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关键词： upward transfer   mixed methods   STEM   computer science   gender  

摘要： Research Questions: While community college transfer (i.e., upward transfer) represents an important mechanism for advancing equity across STEM fields, existing studies of gender and women's participation within computer science have largely excluded the perspectives of upward transfer students. We address this gap in the literature by exploring transfer receptivity and gender discrimination within computer science, guided by the following questions: (1) How do upward transfer computer science students report their receptivity experiences, and how might this differ by gender? (2) How do upward transfer computer science students make meaning of receptivity experiences, and how might that meaning making be shaped by gender? Methods: We use a sequential mixed methods design, relying on longitudinal survey and interview data from upward transfer computer science majors, collected throughout students' first year at the receiving university. Results: Findings reveal that, relative to men, upward transfer women report greater experiences of transfer stigma and challenges accessing resources at the receiving university. Qualitative findings document additional nuances in how upward transfer students-especially women-describe resilience as they navigate the university campus, encounter navigation challenges at the university, and make meaning of various manifestations of transfer stigma on campus. Contributions: In addition to implications for research and theory, we discuss what universities can do to foster a more receptive environment for upward transfer women. Specific recommendations focus on ensuring that spaces for women in computing are inclusive of transfer students and, likewise, creating supportive transfer cohort communities that are inclusive of women.

关键词： 科学合作   机构合作网络   合作绩效   学术影响力  

摘要： [目的/意义]在科学合作中，机构是科学研究中的主要驱动单位。相较机构内合作，跨机构合作往往更具产出高影响力文章的潜力。因此，对机构层面的细粒度合作进行研究具有重要意义。[方法/过程]为了研究不同机构的合作类型与学术影响力之间的关系，本文通过对机构进行分类，定义不同的类型，运用网络分析法，研究不同机构合作类型网络指标与学术影响力之间的关系。[结果/结论]本文以计算机科学领域为例，通过对计算机科学领域不同的机构合作类型网络指标与学术影响力之间的关系分析发现，度中心性和接近中心性与学术影响力呈现正相关的关系，中介中心性则与学术影响力呈负相关关系；而在不同的子领域中，不同的中心性指标与学术影响力之间存在较大差异，与计算机科学领域的整体回归结果也存在较大差异。

关键词： Context modeling   Transformers   Translation   Decoding   Vectors   Training   Neural machine translation   Standards   Computer science   Computational modeling   Machine translation   document embeddings  

摘要： Standard neural machine translation (NMT) assumes that document-level context information is irrespective. Most existing document-level NMT methods are satisfied with a smattering sense of shallow document-level information, such as using a few context sentences surrounding the source sentence as document-level information. Our work focuses on exploiting detailed document-level context in terms of multiple forms of document embeddings, which can sufficiently model deeper and richer document-level context. The proposed document-level NMT is implemented to enhance the Transformer baseline by introducing both global and local document-level clues on the source end. We compress the entire document text with explicit boundaries into a token-size global static document embedding, and the neighboring sentences as a token-size local dynamic document embedding and concatenate with the source tokens. Experiments reveal that the proposed method significantly improves the translation performance over strong baselines and other related studies.

关键词： Activity recognition   Long short term memory   Feature extraction   Context modeling   Computer science   Attention mechanisms   Deep learning   Graph convolutional networks   Computer vision   Adaptation models   Group activity recognition   actor relation graph   spatio-temporal feature extraction   GCN-LSTM-Attention  

