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BACKGROUND There is little evidence about the association of pre-existing hepatitis C infection (HCV) with outcomes in patients with coronavirus disease 2019 (COVID-19). AIM To assess the prevalence of history of HCV among patients with COVID-19 and to study the relationship of in-hospital mortality in relation with other predictors of poor outcomes in the presence or absence of COVID-19 induced acute liver injury. METHODS In a retrospective single-center study design, 1193 patients with COVID-19 infection were studied. Patients were then classified into those with and without a history of HCV, 50 (4.1%) and 1157 (95.9%) respectively. RESULTS Multivariate cox-regression models showed that age, HCV, D-Dimer, and ferritin were the only predictors of in-hospital mortality. Acute liver injury and fibrosis score (Fib-4 score) were not different between both groups. Multivariate cox-regression model for liver profile revealed that aspartate aminotransferase/ alanine aminotransferase ratio, Fib-4 score, and HCV were predictors of in-hospital mortality. After propensity score matching HCV was the only predictor of mortality in the multivariate cox-regression model. A model including HCV was found to add predictive value to clinical and laboratory parameters. CONCLUSION In patients with COVID-19, history of HCV infection leads to an accentuated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virulence, irrespective of baseline comorbidities, admission laboratory variables, or COVID-19-induced liver injury, which may be related to extrahepatic effects of HCV leading to enhanced ACE-2/TMPRSS mechanisms of SARS-CoV-2 viral entry, baseline cytokine-mediated pro-inflammation, and endothelial dysfunction.

Procedures play a key role in ensuring safe operation at nuclear power plants (NPPs). Development and maintenance of a large number of procedures reflecting the best knowledge available in all relevant areas is a complex job. This paper introduces a newly developed methodology and the implemented software, called iExtractor, for the extraction of syntactic and semantic information from NPP procedures utilizing natural language processing (NLP)-based technologies. The steps of the iExtractor integrated with sets of rules and an ontology for NPPs are described in detail with examples. Case study results of the iExtractor applied to selected procedures of a U.S. commercial NPP are also introduced. It is shown that the iExtractor can provide overall comprehension of the analyzed procedures and indicate parts of procedures that need improvement. The rich information extracted from procedures could be further utilized as a basis for their enhanced management.

Purpose The refined process structure tree (RPST), the hierarchy of non-overlapping single-entry single-exit (SESE) regions of a process model, has been utilized for better comprehension and more efficient analysis of business process models. Existing RPST methods, based on the triconnected components of edges, fail to identify a certain type of SESE region. The purpose of this paper is to introduce an alternative method for generating a complete RPST utilizing rather simple techniques. Design/methodology/approach The proposed method first focuses on the SESE regions of bonds and rigids, from the innermost ones to the outermost ones, utilizing dominance and post-dominance relations. Then, any SESE region of a series nested in a bond or a rigid is identified with a depth-first search variation. Two-phase algorithms and their completeness proofs, a software tool incorporating visualization of stepwise outcomes, and the experimental results of the proposed method are provided. Findings The proposed method utilizes simple techniques that allow their straightforward implementation. Visualization of stepwise outcomes helps process analysts to understand the proposed method and the SESE regions. Experiments with 604 SAP reference models demonstrated the limitation of the existing RPST methods. The proposed method, however, completely identified all types of SESE regions, defined with nodes, in less computation time than with the old methods. Originality/value Each triconnected component of the undirected version of a process model is associated with a pair of boundary nodes without discriminating between the entry and the exit. Here, each non-atomic SESE region is associated with two distinct entry and exit nodes from the original model in the form of a directed graph. By specifying the properties of SESE regions in more comprehensible ways, this paper facilitates a deeper understanding of SESE regions rather than relying on the resulting RPST.

