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In the recent Web environment, the key issue is not who can use the resource, but how to control the usage of resources. Usage control (UCON) is a consolidate solution for this problem. UCON can be applied to solve existing authorization problems and support finer-grained control than any other methods. However, there are many situations in e-business and information systems in which the user consent is needed. For example, a contents provider should obtain the consent of the originator in order to modify digital contents. With this trend, the consent is suggested as a new issue that can establish trust in e-business. Unfortunately, the current UCON model does not support user consent. In this paper, we propose a novel method to control user consent in UCON model. We modify the authorization model of the UCON model to support consent handling and discuss the administration of proposed model. The proposed method extends the coverage of UCON model in security area. It also provides a solution for trust relationships on e-Business and individual privacy protection.

Grid is developed to accomplish large and complex computation by gathering distributed computing resources. Grid employs information service to manage and provide these collected resources. The accurate information on resource provider is essential to stable service. In Grid information service, GRIS and GIIS are used to gather and maintain the resource information. These two directory servers store resource information in a cache only for a fixed period to improve fast response and accurate access. Since resource information search is performed in the cache, the system performance depends on the search time in a cache. In this paper, we propose a novel cache management system based on the resource priority. Priority of resource is determined by the frequency of resource usage, and the number of available resources in GRIS. The simulated priority driven schemes provide more accurate information and faster response to a client than the existing cache update scheme of information service.

Recent popularity of hand-held devices and pervasiveness of e-commerce have increased web accesses through mobile devices. However, due to the limited screen size, users have to scroll a lot to locate what they want. Even worse, network connections are usually too slow to support most of the data-intensive applications. Therefore, an efficient scheme for navigating web contents through these devices should be addressed in this arena. In this paper, we propose a web agent system, ASPerson that enables users to browse ordinary web pages efficiently even on the small screen mobile devices. It extracts any specific sections of interest from web pages and reorganizes them based on user preference for convenient browsing.

Since the first computer virus has been found, scanning detection has been used as a primarily method in virus detection systems. As computer viruses and worms become more complex and sophisticated, the scanning detection method is no longer able to detect various forms of viruses and worms effectively. Many anti-virus researchers proposed various detection methods including artificial immune system to cope with these characteristics of viruses and worms. This paper discusses some principle of artificial immune system and proposes artificial immune based virus detection system that can detect unknown viruses.

In a function approximation task using neural network, there exist many cases in which the distributions of data are so complex that the regression with single network does not perform the given task well. Employing multiple modules that performs regression in different regions respectively may be a reasonable solution for this case. This paper proposes a new adaptive modular architecture for regression model, and simulates its performance on difficult problems that are not easily approximated using traditional neural network learning algorithms. Regression modules are added as learning proceeds, depending on the local distribution of data. The use of a local distribution that captures the underlying local structure of the data offers the advantage of adaptive regression.

The composite stock cutting problem is defined as allocating rectangular and irregular patterns onto a large composite stock sheet of finite dimensions in such a way that the resulting scrap will be minimized. In this paper, we propose distributed simulated annealing for this composite stock cutting problem. The new cost error tolerant scheme relaxes synchronization and does move generations asynchronously in a dynamically changed stream length to keep the convergence property of the sequential annealing. This paper also proposes the efficient data structures for pattern related information.

Human bodies defend themselves against harmful invaders by the Natural Immune System (NIS). The NIS possesses a huge set of detectors against many different invaders. Based on the essential principles of the NIS, the idea of Artificial Immune System (AIS) can be used for protecting computer resources. The AIS can detect the nonself-activity using a self-adopted detector set. The detector set is generated randomly and evolved by a Genetic Algorithm (GA). However, the random generation and the blind GA adoption decrease the performance of detector set and generate unnecessary redundancy. To overcome this problem, we propose a more efficient detector generation and evolution scheme. Using the proposed idea, the AIS can produce more effective detector set with time advantages.

