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关键词： Task analysis   Internet of Things   Cloud computing   Computer science   Computational modeling   Topology   System performance   Auction mechanism   dependent tasks   edge cloud   Internet of Things (IoT) users   task offloading  

摘要： The rapid proliferation of latency-sensitive Internet of Things (IoT) applications boosts the frequency of offloading compute-intensive tasks from IoT users to mobile edge computing (MEC) due to the limitation resources of IoT devices. It is inevitably for IoT users to compete for the computing resources of the MEC, especially when the computation tasks are dependent and have hard deadline constraints. However, most existing dependent task offloading schemes may not well consider the resource competition issues among IoT users, and possibly lead to limited system performance in multiuser scenario. To address this issue, we intend to design an auction-based dependent task-offloading mechanism to improve the efficiency of task offloading for multiple IoT users. First, we formulate the dependent task offloading as a valuation maximization problem in the trade of computing resources satisfying users' latency requirements, which has been proved to be NP-hard. Then, by jointly considering the task graph structure and the current status of the MEC, we propose a truthful auction mechanism, named greedy winner selection strategy, in which a heuristic dependent task assignment for winners is designed to improve the efficiency of the task offloading. By conducting extensive simulations, we validate that the performance of the proposed dependent task offloading strategy is superior to existing competition algorithms, in terms of total valuations, average makespans, and success rates.

关键词： Students  

摘要： Modern society is diverse, multicultural, and multifaceted. Because of these characteristics, we are currently observing an increase in the debates about equity, diversity, and inclusion in different areas, especially because several groups of individuals are underrepresented in many environments. In computer science and software engineering, it seems counterintuitive that these areas, which are responsible for creating technological solutions and systems for billions of users around the world, do not reflect the diversity of the society to which it serves. In trying to solve this diversity crisis in the software industry, researchers started to investigate strategies that can be applied to increase diversity and improve inclusion in academia and the software industry. However, the lack of diversity in computer science and related courses, including software engineering, is still a problem, in particular when some specific groups are considered. LGBTQIA+ students, for instance, face several challenges to fit into technology courses, even though most students in universities right now belong to Generation Z, which is described as open-minded to aspects of gender and sexuality. In this study, we aimed to discuss the state-of-art of publications about the inclusion of LGBTQIA+ students in computer science education. Using a mapping study, we identified eight studies published in the past six years that focused on this public. We present strategies developed to adapt curricula and lectures to be more inclusive to LGBTQIA+ students and discuss challenges and opportunities for future research. © 2023, CC BY.

关键词： Computer science   Information technology   Research management  

关键词： Students  

摘要： Involving integrated development environments (IDEs) in introductory-level (CS1) programming courses is critical. However, it is difficult for instructors to find a suitable IDE that is beginner friendly and supports strong functionality. In this paper, we report the experience of using Visual Studio Code (VS Code) in a CS1 programming course. We describe our motivation for choosing VS Code and how we introduce it to students. We create comprehensive guidance with hierarchical indexing to help students with diverse programming backgrounds. We perform an experimental evaluation of students’ programming experience of using VS Code and validate the VS Code together with guidance as a promising solution for CS1 programming courses. Copyright © 2023, The Authors. All rights reserved.

关键词： Students  

摘要： We asked ChatGPT to participate in an undergraduate computer science exam on"Algorithms and Data Structures". We evaluated the program on the entire exam as posed to the students. We hand-copied its answers onto an exam sheet, which was subsequently graded in a blind setup alongside those of 200 participating students. We find that ChatGPT narrowly passed the exam, obtaining 20.5 out of 40 points. This impressive performance indicates that ChatGPT can indeed succeed in challenging tasks like university exams. At the same time, the tasks in our exam are structurally similar to those on other exams, solved homework problems, and teaching materials that can be found online. Therefore, it would be premature to conclude from this experiment that ChatGPT has any understanding of computer science. The transcript of our conversation with ChatGPT is available at https://***/tml-tuebingen/chatgpt-algorithm-exam, and the entire graded exam is in the appendix of this paper. Copyright © 2023, The Authors. All rights reserved.

关键词： Computer science   Information technology   Scientific data  

摘要： In the issue of few-shot image classification, due to lack of sufficient data, directly training the model will lead to overfitting. In order to alleviate this problem, more and more methods focus on non-parametric data augmentation, which uses the information of known data to construct non-parametric normal distribution to expand samples in the support set. However, there are some differences between base class data and new ones, and the distribution of different samples belonging to same class is also different. The sample features generated by the current methods may have some deviations. A new few-shot image classification algorithm is proposed on the basis of information fusion rectification (IFR), which adequately uses the relationship between the data (including the relationship between base class data and new ones, and the relationship between support set and query set in the new class data), to rectify the distribution of support set in the new class data. In the proposed algorithm, feature of support set is expanded through sampling from the rectified normal distribution, so as to augment the data. Compared with other image augmentation algorithms, the experimental results on three few-shot datasets show that the accuracy of the proposed IFR algorithm is improved by 1.84-4.66% on 5-way 1-shot task and 0.99-1.43% on 5-way 5-shot task.

关键词： Qubits  

摘要： This paper provides a short introduction to the mathematical foundation of quantum computation for researchers in computer science by providing an introduction fo the mathematical basis of calculations. This paper concerns the mathematical foundations of quantum computation addressing first the representation of qubit using the Bloch sphere and second the special relations between SU(2) and SO(3). The properties of SU(2) are introduced focusing especially about the double-covering of SO(3) and explaining how to map rotations of SO(3) into matrices of SU(2). Quantum physic operators are based on SU(2) since we have a direct relationship to SO(3) namely one isomorphism. We start first from basic representations of qubit in 3 and representations of operators in SU(2) and we next discuss with operators that permit to move from one SU(2) to another one according to a specific operator of SU(2) that is related to rotation into 3 Copyright © 2023, The Authors. All rights reserved.

关键词： Computer science   Mathematics and computing   Machine learning  

摘要： Machine-learning tools already help mathematicians to formulate new theories and solve tough problems. But they’re set to shake up the field even more.

关键词： Computer science   Scientific community   Machine learning  

摘要： Eighty per cent of respondents have used AI chatbots — and 57% say they use them for ‘creative fun’.

关键词： Feature extraction   Manuals   Semantics   Training   Data models   Convex functions   Computer science   Semi-supervised learning   feature selection   binary hashing   spectral embedding   importance differentiation  

摘要： Semi-supervised feature selection methods jointly exploit the labelled and unlabelled samples when selecting the features. Under the semi-supervised learning scenario, the number of labelled data significantly impacts the feature selection performance. In this paper, we introduce the label learning with binary hashing to the research field of feature selection and propose a novel Semi-supervised Feature Selection with Binary Label Learning (SFS-BLL) model. Specifically, we learn the binary hash codes as the pseudo labels by specially imposing binary hash constraints on the spectral embedding process to increase the number of labels. Meanwhile, we propose a self-weighted sparse regression module which exploits the learned labels and given manual labels together with importance differentiation to guide the feature selection process. Finally, we develop an effective discrete optimization method based on the Alternating Direction Method of Multipliers (ADMM) to iteratively optimize the binary labels and the feature selection matrix. Extensive experiments on widely tested benchmarks demonstrate the superiority of the proposed method from various aspects.