Miracle-dz/UniFAR-FTUs huggingface dataset:
Open Source vs. Proprietary Collaborative Virtual Learning Environments

This chapter is an evaluation of the efficiency of 3D Collaborative Virtual Learning Environments to facilitate the implementation of collaborative learning activities. Firstly, there is a presentation of the state of the art regarding open source as well as proprietary platforms. Afterwards, the use of a case study reveals issues concerning the suitability of open source Collaborative Virtual Learning Environments, rationalizing the choice of executing a collaborative learning scenario in Second Life. The specific scenario is then presented culminating in evaluation results related to the appropriateness of Second Life with regards to its technical and pedagogical affordances. Finally, students' suggestions and reactions towards such a novel didactical approach are discussed.
Defining and Describing Reason

Approximately a year ago I began to believe that reasoning was problem solving's poor cousin. In comparison to problem solving, which involves strategically overcoming obstacles in pursuit of a solution, reasoning seemed vague in function. In fact, reasoning has undergone a variety of definitions. As Raymond Nickerson indicates in this volume, reasoning has, on the one hand, been defined narrowly as the process of drawing deductive inferences and, on the other hand, as an aspect of thinking that is involved not only in drawing inferences but in making decisions and solving problems as well. Has reasoning been defined so broadly and redefined so frequently that it has lost its significance? Put another way, could reasoning be legitimately subsumed under problem solving or decision making without any loss? I no longer think so. The purpose of the present book, The Nature of Reasoning , is to provide a comprehensive examination of the significance and distinctiveness of reasoning and, of course, at the same time, to indicate how it mediates other cognitive operations, such as problem solving and decision making. The book organizes in one volume what is known about reasoning, including its structural prerequisites, mechanisms, susceptibility to pragmatic influences and pitfalls, and bases for development. By focusing on factors that are pertinent to reasoning across domains, we present a united and comprehensive analysis of reasoning – an analysis that will inform anyone who is interested in learning about the fundamental factors and critical issues in reasoning research today.
The View Growth Pattern of User Generated Videos on YouTube

With the rapid development of social media, video sharing sites like YouTube are getting more and more attention. Discovering the view growth pattern have become interesting topics for researchers as well as advertisers, media companies. In this paper, we analyze two aspects about video view growth pattern of YouTube videos. Firstly, the pattern of aggregated view is studied. It is found that the aggregated view rate peaks in the first few days, and falls quickly in the following days, and then decrease slowly during the consecutive weeks. Finally, the view rate tends to be a constant on the long run. The aggregated view count after a period of two months can be fitted with a linear line. Secondly, the view growth pattern of individual video is explored. The results indicate that the majority of videos peak at the very beginning of videos’ lifetime, and the category of view sources causes the peak is different. The view count of individual video and the view count from each source item also stabilize after a period of two months, and we finally show the referring time and active period of each source item.
Hierarchical distributed optimization and predictive control of a smart grid

: The energy transition, from a centralized to a decentralized and sustainable power supply using small scale power plants, presents new challenges to the distribution grid provider who is responsible for maintaining the stability of the electricity network. New procedures to ensure the overall network stability need to be developed, which are flexible with respect to the underlying network and scalable, to be able to handle the amount of data of a fast growing network of renewable energy producers. To this end, we examine model predictive control (MPC) and hierarchical distributed optimization algorithms. In particular, we use a network of residential energy systems (RESs), connected to a grid provider through a point of common coupling, where every resident is equipped with solar photovoltaic panels and local storage devices to examine three different hierarchical distributed optimization algorithms. The flexibility of the algorithms allows for a plug and play manner of implementation. Scalability is obtained by solving the optimization problems on the level of the RESs and not on the level of the grid provider. Furthermore, with respect to a specific centralized optimization problem, convergence of the distributed optimization algorithms to the central optimum can be proven. The performance of the distributed optimization algorithms and the corresponding MPC schemes are illustrated using a dataset on power generation and power consumption of residential customers of the company Ausgrid.
An Integrated Incentive Framework for Mobile Crowdsourced Sensing

