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关键词： Chatbots   Artificial intelligence   Measurement   Education   Computer science   Writing   Object recognition   Electrical engineering   Translation   Robot sensing systems   ChatGPT   education   LLM   computer science and engineering   electrical engineering  

摘要： Large Language Models (LLMs) like ChatGPT have become the most popular regenerative AI applications, used for obtaining responses for queries in different domains. The responses of ChatGPT are already becoming mainstream and are challenging conventional methods of learning. This article focuses on the application of ChatGPT for academic instructional purposes in the field of computer engineering and related majors. The capability of ChatGPT for instructional purposes is evaluated based on the responses to different questions about these engineering streams. This article explores different opportunities (with use cases), that ChatGPT can provide in augmenting the learning experience. It also provides scenarios of limitations and modifying the evaluation process to prevent the use of ChatGPT, which may lead to an inaccurate dissemination of accepted facts. In this paper, common classroom problems and their respective responses from ChatGPT in the domains of Computer Science, Cyber Security, Data Science, and Electrical Engineering are analyzed to determine the categories of queries for which ChatGPT offers reliable responses and those for which it may be factually incorrect. A student survey is performed to demonstrate that students must be made aware that ChatGPT may not be suitable for certain types of queries and means of upgrading the evaluation process.

关键词： Knowledge engineering   Semantics   Data models   Predictive models   Attention mechanisms   Self-supervised learning   Analytical models   Deep learning   Computer science   Computational modeling   Intelligent systems   self-supervised learning   attention mechanisms   graph convolutional networks   knowledge tracing  

摘要： As intelligent education advances and online learning becomes more prevalent, Knowledge Tracing (KT) has become increasingly important. KT assesses students' learning progress by analysing their historical performance in related exercises. Despite significant advances in the field, there are still shortcomings in two aspects: first, a lack of effective integration between exercises and knowledge points;second, an overemphasis on nodal information, neglecting deep semantic relationships. To address these, we propose a self-supervised learning approach that uses an enhanced heterogeneous graph attention network to represent and analyse complex relationships between exercises and knowledge points. We introduce an innovative surrogate view generation method to optimise the integration of local structural information and global semantics within the graph, addressing relational inductive bias. In addition, we incorporate the improved representation algorithm into the loss function to handle data sparsity, thereby improving prediction accuracy. Experiments on three real-world datasets show that our model outperforms baseline models.

关键词： Teamwork   Computer science   Software engineering   Education   Industries   Software   Software development management   Instruments   Systematic literature review   Companies   Software engineering education   teamwork   women in computer science  

摘要： Teamwork is recognized as a highly valuable skill for any software engineer. In computer science programs it is typically developed through project-based courses, though these courses often do not adopt a gender-oriented approach. Women are underrepresented in the software industry, and there is limited knowledge about their teamwork competencies. While some studies compare teamwork skills between men and women, few have explored the impact of education on the development of this skill, particularly in relation to different aspects of teamwork. By comparing teamwork quality between genders at different program levels, we intend to gain valuable insights about students' initial teamwork skills and the effect of software engineering education over time. To do this, we have carried out an empirical study of two project-based software engineering courses in our Computer Science and Engineering program: an introductory software engineering course and a capstone course. In each case we evaluate different aspects of teamwork by applying the same peer assessment instrument along 13 semesters. Our study revealed no significant gender-based disparities within the courses under study, indicating that such differences are absent both in students' initial abilities and final competencies. What we did observe, however, is that women's teamwork assessments are more homogeneous over time than the men's, where we see significant improvements in Attitude, Motivation, Coordination and Communication between courses. Our results lead us to believe that, it takes men longer to reach the same capacity for working in teams than women, but in the end, students of both genders reach similar teamwork capabilities in all teamwork quality dimensions. Therefore, women have just as much potential to excel as software engineers as men, especially when it comes to teamwork.

关键词： Stock markets   Portfolios   Data models   Prediction algorithms   Profitability   Indexes   Heuristic algorithms   Deep reinforcement learning   Computer science   Supervised learning   Deep reinforcement learning (DRL)   stock market trading   portfolio management   max drawdown rewards   advantage actor-critic (A2C)   deep deterministic policy gradient (DDPG)   proximal policy optimization (PPO)  

