Large language models (LLMs) have shown great potential in the medical domain. However, existing models still fall short when faced with complex medical diagnosis task in the real world. This is mainly because they lack sufficient reasoning depth, which leads to information loss or logical jumps when processing a large amount of specialized medical data, leading to diagnostic errors. To address these challenges, we propose Tree-of-Reasoning (ToR), a novel multi-agent framework designed to handle complex scenarios. Specifically, ToR introduces a tree structure that can clearly record the reasoning path of LLMs and the corresponding clinical evidence. At the same time, we propose a cross-validation mechanism to ensure the consistency of multi-agent decision-making, thereby improving the clinical reasoning ability of multi-agents in complex medical scenarios. Experimental results on real-world medical data show that our framework can achieve better performance than existing baseline methods.

A technical interview is the culmination of the recruiting process for hiring software engineers in the tech industry. Many well-known companies, including Amazon, Meta (formerly Facebook), Alphabet (Google), and Microsoft, use it to filter candidates. However, the drawbacks of technical interviews are well-documented, including their lack of real-world relevance, bias towards newer developers, demanding time commitment, and potential to induce unnecessary anxiety and frustration. De-spite these criticisms, there is no clear indication that the industry will alter the format of technical interviews in the near future. To assist student developers in preparing for these challenges, we conducted a quantitative analysis using over 300,000 user profiles from LeetCode, arguably the most popular online platform for preparing software development candidates for interviews. Our analysis aims to provide developers with insights into the effort required to prepare for technical interviews, especially in terms of solving programming questions, to secure a position at a renowned company.

This is a research-to-practice full paper. Several research studies indicate that women who have opted into a computing career path must regularly contend with negative stereotypes about their technical abilities. These stereotypes are often cited as contributing factors to the underrepresentation of women in computing. To counter these stereotypes and enhance female participation in computer science, numerous interventions have been designed. However, most existing research tends to rely on anecdotal evidence and questionnaires to study these stereotypes. In contrast, our study collected data from over 900 students over a span of eight years and adopted a comprehensive quantitative approach to examine these stereotypes about female students. We utilized pre-class GitHub contribution metrics to evaluate students' programming experience and an array of in-class grading items to measure students' performance. Additionally, we mined the project repositories' git logs to gain insights into students' contributions to team projects. Our investigation began by probing whether there was a notable difference in the technical backgrounds or preparedness between female and male students. The results indicated that males tended to be better prepared. Next, we explored potential disparities in class performance between the two genders. Our findings revealed that males and females each excelled in different areas. We were also interested in discerning if female and male students contributed equally to team projects; our analysis affirmed that the contributions were comparable between the two groups. If allowed to choose their teammates, we examined whether they showed a preference for single-gender teams or mixed-gender teams. Our conclusions indicated no marked preference. This paper aims to augment the body of research on computing education by assisting educators in gaining a better understanding of female students in the class. Moreover, it tests the stereotypes by comparing them with empirical results.

In modern software engineering education, team formation is crucial for mimicking real-world collaborative scenarios and boosting project-based learning outcomes. This paper introduces a simple, innovative, and universally adaptable method for forming student teams within a software engineering class. We utilize publicly available pre-class GitHub metrics as our input variables (e.g., number of commits, pull requests, code size, etc.). For team formation, the constrained k-means algorithm is employed. This algorithm embraces domain-specific constraints, ensuring the resulting teams not only resonate with the inherent data clusters but also meet educational requirements. Preliminary results suggest that our methodology yields teams with a harmonious blend of skills, experiences, and collaborative potentials, thereby setting the stage for enhanced project success and enriched learning experiences. Quantitative analyses show that teams formed via our approach outperform both randomly assembled teams and student self-selected teams concerning project grades. Moreover, teams created using our method also display a reduced standard deviation in grades, suggesting a more consistent performance across the board.

This study presents a comparative analysis of contributions to GitHub by students before and after participating in a Software Engineering class based on Open Source Software (OSS). The primary objective is to understand the influence of formal software engineering education on students' engagement in OSS projects, as reflected in their GitHub activities. The research addresses two key questions. Firstly, it examines how GitHub contributions change before and after the class. The corresponding hypothesis posits that students' average GitHub contributions will exhibit a distinct pattern post-class compared to pre-class. Additionally, the study explores the potential association between students' academic performance in the class and their level of GitHub contributions after the class. The strength and direction of the potential association are quantified using the Spearman correlation coefficient, considering the potential non-linear nature of the data. This analysis uses data from over 1000 students across more than 10 years, encompassing their GitHub contribution data over multiple timeframes and their grades in the class. The study employs a combination of statistical methods, including paired tests and correlation analysis, to explore these dynamics. While causality cannot be established due to the absence of a control group, the findings offer valuable insights into the correlation between academic engagement and practical contributions in the realm of OSS development. This research contributes to the understanding of how theoretical software engineering education might relate to practical application and engagement in real-world projects.