Automating information extraction from legal documents and formalising them into a machine understandable format has long been an integral challenge to legal reasoning. Most approaches in the past consist of highly complex solutions that use annotated syntactic structures and grammar to distil rules. The current research trend is to utilise state-of-the-art natural language processing (NLP) approaches to automate these tasks, with minimum human interference. In this paper, based on its functional aspects, we propose a legal taxonomy of semantic types in Korean legislation, such as definitional provision, deeming provision, penalty, obligation, permission, prohibition, etc. In addition to this, a NLP classifier has been developed to facilitate the automated legal norms classification process and an overall F1 score of 0.97 has been achieved.

Automating information extraction from legal documents and formalising them into a machine understandable format has long been an integral challenge to legal reasoning. Most approaches in the past consist of highly complex solutions that use annotated syntactic structures and grammar to distil rules. The current research trend is to utilise state-of-the-art natural language processing (NLP) approaches to automate these tasks, with minimum human interference. In this paper, based on its functional aspects, we propose a legal taxonomy of semantic types in Korean legislation, such as definitional provision, deeming provision, penalty, obligation, permission, prohibition, etc. In addition to this, a NLP classifier has been developed to facilitate the automated legal norms classification process and an overall F1 score of 0.97 has been achieved.

Robust design techniques, which are based on the concept of building quality into products or processes, are increasingly popular in many manufacturing industries. In this paper, we propose a new robust design model in the context of pharmaceutical production research and development. Traditional robust design principles have often been applied to situations in which the quality characteristics of interest are typically time insensitive. In pharmaceutical manufacturing processes, time-oriented quality characteristics, such as the degradation of a drug, are often of interest. As a result, current robust design models for quality improvement which have been studied in the literature may not be effective in finding robust design solutions. To address such practical needs, this paper develops a robust design model using censored data, which is perhaps the first attempt in the robust design field. We then study estimation methods, such as the expectation–maximization algorithm and the maximum likelihood method, in the robust design context. Finally, comparative studies are discussed for model verification via a numerical example.

Existence of cycles (or loops) is one of the main sources that make the analysis of workflow models difficult. Several approaches of structural verification exist in the literature, but how to verify cyclic workflow models efficiently in a comprehensible form remains an open research question. Thus, a novel structural verification approach for cyclic workflow models by means of acyclic decomposition and reduction of loops is introduced in this paper with the following contributions. First, acyclic decomposition of natural loops, further enhanced by reduction of nested loops, enables existing verification techniques, normally dealing with acyclic models, to handle workflow models with natural loops. Second, instantiation of an irreducible loop into natural loops, altogether with reduction of concurrent loop entries, enables the proposed approach to handle workflow models with irreducible loops. Last, diagnostic information, provided by the proposed approach, helps stakeholders correct and improve their workflow models. Two examples are provided to show that the proposed approach is systematic and practical. In addition, a prototype of the proposed approach is developed. Its execution result shows that, while providing diagnostic information, the proposed approach can handle workflow models with arbitrary cycles effectively.

Groundwater is one of the major sources of water supply for domestic, industrial, and agricultural purposes. Intensive water resources constructions in past decades have had huge impacts on hydrological systems. Recently, groundwater dams have received consistent attention as alternative water supply systems with minimal environmental destructions. Groundwater dams are usually of smaller capacity and costs much less compared with river dams. Therefore, it can be a very attractive solution especially for those small provincial cities suffering severe months-long drought every year. As an application of computer science technologies develops, a number of information systems are utilized for the sustainable development for water resources. Recently, groundwater dams have received consistent attention as alternative water supply systems with minimal environmental destruction. Since groundwater dams are constructed at the height close to sea level, optimal water-pumping strategy based on accurate forec...

