Chao-Hsien Chu

Anonymous Two-Factor Authentication in Distributed Systems: Certain Goals Are Beyond Attainment

Despite two decades of intensive research, it remains a challenge to design a practical anonymous two-factor authentication scheme, for the designers are confronted with an impressive list of security requirements (e.g., resistance to smart card loss attack) and desirable attributes (e.g., local password update). Numerous solutions have been proposed, yet most of them are shortly found either unable to satisfy some critical security requirements or short of a few important features. To overcome this unsatisfactory situation, researchers often work around it in hopes of a new proposal (but no one has succeeded so far), while paying little attention to the fundamental question: whether or not there are inherent limitations that prevent us from designing an “ideal” scheme that satisfies all the desirable goals? In this work, we aim to provide a definite answer to this question. We first revisit two foremost proposals, i.e. Tsai et al.s scheme and Lis scheme, revealing some subtleties and challenges in designing such schemes. Then, we systematically explore the inherent conflicts and unavoidable trade-offs among the design criteria. Our results indicate that, under the current widely accepted adversarial model, certain goals are beyond attainment. This also suggests a negative answer to the open problem left by Huang et al. in 2014. To the best of knowledge, the present study makes the first step towards understanding the underlying evaluation metric for anonymous two-factor authentication, which we believe will facilitate better design of anonymous two-factor protocols that offer acceptable trade-offs among usability, security and privacy.

A genetic algorithm for cellular manufacturing design and layout

Abstract Cellular manufacturing (CM) is an approach that can be used to enhance both flexibility and efficiency in today’s small-to-medium lot production environment. The design of a CM system (CMS) often involves three major decisions: cell formation, group layout, and group schedule. Ideally, these decisions should be addressed simultaneously in order to obtain the best results. However, due to the complexity and NP-complete nature of each decision and the limitations of traditional approaches, most researchers have only addressed these decisions sequentially or independently. In this study, a hierarchical genetic algorithm is developed to simultaneously form manufacturing cells and determine the group layout of a CMS. The intrinsic features of our proposed algorithm include a hierarchical chromosome structure to encode two important cell design decisions, a new selection scheme to dynamically consider two correlated fitness functions, and a group mutation operator to increase the probability of mutation. From the computational analyses, these proposed structure and operators are found to be effective in improving solution quality as well as accelerating convergence.

The Adoption and Implementation of RFID Technologies in Healthcare: A Literature Review

Radio Frequency Identification (RFID) technology not only offers tracking capability to locate equipment, supplies and people in real time, but also provides efficient and accurate access to medical data for health professionals. However, the reality of RFID adoption in healthcare is far behind earlier expectation. This study reviews literature on the use of RFID in healthcare/hospitals following a formal innovation-decision framework. We aim to identify the common applications, potential benefits, barriers, and critical success factors. Our study facilitates quick assessment and provides guidance for researchers and practitioners in adopting RFID in medical arenas. Many earlier adopters in healthcare found RFID to be functional and useful in such areas as asset tracking and patient identification. Major barriers to adoption include technological limitations, interference concerns, prohibitive costs, lack of global standards and privacy concerns. Better designed RFID systems with low cost and privacy issues addressed are needed to increase acceptance of RFID in healthcare.

A fuzzy clustering approach to manufacturing cell formation

Cell formation, one of the most important problems faced in designing cellular manufacturing systems, is to group parts with similar geometry, function, material and process into part families and the corresponding machines into machine cells. There has been an extensive amount of work in this area and, consequently, numerous analytical approaches have been developed. One common weakness of these conventional approaches is that they implicitly assume that disjoint part families exist in the data; therefore, a part can only belong to one part family. In practice, it is clear that some parts definitely belong to certain part families, whereas there exist parts that may belong to more than one family. In this study, we propose a fuzzy c-means clustering algorithm to formulate the problem. The fuzzy approach offers a special advantage over conventional clustering. It not only reveals the specific part family that a part belongs to, but also provides the degree of membership of a part associated with each part...