摘要： The challenge of understanding and recognizing group activities through human behavior and interactions is a prominent issue in the realm of computer vision research. This area boasts a wide range of applications, including security surveillance, healthcare monitoring, and human-computer interaction systems. However, accurately deciphering complex group dynamics continues to pose significant difficulties. Traditional video-based activity recognition methods often grapple with persistent obstacles such as environmental noise, background clutter, and the inter-class similarities inherent in activity patterns. Recent advancements in this field have largely focused on spatial feature extraction, which alone proves inadequate for thorough group activity analysis. In response to these limitations, we propose a novel deep learning framework that effectively captures both temporal and spatial characteristics of group interactions. Our architecture employs a Convolutional Neural Network (CNN) with Inception-V3 as the foundational model for initial feature extraction. This is complemented by the development of an Actor Relation Graph (ARG) utilizing Zero Normalized Cross Correlation (ZNCC), which adeptly illustrates both appearance-based and positional relationships among participants. By integrating the ARG with a hybrid model that combines Graph Convolutional Network (GCN), Long Short-Term Memory (LSTM), and Attention mechanisms, our approach significantly enhances the extraction of spatial and relational features compared to conventional techniques. Experimental evaluations conducted on two benchmark datasets-the Collective Activity Dataset (CAD) and the Volleyball dataset-demonstrate the efficacy of our framework. Our proposed model achieves state-of-the-art performance, attaining prediction accuracies of 94.32% and 94.47% on the CAD and Volleyball datasets, respectively, thereby surpassing existing methodologies in the realm of group activity recognition.

关键词： User experience   Usability   Open source software   Guidelines   Biological system modeling   Standards organizations   Companies   Computer science   Computational modeling   User centered design   Exploratory factor analysis   free and open-source software   FOSS UX enhancement model   Kaiser Meyer Olkin   principles axis factoring   user experience   user experience maturity influencing factor  

摘要： Free and open-source software has rapidly grown and garnered considerable attention in recent years. However, the design complexities and guidelines inherent to Free and Open Source Software (FOSS) pose a challenging task in enhancing the User Experience (UX) of FOSS. Several pre-existing models have significantly contributed to the existing body of knowledge, yet they often overlook FOSS-specific attributes that establish a direct correlation with the efficiency of the UX in FOSS. To address this gap, the study proposes a FOSS UX enhancement model (FUEM) designed to align with current FOSS practices and guidelines, aiming to improve user experience in free and open-source software. The proposed model consists of six levels, with each level encompassing UX maturity influencing factors (UMXIF) identified in prior research. These factors are re-evaluated through a Likert scale questionnaire to gather feedback from developers, project managers, and UI/UX designers, as the previous study solely included the perspectives of UX experts. Our findings reveal that these factors positively impact FOSS projects, and integrating them into FUEM will enable the FOSS community to ascertain their level of UX maturity and identify which missing UMXIF they must adopt to progress to the next stage of UX maturity in FUEM. Furthermore, the tailored model developed for FOSS is validated through feedback analysis of expert reviews and quantitative analysis employing a one-way ANOVA test. The ANOVA test indicates that the p-value exceeds the significant threshold of. 05, suggesting that the FOSS UX model can considerably enhance UX in FOSS projects by providing a structured pathway for improving UX maturity, serving as a valuable resource for the FOSS community, guiding the development of more user-centric open-source software, and fostering a deeper understanding of UX maturity practices explicitly tailored for FOSS environments.

关键词： Artificial intelligence  

摘要： The overwhelming popularity and widespread use of generative artificial intelligence has forced universities to revise their academic policies. This is a brief summary of the changes made by universities with leading computer science programs as of summer 2025. © 1970-2012 IEEE.

摘要： 《中国教育现代化2035》提出，要建设智能化校园，统筹建设一体化智能化教学、管理与服务平台，利用现代技术加快推动人才培养模式改革，实现规模化教育与个性化培养的有机结合。近年来，智慧课堂建设不断加速，已成为教师教学与学生学习的重要组成部分。随着以ChatGPT、Deepseek为代表的人工智能技术迅速发展，大语言模型有能从海量数据中学习语言规律，能提供丰富的文本生成和智能问答功能，并能进行自然语言人机对话等特点，为英语阅读教学带来了更大的机遇和想象。因此，生成式人工智能技术如何赋能支持初中英语课堂教学成为当前重要课题。本文尝试基于生成式人工智能的初中英语阅读教学，破解英语阅读教学中遇到的卡点，以更好培养学生英语学科核心素养。

关键词： IP networks   Control systems   Protocols   Software   Decision trees   Classification algorithms   Memory management   Software defined networking   Field programmable gate arrays   Computer science   Packet classification   hash table   tuple space search   linear search   OpenFlow  