摘要： Algorithmic stock trading has improved tremendously, with Reinforcement Learning (RL) algorithms being more adaptable than classic approaches like mean reversion and momentum. However, challenges remain in adequately depicting market events and generating suitable rewards to influence the trading decisions of an agent in a dynamic environment. This study proposed an improved stock market trading framework termed the RMS-Driven Deep Reinforcement Learning (DRL) model for optimal portfolio management. The research attempts to give a more comprehensive view of the market and, as a result, enhance trading decisions by including consumer information and incorporating news sentiments into the model, in addition to data from typical earnings reports. More specifically, three kinds of DRL models are presented, combined with data from stock earnings reports, Max Drawdown rewards, and sentiment indicators (RMS), known as PPO_RMS, A2C_RMS, and DDPG_RMS, respectively. The findings of this research indicate that the integrated model, mainly the DDPG_RMS effectively outperforms the baseline <^>DJI index in many risk-return analyses in ratios showing better risk management, and profitability. The proposed stock trading model generates a maximum cumulative return of 27% with a Sharpe ratio of 0.66, showing an appropriate trade-off between risk and return. This approach, which incorporates sentiment analysis and Max Drawdown rewards, significantly enhances the model's performance in adapting to changing market conditions. Therefore, the results emphasize the appropriateness of integrating sentiment indices with traditional financial data to enhance a trader's performance while also offering essential information to aid in the development of improved trading tactics in continuously changing financial markets.

关键词： postdoctoral   mentorship   content analysis   engineering   computer science  

摘要： Postdoctoral training holds an increasingly important position in securing high-impact research and academic positions in engineering and computer science. High-quality mentorship contributes to the success of postdoctoral trainees, but existing literature demonstrates postdocs from outside of the US, women, and racial minorities who experience stereotypes are provided less mentorship, and less successful outcomes than their white and male counterparts. Further, trainees who originate from outside of the US hold more than half of the postdoctoral positions in the US. Increasingly, funding agencies within the US require postdoctoral mentorship plans, but it is unclear to what end and if they are helpful or simply performative. Specifically, to date, no research has investigated the content of mentorship plans, their alignment to theory and existing research, or the needs of postdoctoral trainees particularly those from outside of the US, women, and racial minorities. As part of a larger project investigating postdoctoral mentorship, we analyzed 54 postdoctoral mentoring plans to identify how they align with recommendations from content supported by theory and literature. We find marginal alignment with best practices, with much room for improvement, including tailoring and specificity. From this work, a postdoctoral mentorship blueprint and rubric for plan evaluation are offered as tools to improve the uniqueness, specificity, and utility of mentorship plans.

关键词： Surveys   Engineering profession   Psychology   Computer science   STEM   Particle measurements   Ethnicity   Cause effect analysis   Atmospheric measurements   Analysis of variance   Applied research   basic research   computing education   gender  

摘要： Women remain under-represented in academic science, and this is especially true in computing. While there is limited research on gender differences in research focus, there is evidence that women may be more likely to conduct applied research. We surveyed tenured and tenure-track faculty in the United States to understand perceptions of hypothetical researchers who engage in applied or theoretical work. Faculty rated researchers engaged in applied research as less likely to publish their work, receive tenure/be promoted, obtain awards, and get funding for their work. Faculty further rated these researchers as less brilliant, creative, and technically skilled than they rated their theory-focused counterparts. Data from publications, hiring, funding, and awards suggests that applied research may indeed lead to worse career outcomes. We further show that women are more highly represented in applied research areas than theoretical ones. Negative perceptions of applied researchers must now be addressed to avoid exacerbating the gender gap.

关键词： Privacy   Encryption   Data privacy   Servers   Iron   Web services   Electronic mail   Computer science   Proposals   Public key   Multi-input functional encryption   maximum similarity   intermediate result privacy   function privacy   web service  

摘要： As a common metric, maximum similarity between two objects is widely employed by web platforms to provide matching services. However, the calculation of maximum similarity involves numerous sensitive or confidential users' data, and the web platform server is often not trusted who might peep these data out of curiosity, or even worse sell them to unauthorized entities to make profits. Therefore, many research lines on functional encryption have been suggested and studied on how to calculate the maximum similarity while ensure the privacy of users' data. Unfortunately, all of them will divulge some intermediate results to the web platform server when processing this issue. In this paper we present a new functional encryption scheme supporting privacy-preserving maximum similarity, which enables the web service platforms to figure out the maximum similarity without learning anything else about their data. Moreover, we provide a formal analysis to prove the security of the proposed scheme, followed by some experimental evaluations and comprehensive comparisons with the related works. It shows that, our scheme is the first functional encryption realization on maximum similarity without divulging the intermediate result and meanwhile achieve a higher security-function privacy, as well as a traditional data privacy.

