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关键词： Codes   Software   Software algorithms   Visualization   Java   Computer science   Source coding   Software quality   Reviews   History   Software refactoring   history   detection   entity matching   software evolution  

摘要： Software developers and maintainers frequently conduct software refactorings to improve software quality. Identifying the conducted software refactorings may significantly facilitate the comprehension of software evolution, and thus facilitate software maintenance and evolution. Besides that, the identified refactorings are also valuable for data-driven approaches in software refactoring. To this end, researchers have proposed a few approaches to identifying software refactorings automatically. However, the performance (especially precision) of such approaches deserves substantial improvement. To this end, in this paper, we propose a novel refactoring detection approach, called ReExtractor+. At the heart of ReExtractor+ is a reference-based entity matching algorithm that matches coarse-grained code entities (e.g., classes and methods) between two successive versions, and a context-aware statement matching algorithm that matches statements within a pair of matched methods. We evaluated ReExtractor+ on a benchmark consisting of 400 commits from 20 real-world projects. The evaluation results suggested that ReExtractor+ significantly outperformed the state of the art in refactoring detection, reducing the number of false positives by 57.4% and improving recall by 18.4%. We also evaluated the performance of the proposed matching algorithms that serve as the cornerstone of refactoring detection. The evaluation results suggested that the proposed algorithms excel in matching code entities, substantially reducing the number of mistakes (false positives plus false negatives) by 67% compared to the state-of-the-art approaches.

关键词： Navigation   Accuracy   Encryption   Vectors   Computer science   Urban areas   Web services   Satellite images   Photography   Complexity theory   Navigation services   unordered stops   accuracy   security   efficiency  

摘要： Navigation services enable users to find the shortest path from a starting point $S$S to a destination $D$D, reducing time, gas, and traffic congestion. Still, navigation users risk the exposure of their sensitive location data. Our motivation arises from how users can accurately, securely, and efficiently navigate from $S$S to $D$D while passing through $k$k unordered stops, i.e., midway locations with a non-fixed visiting order. In this work, we formally define Semi-Constrained Navigation (SCN) and present a novel scheme Hermes to achieve accurate, secure, and efficient SCN. Specifically, we propose a divide-and-conquer approach to strike a good balance between accuracy and efficiency. It recursively depth-first-searches the whole area (a navigation tree) and invokes five carefully-crafted strategies stop-by-stop to compute three subpaths in three sequential subareas. We construct a path-distance oracle to encrypt the road graph and securely implement the strategies by using homomorphic encryption and garble circuits. We formally prove the security in the random oracle model and analyze the search complexity to be less than $O(k<^>{2})$O(k2). We experiment over a real-world city map and compare with six baselines. Results show that path search with $k=4$k=4 among $N=1000$N=1000 intersections requires 5.58 seconds with a 3.2% distance deviation rate and an 82.5% path similarity.

关键词： Image color analysis   Computer science   Hands   Training   STEM   Interference   Faces   Emotional responses   Virtual environments   Environment design   study design. design bias   gender bias   avatars   avatar embodiment   avatar embodiment  

摘要： Extended Reality (XR) is a powerful tool for training, education, and gaming. Research suggests that gender differences exist in XR environments including women having a lower sense of subjective presence and being more susceptible to motion sickness. However, the underrepresentation of women both as participants and researchers could lead to potential design biases, impacting the accuracy and inclusivity of XR systems. This work investigates subtle design differences in virtual environments on women's performance on a cognitive test. Non-male participants (n = 40) completed the Stroop Interference Task in two virtual classroom environments: a neutral and a stereotypically STEM environment. The environments were altered by four wall posters depicting positive gender-neutral and nature posters to science-fiction and positive male figures, such as Albert Einstein. Results support that when participants were in the stereotypical environment they were more distracted and responded more slowly and less accurately than when they were in the neutral environment. Additionally, positive female self-avatars buffered participants from the negative impacts of the stereotypical environment. These results highlight the need for more inclusive research practices. Minor adjustments can significantly improve or harm women's engagement and performance in XR settings. We emphasize the importance of bias awareness in study design, and recommend that researchers consider how their experiments could impact participants of all demographics, in order to enhance inclusivity and non-biased results.

