33974786
Mining a Complete Set of Both Positive and Negative Association Rules from Large Databases
Association rule mining is one of the key issues in knowledge discovery. In recent years, negative association rule mining has attracted remarkable attention. This paper presents a notion of validity for both positive and negative association rules, which is considered intuitive and necessary. Then, a mining algorithm to find all rules in light of completeness is proposed. In doing so, several pruning strategies based on the upward closure property are developed and incorporated into the algorithm so as to guarantee the computational efficiency.
14004456
Enhancing multimedia streaming performance through peer-paired collaboration
In this paper, a novel multimedia streaming framework called peer-paired pyramid streaming (P/sup 3/S) is proposed. The philosophy of P/sup 3/S is to enable collaboration between clients so as to bring in better performance. The structure of P/sup 3/S is basically a hybrid client/server and peer-to-peer structure and exhibits a triangle-cell based hierarchy. Based on P/sup 3/S, performance enhancement techniques are designed to increase the aggregated bandwidth of all participants. We present an optimal data allocation algorithm, which maximizes the overall throughput of the whole streaming session. We also present a greedy data allocation algorithm that is slightly suboptimal but much simpler. Extensive simulations were performed to demonstrate the effectiveness of proposed techniques.
1622406
Structure Analysis and SNR-Variance Evolution for Parallel Concatenated Coded IDMA Systems
This paper is concerned with parallel concatenated coded interleave-division multiple-access (IDMA) systems. We briefly present the transmitter/receiver structure of these systems and propose an efficient SNR-variance evolution scheme for bit-error-rate (BER) performance evaluation. The proposed evolution scheme is also useful for system performance optimization. Numerical results show that the BER performance curves obtained by simulation and evolution are quite close when the data length is long enough.
39099940
Efficient error recovery techniques in a novel multimedia streaming framework with peer-paired collaboration
Recently, active research has gone to application-level multicast that tries to bring all the merits of unicast and IP multicast together while avoiding their drawbacks. How to efficiently recover from errors in such a system is a difficult question yet to be answered. The current research focus is primarily on developing local error recovery techniques, but these techniques would not work in case of shared losses, which often occur in application-level multicast. In this paper, a novel multimedia streaming framework called peer-paired pyramid streaming (P/sup 3/S) is proposed. It is basically a hybrid client/server and peer-to-peer structure. In P/sup 3/S, clients are hierarchically organized and those at the same level are coupled and peer-pairs are then formed. Based on P/sup 3/S, we introduce some controlled delay between packets that are vulnerable to shared losses to combat with the shared losses. The proposed technique is supported by both theoretical and experimental results presented in this paper.
37867207
A Low-Latency Peer-to-Peer Approach for Massively Multiplayer Games
This paper focuses on the latency reduction problem in massively multiplayer games (MMGs). As the client-server (CS) architecture in use today in most commercial MMGs applications are exposing its weakness in scalability as the number of players increases, researchers start to consider the peer-to-peer (P2P) model that has inherent high scalability for MMGs. However, existing P2P models generally lead to high latency that significantly detracts from the playing experience. To improve this, we present a novel communication model intended to reduce the latency of network communication on game states. The model, based on DHT protocol, can select the best candidate node to perform server's role for a game zone. In contrast, existing P2P models designate 'peer servers' randomly without considering latency effects. The simulation shows that our approach obtains much lower latency than existing P2P models. The performance of our approach is shown to be even better than that of CS models.
16268564
FreeRank: implementing independent ranking service for multiplayer online games
Ranking is necessary for multiplayer online games to provide players with self-complacence and reference for choosing game counterparts. Most existing ranking solutions are tightly coupled with game applications of client-server architectures. In this paper, a novel scheme named FreeRank is proposed as a ranking service independent to specific architecture and detailed implementation of each individual game application. Based on a certificate-based framework and a reputation-based score-computing algorithm. FreeRank resolves the challenge of cheating prevention. Preliminary analysis and simulation results show that FreeRank is feasible and effective.
