This special section is based on collaborative research results accomplished by the two teams under the support by the International Partnership Programs for Creative Research Teams, funded by the Chinese Academy of Sciences and State Administration of Foreign Experts Affairs. The two teams are on Intelligence and Security Informatics and Advanced Computer Systems and Architectures, respectively. The papers selected for this special section are peer-reviewed through the normal review process of the JCST.
The first group consists of five contributions, which record recent progress in the representative growing fields of security informatics and social computing. The first paper, "Customer Activity Sequence Classification for Debt Prevention in Social Security'', by Huaifeng Zhang et al., proposes a novel hierarchical algorithm for sequence classification using discriminative sequential patterns. The algorithm first mines for the sequential patterns that are most strongly correlated to each target class, and then employs pattern pruning and coverage test to select the mined patterns. The patterns that pass the test in each loop are used to form sub-classifiers at different levels of the final classifier. The authors apply the proposed algorithm to the prediction of debt occurrences based on customer activity sequence data, and show the efficiency and effectiveness of the algorithm in real-world social security application.
Security informatics and social computing are faced with the same research challenge of analyzing huge amounts of data from various information sources. Machine learning methods play a critical role in such domains. "Performance Evaluation of Machine Learning Methods in Cultural Modeling'', by Xiao-Chen Li et al., investigates the performance of representative classification methods in cultural modeling and analyzes the empirical results as to group behavior forecasting using benchmark cultural data sets. Cultural modeling addresses important research issues such as the discovery of the correlation between cultural factors and organizations' behavior, efficient and effective identification of behavioral patterns of organizations from tons of cultural-related data, and the prediction of organizational behavior based on the cultural context.
Due to the data characteristics and information overload, data mining techniques have become popular in recent years. However, many information-intensive, knowledge-critical domains in security informatics and social computing require a pervasive data analysis platform so that decisions can be made rapidly under distributed and dynamic system environments. "Ubiquitous Mining with Interactive Data Mining Agents'', by Xin-Dong Wu et al., presents an interactive data mining agent --- OIDM, which provides three categories (i.e., classification, association analysis and clustering) of data mining tools, and interacts with the user to facilitate the mining process. OIDM can help users find appropriate mining algorithms, refine and compare the mining process and finally achieve the best mining results, so that security informatics and social computing applications can benefit from it.
As a typical representation of social computing and Web 2.0 application, collaborative social annotation systems allow users to record and share their original keywords or tag attachments to Web resources. These annotations are a method for organizing and labeling information, however, there exist problems such as spam and synonymous annotations. "Exploring Social Annotations with the Application to Web Page Recommendation'', by Hui-Qian Li et al., discusses the advantage of organizing social annotations from semantic perspective and embedding them into algorithms for knowledge discovery. The authors propose four graphic models in which user, Web page and annotation clusters are associated based on real users' surfing and annotating activity patterns. The experimental results show that the graphic models outperform the classical methods and are robust for the real applications.
In recent years, the scope of social computing has expanded tremendously, with almost all branches of software research and practice strongly feeling its impact. The last paper in this group, "Innovative Batik Design with an Interactive Evolutionary Art System'', by Yang Li et al., explores the use of Interactive Evolutionary Algorithm (IEA) in an art system, with the goal of enhancing user's creativity to generate innovative Batik-like patterns. In the paper, first, a new representation is proposed to capture the features in Batik and create innovative patterns through evolutionary processes; second, an out-breeding mechanism is applied to the system, in order to sustain user's interests for a longer period. The authors develop the first Batik design system, and the experimental results show its effectiveness and potential in evolving novel Batik design.
The second group of this special section contains four research papers on advanced computer systems and architectures. They represent recent progress in system-level simulation, many-core architecture, design for reliable Network-on-Chip (NOC), and DRAM row buffer locality optimization.
The paper "SimK: A Large-Scale Parallel Simulation Engine'' by Jian-Wei Xu et al. proposes a parallel simulator engine (SimK) towards the prevalent SMP/CMP platform, which aims at large-scale fine-grained computer system simulation. Based on SimK, large-scale parallel simulators HppSim and HppNetSim have been developed, which can simulate a full supercomputer system and its interconnection network respectively.
The paper "Godson-T: An Efficient Many-Core Architecture for Parallel Program Executions'' by Dong-Rui Fan et al. proposes a many-core architecture, Godson-T. It features a region-based cache coherence protocol, asynchronous data transfer agents and hardware-supported synchronization mechanisms, to provide full potential for high efficiency of the on-chip resource utilization.
The paper "Selected Crosstalk Avoidance Code for Reliable Network-on-Chip'' by Ying Zhang et al. proposes a reliable NOC design using a code with the capability of both crosstalk avoidance and single error correction. It can handle possible error caused by either crosstalk effects or single event upset (SEU). In comparison with previous crosstalk avoidance methods, the proposed method can reduce wire overhead, power dissipation and the total delay.
The paper "PARBLO: Page-Allocation-Based DRAM Row Buffer Locality Optimization '' by Wei Mi et al. proposes a new page-allocation-based optimization that works seamlessly together with some existing hardware and software optimizations to eliminate significantly more row buffer conflicts. The proposed method can reduce row buffer miss rates by up to 76%, and translates into performance speedups by up to 15%.
The guest editors hope that the perspectives, technological developments, research findings and empirical findings as presented in this special section will help encourage exciting research on the related fields. We thank all contributing authors and reviewers for their hard work. In particular, we thank the Journal of Data Acquisition and Processing editorial staffs for their significant assistance in the process.