关键词： Computer science   Latent profile analysis   Latent transition analysis   Motivation   Undergraduates  

摘要： Although achievement motivation theories like the Situated Expectancy-Value Theory highlight domain-general and course-specific motivation, few studies have examined students' motivational beliefs at both levels. This study explores domain-general and course-specific motivational profiles in undergraduate computer science (CS) courses, focusing on expectancy for success, values, and belonging. Using latent profile analysis, we identified four domain-level profiles and five course-level profiles, with the profiles representing different combinations of positive motivation and cost. We examined how these profiles evolved during the semester, finding via latent transition analysis that domain-general motivations appear to be more stable over time. Additionally, gender differences in profile membership showed women and other non-men often in higher cost profiles compared to men, and there was more variability in mean grades across course profiles than across CS domain profiles. These findings enhance our understanding of how combinations of motivational beliefs can influence student engagement and performance. Educational Relevance and Implications Statement: Our study explored the motivational profiles of undergraduate computer science (CS) students, revealing that these profiles vary by domain and course and change from the beginning to the end of the semester. We found that domain-general motivational beliefs, which encompass general attitudes toward the field of CS, remain more stable over time compared to course-specific motivational beliefs, which reflect specific attitudes toward individual CS courses. The stability of domain-general motivational beliefs suggest that foundational attitudes formed early could influence long-term engagement with CS, emphasizing the need for early interventions. We also found gender differences in motivational profiles;women and other non-men were more likely to be in high-cost profiles compared to men. The gender disparities in per

关键词： Proteins   Orbits   Databases   Clustering algorithms   Vectors   Predictive models   Feature extraction   Prediction algorithms   Electronic mail   Computer science   Protein complex prediction   protein-protein interaction networks   graphlet feature   graph neural network   consensus   ensemble model  

摘要： Proteins interact with other proteins and bio-molecules to form a complex and execute key biological functions in a living organism, and respond to several environmental signals. Designing efficient predictive models for protein complexes is a challenging task with limited coverage in the contemporary literature. With this motivation, we have developed a novel method, PCPredG, for 3-node protein complex prediction from PPI networks using 5-node graphlet features. CORUM protein complex repository has been used to curate positive and negative data samples with the help of MCODE and MCL clustering algorithms. During experiments, Random Forest(RF) and SVM classifiers are trained with 1000 positive 3-node complexes in 10-fold cross-validation setup and with 1:1 to 1:10 positive-negative proportions. In parallel, we have implemented the state-of-the-art GCN with polarised message-passing, GAT and an ensemble of GCN and GAT in both balanced and imbalanced setups. We also introduced a 10-fold quality consensus on the hold-out set across all the experiments. We have achieved the best performances with the RF classifier in both balanced and imbalanced experiments.

关键词： Blockchains   Authentication   Costs   Protocols   Security   Privacy   Surveys   Electronic voting   Computer science   Information security   k-TAA   accumulator   blockchain   e-vote   supervision  

摘要： The e-vote is regarded as a way to express the opinion that the voters ask for. Actually, the e-vote could be applied wildly like questionnaire, survey and feedback. Moreover, the coexistences of efficiency and security as well as transparency and privacy could be considered as building blocks in the e-vote system. The blockchain could provide a public access board to reduce the storage costs for the field consisted of the vote group manager (GM) with its vote assistants (VA). Particularly the k-times anonymous authentication (k-TAA) could also be a practical approach to preserve voters' privacy and reduce the computation costs during the vote process. However, the e-vote scheme with pure k-TAA strategy could damage either the supervision of voting or the efficiency and consistency of authentication process. What's more, the impacts of dishonest voters couldn't be stopped until the vote end. To tackle these problems, we apply the accumulator technology to add or revoke the voters at any time and extend the framework of k-TAA with the update process for the e-vote on blockchain for supervision (k-TEVS). In our scheme, the voter updates his membership witness and proves the fact that he is still a valid member with respective VA under the latest accumulator value. What's more, this witness update operation is not contained in the authentication process, which means that the authentication process is still constant and efficient. Moreover, our add or delete update process with signature of knowledge needs only one pairing operation. For the security, we prove that the relaxed anonymity still holds in the k-TEVS framework. Finally, We implement k-TEVS scheme, the Emura's work [1] and the Huang's work [2] for comparison. Then we make time cost and communication cost experiments, which present the feasibility and practicality of this scheme.

关键词： Optimization   Manifolds   Cost accounting   Convergence   Fuzzy systems   Deep learning   Decision theory   Computer science   Vectors   Stability criteria   Fuzzy valuation step   deep learning   dual function   fuzzy optimization   manifold optimization  

摘要： Fuzzy mathematical theory is widely used, fuzzy optimization is a branch of fuzzy mathematical theory, the significant application area is artificial intelligence in computer science, especially machine learning (deep learning) and pattern recognition. Fuzzy mathematics, especially fuzzy optimization, has become a bridge between the manifold optimization theory and deep learning applications, which is an essential theoretical foundation. The manifold optimization algorithm employs the projection method, which is unstable. In order to resolve the problem, in this article, the theory and methodology of manifold optimization concerning real and complex spaces is fully considered. Our primary focus is on the Riemannian manifold, where a groundbreaking optimization algorithm with the dual function and a fuzzy valuation step is proposed. To accelerate the convergence and enhance the stability of the optimization algorithm, a novel learning rate is present, which is referred as bivariate gradual learning rate warm-up. A comprehensive analysis of its convergence rates is conducted in various scenarios and the experiments results substantiate our discoveries, and demonstrate the correctness and effectiveness of our devised algorithm.

