15447660
A logical replication-based intrusion detection approach for CSCW system
With the rapid development of CSCW techniques, CSCW system security has been of much attention. Its major challenge comes from malicious attacks, which cannot be handled by traditional security mechanisms, such as authorization, access control, encryption, etc. Although there existed some intrusion detection systems, current researches on intrusion detection are still insufficient in accuracy and efficiency for two reasons: firstly, most of them implement malicious transaction detection only by matching samples with predefined patterns; secondly, most current researches mainly resort to semantic analyzing or even only manual interventions to judge the malicious attacks. Inspired by the replication technique, we propose and implement a novel logical replication-based intrusion detection solution for CSCW system in this paper. It provides an additional layer of defense against application attacks, especially program attacks. The approach entitles the system to detect malicious intrusions efficiently at low false positive rate.
1107258
Improving image clustering: An unsupervised feature weight learning framework
We address the problem of feature weight learning for image clustering. In practice, before clustering data, we generally normalize all data features between 0 and 1, because we cannot determine which features are more important. In this paper, we provide a feature weight learning framework for clustering which can obtain the feature weights and cluster labels simultaneously. An alternative optimization algorithm is adopted to solve this problem. Empirical studies on the toy data and real image data demonstrate our algorithm's effectiveness in improving the clustering performance.
8051159
A Web-based Framework For On-line Collaborative Learning
This paper presents a framework for design of online courses and collaborative learning based on Web technologies. It provides the following features: construction of structured learning materials in a distributed environment; personalization of course contents in the run-time that adapts to the progress of individual students; it can supply relevant Web resources to the students by analyzing the learning history dynamically; students in a group could exchange their learning experiences and discover relevant knowledge via the communication channels provided by the framework. The proposed techniques could be deployed to a variety of online computer-assisted modules to improve the effectiveness of Web-based e-learning
26669443
Bottom-up discovery of frequent rooted unordered subtrees
In the past decade, XML has emerged as the standard language for information exchanging over the Internet. Due to its tree-structure paradigm, XML is superior for its capability of storing, querying, and manipulating complex data. Therefore, discovering frequent tree patterns over tree-structured data has become an interesting topic for XML data management. In this paper, we propose a tree mining algorithm, named BUXMiner, for finding a special class of frequent trees, called rooted unordered trees, from a tree-structured database. BUXMiner employs an efficient bottom-up approach to enumerate all candidate trees over a compact global tree guide and computes the frequent trees based on the tree guide. In addition to BUXMiner, we also propose a mining approach called BUMXMiner to discover the maximal frequent rooted unordered trees. We compare BUXMiner with previous tree-structure mining algorithms, namely XQPMinerTID and FastXMiner, which were also proposed to discover rooted unordered trees. The experimental results show that our algorithm outperforms XQPMinerTID and FastXMiner in terms of efficiency. The performance results from real-world applications also indicate the usefulness of our proposed tree mining algorithms in a variety of web applications, such as analysis of web page access patterns and mining frequent XML query patterns for caching.
8340253
UGE4B: An Universal Grid Environment for Bioinformatics Research
Recent years, grid technology has been used widely in the fields of scientific research. In this paper, we present a universal grid environment for bioinformatics research (UGE4B). Although there have been several similar projects successfully constructed in the world, we need to meet some unique requirements from bioinformatics research in China. UGE4B consists of 6 components, including portal, task management, data management, information server, security and computing node. As a national-wide bioinformatics research platform, UGE4B is built through interconnecting multiple sub grids provided by each bioinformatics institutes. Besides provides a universal portal to end users for bioinformatics computing and data accessing, UGE4B also supported some special bioinformatics applications
116588251
On-line evolutionary exponential family mixture
This paper deals with evolutionary clustering, which refers to the problem of clustering data with distribution drifting along time. Starting from a density estimation view to clustering problems, we propose two general on-line frameworks. In the first framework, i.e., historical data dependent (HDD), current model distribution is designed to approximate both current and historical data distributions. In the second framework, i.e., historical model dependent (HMD), current model distribution is designed to approximate both current data distribution and historical model distribution. Both frameworks are based on the general exponential family mixture (EFM) model. As a result, all conventional clustering algorithms based on EFMs can be extended to evolutionary setting under the two frameworks. Empirical results validate the two frameworks.
