Scholarly Works, Computer Science

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Research articles, presentations, and other scholarship

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SecQuant: Quantifying Container System Call Exposure
Jang, Sunwoo; Song, Somin; Tak, Byungchul; Suneja, Sahil; Le, Michael V.; Yue, Chuan; Williams, Dan (Springer, 2022-09-22)
Despite their maturity and popularity, security remains a critical concern in container adoption. To address this concern, secure container runtimes have emerged, offering superior guest isolation, as well as host protection, via system call policing through the surrogate kernel layer. Whether or not an adversary can bypass this protection depends on the effectiveness of the system call policy being enforced by the container runtime. In this work, we propose a novel method to quantify this container system call exposure. Our technique combines the analysis of a large number of exploit codes with comprehensive experiments designed to uncover the syscall pass-through behaviors of container runtimes. Our exploit code analysis uses information retrieval techniques to rank system calls by their risk weights. Our study shows that secure container runtimes are about 4.2 to 7.5 times more secure than others, using our novel quantification metric. We additionally uncover changing security trends across a 4.5 year version history of the container runtimes.
Message from the USENIX ATC’23 Program Co-Chairs
Lawall, Julia; Williams, Dan (2023-01-01)
High-Quality Dataset-Sharing and Trade Based on A Performance-Oriented Directed Graph Neural Network
Zeng, Yingyan; Zhou, Xiaona; Chilukuri, Premith; Lourentzou, Ismini; Jin, Ran (2025)
The advancement of Artificial Intelligence (AI) models heavily relies on large high-quality datasets. However, in advanced manufacturing, collecting such data is time-consuming and labor-intensive for a single enterprise. Hence, it is important to establish a context-aware and privacy-preserving data sharing system to share small-but-high-quality datasets between trusted stakeholders. Existing data sharing approaches have explored privacy-preserving data distillation methods and focused on valuating individual samples tied to a specific AI model, limiting their flexibility across data modalities, AI tasks, and dataset ownership. In this work, we propose a performance-oriented representation learning (PORL) framework in a Directed Graph Neural Network (DiGNN). PORL distills raw datasets into privacy-preserving proxy datasets for sharing and learns compact meta data representations for each stakeholder locally. The meta data will then be used in DiGNN to forecast the AI model performance and guide the sharing via graph-level supervised learning. The effectiveness of the PORL-DiGNN is validated by two case studies: data sharing in the semiconducting manufacturing network between similar processes to create similar quality defect models; and data sharing in the design and manufacturing network of Microbial Fuel Cell anodes between upstream (design) and downstream (Additive Manufacturing) stages to create distinct but related AI models.
Computing Education in African Countries: A Literature Review and Contextualised Learning Materials
Hamouda, Sally; Marshall, Linda; Sanders, Kate; Tshukudu, Ethel; Adelakun-Adeyemo, Oluwatoyin; Becker, Brett; Dodoo, Emma; Korsah, G.; Luvhengo, Sandani; Ola, Oluwakemi; Parkinson, Jack; Sanusi, Ismaila (ACM, 2025-01-22)
This report begins with a literature review of computing education in Africa.We found a substantial body of work, scattered over more than 80 venues, which we have brought together here for the first time. Several important themes emerge in this dataset, including the need to contextualise computing education. In the second part of this report we investigate contextualisation further. We present a pilot study, grounded in the literature review, of the development of course materials, sample code, and programming assignments for introductory programming, contextualised for six African countries: Botswana, Egypt, Ghana, Nigeria, South Africa, and Zambia. We include the materials, report on a preliminary evaluation of the materials by fellow educators in African countries, and suggest a process by which other educators could develop materials for their local contexts.
