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Showing papers in "Journal of Software in 2011"



Journal ArticleDOI
TL;DR: A shift from a more conventional use of URN for telecommunications and reactive systems to business process management and aspect-oriented modeling, with relevant extensions to the language being proposed is observed.
Abstract: The User Requirements Notation (URN), standardized by the International Telecommunication Union in 2008, is used to model and analyze requirements with goals and scenarios. This paper describes the first ten years of development of URN, and discusses ongoing efforts targeting the next ten years. We did a study inspired by the systematic literature review approach, querying five major search engines and using the existing URN Virtual Library. Based on the 281 scientific publications related to URN we collected and analyzed, we observe a shift from a more conventional use of URN for telecommunications and reactive systems to business process management and aspect-oriented modeling, with relevant extensions to the language being proposed. URN also benefits from a global and active research community, although industrial contributions are still sparse. URN is now a leading language for goal-driven and scenario-oriented modeling with a promising future for many application domains.

178 citations


Journal Article
TL;DR: In this article, the authors describe the requirements in cloud computing, security key technology, standard and regulation etc., and provide a cloud computing security framework, arguing that the changes in the above aspects will result in a technical revolution in the field of information security.
Abstract: Cloud Computing is the fundamental change happening in the field of Information Technology.It is a representation of a movement towards the intensive,large scale specialization.On the other hand,it brings about not only convenience and efficiency problems,but also great challenges in the field of data security and privacy protection.Currently,security has been regarded as one of the greatest problems in the development of Cloud Computing.This paper describes the great requirements in Cloud Computing,security key technology,standard and regulation etc.,and provides a Cloud Computing security framework.This paper argues that the changes in the above aspects will result in a technical revolution in the field of information security.

118 citations


Journal ArticleDOI
TL;DR: The positive impact of a train-the-trainer model used in this study in a variety of schools under naturally occurring conditions holds promise for low-cost, preventive mental health programs.
Abstract: This research investigated the impact of a social and emotional learning program, You Can Do It! Education (YCDI), on different aspects of student social and emotional wellbeing. YCDI was implemented on a whole-school basis in six primary schools with six matched schools serving as controls. At the end of the school year, students in grade 5 in both types of schools completed the Attitudes to School Survey (Victorian Department of Education) and, again, at the end of the following school year when they were in grade 6. Results indicated significant improvements over time on different aspects of student well-being in the YCDI schools and not in the non-YCDI schools. The positive impact of a train-the-trainer model used in this study in a variety of schools under naturally occurring conditions holds promise for low-cost, preventive mental health programs.

60 citations


Journal ArticleDOI
TL;DR: A novel hybrid Hooke Jeeves ABC (HJABC) algorithm with intensification search based on the HookeJeeves pattern search and the ABC is presented to demonstrate how the standard ABC can be improved by incorporating a hybridization strategy.
Abstract: Artificial bee colony (ABC) algorithm is one of the most recently proposed swarm intelligence algorithms for global numerical optimization. It performs well in most cases; however, there still exist some problems it cannot solve very well. This paper presents a novel hybrid Hooke Jeeves ABC (HJABC) algorithm with intensification search based on the Hooke Jeeves pattern search and the ABC. The main purpose is to demonstrate how the standard ABC can be improved by incorporating a hybridization strategy. The proposed algorithm is tested on a comprehensive set of 3 6 complex benchmark functions and a slope stability analysis problem including a wide range of dimensions. Comparisons are made with the basic ABC and some recent algorithms. Numerical results show that the new algorithm is promising in terms of convergence speed, success rate and solution accuracy .

59 citations


Journal ArticleDOI
TL;DR: This paper proposes the access control policies based on UCON to meet the needs of requirements for security authorization and control in the internet of things.
Abstract: In this paper, we introduce the internet of things and related architecture and protocols, and the family of usage control (UCON) models, which integrate authorizations (A), obligations (B), and conditions (C). The UCON is a generalization of access control to cover authorizations, obligations, conditions, continuity (ongoing controls), and mutability. In the internet of things' highly dynamic, distributed environment, obligations and conditions are also crucial decision factors for secure controls on usage of resources, such as digital resources for cars and other moving devices and so on. In order to meet the needs of privacy and authorization flexible access control in the internet of things, we propose the access control policies based on UCON to meet the needs of requirements for security authorization and control.