Currently, mobile devices (e.g., smartphones) are equipped with multiple wireless interfaces and rich builtin functional sensors that possess powerful computation and communication capabilities, and enable numerous Mobile Crowdsourced Sensing (MCS) applications. Generally, an MCS system is composed of three components: a publisher of sensing tasks, crowd participants who complete the crowdsourced tasks for some kinds of rewards, and the crowdsourcing platform that facilitates the interaction between publishers and crowd participants. Incentives are a fundamental issue in MCS. This paper proposes an integrated incentive framework for MCS, which appropriately utilizes three widely used incentive methods: reverse auction, gamification, and reputation updating. Firstly, a reverse-auction-based two-round participant selection mechanism is proposed to incentivize crowds to actively participate and provide high-quality sensing data. Secondly, in order to avoid untruthful publisher feedback about sensing-data quality, a gamification-based verification mechanism is designed to evaluate the truthfulness of the publisher's feedback. Finally, the platform updates the reputation of both participants and publishers based on their corresponding behaviors. This integrated incentive mechanism can motivate participants to provide high-quality sensed contents, stimulate publishers to give truthful feedback, and make the platform profitable.
A Strategy of CDN Traffic Optimization Based on the Technology of SDN

Modern Content Delivery Network(CDN)[1,2] by caching and scheduling strategy to reduce user-perceived latency, because CDN can schedule user to the CDN cache node which closest to. At the same time, this strategy can reduce the data flow which has to be sent back to the source station, and decrease the calculation pressure of source station. For some content which is dynamic and cannot be cached, the current acceleration approach is to create a GRE tunnel between the CDN nodes, using the static route scheduling dynamic data flow, improve the speed of data flow which back to the source station. It has been a recent trend of CDN to separate the control flow and data flow through technical, in order to change traditional dynamic/static content acceleration model, to optimize the CDN traffic. The core idea of Software Defined Network(SDN)[3,4]is through the network control plane and data plane separation equipment, so as to realize the flexible control of network traffic. SDN technology is used to transform the CDN, Make the CDN have an intelligent traffic scheduling environment which can focus on monitoring network and scheduling flow path real-time According to the different business. Ultimate aim is to avoid local congestion, optimize the CDN traffic, and improve the quality of the CDN service effect.
An empirical study on code comprehension: DCI compared to OO

B. Thomas Golisano College of Computing and Information Sciences Department of Software Engineering Rochester, New York Master of Science in Software Engineering An empirical study on code comprehension: DCI compared to OO by Héctor A. VALDECANTOS Comprehension of source code affects software development, especially its maintenance where reading code is the most time consuming performed activity. A programming paradigm imposes a style of arranging the source code that is aligned with a way of thinking toward a computable solution. Then, a programming paradigm with a programming language represents an important factor for source code comprehension. Object-Oriented (OO) is the dominant paradigm today. Although, it was criticized from its beginning and recently an alternative has been proposed. In an OO source code, system functions cannot escape outside the definition of classes and their descriptions live inside multiple class declarations. This results in an obfuscated code, a lost sense the run-time, and in a lack of global knowledge that weaken the understandability of the source code at system level. A new paradigm is emerging to address these and other OO issues, this is the Data Context Interaction (DCI) paradigm. We conducted the first human subject related controlled experiment to evaluate the effects of DCI on code comprehension compared to OO. We looked for correctness, time consumption, and focus of attention during comprehension tasks. We also present a novel approach using metrics from Social Network Analysis to analyze what we call the Cognitive Network of Language Elements (CNLE) that is built by programmers while comprehending a system. We consider this approach useful to understand source code properties uncovered from code reading cognitive tasks. The results obtained are preliminary in nature but indicate that DCI-trygve approach produces more comprehensible source code and promotes a stronger focus the attention in important files when programmers are reading code during program comprehension. Regarding reading time spent on files, we were not able to indicate with statistical significance which approach allows programmers to consume less time.
Cloud Computing and Information Resources Sharing Management

Science and technology increasingly developed today, is a new - cloud computing is a new technology, put forward by the study of cloud computing most came up from the cloud structure. For specific to give full play to the advantages of cloud computing, this paper for the use of cloud computing in information resource sharing management issues related to the corresponding elaboration, make the development of information resource sharing management more explicit, to show the readers the cloud computing technology and features in the future.
Web Service Evaluation Using Probabilistic Models

Information system users, administrators, and designers are all interested in performance evaluation since their goal is to obtain or provide the highest performance at the lowest cost. This goal has resulted in continuing evolution of higher performance and lower cost systems leading to today’s proliferation of workstations and personal computers, many of which have better performance than earlier supercomputers. As the variety of Web services applications (Websites) increases, it gets more important to have a set of evaluation criteria that should evaluate the performance of their effectiveness. Based on those criteria, the quality of the services that the Web applications are providing could be analysed. This work represents software metrics that could (or need) be used to quantify the quality of the information that the Web services are providing. These measures could be useful to understand problematic frameworks during the implementation of the Websites and could lead to solutions preventing those problems.
Scientific middleware for abstracted parallelisation

