Reviewerly/exHarmony huggingface dataset:
A Failure in Decryption Process for Bivariate Polynomial Reconstruction Problem Cryptosystem

Authors:
raw_author_name: Siti Nabilah Yusof
raw_affiliation_string: Institute for Mathematical Research, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
raw_author_name: Muhammad Rezal Kamel Ariffin
raw_affiliation_string: Institute for Mathematical Research, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
raw_author_name: Sook-Chin Yip
raw_affiliation_string: Faculty of Engineering, Multimedia University, Cyberjaya 63100, Selangor, Malaysia
raw_author_name: Terry Shue Chien Lau
raw_affiliation_string: Faculty of Computing and Informatics, Multimedia University, Cyberjaya 63100, Selangor, Malaysia
raw_author_name: Zahari Mahad
raw_affiliation_string: Institute for Mathematical Research, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
raw_author_name: Ji-Jian Chin
raw_affiliation_string: School of Engineering, Computing and Mathematics (Faculty of Science and Engineering), University of Plymouth, Drake Circus, Plymouth PL 48AA, UK
raw_author_name: Choo-Yee Ting
raw_affiliation_string: Faculty of Computing and Informatics, Multimedia University, Cyberjaya 63100, Selangor, Malaysia

Abstract: In 1999, the Polynomial Reconstruction Problem (PRP) was put forward as a new hard mathematics problem. A univariate PRP scheme by Augot and Finiasz was introduced at Eurocrypt in 2003, and this cryptosystem was fully cryptanalyzed in 2004. In 2013, a bivariate PRP cryptosystem was developed, which is a modified version of Augot and Finiasz's original work. This study describes a decryption failure that can occur in both cryptosystems. We demonstrate that when the error has a weight greater than the number of monomials in a secret polynomial, p, decryption failure can occur. The result of this study also determines the upper bound that should be applied to avoid decryption failure.
Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study

Authors:
raw_author_name: Junming Cao
raw_affiliation_string: Fudan University, Shanghai, China
raw_author_name: Xiaofei Xie
raw_affiliation_string: Singapore Management University, Singapore, Singapore
raw_author_name: Shangqing Liu
raw_affiliation_string: Nanyang Technological University, Singapore, Singapore
raw_author_name: Bihuan Chen
raw_affiliation_string: Fudan University, Shanghai, China
raw_author_name: Xin Peng
raw_affiliation_string: Fudan University, Shanghai, China

Abstract: Code review is an essential activity for ensuring the quality and maintainability of software projects. However, it is a time-consuming and often error-prone task that can significantly impact the development process. Recently, ChatGPT, a cutting-edge language model, has demonstrated impressive performance in various natural language processing tasks, suggesting its potential to automate code review processes. However, it is still unclear how well ChatGPT performs in code review tasks. To fill this gap, in this paper, we conduct the first empirical study to understand the capabilities of ChatGPT in code review tasks, specifically focusing on automated code refinement based on given code reviews. To conduct the study, we select the existing benchmark CodeReview and construct a new code review dataset with high quality. We use CodeReviewer, a state-of-the-art code review tool, as a baseline for comparison with ChatGPT. Our results show that ChatGPT outperforms CodeReviewer in code refinement tasks. Specifically, our results show that ChatGPT achieves higher EM and BLEU scores of 22.78 and 76.44 respectively, while the state-of-the-art method achieves only 15.50 and 62.88 on a high-quality code review dataset. We further identify the root causes for ChatGPT's underperformance and propose several strategies to mitigate these challenges. Our study provides insights into the potential of ChatGPT in automating the code review process, and highlights the potential research directions.
CoderEval: A Benchmark of Pragmatic Code Generation with Generative Pre-trained Models

Authors:
raw_author_name: Hao Yu
raw_affiliation_string: Peking University, Beijing, China
raw_author_name: Dezhi Ran
raw_affiliation_string: Peking University, Beijing, China
raw_author_name: Ying Li
raw_affiliation_string: Peking University, Beijing, China
raw_author_name: Qianxiang Wang
raw_affiliation_string: Huawei Cloud Computing Technologies Co., Ltd., Beijing, China
raw_author_name: Tao Xie
raw_affiliation_string: Peking University, Beijing, China

