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摘要： Jigsaw is a cooperative learning approach in which a topic is broken into several segments, each student learns one segment of a topic individually, then students work in small groups to review what they learned, to teach their segments to each other, and to construct a complete picture of the topic. This paper aims to help computer science instructors who are interested in developing jigsaw activities by providing a model activity and by sharing the experience of implementing it in the classroom.

关键词： Collaboration   Recommender systems   Computer science   Task analysis   Learning systems   Uncertainty   Matrix decomposition   Recommender systems   active learning   cold-start   exploitation and exploration  

摘要： In recommender systems, cold-start issues are situations where no previous events, e.g., ratings, are known for certain users or items. In this paper, we focus on the item cold-start problem. Both content information (e.g., item attributes) and initial user ratings are valuable for seizing users' preferences on a new item. However, previous methods for the item cold-start problem either (1) incorporate content information into collaborative filtering to perform hybrid recommendation, or (2) actively select users to rate the new item without considering content information and then do collaborative filtering. In this paper, we propose a novel recommendation scheme for the item cold-start problem by leveraging both active learning and items' attribute information. Specifically, we design useful user selection criteria based on items' attributes and users' rating history, and combine the criteria in an optimization framework for selecting users. By exploiting the feedback ratings, users' previous ratings and items' attributes, we then generate accurate rating predictions for the other unselected users. Experimental results on two real-world datasets show the superiority of our proposed method over traditional methods.

摘要： This article demonstrates a pedagogy of a research-focused Computer Science course for undergraduates in a liberal arts environment. The long term benefits of Course-based Undergraduate Research Experiences (CUREs) in different STEM fields are the driving forces for modeling such a course. The course balances a research-focused, semi-supervised, active learning experience and a traditional lecture-focused, supervised classroom-environment. This article summarizes the current semester's student experiences and suggests possible scope for improvements.

摘要： The Gartner group [3] notes that most in the IT industry will be using cloud-based resources in 2020 and that future use of cloud computing is only expected to grow. Furthermore, 75% of surveyed organizations currently using cloud-services plan to adopt a "cloud-first" strategy, in which cloud computing solutions are evaluated first before making new investments.

关键词： 计算机科学   物联网   推动  

摘要： 物联网的出现使不相干的事物产生了联系,改变了人们的生活方式。物联网相关技术的不断完善和创新发展,对我国经济发展起到重要的促进作用,为社会和谐发展提供了便利。文章分析了物联网的关键技术,并研究了计算机技术对物联网的推动作用。

关键词： Singular value decomposition   Social networking (online)   Matrix decomposition   Computer science   Eigenvalues and eigenfunctions   Sparse matrices   Cyber-Physical-Social Network   Tensor   Orthogonal tensor basis   Eigentensor   Recommendation  

摘要： The development of social networks and ubiquitous sensing promotes the network space into a new stage, which integrates the cyber network, physical network, and social network into cyber-physical-social networks (CPSN). In this paper, we propose a CPSN-based service framework. The framework firstly represents CPSN as an adjacency tensor. Then, a novel tensor decomposition method named high-order orthogonal tensor singular value decomposition (HO-OTSVD) is proposed for knowledge discovery. To cope with the dynamic CPSN, an incremental HO-OTSVD (IHO-OTSVD) is developed to update the orthogonal tensor basis and the core tensor. Furthermore, we propose high-order bidiagonal Lanczos algorithm to cope with the orthogonalization of HO-OTSVD, wherein the complexity reduces from cubic execution time to quadratic execution time. Finally, we use a recommendation system as a case study to evaluate the effectiveness and efficiency of the proposed CPSN-based framework. The results show that HO-OTSVD method outperforms the existing methods.

关键词： Social network services   Computer science   Computational modeling   Measurement   Cyber-physical systems   Social cyber-physical system   trustworthiness management   service discovery   satisfaction rate  

摘要： The concept of social networking is integrated into the Cyber-Physical Systems (CPSs) for the purpose of allowing smart objects to establish social relationships in an autonomous way. Solutions based on social relationships are considered to be promising approaches for resource/service discovery. The primary issues in a social-CPS (SCPS) involve how to define, quantify and eventually establish social relationships between objects, and how to build a reliable system. In this paper, we first put forward a definition of the trustworthiness management in a SCPS. And we subsequently build a reputation-based trust mechanism that quantifies the direct trustworthiness between objects, using a fuzzy logic model to address the perceptions of uncertainty and risk contained in this concept of trust. Then we propose a light-weight paradigm for service discovery which makes use of indirect trustworthiness for service provider ranking, based on the theories of the directed acyclic graph. Several rules are introduced for the weighted recommendation paths, and an expurgation mechanism is proposed to simplify the process of indirect trustworthiness evaluation. The convincing experimental results show that our mechanism performs better than the other two methods with four aspects of satisfaction rate, quality of recommended information, accuracy of trust assessment and convergence time.

