This paper presents a novel approach for extracting simultaneously human daily activity patterns and discovering the temporal relations of these activity patterns. It is necessary to resolve the services conflict and to satisfy a user who wants to use multiple services. To extract the simultaneous activity patterns, context has been collected from physical sensors and electronic devices. In addition, a context model is organized by the proposed incremental statistical method to determine conflicts and to infer user intentions through analyzing the daily human activity patterns. The context model is represented by the sets of the simultaneous activity patterns and the temporal relations between the sets. To evaluate the method, experiments are carried out on a test-bed called the Ubiquitous Smart Space. Furthermore, the user-intention simulator based on the simultaneous activity patterns and the temporal relations from the results of the inferred intention is demonstrated.

An understanding of performance of database driven network applications is critical in combating slow performance of e-commerce web sites, besides efficient web page design, and high quality and well-maintained web server equipment. This paper describes a method of measuring performance from the user viewpoint, which can help enormously in making realistic assessment of true performance of database driven applications. The performance measurements were taken at user locations by using several specially designed JavaScript functions along with ASP scripts. A performance study is presented in this paper, comparing performance of data access using stored procedures with the traditional way of querying a database. It is generally believed that stored procedures have performance benefits as they are pre-compiled. However, our study shows that the data access approach using stored procedures provides significant benefits(by about 30%) over the traditional approach for querying a commercial MySQL database, only when retrieving a substantial amount of data(at least 10,000 rows of data).

VoIP service is the transmission of voice data using SIP protocol on an IP-based network. The SIP protocol has many advantages, such as providing IP-based voice communication and multimedia service with low communication cost. Therefore, the SIP protocol disseminated quickly. However, SIP protocol exposes new forms of vulnerabilities to malicious attacks, such as message flooding attack. It also incurs threats from many existing vulnerabilities as occurs for IP-based protocol. In this paper, we propose a new virtual proxy to cooperate with the existing Proxy Server to provide state monitoring and detect SIP message flooding attack with IP/MAC authentication. Based on a proposed virtual proxy, the proposed system enhances SIP attack detection performance with minimal latency of SIP packet transmission.

The challenges of rapidly growing numbers of mobile nodes in IPv6-based networks are being faced by mobile computing researchers worldwide. Recently, IETF has standardized Mobile IPv6 and Fast Handover for Mobile IPv6(FMIPv6) for supporting IPv6 mobility. Even though existing literatures have asserted that FMIPv6 generally improves MIPv6 in terms of handover speed, they did not carefully consider the details of the whole handover procedures. Therefore, in conventional protocols, the handover process reveals numerous problems manifested by a time-consuming network layer based movement detection and latency in configuring a new care of address with confirmation. In this article, we study the impact of the address configuration and confirmation procedure on the IP handover latency. To mitigate such effects, we propose a new scheme which can reduce the latency taken by the movement detection, address configuration and confirmation from the whole handover latency. Furthermore, a mathematical analysis is provided to show the benefits of our scheme. In the analysis, various parameters are used to compare our scheme with the current procedures, while our approach is focused on the reduction of handover latency. Finally, we demonstrate total handover scenarios for the proposed techniques and discussed the major factors which contribute to the handover latency.

This paper introduces DICE, a Domain-Independent text Classification Engine. DICE is robust, efficient, and domain-independent in terms of software and architecture. Each module of the system is clearly modularized and encapsulated for extensibility. The clear modular architecture allows for simple and continuous verification and facilitates changes in multiple cycles, even after its major development period is complete. Those who want to make use of DICE can easily implement their ideas on this test bed and optimize it for a particular domain by simply adjusting the configuration file. Unlike other publically available tool kits or development environments targeted at general purpose classification models, DICE specializes in text classification with a number of useful functions specific to it. This paper focuses on the ways to locate the optimal states of a practical text classification framework by using various adaptation methods provided by the system such as feature selection, lemmatization, and classification models.

In this paper, we introduce the architecture of Genetic Algorithm(GA) based Feed-forward Polynomial Neural Networks(PNNs) and discuss a comprehensive design methodology. A conventional PNN consists of Polynomial Neurons, or nodes, located in several layers through a network growth process. In order to generate structurally optimized PNNs, a GA-based design procedure for each layer of the PNN leads to the selection of preferred nodes(PNs) with optimal parameters available within the PNN. To evaluate the performance of the GA-based PNN, experiments are done on a model by applying Medical Imaging System(MIS) data to a multi-variable software process. A comparative analysis shows that the proposed GA-based PNN is modeled with higher accuracy and more superb predictive capability than previously presented intelligent models.

Many types of sensor data exhibit strong correlation in both space and time. Both temporal and spatial suppressions provide opportunities for reducing the energy cost of sensor data collection. Unfortunately, existing clustering algorithms are difficult to utilize the spatial or temporal opportunities, because they just organize clusters based on the distribution of sensor nodes or the network topology but not on the correlation of sensor data. In this paper, we propose a novel clustering algorithm based on the correlation of sensor data. We modify the advertisement sub-phase and TDMA schedule scheme to organize clusters by adjacent sensor nodes which have similar readings. Also, we propose a spatio-temporal suppression scheme for our clustering algorithm. In order to show the superiority of our clustering algorithm, we compare it with the existing suppression algorithms in terms of the lifetime of the sensor network and the size of data which have been collected in the base station. As a result, our experimental results show that the size of data is reduced and the whole network lifetime is prolonged.