59 citations


Journal ArticleDOI
TL;DR: It is found that, in a single document, a term with higher frequency and close to hypo-dispersion distribution usually contains much semantic information and should be given higher weight, and by leveraging the Pearson Chi-square Test Statistic, a Term Distribution based Local Term Weight Algorithm and Global Term weight Algorithm are put forward.
Abstract: In the process of document formalization, term weight algorithm plays an important role. It greatly interferes the precision and recall results of the natural language processing(NLP) systems. Currently, TF-IDF term weight algorithm is widely applied into language models to build NLP Systems. Since term frequency is not the only discriminator which is necessary to be considered in term weight ing and make each weight suitable to indicate the term’s importance, we are motivated to investigate other statistical characteristics of terms and found an important discriminator: term distribution. Furthermore, we found that , in a single document, a term with higher frequency and close to hypo-dispersion distribution usually contains much semantic information and should be given higher weight. One the other hand, in a document collection, the term with higher frequency and hypo-dispersion distribution usually contains less information. Based on this hypothesis, by leveraging the Pearson Chi-square Test Statistic, a Term Distribution based Local Term Weight Algorithm and Global Term Weight Algorithm are put forward respectively in this paper. Also, the experiment results at the end of this paper approve the reliability and efficiency of the algorithm s .

54 citations


Journal ArticleDOI
TL;DR: A novel fuzzy c-means clustering method based on the enhanced Particle Swarm Optimization algorithm that distributes the memberships on the basis of the distance between the sample and cluster centers, making memberships meet the constraints of FCM.
Abstract: To deal with the problem of premature convergence of the fuzzy c-means clustering algorithm based on particle swarm optimization, which is sensitive to noise and less effective when handling the data set that dimensions greater than the number of samples, a novel fuzzy c-means clustering method based on the enhanced Particle Swarm Optimization algorithm is presented. Firstly, this approach distributes the memberships on the basis of the distance between the sample and cluster centers, making memberships meet the constraints of FCM. Then, optimization strategy is presented that the optimal particle can be guided to close the group effectively. The experimental results show the proposed method significantly improves the clustering effect of the PSO-based FCM that encoded in membership.

49 citations


Journal Article
TL;DR: This study elaborates the research problems relating to ABE systems, including access structure design for CP-ABE, attribute key revocation, key abuse and multi-authorities ABE with an extensive comparison of their functionality and performance.
Abstract: Attribute-Based encryption (ABE) scheme takes attributes as the public key and associates the ciphertext and user’s secret key with attributes,so that it can support expressive access control policies.This dramatically reduces the cost of network bandwidth and sending node’s operation in fine-grained access control of data sharing.Therefore,ABE has a broad prospect of application in the area of fine-grained access control.After analyzing the basic ABE system and its two variants,Key-Policy ABE (KP-ABE) and Ciphertext-Policy ABE (CP-ABE),this study elaborates the research problems relating to ABE systems,including access structure design for CP-ABE,attribute key revocation,key abuse and multi-authorities ABE with an extensive comparison of their functionality and performance.Finally,this study discusses the need-to-be solved problems and main research directions in ABE.

45 citations


Journal ArticleDOI
TL;DR: This paper presents data collected over a three day teaching and learning event in which students were invited to share their understandings of wellbeing as they worked to accomplish tasks related to their school examinations, and attempts to create space to consider wellbeing's role in the senior secondary context.
Abstract: In recent years, the term wellbeing has become more common as an explicit educational aim. Despite its frequent use, it is often broadly applied, and rarely explicitly defined. Typically, wellbeing is described in education policy in ways that align conceptual pairings common in political discourse, including wealth, health, and happiness. Given the attention wellbeing is receiving by politicians around the world, this is an important time to consider if common uses of the term are relevant to and resonate with those in the school context, particularly amongst those on the cusp of entry into their adult lives. Here, I present data collected over a three day teaching and learning event in which students were invited to share their understandings of wellbeing as they worked to accomplish tasks related to their school examinations. Soutter, Gilmore, & O’Steen’s (2010) framework for wellbeing served as the conceptual lens through which data were analysed. The central finding to emerge was that wellbeing is conceptualised by students as a multi-dimensional, complex construct that holds both instrumental and intrinsic value for them as individuals, but that educational experiences did not play a prominent role in their visual or verbal communication about wellbeing. Through the discussion, this paper attempts to “create space” to consider wellbeing’s role in the senior secondary context.