关键词： Tools   Predictive models   Computer science   Psychology   Business   Training   Agent-based simulator   agent-based social simulation   higher education   multi-agent system   sociogram  

摘要： Most novice teachers and even some experienced teachers can lack appropriate tools for designing teaching strategies that ensure the quality of education. The ability of working in teams is crucial in educating professionals. The literature proves that social relations influence the performance of teams. For instance, the team cohesion is directly related with its performance. In this paper, we have developed an agent-based tool for assisting teachers in simulating their teaching strategies to estimate their influence on the group sociometrics like cohesion, coherence of reciprocal relations, dissociation, and density of relations. The experiments with nine scenarios in disciplines of computer science, electronic, psychology, business, tourism, and renewal energies with 239 students and 6 teachers including experienced and novice ones show that its underlying agent-based framework can adapt to different disciplines obtaining similar outcomes to the real ones. We learned that the tool was especially reliable in predicting the density of relations and the cohesion, being the latter one probably the most relevant due to its known relation with academic performance. In addition, we also learned that it was difficult to assess the prediction quality of the dissociation in higher education, due to the usual low amounts or absence of reciprocal rejections in the students' groups in this educational stage. The presented agent-based tool is publicly distributed as open source for facilitating other researchers in following this research line.

关键词： Internet of Things   Big Data   Artificial intelligence   Computer science   Heuristic algorithms   Informatics   Optimization  

摘要： These redundant data in IoT should be compressed or removed. Furthermore, the unstructured data in IoT plays an important role for analyzing the user behaviors, while transmitting and processing these unstructured data consumption of substantial energy. These unstructured data should be mined or restructured. In addition, the increasing number of users in IoT leads to a fast-growing data in IoT, while the Quality of Service (QoS) of IoT should be maintained regardless of the number of IoT users. Therefore, the data transmission and processing in IoT should be performed in a more intelligent manner. All these observations indicate that the integration of big data and artificial intelligence (AI) for IoT is a good propellant to improve the data transmission and processing in IoT, since big data technology (e.g., data integration, data mining, data prediction) could effectively handle various data while AI technology could further facilitate capturing and structuring big data.

关键词： Biomedical engineering   Computer science   Mechanical engineering   Applied computing in medical science   Artificial intelligence   Computational mechanics   Process modeling   Biomedical devices   Surface electromyography   Data visualization   Correlation   Mutual information   Artificial neural networks   Kinematics estimation  

摘要： The inputs to the outputs of nonlinear systems can be modeled using machine and deep learning approaches, among which artificial neural networks (ANNs) are a promising option. However, noisy signals affect ANN modeling negatively;hence, it is important to investigate these signals prior to the modeling. Herein, two customized and simple approaches, visual inspection and absolute correlation, are proposed to examine the relationship between the inputs and outputs of a nonlinear system. The system under consideration uses bio-signals from surface electromyography as inputs and human finger joint angles as outputs, acquired from eight intact participants performing movements and grasping tasks in dynamic conditions. Furthermore, the results of these approaches are tested using the standard mutual information measure. Hence, the system dimensionality is reduced, and the ANN learning (convergence) is accelerated, where the most informative inputs are selected for the next phase. Subsequently, four ANN types, i.e., feedforward, cascade-forward, radial basis function, and generalized regression ANNs, are used to perform the modeling. Finally, the performance of the ANNs is compared with findings from the signal analysis. Results indicate a high level of consistency among all the aforementioned signal pre-analysis techniques from one side, and they also indicate that these techniques match the ANN performances from the other side. As an example, for a certain movement set, the ANN models resulted in the rotation estimation accuracy of the joints in the following descending order: carpometacarpal, metacarpophalangeal, proximal interphalangeal, and distal interphalangeal. This information has been indicated in the signal pre-analysis step. Therefore, this step is crucial in input-output variable selections prior to machine-/deeplearning-based modeling approaches.