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关键词： ChatGPT   generative AI   cheating   quality assurance   university assessment  

摘要： Cheating has been a long-standing issue in university assessments. However, the release of ChatGPT and other free-to-use generative AI tools has provided a new and distinct method for cheating. Students can run many assessment questions through the tool and generate a superficially compelling answer, which may or may not be accurate. We ran a dual-anonymous "quality assurance" marking exercise across four end-ofmodule assessments across a distance university computer science (CS) curriculum. Each marker received five ChatGPT-generated scripts alongside 10 student scripts. A total of 90 scripts were marked;every ChatGPTgenerated script for the undergraduate modules received at least a passing grade (> 40%), with all of the introductory module CS1 scripts receiving a distinction (> 85%). None of the ChatGPT-taught postgraduate scripts received a passing grade (> 50%). We also present the results of interviewing the markers and of running our sample scripts through a GPT-2 detector and the TurnItIn AI detector, which both identified every ChatGPT-generated script but differed in the number of false positives. As such, we contribute a baseline understanding of how the public release of generative AI is likely to significantly impact quality assurance processes. Our analysis demonstrates that in most cases, across a range of question formats, topics, and study levels, ChatGPT is at least capable of producing adequate answers for undergraduate assessment.

关键词： Security   Autonomous vehicles   Encryption   Games   Internet of Things   Computer science   Sun   Internet of the autonomous vehicle   secure communication   tamper-resistant   identity-based broadcast signcryption  

摘要： As increasingly prevalent technologies in autonomous driving, 5G and the Internet of Things (IoT), Internet of autonomous vehicle (IoAV) technology is recognized as a technique that is capable of disruptively changing the way people travel and greatly improving the travel experience. In the IoAV scenarios, information dissemination is inseparable from the interaction between autonomous vehicles and smart infrastructure. However, existing efforts rarely focus on the secrecy, authenticity of interactive data and flexible one-to-many communication between autonomous vehicles. In this paper, we propose a tamper-resistant broadcasting (TRBS) scheme for secure communication, which handles the inefficiencies and insecurity of existing identity-based broadcast signcryption solutions. Not only can our TRBS protect communication data from being illegally accessed, forged, or tampered with by malicious vehicles, but it can also enable efficient and flexible secure information dissemination between autonomous vehicles. We also exhibit strict security proofs and experimental evaluations to demonstrate our TRBS is secure and efficient for real-world applications.

关键词： Oscillators   Task analysis   Machine learning algorithms   Convergence   Computer science   Standards   Search problems   Bipartite networks   coarsening   label propagation   multilevel approach   multi-label propagation  

摘要： Complex machine learning tasks for knowledge discovery in networked data, such as community detection, node categorization, network visualization, and dimension reduction, have been successfully addressed by coarsening algorithms. It iteratively reduces the original network into a hierarchy of smaller networks, resulting in informative simplifications of the original network at various degrees of detail. Few of these algorithms, however, have been specially built to cope with bipartite networks. Besides of this, current coarsening algorithms present the following theoretical limitations that should be addressed: 1) A high-cost search strategy in dense networks;2) current coarsening algorithms are usually based on label propagation, which is limited to propagate single-labels;and 3) the synchronous label propagation scheme yields the cyclic oscillation problem. To overcome such limitations, we propose a coarsening algorithm based on multi-label propagation, which is more suitable for large-scale bipartite networks and allows a time-effective implementation. Furthermore, our proposal improves the standard semi-synchronous strategy and simultaneously propagates multiple labels to create the coarsened network representation. The empirical analysis of synthetic and real-world networks provides evidence that our coarsening strategy leads to significant gains regarding accuracy and runtime against standard techniques.

