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Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on

Issue 5 • Date Sept. 2010

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Displaying Results 1 - 15 of 15
  • Table of contents

    Page(s): C1
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  • IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews publication information

    Page(s): C2
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  • Survey on Contemporary Remote Surveillance Systems for Public Safety

    Page(s): 493 - 515
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1500 KB) |  | HTML iconHTML  

    Surveillance systems provide the capability of collecting authentic and purposeful information and forming appropriate decisions to enhance safety. This paper reviews concisely the historical development and current state of the three different generations of contemporary surveillance systems. Recently, in addition to the employment of the incessantly enlarging variety of sensors, the inclination has been to utilize more intelligence and situation awareness capabilities to assist the human surveillance personnel. The most recent generation is decomposed into multisensor environments, video and audio surveillance, wireless sensor networks, distributed intelligence and awareness, architecture and middleware, and the utilization of mobile robots. The prominent difficulties of the contemporary surveillance systems are highlighted. These challenging dilemmas are composed of the attainment of real-time distributed architecture, awareness and intelligence, existing difficulties in video surveillance, the utilization of wireless networks, the energy efficiency of remote sensors, the location difficulties of surveillance personnel, and scalability difficulties. The paper is concluded with concise summary and the future of surveillance systems for public safety. View full abstract»

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  • Toward Credible Evaluation of Anomaly-Based Intrusion-Detection Methods

    Page(s): 516 - 524
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (341 KB) |  | HTML iconHTML  

    Since the first introduction of anomaly-based intrusion detection to the research community in 1987, the field has grown tremendously. A variety of methods and techniques introducing new capabilities in detecting novel attacks were developed. Most of these techniques report a high detection rate of 98% at the low false alarm rate of 1%. In spite of the anomaly-based approach's appeal, the industry generally favors signature-based detection for mainstream implementation of intrusion-detection systems. While a variety of anomaly-detection techniques have been proposed, adequate comparison of these methods' strengths and limitations that can lead to potential commercial application is difficult. Since the validity of experimental research in academic computer science, in general, is questionable, it is plausible to assume that research in anomaly detection shares the above problem. The concerns about the validity of these methods may partially explain why anomaly-based intrusion-detection methods are not adopted by industry. To investigate this issue, we review the current state of the experimental practice in the area of anomaly-based intrusion detection and survey 276 studies in this area published during the period of 2000-2008. We summarize our observations and identify the common pitfalls among surveyed works. View full abstract»

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  • A Biologically Inspired Sensor Wakeup Control Method for Wireless Sensor Networks

    Page(s): 525 - 538
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (793 KB) |  | HTML iconHTML  

    This paper presents an artificial ant colony approach to distributed sensor wakeup control (SWC) in wireless sensor networks (WSN) to accomplish the joint task of surveillance and target tracking. Each sensor node is modeled as an ant, and the problem of target detection is modeled as the food locating by ants. Once the food is found, the ant will release pheromone. The communication, invalidation, and fusion of target information are modeled as the processes of pheromone diffusion, loss, and accumulation. Since the accumulated pheromone can measure the existence of a target, it is used to determine the probability of ant-searching activity in the next round. To the best of our knowledge, this is the first biologically inspired SWC method in the WSN. Such a biologically inspired method has multiple desirable advantages. First, it is distributive and does not require a centralized control or cluster leaders. Therefore, it is free of the problems caused by leader failures and can save the communication cost for leader selection. Second, it is robust to false alarms because the pheromone is accumulated temporally and spatially and thus is more reliable for wakeup control. Third, the proposed method does not need the knowledge of node position. Two theorems are presented to analytically determine the key parameters in the method: the minimum and maximum pheromone. Simulations are carried out to evaluate the performance of the proposed method in comparison with representative methods. View full abstract»

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  • Anonymity and Monitoring: How to Monitor the Infrastructure of an Anonymity System

    Page(s): 539 - 546
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (518 KB) |  | HTML iconHTML  

    The Tor network is a widely deployed anonymity system on the Internet used by thousands of users every day. A basic monitoring system has been designed and implemented to allow long-term statistics, provide feedback to the interested user, and detect certain attacks on the network. The implementation has been added to TorStatus, a project to display the current state of the Tor network. During a period of six months, this monitoring system collected data, where information and patterns have been extracted and analyzed. Interestingly, the Tor network is very stable with more than half of all the servers located in Germany and the United States. The data also shows a sinusoidal pattern every 24 h in the total number of servers. View full abstract»

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  • An Adaptive Q -Learning Algorithm Developed for Agent-Based Computational Modeling of Electricity Market

    Page(s): 547 - 556
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (278 KB) |  | HTML iconHTML  

    Balancing between exploration and exploitation with adaptation of the Q-learning (QL) parameters to the condition of dynamic uncertain environment has always been a significant subject of interest in the context of reinforcement learning. The peculiarities of the electricity market have provided such complex dynamic economic environment, and consequently have increased the requirement for advancement of the learning methods. In this economic system, the agent's market power plays a vital role in bidding decision-making problem. In order to improve the QL method, as main idea, adaptation of its parameters to the market power is proposed for making a good balance between exploration and exploitation. To implement this adaptation process, due to the fuzzy nature of human's decision-making process, a fuzzy system is designed to map each agent's market power into the QL parameters. Therefore, a fuzzy QL method is developed to model the power supplier's strategic bidding behavior in a computational electricity market. In the simulation framework, the QL algorithm selects the power supplier's bidding strategy according to the past experiences and the values of the parameters, which show the human's risk characteristic. The application of the proposed methodology for the power supplier in a multiarea power system shows the performance improvement in comparison to the QL with fixed parameters. View full abstract»