Robust design techniques, which are based on the concept of building quality into products or processes, are increasingly popular in many manufacturing industries. In this paper, we propose a new robust design model in the context of pharmaceutical production research and development. Traditional robust design principles have often been applied to situations in which the quality characteristics of interest are typically time insensitive. In pharmaceutical manufacturing processes, time-oriented quality characteristics, such as the degradation of a drug, are often of interest. As a result, current robust design models for quality improvement which have been studied in the literature may not be effective in finding robust design solutions. To address such practical needs, this paper develops a robust design model using censored data, which is perhaps the first attempt in the robust design field. We then study estimation methods, such as the expectation–maximization algorithm and the maximum likelihood method, in the robust design context. Finally, comparative studies are discussed for model verification via a numerical example.

Pharmaceutical formulation design with the development of analytical technologies and methodologies is an integral part of a drug’s research and development. Conventional pharmaceutical formulation design problems are typically studied under the assumption of an unconstrained experimental region with a single quality characteristic. In real world pharmaceutical formulation design problems, however, we are often faced with multiple quality characteristics with a constrained experimental region due to physical limitations. In order to address these needs, we propose a multidisciplinary optimization process for the optimal design of pharmaceutical formulations by developing a response surface simplex design (RSSD) and a response surface D-optimal design (REDD) for experimental modeling and by developing a partitioned composite desirability function (PCDF) for multidisciplinary optimization. Two actual case studies are also presented here for model verification purposes.

The growing availability of Web services provides a new opportunity for enabling new ways of system integration and interoperability, which are needed to meet the needs of global e-business. In this study, we propose an extensible workflow architecture based on Web services and object-oriented techniques. The proposed architecture contains three components, the Open Kernel Framework, the Universal Resource Manager, and the Process Definition Publisher. We show how this architecture can help extend workflow systems with new features dynamically such as new workflow patterns, new process semantics, new workflow resources, and even new process languages.

In today’s process-centered business organization, it is imperative that enterprise information system must be converted from task-centered to process-centered system. Traditional software development methodology is function-oriented, in which each function manages its own data and it results in redundancy because data that belongs to one object are stored by several functions. Proposed in this paper is a process-driven software development methodology, in which business process is a major concern and workflow functionalities are identified and specified throughout the entire development life cycle. In the proposed methodology, the development process, modeling tools and deliverables are clarified explicitly. Proposed methodology can be a guideline to practitioners involved in enterprise software development, of which workflow is an essential part.

The reliable in-line monitoring of pharmaceutical processes has been regarded as a key tool toward the full implementation of process analytical technology. In this study, near-infrared (NIR) spectroscopy was examined for use as an in-line monitoring method of the paracetamol cooling crystallization process. The drug powder was dissolved in ethanol-based cosolvent at 60°C and was cooled by 1°C/min for crystallization. NIR spectra acquired by in-line measurement were interpreted by principal component analysis combined with off-line characterizations via X-ray diffraction, optical microscopy, and transmission electron microscopy. The whole crystallization process appeared to take place in three steps. A metastable form II polymorph of paracetamol was formed and transformed into the stable form I polymorph on the way to the growth of pure form I by cooling crystallization. These observations are consistent with a previous focused beam reflectance method-based study (Barthe et al., Cryst Growth Des 8:3316–3322, 2008).