In this paper we introduce a class of problems that arise when the analysis of data split into an unknown number of pieces is attempted. Such analysis falls under the definition of Grid computing, but fails to be addressed by the current Grid computing projects, as they do not provide the appropriate abstractions. We then describe a distributed web service based middleware platform, which solves these problems by supporting construction of parallel data analysis functions for datasets with an unknown level of distribution. This analysis is achieved through the combination of Martlet, a work-flow language that uses constructs from functional programming to abstract the parallelisation in computations away from the user, and the construction of supporting middleware. To construct such a supporting middleware it is necessary to provide the capability to reason about the data structures held without restricting their nature. Issues covered in the development of this supporting middleware include the ability to handle distributed data transfer and management, function deployment and execution.
PEGASIS : Power-Efficient Gathering in Sensor Information Systems

Sensor network consisting of nodes with limited battery power and wireless communications are deployed to collect useful information from the field. The main idea in PEGASIS is for each node to receive from and transmit to close neighbors and take turns being the leader for transmission to the BS. This approach distributes the energy load evenly among the sensor nodes in the network. Sensor nodes are randomly deployed in the sensor field, and therefore, the i th node is at a random location. The nodes will be organized to form a chain, which can either be accomplished by the sensor nodes themselves using a greedy algorithm. The algorithm to resolve the unbalanced energy consumption problem caused by long distance data transmission of some nodes in a chain formed by the greedy algorithm.
Analysis of Lightweight Cryptographic Solutions for Internet of Things

Internet of Things (IoT) enables physical things to communicate, compute and take decisions based on any network activity. This calls for a secure solution for communication among heterogeneous devices. With the development in Information and Communication Technology (ICT), a unique impact of smart things is observed in our everyday life. IoT can consider users that interact in heterogeneous environment. In heterogeneous environment motive of each user in IoT can be different in form of communication and computation and is difficult to be judged. A malicious user can destroy the security and privacy of the network. This study gives a detailed analysis of existing security solutions for IoT. Firstly, a comparison of lightweight cryptography algorithms is made on basis of block size, key size, number of rounds, and probable attacks. Later, the various security issues in IoT are discussed along with possible solution. Security solutions in IoT will improve the trust over IoT. A secure solution that will require less computational power and is less vulnerable to existing attacks is desired.
Implementation of Highly Efficient OCSP Server

OCSP server is a key component of PKI. The response interval is influenced mainly by servers signature speed. In this article, a highly efficient implementation of OCSP server is brought forward, which applies Hash-table search, cache, and multi-thread technology. In addition to signing response messages correctly, ensuring security, and keeping compatibility, servers efficiency is improved greatly.
Kansei Information Security Assessment (KISA): Characterizing Trust as Stimuli for User Emotional Assessment in Information Security

This paper reports the preliminary study of implementing Kansei Engineering as emotion assessment platform in information security. Trust was chosen as the initial emotional traits for the study. The development and characterization of trust as Kansei stimuli is presented in this paper. In the development of trust as Kansei stimuli, literature reviews of previous published papers on trust as emotional aspect in information security were done in online databases, which are Science Direct, IEEE Xplore, Springer Link and Google Scholar. A number of 52 related articles were found to have reported on the topic. Based from literature reviews, the basic of trust elements in the information security were found to be integrity, benevolence and ability of the humans and technical entities in the information security implementations. Furthermore, trust assessments will also need to cover the element of cultural and religion influence in affecting the trust among the human users. Therefore, in characterizing trust as Kansei stimuli, the stimuli will need to assess the trust level of integrity, benevolence and ability of the humans and technical components, and also on cultural and religion background among the human users. With this findings, the development and characterization of trust as Kansei stimuli in information security domain was proven possible, thus provides novel findings that Kansei Engineering could be implemented as emotion assessment in information security domain. This paper shows and proven the possibility of the applying Kansei Engineering as emotion assessment platform in information security. It opened a new chapter in the research in Kansei Engineering and information security.
A study of active usage of mobile instant messaging application