45 citations


Journal ArticleDOI
TL;DR: A novel AHP- based ELECTRE I method of reliability design scheme decision for computer numerical control (CNC) machine is proposed and computational results show that the proposed approach is reliable and performs well.
Abstract: The ELECTRE I is one of the most extensively used methods to solve multiple criteria decision making (MCDM) problems. In this paper, we propose a novel AHP- based ELECTRE I method of reliability design scheme decision for computer numerical control (CNC) machine. Based on the AHP method combined with ELECTRE I, the decision model is built to select the optimal design scheme. The AHP method is applied to determinate the weights of reliability design factors through the decision model. ELECTRE I method is then designed to rank reliability design scheme in order of decision maker's preference. To evaluate performance of the developed algorithm, an illustrative example of CNC machine is given. The computational results show that the proposed approach is reliable and performs well.

Journal Article
TL;DR: This paper proposes an approach to improve the awareness of network security, based on the Markov Game Model, which gains a standard data of assets, threats, and vulnerabilities via fusing a variety of system security data collected by multi-sensors.
Abstract: To analyze the influence of propagation on a network system and accurately evaluate system security,this paper proposes an approach to improve the awareness of network security,based on the Markov Game Model(MGM).This approach gains a standard data of assets,threats,and vulnerabilities via fusing a variety of system security data collected by multi-sensors.For every threat,it analyzes the rule of propagation and builds a threat propagation network(TPN).By using the Game Theory to analyze the behaviors of threats,administrators,and ordinary users,it establishes a three player MGM.In order to make the evaluation process a real-time operation,it optimizes the related algorithm.The MGM can dynamically evaluate system security situation and provide the best reinforcement schema for the administrator.The evaluation of a specific network indicates that the approach is suitable for a real network environment,and the evaluation result is precise and efficient.The reinforcement schema can effectively curb the propagation of threats.

Journal ArticleDOI
TL;DR: This paper introduces the use of social network analysis for socially constructed data to study inter-organizational systems of innovation and their value-add supply chain and describes a Euro- American perspective on innovation ecosystems that link China globally.
Abstract: This paper introduces the use of social network analysis for socially constructed data to study inter-organizational systems of innovation and their value-add supply chain Through social network analysis, we explore the structure of relationships among Chinese technology-based companies, foreign technology- based companies with Chinese locations, Chinese investment firms, and foreign firms investing in Chinese companies – with particular attention to the business sectors related to e-commerce and internet security We use an organizational sociology frame- work and socially constructed data in English to describe a Euro- American perspective on innovation ecosystems that link China globally

Journal ArticleDOI
TL;DR: The chaotic distributed superiority of the cat map is analyzed and the detailed implementation of CDE is introduced and the effectiveness of the CDE-SVR is verified in the numerical tests.
Abstract: The Differential Evolution (DE) population-based algorithm is an optimal algorithm with powerful global searching capability, but it is usually in low convergence speed and presents bad searching capability in the later evolution stage. A new Chaos Differential Evolution algorithm (CDE) based on the cat map is proposed which combines DE and chaotic searching algorithm. Firstly, the chaotic distributed superiority of the cat map is analyzed in this paper. Secondly, the detailed implementation of CDE is introduced. Finally, the effectiveness of CDE is verified in the numerical tests. The Support Vector Regression machine (SVR) is an effective tool to solve the problem of nonlinear prediction, but its prediction accuracy and generalization performances depend on the selection of parameters greatly. So, the CDE is applied to SVR to build an optimized prediction model called CDE-SVR. Then the new prediction model is applied to the short-time regression prediction of the chaotic time series and the boundary extension of the mechanical vibration signals. The results of the two experiments demonstrate the effectiveness of the CDE-SVR.