关键词： Game-based learning   Minecraft Education Edition   Educational technology   Lesson planning   Program evaluation  

摘要： The purpose of this research is to evaluate the potential of use for computer science course designs (CSCD) created by game-based learning method. Qualitative case study was used as the research design. CSCDs created for Minecraft Education Edition (MinecraftEdu) were examined in the context of various criteria by document analysis method, and semi-structured interviews were conducted with teachers actively using MinecraftEdu in their classes. The research revealed that the CSCDs created for MinecraftEdu primarily aimed at developing coding and design skills, underscored interdisciplinary collaboration, laid more emphasis on cognitive-psychomotor achievements with curriculum eligibility. It was acknowledged that introduction, body and conclusion parts have an elaborate design in majority of course designs according to Gagne's Events of Instruction Model, and yet, more attention should be paid to gaining the learner's attention, stimulating recall of prior learning, giving feedback, and ensuring retention and transfer. Despite increasing interest and trend towards educational games and game-based learning environments, there are limited number of studies that can help teachers evaluate the games that can be most effective in their fields and different learning contexts. Therefore, it is anticipated that this study will contribute to the related literature on game-based course designs and applicability.

关键词： Artificial intelligence   Operations research   Computer science   Optimization   Graph theory   Minimum Spanning Tree Problem   Dhouib-Matrix  

摘要： The Minimum Spanning Tree problem aims to create a subset of a graph where all the vertices are connected with the minimum edge weights and with no cycle. In this field, an innovative method entitled Dhouib-Matrix-MSTP (DM-MSTP) is designed in this research work with a time complexity independently of the number of edges O(n*log(n)) where n is the number of vertices. DM-MSTP is a constructive algorithm based on a matrix navigation with two new lists (Min-Columns and MST-Path) in order to organize the steering flow. DM-MSTP is composed of four simple steps where the first and the fourth steps are repeated only once, whereas the second and third are reiterated (n-1). For more clarification, a step-by-step application of the proposed method is presented in details. Besides, the performance of DM-MSTP is proved through different examples from the literature including a graph with negative weighted edges and complete graphs from TSP-LIB. Moreover, with a simple modification (Min by Max) the DM-MSTP is tested on the Maximum (Largest) Spanning Tree Problem. Also, DM-MSTP is applied on eight case studies and compared to six methods developed in the literature. All these experimental results in the above different environments show that DM-MSTP can rapidly plan the shortest spanning tree with a stable performance and convivial representation of the optimal tree. Hence, DM-MSTP is developed under Python programming language using Matplotlib and Numpy standard libraries.

关键词： Controllability   Color   Heuristic algorithms   Network systems   Games   Process control   Computer science   Dynamics over graphs   edge augmentation   strong structural controllability   zero forcing sets (ZFSs)  

摘要： This article studies the problem of computing a minimum zero forcing set (ZFS) in undirected graphs and presents new approaches to reducing the size of the minimum ZFS via edge augmentation. The minimum ZFS problem has numerous applications;for instance, it relates to the minimum leader selection problem for the strong structural controllability of networks defined over graphs. Computing a minimum ZFS is an NP-hard problem in general. We show that the greedy heuristic for the ZFS computation, though it typically performs well, could give arbitrarily bad solutions for some graphs. We provide a linear-time algorithm to compute a minimum ZFS in trees and a complete characterization of the minimum ZFS in the clique chain graphs. We also present a game-theoretic solution for general graphs by formalizing the minimum ZFS problem as a potential game. In addition, we consider the effect of edge augmentation on the size of the ZFS. Adding edges could improve network robustness;however, it could increase the size of the ZFS. We show that adding a set of carefully selected missing edges to a graph may actually reduce the size of the minimum ZFS. Finally, we numerically evaluate our results on random graphs.

关键词： Computer science   Neural networks   Time series   Load forecasting  

摘要： In natural language processing (NLP), transformer based models have surpassed recurrent neural networks (RNN) as state of the art, being introduced specifically to address the limitations of RNNs originating from its sequential nature. As a similar sequence modeling problem, transformer methods can be readily adapted for deep learning time series prediction. This paper proposes a sparse transformer based approach for electricity load prediction. The layers of a transformer addresses the shortcomings of RNNs and CNNs by applying the attention mechanism on the entire time series, allowing any data point in the input to influence any location in the output of the layer. This allows transformers to incorporate information from the entire sequence in a single layer. Attention computations can also be parallelized. Thus, transformers can achieve faster speeds, or trade this speed for more layers and increased complexity. In experiments on public datasets, the sparse transformer attained comparable accuracy to an RNN-based SOTA method (Liu et al., 2022) while being up to 5x faster during inference. Moreover, the proposed model is general enough to forecast the load from individual households to city levels as shown in the extensive experiments conducted.