Instructors' Perspectives on Capstone Courses in Computing Fields: A Mixed-Methods Study
Hooshangi, Sara; Shakil, Asma; Dasgupta, Subhasish; Davis, Karen C.; Farghally, Mohammed; Fitzpatrick, KellyAnn; Gutica, Mirela; Hardt, Ryan; Riddle, Steve; Seyam, Mohammed (ACM, 2025-01-22)
Team-based capstone courses are integral to many undergraduate and postgraduate degree programs in the computing field. They are designed to help students gain hands-on experience and practice professional skills such as communication, teamwork, and selfreflection as they transition into the real world. Prior research on capstone courses has focused primarily on the experiences of students. The perspectives of instructors who teach capstone courses have not been explored comprehensively. However, an instructor’s experience, motivation, and expectancy can have a significant impact on the quality of a capstone course. In this working group, we used a mixed methods approach to understand the experiences of capstone instructors. Issues such as class size, industry partnerships, managing student conflicts, and factors influencing instructor motivation were examined using a quantitative survey and semistructured interviews with capstone teaching staff from multiple institutions across different continents. Our findings show that there are more similarities than differences across various capstone course structures. Similarities include team size, team formation methodologies, duration of the capstone course, and project sourcing. Differences in capstone courses include class sizes and institutional support. Some instructors felt that capstone courses require more time and effort than regular lecture-based courses. These instructors cited that the additional time and effort is related to class size and liaising with external stakeholders, including industry partners. Some instructors felt that their contributions were not recognized enough by the leadership at their institutions. Others acknowledged institutional support and the value that the capstone brought to their department. Overall, we found that capstone instructors were highly intrinsically motivated and enjoyed teaching the capstone course. Most of them agree that the course contributes to their professional development. The majority of the instructors reported positive experiences working with external partners and did not report any issues with Non-Disclosure Agreements (NDAs) or disputes about Intellectual Property (IP). In most institutions, students own the IP of their work, and clients understand that. We use the global perspective that this work has given us to provide guidelines for institutions to better support capstone instructors.
Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools
Prather, James; Leinonen, Juho; Kiesler, Natalie; Gorson Benario, Jamie; Lau, Sam; MacNeil, Stephen; Norouzi, Narges; Opel, Simone; Pettit, Vee; Porter, Leo; Reeves, Brent; Savelka, Jaromir; Smith, David; Strickroth, Sven; Zingaro, Daniel (ACM, 2025-01-22)
Generative AI (GenAI) is advancing rapidly, and the literature in computing education is expanding almost as quickly. Initial responses to GenAI tools were mixed between panic and utopian optimism. Many were fast to point out the opportunities and challenges of GenAI. Researchers reported that these new tools are capable of solving most introductory programming tasks and are causing disruptions throughout the curriculum. These tools can write and explain code, enhance error messages, create resources for instructors, and even provide feedback and help for students like a traditional teaching assistant. In 2024, new research started to emerge on the effects of GenAI usage in the computing classroom. These new data involve the use of GenAI to support classroom instruction at scale and to teach students how to code with GenAI. In support of the former, a new class of tools is emerging that can provide personalized feedback to students on their programming assignments or teach both programming and prompting skills at the same time. With the literature expanding so rapidly, this report aims to summarize and explain what is happening on the ground in computing classrooms. We provide a systematic literature review; a survey of educators and industry professionals; and interviews with educators using GenAI in their courses, educators studying GenAI, and researchers who create GenAI tools to support computing education. The triangulation of these methods and data sources expands the understanding of GenAI usage and perceptions at this critical moment for our community.
User-based I/O Profiling for Leadership Scale HPC Workloads
Yazdani, Ahmad Hossein; Paul, Arnab; Karimi, Ahmad; Wang, Feiyi; Butt, Ali (ACM, 2025-01-04)
I/O constitutes a significant portion of most of the application runtime. Spawning many such applications concurrently on an HPC system leads to severe I/O contention. Thus, understanding and subsequently reducing I/O contention induced by such multi-tenancy is critical for the efficient and reliable performance of the HPC system. In this study, we demonstrate that an application’s performance is influenced by the command line arguments passed to the job submission. We model an application’s I/O behavior based on two factors: past I/O behavior within a time window and userconfigured I/O settings via command-line arguments. We conclude that I/O patterns for well-known HPC applications like E3SM and LAMMP are predictable, with an average uncertainty below 0.25 (A probability of 80%) and near zero (A probability of 100%) within a day. However, I/O pattern variance increases as the study time window lengthens. Additionally, we show that for 38 users and at least 50 applications constituting approximately 93000 job submissions, there is a high correlation between a submitted command line and the past command lines made within 1 to 10 days submitted by the user. We claim the length of this time window is unique per user.