Journal ArticleDOI
TL;DR: The paper analyzes information flow management of VMI system in automobile parts inbound logistics based on the environment of Internet of Things.
Abstract: Reducing inventory levels is a major supply chain management challenge in automobile industries. With the development of information technology new cooperative supply chain contracts emerge such as Vendor-Managed Inventory (VMI). This research aims to look at the literature of information management of VMI and the Internet of Things, then analyzes information flow model of VMI system. The paper analyzes information flow management of VMI system in automobile parts inbound logistics based on the environment of Internet of Things.

Journal ArticleDOI
TL;DR: Experimental result shows that feature vector extracted by the wavelet transform can characterize emotional patterns through the comparison with the BP neural network classifier and Support vector Machine, indicating that the Support Vector Machine have a stronger emotional recognition effect.
Abstract: This paper compares the emotional pattern recognition method between standard BP neural network classifier and BP neural network classifier improved by the L-M algorithm. Then we compare the method Support Vector Machine (SVM) to them. Experiment analyzes wavelet transform of surface Electromyography (EMG) to extract the maximum and minimum wavelet coefficients of multi-scale firstly. We then input the two kinds of classifier of the structural feature vector for emotion recognition. The experimental result shows that the standard BP neural network classifier, L-M improved BP neural network classifier and support vector machine's overall pattern recognition rate is 62.5%, 83.33% and 91.67 respectively. Experimental result shows that feature vector extracted by the wavelet transform can characterize emotional patterns through the comparison with the BP neural network classifier and Support Vector Machine, indicating that the Support Vector Machine have a stronger emotional recognition effect.

Journal ArticleDOI
TL;DR: The paper discusses the Secure Tropos modeling language, the security aware process of the methodology, and it also introduces thesecTro, an automated tool to support the methodology.
Abstract: This paper discusses the secure Tropos methodology. This is the first paper in the literature that discusses all the aspects of the methodology as it has evolved over the last 10 years. In particular, the paper discusses the Secure Tropos modeling language, the security aware process of the methodology, and it also introduces the secTro, an automated tool to support the methodology.

Journal ArticleDOI
TL;DR: A comprehensive review of the existing works done in the field of multimodal function optimization was given and a critical analysis of existing methods was also provided as discussed by the authors, which summarized defects of existing algorithms: lacking of self-adaptive adjustment function, requiring setting some parameters according to different problems, lacking of unified theoretical and experimental system to guide algorithms design and not maintaining the diversity of swarm.
Abstract: Many scientific and engineering applications involve finding more than one optimum. A comprehensive review of the existing works done in the field of multimodal function optimization was given and a critical analysis of the existing methods was also provided. Several techniques in solving multimodal function optimization problems were introduced, such as clearing, deterministic crowding, sharing, species conserving and so on. And we summarized defects of existing algorithms: lacking of self-adaptive adjustment function, requiring setting some parameters according to different problems, lacking of unified theoretical and experimental system to guide algorithms design and not maintaining the diversity of swarm. Moreover, most of existing multimodal particle swarm optimization algorithms which include SPSO, MSPSO, ESPSO, ANPSO, kPSO, MGPSO, AT-MGPSO, rpso, and SDD-PSO were described and compared and advantages and disadvantages existing in these algorithms were pointed out. Therefore, some ideas to improve the performance of multimodal function optimization algorithms were proposed.

Journal ArticleDOI
TL;DR: This paper presented a personalized collaborative filtering recommendation method combining the association rules mining and self-organizing map which can alleviate the data sparsity problem in the recommender systems.
Abstract: With the development of the Internet, the problem of information overload is becoming increasing serious. People all have experienced the feeling of being overwhelmed by the number of new books, articles, and proceedings coming out each year. Many researchers pay more attention on building a proper tool which can help users obtain personalized resources. Personalized recommendation systems are one such software tool used to help users obtain recommendations for unseen items based on their preferences. The commonly used personalized recommendation system methods are content-based filtering, collaborative filtering, and association rules mining. Unfortunately, each method has its drawbacks. This paper presented a personalized collaborative filtering recommendation method combining the association rules mining and self-organizing map. It used the association rules mining to fill the vacant where necessary. Then, it employs clustering function of self-organizing map to form nearest neighbors of the target item and it produces prediction of the target user to the target item using item-based collaborative filtering. The recommendation method combining association rules mining and collaborative filtering can alleviate the data sparsity problem in the recommender systems.

