Journal Articles, Association for Computing Machinery (ACM)

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'Do I Have to Take This Class?': A Review of Ethics Requirements in Computer Science Curricula
Weichert, James; Kim, Dayoung; Zhu, Qin; Eldardiry, Hoda (ACM, 2025-02-12)
ABET criteria for accreditation of undergraduate computer science (CS) degrees require universities to cover within their curricula topics including “local and global impacts of computing solutions on individuals, organizations, and society,” and to prepare their students to “make informed judgments in computing practice, taking into account legal, ethical, diversity, equity, inclusion, and accessibility principles” [1]. A growing body of research similarly identifies the need for CS programs to integrate ethics into their degree requirements, both through standalone ethics-related courses and embedded modules or case studies on the ethical impacts in ‘technical’ courses. The calls for increased attention to CS ethics education have become more pressing with the emergence of sophisticated consumer-ready AI technologies, which pose new ethical challenges in the forms of bias, hallucination, and autonomous decision-making. Yet it remains unclear whether current university curricula are adequately preparing future graduates to confront these challenges. This paper presents a systematic review of the degree requirements of 250 computer science bachelor’s degree programs worldwide. We categorize each program according to whether a CS-related ethics course is offered and/or required by the department, finding that almost half of all universities we review do not offer any computing ethics courses, and only 33% of universities require students to take an ethics course to obtain their degree. We analyze differences among public US, private US, and non-US universities and discuss implications for curricular changes and the state of undergraduate computing ethics education.
Diary Study as an Educational Tool: An Experience Report from an HCI Course
Fan, Jixiang; Haqq, Derek; Saaty, Morva; Wang, Wei-Lu; McCrickard, D. Scott (ACM, 2025-02-12)
With the rapid advancement and widespread adoption of computer technology, it has become an indispensable component in the development of human society. Therefore, computer science education’s focus extends beyond merely teaching students to read and write code; it is crucial to assist them in gaining an accurate and deep understanding of the applications of technology in the real world, its conveniences, and potential risks. Furthermore, it involves exploring how to design, improve, and innovate computer technologies to meet practical demands. Consequently, Human-Computer Interaction (HCI) has grown increasingly significant in the curriculum of computer science. However, research indicates that computing students face numerous challenges in learning HCI. To enhance students’ ability to experience, discover, and understand user needs, the authors of this paper recommend incorporating diary studies in HCI education. In the field of HCI, diary studies are a method for collecting long-term data on user behavior and experiences in a natural environment. Participants are required to record their daily activities, product usage, encountered issues, and personal impressions over specific periods. This paper will detail the process and steps implemented in our diary studies and present student feedback and evaluations. Through this experience report, we hope to encourage more educators to adopt and refine the diary study methodology in their courses, thereby aiding computer science students in better understanding and embracing the concepts and knowledge of HCI.
The Impact of Group Discussion and Formation on Student Performance: An Experience Report in a Large CS1 Course
Wu, Tong; Tang, Xiaohang; Wong, Sam; Chen, Xi; Shaffer, Clifford A.; Chen, Yan (ACM, 2025-02-12)
Programming instructors often conduct collaborative learning activities, such as Peer Instruction (PI), to enhance student motivation, engagement, and learning gains. However, the impact of group discussion and formation mechanisms on student performance remains unclear. To investigate this, we conducted an 11- session experiment in a large, in-person CS1 course. We employed both random and expertise-balanced grouping methods to examine the efficacy of different group mechanisms and the impact of expert students’ presence on collaborative learning. Our observations revealed complex dynamics within the collaborative learning environment. Among 255 groups, 146 actively engaged in discussions, with 96 of these groups demonstrating improvement for poor-performing students. Interestingly, our analysis revealed that different grouping methods (expertise-balanced or random) did not significantly influence discussion engagement or poor-performing students’ improvement. In our deeper qualitative analysis, we found that struggling students often derived benefits from interactions with expert peers, but this positive effect was not consistent across all groups.We identified challenges that expert students face in peer instruction interactions, highlighting the complexity of leveraging expertise within group discussions.