Distributed denial-of-service (DDoS) attacks pose a serious threat to network security. There have been a lot of methodologies and tools devised to detect DDoS attacks and reduce the damage they cause. Still, most of the methods cannot simultaneously achieve (1) efficient detection with a small number of false alarms and (2) real-time transfer of packets. Here, we introduce a method for proactive detection of DDoS attacks, by classifying the network status, to be utilized in the detection stage of the proposed anti-DDoS framework. Initially, we analyse the DDoS architecture and obtain details of its phases. Then, we investigate the procedures of DDoS attacks and select variables based on these features. Finally, we apply the k-nearest neighbour (k-NN) method to classify the network status into each phase of DDoS attack. The simulation result showed that each phase of the attack scenario is classified well and we could detect DDoS attack in the early stage., {"references":["J.B.D. Cabrera, et al. \"Proactive detection of distributed denial of service\nattacks using MIB traffic variablesÔÇöa feasibility study\", Proceedings of\nthe seventh IFIP/IEEE International Symposium on Integrated Network\nManagement, Seattle, May, 2001, pp. 1-14.","S. Chebrolu, A. Abraham, and P. J. Thomas, \"Feature deduction and\nensemble design of intrusion detection systems\", Computers & Security,\nVol. 24, issue 4, pp. 295-307. 2005.","D. Gavrilis, and E. Dermatas, \"Real-time detection of distributed denialof-\nservice attacks using RBF networks and statistical features\",\nComputer Networks, Vol. 48, issue 2, pp. 235-245. 2005.","G. Guo, H. Wang, D. Bell, Y. Bi, and K. Greer, \"Using kNN model for\nautomatic text categorization\", Soft Computing - A Fusion of\nFoundations, Methodologies and Applications, Vol. 10, No. 5, pp. 423-\n430. 2006.","S. Haykin, Neural Networks: A Comprehensive Foundation, Upper\nSaddle River, Prentice Hall, New Jersey, 1994.","J. Ioannidis, and S. M. Bellovin, \"Implementing pushback: router-based\ndefense against DDoS attacks\", Presented at Network and Distributed\nSystem Security Symposium, 2002.","M. Kim, H. Na, K. Chae, H. Bang, and J. Na, \"A Combined Data Mining\nApproach for DDoS Attack Detection\", ICOIN 2004, LNCS 3090,\nSpringer-Verlag, Berlin Heidelberg, pp. 943-950.","K. Lee, J. Kim, K. H. Kwon, Y. Han, and S. Kim, \"DDoS attack\ndetection method using cluster analysis\", Expert Systems with\nApplications, 2007, Vol. 34, pp. 1659-1665.","H. W. Lee, \"SVM Based Packet Marking Technique for Traceback on\nMalicious DDoS Traffic\", ICOIN 2006, LNCS 3961, Springer-Verlag,\nBerlin Heidelberg, pp. 754-763.\n[10] S. C. Lin, and S. S. Tseng, \"Constructing detection knowledge for DDoS\nintrusion tolerance\", Expert Systems with Applications, 2004, Vol. 27,\npp. 379-390.\n[11] R. Mahajan, S. M. Bellovin, S. Floyd, J. Ioannidis, V. Paxson, and S.\nShenker, \"Controlling high bandwidth aggregate in the network\", ACM\nSIGCOMM Computer Communication Review, 2002, Vol. 32, No. 3\npp. 62 - 73.\n[12] J. May, J. Peterson, and J. Bauman, \"Attack detection in large\nnetworks\", Proceedings of the DARPA Information Survivability\nConference & Exposition II (DISCEX -01), 2001, Vol. 1, pp.15-21.\n[13] MIT Lincoln Lab, 2000, DARPA intrusion detection scenario specific\ndatasets,\nhttp://www.ll.mit.edu/IST/ideval/data/2000/2000_data_index.html.\n[14] T. M. Mitchell, Machine Learning, MacGraw Hill, New York, 1996.\n[15] K. Park, and H. Lee, \"A proactive approach to distributed DoS attack\nprevention using route-based packet filtering\", Tech. Rep. CSD-00-017,\nDepartment of Computer Sciences, Purdue University, 2000.\n[16] F. Sebastiani, \"Machine learning in automated text categorization\",\nACM Computing Surveys, Vol. 34, issue 1, Consiglio Nazionale delle\nRicerche, Italy, 2002, pp. 1-47.\n[17] A. Sharma, A. K. Pujari, and K. K. Paliwal, \"Intrusion detection using\ntext processing techniques with a kernel based similarity measure\",\nComputers & Security, 2007, Vol. 26, issue 7-8, 2007, pp. 488-495.\n[18] B. Todd, \"Distributed Denial of Service Attacks\", 2000.\nhttp://www.linuxsecurity.com/resource_files/intrusion_detection/ddosfaq.\nhtml\n[19] X. Xu, Y. Sun, and Z. Huang, \"Defending DDoS Attacks Using Hidden\nMarkov Models and Cooperative Reinforcement Learning\", Yang C.C.\net al. (Eds.): PAISI 2007, LNCS 4430, Springer-Verlag, Berlin\nHeidelberg, pp. 196-207.\n[20] A. Yaar, A. Perrig, and D. Song, \"Pi: a path identification mechanism to\ndefend against DDos attack\", Proceedings of the IEEE Symposium on\nSecurity and Privacy, 2003, pp. 93-107."]}

