1755728
An evolutionary autonomous agents approach to image feature extraction
This paper presents a new approach to image feature extraction which utilizes evolutionary autonomous agents. Image features are often mathematically defined in terms of the gray-level intensity at image pixels. The optimality of image feature extraction is to find all the feature pixels from the image. In the proposed approach, the autonomous agents, being distributed computational entities, operate directly in the 2-D lattice of a digital image and exhibit a number of reactive behaviors. To effectively locate the feature pixels, individual agents sense the local stimuli from their image environment by means of evaluating the gray-level intensity of locally connected pixels, and accordingly activate their behaviors. The behavioral repository of the agents consists of: 1) feature-marking at local pixels and self-reproduction of offspring agents in the neighboring regions if the local stimuli are found to satisfy feature conditions, 2) diffusion to adjacent image regions if the feature conditions are not held, or 3) death if the agents exceed their life span. As part of the behavior evolution, the directions in which the agents self-reproduce and/or diffuse are inherited from the directions of their selected high-fitness parents. Here the fitness of a parent agent is defined according to the steps that the agent takes to locate an image feature pixel.
33842812
Anycast-Based Cooperative Proxy Caching
As one of the most popular applications currently running on the Internet, the World Wide Web is of an exponential growth in size, which results in the network congestion and server overloading. Web caching as an important technique aims at reducing network traffic, server load, and user-perceived retrieval delays by replicating popular contents on proxy caches. In our paper, we develop an anycast-based cooperative web proxy caching mechanism. It puts response time, hop counts, self-organized load balancing, adaption, and self-configuration as primary concerns. In our scheme, anycast is used to bring a server “nearest” to a client to improve overall performance, all proxies in a local network consists an anycast group. The object placement and replacement problems are formulated as an optimization problem and the solution is obtained by using an anycast-based cooperative proxy caching algorithm. Some simulation experiments have been conduced to evaluate the proposed scheme in terms of a wide range of performance metrics. Analytical and experimental results show that our algorithm outperforms some existing algorithms in response time, hop counts.
5692814
Mining associated implication networks: computational intermarket analysis
Current attempts to analyze international financial markets include the use of financial technical analysis and data mining techniques. In this paper, we propose a new approach that incorporates implication networks and association rules to form an associated network structure. The proposed approach explicitly addresses the issue of local vs. global influences between financial markets.
37683026
Web Intelligence (WI)
The 21st century is the age of the Internet and the World Wide Web. The Web revolutionizes the way we gather, process, and use information. At the same time, it also redefines the meanings and processes of business, commerce, marketing, finance, publishing, education, research, development, as well as other aspects of our daily life. The revolution is just beginning. Although individual Web based information systems are constantly being deployed, advanced issues and techniques for developing and for benefiting from Web intelligence still remain to be systematically studied. The article defines a new research field, namely Web Intelligence (WI) by giving a complete picture of WI related topics for systematic study on advanced Web technology and developing Web based intelligent information systems. Roughly speaking, WI exploits AI and advanced information technology on the Web and Internet. It is the key and the most urgent research field of IT for business intelligence.
14178256
Distributed Problem Solving Without Communication - An Examination of Computationally Hard Satisfiability Problems
In this paper, we extend and modify the ERA approach proposed in Ref. 13 to solve Propositional Satisfiability Problems (SATs). The new ERA approach involves a multiagent system where each agent only senses its local environment and applies some self-organizing rules for governing its movements. The environment, which is a two-dimensional cellular environment, records and updates the local values that are computed and affected according to the movements of individual agents. In solving a SAT with the ERA approach, we first divide variables into several groups, and represent each variable group with an agent whose possible positions correspond to the elements in a Cartesian product of variable domains, and then randomly place each agent onto one of its possible positions. Thereafter, the ERA system will keep on dispatching agents to choose their movements until an exact or approximate solution emerges. The experimental results on some benchmark SAT test-sets have shown that the ERA approach can obtain comparable results as well as stable performances for SAT problems. In particular, it can find approximate solutions for SAT problems in only a few steps. The real value of this approach is that it is a distributed asynchronous approach without any centralized control or evaluation, where the agents can cooperate to solve problems without explicit communication.