摘要： Packet classification that attracts wide interests in router designs is one of the most important functionalities to provide services such as QoS, packet forwarding, firewall, and VPN. With the expanding network and the rise of software-defined networks, software routers/switches are needed to enable a wide variety of network applications in physical or virtual networks. Existing packet classification schemes used in software switches had become difficult to support a fast classification process for large multi-field rulesets. In addition, it is necessary to have fast updates that are frequently performed. In this paper, we propose a hash-based partitioning scheme for a multi-field packet classification problem. The proposed design is a 4-level data structure based on hashing. It selects the suitable fields to construct the data structures so that we can quickly narrow down the search space to find matched rules. The hash table used in the proposed design is more effective than the existing search data structures. Our experimental results show that the proposed scheme performs best in all four-performance metrics of memory consumption, construction time, classification time, and update time compared with the six most recently proposed schemes, CutTSS, TabTree, TupleMerge, CutSplit, PartitionSort, and PSTSS. The average number of bytes required per rule is only 27-99% of that needed in these schemes. The construction time of our scheme is 2.06 to 327.82 times faster than these schemes. Most importantly, the proposed scheme is 1.92-16.65 and 2.22-4.54 times faster than these six schemes in throughput and update time, respectively.

关键词： Optimization   Feature extraction   Text summarization   Particle swarm optimization   Genetic algorithms   Clustering algorithms   Support vector machines   Deep learning   Data mining   Computer science   single document summarization   pre-processing   feature extraction   vectorization   hybrid CSO-HHO algorithm  

摘要： Presently, the exponential growth of unstructured data on the web and social networks has made it increasingly challenging for individuals to retrieve relevant information efficiently. Over the years, various text summarization techniques have been developed to address this issue. However, traditional approaches that rely on directly extracting words often lead to redundancies and fail to establish a strong connection between the summary and the original document. This paper presents a novel Deep Learning (DL)-based text summarization approach incorporating the following phases: pre-processing, feature extraction, vectorization, and summarization using a hybrid Cat Swarm Optimization (CSO) and Harris Hawk Optimization (HHO) algorithm. Initially, input documents undergo pre-processing steps, including sentence segmentation, word tokenization, stop word removal, and lemmatization, to enhance text quality. Features are then extracted using a Restricted Boltzmann Machine (RBM) to obtain nine key attributes. Vectorization is performed using Term Frequency-Inverse Document Frequency (TF-IDF) to represent sentences in vector form. The hybrid CSO-HHO algorithm is subsequently applied to generate summaries. The proposed method's efficiency was evaluated using datasets from the Document Understanding Conference (DUC), specifically DUC-2002, DUC-2003, and DUC-2005. Metrics such as sensitivity, readability, coherence, precision, BLEU score, ROUGE score, and F-score were analyzed to assess performance. The proposed approach's results were compared with existing methods, including CSO, QABC, PSO, GJO, FF, and machine learning techniques like SVM and RF. The hybrid CSO-HHO algorithm achieved an accuracy of 99.56%, demonstrating its superiority in text summarization tasks.

关键词： Logic   Databases   Data mining   Systematic literature review   Hardware   Formal specifications   Specification languages   Software systems   Computer science   Taxonomy   Automata learning   linear temporal logic   specification mining   SVA generation   API rule inference   software reliability   assertion mining   behavior detection   specification   property discovery  

摘要： Linear Temporal Logic (LTL) specifications play a crucial role in the verification process of cyber-physical systems, increasing the guarantees of their correctness. These specifications are vital for ensuring that both hardware and software components behave as expected, especially in complex real-world scenarios. In the last decades, researchers have developed several methodologies and tools to automatically generate LTL specifications, creating an urgent need to organize and synthesize existing literature to ease entry into this field and guide future research efforts. Therefore, starting from a pool of over 3000 papers extracted from the Scopus database in the temporal range 2000-2024, this paper employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to produce a systematic review of mining LTL specifications of hardware and software systems. In particular, we provide a taxonomy of the methods and describe with significant detail all the relevant techniques present at the state of the art. Finally, we discuss the challenges of mining LTL specifications and explore potential directions and opportunities for future research.