In regenerative medicine industry, one of the key problems is to reduce variation of output characteristics so that a generic drug can pass the statistical criteria for bioequivalence tests. Quality by design (QbD) is a set of offline tools in which robust design (RD) plays significant role in controlling variance in the pharmaceutical process recently. The conventional RD approach basically deals with static data while the bioequivalence tests require dynamic (time-oriented) data. The primary objective of this paper is to develop a new RD approach with time-oriented responses to bioequivalence tests for generic drug. Because the responses are a function of control factors and time, it is reasonable that the tentative relationship can be analyzed according to both vertical and horizontal directions in which the response surface methodology (RSM) is utilized. For seeking the optimal control factors setting, a mean squared error (MSE) robust design model is chosen and optimized by utilizing Matlab package. A experimental study is conducted to demonstrate how to apply proposed RD approach in practice and how can it be used to improve the quality of generic drug for meeting statistical criteria of bioequivalence tests.

Project selection problems on management information system (MIS) are often considered a multi-criteria decision-making (MCDM) for a solving method. These problems contain two aspects, such as interdependencies among criteria and candidate projects and qualitative and quantitative factors of projects. However, most existing methods reported in literature consider these aspects separately even though these two aspects are simultaneously incorporated. For this reason, we proposed a hybrid method using analytic network process (ANP) and fuzzy logic in order to represent both aspects. We then propose a goal programming model to conduct an optimization for the project selection problems interpreted by a hybrid concept. Finally, a numerical example is conducted as verification purposes., {"references":["Kuanchin C., Narasimhaiah G., Information System Project Selection\nUsing Fuzzy Logic, IEEE transaction on Systems, Man and\nCybernetics , Vol.28, No.6, 1998, pp. 849-855.","Chen T.C., A Decision Model for Information System Project Selection,\nEngineering Management Conference,IEMC '02, Vol.2, 2002, pp. 585-\n589.","Buss M.D.J., How to rank computer projects, Harvard Business Review,\nVol. 61, No. 1, 1983, pp. 118-125.","Lee J.W, Kim S.H, Using analytic network process and goal\nprogramming for interdependent information system project selection,\nComputers & Operations Research, Vol.27, No.4, 2000, pp. 367-382.","Lucas HC, Moor Jr, A multiple-criterion scoring approach to\ninformation system project selection, INFOR, Vol.14, No.1, 1976, pp. 1-\n12.","Muralidhar K, Santhnanm R, Wilson RL, Using the analytic hierarchy\nprocess for information system project selection, Information and\nManagement Vol.18, Nol.1, 1990, pp. 87-95.","Marc JS, Wilson RL, Using the analytic hierarchy process and goal\nprogramming for information system project selection, Information and\nManagement, Vol.20, 1991, pp. 333-342.","Sanathanam R, Kyparisis GJ, A decision model for interdependent\ninformation system project selection, European Journal of Operational\nResearch, Vol.89, 1996, pp. 380-399.","Sanathanam R, Kyparisis GJ, A multiple criteria decision model for\ninformation system project selection, Computers and Operations\nResearch Vol.22, No.8, 1995, pp. 807-818.\n[10] Santhanam R, Muralidhar K, Schniederjans M, A zero-one goal\nprogramming approach for information system project selection,\nOMEGA, Vol.17, No.6, 1989, pp. 583-93.\n[11] Saaty TL, The analytic hierarchy process. McGraw-Hill, New York,\n1980.\n[12] Saaty TL, The analytic network process, RWS Publications, Expert\nChoice, Inc. 1996.\n[13] Agarwal R., Tanniru M. R., Dacruz M., Knowledge-based support for\ncombining qualitative and quantitative judgment in resource allocation\ndecisions, J. Manage. Inf. Syst., Vol.9, No.1, 1992, pp. 165-184.\n[14] Whalen T., Decision making under uncertainty with various assumptions\nabout available information, IEEE Trans. Syst., Man, Cybern., Vol.14,\n1984, pp. 888-990.\n[15] L. A. Zadeh, The concept of a linguistic variable and its application to\napproximate reasoning I and II, Inf. Sci., Vol.8, 1975, pp. 199-249."]}