120992950
A mathematical model for bifurcations in a Belousov-Zhabotinsky reaction
Abstract In our investigation we find that there are three stages during the whole lifespan of a Belousov–Zhabotinsky (BZ) system with 1,4-cyclohexanedione (CHD) and ferroin: transitional period (TP), induction period (IP), and main period (MP). When the system goes from the TP to the IP, there is a bifurcation from monostability (orange steady-state) to bistability (both orange and blue steady-states). When the system shifts from the IP to the MP, a different bifurcation occurs from the bistability back to the monostability (orange steady-state). A reduction step before the second bifurcation propagates slower than oxidation steps and pulses during the whole lifespan of the medium. This sharp difference of propagation speed has not been observed in other BZ systems, and cannot be interpreted by the prototype Oregonator model. We construct here an Oregonator-type model that simulates the two bifurcations and the propagation speed difference of the two kinds of step before the second bifurcation. Furthermore, our model demonstrates other bifurcations that are not observable in experiment. Our discovery not only unfolds a new aspect of the well-studied BZ reaction, but also leads to a new demand for further understanding of a reaction–diffusion equation system as the corresponding ordinary differential equation system bifurcates.
6208997
Network Attack-Defense Simulation Training System Based on HLA
Soldiers possessing of network attack-defense ability are the key factor for future information war. Aiming at the problem of lacking daily training and drilling environment, a method of building Network Attack-Defense Simulation Training System (NADSTS) based on HLA is put forward. With the method, attack and defense training are designed as different federation member. The system is divided to presentation, application and adapter layer clearly. Key technologies involves network attack-defense, network simulation and simulation driving are also presented. Software is developed based on plug-in framework. Its simulation examples show greatest traits on building similar large-scale simulation system.
15486362
Simulation Training Platform for Large Tactical Communication Equipment Based on VR
A simulation training platform aiming at the difficult problem of training is presented. Platform is built based on HLA and its framework is designed to hierarchical structure. Models of training equipment are created with software named GL Studio and Multigen Creator. Key technologies of modeling process are detailed. Equipment operations are layered and its rules are described with XML document. Driving framework programming with Vega Prime API can integrate 3D models, operation rules, special effect and whole scene to a platform system based on VR. Finally, plug-in soft framework is adopted to implement the system and simulation examples are introduced to prove the feasibility of the platform.
3708864
A New Systolic Architecture for Modular Division
A new systolic architecture for modular division is proposed in this paper. The architecture is based on a new hardware algorithm for modular division that is extended from the plus-minus algorithm for the greatest common divisor computation. Both the area complexity and the time complexity of the new architecture are linear with respect to the operand bit length. Compared to the architecture using a redundant number representation proposed by Kaihara and Takagi, the new architecture allows a doubled throughput due primarily to the greatly reduced critical path delay, while the area is only about 20 percent larger. Moreover, it is shown that, with a small addition to the control logic, the architecture can also be used to perform Montgomery modular multiplication, thereby efficiently realizing a unified modular multiplier/divider
41685688
A Gradient BYY Harmony Learning Algorithm for Straight Line Detection
Straight line detection is a basic problem in image processing and has been extensively studied from different aspects, but most of the existing algorithms need to know the number of straight lines in an image in advance. However, the Bayesian Ying-Yang (BYY) harmony learning can make model selection automatically during parameter learning for the Gaussian mixture modeling, which can be further applied to detecting the correct number of straight lines automatically by representing the straight lines with Gaussians or Gaussian functions. In this paper, a gradient BYY harmony learning algorithm is proposed to detect the straight lines automatically from an image as long as the pre-assumed number of straight lines is larger than the true one. It is demonstrated by the simulation and real image experiments that this gradient BYY harmony learning algorithm can not only determine the number of straight lines automatically, but also detect the straight lines accurately against noise.