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关键词： Computer science research   Temporal evolution   Gender gap in computer science   National scientific productivity   Research areas  

摘要： In this article, we study the evolution of the computer science research community over the past 30 years. Analyzing data from the full Scopus database, we investigate how aspects such as the community size, gender composition, and academic seniority of its members changed over time. We also shed light on the varying popularity of specific research areas, as derived from the ACM's Special Interest Groups and IEEE classifications. Our analysis spans 19 nations (all members of the G20 group, excluding the EU) and involves a total of 728,374 authors and 8,412,543 publications. This work shows that the overall size of the computer science community has increased by a factor of ten in the time period 1991-2020, with China and India enjoying the highest growth. At the same time, this increase has not been uniform across research areas. Female participation has also increased, but more slowly than expected and not uniformly across countries and areas.

关键词： Random access memory   Ash   Indexes   Computer science   Time factors   Hard disks   Engines   Flash memory   hit ratio   mapping table   performance  

摘要： In modern solid-state drives (SSDs), a flash translation layer (FTL) is the core engine performing the logical-to-physical address translations. However, the increasing capacity of SSDs requires a large DRAM space to hold the mapping table in a page-level FTL. Due to the limited size of built-in DRAM, existing FTL schemes selectively cache some active mapping entries in DRAM, while store the entire mapping table on flash. However, the low cache hit ratio introduced by the traditional mapping entry eviction policy degrades the access performance of SSDs. In this article, we propose a novel FTL, hot-clusterity FTL (HCFTL), to improve the hit ratio of cached mapping table (CMT) of SSDs with limited cache space. HCFTL clusters mapping entries, which are newly evicted from the cache, into dynamic translation pages (DTPs). Reading entries from DTPs will increase the CMT hit ratio due to spatial and temporal localities. Because the logical page numbers (LPNs) in a DTP are not consecutive, we introduce two different efficient index structures to speedup the lookup of mapping entries in DTPs. Furthermore, to make the DTP index highly accurate, we additionally allocate an auxiliary cache to buffer the mapping entries newly evicted from CMT, and produce dynamic pages with minimum difference between the maximal and the minimal LPNs of entries in those pages. Our experiments show that HCFTL can improve the CMT hit ratio by up to 41.1% and decrease the system response time by up to 33.3%, compared to baseline FTL schemes.

关键词： Writing   Education   Tools   Natural language processing   Task analysis   Computer science   Systematics   Argumentation   content annotation   experiential learning (EL)   higher education (HE)   natural language processing (NLP)   rubric reliability   STEM   written communication  

摘要： Contribution: Demonstrates how to use experiential learning (EL) to improve argumentative writing. Presents the design and development of a natural language processing (NLP) application for aiding instructors in providing feedback on student essays. Discusses how EL combined with automated support provides an analytical approach to improving written-communication skills. Background: High-quality, timely, feedback is an effective way to improve students' writing. However, large class sizes and limited instructor backgrounds often make formative feedback impossible. Recent trends, including lowering entry requirements, have added to these challenges. Assistive technologies for implementing inclusive education provide viable solutions. Research Questions: 1) How and why can EL be used to develop argumentative writing skills in university STEM students? 2) How can technologies be developed to support using EL in teaching writing? and 3) How might the holistic impact of using such analytic techniques be evaluated? Methodology: Participants in an EL project were assigned two essays in sequence. They were given instructions on making good arguments and shown how to use an analytic rubric to maximize their scores. The essays were hand scored by tutors who provided scores for each dimension of the rubric. Subsequently, the content and argumentation of the essays were analyzed using NLP techniques to obtain independent scores. Qualitative data were also collected. Findings: The project produced transformative writing experiences for the participants. It showed how analytical techniques help improve writing skills and how relevant automated instructor assistance can be developed using NLP technologies.