The continuous improvement and application of information system technologies have become widely recognized by the industry as critical for maintaining a competitive advantage in the marketplace (Shin et al., 2006). It is also recognized that improvement and application activities are the most efficient and cost-effective when implemented during an early process/product design stage. Data mining (DM) has emerged as one of the key features of many applications in computer science. Often used as a means for predicting the future directions and extracting hidden limitations and specifications of a product/process, DM involves the use of data analysis (DA) tools to discover previously unknown and valid patterns and relationships from a large database. Most DM methods for factor selection reported in literature may yield a number of factors associated with interesting response factors without providing detailed information, such as relationships between the input factor and response, statistical inferences, and analyses (Yang et al., 2007; Witten & Frank, 2005). Based on this, Gardner and Bieker (Gardner & Bieker, 2000) suggested an alternative DA approach toward resolving semiconductor manufacturing problems in order to determine the significant factors. Furthermore, Su et al. (Su et al., 2005) developed an integrated procedure combining a DM method and Taguchi methods. DA is a term coined to describe the process of sifting through large databases for discovering interesting patterns and relationships. This field spans several disciplines such as databases, machine learning, intelligent information systems, statistics, and expert systems. Two approaches that enable the application of standard machine learning algorithms to large databases are factor selection and sampling. Factor selection is known to be an effective method for reducing dimensionality, removing irrelevant and redundant data, increasing mining accuracy, and improving result comprehensibility (Yu & Liu, 2003). Consequently, factor selection has been a fertile field for research and development since the 1970s and proven to be efficient in removing irrelevant and redundant features, increasing efficiency in mining tasks, improving mining performance like predictive accuracy, and enhancing comprehensibility of the learned results. The factor selection algorithm performs a search through the space of feature subsets (Allen, 1974). In general, two categories of the algorithm have been proposed to resolve the factor selection problem. The first category is based on a filter approach that is independent of the learning algorithms and serves as a filter to sieve out the irrelevant factors. The second category is based on a wrapper approach, which uses an induction algorithm itself as part of the function evaluating the factor subset (Langley, 1994). Since most of the filter methods are based on a heuristic O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

There is no doubt that business process management (BPM) has added tremendous value to organizations in virtually every industry. By utilizing business process management systems (BPMS), organizations can streamline and automate tasks, reduce turnaround time, and help ensure regulatory compliance. The foremost considerations when enterprises deploy BPM solutions involve supportive system functions, friendly graphical user interfaces (GUI) and maximal return on investment (ROI), based on the specific processes required, organizational management, and working style of their particular business environment. In order to address the problems associated with BPMS customization, for the flexible coverage of various requirements, this paper takes a three step approach. First, limited customization support of commercial and academic BPMS is reviewed in favor of open source BPMS. Then, advantageous features of uEngine, an open source BPMS which supports flexible customization, are introduced. Finally, API-based and component-based customization in building a BPM application utilizing uEngine is introduced with illustrative examples.

