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关键词： 计算机仪表   尺寸变化   精度稳定性  

摘要： 本文论述了材料的变化对计算机仪表精度长期稳定的影响。随着对这些传感器的精确度和持久稳定性研究的不断加深,人们认识到材料尺寸的不稳定性是造成精度退化的关键因素。本文以常见的计算机传感器组件材料为对象,从材料科学的角度全面回顾了微观瑕疵、第二相颗粒、晶体结构和纹理、内在应力以及环境条件如何影响材料的尺寸稳定性。同时,也探讨了如何基于材料的固有属性来提升传感器的长期精度稳定性,并对计算机传感器的材料和制造工艺的未来发展进行了展望。

关键词： Computer science   Privacy   Data Compliance   Hospital   OCT Images   Federated Learning   Internal audit   GDPR   digital sovereignty  

摘要： Corporate governance privacy policies that follow the digital sovereignty, have an impact on firm’s value and it will reflect on M & A. The best practices and access to new technologies of data privacy policies after acquisition will be applied as per GDPR recommendations. The objective of this paper is to apply a privacy model to protect patients as per privacy policies and maintain cyber security data compliance. The checklist of the acquirer cyber security data compliance policy is applied to the acquired hospital by considering the GDPR articles. Federated learning privacy model is applied to protect and preserve the data during training and testing on OCT image dataset. The dataset was split for training and testing, and gaussian noise was added for privacy preservation from inversion attack. Further, to mitigate the label flipping attack, non-linear aggression method is applied and prediction was measured. Internal audit effectiveness for data compliance and noncompliance was explored and multiple linear regression model was applied to check the variance score. The variance score of 0.59 was obtained using the stimulated dataset and further it can be improved by considering the real time dataset.

关键词： 计算机专业   大学生程序设计竞赛   课赛一体化   教学模式  

摘要： 随着计算机技术的不断发展,培养高质量的计算机专业人才成为高等教育的重要任务之一。文章对基于大学生程序设计竞赛系列赛事的课赛一体化教学模式进行了深入研究,在梳理现有教学模式存在的问题和挑战的基础上,结合学科特点和学生需求,提出了一个中心、两个方向、三年联动、全面参与的课赛一体化创新教学模式,并通过实践验证其有效性,旨在为计算机专业教学改革提供有益的借鉴和参考。

关键词： 应用型   人才培养   大学生   就业  

摘要： 本文简述了应用型人才培养模式的定义及其在提升学生就业竞争力中的重要性,探讨了当前人才培养与就业工作中存在的问题,基于职业要求和市场需求对大学课程改革的指导作用、校企合作对技能提升的影响,以及应用型教育模式和国家政策对人才培养的推动作用,提出了促进大学生就业的具体措施,以提高大学生的就业质量,使大学生能够在毕业后迅速融入社会生产体系,成为满足现代企业需求的复合型、高技能型人才。This paper outlines the definition of the application-oriented talent cultivation model and its importance in enhancing students’ employment competitiveness. It examines the current issues in talent cultivation and employment practices, focusing on the guiding role of professional requirements and market demands in university curriculum reform, the impact of university-industry collaboration on skill development, and the contributions of application-oriented education models and national policies to talent cultivation. The paper proposes specific measures to improve university graduates’ employment quality, enabling them to integrate quickly into the social production system after graduation and meet the needs of modern enterprises as versatile, high-skilled professionals.

摘要： The authors regret that the name of Dr. Brindha M was inadvertently omitted from the author list in the final published version of the article. She was included in the original submission and has contributed significantly to the research work. The authors would like to apologise for any inconvenience caused.

关键词： educational technologies   ChatGPT   GenAI   LLM   computer science education   database administration  

摘要： GenAI tools like ChatGPT have changed the educational landscape, and empirical experiences are needed to better understand how to use them to their fullest potential. This article empirically explores the usage of ChatGPT 3.5 in database administration education through a case study conducted with 40 computer science students. Specifically, it inspects how widespread the use of ChatGPT is and students' perceptions of this tool, how prior knowledge on a topic affects the use of ChatGPT, and the relationship between the usage of ChatGPT and success in solving practical problems. The student's grades in a computer practical exam, a set of theoretical tests to assess progression in knowledge acquisition, and a comprehensive questionnaire are employed as research instruments. The obtained results indicate that students use ChatGPT moderately but more frequently than traditional internet learning resources such as official documentation, Stack Overflow or googling. However, the usage is uneven among students, and those who end up getting better grades use ChatGPT more. Beyond prompting skills, one of the elements that is key to the students' productive use of this tool is their prior knowledge about database administration. This article concludes that ChatGPT is an excellent educational instrument in the context of database administration and that in order to use it properly, it is necessary for students to have good prompting skills as well as a sound theoretical basis. Training students in the use of GenAI tools like ChatGPT, for example, with a guided practice strategy where prompting and conducted step-by-step practice are employed is key to prevent the appearance of new digital trenches.