Conversate: Supporting Reflective Learning in Interview Practice Through Interactive Simulation and Dialogic Feedback
Daryanto, Taufiq; Ding, Xiaohan; Wilhelm, Lance; Stil, Sophia; Knutsen, Kirk; Rho, Eugenia (ACM, 2025-01-10)
Job interviews play a critical role in shaping one’s career, yet practicing interview skills can be challenging, especially without access to human coaches or peers for feedback. Recent advancements in large language models (LLMs) present an opportunity to enhance the interview practice experience. Yet, little research has explored the effectiveness and user perceptions of such systems or the benefits and challenges of using LLMs for interview practice. Furthermore, while prior work and recent commercial tools have demonstrated the potential of AI to assist with interview practice, they often deliver one-way feedback, where users only receive information about their performance. By contrast, dialogic feedback, a concept developed in learning sciences, is a two-way interaction feedback process that allows users to further engage with and learn from the provided feedback through interactive dialogue. This paper introduces Conversate, a web-based application that supports reflective learning in job interview practice by leveraging large language models (LLMs) for interactive interview simulations and dialogic feedback. To start the interview session, the user provides the title of a job position (e.g., entry-level software engineer) in the system. Then, our system will initialize the LLM agent to start the interview simulation by asking the user an opening interview question and following up with questions carefully adapted to subsequent user responses. After the interview session, our back-end LLM framework will then analyze the user’s responses and highlight areas for improvement. Users can then annotate the transcript by selecting specific sections and writing self-reflections. Finally, the user can interact with the system for dialogic feedback, conversing with the LLM agent to learn from and iteratively refine their answers based on the agent’s guidance. To evaluate Conversate, we conducted a user study with 19 participants to understand their perceptions of using LLM-supported interview simulation and dialogic feedback. Our findings show that participants valued the adaptive follow-up questions from LLMs, as they enhanced the realism of interview simulations and encouraged deeper thinking. Participants also appreciated the AI-assisted annotation, as it reduced their cognitive burden and mitigated excessive self-criticism in their own evaluation of their interview performance. Moreover, participants found the LLM-supported dialogic feedback to be beneficial, as it promoted personalized and continuous learning, reduced feelings of judgment, and allowed them to express disagreement.
Exploring Large Language Models Through a Neurodivergent Lens: Use, Challenges, Community-Driven Workarounds, and Concerns
Carik, Buse; Ping, Kaike; Ding, Xiaohan; Rho, Eugenia (ACM, 2025-01-10)
Despite the increasing use of large language models (LLMs) in everyday life among neurodivergent individuals, our knowledge of how they engage with, and perceive LLMs remains limited. In this study, we investigate how neurodivergent individuals interact with LLMs by qualitatively analyzing topically related discussions from 61 neurodivergent communities on Reddit. Our findings reveal 20 specific LLM use cases across five core thematic areas of use among neurodivergent users: emotional well-being, mental health support, interpersonal communication, learning, and professional development and productivity. We also identified key challenges, including overly neurotypical LLM responses and the limitations of text-based interactions. In response to such challenges, some users actively seek advice by sharing input prompts and corresponding LLM responses. Others develop workarounds by experimenting and modifying prompts to be more neurodivergent-friendly. Despite these efforts, users have significant concerns around LLM use, including potential overreliance and fear of replacing human connections. Our analysis highlights the need to make LLMs more inclusive for neurodivergent users and implications around how LLM technologies can reinforce unintended consequences and behaviors.