There have been many researches in Computer Science that their fundamental ideas were based on Biology. Genetic algorithm and neural network are best-known paradigms in this category. Recently, many ideas from immune system have been used in detecting computer virus and worm. Since the first computer virus has been found, scanning detection has been used as a primarily method in virus detection systems. As computer viruses and worms become more complex and sophisticated, the scanning detection method is no longer able to detect various forms of viruses and worms effectively. Many anti-virus researchers proposed various detection methods including artificial immune system to cope with these characteristics of computer viruses and worms. This paper discusses the principle of artificial immune system and proposes artificial immune based virus detection system that can detect unknown viruses.

This paper presents a distributed genetic algorithm for the channel routing problem in MPI environments. This system is implemented on a network of personal computers running Linux operating system connected via 10Mbps Ethernet. Each slave processor generates its own sub-population using genetic operations and communicates with the master processor in an asynchronous manner to form the global population. The experimental results show that the proposed algorithm maintains the convergence properties of sequential genetic algorithm while it achieves linear speedup as the nets of the channel routing and the number of computing processors increase.

There are various techniques to safeguard computer systems and networks against illegal actions. Secure OS based on Role Based Access Control (RBAC) is one of the systems that reflect these techniques. The RBAC system controls access to system resources based on roles. Recently, many systems employ more fine-grained access control on system resources to enhance system security. However, this approach in access control level may cause unexpected problems, since most systems acquire system resources through system call that is hooked on kernel. In this paper, we propose a novel approach to Intrusion Detection System (IDS). The proposed Autonomic Protection System (APS) supports fine-grained intrusion detection. It resides above Secure OS based on RBAC that provides general-grained access control. The system detects intrusions using security policy based on RBAC model. The system performs double checking for intrusions using positive and negative intrusion detection policy. Additionally, as one of active responses, the system supports the self-adaptation of security policy depending on various computing environments. Therefore, the system can detect intrusions more accurately and respond to the attacks actively and appropriately.

In this paper, a novel method for intrusion detection is presented. The presented method uses a clustering method based on the transformed features, which can enhance the effectiveness of the clustering. Clustering is used in anomaly detection systems to separate attack and normal samples. In general, separating attack and normal samples in the original input space is not an easy task. For better separation of the samples, a transformation that maps input data into a different feature space should be performed. In this paper, we propose a novel method for obtaining proper transformation function that reflects the characteristics of the given domain. The transformation function is obtained from the hidden layer of the trained three-layer neural network. Experiments over network connection records from KDD CUP 1999 data set are used to evaluate the proposed method. The result of the experiment clearly shows the outstanding performance of the proposed method.

Most documents on the web today contain image as well as text data. So far, classification of images has been dependent on the annotation. For the more efficient and accurate classification, not only textual data but also image contents should be considered. In this paper, we propose a novel method for classifying specific image data; sports images. The proposed method is based on the Bayesian framework and employs four important color features to exploit the properties of sports images.

The recent trend of access control on the Web-related resource is that the service provider differentiates services according to the user’s attributes such as membership class or job position. For proper and simple representation of the relationships between user’s attributes and permission, role-base access control is used on the Web. As the size of the organization increases, does the number of membership classes or job positions. Naturally, the role hierarchy becomes too complicated to administrate properly. The lack of proper role administration makes the system vulnerable in access control. Moreover, the web services are distributed and categorized by the type of service. It is difficult that a centralized server controls the distribute environment properly. In this paper, we propose a distributed role hierarchy module that can manage the role hierarchy effectively and practically. The proposed system employs the distributed local role hierarchy, and the combination of them represents a logical global role structure. Distributed role hierarchy modules manage local role hierarchy that is related to each of them.

The paper presents a fuzzy version of the Adaptive Multi-module Approximation Network. New modules are generated when performance of existing modules is inadequate for some training data, and the applicability of a module to each input vector is determined based on the fuzzy membership of that vector in the possibly asymmetric clusters represented by the reference vectors associated with different modules. The main idea is that for neural networks that rely on a reference vector (for vector quantization, clustering, and similar tasks), the use of fuzzy membership criterion based on the distribution of data (inside different Voronoi cells) may be more appropriate than the traditional approach using a Euclidean metric to determine to which cell each data point belongs.

