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关键词： Ranking of doctoral programs   Ranking of computer science departments   Peer assessment   Clustering   ablation study  

摘要： This paper focuses on ranking computer science departments based on the quality of publications by the faculty in those departments. There are multiple strategies to convert publication lists into ranking scores for the departments. Important open questions include handling multi-author publications, inclusion criteria for publications and publication venues, accounting for the quality of publication venues, and accounting for the sub-areas of computer science. An ablation study is performed to evaluate the importance of different decisions for department ranking. The correlation between the resulting rankings and the peer assessment of computer science departments provided by the U.S. News was measured to evaluate the importance of different decisions. The results show that the selection of publication venues has the highest impact on the ranking. In contrast, decisions related to publication recency, multi-author publications, and clustering publications into subareas have less impact. Overall, Pearson's correlation coefficient between the publication-based scores and the U.S. News ranking is above 0.90 for a large range of decisions, indicating a strong agreement between the objective measure and the subjective opinion of peers.

关键词： Fair decisions   Explainable decisions   Autonomous artefacts   Legal and technical perspective   European and US legal system  

摘要： The paper offers a contribution to the interdisciplinary constructs of analyzing fairness issues in automatic algorithmic decisions. Section 2 shows that technical choices in supervised learning have social implications that need to be considered. Section 3 proposes a contextual approach to the issue of unintended group discrimination, i.e. decision rules that are facially neutral but generate disproportionate impacts across social groups (e.g., gender, race or ethnicity). The contextualization will focus on the legal systems of the United States on the one hand and Europe on the other. In particular, legislation and case law tend to promote different standards of fairness on both sides of the Atlantic. Section 4 is devoted to the explainability of algorithmic decisions;it will confront and attempt to cross-reference legal concepts (in European and French law) with technical concepts and will highlight the plurality, even polysemy, of European and French legal texts relating to the explicability of algorithmic decisions. The conclusion proposes directions for further research.

关键词： Computer bugs   Maintenance engineering   Data models   Mobile applications   Libraries   Data mining   Computer science   App reviews   maintenance   mobile applications  

摘要： It is well known that user feedback (i.e., reviews) plays an essential role in mobile app maintenance. Users upload their troubles, app issues, or praises, to help developers refine their apps. However, reading tremendous amounts of reviews to retrieve useful information is a challenging job. According to our manual studies, reviews are full of repetitive opinions, thus developers could stop reading reviews when no more new helpful information appears. Developers can extract useful information from partial reviews to ameliorate their app and then develop a new version. However, it is tough to have a good trade-off between getting enough useful feedback and saving more time. In this paper, we propose a novel approach, named STRE, which utilizes historical reviews to suggest the time when most of the useful information appears in reviews of a certain version. We evaluate STRE on 62 recent versions of five apps from Apple's App Store. Study results demonstrate that our approach can help developers save their time by up to 98.33% and reserve enough useful reviews before stopping to read reviews such that developers do not spend additional time in reading redundant reviews over the suggested stopping time. At the same time, STRE can complement existing review categorization approaches that categorize reviews to further assist developers. In addition, we find that the missed top-word-related reviews appearing after the suggested stopping time contain limited useful information for developers. Finally, we find that 12 out of 13 of the emerging bugs from the studied versions appear before the suggested stopping time. Our approach demonstrates the value of automatically refining information from reviews.

关键词： Data science   Data mining   Computer science   Business   Technological innovation   Data models   Trajectory   project based-learning   assessment tools  

摘要： The concepts of innovation, creativity, problem solving, effective communication, autonomy and critical thinking are at the core of becoming a good data scientist. Adapting to new technological resources and tools is also an important skill, which also builds on the curious and inquisitive nature associated with data science, and is fuelled by rapidly changing data science ecosystems in industry. In this regard, Project-based learning (PBL) has clear benefits for engaging students in data science courses. However, the exploratory character of data science projects, which do not start with a clear specification of what to do, but some data to analyse, pose some challenges to the application of PBL. Our aim is to improve students' data science learning experiences and outcomes through the use of PBL. In this paper, we share our experiences with PBL and present an assessment rubric that focuses on value, innovation and narrative, which can be used as a scaffolding structure for data science courses. Our analysis of a PBL data science course at MSc level, together with data from student surveys, shows how the methodology and rubric align well with the exploratory nature of data science and the proactive, curious, and inquisitive skills required of data scientists.

关键词： zero-shot learning   computer science   pattern recognition   deep learning  

摘要： Existing zero-shot learning (ZSL) methods typically focus on mapping from the feature space (e.g., visual space) to class-level attributes, often leading to a non-injective projection. Such a mapping may cause a significant loss of instance-level information. While an ideal projection to instance-level attributes would be desirable, it can also be prohibitively expensive and thus impractical in many scenarios. In this work, we propose a variational disentangle zero-shot learning (VDZSL) framework that addresses this problem by constructing variational instance-specific attributes from a class-specific semantic latent distribution. Specifically, our approach disentangles each instance into class-specific attributes and the corresponding variant features. Unlike transductive ZSL, which assumes that unseen classes' attributions are known beforehand, our VDZSL method does not rely on this strong assumption, making it more applicable in real-world scenarios. Extensive experiments conducted on three popular ZSL benchmark datasets (i.e., AwA2, CUB, and FLO) validate the effectiveness of our approach. In the conventional ZSL setting, our method demonstrates an improvement of 12 similar to 15% relative to the advanced approaches and achieves a classification accuracy of 70% on the AwA2 dataset. Furthermore, under the more challenging generalized ZSL setting, our approach can gain an improvement of 5 similar to 15% compared with the advanced methods.

