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关键词： 职业院校   计算机专业青年教师   教学能力  

摘要： 近年来,国家对职业教育的重视程度不断加深,职业院校青年教师的教学能力直接影响着人才培养的质量。特别是在计算机专业快速发展的背景下,人工智能、机器学习、区块链、物联网等前沿研究方向层出不穷,青年教师的教学能力水平显得尤为重要。文章研究了职业院校计算机专业青年教师的教学能力现状,分析了其面临的问题,并针对性地提出了提升教学能力的策略,其中包括加强计算机专业知识的学习和教育心理学的学习,开展岗前培训以帮助教师顺利转型,完善考核机制以促进教学相长,开设科研课程提升教师的科研基本能力,并建立和完善科研奖励机制。职业院校可以参考这些策略,以提升计算机专业青年教师的教学能力。

关键词： Developing country education   Educational outcomes   Interdisciplinary education   Program evaluation   Skill gap analysis  

摘要： This paper evaluates the impact of an English and Business Computer Science program in the Democratic Republic of Congo (DRC), the world’s second-largest French-speaking country after France. The program addresses the gap between academic training and market needs by combining English language proficiency with essential business and computer science skills. This combination aims to equip students for success in the business and technology sectors, where English is a key language. The paper details the program’s implementation, outcomes, and evaluation, using surveys of employers and graduates, along with an analysis of student competencies and employability. The findings underscore the importance of English proficiency, along with business and technological skills, in aligning educational outcomes with job market demands, thereby improving student success. © The Author(s) 2024.

关键词： 黑神话:悟空   游戏产业   中华优秀传统文化  

摘要： 中华优秀传统文化的创造性转化和创新性发展是习近平文化思想的关键内容,更是新时代文化建设亟待解决的重大课题。游戏作为一种跨文化的传播媒介,为中华优秀传统文化的传承与发展赋予诸多可能。以《黑神话:悟空》游戏为例,文章借助现代游戏技术生动再现了中国文化元素、中式美学元素和中国哲学文化思想,实现游戏与文化的有机结合。在此基础上,其突破旧有模式,实现价值观传递;打破文化壁垒,推动文化交流传播;打造“线上+线下”模式,助力文旅产业发展。在新时代语境下,游戏产业应以习近平文化思想为指引,以此款游戏为鉴,做到结合新时代特点,展现人民情怀;深挖中华传统文化内核,营造浓厚的文化意象;坚守传统文化之正,创新游戏表达之新;利用自身优势讲好中国故事,传递中国声音。

关键词： 黑神话:悟空   文旅融合   游戏驱动   创新发展  

摘要： 在数字化时代,游戏作为新兴文化载体,对文旅融合发展产生重要影响。国产3A游戏《黑神话:悟空》的成功提升了本土文化认同,带动了旅游热潮,促进了玩家向游客的转变,为山西文旅融合发展注入了新活力,扩大了山西文化的传播范围和影响力。为进一步推动“旅游+游戏”创新发展模式,实现山西文旅融合高质量发展,要在三方面发力:突破业态局限,打造多元文旅体验;补齐服务短板,提升游客体验质量;加强宣传推广,扩大文旅品牌影响力。

关键词： Service-Learning   Computational Thinking   Information Literacy   STEM Education   Higher Education   China  

摘要： Service-Learning (S-L) has been recognized as a promising educational strategy in higher education in China. This study explores the integration of an academic S-L component within a Computer Science curriculum at a university on China's east coast, focusing on student experiences. We thematically analyzed qualitative data encompassing reflection reports and final essays from 53 enrolled undergraduates, transcripts from 11 semi-structured interviews with students, and community feedback. The findings illuminate the multifaceted benefits of embedding S-L in Computer Science education. Students not only acquired theoretical knowledge but also developed crucial cognitive skills through practical, real-world service experiences. The S-L component was instrumental in enhancing students' connections with peers, service recipients, and the broader community, fostering a deep sense of engagement and empathy. Additionally, the study highlights the role of academic S-L in stimulating creativity and student empowerment in the rapidly evolving domains of information technology and higher education.