Journal ArticleDOI
TL;DR: A novel algorithm for SVDD incremental learning is proposed, in which the useless sample is discarded and useful information in training samples is accumulated and results indicate the effectiveness of the proposed algorithm.
Abstract: Support vector data description (SVDD) has become a very attractive kernel method due to its good results in many novelty detection problems.Training SVDD involves solving a constrained convex quadratic programming,which requires large memory and enormous amounts of training time for large-scale data set.In this paper,we analyze the possible changes of support vector set after new samples are added to training set according to the relationship between the Karush-Kuhn-Tucker (KKT) conditions of SVDD and the distribution of the training samples.Based on the analysis result,a novel algorithm for SVDD incremental learning is proposed.In this algorithm,the useless sample is discarded and useful information in training samples is accumulated.Experimental results indicate the effectiveness of the proposed algorithm.

Journal ArticleDOI
TL;DR: How nutrition workshops and cooking classes in primary schools can influence healthy eating habits among schoolaged children is discussed.
Abstract: Schools are in the unique position of being able to influence students’ eating behaviours in a beneficial manner. Positive peer and teacher modelling can encourage students to try foods they exhibited distaste for previously. Pilot research has shown that when nutrition and cooking sessions are conducted in primary schools, foods refused or untried at home during meal times, such as vegetables, were asked for following the school cooking classes using that same food. In this paper I will discuss how nutrition workshops and cooking classes in primary schools can influence healthy eating habits among schoolaged children. The research indicated that there was a transfer of knowledge around healthy nutrition from a school environment to a home environment through students as agents of change.

Journal ArticleDOI
TL;DR: A novel web page segmentational algorithm based on finding the Gomory-Hu tree in a planargraph that improves upon the other two with much higher precision and recall, and its running time is far lower than Chakrabarti et al.
Abstract: We propose a novel web page segmentationalgorithm based on finding the Gomory-Hu tree in a planargraph. The algorithm firstly distills vision and structureinformation from a web page to construct a weightedundirected graph, whose vertices are the leaf nodes of theDOM tree and the edges represent the visible positionrelationship between vertices. Then it partitions the graphwith the Gomory-Hu tree based clustering algorithm.Experimental results show that, compared with VIPS andChakrabarti et al.’s graph theoretic algorithm, ouralgorithm improves upon the other two with much higherprecision and recall, and its running time is far lower thanthat of Chakrabarti et al.’s graph theoretic algorithm.


Journal ArticleDOI
TL;DR: An approach based on the bug tossing history and textual similarities between bug reports can significantly improve the efficiency of bug assignment: the bug resolver is often identified with fewer tos-sing events.
Abstract: In open-source software development a new bug firstly is found by developers or users. Then the bug is described as a bug report, which is submitted to a bug repository. Finally the bug triager checks the bug report and typically assigns a developer to fix the bug. The assignment process is time-consuming and error-prone. Furthermore, a large number of bug reports are tossed (reassigned) to other developers, which increases bug-fix time. In order to quickly identify the fixer to bug reports we present an approach based on the bug tossing history and textual similarities between bug reports. This proposed approach is evaluated on Eclipse and Mozilla. The results show that our approach can significantly improve the efficiency of bug assignment: the bug fixer is often identified with fewer tossing events.

Journal ArticleDOI
TL;DR: A new similarity measure named adjusted Euclidean distance (AED) method is proposed which unifies all Euclidesan distances between vectors in different dimensional vector spaces and improves the accuracy of prediction and recommendation.
Abstract: Memory-based collaborative filtering (CF) is applied to help users to find their favorite items in recommender systems. Up to now, this approach has been proven successful in recommender systems, such as e-commerce systems. The idea of this approach is that the interest of a particular user will be more consistent with those who share similar preference with him or her. Therefore, it is critical that an appropriate similarity measure should be selected for making recommendations. This paper proposes a new similarity measure named adjusted Euclidean distance (AED) method which unifies all Euclidean distances between vectors in different dimensional vector spaces. Our AED enjoy the advantages that it takes both the length of vectors and different dimension-numbers of vector spaces into consideration. Based on two datasets MovieLens and Book-Crossing, we conduct experiments comparing our AED with two notable existing methods. The experimental results demonstrate that our AED improves the accuracy of prediction and recommendation.