关键词： cryptography   electric curve cryptographic   key management   underwater wireless sensor networks   computer science  

摘要： Underwater Wireless Sensor Networks (UWSNs) obtains more attention due to their wide range of applications such as underwater oil field discovery, Tsunami monitoring systems, surveillance systems, and many more. In such a resource-constrained environment, sensors are more vulnerable to malicious attacks. Node authentication and secure communication is one of the vital issues in UWSNs. In this study, a secure and lightweight key management framework for UWSNs is proposed. The proposed framework includes key generation, key distribution, revocation, and authentication mechanisms along with lightweight implementation, and scalability. We use an elliptic curve-based algorithm for key distribution, and certificate revocation list (CRL) for key revocation. We also examine the performance of the proposed framework taking into account the amount of communication overhead as well as the level of security. The simulation results show that the proposed framework provides better security with less communication overhead compared to existing frameworks. This framework can be used for secure data communication in UWSNs, which has various applications in oceanography, environmental monitoring, and military operations.

关键词： analogy-based teaching   computer science education   experimental evaluation  

摘要： Analogy-based learning methods map the concept being learned to a concept well understood by the learner. An analogy is primarily useful when learners do not know the topic being studied. Computer science is an area where the concepts exhibit a high level of abstraction and, hence, are hard for students to comprehend. The use of analogies in instruction can significantly reduce the cognitive load a student faces in learning abstract computer science concepts. The role of analogies in helping students learn computer science topics has not been explored adequately. This paper presents our efforts related to using analogy-based teaching in computer science. Over the last several years, we have developed extensive analogies for many advanced computer science concepts. We have used these analogies extensively in classroom teaching at our institution. We list the analogies that we have developed and used in our classroom teaching and, as illustration, discuss two analogies: one from the field of operating systems and another one in modular software design. We have also conducted experiments to evaluate the impact of using these two analogies on student learning outcomes. Our results confirm our hypothesis that analogy-based instruction techniques are effective and result in improved student learning outcomes.

关键词： Active learning   Game-based learning   Educational technology   Educational games   Serious games  

摘要： Educational video games have been proven effective for many purposes. However, little research has been conducted in order to analyze the effect of the game genre on the pedagogical effectiveness of the game-based learning experiences conducted using them. This article aims to fill this gap by analyzing and comparing, for the first time, the instructional and motivational effectiveness of two educational video games of different genres: third-person shooter and endless runner. The research questions addressed in this article are as follows: Is Game-Based Learning using teacher-authored video games effective in terms of knowledge acquisition and motivation to learn about database administration in computer science education? Does the effectiveness of Game-Based Learning in computer science education depends on playing a third-person shooter or an endless runner game? An experimental design (randomized controlled trial with pre-post test design) supported by instruments to gather the knowledge acquisition and opinion of the students allowed for a rigorous analysis of the conducted experience. A total of 45 computer science students organized in two groups learned about database administration by playing two educational video games created through a teacher-oriented authoring tool. One group played a third-person shooter game, whereas the other group played an endless runner game. Both games integrated the same learning content and were played by the students the same amount of time. The results show that the analyzed video games are highly and equally effective in terms of knowledge acquisition (learning gains were statistically significant with a medium effect size in both groups) and motivation (average student ratings above 4.5 out of 5 in both groups). Although the students expressed a preference for third person shooter games, they also expressed that they would have learned the same with other types of games. In conclusion, the evidence reported in this article

关键词： Peer review   Rebuttal analysis   Social network analysis   Social interaction   Rebuttal strategy   Rebuttal success prediction  

摘要： With an exponential increase in submissionsto top-tier Computer Science (CS) conferences, more and more conferences have introduced a rebuttal stage to the conference peer review process. The rebuttal stage can be modeled as social interactions between authors and reviewers. A successful rebuttal often results in an increased review score after the rebuttal stage. In this paper, we con-duct an empirical study to determine the factors contributing to a successful rebuttal using over 3000 papers and 13,000 reviews from ICLR2022, one of the most prestigious computer science conferences. First, we observe a significant difference in review scores before and after the re-buttal stage, which is crucial for paper acceptance. Furthermore, we investigate factors from the reviewer's perspective using signed social network analysis. A notable finding is the increase in balanced network structure after the rebuttal stage. Subsequently, we evaluate several quantifi-able author rebuttal strategies and their effects on review scores. These strategies can help authors in improving their review scores. Finally, we used machine learning models to predict rebuttal success and validated the impact of potential factors analyzed in this paper. Our experiments demonstrate that the utilization of all features proposed in this study can aid in predicting the success of the rebuttal. In summary, this work presents a study on the impact factors of successful rebuttals from both reviewers' and authors' perspectives and lays the foundation for analyzing rebuttals with social network analysis.

关键词： Task analysis   Superresolution   Measurement   Indexes   PSNR   Computer architecture   Satellites   Artificial intelligence   computer science   deep learning   IEEE   super-resolution   survey   TPAMI  

摘要： With the advent of Deep Learning (DL), Super-Resolution (SR) has also become a thriving research area. However, despite promising results, the field still faces challenges that require further research, e.g., allowing flexible upsampling, more effective loss functions, and better evaluation metrics. We review the domain of SR in light of recent advances and examine state-of-the-art models such as diffusion (DDPM) and transformer-based SR models. We critically discuss contemporary strategies used in SR and identify promising yet unexplored research directions. We complement previous surveys by incorporating the latest developments in the field, such as uncertainty-driven losses, wavelet networks, neural architecture search, novel normalization methods, and the latest evaluation techniques. We also include several visualizations for the models and methods throughout each chapter to facilitate a global understanding of the trends in the field. This review ultimately aims at helping researchers to push the boundaries of DL applied to SR.