Complex Analysis of Anisotropic Swarms
This paper considers a continuous time swarm model with individuals moving with a nutrient profile (or an attractant/repellent) in an n-dimensional space. The swarm behavior is a result of a balance between inter-individual interplays as well as the interplays of the swarm agents with their environment. It is proved that the swarm members aggregate and eventually form a cohesive cluster of finite size around the swarm weighted center in a finite time under certain conditions.

title: Collective Behavior Analysis of a Class of Social Foraging Swarms
abstract: This paper considers an anisotropic swarm model that consists of a group of mobile autonomous agents with an attraction-repulsion function that can guarantee collision avoidance between agents and a Gaussian-type attractant/repellent nutrient profile. The swarm behavior is a result of a balance between inter-individual interplays as well as the interplays of the swarm individuals (agents) with their environment. It is proved that the members of a reciprocal swarm will aggregate and eventually form a cohesive cluster of finite size. It is shown that the swarm system is completely stable, that is, every solution converges to the equilibrium point set of the system. Moreover, it is also shown that all the swarm individuals will converge to more favorable areas of the Gaussian profile under certain conditions. The results of this paper provide further insight into the effect of the interaction pattern on self-organized motion for a Gaussian-type attractant/repellent nutrient profile in a swarm system.

title: Complex Analysis of Anisotropic Swarms with Gaussian Profiles
abstract: This paper considers an M-member "individual-based" continuous time swarm model with individuals moving with a nutrient profile (or an attractant/repellent) in an n- dimensional space. It is proved that the swarm members aggregate and eventually form a cohesive cluster of finite size for Gaussian Profiles. Moreover, all the swarm members will converge to more favorable areas of the Gaussian profile under certain conditions.

title: Flocking of Multi-Vehicle Systems With A Leader
abstract: We study the coordinated motion of a group of vehicles with a leader based on nearest neighbor rules. The leader may be a special vehicle in the group or an external signal (virtual leader) to steer the vehicle group. The control law consists of two parts: a potential field force that makes the vehicles attract to each other and at the same time avoid collision in the group, and an alignment force that makes all vehicles' headings and speeds to converge respectively to common values. We assume that the interaction patterns are either fixed or switching based on the information topologies of the leader and member vehicles. Our approach is based on the Lyapunov theory and basic graph theory. We also consider the effect of noise on the collective dynamics of the group. Numerical simulations are worked out to illustrate the analytical results

title: Controllability of a Leader-Follower Dynamic Network with Interaction Time Delays
abstract: This paper studies the controllability of a leader-follower network of dynamic agents in the presence of communication delays. The network dynamics is governed by nearest neighbor rules over a fixed communication topology. The leader is a particular agent acting as an external input to steer the other member agents. We derive sufficient conditions of the controllability of the dynamic network and give an example to illustrate the main results.

title: Stability Analysis of Swarm with Interaction Time Delays Using Nearest Neighbors Information
abstract: This paper investigates the collective behavior of a leader-follower system with communication time lags based on nearest neighbors information. The leaders proposed in the swarm can obtain the information from the environment, but the followers can not. Under the common assumptions, we prove that the individuals of the swarm with the local information will aggregate and form a cohesive cluster of finite size. Moreover, we can clearly see the effects of the time delays on the dynamic of the swarm from the simulations.

title: Opinion observer: analyzing and comparing opinions on the Web
abstract: The Web has become an excellent source for gathering consumer opinions. There are now numerous Web sites containing such opinions, e.g., customer reviews of products, forums, discussion groups, and blogs. This paper focuses on online customer reviews of products. It makes two contributions. First, it proposes a novel framework for analyzing and comparing consumer opinions of competing products. A prototype system called Opinion Observer is also implemented. The system is such that with a single glance of its visualization, the user is able to clearly see the strengths and weaknesses of each product in the minds of consumers in terms of various product features. This comparison is useful to both potential customers and product manufacturers. For a potential customer, he/she can see a visual side-by-side and feature-by-feature comparison of consumer opinions on these products, which helps him/her to decide which product to buy. For a product manufacturer, the comparison enables it to easily gather marketing intelligence and product benchmarking information. Second, a new technique based on language pattern mining is proposed to extract product features from Pros and Cons in a particular type of reviews. Such features form the basis for the above comparison. Experimental results show that the technique is highly effective and outperform existing methods significantly.

title: Filtering Spam in Social Tagging System with Dynamic Behavior Analysis
abstract: Spam in social tagging systems introduced by some malicious participants has become a serious problem for its global popularizing. Some studies which can be deduced to static user data analysis have been presented to combat tag spam, but either they do not give an exact evaluation or the algorithms’ performances are not good enough. In this paper, we proposed a novel method based on analysis of dynamic user behavior data for the notion that users’ behaviors in social tagging system can reflect the quality of tags more accurately. Through modeling the different categories of participants’ behaviors, we extract tag-associated actions which can be used to estimate whether tag is spam, and then present our algorithm that can filter the tag spam in the results of social search. The experiment results show that our method indeed outperforms the existing methods based on static data and effectively defends against the tag spam in various spam attacks.

title: An Effective Approach for Hiding Sensitive Knowledge in Data Publishing
abstract: Recent efforts have been made to address the problem of privacy preservation in data publishing. However, they mainly focus on preserving data privacy. In this paper, we address another aspect of privacy preservation in data publishing, where some of the knowledge implied by a dataset are regarded as private or sensitive information. In particular, we consider that the data are stored in a transaction database, and the knowledge is represented in the form of patterns. We present a data sanitization algorithm, called SanDB, for effectively protecting a set of sensitive patterns, meanwhile attempting to minimize the impact of data sanitization on the non-sensitive patterns. The experimental results show that SanDB can achieve significant improvement over the best approach presented in the literature.

title: Constrained Nonlinear State Estimation - A Differential Evolution Based Moving Horizon Approach
abstract: A solution is proposed to estimate the states in the nonlinear discrete time system. Moving Horizon Estimation (MHE) is used to obtain the approximated states by minimizing a criterion that is the Euclidean form of the difference between the estimated outputs and the measured ones over a finite time horizon. The differential evolution (DE) algorithm is incorporated into the implementation of MHE in order to solve the optimization problem which is presented as a nonlinear programming problem due to the constraints. The effectiveness of the approach is illustrated in simulated systems that have appeared in the moving horizon estimation literature.

title: A Novel Worm Detection Model Based on Host Packet Behavior Ranking
abstract: Traditional behavior-based worm detection can't eliminate the influence of the worm-like P2P traffic effectively, as well as detect slow worms. To try to address these problems, this paper first presents a user habit model to describe the factors which influent the generation of network traffic, then a design of HPBRWD (Host Packet Behavior Ranking Based Worm detection) and some key issues about it are introduced. This paper has three contributions to the worm detection: 1) presenting a hierarchical user habit model; 2) using normal software and time profile to eliminate the worm-like P2P traffic and accelerate the detection of worms; 3) presenting HPBRWD to effectively detect worms. Experiments results show that HPBRWD is effective to detect worms.