关键词： Social networking (online)   Computational modeling   Query processing   Knowledge engineering   Formal concept analysis   Computer science   Quality control   Clique   formal concept analysis (FCA)   fuzzy attributed social network   skyline  

摘要： Identifying the optimal groups of users that are closely connected and satisfy some ranking criteria from an attributed social network attracts significant attention from both academia and industry. Skyline query processing, a multicriteria decision-making optimized technique, is recently embedded into cohesive subgraphs mining in graphs/social networks. However, the existing studies cannot capture the fuzzy property of connections between users in social networks. To fill this gap, in this article, we formulate a novel model of the skyline (lambda,k)-cliques over a fuzzy attributed social network and develop a formal concept analysis (FCA)-based skyline (lambda,k)-cliques identification algorithm. Specifically, lambda can be regarded as a quality control parameter for measuring the stability of the cohesive groups. Extensive experimental results conducted on three real-world datasets demonstrate the effectiveness of the skyline (lambda,k)-clique model in a fuzzy attributed social network. Furthermore, an illustrative example is executed for revealing the usefulness of our model. It is expected that our proposed skyline (lambda,k)-clique model can be widely used in various graph-based computational social systems, such as optimal team formation in crowdsourcing, and group recommendation in social networks.

关键词： Convolution   Shape   Computer architecture   Computational modeling   Lenses   Redundancy   Computer science   Channelwise attention   convolutional networks   grouped relationships   inner-imaging (InI)  

摘要： Despite the tremendous success in computer vision, deep convolutional networks suffer from serious computation costs and redundancies. Although previous works address that by enhancing the diversities of filters, they have not considered the complementarity and the completeness of the internal convolutional structure. To respond to this problem, we propose a novel inner-imaging (InI) architecture, which allows relationships between channels to meet the above requirement. Specifically, we organize the channel signal points in groups using convolutional kernels to model both the intragroup and intergroup relationships simultaneously. A convolutional filter is a powerful tool for modeling spatial relations and organizing grouped signals, so the proposed methods map the channel signals onto a pseudoimage, like putting a lens into the internal convolution structure. Consequently, not only is the diversity of channels increased but also the complementarity and completeness can be explicitly enhanced. The proposed architecture is lightweight and easy to be implement. It provides an efficient self-organization strategy for convolutional networks to improve their performance. Extensive experiments are conducted on multiple benchmark datasets, including CIFAR, SVHN, and ImageNet. Experimental results verify the effectiveness of the InI mechanism with the most popular convolutional networks as the backbones.

关键词： app development   behavior change technique   health promotion   mobile health   mobile application   application development   design platform   platform development   no-code mHealth app   no-code app   no-code   end user   participatory research   Pathverse   agile   hybrid-agile   software design   software development   software developer   computer science   BCT   behavior change   research tool   research instrument   digital platform   mHealth   mobile app  

摘要： Background: A challenge facing researchers conducting mobile health (mHealth) research is the amount of resources required to develop mobile apps. This can be a barrier to generating relevant knowledge in a timely manner. The recent rise of "no-code" software development platforms may overcome this challenge and enable researchers to decrease the cost and time required to develop mHealth research apps. Objective: We aimed to describe the development process and the lessons learned to build Pathverse, a no-code mHealth app design platform. Methods: The study took place between November 2019 and December 2021. We used a participatory research framework to develop the mHealth app design platform. In phase 1, we worked with researchers to gather key platform feature requirements and conducted an exploratory literature search to determine needs related to this platform. In phase 2, we used an agile software framework (Scrum) to develop the platform. Each development sprint cycle was 4 weeks in length. We created a minimum viable product at the end of 7 sprint cycles. In phase 3, we used a convenience sample of adults (n=5) to gather user feedback through usability and acceptability testing. In phase 4, we further developed the platform based on user feedback, following the V-model software development process. Results: Our team consulted end users (ie, researchers) and utilized behavior change technique taxonomy and behavior change models (ie, the multi-process action control framework) to guide the development of features. The first version of the Pathverse platform included features that allowed researchers to (1) design customized multimedia app content (eg, interactive lessons), (2) set content delivery logic (eg, only show new lessons when completing the previous lesson), (3) implement customized participant surveys, (4) provide self-monitoring tools, (5) set personalized goals, and (6) customize app notifications. Usability and acceptability testing revealed that r