Today the fastest growing communities of web users are mobile visitors who browse web pages using wireless PDAs and cellular phones. However, most web pages are optimized exclusively for desktop clients on the broadband network and are inconvenient to users with small screen mobile devices. They display only a few lines of text and cannot run client-side programs or scripts due to lack of system resources. Even worse, their connections are usually too slow to support most of the data-intensive applications. To address this problem, in this paper, we propose a newslet scheme that makes it feasible to browse ordinary news web pages on small screen mobile devices. It first extracts news sections of user preference from news web pages and then automatically reorganizes them for convenient browsing on the mobile devices.

With the rapid growth of distributed and network systems, sharing resources among many users become more common. As a result of that, we encounter with new problems concerning security and privacy on the shared resources. An access control mechanism such as role-based access control (RBAC) is one of the solutions to cope with these problems. RBAC is an efficient access control mechanism for organization data with role and permission management. In this paper, we propose a new implementation method for RBAC, which uses neural networks instead of tables. By employing neural network, it has advantages of not using multiple storages for rolepermission tables and extra mutual exclusive data tables. It also reduces access time for requested role and permission sets.

The survival, self-renewal and differentiation of hematopoietic stem and progenitor cells (HSPC) are tightly regulated by extrinsic signals, and intrinsically by transcription factors and their regulatory networks. The molecular and cellular mechanisms, which regulate the complex process of hematopoiesis, depend upon the correct expression of transcription factors and their regulators. One such family of regulators is the inhibitor of DNA binding/differentiation (Id), which is helix-loop-helix proteins that function by acting as dominant negative regulators of transcription factors such as E proteins, ETS, Pax, and retinoblastoma proteins. Expression of Id2, one of the Id family proteins, is regulated by growth factor independence-1 (Gfi-1) encoding a transcriptional repressor. Gfi-1 is required for the development of multiple cell lineages including HSPC and ultimately differentiated blood cells. Although genes have been identified to mediate hematopoietic defects observed in Gfi-1 knockout (Gfi-1 KO) mice including the maturational and developmental defects in granulocyte (CSF-1, RasGRP1, and PU.1) and B cell (PU.1 or Id2), and myeloid hyperplasia (Id2 or HoxA9), Gfi-1-target genes that mediate the defects in radioprotection, maintenance of HSC, and erythroid hyperplasia in Gfi-1 KO mice are unknown. Since Id2 expression is elevated in HSPC of Gfi-1 KO mice and Id2 promotes cell proliferation, we hypothesized that lowering Id2 expression could rescue the HSPC defects in the Gfi-1 KO mice. By transplanting Gfi-1 KO mouse bone marrow cells (BMC) into lethally-irradiated recipient mice, we observed that short-term reconstituting cell (STRC) activity in Gfi-1 KO BMC is rescued by transplanting Gfi-1 KO; Id2 Het (heterozygosity at the Id2 locus) BMC, while the long-term reconstitution defect of HSC was not. Interestingly, lineage- Sca-1- c-Kithi HPC, which enriched for megakaryocyte-erythroid progenitor (MEP) as one of the STRC, were fully restored in mice transplanted with Gfi-1 KO; Id2 Het BMC, in contrast to lack of the HPC in Gfi-1 KO BM-transplanted mice. The restoration of donor c-Kithi HPC was directly correlated with increased red blood cell (RBC) levels in recipient mice, which was produced after donor BM engraftment. Furthermore, we identified that reduced Id2 levels restore erythroid cell development by rescuing short-term hematopoietic stem cell, common myeloid progenitor and MEP in the Gfi-1 KO mice. In addition, burst forming unit-erythroid (BFU-E) colony assay showed that hemoglobinized BFU-E development was restored in Gfi-1 KO BM and spleen by lowering Id2 levels. Unlike Id2 reduction, reducing other Id family (Id1 or Id3) levels in Gfi-1 KO mice does not rescue the impaired development of erythroid and other hematopoietic lineages including myeloid, T and B cells. Abnormal expansion of CD71+ Ter119-/low erythroid progenitor cells was rescued in Gfi-1 KO; Id2 Het BMC compared to those in Gfi-1 KO mice. Thus, we hypothesized that erythroid development was blocked at the early stage of erythropoiesis due to the ectopic expression of Id2 in Gfi-1 KO mice. Using Id2 promoter-driven YFP reporter mice, we found that Id2 is highly expressed in the CD71+ Ter119-/low erythroid progenitors, and decreases as the cells mature to pro-erythroblasts and erythroblasts, suggesting that repression of Id2 expression is required for proper erythroid differentiation in the later stages. The dramatic changes of Id2 expression during erythroid development support our findings that the overexpression of Id2 in the absence of Gfi-1-mediated transcriptional repression causes impaired erythropoiesis at the early stage. To identify the molecular mechanisms that could account for how reduced Id2 levels rescue erythropoiesis in Gfi-1 KO mice, we compared the expression of genes and proteins in Gfi-1 KO; Id2 Het and Gfi-1 KO BMC. Using microarray, qRT-PCR and western blot, we discovered that reduction of Id2 expression in Gfi-1 KO BMC results in increased expression of Gata1, EKlf, and EpoR genes, which are required for erythropoiesis. However, the expression levels of cell cycle regulators were not altered by lowering Id2 expression in Gfi-1 KO mice. These data suggest a novel molecular mechanism in which Gfi-1 modulates erythropoiesis by repressing the expression of Id2 that reduce the levels of Id2 protein, binding to E2A and inhibiting the formation of E2A/Scl transcription enhancer complex. Disclosures: No relevant conflicts of interest to declare.

