740943
Fitness calculation approach for nested if-else construct in evolutionary testing
This poster paper addresses the fitness calculation problem fornested if-else constructs. A new term "optimism level" is incorporated into the fitness function to assess the branch distance of nested branches for a given test data.
171642
Function Call Flow based Fitness Function Design in Evolutionary Testing
Evolutionary Testing has been shown a promising technology for the automatic test data generation. It reformulates test data generation as a metaheuristic search. A well- designed fitness function is essential to the efficiency of evolutionary search. Many efforts have been directed at the design and implementation of fitness function in recent years. However, previous work has just focused on the control dependency of the target on the branches in the same function. When function calls exist in the desired execution trace to the target, the evaluation of the test data on the coverage of these function calls, which should be provided to the evolutionary search, is not captured by the existing fitness function. In this case, the existing fitness function can not fairly evaluate the test data. And the evolutionary search will be hampered or even fail in severe cases. In this paper, a new term is first proposed to incorporate into the existing fitness function. It is applied to evaluate the test data's coverage of function calls along the desired path to the target. The new fitness function can evaluate the test data more fairly, resulting in a better guidance to the evolutionary search. This can be seen by the experiments carried out on two cases.
60472167
A RM-Based Static Deployment System for Cluster
In this paper, a Reliable-Multicast (RM) based static deployment system for cluster is designed which can be functionally divided into three parts: image capture, image transference, and node configuration. Image capture takes the snapshot of the operating system in the source node. Image transference distributes the captured image to target nodes. Node configuration mainly finishes the configuration of some identified information to target nodes. Applying the technology of Image-Based Installation [1] and an idea of separating the operating system image from service software, this system solves the tedious problems involved when the large-scale cluster is divided into a number of functional pools; using the network transmit model of RM, this system reduces the cost of network bandwidth and significantly improves the efficiency in deployment; combining WOL [2] and PXE [3], the system provides the function of single control point and lightens the workload of the system administrator. So this system can solve the problems of current cluster deployment mechanisms, such as inefficiency, imperfection of automated node configuration, and only supporting the Linux-based operating system deployment, etc. The deployment system consists of the deployment server, image servers, and target nodes. The deployment server is the core of the entire deployment system, which provides DHCP and TFTP service to ensure that the deployment control program can finish the deployment of target nodes after the cluster nodes start up from the NIC. Image servers mainly store the images and applications which can be accessed by the deployment server or target nodes by the NFS service. Target nodes are the nodes which need to be deployed in the cluster. Before the deployment process, the image of the source node and the service environment of the deployment server and image server should be prepared. During the deployment, the target node communicates with the deployment server by using PXE and WOL, and then the deployment server deploys the corresponding image and application service to the target nodes through the RM method. Finally, the target nodes finish their configurations. In the same experimental environment, some experiments respectively for this system, clonesys, and a deployment system identified by X have been built. As proved in experiments, this system can build a cluster more quickly and efficiently.
19453960
Integrating Case based Reasoning and Explanation based Learning in an Apprentice Agent
The problem in applications of case based reasoning (CBR) is its utility problem, that is, the cost of retrieving the most appropriate case from the case library for a new given problem and the cost of adapting the retrieved case for solving the new given problem. This paper proposes an approach to solve the utility problem of CBR by integrating CBR and explanation based learning (EBL) from a perspective that emphasizes the function of learning in CBR. In this paper, CBR and EBL are integrated in an apprentice agent, and the application of this apprentice agent in the robotic assembly domain is given as an example.
33698748
Clustering and Visualizing Audiovisual Dataset on Mobile Devices in a Topic-Oriented Manner
With the significant enhancement of telecom bandwidth and multimedia-supported mobile devices occupying the market, consuming audiovisual contents on the move is no longer a hype. A lot of telecom operators are now porting traditional TV service to PDAs, 3G cell phones. However, several surveys suggest that direct migration of service from large screen to small screen may not comply with mobile users' consuming behaviors. In this paper, we first elaborate existing surveys together with our survey result to understand consumer interests in terms of consuming audiovisual contents on mobile devices. Based on the findings, we propose a novel solution to help user locate, gather, and cross-relate topics scattered in an array of contents across various domains. A web-based demo application has been implemented for PDA. The system evaluation indicates that this paradigm of organizing and presenting multimedia archives is very welcomed.
12907377
Body Detection and Tracking with Hierarchical Scheme in Dynamic Scenes
In this paper, we propose an approach for body detection and tracking with a hierarchical and salient-half-body-prior scheme and apply it in real dynamic scenes. The human region is firstly located by an SVM classifier with histogram of oriented gradients (HOG). Two image observations, motion foreground and edge distance map, are then applied for body detection. In detail, the torso part is first roughly initialized, and then parts are localized in a salient-prior half-body sampling scheme by maximizing body configuration probability with both parts likelihood and body geometry constraints. For body tracking, the color-texture appearance templates of parts extracted in detection phase are further used in tracking process. The approach has been tested on soccer and skating videos and got good results.
62767012
Incorporating prior knowledge into SVM for image retrieval
SVM based image retrieval suffers from the scarcity of labelled samples. In this paper, this problem is solved by incorporating prior knowledge into SVM. Firstly, some prior knowledge of image retrieval is discussed and constructed. After that, the knowledge is incorporated into SVM optimization as a constraint, and a new knowledge-based target function is formulated. Based on this, a framework of image retrieval with knowledge based SVM is proposed. Experimental results demonstrate that the proposed method can effectively improve the learning and retrieval performance of SVM, especially when the number of labelled samples is small.