Cross-lingual thesaurus for multilingual knowledge management

The Web is a universal repository of human knowledge and culture which has allowed unprecedented sharing of ideas and information in a scale never seen before. It can also be considered as a universal digital library interconnecting digital libraries in multiple domains and languages. Beside the advance of information technology, the global economy has also accelerated the development of inter-organizational information systems. Managing knowledge obtained in multilingual information systems from multiple geographical regions is an essential component in the contemporary inter-organization information systems. An organization cannot claim itself to be a global organization unless it is capable to overcome the cultural and language barriers in their knowledge management. Cross-lingual semantic interoperability is a challenge in multilingual knowledge management systems. Dictionary is a tool that is widely utilized in commercial systems to cross the language barrier. However, terms available in dictionary are always limited. As language is evolving, there are new words being created from time to time. For examples, there are new technical terms and name entities such as RFID and Baidu. To solve the problem of cross-lingual semantic interoperability, an associative constraint network approach is investigated to construct an automatic cross-lingual thesaurus. In this work, we have investigated the backmarking algorithm and the forward evaluation algorithm to resolve the constraint satisfaction problem represented by the associative constraint network. Experiments have been conducted and show that the forward evaluation algorithm outperforms the backmarking one in terms of precision and recall but the backmarking algorithm is more efficient than the forward evaluation algorithm. We have also benchmarked with our earlier technique, Hopfield network, and showed that the associate constraint network (either backmarking or forward evaluation) outperforms in precision, recall, and efficiency.     

An information-based neural approach to generic constraint satisfaction

A novel artificial neural network heuristic (INN) for general constraint satisfaction problems is presented, extending a recently suggested method restricted to boolean variables. In contrast to conventional ANN methods, it employs a particular type of non-polynomial cost function, based on the information balance between variables and constraints in a mean-field setting. Implemented as an annealing algorithm, the method is numerically explored on a testbed of Graph Coloring problems. The performance is comparable to that of dedicated heuristics, and clearly superior to that of conventional mean-field annealing.     

An Iterative local-search framework for solving constraint satisfaction problem

In this article, we introduce a new solving framework based on using alternatively two local-search algorithms to solve constraint satisfaction and optimization problems. The technique presented is based on the integration of local-search algorithm as a mechanism to diversify the search instead of using a build on diversification mechanisms. Thus, we avoid tuning the multiple parameters to escape from a local optimum. This technique improves the existing methods: it is generic especially when the given problem can be expressed as a constraint satisfaction problem. We present the way the local-search algorithm can be used to diversify the search in order to solve real examination timetabling problems. We describe how the local-search algorithm can be used to assist any other specific local-search algorithm to escape from local optimality. We showed that such framework is efficient on real benchmarks for timetabling problems.     

A constraint satisfaction neural network and heuristic combined approach for concurrent activities scheduling

Scheduling activities in concurrent product development process is of great sig-nificance to shorten developements lead time and minimize the cost.Moreover,it can eliminate the unnecessary redesign periods and guarantee that serial activities can be executed as concurrently as possible,This paper presents a constraint satisfaction neural network and heuristic combined approach for concurrent activities scheduling.In the combined approack,the neural network is used to obtain a feasible starting time of all the activities based on sequence constraints ,the heuristic algorithm is used to obtain a feasible solution of the scheduling problem based on resource constrainsts.The feasible scheduling solution is obtained by a gradient optimization function .Sim-ulations have shown that the proposed combined approach is efficient and fasible with respect to concurrent activities scheduling.     

约束满足问题的预处理方法研究

搜索控制问题是大多数人工智能问题求解面临的一个根本间题,而约束满足是解决这一问题的常用方法之一它源于机器视觉领域中的情景标识任务,如今在人工智能的众多领域(如规划、调度、时序推理)中获得了广泛的应用,受到了人工智能界的高度重视.在近几期的UCAI和AAAI等国际人工智能会议上这方面的内容均占有一定的比重,《A币ficial In-telligence》杂志曾于1992年出了一期约束满足问题的专辑     

基于动态约束网络的约束优化分解

约束网络通过对较大规模的任务和问题的分解,传递了并行工程产品开发过程中的各多功能小组之间必然存在的相互制约、相互依赖的关系。在任务和问题简单分解的过程中,由于所得到的子问题之间通常是不能完全独立的,因而只能在有限程度上降低难度、简化问题。在分析了约束求解的研究现状和不足的基础上,提出了一种基于动态约束的约束优化分解方法,使得各个子任务、子问题间的关系得到进一步的分解,从而在满足原要求的基础上,得到优化的结果。     

浅谈计算机网络信息安全分析与管理探讨

随着计算机科学技术的发展,计算机互联网技术已经成为了人类社会网络信息共享的最大平台,世界各个国家和大型公司也非常重视计算机互联网数据库系统的发展.截至目前为止,经过多年的科学研究,许多大型互联网企业已经初步了解了互联网的发展构架,但是,由于互联网是一个将全世界连接起来的大网络,这就导致在互联网中潜藏着很多安全问题.针对这样的情况,本文将具体结合目前计算机网络信息安全管理的实际情况,谈一谈应当如何加强计算机网络信息安全管理.     