Journal ArticleDOI
TL;DR: This hybrid algorithm utilizes the combination of ACO and Simulated Annealing algorithm and proposes a n adaptive control mechanism based on ant flow of route choice to improve the global search ability.
Abstract: This paper presents a scheduling approach, based on Ant Colony Optimization (ACO), developed to address the scheduling problem in manufacturing systems constrained by both machines and heterogeneous workers called as Dual Resource Constrained Job Shop Scheduling Problem with Heterogeneous Workers. This hybrid algorithm utilizes the combination of ACO and Simulated Annealing (SA) algorithm and proposes a n adaptive control mechanism based on ant flow of route choice to improve the global search ability. Two adaptive adjusting schemes of parameter s based on iteration times and quality of solutions respectively are imposed to actualize the performance optimization by stages. Then the performances of different optimization methods with different resource allocation strategies are compared according to simulation experiments on both concrete instance and random benchmarks while related discussion are represented at last.

Journal ArticleDOI
TL;DR: Results show that using the improved Gini index algorithm to feature weight can improve the performance of Naive Bayesian classifier effectively and obtains good application in the sensitive information recognition system.
Abstract: Text categorization is a fundamental methodology of text mining and a hot topic of the research of data mining and web mining in recent years. It plays an important role in building traditional information retrieval, web indexing architecture, Web information retrieval, and so on. This paper presents an improved algorithm of text categorization that combines the feature weighting technique with Naive Bayesian classifier. Experimental results show that using the improved Gini index algorithm to feature weight can improve the performance of Naive Bayesian classifier effectively. This algorithm obtains good application in the sensitive information recognition system.

Journal ArticleDOI
TL;DR: The study adopts 2- exchange mutation operator, combine hill-climbing algorithm to strengthen the partial searching ability of chromosome, and Boltzmann simulated annealing mechanism for control genetic algorithm crossover and mutation operations improve the convergence speed and search efficiency.
Abstract: The present study is focused on the Min-Max Vehicle Routing Problem (MMVRP). According to the characteristics of model, hybrid genetic algorithm is used to get the optimization solution. First of all, use natural number coding so as to simplify the problem; apply improved insertion method so as to improve the feasibility of the solution; retain the best selection so as to guard the diversity of group. The study adopts 2- exchange mutation operator, combine hill-climbing algorithm to strengthen the partial searching ability of chromosome. Secondly, Boltzmann simulated annealing mechanism for control genetic algorithm crossover and mutation operations improve the convergence speed and search efficiency. At last, it uses simulated experiments to prove the effectiveness and feasibility of this algorithm, and provides clues for massively solving practical problems.

Journal ArticleDOI
TL;DR: An operation optimization model of gas network with consideration of quantity input (output) constraints of each node, operation pressure constraints of pipelines, compressor constraints, valve constraints and hydraulic constraints of the pipeline system is proposed.
Abstract: Natural gas is normally transported through a vast network of pipelines. A pipeline network is generally established to connect gas wells with gas processing fields (gathering network) or to transmit gas at high pressure from gas sources to regional demand points (trunk network) or to distribute gas to consumers at low pressure from regional demand points (distribution network). The problems involved in optimizing the operation conditions of networks to promote benefit belong to a class of non-liner optimization problems. The operation benefit of gas network is combined with the purchase and sale prices of gas, the quantity bought and sold of gas and the management costs. Aimed at the maximum operation benefit, the paper proposes an operation optimization model of gas network with consideration of quantity input (output) constraints of each node, operation pressure constraints of pipelines, compressor constraints, valve constraints and hydraulic constraints of the pipeline system. The model adapts to all kinds of pipeline structures, followed with our presentation of a global approach, which is based on the method of adaptive genetic algorithm, to the optimization model. Afterwards, omputer software is developed to optimize the operation conditions of gas trunk networks , gas gathering and distribution network s . Finally, an application example will be demonstrated .