This paper studies the antecedents of active usage of mobile instant messaging (MIM) applications based on an attachment theory perspective. It proposes MIM identification; self-congruence, design aesthetics, interactivity, mobility and feedback can be the factors that influence active usage of MIM from a user-device attachment perspective. It also explores two working mechanisms of these factors: emotional attachment and functional dependence. By using a survey method, 294 valid responses were collected from Chinese active smartphone users and were analyzed using structural equation modelling. The results of the analysis indicate that MIM identification, self-congruence and design aesthetics influence emotional attachment significantly, and mobility and design aesthetics influence functional dependence significantly. Besides, interactivity and feedback do not have any significant impact on functional dependence. Implications for theory and practice are discussed.
Manutenção Corretiva Baseada em Padrões e Antipadrões de Casos de Uso

The deployment of use cases has been growing rapidly as the main technique that offers an intuitive and systematic mean to describe business processes and the capture of functional requirements. Concerning the development of object-oriented systems, use cases drive the whole software development process. Therefore, in a process for object-oriented systems driven by use cases, the use cases inspection is essential to the capture of errors. The objective of this paper is to present a strategy for the review of the use cases, employing use case patterns and anti-patterns, that emerged as the result of a work that had as goal the review of models related to the development of a Workflow product during corrective maintenance. Resumo. A utilizacao de casos de uso tem crescido rapidamente como a principal tecnica que oferece um meio sistematico e intuitivo para descrever processos de negocios e de captura de requisitos funcionais. No desenvolvimento de software orientado a objetos, casos de uso direcionam todo o processo de desenvolvimento de software. Assim, num processo de software orientado por casos de uso, a inspecao de casos de uso e essencial para a captura erros. O objetivo desse trabalho e apresentar uma estrategia de revisao de casos de uso baseada em padroes e anti-padroes de casos de uso, que resultou de um trabalho de revisao dos modelos de desenvolvimento de um produto de Workflow, durante a manutencao corretiva.
Pruning Large Data Sets for Finding Association rule in cloud: CBPA (Count Based Pruning Algorithm )

Organizations are more interested in the interesting data rather than the bulk of data. So they need a systematic and scientific approach to extract meaningful data out of heaps of the data and to find out the relations among these patterns. To analyze "big data" on clouds, it is very important to research data mining strategies based on cloud computing paradigm from both theoretical and practical views. In this paper, based on the original Apriori algorithm, an improved algorithm is proposed which adopts a new count-based method to prune candidate item sets and uses generation record to reduce total data scan amount and also make it more modeling oriented. Experiments demonstrate that by performing our algorithm of on given datasets we will find the Solution of problems in association and apriori algorithm.
HMM based spectrum access options for cognitive radio networks

Cognitive radio networks have managed the radio spectrum more efficiently by using the idle spectrum in primary users' licensed frequency bands. Recent researches tell us the spectrum utilization can be improved significantly if it is possible for cognitive users to access the free spectrum bands. In this paper, we formulate the spectrum access selection optimization problem as a hidden Markov model (HMM), which helps the cognitive users evaluate the channel quality. We will implement HMM predict next channel state based on past ones. Then we adopt the restless bandit method to obtain the optimal solution that makes better spectral efficiency and reduces the energy consumption.
Prediction Algorithm of User Behavior Based on Community Mining in Telecom Network

This paper uses the social network analysis to solve the problem of the prediction of user behavior in telecommunications network and transforms the prediction of user behavior into two steps of community mining and intra-community user behavior prediction community.Then it uses the improved clustering technology of coagulation level to achieve community mining of the users in telecommunications network,and designs a prediction algorithm of user behavior within community through sorting in order of the degree of adjacency within the community.Simulation results prove that the algorithm can effectively predict the user behavior in the specified communities.
Study on s-Tree Algorithm for Personalized Recommendation

This article proposes a new algorithm based on the connection rule excavation most greatly frequent visit—— s-Tree algorithm, and analyzes the user by this visit pattern, excavates the specific user visit pattern and the browsing is partial to routing information, then optimizes the stand structure, provides “one to one” the personalized Web page visit forecast and the content recommendation.
A Review on Enhancement of Face Recognition Technology in Biometrics

Idea for research came up while studying soft computing. Mainly research is based upon MATLAB including image processing. We have read many algorithms which are based on image processing like Adam’s algorithm & fisher’s algorithm for image processing etc. images based on these algorithms as mentioned above had low quality & contrary to this set of facts we try to improve quality of images. The objective of research is to implement Face recognition within MATLAB using edge detection mechanism like Canny. Face reorganization system is a biometric system & this s working depends on working of biometric devices.In this research we have studied & evaluate different edge detection techniques. We have seen that canny edge detector gives better result as compared to others with some positive points.