关键词： Codes   Chatbots   Maintenance engineering   Software development management   Software   Computer bugs   Urban areas   Computer science   Accuracy   Source coding   Empirical study   ChatGPT   self-verification   code generation   code completion   program repair  

摘要： With the increasing utilization of large language models such as ChatGPT during software development, it has become crucial to verify the quality of code content it generates. Recent studies proposed utilizing ChatGPT as both a developer and tester for multi-agent collaborative software development. The multi-agent collaboration empowers ChatGPT to produce test reports for its generated code, enabling it to self-verify the code content and fix bugs based on these reports. However, these studies did not assess the effectiveness of the generated test reports in validating the code. Therefore, we conduct a comprehensive empirical investigation to evaluate ChatGPT's self-verification capability in code generation, code completion, and program repair. We request ChatGPT to (1) generate correct code and then self-verify its correctness;(2) complete code without vulnerabilities and then self-verify for the presence of vulnerabilities;and (3) repair buggy code and then self-verify whether the bugs are resolved. Our findings on two code generation datasets, one code completion dataset, and two program repair datasets reveal the following observations: (1) ChatGPT often erroneously predicts its generated incorrect code as correct, its vulnerable completed code as non-vulnerable, and its failed program repairs as successful during its self-verification. (2) The self-contradictory hallucinations in ChatGPT's behavior arise: (a) ChatGPT initially generates code that it believes to be correct but later predicts it to be incorrect;(b) ChatGPT initially generates code completions that it deems secure but later predicts them to be vulnerable;(c) ChatGPT initially outputs code that it considers successfully repaired but later predicts it to be buggy during its self-verification. (3) The self-verification capability of ChatGPT can be enhanced by asking the guiding question, which queries whether ChatGPT agrees with assertions about incorrectly generated or repaired code and vulnerabil

关键词： Contractive autoencoders   interactive prognosis   nonlinear degradation model   remaining useful life (RUL)   turbofan engines  

摘要： Uncertainty quantification of the remaining useful life (RUL) for degraded systems under the big data era has been a hot topic in recent years. A general idea is to execute two separate steps: deep-learning-based health indicator (HI) construction and stochastic process-based degradation modeling. However, there exists a critical matching defect between the constructed HI and a degradation model, which seriously affects the RUL prediction accuracy. Toward this end, this article proposes an interactive prognosis framework between deep learning and a stochastic process model for the RUL prediction. First, we resort to stacked contractive autoencoders to fuse multiple sensor information of historical systems for constructing the HI in a typical unsupervised manner. Then, considering the nonlinear characteristic of the constructed HI, an exponential-like degradation model is introduced to construct its degradation evolving model, and theoretical expressions of the prediction results are derived under the concept of the first hitting time. Furthermore, we design an optimization objective function by integrating the HI construction and degradation modeling for the RUL prediction. To minimize the designed objective function of the proposed interactive prognosis framework, a gradient descent algorithm is employed to update the model parameters. Based on the well-trained interactive prognosis model, we can obtain the HI of a field system from stacked contractive autoencoders with sensor data and the probability density function (pdf) of the predicted RUL on the basis of the estimated parameters. Finally, the effectiveness and superiority of the proposed interactive prognosis method are verified by two case studies associated with turbofan engines.

