Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

[서평]「The Unified Modeling Language User Guide」

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. Although academic research on recommender systems has increased significantly over the past 10years, there are deficiencies in the comprehensive literature review and classification of that research. For that reason, we reviewed 210 articles on recommender systems from 46 journals published between 2001 and 2010, and then classified those by the year of publication, the journals in which they appeared, their application fields, and their data mining techniques. The 210 articles are categorized into eight application fields (books, documents, images, movie, music, shopping, TV programs, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). Our research provides information about trends in recommender systems research by examining the publication years of the articles, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this paper helps anyone who is interested in recommender systems research with insight for future research direction.

A literature review and classification of recommender systems research

liams for their clarifi cation on state and national regulations aimed at reducing the risks of salmonellosis and their advice on conducting this survey. We also thank members and staff of the National Association of State Public Health Veterinarians and the National Resource Center for Health and Safety in Child Care and Early Education for their assistance. a population-based, case-control study.nas and Salmonella Marina infection in children: a refl ection of the increasing incidence of reptile-associated sal-monellosis in the United States. 6. Centers for Disease Control and Prevention. Multistate outbreak of human Salmonella Typhimurium infections associated with aquatic frogs—United States, Wild Boars as Hosts of Human-Pathogenic Anaplasma phagocytophilum Variants To the Editor: Michalik et al. (1) reported a 12% prevalence of Ana-plasma phagocytophilum, the caus-ative agent of human granulocytic anaplasmosis and tick-borne fever of ruminants, in wild boars in Poland. A. phagocytophilum has been reported with low prevalence among wild boar in the Czech Republic, Slovenia (2), and Japan (3). In Spain and Missis-sippi, United States, A. phagocyto-philum in wild boars or feral pigs, respectively , has not been reported (4,5). Furthermore, in Slovenia and Poland, the A. phagocytophilum gene sequences found in samples from wild boars were identical to those found in samples from humans and the tick vector Ixodes ricinus (1). These results suggested , as pointed out by Michalik et al. (1), that wild boar might play a role in the epizootiology of A. phagocyto-philum by serving as a natural reservoir host, at least in some regions. To test this hypothesis, we conducted transcriptomics studies to characterize host response to A. phagocy-tophilum infection in naturally and experimentally infected boars (6,7). The results suggested that boars are susceptible to A. phagocytophilum, but are able to control infection, mainly through activation of innate immune responses and cytoskeleton rearrangement to promote phagocy-tosis and autophagy. Control of A. phagocytophilum infection in boars might result in infection levels below PCR detection or infection clearance, contributing to the low percentage of infection prevalence detected for this species in most regions. The low detection levels suggest that boars have a low or no impact as a reservoir host for A. phagocytophilum. Even if boars remain persistently infected with A. phagocytophilum at low levels by downregulating some adap-tive immune genes and delaying the apoptotic death of neutrophils through activation of the Jak-STAT pathway, among other mechanisms (6), their role as a source of infection for …

In References to References

The blooming trend of smart grid deployment is engaged by the evolution of the network technology, as the connected environment offers various alternatives for electrical data collections. Having diverse data sharing/transfer means is deemed an important aspect in enabling intelligent controls/governance in smart grid. However, security and privacy concerns also are introduced while flexible communication services are provided, such as energy depletion and infrastructure mapping attacks. This paper proposes a model permissioned blockchain edge model for smart grid network (PBEM-SGN) to address the two significant issues in smart grid, privacy protections, and energy security, by means of combining blockchain and edge computing techniques. We use group signatures and covert channel authorization techniques to guarantee users’ validity. An optimal security-aware strategy is constructed by smart contracts running on the blockchain. Our experiments have evaluated the effectiveness of the proposed approach.