18067929
Multiagent SAT (MASSAT): Autonomous Pattern Search in Constrained Domains
In this paper, we present an autonomous pattern search approachto solving Satisfiability Problems (SATs). Our approach is essentially a multiagent system. To solve a SAT problem, we first divide variables into groups, and represent each variable group with an agent. Then, we randomly place each agent onto a position in the correspoding local space which is composed of the domains of the variables that are represented by this agent. Thereafter, all agents will autonomously make searchdecisions guided by some reactive rules in their local spaces until a special pattern (i.e., solution) is found or a time step threshold is reached. Experimental results on some benchmark SAT test-sets have shown that by employing the MASSAT approach, we can obtain performances comparable to those of other popular algorithms.
13352152
Software architecture for mobile distributed computing
Today's wireless networks and devices support the dynamic composition of mobile distributed systems, according to device connectivity. This has in particular led to the introduction of a number of supporting middleware. However, such solutions need to be complemented with adequate modeling and verification support towards enforcing the correctness of the dynamic mobile systems with respect to both functional and nonfunctional properties. Building on the elegant properties of software architecture modeling, this paper introduces base modeling of mobile software components, which integrates key features of the wireless infrastructure and allows for reasoning about the behavior of dynamically composed systems.
6371621
3D object reconstruction from a single 2D line drawing without hidden lines
The human vision system can interpret a single 2D line drawing as a 3D object without much difficulty even if the hidden lines of the object are invisible. Several reconstruction approaches have tried to emulate this ability, but they cannot recover the complete object if the hidden lines of the object are not shown. This paper proposes a novel approach for reconstructing complete 3D objects from line drawings without hidden lines. First, we develop some constraints and properties for the inference of the topology of the invisible edges and vertices of an object. Then we present a reconstruction method based on perceptual symmetry and planarity of the object. We give a number of examples to demonstrate the ability of our approach.
864760
Bayesian Tensor Inference for Sketch-Based Facial Photo Hallucination
This paper develops a statistical inference approach, Bayesian Tensor Inference, for style transformation between photo images and sketch images of human faces. Motivated by the rationale that image appearance is determined by two cooperative factors: image content and image style, we first model the interaction between these factors through learning a patch-based tensor model. Second, by introducing a common variation space, we capture the inherent connection between photo patch space and sketch patch space, thus building bidirectional mapping/inferring between the two spaces. Subsequently, we formulate a Bayesian approach accounting for the statistical inference from sketches to their corresponding photos in terms of the learned tensor model. Comparative experiments are conducted to contrast the proposed method with state-of-the-art algorithms for facial sketch synthesis in a novel face hallucination scenario: sketch-based facial photo hallucination. The encouraging results obtained convincingly validate the effectiveness of our method.
828752
Generalized monotone schemes, discrete paths of extrema, and discrete entropy conditions
Solutions of conservation laws satisfy the monotonicity property: the number of local extrema is a non-increasing function of time, and local maximum/minimum values decrease/increase monotonically in time. This paper investigates this property from a numerical standpoint. We introduce a class of fully discrete in space and time, high order accurate, difference schemes, called generalized monotone schemes. Convergence toward the entropy solution is proven via a new technique of proof, assuming that the initial data has a finite number of extremum values only, and the flux-function is strictly convex. We define discrete paths of extrema by tracking local extremum values in the approximate solution. In the course of the analysis we establish the pointwise convergence of the trace of the solution along a path of extremum. As a corollary, we obtain a proof of convergence for a MUSCL-type scheme that is second order accurate away from sonic points and extrema.
1291895
A proxy-assisted adaptation framework for object video multicasting
Video multicast is a challenging problem due to the heterogeneous and best-effort nature of the Internet. In this paper, we present a novel video multicast framework that exploits the potential of object scalability offered by MPEG-4. Specifically, we introduce the concept of object transmission proxy (OTP), which filters incoming streams using object-based bandwidth adaptation to meet dynamic network conditions. Multiple OPTs can form an overlay network that interconnects diverse multicast islands with semi-uniform demands within each single island. We concur with the wisdom that an application best knows the utility of its data. Hence, the bandwidth-adaptation algorithm for the OTPs adaptively allocates bandwidth among video objects according to their respective utilities and then performs application-level filtering based on an effective stream classification and packetization scheme. Extensive simulation results demonstrate that our framework has substantial performance improvement over conventional bandwidth-adaptation schemes. It is particularly suitable for object-based video multicasting where the objects are of different importance.