Abstract: Code generation models based on the pre-training and fine-tuning paradigm have been increasingly attempted by both academia and industry, resulting in well-known industrial models such as Codex, CodeGen, and PanGu-Coder. To evaluate the effectiveness of these models, multiple existing benchmarks (e.g., HumanEval and AiXBench) are proposed, including only cases of generating a standalone function, i.e., a function that may invoke or access only built-in functions and standard libraries. However, non-standalone functions, which typically are not included in the existing benchmarks, constitute more than 70% of the functions in popular open-source projects, and evaluating models' effectiveness on standalone functions cannot reflect these models' effectiveness on pragmatic code generation scenarios (i.e., code generation for real settings of open source or proprietary code).
TRACED: Execution-aware Pre-training for Source Code

Authors:
raw_author_name: Benjamin Steenhoek
raw_affiliation_string: Iowa State University, Ames, IA, USA
raw_author_name: Gail Kaiser
raw_affiliation_string: Columbia University, New York, NY, United States
raw_author_name: Wei Le
raw_affiliation_string: Iowa State University, Ames, IA, United States
raw_author_name: Baishakhi Ray
raw_affiliation_string: Columbia University, New York, NY, United States

Abstract: Most existing pre-trained language models for source code focus on learning the static code text, typically augmented with static code structures (abstract syntax tree, dependency graphs, etc.). However, program semantics will not be fully exposed before the real execution. Without an understanding of the program execution, statically pre-trained models fail to comprehensively capture the dynamic code properties, such as the branch coverage and the runtime variable values, and they are consequently less effective at code understanding tasks, such as retrieving semantic clones and detecting software vulnerabilities.
Modularizing while Training: A New Paradigm for Modularizing DNN Models

Authors:
raw_author_name: Binhang Qi
raw_affiliation_string: Beihang University, Beijing, China
raw_author_name: Hailong Sun
raw_affiliation_string: Beihang University, Beijing, China
raw_author_name: Hongyu Zhang
raw_affiliation_string: Chongqing University, Chongqing, China
raw_author_name: Xiang Gao
raw_affiliation_string: Beihang University, Beijing, China

Abstract: Deep neural network (DNN) models have become increasingly crucial components of intelligent software systems. However, training a DNN model is typically expensive in terms of both time and computational resources. To address this issue, recent research has focused on reusing existing DNN models - borrowing the concept of software reuse in software engineering. However, reusing an entire model could cause extra overhead or inherit the weaknesses from the undesired functionalities. Hence, existing work proposes to decompose an already trained model into modules, i.e., modularizing-after-training, to enable module reuse. Since the trained models are not built for modularization, modularizing-after-training may incur huge overhead and model accuracy loss. In this paper, we propose a novel approach that incorporates modularization into the model training process, i.e., modularizing-while-training (MwT). We train a model to be structurally modular through two loss functions that optimize intra-module cohesion and inter-module coupling. We have implemented the proposed approach for modularizing Convolutional Neural Network (CNN) models. The evaluation results on representative models demonstrate that MwT outperforms the existing state-of-the-art modularizing-after-training approach. Specifically, the accuracy loss caused by MwT is only 1.13 percentage points, which is less than that of the existing approach. The kernel retention rate of the modules generated by MwT is only 14.58%, with a reduction of 74.31% over the existing approach. Furthermore, the total time cost required for training and modularizing is only 108 minutes, which is half the time required by the existing approach. Our work demonstrates that MwT is a new and more effective paradigm for realizing DNN model modularization, offering a fresh perspective on achieving model reuse.
How do Developers Talk about GitHub Actions? Evidence from Online Software Development Community

Authors:
raw_author_name: Yang Zhang
raw_affiliation_string: College of Computer, National University of Defense Technology, Changsha, China
raw_author_name: Huaimin Wang
raw_affiliation_string: College of Computer, National University of Defense Technology, Changsha, China