关键词： Data mining   Systematics   Manuals   Libraries   Education   Licenses   Programming profession   Computer Science Education (CSEd)   open educational resources (OERs)   OER classification   OER digital source  

摘要： Contribution: This study presents the results of a systematic mapping on the classification and organization of open educational resources (OERs) focused on Computer Science Education (CSEd) in digital sources. Background: The number of open resources (e.g., images, videos, and websites) available on the Internet for the teaching of Computer Science has increased in recent years. As a consequence, several authors have proposed different ways of classifying them, which include lists of disciplines, classification algorithms, and semantic enrichment. However, such studies are dispersed and fragmented in the literature, making the current state-of-the-art unclear. Research Questions: 1) What digital sources have been used to classify OERs for CSEd? 2) What CSEd domains have been explored for the classification of OERs? and 3) What approaches have been adopted for the classification of OERs for CSEd? Methodology: Three search strategies were performed: 1) automatic search in the five major digital libraries of CSEd;2) manual search in the major CSEd conferences and journals;and 3) snowballing on the references and citations. As a result, 22 relevant studies were selected. Findings: The main findings were systematization of the main OER sources for CSEd, identification of 64 CSEd domains covered by OERs organized in a taxonomy, the immaturity level of investigations and tools, and lack of mechanisms that organize OERs for CSEd.

关键词： Task analysis   Optimization   Knowledge transfer   Sociology   Evolutionary computation   Multitasking   Computer science   Evolutionary multitasking (EMT)   helper-task   knowledge transfer   multifactorial evolutionary algorithm (MFEA)   multiobjectivization   multiobjectivization via decomposition (MVD)  

摘要： Evolutionary multitasking (EMT) is an emerging research direction in the field of evolutionary computation. EMT solves multiple optimization tasks simultaneously using evolutionary algorithms with the aim to improve the solution for each task via intertask knowledge transfer. The effectiveness of intertask knowledge transfer is the key to the success of EMT. The multifactorial evolutionary algorithm (MFEA) represents one of the most widely used implementation paradigms of EMT. However, it tends to suffer from noneffective or even negative knowledge transfer. To address this issue and improve the performance of MFEA, we incorporate a prior-knowledge-based multiobjectivization via decomposition (MVD) into MFEA to construct strongly related meme helper-tasks. In the proposed method, MVD creates a related multiobjective optimization problem for each component task based on the corresponding problem structure or decision variable grouping to enhance positive intertask knowledge transfer. MVD can reduce the number of local optima and increase population diversity. Comparative experiments on the widely used test problems demonstrate that the constructed meme helper-tasks can utilize the prior knowledge of the target problems to improve the performance of MFEA.

关键词： Web services   Robustness   Computer science   Collaboration   Social networking (online)   Recommender systems   Proposals   Cold-start problems   collaborative filtering (CF)   rating prediction   recommendation   user trust  

摘要： The ever-increasing popularity of recommendation systems allows users to find appropriate services without excessive effort. However, due to the unstable and complex network environment, the historical behavior data of users are quite sparse in most cases. The inherent drawbacks render preference prediction infeasible for cold-start users and have become a crucial issue to be resolved in recommendation systems. To deal with the problems, we first present a Trust-based Collaborative Filtering (TbCF) algorithm to perform basic rating prediction in a manner consistent with the existing CF methods. Then, we propose the Hybrid Collaborative Filtering Recommendation approach with User-Item-Trust Records (UIThybrid), a novel approach that incorporates user trust into the existing CF-based methods in a harmonious way to supplement rating information. UIThybrid employs multiple perspectives to extract proper services and achieves a good tradeoff between the robustness, accuracy, and diversity of the recommendation. We conduct extensive real-world experiments on the Epinions data set to demonstrate the feasibility and efficiency of UIThybrid.