From a data mining perspective, sequence classification is to build a classifier using frequent sequential patterns. However, mining for a complete set of sequential patterns on a large dataset can be extremely time-consuming and the large number of patterns discovered also makes the pattern selection and classifier building very time-consuming. The fact is that, in sequence classification, it is much more important to discover discriminative patterns than a complete pattern set. In this paper, we propose a novel hierarchical algorithm to build sequential classifiers using discriminative sequential patterns. Firstly, we mine for the sequential patterns which are the most strongly correlated to each target class. In this step, an aggressive strategy is employed to select a small set of sequential patterns. Secondly, pattern pruning and serial coverage test are done on the mined patterns. The patterns that pass the serial test are used to build the sub-classifier at the first level of the final classifier. And thirdly, the training samples that cannot be covered are fed back to the sequential pattern mining stage with updated parameters. This process continues until predefined interestingness measure thresholds are reached, or all samples are covered. The patterns generated in each loop form the sub-classifier at each level of the final classifier. Within this framework, the searching space can be reduced dramatically while a good classification performance is achieved. The proposed algorithm is tested in a real-world business application for debt prevention in social security area. The novel sequence classification algorithm shows the effectiveness and efficiency for predicting debt occurrences based on customer activity sequence data.

Cultural modeling (CM) is an emergent and promising research area in social computing. It aims to develop behavioral models of human groups and analyze the impact of culture factors on human group behavior using computational methods. Machine learning methods, in particular classification, play a critical role in such applications. Since various cultural-related data sets possess different characteristics, it is important to gain a computational understanding of performance characteristics of various machine learning methods. In this paper, we investigate the performance of seven representative classification algorithms using a benchmark cultural modeling data set and analyze the experimental results as to group behavior forecasting.

Due to the increasing availability and sophistication of data recording techniques, multiple information sources and distributed computing are becoming the important trends of modern information systems. Many applications such as security informatics and social computing require a ubiquitous data analysis platform so that decisions can be made rapidly under distributed and dynamic system environments. Although data mining has now been popularly used to achieve such goals, building a data mining system is, however, a nontrivial task, which may require a complete understanding on numerous data mining techniques as well as solid programming skills. Employing agent techniques for data analysis thus becomes increasingly important, especially for users not familiar with engineering and computational sciences, to implement an effective ubiquitous mining platform. Such data mining agents should, in practice, be intelligent, complete, and compact. In this paper, we present an interactive data mining agent --- OIDM (online interactive data mining), which provides three categories (classification, association analysis, and clustering) of data mining tools, and interacts with the user to facilitate the mining process. The interactive mining is accomplished through interviewing the user about the data mining task to gain efficient and intelligent data mining control. OIDM can help users find appropriate mining algorithms, refine and compare the mining process, and finally achieve the best mining results. Such interactive data mining agent techniques provide alternative solutions to rapidly deploy data mining techniques to broader areas of data intelligence and knowledge informatics.