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  • A Hybrid Recommendation Method with Reduced Data for Large-Scale Application

    Page(s): 557 - 566
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (603 KB) |  | HTML iconHTML  

    Most recommendation algorithms attempt to alleviate information overload by identifying which items a user will find worthwhile. Content-based (CB) filtering uses the features of items, whereas collaborative filtering (CF) relies on the opinions of similar customers to recommend items. In addition to these techniques, hybrid methods have also been suggested to improve the performance of recommendation algorithms. However, even though recent hybrid methods have helped to avoid certain limitations of CB and CF, scalability and sparsity are still major problems in large-scale recommendation systems. In order to overcome these problems, this paper proposes a novel hybrid recommendation algorithm HYRED, which combines CF using the modified Pearson's binary correlation coefficients with CB filtering using the generalized distance-to-boundary-based rating. In the proposed recommendation system, the nearest and farthest neighbors of a target customer are utilized to yield a reduced dataset of useful information by avoiding scalability and sparsity problem when confronted by tremendous volumes of data. The use of reduced datasets enables us not only to lessen the computing effort, but also to improve the performance of recommendations. In addition, a generalized method to combine CF and CB system into a hybrid recommendation system is proposed by developing on the normalization metric. We have used this HYRED algorithm to experiment with all possible combination of CF and statistical-learning-based CB filtering. These experiments have shown that the use of reduced datasets saves computational time, and neighbor information improves performance. View full abstract»

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  • Provably Secure Integrated On/Off-Line Electronic Cash for Flexible and Efficient Payment

    Page(s): 567 - 579
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (892 KB) |  | HTML iconHTML  

    Due to the ubiquity of the Internet and wireless networks, the development of electronic commerce is growing up rapidly. Many payment mechanisms, such as electronic cash (e-cash), credit cards, and electronic wallets, for electronic transactions have been proposed. Especially, e-cash has become popular since it can fully protect the privacy of customers in various electronic transactions. In general, e-cash can be classified into two types, which are on-line e-cash and off-line e-cash, and they are suitable for different applications and environments. All of the proposed e-cash schemes only focus on on-line or off-line e-cash, but not both. In these schemes, users must decide which type of e-cash they will use later when withdrawing. In this paper, we will propose a novel e-cash scheme, which can support each user to withdraw a generic e-cash and decide to spend it as an on-line e-cash or an off-line e-cash when paying. Owing to the integration of on-line and off-line e-cash, our proposed scheme is more convenient for users and more flexible for the bank and shops as compared with the previous schemes. Furthermore, we consider anonymity control, no swindling, tamper resistance, and other key features of e-cash in the proposed scheme. Finally, we also provide formal proofs for the security of the proposed scheme. View full abstract»

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  • Incremental Embedding and Learning in the Local Discriminant Subspace With Application to Face Recognition

    Page(s): 580 - 591
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (551 KB) |  | HTML iconHTML  

    Dimensionality reduction and incremental learning have recently received broad attention in many applications of data mining, pattern recognition, and information retrieval. Inspired by the concept of manifold learning, many discriminant embedding techniques have been introduced to seek low-dimensional discriminative manifold structure in the high-dimensional space for feature reduction and classification. However, such graph-embedding framework-based subspace methods usually confront two limitations: (1) since there is no available updating rule for local discriminant analysis with the additive data, it is difficult to design incremental learning algorithm and (2) the small sample size (SSS) problem usually occurs if the original data exist in very high-dimensional space. To overcome these problems, this paper devises a supervised learning method, called local discriminant subspace embedding (LDSE), to extract discriminative features. Then, the incremental-mode algorithm, incremental LDSE (ILDSE), is proposed to learn the local discriminant subspace with the newly inserted data, which applies incremental learning extension to the batch LDSE algorithm by employing the idea of singular value-decomposition (SVD) updating algorithm. Furthermore, the SSS problem is avoided in our method for the high-dimensional data and the benchmark incremental learning experiments on face recognition show that ILDSE bears much less computational cost compared with the batch algorithm. View full abstract»

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  • Age-Related Physical and Emotional Characteristics to Safety Warning Sounds: Design Guidelines for Intelligent Vehicles

    Page(s): 592 - 598
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (826 KB) |  | HTML iconHTML  

    Recent technological advances have made motor vehicles more intelligent to increase safety and comfort. An intelligent vehicle provides drivers with safety warning information through audible sounds, visual displays, and tactile feedback. However, elderly drivers often have decreased cognitive and psychomotor abilities in the areas such as hearing, eyesight, short-term memory, and spatial perception. Therefore, possible age-related deficits should be considered when designing effective warning systems. This paper evaluates the impact of advancing age on drivers' physical responses and emotional preferences with regard to audible safety warnings that are widely used to warn about driving hazards. Three sound characteristics (frequency, tempo, and intensity) and three age groups (younger, middle, and older) were considered in investigating the effect of age-related hearing loss and reduced speed of movement. Data were collected from 38 drivers who drove on a simulated rural road in a driving simulator. Experimental results showed that age influenced drivers' responses and emotional preference. An appropriate range of warning sounds is suggested. View full abstract»

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  • Special issue on engineering applications of memetic computing

    Page(s): 599
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  • Proven powerful [advertisement]

    Page(s): 600
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  • IEEE Systems, Man, and Cybernetics Society Information

    Page(s): C3
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  • IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews Information for authors

    Page(s): C4
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Aims & Scope

Overview, tutorial and application papers concerning all areas of interest to the SMC Society: systems engineering, human factors and human machine systems, and cybernetics and computational intelligence. 

This Transactions ceased production in 2012. The current retitled publication is IEEE Transactions on Human-Machine Systems.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Dr. Vladimir Marik
(until 31 December 2012)