Generative Co-Learners: Enhancing Cognitive and Social Presence of Students in Asynchronous Learning with Generative AI
Wang, Tianjia; Wu, Tong; Liu, Huayi; Brown, Chris; Chen, Yan (ACM, 2025-01-10)
Cognitive presence and social presence are crucial for a comprehensive learning experience. Despite the flexibility of asynchronous learning environments to accommodate individual schedules, the inherent constraints of asynchronous environments make augmenting cognitive and social presence particularly challenging. Students often face challenges such as a lack of timely feedback and support, an absence of non-verbal cues in communication, and a sense of isolation. To address this challenge, this paper introduces Generative Co-Learners, a system designed to leverage generative AI-powered agents, simulating co-learners supporting multimodal interactions, to improve cognitive and social presence in asynchronous learning environments.We conducted a study involving 12 student participants who used our system to engage with online programming tutorials to assess the system’s effectiveness. The results show that by implementing features to support textual and visual communication and simulate an interactive learning environment with generative agents, our system enhances the cognitive and social presence in the asynchronous learning environment. These results suggest the potential to use generative AI to support student learning and transform asynchronous learning into a more inclusive, engaging, and efficacious educational approach.
WiFi Received Signal Strength (RSS) Based Automated Attendance System for Educational Institutions
Khan, Sidratul Muntaha; Maliha, Mehreen Tabassum; Haque, Md Shahedul; Rahman, Ashikur (ACM, 2024-12-19)
Smartphones are becoming part of people’s day-to-day activities now-a-days. Globally, almost 90% of cellular phones are smartphones. “Personnel tracking” is a viable usage of smartphones. Automated attendance system, an application of personnel tracking, is efficient and essential for enhancing productivity and streamlining operations in modern workplaces and educational institutions. While traditional methods of attendance tracking are prone to inaccuracies and inefficiencies, smartphone-based systems offer a smoother approach. In this paper, we present a smartphone-based attendance system that leverages Wi-Fi signal strength for indoor localization. Instead of requiring precise positioning, we propose a system with zone-based localization approach. We divide the whole area into distinct smaller zones and determine the users’ location within these zones. Through real-world deployment, we demonstrate that our novel approach reduces the complexity of exact positioning while still achieving high accuracy in identifying a user’s presence within specific areas, which are often enclosed by boundaries. Comparative evaluations with implementing an algorithm show the superiority of our proposed method in terms of accuracy and practicality, making it suitable for deployment in large-scale organizational settings.
Assessing ChatGPT's Code Generation Capabilities with Short vs Long Context Programming Problems
Shuvo, Uddip Acharjee; Dip, Sajib Acharjee; Vaskar, Nirvar Roy; Al Islam, A. B. M. Alim (ACM, 2024-12-19)
This study assesses the code generation capabilities of ChatGPT using competitive programming problems from platforms such as LeetCode, HackerRank, and UVa Online Judge. In a novel approach, we contrast ChatGPT’s performance on concise problems from LeetCode against more complex, narrative-driven problems from Codeforces. Our results reveal significant challenges in addressing the intricate narrative structures of Codeforces, with difficulties in problem recognition and strategic planning in extended contexts. While initial code accuracy for LeetCode problems stands at 72%, it drops to 31% for complex Codeforces problems using Python. Additionally, we explore the impact of targeted instructions aimed at enhancing performance, which increased LeetCode accuracy to 73.53% but saw a decrease in Codeforces performance to 29%. Our analysis further extends across multiple programming languages, examining if iterative prompting and specific feedback can enhance code precision and efficiency. We also delve into ChatGPT’s performance on challenging problems and those released post its training period. This research provides insights into the strengths and weaknesses of AI in code generation and lays groundwork for future developments in AI-driven coding tools.
Global stability analysis using the method of Reduction Of Dissipativity Domain
Jafari, Reza; Hagan, Martin (IEEE, 2011-07)
This paper describes a modification to the method of Reduction Of Dissipativity Domain with Linear Boundaries (RODD-LB1) which was introduced by Barabanov and Prokharov [7]. The RODD method is a computational technique for the global stability analysis of nonlinear dynamic systems. In this paper we introduce an extension to the original RODD method that is designed to speed up convergence. The efficiency of the extended algorithm is demonstrated through numerical examples.