Collaborative social annotation systems allow users to record and share their original keywords or tag attachments to Web resources such as Web pages, photos, or videos. These annotations are a method for organizing and labeling information. They have the potential to help users navigate the Web and locate the needed resources. However, since annotations are posted by users under no central control, there exist problems such as spam and synonymous annotations. To efficiently use annotation information to facilitate knowledge discovery from the Web, it is advantageous if we organize social annotations from semantic perspective and embed them into algorithms for knowledge discovery. This inspires the Web page recommendation with annotations, in which users and Web pages are clustered so that semantically similar items can be related. In this paper we propose four graphic models which cluster users, Web pages and annotations and recommend Web pages for given users by assigning items to the right cluster first. The algorithms are then compared to the classical collaborative filtering recommendation method on a real-world data set. Our result indicates that the graphic models provide better recommendation performance and are robust to fit for the real applications.

This paper describes an evolutionary art system, which explores the potential ability of evolutionary computation in Batik design. We investigate the use of Interactive Evolutionary Algorithm (IEA) in our system, with the goal of enhancing user's creativity to generate innovative Batik-like patterns. We focus mainly on two crucial aspects of the system. First, a new representation is proposed to capture the features in Batik and create innovative patterns through evolutionary processes. Second, an out-breeding mechanism is applied to our system, in order to sustain user's interest for a longer period. Our system can search a much larger design space than other systems and can avoid being trapped in a local optimum. We describe the system in detail and the methodology we have adopted in the system. Our experimental results have shown that our newly developed system is effective and has great potentials in evolving novel Batik design. To our best knowledge, this is the first Batik design tool in the world.

Simulation is an important method to evaluate future computer systems. Currently microprocessor architecture has switched to parallel, but almost all simulators remained at sequential stage, and the advantages brought by multi-core or many-core processors cannot be utilized. This paper presents a parallel simulator engine (SimK) towards the prevalent SMP/CMP platform, aiming at large-scale fine-grained computer system simulation. In this paper, highly efficient synchronization, communication and buffer management policies used in SimK are introduced, and a novel lock-free scheduling mechanism that avoids using any atomic instructions is presented. To deal with the load fluctuation at light load case, a cooperated dynamic task migration scheme is proposed. Based on SimK, we have developed large-scale parallel simulators HppSim and HppNetSim, which simulate a full supercomputer system and its interconnection network respectively. Results show that HppSim and HppNetSim both gain sound speedup with multiple processors, and the best normalized speedup reaches 14.95X on a two-way quad-core server.

Moore's law will grant computer architects ever more transistors for the foreseeable future, and the challenge is how to use them to deliver efficient performance and flexible programmability. We propose a many-core architecture, Godson-T, to attack this challenge. On the one hand, Godson-T features a region-based cache coherence protocol, asynchronous data transfer agents and hardware-supported synchronization mechanisms, to provide full potential for the high efficiency of the on-chip resource utilization. On the other hand, Godson-T features a highly efficient runtime system, a Pthreads-like programming model, and versatile parallel libraries, which make this many-core design flexibly programmable. This hardware/software cooperating design methodology bridges the high-end computing with mass programmers. Experimental evaluations are conducted on a cycle-accurate simulator of Godson-T. The results show that the proposed architecture has good scalability, fast synchronization, high computational efficiency, and flexible programmability.

With the shrink of the technology into nanometer scale, network-on-chip (NOC) has become a reasonable solution for connecting plenty of IP blocks on a single chip. But it suffers from both crosstalk effects and single event upset (SEU), especially crosstalk-induced delay, which may constrain the overall performance of NOC. In this paper, we introduce a reliable NOC design using a code with the capability of both crosstalk avoidance and single error correction. Such a code, named selected crosstalk avoidance code (SCAC) in our previous work, joins crosstalk avoidance code (CAC) and error correction code (ECC) together through codeword selection from an original CAC codeword set. It can handle possible error caused by either crosstalk effects or SEU. When designing a reliable NOC, data are encoded to SCAC codewords and can be transmitted rapidly and reliably across NOC. Experimental results show that the NOC design with SCAC achieves higher performance and is reliable to tolerate single errors. Compared with previous crosstalk avoidance methods, SCAC reduces wire overhead, power dissipation and the total delay. When SCAC is used in NOC, it can save 20% area overhead and reduce 49% power dissipation.% than the previous methods could.