Abstract: Continuous integration, deployment and delivery (CI/CD) have become cornerstones of DevOps practices. In recent years, GitHub Action (GHA) has rapidly replaced the traditional CI/CD tools on GitHub, providing efficiently automated workflows for developers. With the widespread use and influence of GHA, it is critical to understand the existing problems that GHA developers face in their practices as well as the potential solutions to these problems. Unfortunately, we currently have relatively little knowledge in this area. To fill this gap, we conduct a large-scale empirical study of 6,590 Stack Overflow (SO) questions and 315 GitHub issues. Our study leads to the first comprehensive taxonomy of problems related to GHA, covering 4 categories and 16 sub-categories. Then, we analyze the popularity and difficulty of problem categories and their correlations. Further, we summarize 56 solution strategies for different GHA problems. We also distill practical implications of our findings from the perspective of different audiences. We believe that our study contributes to the research of emerging GHA practices and guides the future support of tools and technologies.
Improving Testing of Deep-Learning Systems

Authors:
raw_author_name: Raghvinder S. Sangwan
raw_affiliation_string: Carnegie Mellon University, Pittsburgh, PA, USA
raw_author_name: Satish M. Srinivasan
raw_affiliation_string: Pennsylvania State University Great Valley, Malvern, PA, USA

Abstract: A combination of differential and mutation testing results in better test data.
Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning

Authors:
raw_author_name: Mingyang Geng
raw_affiliation_string: National University of Defense Technology, Changsha, China
raw_author_name: Shangwen Wang
raw_affiliation_string: National University of Defense Technology, Changsha, China
raw_author_name: Dezun Dong
raw_affiliation_string: National University of Defense Technology, Changsha, China
raw_author_name: Haotian Wang
raw_affiliation_string: National University of Defense Technology, Changsha, China
raw_author_name: Ge Li
raw_affiliation_string: Peking University, Beijing, China
raw_author_name: Zhi Jin
raw_affiliation_string: Peking University, Beijing, China
raw_author_name: Xiaoguang Mao
raw_affiliation_string: National University of Defense Technology, Changsha, China
raw_author_name: Xiangke Liao
raw_affiliation_string: National University of Defense Technology, Changsha, China

Abstract: Code comment generation aims at generating natural language descriptions for a code snippet to facilitate developers' program comprehension activities. Despite being studied for a long time, a bottleneck for existing approaches is that given a code snippet, they can only generate one comment while developers usually need to know information from diverse perspectives such as what is the functionality of this code snippet and how to use it. To tackle this limitation, this study empirically investigates the feasibility of utilizing large language models (LLMs) to generate comments that can fulfill developers' diverse intents. Our intuition is based on the facts that (1) the code and its pairwise comment are used during the pre-training process of LLMs to build the semantic connection between the natural language and programming language, and (2) comments in the real-world projects, which are collected for the pre-training, usually contain different developers' intents. We thus postulate that the LLMs can already understand the code from different perspectives after the pre-training. Indeed, experiments on two large-scale datasets demonstrate the rationale of our insights: by adopting the in-context learning paradigm and giving adequate prompts to the LLM (e.g., providing it with ten or more examples), the LLM can significantly outperform a state-of-the-art supervised learning approach on generating comments with multiple intents. Results also show that customized strategies for constructing the prompts and post-processing strategies for reranking the results can both boost the LLM's performances, which shed light on future research directions for using LLMs to achieve comment generation.
PonziGuard: Detecting Ponzi Schemes on Ethereum with Contract Runtime Behavior Graph (CRBG)

Authors:
raw_author_name: Jing Chen
raw_affiliation_string: Wuhan University, Wuhan, China
raw_author_name: Kun He
raw_affiliation_string: Wuhan University, Wuhan, China
raw_author_name: Yueming Wu
raw_affiliation_string: Nanyang Technological University, Singapore, Singapore
raw_author_name: Gelei Deng
raw_affiliation_string: Nanyang Technological University, Singapore, Singapore
raw_author_name: Ruiying Du
raw_affiliation_string: Wuhan University, Wuhan, China
raw_author_name: Cong Wu
raw_affiliation_string: Nanyang Technological University, Singapore, Singapore