关键词： Hidden Markov models   Tensors   Data models   Algebra   Data analysis   Computational modeling   Prediction algorithms   Analytical models   Data mining   Computer science   Tensor-based factorial hidden markov model   cyber-physical-social systems   tensor algebra   data analytic micro-services  

摘要： With the rapid development and widespread application of information, computer, and communication technologies, Cyber-Physical-Social Systems (CPSS) have gained increasing importance and attention. To enable intelligent applications and provide better services for CPSS users, efficient data analytical models are crucial. This paper presents a novel data analytic framework for CPSS services. First, a Tensor-Based Factorial Hidden Markov Model (T-FHMM) is introduced to comprehensively analyze multi-user activity features, enhancing CPSS activity analytics. A tensor-based Forward-Backward algorithm is then designed for T-FHMM to efficiently perform evaluation tasks using multiple probabilistic computing micro-services. Additionally, a tensor-based Baum-Welch algorithm is developed to accurately learn model parameters via parameter optimization micro-services. Furthermore, a tensor-based Viterbi algorithm is implemented with specific micro-services to improve prediction tasks. Finally, the comprehensive performance of the proposed model and algorithms is validated on three open datasets through self-comparison and other-comparison. Experimental results demonstrate that the proposed method outperforms compared methods in terms of accuracy, precision, recall, and F1-score.

关键词： Students  

摘要： Background and Context. Generative AI (GenAI) is becoming increasingly common in computer science education, yet its effects on student learning, skill development, and perceptions remain relatively underexplored. Despite widespread concerns about overreliance on GenAI, few studies have systematically examined how structured scaffolding can shift students’ engagement with GenAI tools in formal educational settings. Objectives. We investigate whether and how a dedicated "AI-Lab" intervention, which emphasizes scaffolding and mindful usage, shapes students’ understanding, perceptions, and applications of GenAI tools in undergraduate computer science and first-year engineering courses. Our main goal is to determine whether students develop a more deliberate approach to using GenAI that preserves and enhances essential skill building. Method. Over three regular semesters at Purdue University, we implemented the AI-Lab in two mandatory Data Structures and Algorithms courses, one elective Competitive Programming course, and one first-year engineering course, yielding 831 valid student responses. Using a mixed-methods approach, we collected and analyzed quantitative survey data (pre- and post-intervention) as well as qualitative focus group transcripts to capture both broad trends and in-depth perspectives. Findings. While the overall frequency of GenAI usage for homework or programming projects remained largely stable, we observed large effect sizes in comfort and openness across conceptual, debugging, and homework problems. Notably, usage patterns for debugging also shifted statistically significantly, reflecting students’ more mindful and deliberate approach. Focus group discussions corroborated these results, suggesting that the intervention "bridged the gap" between naive GenAI usage and more nuanced, reflective integration of AI tools into coursework, ultimately heightening students’ awareness of their own skill development. Implications. Our findings underscore that

关键词： Prototypes   Vectors   Pipelines   Genetic algorithms   Computer science   Problem-solving   Heuristic algorithms   Binary trees   Space exploration   Metaheuristic   optimization   automated algorithm design   automated machine learning   evolutionary algorithm   evolutionary algorithm  

摘要： Metaheuristics are widely recognized gradient-free solvers to hard problems that do not meet the rigorous mathematical assumptions of conventional solvers. The automated design of metaheuristic algorithms provides an attractive path to relieve manual design effort and gain enhanced performance beyond human-made algorithms. However, the specific algorithm prototype and linear algorithm representation in the current automated design pipeline restrict the design within a fixed algorithm structure, which hinders discovering novelties and diversity across the metaheuristic family. To address this challenge, this paper proposes a general framework, AutoOpt, for automatically designing metaheuristic algorithms with diverse structures. AutoOpt contains three innovations: (i) A general algorithm prototype dedicated to covering the metaheuristic family as widely as possible. It promotes high-quality automated design on different problems by fully discovering potentials and novelties across the family. (ii) A directed acyclic graph algorithm representation to fit the proposed prototype. Its flexibility and evolvability enable discovering various algorithm structures in a single run of design, thus boosting the possibility of finding high-performance algorithms. (iii) A graph representation embedding method offering an alternative compact form of the graph to be manipulated, which ensures AutoOpt's generality. Experiments on numeral functions and real applications validate AutoOpt's efficiency and practicability.