Understanding the Effects of Integrating Music Programming and Web Development in a Summer Camp for High School Students
Manesh, Daniel; Jelson, Andrew; Altland, Emily; Freeman, Jason; Lee, Sang Won (ACM, 2025-02-18)
This poster presents the development and implementation of a 10- day remix-based summer camp curriculum designed to introduce high school students, particularly a multinational cohort of young women, to programming through creative coding. The curriculum integrates music composition using EarSketch and web development with HTML and CSS. The camp aims to inspire participants to gain self-efficacy in programming and motivate them to explore STEM/computing careers. Preliminary results from surveys and interviews indicate increased confidence in programming skills. This ongoing research explores the impact of remixing as a gateway for transitioning into more general-purpose computing domains such as web development.
RT-BarnesHut: Accelerating Barnes–Hut Using Ray-Tracing Hardware
Nagarajan, Vani; Gangaraju, Rohan; Sundararajah, Kirshanthan; Pelenitsyn, Artem; Kulkarni, Milind (ACM, 2025-03)
The 𝑛-body problem involves calculating the effect of bodies on each other. 𝑛-body simulations are ubiquitous in the fields of physics and astronomy and notoriously computationally expensive. The naïve algorithm for 𝑛-body simulations has the prohibiting 𝑂(𝑛2) time complexity. Reducing the time complexity to 𝑂(𝑛 · lg(𝑛)), the tree-based Barnes–Hut algorithm approximates the effect of bodies beyond a certain threshold distance. Other than algorithmic improvements, extensive research has gone into accelerating 𝑛-body simulations on GPUs and multi-core systems. However, Barnes– Hut is a tree-traversal algorithm, which makes it a poor target for acceleration using traditional GPU shader cores. In contrast, recent work shows that, for tree-based computations, GPU ray-tracing (RT) cores dominate shader cores. In this work, we reformulate the Barnes–Hut algorithm as a ray-tracing problem and implement it with NVIDIA OptiX. Our evaluation shows that the resulting system, RT-BarnesHut, outperforms current state-of-the-art GPU-based implementations.
Making Software Development More Diverse and Inclusive: Key Themes, Challenges, and Future Directions
Hyrynsalmi, Sonja; Baltes, Sebastian; Brown, Chris; Prikladnicki, Rafael; Rodriguez-Perez, Gema; Serebrenik, Alexander; Simmonds, Jocelyn; Trinkenreich, Bianca; Wang, Yi; Liebel, Grischa (ACM, 2025)
Introduction: Digital products increasingly reshape industries, influencing human behavior and decision-making. However, the software development teams developing these systems often lack diversity, which may lead to designs that overlook the needs, equal treatment or safety of diverse user groups. These risks highlight the need for fostering diversity and inclusion in software development to create safer, more equitable technology. Method: This research is based on insights from an academic meeting in June 2023 involving 23 software engineering researchers and practitioners. We used the collaborative discussion method 1-2-4-ALL as a systematic research approach and identified six themes around the theme ?challenges and opportunities to improve Software Developer Diversity and Inclusion(SDDI)'. We identified benefits, harms, and future research directions for the four main themes. Then, we discuss the remaining two themes, Artificial Intelligence&SDDI and AI&Computer Science education, which have a cross-cutting effect on the other themes. Results: This research explores the key challenges and research opportunities for promoting SDDI, providing a roadmap to guide both researchers and practitioners. We underline that research around SDDI requires a constant focus on maximizing benefits while minimizing harms, especially to vulnerable groups. As a research community, we must strike this balance in a responsible way.