Security services for workflow systems are becoming increasingly important for the interoperability of cross- enterprises in insecure environments. As a security service, the authorization mechanism for confidentiality and integrity has gained growing attention in both the research world and the industry. However, the traditional workflow authorization model (WAM) mainly focuses on the synchronization of authorization flow by using authorization templates (ATs) and is limited in encountering the consequences of workflow changes and/or exceptions, which are very common in today's dynamic business environment. In this paper, we propose an integrated approach of a flexible WAM with dynamic checking features to handle workflow changes and exceptions. Extended temporal role-based access control and flexible workflow authorization template are adopted to further enhance the traditional AT ensuring information confidentiality and integrity. The method of implementing the proposed model is also introduced.

Security issues pertaining to workflow systems are becoming increasingly important for the cross-enterprises interoperability in insecure environments. Among them, access control for information confidentiality and integrity has attracted widespread attention. However, in the context of the contemporary dynamic business environment, the traditional workflow authorization model (WAM) faces limitations in handling the consequences of dynamic workflow changes and exceptions, since it focuses primarily on the synchronization of authorization flow by using authorization templates (ATs). In this paper, we propose a flexible access control with dynamic checking features for handling workflow changes and exceptions. Extended temporal role-based access control and flexible workflow authorization template are adopted in order to further enhance the traditional AT, thereby ensuring information confidentiality and integrity. Additionally, a case study applying the proposed model to uEngine, an open source workflow management system, is presented.

Data mining (DM) has emerged as one of the key features of many applications on information system. While Data Analysis (DA) represents a significant advance in the type of analytical tools currently available, there are limitations to its capability. In order to address one of the limitations on the DA capabilities of identifying a causal relationship, we propose an integrated approach, called robust data mining (RDM), which can reduce dimensionality of the large data set, may provide detailed statistical relationships among the factors and robust factor settings. The primary objective of this paper is two-fold. First, we show how DM techniques can be effectively applied into a wastewater treatment process design by applying a correlation-based feature selection (CBFS) method. This method may be far more effective than any other methods when a large number of input factors are considered on a process design procedure. Second, we then show how DM results can be integrated into a robust design (RD) paradigm based on the selected significant factors. Our numerical example clearly shows that the proposed RDM method can efficiently find significant factors and the optimal settings by reducing dimensionality.

Graphical user interface (GUI) for applications of embedded devices (ED) has been increasingly in demand. Rapid GUI development tools are being required more than ever. However, traditional way of GUI development for ED applications has some limitation in support of visual environment and blocks rapid development. In this paper, we introduce a new mini visual IDE, called VY, which supports rapid development utilizing control library, and simulation of GUI. VY can realize the development of GUI in ED efficiently by means of "what you see is what you get"(WYSIWYG). It consists of five modules: simulator, control library, script complier, script virtual machine (SVM) and visual window. By utilizing the hardware-independent control library of graphical interfaces, an attractive GUI can be produced with much less script effort on the visual window. Then, the script file is compiled into script data (including instructions) which will be executed by the SVM with visiting system calls in ED, and the Simulator will display the resulting GUI. After all these steps, the final GUI can be downloaded into the ED system. In this way, VY supports rapid development of hardware-independent GUI for ED applications on a visual metaphor.

Most data mining (DM) methods reviewed in literature for the factor selection may obtain a number of input factors associated with the interesting response without providing the detailed information, such as relationship between the input factors and response, statistical inferences, and analysis. These DM methods also may not discuss the robustness of solutions, either by considering data preprocesses for outliers and missing values, or by considering uncontrollable noise factors. In order to address these problems, we propose an extended robust data mining (ERDM) model. The main concerns of this model are three-fold. The proposed ERDM conducts outlier test and expectation maximum (EM) algorithm to carry out the data preprocess. The proposed ERDM then reduces the dimensionality to find the significant factors among a large number of input factors using correlation-based feature selection (CBFS) method and best first search (BFS) algorithm. Finally, the proposed model utilizes the theory of robust design to handle the noise factors using the concept of surrogate variable and the response surface methodology (RSM).