8422890
Spatiotemporal Phase Congruency Based Invariant Features for Human Behavior Classification
In applications of behavior recognition, the use of spatiotemporal invariant feature points can improve the robustness to noise, illumination and geometric distortions. In this paper, we develop a novel detection model of spatiotemporal invariant feature by generalizing the notion of image phase congruency to video volume phase congruency. The proposed model detects feature points by measuring the spatiotemporal phase congruency of Fourier series components along with their characteristic scale and principal orientation. Compared with other state-of-the-art methods, the key advantages of this interest point detector include the invariance to contrast variations and more precise feature location. Furthermore, an invariant feature descriptor is advanced based on the phase congruency map, resulting in enhanced discriminative power in classification tasks. Experimental results on KTH human motion dataset demonstrate the validity and effectiveness of the extracted invariant features in the human behavior recognition scheme.
20063786
Implementation of a graphics design framework on the Web
We propose a client-server graphics design framework and implement a room design system using CORBA. We have integrated technologies used in distributed computation (Java and CORBA) and computer graphics (VRML). To support multiuser collaborative design, we propose a supplier-consumer communication approach and implement it using the event service in CORBA. Our system can be applied in electronic commerce to improve the existing systems by allowing customers to select furniture and to visualize the whole furnished room in 3D.
15500084
Understanding the Prediction of Transmembrane Proteins by Support Vector Machine using Association Rule Mining
With the efforts to understand protein structure, many computational approaches have been made recently. Among them, the support vector machine (SVM) methods have been recently applied and showed successful performance compared with other machine learning schemes. However, despite the high performance, the SVM approaches suffer from the problem of understandability since it is a black-box model. To overcome this limitation, this study attempted to combine the SVM with the association rule based classifier which can present the meaningful explanation about the prediction. To perform this task, a new association rule based classifier (PCPAR) was devised based on the existing classifier, CPAR, to handle the sequential data. PCPAR creates the patterns by merging the generated rules and then classifies the sequential data based on the pattern match. The experimental result presents the following: with sequential data, the PCPAR scheme shows better performance with respect to the accuracy and the number of generated patterns than CPAR method whether applied alone or combined with SVM. The combined scheme of SVMPCPAR generates more compact patterns than the combined scheme of SVM with decision tree, SVM DT, with similar performance. These patterns are easily understandable and biologically meaningful
10702245
A wireless LAN-based indoor positioning technology
Context-aware computing is an emerging computing paradigm that can provide new or improved services by exploiting user context information. In this paper, we present a wireless-local-area-network-based (WLAN-based) indoor positioning technology. The wireless device deploys a position-determination model to gather location information from collected WLAN signals. A model-based signal distribution training scheme is proposed to trade off the accuracy of signal distribution and training workload. A tracking-assistant positioning algorithm is presented to employ knowledge of the area topology to assist the procedure of position determination. We have set up a positioning system at the IBM China Research Laboratory. Our experimental results indicate an accuracy of 2 m with a 90% probability for static devices and, for moving (walking) devices, an accuracy of 5 m with a 90% probability. Moreover, the complexity of the training procedure is greatly reduced compared with other positioning algorithms.
31706841
A Fast Subspace Clustering Algorithm Based on Pattern Similarity
Traditional clustering models define similarity by distance over dimensions. However, distance functions are not always adequate in capturing correlations among the objects. Pattern-based clustering can discover this kind of clusters. But state-of-the-art pattern-based clustering methods are inefficient and haven't criteria to evaluate the quality of clusters. This paper presents a novel pattern similarity-based subspace clustering with the pattern tree (PPSC for short) that offers these capabilities. The method uses new evaluation criteria to discover best clusters, which enables user to find clusters according to different needs. Meanwhile, observing the analogy between mining frequent itemsets and discovering subspace clusters around random points, we apply the pattern-tree to determine subspace by scanning the database once, so it can perform efficiently in large datasets.