Test Case-Informed Knowledge Tracing for Open-ended Coding Tasks
Duan, Zhangqi; Fernandez, Nigel; Hicks, Alexander; Lan, Andrew (ACM, 2025-03-03)
Open-ended coding tasks, which ask students to construct programs according to certain specifications, are common in computer science education. Student modeling can be challenging since their open-ended nature means that student code can be diverse. Traditional knowledge tracing (KT) models that only analyze response correctness may not fully capture nuances in student knowledge from student code. In this paper, we introduce Test case-Informed Knowledge Tracing for Open-ended Coding (TIKTOC), a framework to simultaneously analyze and predict both open-ended student code and whether the code passes each test case. We augment the existing CodeWorkout dataset with the test cases used for a subset of the open-ended coding questions, and propose a multitask learning KT method to simultaneously analyze and predict 1) whether a student’s code submission passes each test case and 2) the student’s open-ended code, using a large language model as the backbone. We quantitatively show that these methods outperform existing KT methods for coding that only use the overall score a code submission receives. We also qualitatively demonstrate how test case information, combined with open-ended code, helps us gain fine-grained insights into student knowledge.
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.
A Fully Polynomial Time Approximation Scheme for Adaptive Variable Rate Task Demand
Willcock, Aaron; Fisher, Nathan; Chantem, Thidapat (Tam) (ACM, 2024-11-06)
The Adaptive Variable Rate (AVR) task model defines a task where job WCET and period are a function of engine speed. Motivated by a lack of tractable AVR task demand methods, this work uses predefined job sequences for the Bounded Precedence Constraint Knapsack Problem inherent in AVR task demand calculation instead of enumerating all considered speeds as in existing work. A new, exact approach is proposed and approximated, enabling the derivation of a Fully Polynomial Time Approximation Scheme that outperforms the state-of-the-art in runtime (7,800x improvement) and RAM use (99% reduction) with less than 8% demand overestimate.
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.
On Extending Incorrectness Logic with Backwards Reasoning
Verbeek, Freek; Sefat, Md Syadus; Fu, Zhoulai; Ravindran, Binoy (ACM, 2025-01-07)
This paper studies an extension of O'Hearn's incorrectness logic (IL) that allows backwards reasoning. IL in its current form does not generically permit backwards reasoning. We show that this can be mitigated by extending IL with underspecification. The resulting logic combines underspecification (the result, or postcondition, only needs to formulate constraints over relevant variables) with underapproximation (it allows to focus on fewer than all the paths). We prove soundness of the proof system, as well as completeness for a defined subset of presumptions. We discuss proof strategies that allow one to derive a presumption from a given result. Notably, we show that the existing concept of loop summaries -- closed-form symbolic representations that summarize the effects of executing an entire loop at once -- is highly useful. The logic, the proof system and all theorems have been formalized in the Isabelle/HOL theorem prover.
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.
Experimental Validation of a 3GPP compliant 5G-based Positioning System
Dhungel, Sarik; Duggal, Gaurav; Ron, Dara; Tripathi, Nishith; Buehrer, R. Michael; Reed, Jeffrey H.; Shah, Vijay K. (ACM, 2024-12-04)
The advent of 5G positioning techniques by 3GPP has unlocked possibilities for applications in public safety, vehicular systems, and location-based services. However, these applications demand accurate and reliable positioning performance, which has led to the proposal of newer positioning techniques. To further advance the research on these techniques, in this paper, we develop a 3GPP-compliant 5G positioning testbed, incorporating gNodeBs (gNBs) and User Equipment (UE). The testbed uses New Radio (NR) Positioning Reference Signals (PRS) transmitted by the gNB to generate Time of Arrival (TOA) estimates at the UE. We mathematically model the inter-gNB and UE-gNB time offsets affecting the TOA estimates and examine their impact on positioning performance. Additionally, we propose a calibration method for estimating these time offsets. Furthermore, we investigate the environmental impact on the TOA estimates. Our findings are based on our mathematical model and supported by experimental results.

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