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关键词： Computer science   Training   Problem-solving   Programming profession   Engineering profession   Licenses   Europe   computational thinking   primary education   secondary education   syllabus  

摘要： Although Computer Science has grown to become one of the most highly demanded professional careers, every year, only a small percentage of students choose a degree directly related to Computer Science. Perhaps the problem lies in the lack of information that society has about Computer Science itself, and particularly about the work computer scientists do. No one doubts the role of Mathematics or Languages as core subjects in every primary and secondary education syllabus;however, Computer Science plays a negligible role in most current syllabuses. Only in a few countries have governments paid special attention to content related to Computer Science and to learning to analyze and solve problems the way computer scientists do (Computational Thinking). In this article, we present Piens@ Computacion@ULLmente, a project that provides a methodology to promote Computer Science through Computational Thinking activities among primary and secondary education students. The results obtained from an exhaustive statistical analysis of the data we collected demonstrate that the perception of Computer Science that pre-university students have can be improved through specific training. Moreover, we can also confirm that the performance of pre-university students involving Computational Thinking skills is independent of gender, particularly at the primary education level.

关键词： 计算机科学与技术   发展   信息化  

摘要： 21世纪,以计算机网络和多媒体为代表的信息技术快速地进入了人们的生活之中,使得当今成为了以信息化为创新载体、以知识为基础的知识时代。随着计算机通信多媒体和网络技术的快速发展,其已经成为信息化的重要支柱,信息化也将促进人类社会不断进步与发展。计算机科学技术和信息化之间有非常紧密的联系,能够为社会发展提供非常大的帮助,形成更强的信息价值,从而改善人们的精神水平,促进精神消费的有效转变。

关键词： Adaptive systems   Neurons   Clustering algorithms   Protocols   Entropy   Computer science   Wireless sensor networks   LDoS attack   fine-grained detection   Smith-Waterman algorithm   adaptive Kohonen network   traditional network   software-defined network  

摘要： Low-rate denial of service (LDoS) attacks exploit the security vulnerabilities of network protocols adaptive mechanisms to launch periodic bursts. These attacks result in the severe destruction of the quality of service of TCP applications. Therefore, detection of LDoS attacks is a concern among scientific communities. However, the existing coarse-scale detection methods yield poor detection performance and adaptability. To achieve the accurate detection of LDoS attacks, an adaptive Kohonen Network based fine-grained detection (AKN-FGD) model for LDoS attacks is proposed. Based on the burst and periodicity characteristics of attack traffic, the Smith-Waterman (SW) algorithm is used to estimate the pulse period, which is the length of the detection unit. Subsequently, cluster analysis is performed for each detection unit using the adaptive Kohonen network (AKN) algorithm because the discreteness of traffic suffering from LDoS attacks is more pronounced than that of legitimate traffic. Finally, the existence of LDoS attacks can be verified in view of a novel decision metric, denoted as the anomaly degree, based on the clustering results. We conducted experiments not solely in traditional networks using NS3 and in a test-bed environment but also in a software-defined network (SDN), with accuracies of 99.7%, 99.8%, and 95.6% for detecting LDoS bursts, respectively. The experimental results show that the AKN-FGD scheme not only enables accurate fine-grained detection, that is, it can detect every attack burst, but also estimates the start and end times of the attacks. Moreover, we have compared the AKN-FGD scheme with some other detection methods, and a comparison of the results show that our proposed approach displays better detection performance.

关键词： Biographies   History   Computer science education   Keller,M.K   Gender issues   Engineering profession  

摘要： Profiles Mary Kenneth Keller who received the first Ph.D in Computer Science (1965, University of Wisconsin). Born Evelyn Marie Keller, she entered the Catholic congregation of the Sisters of Charity of the Blessed Virgin Mary (BVMs) and was given the name Sister Mary Kenneth. In her publications, she used the name Sister Mary K. Keller, Sister Mary Kenneth, Mary K. Keller, and (her complete name) Sister Mary Kenneth Keller. Presents a biography of her early life, family, education, and professional career.

关键词： Business   Logic gates   Informatics   Social networking (online)   Optimization   Mathematical models   Computer science   Business networks   community structure   influential nodes   node centrality   smart enterprises  

摘要： Finding influential nodes reshuffles the very notion of linear paths in business processes and replaces it with networks of business value within a smart enterprise system. There are many existing algorithms for identifying influential nodes with certain limitations for applying in large-scale networks. In this article, we propose a community structure with integrated features ranking (CIFR) algorithm to find influential nodes in the network. First, we use the community detection algorithm to find communities in the system, and then we rank the nodes of network based on three factors, namely local ranking, gateway ranking, and community ranking, collectively termed as integrated features. Our algorithm intends to select influential nodes, which are both globally and locally optimal, leading to overall high information propagation. We perform the experimental results on total eight networks using various evaluation parameters. The obtained results validate superior performance against contemporary algorithms adding value to smart enterprises.