Ontology-based similarity for product information retrieval

Product development of today is becoming increasingly knowledge intensive. Specifically, design teams face considerable challenges in making effective use of increasing amounts of information. In order to support product information retrieval and reuse, one approach is to use case-based reasoning (CBR) in which problems are solved “by using or adapting solutions to old problems.” In CBR, a case includes both a representation of the problem and a solution to that problem. Case-based reasoning uses similarity measures to identify cases which are more relevant to the problem to be solved. However, most non-numeric similarity measures are based on syntactic grounds, which often fail to produce good matches when confronted with the meaning associated to the words they compare. To overcome this limitation, ontologies can be used to produce similarity measures that are based on semantics. This paper presents an ontology-based approach that can determine the similarity between two classes using feature-based similarity measures that replace features with attributes. The proposed approach is evaluated against other existing similarities. Finally, the effectiveness of the proposed approach is illustrated with a case study on product–service–system design problems.     

一种基于形式概念分析的问答系统答案抽取的研究

为了在问答系统中尝试回答更加复杂的问题,有必要存在一种原理性的方法来动态产生不同的问答策略。提出在问答系统中使用形式概念分析（FCA）来抽取答案。在抽取过程中,首先在常问问题集（FAQs）中寻找已经存在的问题,如果在FAQs中的答案不满足用户的需求,再通过搜索引擎获取相关的文档。接着利用这些文档中前N个文档构建概念格,进而采用概念匹配在格中抽取答案。对于不同的问题,使用不同的策略进行匹配和抽取答案。     

基于贝叶斯网络和互信息的检索用户模型

个性化信息服务越来越成为信息检索领域研究的热点.将贝叶斯网络和互信息相结合,用于个性化检索的用户建模中,建立了一个能同时表达特征词的统计分布和特征词间的语义相关性的用户模型.模型以贝叶斯网络结构为框架,包含了特征词的概率统计信息和特征词间互信息,并引入了时间机制.实验结果表明,用该模型进行信息检索,在查全率和查准率方面都得到了提高.     

An approach to identify influential building blocks and linkages in an information resource network

An information resource network (IRN) is a time-ordered and potentially interrelated set of information elements. Examples include papers within a research domain, blog postings dealing with a certain topic, and information records within a company. We present a structured analysis to identify influential building blocks and linkages in a general IRN and show that our approach can be used for large networks of information nodes. Our method compensates for biases that can emerge at the edges of such time-dependent networks. Importantly, our focus is on the information elements and not on the authors of such information. We illustrate this process using one example of a resource network — research papers in a given domain. Our method can be implemented in any domain that can be represented as time-ordered, interrelated components of information sets.     

An objective-oriented approach to program comprehension using multiple information sources

Program comprehension is a key activity throughout software maintenance and reuse. The knowledge acquired through comprehending programs can guide engineers to perform various kinds of software maintenance and reuse tasks. The effective comprehension strategy and the associated efficient approach, as well as the sophisticated tool support, are the indispensable elements for an entire solution to program comprehension to reduce the high costs of this nontrivial activity. This paper presents an objective-oriented comprehension strategy, contrasting to the traditional comprehensive understanding strategy in the literature. It is a kind of on-demand understanding for specific tasks and more effective in practice. In addition, using multiple information sources to understand programs is proposed with the corresponding framework. From these two points of views, we propose a feature-oriented program comprehension approach using requirement documentation. This approach aims at a specific category of feature-related software maintenance and reuse tasks. Case studies are conducted to evaluate the proposed solution. Results from the studied cases show that the experimental prototype provides more explicit advices for software engineers when performing these tasks.     

在线社交网络中信息传播模式的特征分析

在线社交网络以其独特的传播优势,已成为一种流行的社交媒体平台。针对在线社交网络中信息传播模式的形式特点,结合传染病动力学原理,提出了在线社交网络中的信息传播模型。模型考虑了不同用户行为对传播机理的影响,并建立了不同用户节点的演化方程组,模拟了信息传播的过程,分析了不同类型的用户在网络中的行为特征以及影响信息传播的主要因素。实验结果表明：不同类型的用户在信息传播过程中有着特定的行为规律,信息不会无限制地传播,并在最终达到平稳状态,并且传播系数和免疫系数越大,信息传播达到稳态的速度就越快。     

空间信息网络多址接入技术研究进展

空间信息网络具有高动态拓扑,大时空尺度、多样化业务和星上资源受限等特点。随着星间组网技术的日益成熟和航天活动的不断增加,航天器用户的动态多址接入问题已经成为影响网络性能的关键因素,得到了国内外研究机构的广泛关注。首先分析了典型多址接入技术的主要特点;然后在空间信息网络场景中,对基于竞争的分布式接入控制、基于无冲突的集中式接入控制以及多协议混合接入控制等三种多址接入技术进行了性能分析,对比剖析了三种多址接入方法的优势与不足。最后,分别提出了典型多址接入技术在未来空间信息网络中的典型应用场景和适应性改造方法。     