Leveraging complex event processing for smart hospitals using RFID

RFID technology has been examined in healthcare to support a variety of applications such as patient identification and monitoring, asset tracking, and patient-drug compliance. However, managing the large volume of RFID data and understanding them in the medical context present new challenges. One effective solution for dealing with these challenges is complex event processing (CEP), which can extract meaningful events for context-aware applications. In this paper, we propose a CEP framework to model surgical events and critical situations in an RFID-enabled hospital. We have implemented a prototype system with the proposed approach for surgical management and conducted performance evaluations to test its scalability and capability. Our study provides a feasible solution to improve patient safety and operational efficiency for an RFID-enabled hospital, by providing sense and response capability to detect medically significant events.

Cluster analysis in manufacturing cellular formation

Cluster analysis, an analytical method used for classifying objects, has been developed over the last century, and the literature on cluster analysis has exploded during the past two decades. This technique has been broadly used in the fields of biology, social science, and psychology, but only very little in manufacturing. In this paper, we provide a state-of-the-art review on the use of cluster analysis in cellular formation--one of the first and most important issues in designing cellular manufacturing systems. The problems, research directions, and related literature are presented for further studies.

A comparison of three array-based clustering techniques for manufacturing cell formation

SUMMARY This paper examines three array-based clustering algorithms—rank order clustering (ROC), direct clustering analysis (DCA), and bond energy analysis (BEA)—for manufacturing cell formation. According to our test, bond energy analysis outperforms the other two methods, regardless of which measure or data set is used. If exceptional elements exist in the data set, the BEA algorithm also produces better results than the other two methods without any additional processing. The BEA can compete with other more complicated methods that have appeared in the literature.

The use of RFID in healthcare: Benefits and barriers

Radio Frequency Identification (RFID) technology not only offers tracking capability to locate equipment and people in real time, but also provides efficient and accurate access to medical data for doctors and other health professionals. However, the reality of RFID adoption is far behind earlier expectation. This study reviews the literature on RFID applications in healthcare based on a formal research framework. We aim to identify current opportunities, potential benefits and adoption barriers. Our study shows that most care providers indicated that RFID to be functional and useful in asset tracking and patient identification. Major barriers to RFID adoption in healthcare include prohibitive costs, technological limitations, and privacy concerns. Although RFID offers healthcare practitioners advantages to enhance clinical practice, better designed RFID systems are needed to increase acceptance and proper use of RFID in healthcare.

Data Mining for Network Intrusion Detection: A Comparison of Alternative Methods*

Intrusion detection systems help network administrators prepare for and deal with network security attacks. These systems collect information from a variety of systems and network sources, and analyze them for signs of intrusion and misuse. A variety of techniques have been employed for analysis ranging from traditional statistical methods to new data mining approaches. In this study the performance of three data mining methods in detecting network intrusion is examined. An experimental design (3times2x2) is created to evaluate the impact of three data mining methods, two data representation formats, and two data proportion schemes on the classification accuracy of intrusion detection systems. The results indicate that data mining methods and data proportion have a significant impact on classification accuracy. Within data mining methods, rough sets provide better accuracy, followed by neural networks and inductive learning. Balanced data proportion performs better than unbalanced data proportion. There are no major differences in performance between binary and integer data representation.

Genetic algorithms for integrating cell formation with machine layout and scheduling

Abstract Cellular manufacturing (CM) has been recognized as an innovative practice for companies to gain efficiency as well as flexibility under today’s small-to-medium lot and customization-oriented manufacturing environment. Among the necessary decisions for a successful CM implementation, cell formation (CF), group layout (GL) and group scheduling (GS) are the three most popular ones. These decisions are interrelated and may impact each other but they are often treated separately or as a sequential decision in prior research. In this paper, we propose a new approach to concurrently make the CF, GL and GS decisions. A conceptual framework and mathematical model, which integrates these decisions, are proposed. A hierarchical genetic algorithm (HGA) is developed to solve the integrated cell design problem. Two heuristic operators are proposed to enhance its computational performance. The results from our study indicate that: (1) the concurrent approach often found better solutions than the sequential one, and (2) with the proposed heuristic operators, the HGA procedure performed better than without them.