关键词： Optimization   Predictive models   Computational modeling   Training   Linear programming   Gaussian processes   Computer science   Adaptive lower confidence bound (ALCB)   expensive optimization   multiobjective optimization   multioutput Gaussian process (GP)   subproblem selection  

摘要： In dealing with the expensive multiobjective optimization problem, some algorithms convert it into a number of single-objective subproblems for optimization. At each iteration, these algorithms conduct surrogate-assisted optimization on one or multiple subproblems. However, these subproblems may be unnecessary or resolved. Operating on such subproblems can cause server inefficiencies, especially in the case of expensive optimization. To overcome this shortcoming, we propose an adaptive subproblem selection (ASS) strategy to identify the most promising subproblems for further modeling. To better leverage the cross information between the subproblems, we use the collaborative multioutput Gaussian process surrogate to model them jointly. Moreover, the commonly used acquisition functions (also known as infill criteria) are investigated in this article. Our analysis reveals that these acquisition functions may cause severe imbalances between exploitation and exploration in multiobjective optimization scenarios. Consequently, we develop a new acquisition function, namely, adaptive lower confidence bound (ALCB), to cope with it. The experimental results on three different sets of benchmark problems indicate that our proposed algorithm is competitive. Beyond that, we also quantitatively validate the effectiveness of the ASS strategy, the CoMOGP model, and the ALCB acquisition function.

关键词： Computer science   Industries   Translational research   Research initiatives  

摘要： Although not labeled as such, the three research centers presented in this article have been performing translational computer science (TCS) for more than 20 years. SBA Research, Software Competence Center Hagenberg, and VRVis Zentrum fuer Virtual Reality und Visualisierung, all funded by the Austrian COMET Competence Centers for Excellent Technologies program, each selected one science-industry partnership to exemplify how they do TCS in their day-to-day work. We show how the COMET funding program provides a framework for establishing long-term research relationships with industry, thus facilitating TCS: research done by the COMET centers is directly put in the practical context of the industry partners, and successes and failures are immediately reported back. This constant feedback loop positively impacts the know-how at the involved companies, while allowing the computer scientists to continuously test, improve, and publish their research based on real-world scenarios.

关键词： Special issues and sections   Scientific computing   Education   High performance computing   Computer science   Information science   Computational modeling   Research initiatives   Data models   Data analysis  

摘要： Most scientific fields have seen a remarkable increase in the use of computational and data-driven research, including physics, chemistry, biology, and astronomy. However, many researchers who lack specialized training in computer science or high-performance computing (HPC) face difficulties in accessing and effectively utilizing these resources. A science gateway serves as a crucial connection point that brings together the various components of advanced cyber infrastructure, such as data collections, instruments, HPC, clouds, and analytical tools, behind user-friendly interfaces. These gateways are developed by communities to simplify the process of accessing and utilizing resources, making them more accessible to researchers who lack technical expertise or resources of their own.

关键词： machine learning   neural networks   ethics   reading   politics   computer science  

摘要： Computer science tends to foreclose the reading of its texts by social science and humanities scholars - via code and scale, mathematics, black box opacities, secret or proprietary models. Yet, when computer science papers are read in order to better understand what machine learning means for societies, a form of reading is brought to bear that is not primarily about excavating the hidden meaning of a text or exposing underlying truths about science. Not strictly reading to make sense or to discern definitive meaning of computer science texts, reading is an engagement with the sense-making and meaning-making that takes place. We propose a strategy for reading computer science that is attentive to the act of reading itself, that stays close to the difficulty involved in all forms of reading, and that works with the text as already properly belonging to the ethico-politics that this difficulty engenders. Addressing a series of three "reading problems" - genre, readability, and meaning - we discuss machine learning textbooks and papers as sites where today's algorithmic models are actively giving accounts of their paradigmatic worldview. Much more than matters of technical definition or proof of concept, texts are sites where concepts are forged and contested. In our times, when the political application of AI and machine learning is so commonly geared to settle or predict difficult societal problems in advance, a reading strategy must open the gaps and difficulties of that which cannot be settled or resolved.