Damage assessment and repair in attack resilient distributed database systems

Preventive measures sometimes fail to defect malicious attacks. With attacks on data-intensive applications becoming an ever more serious threat, intrusion tolerant database systems are a significant concern. The main objective of such systems is to detect attacks, and to assess and repair the damage in a timely manner. This paper focuses on efficient damage assessment and repair in distributed database systems. The complexity caused by data partition, distributed transaction processing, and failures makes intrusion recovery much more challenging than in centralized database systems. This paper identifies the key challenges and presents an efficient algorithm for distributed damage assessment and repair.     

Several multi-criteria programming methods for classification

In this paper, we propose a general optimization-based model for classification. Then we show that some well-known optimization-based methods for classification, which were developed by Shi et al. [Data mining in credit card portfolio management: a multiple criteria decision making approac. In: Koksalan M, Zionts S, editors. Multiple criteria decision making in the new millennium. Berlin: Springer; 2001. p. 427–36] and Freed and Glover [A linear programming approach to the discriminant problem. Decision Sciences 1981; 12: 68–79; Simple but powerful goal programming models for discriminant problems. European Journal of Operational Research 1981; 7: 44–60], are special cases of our model. Moreover, three new models, MCQP (multi-criteria indefinite quadratic programming), MCCQP (multi-criteria concave quadratic programming) and MCVQP (multi-criteria convex programming), are developed based on the general model. We also propose algorithms for MCQP and MCCQP, respectively. Then we apply these models to three real-life problems: credit card accounts, VIP mail-box and social endowment insurance classification. Extensive experiments are done to compare the efficiency of these methods.     

Strong stability of the mono-tubular heat exchanger equation with output feedback

We investigate asymptotic behavior of the C₀-semigroup T(t) associated with the mono-tubular heat exchanger equation with output feedback by a perturbation method. It is shown that T(t) is bounded if a constraint is satisfied by the parameters and the spatial distribution function. Further, applying the Arendt-Batty-Lyubich-Vu theorem, a criterion is established to judge strong stability of T(t).     

面向高速工业无线网络的TDMA MAC协议设计与实现

随着无线技术的发展,在工业自动化中引入无线技术已经成为一种潮流,同时TDMA机制由于可以完全避免冲突,因此在大规模高实时要求的面向车间的无线技术研究中受到了广泛关注,但现在面向车间的高速实时无线技术的研究大都局限于软件仿真。通过在商用802.11硬件上设计并实现面向车间级工业应用的高速工业无线TDMAMAC层协议,建立了一个基于TDMA的面向工业应用的高速无线网络原型系统,实验结果表明实验平台完全能够支撑毫秒级时隙调度的TDMA应用。     

基于Mapobjects的双地图窗口的交互查询设计

介绍基于Ｍａｐｏｂｊｅｃｔｓ的双地图窗口的交互查询的设计方法。     

VB教学方法浅析

程序设计是计算机专业课程中的重要内容之一。在程序设计课程教学中,如何解决程序设计本身枯燥、难懂这个问题,找到一条比较新颖的教学方式,一直是广大计算机教师颇感兴趣的课题。结合几年来VB程序设计的实际经验,并进行深刻的反思,总结出一个四步教学法。     

Group interaction analysis in dynamic context.

Computer understanding of human actions and interactions is one of the key research issues in human computing. In this regard, context plays an essential role in semantic understanding of human behavioral and social signals from sensor data. This paper put forward an event-based dynamic context model to address the problems of context awareness in the analysis of group interaction scenarios. Event-driven multilevel dynamic Bayesian network is correspondingly proposed to detect multilevel events, which underlies the context awareness mechanism. Online analysis can be achieved, which is superior over previous works. Experiments in our smart meeting room demonstrate the effectiveness of our approach.     

An Artificial Neural Network Classifier Design Based-on Variable Kernel and Non-Parametric Density Estimation

This paper proposes a probabilistic variant of the SOM-kMER (Self Organising Map-kernel-based Maximum Entropy learning Rule) model for data classification. The classifier, known as pSOM-kMER (probabilistic SOM-kMER), is able to operate in a probabilistic environment and to implement the principles of statistical decision theory in undertaking classification problems. A distinctive feature of pSOM-kMER is its ability in revealing the underlying structure of data. In addition, the Receptive Field (RF) regions generated can be used for variable kernel and non-parametric density estimation. Empirical evaluation using benchmark datasets shows that pSOM-kMER is able to achieve good performance as compared with those from a number of machine learning systems. The applicability of the proposed model as a useful data classifier is also demonstrated with a real-world medical data classification problem.     

Studying the protein-protein interactions in the postsynaptic density by means of immunoabsorption and chemical crosslinking

Postsynaptic densities (PSDs), isolated from porcine cerebral cortices, are large disk-shaped aggregates consisting of hundreds of different proteins. To study the protein-protein interactions in such complex supramolecules, we developed a procedure to break up the PSD's overall structure, while preserving some interactions between individual proteins. Using the resulting PSD sample and an indirect immunoabsorption procedure, PSD-95 was isolated along with the α- and β-subunits of calcium calmodulin-dependent protein kinase II (CaMKIIα and CaMKIIβ), α-tubulin, β-tubulin, and Chapsyn110. Similarly, CaMKIIα was isolated along with CaMKIIβ, α-tubulin, β-tubulin, and small amounts of PSD-95. The proteins isolated from PSDs treated with a cleavable bifunctional crosslinking reagent were further subjected to diagonal gel electrophoresis analysis, and the results indicated that CaMKIIα resides next to α-tubulin in the PSD. Overall, the results obtained here suggest that within the PSD, large aggregates of CaMKIIα, CaMKIIβ, α-tubulin, and β-tubulin may occur that indirectly associate with PSD-95 and Chapsyn110. Such a protein organization would allow interactions with F-actin in the cytoplasm and with proteins, such as N-methyl-D-aspartate receptors, which reside on the postsynaptic membrane. Furthermore, it would facilitate binding to proteins such as the various microtubule-associated proteins that reside in the core region of the PSD.