关键词： Feature extraction   Cloud computing   Fingerprint recognition   Training   Computational modeling   Accuracy   Transforms   Production   Costs   Computer science   Cloud storage   deduplication   resemblance detection  

摘要： Post-deduplication in traditional cloud environments primarily focuses on single-node, where delta compression is performed on the same deduplication node located on server side. However, with data explosion, the multi-node post-deduplication, also called global deduplication, has become a hot issue in research communities, which aims to simultaneously execute delta compression on data distributed across all nodes. Simply setting up single-node deduplication systems on multi-node environments would significantly affect storage utilization and incur secondary overhead from file migration. Nevertheless, existing global deduplication solutions suffer from lower data compression ratios and high computational overhead due to their resemblance detection's inherent limitations and overly coarse granularities. Similar blocks typically have high correlations between sub-blocks;inspired by this observation, we propose IBNR (Intra-Block Neighborhood Relationship-Based Resemblance Detection for High-Performance Multi-Node Post-Deduplication), which introduces a novel resemblance detection based on relationships between sub-blocks and determines the ownership of blocks in entry stage to achieve efficient global deduplication. Furthermore, the by-products of IBNR have shown powerful scalability by replacing internal resemblance detection scheme with existing solutions on practical workloads. Experimental results indicate that IBNR outperforms state-of-the-art solutions, achieving an average 1.99x data reduction ratio and varying degrees of improvement across other key metrics.

关键词： Unified modeling language   Feature extraction   Analytical models   Organizations   Stakeholders   Computer science   Syntactics   Codes   Planning   Guidelines   Feature modeling   software engineering processes   features   product lines   configurable systems  

摘要： Almost any software system needs to exist in multiple variants. While branching or forking-a.k.a. clone & own-are simple and inexpensive strategies, they do not scale well with the number of variants created. Software platforms-a.k.a. software product lines-scale and allow to derive variants by selecting the desired features in an automated, tool-supported process. However, product lines are difficult to adopt and to evolve, requiring mechanisms to manage features and their implementations in complex codebases. Such systems can easily have thousands of features with intricate dependencies. Feature models have arguably become the most popular notation to model and manage features, mainly due to their intuitive, tree-like representation. Introduced more than 30 years ago, thousands of techniques relying on feature models have been presented, including model configuration, synthesis, analysis, and evolution techniques. However, despite many success stories, organizations still struggle with adopting software product lines, limiting the usefulness of such techniques. Surprisingly, no modeling process exists to systematically create feature models, despite them being the main artifact of a product line. This challenges organizations, even hindering the adoption of product lines altogether. We present FM-PRO, a process to engineer feature models. It can be used with different adoption strategies for product lines, including creating one from scratch (pro-active adoption) and re-engineering one from existing cloned variants (extractive adoption). The resulting feature models can be used for configuration, planning, evolution, reasoning about variants, or keeping an overview understanding of complex software platforms. We systematically engineered the process based on empirically elicited modeling principles. We evaluated and refined it in a real-world industrial case study, two surveys with industrial and academic feature-modeling experts, as well as an open-source case st

关键词： Runtime   Accuracy   Fuzzy logic   Training   Predictive models   Servers   Robustness   Computer science   Artificial neural networks   Prediction algorithms   Deep neuro-fuzzy system   edge server   heterogeneous device   prediction  

摘要： Predicting the runtime of tasks is of great significance as it can help users better understand the future runtime consumption of the tasks and make decisions for their heterogeneous devices, or be applied to task scheduling. Learning features from user task history data for predicting task runtime is a mainstream method. However, this method faces many challenges when applied to edge intelligence. In the Big Data era, user devices and data features are constantly evolving, necessitating frequent model retrains. Meanwhile, the noisy data from these devices requires robust methods for valuable insight extraction. In this paper, we propose an edge server-oriented deep neuro-fuzzy system (ESODNFS) that can be trained and inferred on edge servers, for providing users with task runtime prediction services. We divided the dataset and trained it on multiple improved adaptive-network-based fuzzy inference system units (ANFISU), and finally conducted joint training on a deep neural network (DNN). By partitioning the dataset, we reduced the number of parameters for each ANFISU, and at the same time, multiple units can be trained in parallel, supporting fast training and iteration. Additionally, the application of fuzzy inference can effectively learn the features in noisy data and make accurate predictions. The experimental results show that ESODNFS can accurately predict the runtime of real tasks. Compared with other DNN and DNFS, it can achieve good prediction results while reducing training time by over 35%.

关键词： Decoding   Reliability   Encoding   Codes   Reliability theory   Receivers   Hamming weight   Computer science   Channel capacity   Authentication   Multiple access   identification  

摘要： We introduce the problem of determining the identity of a byzantine user (internal adversary) in a communication system. We consider a two-user discrete memoryless multiple access channel where either user may deviate from the prescribed behaviour. Since small deviations may be indistinguishable from the effects of channel noise, it might be overly restrictive to attempt to detect all deviations. In our formulation, we only require detecting deviations which impede the decoding of the non-deviating user's message. When neither user deviates, correct decoding is required. When one user deviates, the decoder must either output a pair of messages of which the message of the non-deviating user is correct or identify the deviating user. The users and the receiver do not share any randomness. The results include a characterization of the set of channels where communication is feasible, and an inner and outer bound on the capacity region. We also show that whenever the rate region has non-empty interior, the capacity region is same as the capacity region under randomized encoding, where each user shares independent randomness with the receiver. We also give an outer bound for this randomized coding capacity region.