Data mining (DM) has emerged as one of the key features of many applications on information system. While a number of data computing and analyzing method to conduct a survey analysis for a job stress evaluation represent a significant advance in the type of analytical tools currently available, there are limitations to its capability such as dimensionality associated with many survey questions and quality of information. In order to address these limitations on the capabilities of data computing and analyzing methods, we propose an advanced survey analysis procedure incorporating DM into a statistical analysis, which can reduce dimensionality of the large data set, and which may provide detailed statistical relationships among the factors and interesting responses by utilizing response surface methodology (RSM). The primary objective of this paper is to show how DM techniques can be effectively applied into a survey analysis related to a job stress evaluation by applying a correlation-based feature selection (CBFS) method. This CBFS method can evaluate the worth of a subset including input factors by considering the individual predictive ability of each factor along with the degree of redundancy between pairs of input factors. Our numerical example clearly shows that the proposed procedure can efficiently find significant factors related to the interesting response by reducing dimensionality.

Role based access control (RBAC) has received much attention for more than a decade as one of the most attractive solutions for access control in web-based information systems. However, its associated characteristics are often not suitable for a varying organization requiring a flexible structure. In order to address this limitation, it may essentially be required to assess more flexibility to access control mechanisms. To this end, we propose a new extended access control mechanisms incorporating three authentication dimensions based on user, department, and role. We then demonstrate that the proposed mechanisms can improve utilization of a web-based knowledge-sharing system.

Perfomrance analysis is a critical component of workflow system management. Workflow models represented in Petri net have advantages of abundant analysis techniques available. Reachability analysis is useful to detect errorneous states of the business logic, however, it has the problem of state explosion. In this paper, we propose the method of hierarchical reachability analysis for workflow models represented in classical Petri net, even for the cases with cycles. Our method allows analyzing with smaller decomposed reachability graphs, each composed of less places, without losing relevant information. In this way, the proposed method conducts reachability analysis efficiently within each smaller block and with simplified upper layer models after abstraction rather than tracing the large one of the whole model.

A critical challenge in workflow analysis and design is the verification of workflow models, considering commercial workflow systems merely provide a simulation tool for validating workflow models through trial and error. As a result, the current workflow technology does not guarantee that workflow models do not fail or will behave in a manner expected by the modeler. While a couple of verification methods have been reported in the recent literature, how to verify cyclic workflow models remains an open research question. In this paper, we propose a novel integrated approach of hierarchical decomposition and verification of cyclic workflows. This result is significant since it helps close the research gap that other known workflow verification methods fail to deal with cyclic workflow models.

This paper introduces a novel method of verifying a cyclic workflow model by partitioning it into acyclic subgraphs. The iterative classification of nodes according to the feedback structure and derivation of acyclic subgraphs are illustrated. We also introduce the concepts of two-phased verification of control flows and decomposition-oriented analysis, which is generally easier to comprehend and verify individually than the whole workflow model.

The internet and local area networks, which are growing rapidly, have witnessed an increase in malicious threads. Therefore, the deployment of security tools is necessary for protecting organisations from external attacks on their information systems, preventing information losses and ensuring secure communication. To this end, the primary objective of this paper is to propose an enhanced methodology for supporting the decision maker in selecting the appropriate security tools for information systems in organisations. First, we construct a security criteria hierarchy as the decision criteria. Then, based on the criteria, we compute the competitiveness score of each security tool and the relative weights among different types of security tools utilising an analytic hierarchy process (AHP). Finally, in order to incorporate a trade-off between all the competitiveness aspects associated with the security tools and costs, a prioritising/weighting mixed integer goal programming (GP) model or a combination of mixed integer GP and weighted-sum model to determine the optimal solutions for the security tools is employed.