In ubiquitous environment that users access resource anytime and anywhere, access control model should consider user's location information. The proposed uT-RBAC includes the location information for user's least privilege. It also supports time related information, which enables the access control model to accommodate various ubiquitous environments. The proposed uT-RBAC can be dynamically applied to various ubiquitous computing envrionment.

Recently the main trend in providing software services has been shifting from an ASP (application service provider)-oriented to a SaaS (software as a service). ASP is a software service model in which the service is provided on a one by one basis according its ownership, while SaaS is a software service model in which the service is provided virtually on a one by one basis, but physically all at once. In a SaaS environment, all users can access the system via Internet without any software installation—examples include Google and Amazon. Now, more companies are shifting their business software service from ASP to SaaS. However effecting the transition of the existing software and data from ASP to SaaS is not an easy task. First, we have to solve the problem of the integration of data for different forms of software, because each data set consists of different data types. Second, the software integration must support a user customizing interface for various users on the Web. Almost all users want customized services, but those require high costs. In this paper we propose a novel method for transferring the existing business software to integrated software that can be used in the SaaS environment. We use a master table and master code to implement the integrated system. The master table is based on the project master table, and other user information tables are connected to collect the necessary information. All information about the project is stored in each column of the project master table. The master table can integrate various software databases. By using this novel methodology, the existing ASP-based software and data can be effectively transferred to the SaaS environment.

Background: Simplified acute physiology score 3 (SAPS3) was developed in 2005 to evaluate intensive care unit (ICU) performance and to predict patient mortality or disease severity. The score is usually calculated by doctors, but it requires substantial human resources. And many nurse-lead studies use this scoring system. In the present study, we examined the inter-rater reliability of SAPS3 among nurses in an ICU. Methods: Five ICU nurses who worked in an ICU for a mean length of 7.8 years were educated for 2 hours about SAPS3 score and its components. Each nurse scored 26 patients, and the intraclass correlation coefficient (ICC) of the total scores and each subset were evaluated. Results: The ICC (95% confidence interval) of SAPS3 score was 0.89 (0.82-0.95), that of subset I was 0.90 (0.82-0.95), subset II was 0.54 (0.35-0.73), and subset III was 0.95 (0.91-0.97). The ICC of predicted mortality was 0.91 (0.85-0.96). Conclusions: The ICC of SAPS3 score and predicted mortality among ICU nurses were reliable. According to these ICC values, SAPS3 score is a reliable scale to be used by nurses. The ICC of subset II was lower than those of the other subsets, suggesting that education of SAPS3 should focus on the definition of each subset II component.