Abstract: Ponzi schemes, a form of scam, have been discovered in Ethereum smart contracts in recent years, causing massive financial losses. Rule-based detection approaches rely on pre-defined rules with limited capabilities and domain knowledge dependency. Additionally, using static information like opcodes and transactions for machine learning models fails to effectively characterize the Ponzi contracts, resulting in poor reliability and interpretability.
Neural Network-Based Forecasting of Student Enrollment With Exponential Smoothing Baseline and Performance Analysis

Authors:
raw_author_name: Joshua Weese
raw_affiliation_string: Computer Science from University of Ilorin -Nigeria and Kansas State University -Kansas USA; Kansas State University.; State University,; Statistics/Computer Science from University of Agriculture, Makurdi -Nigeria.

Abstract: His research interest cuts across the use of machine learning and data science in Computing Science Education to improve teaching and learning.Specifically, he is exploring
Work-in-Progress: Developing an Interactive, Immersive, 360-Degree Virtual Media for Enhancing Student Learning in Additive Manufacturing

Authors:
raw_author_name: Xiangxiong Kong
raw_affiliation_string: Coastal Carolina University
raw_author_name: Alex Fegely
raw_affiliation_string: Coastal Carolina University

Abstract: in South Carolina with a concentration in
Novel multimodal framework for embedding social justice education in technical engineering coursework

Authors:
raw_author_name: Lara Letaw
raw_affiliation_string: Oregon State University

Abstract: Students enrolled in undergraduate Engineering programs are becoming more diverse while engineering science curricula remain unchanged and outdated.Educators and instructional designers of Engineering Science must transform courses so that they are relevant and engaging to students who vary in gender, age, race, sexual orientation, neurodiversity, disability, alterability, and socioeconomic status.Shifting instruction to serve the needs of diverse populations can benefit all students.If institutions fail to meet the academic needs of learners, students might instead choose the increasingly available alternatives to formal education such as vocational and trade schools, apprenticeships, and self-paced free and paid resources.To help instructional designers make the needed shift, we synthesized three known techniques into a novel framework for engineering science curriculum delivery.We utilize: Writing-to-Learn activities, Inclusive Magnification (InclusiveMag/GenderMag) methods, and Engineering for Social Justice criteria.This framework allows plenty of space for fundamental engineering science coursework and results in a deeper understanding of those engineering concepts through the lens of real-world collaboration and contexts.
The Synergy of Intertwining Grant Activities: Cyber Up! and GenCyber Girls

Authors:
raw_author_name: Tobi West
raw_affiliation_string: (Department Chair); Dakota State University

Abstract: Excellence in Cyber Defense education and serves on multiple national
A Reflection on Action Approach to Teamwork Facilitation

Authors:
raw_author_name: Aparajita Jaiswal
raw_affiliation_string: Purdue University at West Lafayette (PPI)
raw_author_name: Devang Patel
raw_affiliation_string: Purdue University at West Lafayette (COE)
raw_author_name: Yi Zhu
raw_affiliation_string: Purdue University at West Lafayette (COE)
raw_author_name: Alejandra Magana
raw_affiliation_string: Purdue University at West Lafayette (COE)