Group or team projects are an essential component of the software engineering curriculum. Earlier studies have explored how prior programming experience influences students' team project performance and overall class performance in software engineering. However, few studies address the impact of prior programming experience on students' contributions to team projects. Previous work has varied in its definitions of prior programming experience or skill, leading to inconsistent findings. In this study, we collected pre-class GitHub contribution metrics from 237 students (forming 79 teams of three) across two academic years to measure their prior programming experience and skills. We also mined students' project repositories' git logs to collect individual student contributions. A central question revolved around whether students with more substantial prior programming experience were indeed more active contributors to their project teams. Interestingly, our data indicated a positive correlation between prior programming experience and contributions to team projects. We further delved into team dynamics. Specifically, we questioned if teams made up of members with comparable skill levels exhibited a more even distribution of contributions. Contrary to expectations, our findings revealed no association between these two variables. Moreover, we investigated the team configurations that might encourage the rise of "free riders"-students who contributed only minimally. This paper seeks to augment the body of research on computing education and assist educators in understanding how prior programming experience impacts students' contributions in team projects.

Many IoT applications require users to share their devices’ location, and enhanced privacy-protection means sharing location anonymously, unlinkably and without relying on any administrators. But under such protection, it is difficult to trust shared location data, which may be from unregistered devices or from the same one’s multiple logins or from the cloned device ID, even be generated by an attacker without any devices! Such untrusted location sharing cheats system, misleads users, even attacks system. To the best of our knowledge, such problems have not been solved in a decentralized system. To solve them in one scheme, we put forward the first decentralized accumulator for device registration and construct the first practical decentralized anonymous authentication for device login. When logging in, the device provides a special knowledge proof, which integrates zero-knowledge (for privacy) with knowledge-leakage (for identifying abnormal behaviors) designing for blockchain (for decentralization). Therefore, in our system, only registered IoT devices can upload location data and their logins are anonymous and unlinkable, while login exceeding${K}$times in a system period or cloning ID to login concurrently can be identified and tracked without any trusted centers. In addition, we provide the security proofs and the application examples of the proposed scheme. And the efficiency analysis and experimental data show that the performance of our scheme can meet the needs of real-world location sharing on IoT.

Research into predicting students' performance in computer science classes has been conducted globally for over five decades. Numerous metrics, including performance in prior courses, demographic information, and programming experience, have been used to predict success in computer science. Various analytical methods, such as linear regression, decision trees, ensemble methods, and even neural networks, have also been explored. In this study, we investigate whether pre-class GitHub contribution metrics, combined with machine learning techniques, can forecast student performance in a software engineering class. We address two research questions in this paper. Firstly, can pre-class GitHub contribution metrics predict students' performance? Secondly, which machine learning technique is most effective in predicting student performance? We collected data from 802 students over five years and 11 semesters, including pre-class GitHub contribution stats, students' exam grades, project grades, documentation grades, and review writing grades. Eight different machine learning methods were then tested to predict in-class performance using pre-class GitHub contributions. Our results indicate that exam performance can be relatively accurately predicted by machine learning methods. Ensemble methods such as Random Forest, AdaBoost, and XGBoost performed better than other methods. This suggests that pre-class GitHub contribution metrics can be a useful tool for predicting students' performance in software engineering classes, carrying significant implications for educators. This approach can help educators identify at-risk students at the earliest point in the class, enabling early intervention strategies to prevent failure. Our study uniquely utilizes pre-class GitHub contributions, providing a preliminary indication of a student's familiarity with the course material. While prior research has focused on using in-class data to predict student performance, our approach identifies struggling students from the very beginning. We believe this can provide the most beneficial support for students.

What skills does a student need to succeed in a programming class? Ostensibly, previous programming experience may affect a student's performance. Most past studies on this topic use self-reporting questionnaires to query students about their programming experience. This paper presents a novel, unified, and replicable way to measure previous programming experience using students' pre-class GitHub contributions. To our knowledge, we are the first to use GitHub contributions in this way. We conducted a comprehensive statistical study of students in an object-oriented design and development class from 2017 to 2022 (n = 751) to explore the relationships between GitHub contributions (commits, comments, pull requests, etc.) and students' performance on exams, projects, designs, etc. in the class. Several kinds of contributions were shown to have statistically significant correlations with performance in the class. A set of two-samplet -tests demonstrate statistical significance of the difference between the means of some contributions from the high-performing and low-performing groups.

Compact E-cash achieves an efficient system by withdrawing 2n coins within O(1) operations and storing the coins in O(n) bits. For preventing a double-spender from cheating again, it is necessary to trace his e-coins. So full-tracing in compact E-cash system means tracing double-spender and tracing his coins. However, the efficiency problem caused by coin-tracing without TTP (trusted third party) has not been solved. For solving this problem, we introduce a non-standard construction into zero-knowledge proof of payment protocol, which leaks coin information when double-spending but is proven to be perfect zero-knowledge to verifier when spending a coin only once. Therefore, it achieves tracing dishonest users' coins and preserving the anonymity of honest users. Comparing with the existing most efficient method of coin-tracing without TTP, we improve computational complexity from O(k) to O(1) with less storage space. In addition, to improve efficiency and practicality further, batch-spending (spending any number of coins in one operation) and compact-spending (spending all coins in one operation) had been proposed. Based on the non-standard zero-knowledge proof, our scheme provides more efficient batch/compact-spending. Moreover, we also make a comparison with Bitcoin and Bitcoin Lightning Network, which have attracted considerable attention.