Enhanced precision in robot arm positioning: A nonlinear damping approach for flexible joint manipulators
Jafari, Amir Hossein; Dhaouadi, Rached; Jafari, Reza (Wiley, 2024-06-22)
This article introduces an advanced nonlinear controller designed for optimizing the performance of a single‐link robot arm featuring a flexible joint. The proposed nonlinear control strategy incorporates a Proportional‐Integral (PI) controller in conjunction with a nonlinear velocity feedback component, aimed at providing effective nonlinear damping and suppressing vibrations. To validate the controller's performance, extensive simulations are conducted utilizing machine learning techniques within the Python environment. The performance of the proposed nonlinear damping controller is benchmarked against a conventional linear cascaded P‐PI control structure, with both controllers fine‐tuned using the Nelder‐Mead algorithm. Simulation results demonstrate that the nonlinear damping controller yields substantial improvements in the dynamic behavior of the robot axis arm, showcasing superior step and sinusoidal position tracking performance, along with active vibration damping capabilities. This research contributes valuable insights into the enhanced nonlinear control strategies for flexible‐joint robot arms, offering promising advancements in their overall dynamic performance.
Nonlinear Adaptive control with High-Gain Observer
Jafari, Reza (2025-06-30)
In this paper we present an adaptive nonlinear output feedback controller with high-gain observer. One of the main challenge with the design of nonlinear adaptive controller is the stability concern. The stability analysis of the overall closed loop system is addressed through the Lyapunov theorem. The performance of the proposed controller with high-gain observer is tested on a single manipulator with flexible joints.
Box-Jenkins Model of Elastic Drive System Using Levenberg-Marquardt Algorithm
Jafari, Reza (Springer Nature, 2024-03-21)
This paper explains the derivation of Box-Jenkins model for the elastic drive system using Levenberg-Marduardt algorithm. The Box-Jenkins model which is the most flexible linear model has been chosen to identify the elastic drive system. The GPAC analysis has been used for the preliminary identification and the Maximum Likelihood Estimator (Levenberg-Marduardt) is used for the parameter estimations. Several models have been developed for the elastic drive system and the simplest model has been chosen. The accuracy of the final model, residual analysis, has been checked using CHI-Square test.
Forward and Converse Lyapunov Theorems for Discrete Dynamical Systems
Jafari, Reza; Kable, Anthony; Hagan, Martin (IEEE, 2014-08-20)
This paper derives the necessary and sufficient conditions for the Lyapunov function such that the equilibrium point of a dynamical system is stable or Globally Asymptotic Stable (GAS). The paper shows that continuity of Lyapunov function at the equilibrium point is the only necessary and sufficient condition for stability. We shows that a certain type of converse theorem can not be proved with continuous Lyapunov function. The Tower of Babel ‘TOB’ is given as an example of stable dynamical system in which no continuous Lyapunov function exists for this system.