Probabilistic performance analysis for parallel search techniques

This paper discusses the performance analysis of two generic fundamental parallel search techniques on shared memory multi-processor systems in solving the constraint satisfaction problem (CSP). Probabilistic analysis on their expected computation steps needed and their inherent load-balancing capability is performed. Corresponding experimental results are alsoprovided to verify the correctness of the proposed analysis. This fundamental analysis approach can be further applied to various advanced parallel search techniques or various problem solving techniques on parallel platforms. This research was supported in part by the University of Texas at San Antonio under the Faculty Research Award program     

应用模糊分析法对评价网络信息安全的有效研究

如今网络的普及率令人惊叹,然而由于非法入侵所造成的信息安全隐患时时刻刻都存在着.为了尽量降低非法入侵给网络带来的损害并避免此类安全隐患,如今研发出了一种名为模糊层次分析法的网络安全态势评估模型,首先完成对网络安全指标体系的构建,运用参差分析法对计算机各评估指标进行权重和分析.最后采用模糊分析法来建立一个网络信息安全态势一致性评判标准,最终达到综合评估网络信息安全态势的目的.相关研究结果表明,这种模糊层次分析法能够在很大程度上提升网络信息安全态势评估的准确度,结果更加精确而客观,实用性很强.文章旨在对网络信息安全评估的问题进行研究.     

An adaptive PNN-DS approach to classification using multi-sensor information fusion

In this paper, an adaptive neural network approach to classification which combines modified probabilistic neural network and D-S evidence theory (PNN-DS) is proposed. It attempts to deal with the drawbacks of information uncertainty and imprecision using single classification algorithm. This PNN-DS approach firstly adopts a modified PNN to obtain posteriori probabilities and make a primary classification decision in feature-level fusion. Then posteriori probabilities are transformed to masses noting the evidence of the D-S evidential theory. Finally advanced D-S evidential theory is utilized to gain more accurate classification results in the last decision-level fusion. In order to implement PNN-DS, covariance matrices are firstly employed in the modified PNN module to replace the singular smoothing factor in the PNN’s kernel function, and linear function is utilized in the pattern of summation layer. Secondly, the whole scheme of the proposed approach is explained in depth. Thirdly, three classification experiments are carried out on the proposed approach and a large amount of comparable analyses are done to demonstrate the effectiveness and robustness of the proposed approach. Experiments reveal that the PNN-DS outperforms BPNN-DS, which provides encouraging results in terms of classification accuracy and the speed of learning convergence.     

Interactive pattern analysis for relevance feedback in multimedia information retrieval

Relevance feedback is a mechanism to interactively learn a users query concept online. It has been extensively used to improve the performance of multimedia information retrieval. In this paper, we present a novel interactive pattern analysis method that reduces relevance feedback to a two-class classification problem and classifies multimedia objects as relevant or irrelevant. To perform interactive pattern analysis, we propose two online pattern classification methods, called interactive random forests (IRF) and adaptive random forests (ARF), that adapt a composite classifier known as random forests for relevance feedback. IRF improves the efficiency of regular random forests (RRF) with a novel two-level resampling technique called biased random sample reduction, while ARF boosts the performance of RRF with two adaptive learning techniques called dynamic feature extraction and adaptive sample selection. During interactive multimedia retrieval, both ARF and IRF run two to three times faster than RRF while achieving comparable precision and recall against the latter. Extensive experiments on a COREL image set (with 31,438 images) demonstrate that our methods (i.e., IRF and RRF) achieve at least a improvement on average precision and recall over the state-of-the-art approaches.     

A timed mobile agent planning approach for distributed information retrieval in dynamic network environments

The number of mobile agents and total execution time are two factors used to represent the system overhead that must be considered as part of mobile agent planning (MAP) for distributed information retrieval. In addition to these two factors, the time constraints at the nodes of an information repository must also be taken into account when attempting to improve the quality of information retrieval. In previous studies, MAP approaches could not consider dynamic network conditions, e.g., variable network bandwidth and disconnection, such as are found in peer-to-peer (P2P) computing. For better performance, mobile agents that are more sensitive to network conditions must be used. In this paper, we propose a new MAP approach that we have named Timed Mobile Agent Planning (Tmap). The proposed approach minimizes the number of mobile agents and total execution time while keeping the turnaround time to a minimum, even if some nodes have a time constraint. It also considers dynamic network conditions to reflect the dynamic network condition more accurately. Moreover, we incorporate a security and fault-tolerance mechanism into the planning approach to better adapt it to real network environments.     

基于大数据分析的互联网公开情报发现技术及系统研究

从网络空间的海量信息中发现公开情报信息,对于维护国家安全和社会稳定具有重要意义.文章通过开发的大数据网络公开情报发现系统,研究多源异构数据的采集、融合、分析及展示;通过设计网络空间对象关联分析系统来分析处理海量信息,从而能够支持不良信息的溯源、重点关注用户账号的关联、网络嫌疑人身份信息的协同搜索等.