关键词： Peer feedback   UTAUT   Technology acceptance   Achievement goals   Learning   Computer science  

摘要： Computer-supported peer feedback offers great potential to enhance students' learning. Yet, students sometimes do not use computer-supported peer feedback opportunities, which partially can be the result of low technology acceptance. The UTAUT-model specifies performance expectancy, effort expectancy, and facilitating conditions as decisive factors for the intention to use a technology. From a motivational perspective, however, it can be expected that also students' achievement goals have an impact on the intention to use an online peer feedback tool. Therefore, we investigated the effects of learning approach, appearance approach, appearance avoidance, work avoidance and relational goals (besides performance expectancy, effort expectancy and facilitating conditions) on 155 computer science students' intentions and actual use of an online peer feedback tool and their performance in an end-of-course exam. Results of path modelling the longitudinal, student and log-data informed data showed that students' intentions predicted actual use, which predicted exam performance. Learning approach goals positively predicted the intention to use the tool, while performance and work avoidance goals did not predict intentions. Relational goals, however, negatively predicted intentions and end-of-course performance, shedding light on the importance of students' social motivations when using online peer feedback tools in their studies (e.g., peer feedback might be perceived as a social threat). Thus, the results point to the importance of an appropriate framing of online peer feedback tool use in educational settings as a learning opportunity and to reduce students' possible concerns about their social relationships when using online peer feedback tools.

关键词： Representation learning   Iterative methods   Optimization   Computer science   Accuracy   Imputation   Diffusion processes   Incomplete multi-view clustering (IMVC)   latent structure-aware view recovery (LaSA)   cross view graph diffusion regularization  

摘要： Incomplete multi-view clustering (IMVC) presents a significant challenge due to the need for effectively exploring complementary and consistent information within the context of missing views. One promising strategy to tackle this challenge is to recover missing views by inferring the missing samples. However, such approaches often fail to fully utilize discriminative structural information or adequately address consistency, as it requires such information to be known or learnable in advance, which contradicts the incomplete data setting. In this study, we propose a novel approach called Latent Structure-Aware view recovery (LaSA) for the IMVC task. Our objective is to recover missing views through discriminative latent representations by leveraging structural information. Specifically, our method offers a unified closed-form formulation that simultaneously performs missing data inference and latent representation learning, using a learned intrinsic graph as structural information. This formulation, incorporating graph structure information, enhances the inference of missing data while facilitating discriminative feature learning. Even when intrinsic graph is initially unknown due to incomplete data, our formulation allows for effective view recovery and intrinsic graph learning through an iterative optimization process. To further enhance performance, we introduce an iterative consistency diffusion process, which effectively leverages the consistency and complementary information across multiple views. Extensive experiments demonstrate the effectiveness of the proposed method compared to state-of-the-art approaches.

关键词： Tensors   Graphics processing units   Indexes   Sparse matrices   Arithmetic   Registers   Computer science   Single instruction multiple data   Optimization   Memory management   Bit optimization   GPU   sparse tensor contraction (SpTC)   sparse tensor format   tensor core  

摘要： Sparse tensor contraction (SpTC) is an important operator in tensor networks, which tends to generate a large amount of sparse high-dimensional data, placing higher demands on the computational performance and storage bandwidth of the processor. Using GPUs with powerful arithmetic characteristics is a reliable choice for accelerating SpTC, however, the high dimensionality and sparsity of tensor makes GPU-accelerated SpTC operators suffer from the difficulties of low computational intensity and high memory consumption. The recent introduction of Tensor Core Units (TCUs) on GPUs brings even more powerful arithmetic, which exacerbates the memory wall problem. To cope with the challenges, this paper proposes a new BCB format that linearizes the indices of multidimensional blocks to reduce block index accesses and uses a bitmap to store the distribution of non-zero elements in a block to reduce the storage overhead. A parallel blocking algorithm of BCB-SpTC is designed to divide the binary linear indices into free and contracted indexes to improve the pairing overhead of computational tasks. Then based on the characteristic computation method of TCUs, the proprietary filling method of TCUs is designed to overcome the inefficiency of parallel computation of sparse data on TCUs. Finally, experimental results on the A100 dataset show that BCB-SpTC improves the acceleration ratio by 1.1x to 21.3x over the existing SpTC GPU method.