"I WANT": Agency and Accessibility in the Age of AI
Borunda Monsivais, Luis; Gipe-Lazarou, Andrew; Meng, Na (2024-06)
"I WANT access to public buildings and technologies"; "I WANT all stairs to have railings"; "I WANT there to be a talking pedestrian sign"; "I WANT curbs to be more noticeable"; "I WANT technology that is dedicated to the blind". Young, vision-impaired learners from across the world, participating in our team’s human-centered research and participatory design initiatives, express an impassioned desire for agency and inclusive space making. Utilizing these statements as a foundational element of the participatory design process, our work continues to explore the intersection of AI and inclusive space-making, the methods employed through human-centered research and computational techniques such as machine learning and app development, and the potential contributions of these interventions to a more accessible future. This paper presents a two-part investigation into the role of advanced technological interventions and participatory design in shaping the future of architecture and design. Part 1 explores the outcomes of AI assistive device research centered on the voices of future professionals. This phase involved interviews and focus group discussions with blind and visually impaired individuals, designers, and computer scientists in an ongoing human subject research, leading to the creation of an AI-driven navigation app. Part 2 anticipates the deployment of working prototypes derived from these participatory design processes during [Affiliation Placeholder]'s annual Blind Design Workshop, in which more than a dozen young people with vision-impairment participate each spring. Its itinerary includes analog exercises in drawing and model-making (using material samples and wax sticks on Braille graph paper), guided tours of multi-sensory learning spaces across [Location Placeholder], accessible training in the production of 3D-prints and embossed drawings, and mentorship from practicing design professionals of the vision-impaired community, culminating in a final presentation and group critique of accessible design proposals. The workshop is a unique career exploration experience in architecture for individuals with vision impairment, designed to empower them with the understanding that they can have agency in the space-making process by giving them a voice and teaching them to architect their ambitions for the future. The synergy of AI and architecture presents profound opportunities to propel young, vision-impaired individuals from passive observers to active participants in crafting inclusive environments. Our paper discusses how innovative approaches to research and learning can seed future generations with the goal of harnessing AI for social impact in design and substantiating their role as the vanguards of a more accessible world. The outcomes of this study hold the potential to shape pedagogical strategies and industry standards, contributing to a profound reimagining of inclusive design education and practice.
Examining Faculty and Student Perceptions of Generative AI in University Courses
Kim, Junghwan; Klopfer, Michelle; Grohs, Jacob R.; Eldardiry, Hoda; Weichert, James; Cox, Larry A., II; Pike, Dale (Springer, 2025-01-24)
As generative artificial intelligence (GenAI) tools such as ChatGPT become more capable and accessible, their use in educational settings is likely to grow. However, the academic community lacks a comprehensive understanding of the perceptions and attitudes of students and instructors toward these new tools. In the Fall 2023 semester, we surveyed 982 students and 76 faculty at a large public university in the United States, focusing on topics such as perceived ease of use, ethical concerns, the impact of GenAI on learning, and differences in responses by role, gender, and discipline. We found that students and faculty did not differ significantly in their attitudes toward GenAI in higher education, except regarding ease of use, hedonic motivation, habit, and interest in exploring new technologies. Students and instructors also used GenAI for coursework or teaching at similar rates, although regular use of these tools was still low across both groups. Among students, we found significant differences in attitudes between males in STEM majors and females in non-STEM majors. These findings underscore the importance of considering demographic and disciplinary diversity when developing policies and practices for integrating GenAI in educational contexts, as GenAI may influence learning outcomes differently across various groups of students. This study contributes to the broader understanding of how GenAI can be leveraged in higher education while highlighting potential areas of inequality that need to be addressed as these tools become more widely used.
A Dynamic Characteristic Aware Index Structure Optimized for Real-world Datasets
Yang, Jin; Yoon, Heejin; Yun, Gyeongchan; Noh, Sam; Choi, Young-ri (ACM, 2024-12)
Many datasets in real life are complex and dynamic, that is, their key densities are varied over the whole key space and their key distributions change over time. It is challenging for an index structure to efficiently support all key operations for data management, in particular, search, insert, and scan, for such dynamic datasets. In this paper, we present DyTIS (Dynamic dataset Targeted Index Structure), an index that targets dynamic datasets. DyTIS, though based on the structure of Extendible hashing, leverages the CDF of the key distribution of a dataset, and learns and adjusts its structure as the dataset grows. The key novelty behind DyTIS is to group keys by the natural key order and maintain keys in sorted order in each bucket to support scan operations within a hash index. We also define what we refer to as a dynamic dataset and propose a means to quantify its dynamic characteristics. Our experimental results show that DyTIS provides higher performance than the state-of-the-art learned index for the dynamic datasets considered. We also analyze the effects of the dynamic characteristics of datasets, including sequential datasets, as well as the effect of multiple threads on the performance of the indexes.

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