关键词： Kalman filters  

摘要： In the field of computer science education, humanistic traits and mental wellness are crucial. However, the role of biomechanics, especially factors like gait and posture, is often overlooked. Biomechanically, proper gait and posture are essential for maintaining physical balance and distributing forces evenly across the body. This study combines DL and biomechanical methods to assess students. Importantly, it delves into how gait and posture, as biomechanical factors, link to educators' mental health. Gait anomalies can signal stress or fatigue. A hesitant or unsteady gait might disrupt teaching focus and flow, inducing anxiety. Poor posture, like prolonged slouching, leads to muscle strain and pain. This physical discomfort can spark negative emotions, reducing teaching enthusiasm and confidence. The questionnaire included 300 computer science professors (180 women and 120 men) from Chinese universities. Data were collected through psychological questionnaires assessing insomnia, mood, fatigue, and depression. Anthropological factors such as social interaction and communication skills. Wearable sensors have been used to collect biomechanical data, including reactions and activities. The proposed Kalman filter algorithm with a modified visual-geometry group (KFA+MVGG) method allows a comprehensive assessment of computer science students’ psychological well-being, personality, and physical activity through activity and by accurately recording mental signals in real-time. Experimental studies show significant increases in classification accuracy and efficiency, outperforming previous benchmarking methods. The findings that sedentary behavior, poor posture, and restricted movement are linked to fatigue and poor mental health emphasize the importance of biomechanics. The KFA+MVGG offers valuable insights. By understanding these biomechanical impacts, teachers can be more aware of their body mechanics. This can potentially enhance their mental health and overall well-be

关键词： Smart cities   Internet of Things   Computer architecture   Urban areas   Transportation   Metadata   Computer science   Systematic literature review   Reliability   Ecosystems   Data provenance   provenance technologies   Internet of Things (IoT)   IoT security   smart city  

摘要： The rapid growth in the quantity of interconnected Internet of Things devices causes the continuous production of complex and diverse heterogeneous data sets, which encounter significant challenges in big data processing and security. This study highlights the critical function that data provenance serves within smart cities and IoT frameworks. An important task solved by data provenance is tracking the entire lifecycle of the data: their creation, transmission, storage, and processing. This paper emphasizes the critical role of data quality in the reliability of IoT applications. This review presents a comprehensive analysis of current IoT provenance methods and technologies, and we propose a taxonomy that classifies the key components of IoT data provenance systems in smart cities. The findings aim to assist researchers and practitioners with the design and application of effective data provenance approaches suited to smart city systems. Additionally, a case study on secure air quality monitoring demonstrates how provenance mechanisms can be implemented in a real-world IoT network.

关键词： Rendering Computer Graphics   Visualization   Visual Perception   Costs   Feature Extraction   Sensitivity   Computer Science   Solid Modeling   Optimization   Predictive Models   Virtual Reality   Foveated Rendering   Perception Driven Rendering   User Study   Visual Perception   Visual Quality   Perceptual Abilities   Human Visual System   Impact Of Concentration   Visual Ability   Visual Content   Head Mounted Display   High Frame Rate   Virtual Reality Headset   Effects Of Virtual Reality   Audiovisual Content   Virtual Reality Content   Visual Impairment   Visual Features   Perception Of Quality   Sound Pressure Level   Transition Region   Sound Pressure   Sound Frequency   Auditory Features   Extract Visual Features   Human Auditory System   Pixel Block   Foveal Region   Contrast Sensitivity Function   Contrast Sensitivity   Perception Scores   Perception Of Frequency  

摘要： With the increasing complexity of geometry and rendering effects in virtual reality (VR) scenes, existing foveated rendering methods for VR head-mounted displays (HMDs) struggle to meet users' demands for VR scene rendering with high frame rates (≥ 60 f ps for rendering binocular foveated images in VR scenes containing over 50M triangles). Current research validates that auditory content affects the perception of the human visual system (HVS). However, existing foveated rendering methods primarily model the HVS's eccentricity-dependent visual perception ability on the visual content in VR while ignoring the impact of auditory content on the HVS's visual perception. In this paper, we introduce an auditory-content-based perceived rendering quality analysis to quantify the impact of visual perception under different auditory conditions in foveated rendering. Based on the analysis results, we propose an audio-visual aware foveated rendering method (AvFR). AvFR first constructs an audio-visual feature-driven perception model that predicts the eccentricity-based visual perception in real time by combining the scene's audio-visual content, and then proposes a foveated rendering cost optimization algorithm to adaptively control the shading rate of different regions with the guidance of the perception model. In complex scenes with visual and auditory content containing over 1.17M triangles, AvFR renders high-quality binocular foveated images at an average frame rate of 116 f ps. The results of the main user study and performance evaluation validate that AvFR achieves significant performance improvement (up to 1.4× speedup) without lowering the perceived visual quality compared with the state-of-the-art VR-HMD foveated rendering method. © 1995-2012 IEEE.