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

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.
CoderEval: A Benchmark of Pragmatic Code Generation with Generative Pre-trained Models

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

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

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

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

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

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)

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

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

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

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.
Comparing the Effects of In-Person and Remote Learning on Student Performance in an Undergraduate Introductory Statics and Mechanics of Materials Course

The COVID-19 pandemic in 2020-2021 compelled revolutionary changes in teaching methodologies for many conventional institutions of higher education.This study explores the implications of these changes on student performance in an undergraduate introductory mechanical engineering course; given that between the Fall of 2019 and the Spring of 2022, this course conducted normal classroom operations, transitioned to the virtual environment, conducted a full semester virtually, then transitioned back into our 'new normal' classroom environment.During this time the course content and grading methodologies remained relatively unchanged, facilitating comparison between semesters.In addition, this course is required for the entire undergraduate student population regardless of academic major.This provides a diverse cross-section of incoming students, academically and otherwise.With over 2600 students completing the course during these semesters, this study objectively compares performance on course graded events such as exams, lab reports, and group design projects.The ultimate goals of this study are to 1) compare student performance across remote and in-person classroom settings and 2) provide future instructors with data to help optimize the student learning environment for all classroom settings.The conclusions of this study indicate that semesters of transition between in-person and remote learning are the most challenging for students, but otherwise student performance was not significantly affected.
The Synergy of Intertwining Grant Activities: Cyber Up! and GenCyber Girls

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

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
Constructing Quadratic Equations modulo N to emphasize the differences between real and modular arithmetic operations

Cadets majoring in Cybersecurity at the United States Coast Guard Academy are typically enrolled in the department's Introductory Cryptography course during the Fall of their Junior year.Many of these students struggled to understand the need to use modular operations to solve quadratic equations modulo N.This was because homework assignments requiring the solution of these equations typically employed equations for which regular real operations were perfectly satisfactory.Quadratic equations modulo N are assigned to help the students recognize the similarities between modular and real arithmetic while also helping them see the differences.These equations are also introduced to prepare the students for the study of elliptic curves.Three specific differences between modular quadratic equations and real arithmetic quadratic equations were identified.Modular arithmetic equations can fail to have any solutions if the modulus for an otherwise acceptable equation does not permit a multiplicative inverse required for solution.Modular quadratic equations can also have solutions where real quadratic equations would fail to have any solutions (complex solutions).And finally, modular quadratic equations with composite moduli can have many more than two solutions.All three of these differences can be exploited to prevent the students from solving a quadratic equation using real arithmetic methods and thus compel them to use modular techniques.It was observed on the homework assignments that a significant fraction of the students failed to use modular techniques to solve the quadratic equations that were assigned and it was suspected that many of those that did were unsure of why they needed to employ them.After constructing quadratic equations that took advantage of the peculiar properties of modular arithmetic the students were unable to use real arithmetic techniques and had to resort to modular methods thus reinforcing the need to use those methods to solve the modular equations.
TreeVisual: Design and Evaluation of a Web-Based Visualization Tool for Teaching and Learning Tree Visualization

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

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

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.
Virtual Reality Field Trip of a Steel Building Under Construction

The objective of this study is to determine if a virtual reality field trip can provide a valuable educational experience to undergraduate students.The format of this virtual reality field trip was a tour of a steel-framed skyscraper under construction narrated by a lead structural engineer, who took the participants to eight stops around a typical floor and explained various steel framing concepts with illustrations.The tour was provided to the students using standalone virtual reality headsets in a classroom setting.The assessment method consisted of pre-and post-tour quizzes to quantify the comprehension improvement of specific structural steel framing concepts and to gauge the impact of the virtual reality tour on inspiring students' interest in the subject.The assessment data was supplemented by additional post survey data on students' opinions of the experience.It was found that students were excited to interact with the virtual site visit and left the experience with a better understanding of structural steel framing.Lessons learned from this experience are discussed along with student recommendations for improvements in the educational module.
Modularity Analysis of Makerspaces to Determine Potential Hubs and Critical Tools in the Makerspace

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
Model-Based 3D Gaze Estimation Using a TOF Camera

Among the numerous gaze-estimation methods currently available, appearance-based methods predominantly use RGB images as input and employ convolutional neural networks (CNNs) to detect facial images to regressively obtain gaze angles or gaze points. Model-based methods require high-resolution images to obtain a clear eyeball geometric model. These methods face significant challenges in outdoor environments and practical application scenarios. This paper proposes a model-based gaze-estimation algorithm using a low-resolution 3D TOF camera. This study uses infrared images instead of RGB images as input to overcome the impact of varying illumination intensity in the environment on gaze estimation. We utilized a trained YOLOv8 neural network model to detect eye landmarks in captured facial images. Combined with the depth map from a time-of-flight (TOF) camera, we calculated the 3D coordinates of the canthus points of a single eye of the subject. Based on this, we fitted a 3D geometric model of the eyeball to determine the subject’s gaze angle. Experimental validation showed that our method achieved a root mean square error of 6.03° and 4.83° in the horizontal and vertical directions, respectively, for the detection of the subject’s gaze angle. We also tested the proposed method in a real car driving environment, achieving stable driver gaze detection at various locations inside the car, such as the dashboard, driver mirror, and the in-vehicle screen.
Development of a Security Engineering Curriculum for a Distance Learning Environment

In this paper, we describe the ongoing development of the undergraduate security engineering program at the University of Central Florida.While a clear need for security education exists, the goals of such a program are not always so clear.Security, like much of engineering, is a discipline that is applied to other fields.Therefore, it is necessary to educate students not only in the technical aspects of security, but also in the situationdependent way that it is applied.Such an education would provide students with the skills needed to enter the field and the in-depth understanding to be upwardly mobile.To this end, the curriculum is being developed to provide a mix of both technical skills and broad knowledge.This is present both in the course material, and our commitments to utilizing the strengths UCF has to offer (National Center for Forensic Studies, Public Safety Technology Center, etc.) and developing tight integration with other departments (Criminal Justice, Business, etc.).Additionally, all of the courses in this program are made available through the FEEDS distance learning system and must be developed to support remote students.The hands-on, applied coursework being developed must be compatible with distance learning.The four primary educational goals of this program are as follows: Technical knowledge (ready-to-apply skills); Broad knowledge (ability to apply current knowledge to unknown future situation); Business and management procedures; and Legal and ethical issues.Rather than have a course, or set of courses, devoted to each of these areas, we believe that each and every course should cover these four concepts as they apply to the specific subject being taught.The end goal is to provide the students a substantial body of knowledge while avoiding pitfalls such as too little applicable knowledge, too much vendor-specific training, and not enough depth in specific areas.