed File System) and we do a series of tests to evaluate our prototype. Here, the solutions to securing data migration between clouds mainly involve in SSL negotiation, migration ticket design and block encryption in distributed file system and cluster parallel computing.

Towards an Automata-Based Semantic Web Services Composition Method in Context-Aware Environment

This paper proposes a service automaton to proactively provide services to users in a context aware pervasive system. First, a context space (CSP) model is proposed based on conceptual space theory, and this model deals with context, situation, services and user preferences in a unified way. In CSP, situation is used to depict the specific scene faced by a user; user preferences can be deemed as a basis to predict the user desired goal situation; to derive a service composition that achieves a given goal, we formalize semantic web services as automata with an emphasize on the relations among contexts and services. Afterwards a composite service automaton (CSA) is defined to synthesis services into a global automata and support the service composition to achieve the goal situation. At last, a temperature control case in smart homes is illustrated to validate the model and approach. The example shows that the service automata can conveniently reflect the state transition of a context aware system from a global perspective and help user achieve the goal situation.

Combination of Multiple Retrieval Systems Using Rank-Score Function and Cognitive Diversity

Combining multiple retrieval systems is a commonly used method to improve the retrieval performance. However, it is still a challenging problem to figure out when and how the combined system can perform better than its individual systems. In this paper, we study these issues by using an information fusion paradigm: Combinatorial Fusion Analysis (CFA). TREC datasets are used as our experiment data. We measure the cognitive diversity between different individual systems by using a rank-score characteristic (RSC) function. Our results demonstrate that: 1) The performance of combination of p systems does not always increase with p, 2) Rank combination is better than score combination in particular when RSC diversity between two individual systems is large enough, and 3) combination of two systems can improve performance only if the two individual systems have relative good performance and are diverse.

On the generation and pruning of skeletons using generalized Voronoi diagrams

Skeletonization is a necessary process in a variety of applications in image processing and object recognition. However, the concept of a skeleton, defined using either the union of centers of maximal discs or the union of points with more than one generating points, was originally formulated in continuous space. When they are applied to situation in discrete space, the resulting skeletons may become disconnected and further works are needed to link them. In this paper, we propose a novel skeletonization method which extends the concept of a skeleton to include both continuous and discrete space using generalized Voronoi diagrams. We also present a skeleton pruning method which is able to remove noisy branches by evaluating their significance. Three experimental results demonstrate that: (1) our method is stable across a wide range of shapes, and (2) it performs better in accuracy and robustness than previous approaches for processing shapes whose boundaries contain substantial noise.