DRAM row buffer conflicts can increase memory access latency significantly. This paper presents a new page-allocation-based optimization that works seamlessly together with some existing hardware and software optimizations to eliminate significantly more row buffer conflicts. Validation in simulation using a set of selected scientific and engineering benchmarks against a few representative memory controller optimizations shows that our method can reduce row buffer miss rates by up to 76% (with an average of 37.4%). This reduction in row buffer miss rates will be translated into performance speedups by up to 15% (with an average of 5%)

The task of selecting the most appropriate method for indexing the data according to application requires a careful comparison study of indices of interests. In particular, we consider object movements by tracing their trajectories within a predefined road network. MV3DR-tree and 3DR-tree constitute our first group indexing the objects moving in free movement scenarios. Besides, Mapping and MON-tree are the second group indexing the locations of objects moving over a network of road. Those access methods mainly organize a group of R-tree in order to index the underlying road network and the object movements. Our goal in this study is to evaluate existing proposals under fair circumstances with respect to storage consumption and spatio-temporal query execution performance. In our comparisons, we discuss the structure's sensibility to query's spatial and/or temporal extent as well as the tradeoff arising between two groups in terms of reliability and disk access performance. We believe that revealing the vulnerabilities of the selected structures, especially Mapping and MON-tree motivates us to design more robust organizations.

This article proposes a graph-theoretic methodology for query approximation in Geographic Information Systems, enabling the relaxation of three kinds of query constraints: topological, semantic and structural. An approximate query is associated with a value corresponding to the degree of similarity with the original query. Such a value is computed for topological constraints on the basis of the topological distance between configurations, for semantic constraints using the information content approach, and for structural constraints revisiting the maximum weighted matching problem in bipartite graphs. Finally, the high correlation of our proposal with human judgment is demonstrated by an experiment.

Logic programming under the stable model semantics is proposed as a non-monotonic language for knowledge representation and reasoning in artificial intelligence. In this paper, we explore and extend the notion of {\em compatibility} and the {\it\Lambda} operator, which were first proposed by Zhang to characterize default theories. First, we present a new characterization of stable models of a logic program and show that an extended notion of compatibility can characterize {\em stable submodels}. We further propose the notion of weak auto-compatibility which characterizes the {\em Normal Forward Chaining Construction} proposed by Marek, Nerode and Remmel. Previously, this construction was only known to construct the stable models of FC-normal logic programs, which turn out to be a proper subclass of weakly auto-compatible logic programs. We investigate the properties and complexity issues for weakly auto-compatible logic programs and compare them with some subclasses of logic programs.

Wireless personal area networks (WPANs) are formed in a relatively small area, and coordinator that serves as a central control device plays an important role in the operation and organization of a piconet. Typical applications of WPANs include home automation systems, security systems and health monitoring systems. In these types of systems, guaranteeing seamless connectivity is very important, and communications cannot take place when the coordinator malfunctions. Thus, in the case of a breakdown, it is necessary to elect a new coordinator as soon as possible. For this reason, a distributed coordinator election scheme (DCES) is proposed that considers not only QoS support but also network connectivity in an effort to avoid possible network partition. Simulation results show that the proposed scheme can elect a new coordinator while maintaining QoS and guaranteeing connectivity for a limited period.

Evaluating the human friendliness of vehicles is essential for designing new vehicles since large numbers of human-machine interactions occur frequently inside vehicles. In this research, we develop an integrated framework for vehicle interior design using a digital human model (DHM). In this framework, the knowledge-based parametric modelling function of vehicles is implemented using a commercial computer-aided design (CAD) system. The combination of the DHM and the CAD system enables designers into carry out ergonomic evaluations of various human-vehicle interactions and understand the effects of modifications of vehicle design parameters on occupants during designing. Further, the information on human-vehicle interaction obtained using this system can be transmitted to dedicated biomechanical analysis software. By analysing human motions inside vehicles using such software, we can obtain optimized interior design parameters.

Image denoising with some second order nonlinear PDEs often leads to a staircasing effect, which may produce undesirable blocky image. In this paper, we present a new gradient fidelity term and couple it with these PDEs to solve the problem. At first, we smooth the normal vector fields (i.e., the gradient fields) of the noisy image by total variation (TV) minimization and make the gradient of desirable image close to the smoothed normals, which is the idea of our gradient fidelity term. Then, we introduce the Euler-Lagrange equation of the gradient fidelity term into nonlinear diffusion PDEs for noise and staircasing removal. To speed up the computation of the vectorial TV minimization, the dual approach proposed by Bresson and Chan is employed. Some numerical experiments demonstrate that our gradient fidelity term can help to avoid the staircasing effect effectively, while preserving sharp discontinuities in images.