Abstract: Abstract Working in teams has been recognized as an essential 21st-century skill. Introducing teamwork in the undergraduate classroom is crucial as it allows the students to work with individuals with diverse skillsets and learn from one another. It is important to note that just creating a team and allowing the students to work does not foster teamwork skills. Inculcating teamwork skills requires a consciousness on the part of the instructor and the teaching assistants. Pedagogies such as cooperative learning have been recognized as effective in helping students develop teamwork skills. We introduced a joint reflection on action approach to developing teamwork skills among novice students as part of a sophomore-level systems analysis and design course. In this evidence-based practice paper, we report on students' reflections regarding their perceptions of teamwork. This study approaches the following research questions: What are students' reflections about the role of communication while working in teams in a cooperative project-based learning environment? The guiding pedagogical framework for this course is cooperative learning. The course requires the students to work in teams in a semester-long software development project. To elicit reflection on action about their teamwork experience. Specifically, we exposed students to concrete experiences as part of their teamwork interactions, which became the basis for observations and reflections. For this, the semester-long project was complemented with one reflection-on-action activity. In the activity, students were asked to watch a video of secrets of successful teamwork and were asked to reflect on their perceptions about the role of communication within teams. The students' reflections on the activity were analyzed using qualitative inductive thematic analysis to understand the students' perceptions regarding teamwork and communication within teams. Keywords: cooperative learning, teamwork, team communication, undergraduate students
TreeVisual: Design and Evaluation of a Web-Based Visualization Tool for Teaching and Learning Tree Visualization

Authors:
raw_author_name: Brendan O'Handley
raw_affiliation_string: University of Notre Dame,
raw_author_name: Yuheng Wu
raw_affiliation_string: Peking University,
raw_author_name: Chaoli Wang
raw_affiliation_string: University of Notre Dame,

Abstract: theoretic algorithms, graph-based techniques, and deep learning solutions for big data analytics.
Sustainable Boutique Research Services in a Mid-sized STEM Library: A Preliminary Study

Authors:
raw_author_name: Brianna Buljung
raw_affiliation_string: Carnegie Mellon University (CMU). At CMU,; Colorado School of Mines

Abstract: Information seekers' increasing comfort in leveraging research technology has reduced demand for traditional models of reference services, while highlighting the role of expert assistance when navigating complex information needs.Librarians at the Colorado School of Mines (Mines) are exploring how to provide meaningful research support to faculty, postdocs and graduate students on campus while maintaining some aspects of traditional reference services that focus on undergraduates.This study explores the concierge or boutique model that provides individualized, flexible, value-added services within the larger scope of the library.A literature review, internal scan of the Mines institutional context and an outward environmental scan are combined to evaluate feasibility of designing boutique research services in a mid-sized STEMfocused library.Lessons learned in this exploration will influence the design of a pilot program to be implemented in the Mines library.
Cluster2Former: Semisupervised Clustering Transformers for Video Instance Segmentation

Authors:
raw_author_name: Ellák Somfai
raw_affiliation_string: Department of Artificial Intelligence, ELTE Eötvös Loránd University, 1053 Budapest, Hungary; HUN-REN Wigner Research Centre for Physics, 1121 Budapest, Hungary

Abstract: A novel approach for video instance segmentation is presented using semisupervised learning. Our Cluster2Former model leverages scribble-based annotations for training, significantly reducing the need for comprehensive pixel-level masks. We augment a video instance segmenter, for example, the Mask2Former architecture, with similarity-based constraint loss to handle partial annotations efficiently. We demonstrate that despite using lightweight annotations (using only 0.5% of the annotated pixels), Cluster2Former achieves competitive performance on standard benchmarks. The approach offers a cost-effective and computationally efficient solution for video instance segmentation, especially in scenarios with limited annotation resources.
Modularity Analysis of Makerspaces to Determine Potential Hubs and Critical Tools in the Makerspace

Authors:
raw_author_name: Astrid Layton
raw_affiliation_string: Department of Mechanical Engineering at Texas A&M University. Dr.; School of Mechanical Engineering at Georgia Institute of Technology in 2014.
raw_author_name: Julie Linsey
raw_affiliation_string: Georgia Institute of Technology
raw_author_name: Samuel Blair
raw_affiliation_string: Georgia Tech; Texas A&M University
raw_author_name: Henry Banks
raw_affiliation_string: Georgia Institute of Technology

Abstract: Layton's research is an expert in bio-inspired system design, with a focus on the use of biological ecosystems as inspiration for achieving sustainability