Permissioned Blockchain and Edge Computing Empowered Privacy-Preserving Smart Grid Networks

Vehicle class is an important parameter in the process of road-traffic measurement. Currently, inductive-loop detectors (ILD) and image sensors are rarely used for vehicle classification because of their low accuracy. To improve the accuracy, the authors suggest a new algorithm for ILD using back-propagation neural networks. In the developed algorithm, the inputs to the neural networks are the variation rate of frequency and frequency waveform. The output is five classified vehicles. The developed algorithm was assessed at test sites, and the recognition rate was 91.5%. The results verified that the proposed algorithm improves the vehicle-classification accuracy compared to the conventional method based on ILD

Vehicle-Classification Algorithm for Single-Loop Detectors Using Neural Networks

Cloud-based electronic health record (EHR) systems enable medical documents to be exchanged between medical institutions; this is expected to contribute to improvements in various medical services in the future. However, as the system architecture becomes more complicated, cloud-based EHR systems may introduce additional security threats when compared with the existing singular systems. Thus, patients may experience exposure of private data that they do not wish to disclose. In order to protect the privacy of patients, many approaches have been proposed to provide access control to patient documents when providing health services. However, most current systems do not support fine-grained access control or take into account additional security factors such as encryption and digital signatures. In this paper, we propose a cloud-based EHR model that performs attribute-based access control using extensible access control markup language. Our EHR model, focused on security, performs partial encryption and uses electronic signatures when a patient’s document is sent to a document requester. We use XML encryption and XML digital signature technology. Our proposed model works efficiently by sending only the necessary information to the requesters who are authorized to treat the patient in question.

Privacy-Preserving Attribute-Based Access Control Model for XML-Based Electronic Health Record System

Vehicle speed is an important parameter in measurements of road traffic. At present, double-loop detectors are generally used for vehicular speed measurement. However, these detectors incur errors caused by scanning time, spacing between double loops, irregular vehicle trajectories, and the presence of multiple vehicles in the detection zone. This paper suggests a new model that uses an error-filtering algorithm to improve the accuracy of speed measurements. In the field tests, all percent errors of the vehicular speeds measured by the proposed model were within the error tolerance limit (plusmn5%). Furthermore, the variance of percent errors was reduced. Therefore, it can be concluded that the proposed model significantly improves vehicle-speed-measuring accuracy

Model for accurate speed measurement using double-loop detectors

With a rapid development of ubiquitous computing technology in the home and community, users can access information anytime and anywhere via personal devices such as PDA and internet mobile phone. Similarly, more flexible access control is required in the ubiquitous applications. In this paper, we propose a context-aware access control mechanism, which dynamically grants and adapts permissions to users according to the current context. We extend the role-based access control(RBAC) model to deal with the context information in ubiquitous environment. Unlike the traditional RBAC, the proposed access control mechanism can consider context information such as location, time and system resources by the context-aware agent and state checking matrix(SCM).

Context-Aware access control mechanism for ubiquitous applications

In this paper, exercise management systems have been introduced, which are generally used to optimize exercise. They create a proper exercise program via an exercise prescription based on the personal physical status of the user. However, exercise programs, generally created at intervals of two weeks to three months, are static and cannot reflect the user's exercise goals, which change dynamically. This paper proposes context-aware exercise architecture (CAEA), which provides an exercise program via a dynamic exercise prescription based on awareness of the user's status. We use sensors of a U-health environment and implement CAEA as an intelligent fitness guide (IFG) system. The IFG system selectively receives necessary parameters as input according to the user's exercise goals. Based on the changes in the user's exercise type, frequency, and intensity, the system creates an exercise program via an exercise optimization algorithm. In this paper, to show the exercise efficiency using the IFG system, we compared a noncontrol group to a control group. An eight-week study was performed comparing the changes of body weight in the two study groups. The study showed that the control group using the IFG system approached the desired body weight 2.57% more closely than the noncontrol group. Since IFG provides a real-time exercise program for users via an exercise optimization algorithm, it enables the user to perform effective and stable exercise according to the user's physical status.

A Context-Aware Fitness Guide System for Exercise Optimization in U-Health

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