Computer Science Research Articles

Admissions in the age of AI: detecting AI-generated application materials in higher education

Recent advances in Artificial Intelligence (AI), such as the development of large language models like ChatGPT, have blurred the boundaries between human and AI-generated text. This has led to a pressing need for tools that can determine whether text has been created or revised using AI. A general and universally effective detection model would be extremely useful, but appears to be beyond the reach of current technology and detection methods. The research described in this study adopts a domain and task specific approach and shows that specialized detection models can attain high accuracy. The study focuses on the higher education graduate admissions process, with the specific goal of identifying AI-generated and AI-revised Letters of Recommendation (LORs) and Statements of Intent (SOIs). Detecting such application materials is essential to ensure that applicants are evaluated on their true merits and abilities, and to foster an equitable and trustworthy admissions process. Our research is based on 3755 LORs and 1973 SOIs extracted from the application records of Fordham University’s Master’s programs in Computer Science and Data Science. To facilitate the construction and evaluation of detection models, we generated AI counterparts for each LOR and SOI using the GPT-3.5 Turbo API. The prompts for AI-generation text were derived from the admission data of the respective applicants, and the AI-revised LORs and SOIs were generated directly from the human-authored versions. We also utilize an open-access GPT-wiki-intro dataset to further validate our hypothesis regarding the feasibility of constructing domain-specific AI content detectors. Our experiments yield promising results in developing classifiers tailored to a specific domain when provided with sufficient training samples. Additionally, we present a comparative analysis of the word frequency and statistical characteristics of the text, which provides convincing evidence that ChatGPT employs distinctive vocabulary and paragraph structure compared to human-authored text. The code for this study is available on GitHub, and the models can be executed on user-provided data via an interactive web interface.

The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning

The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning

Journal of research on technology in education special issue on computer science for all

Journal of research on technology in education special issue on computer science for all

Towards Sustainable Decarbonization: Addressing Challenges in Electric Vehicle Adoption and Infrastructure Development

The transition to electric vehicles (EVs) plays a pivotal role in achieving decarbonization within the transportation sector. However, the widespread adoption of EVs faces multifaceted challenges, particularly concerning infrastructure development. This paper investigates the intersection of sustainability, decarbonization, and EV adoption, with a focus on identifying and analyzing the challenges associated with infrastructure deployment. Strictly adhering to the methodological principles and process of systematic literature reviews, this paper analyzes research spanning the fields of engineering, energy, computer science, environmental science, social sciences, and others to elucidate the barriers hindering EV adoption, ranging from technological limitations to regulatory complexities and market dynamics. Furthermore, it examines the critical role of infrastructure, encompassing charging networks, grid integration, and supportive policies, in facilitating EV uptake and maximizing environmental benefits. The findings are finally used to present the implications for theory, practice, and policies and to highlight the avenues for future research.

A libraries reproducibility hackathon: connecting students to University research and testing the longevity of published code

Reproducibility is a basis of scientific integrity, yet it remains a significant challenge across disciplines in computational science. This reproducibility crisis is now being met with an Open Science movement, which has risen to prominence within the scientific community and academic libraries especially. To address the need for reproducible computational research and promote Open Science within the community, members of the Open Science and Data Collaborations Program at Carnegie Mellon University Libraries organized a single-day hackathon centered around reproducibility. Partnering with a faculty researcher in English and Digital Humanities, this event allowed several students an opportunity to interact with real research outputs, test the reproducibility of data analyses with code, and offer feedback for improvements. With Python code and data shared by the researcher in an open repository, we revealed that students could successfully reproduce most of the data visualizations, but they required completing some manual setup and modifications to address depreciated libraries to successfully rerun the code. During the event, we also investigated the option of using ChatGPT to debug and troubleshoot rerunning this code. By interacting with a ChatGPT API in the code, we found and addressed the same roadblocks and successfully reproduced the same figures as the participating students. Assessing a second option, we also collaborated with the researcher to publish a compute capsule in Code Ocean. This option presented an alternative to manual setup and modifications, an accessible option for more limited devices like tablets, and a simple solution for outside researchers to modify or build on existing research code.

The destruction of Sciences at Massey University and why we need an independent entity that can investigate universities

This article summarises the events that led to the dismantling of the school of Natural and Computational Sciences at Massey University, Albany campus, and the unsuccessful attempts to prevent it by staff, the public and the New Zealand Association of Scientists (NZAS). Although challenging to prove, we argue that the dismantling was unnecessary, senseless and counterproductive. The unsuccessful attempts to save an academically and financially viable school exposed a weakness in the current Education and Training Act and demonstrated why New Zealand (NZ) needs an independent entity that could investigate universities, guard the national interests, ensure accountability of tertiary institutions’ administration, and protect the academic freedom of individual academics.

Centering equity, diversity, and inclusion in youth digital mental health: findings from a research, policy, and practice knowledge exchange workshop

BackgroundYouth mental health service organizations continue to rapidly broaden their use of virtual care and digital mental health interventions as well as leverage artificial intelligence and other technologies to inform care decisions. However, many of these digital services have failed to alleviate persistent mental health disparities among equity-seeking populations and in some instances have exacerbated them. Transdisciplinary and intersectional knowledge exchange is greatly needed to address structural barriers to digital mental health engagement, develop and evaluate interventions with historically underserved communities, and ultimately promote more accessible, useful, and equitable care.MethodsTo that end, the Digital, Inclusive, Virtual, and Equitable Research Training in Mental Health Platform (DIVERT), the Maritime Strategy for Patient Oriented Research (SPOR) SUPPORT (Support for People and Patient-Oriented Research and Trials) Unit and IWK Mental Health Program invited researchers, policymakers, interprofessional mental health practitioners, trainees, computer scientists, health system administrators, community leaders and youth advocates to participate in a knowledge exchange workshop. The workshop aimed to (a) highlight local research and innovation in youth-focused digital mental health services; (b) learn more about current policy and practice issues in inclusive digital mental health for youth in Canada, (c) participate in generating action recommendations to address challenges to inclusive, diverse and equitable digital mental health services, and (d) to synthesize cross-sector feedback to inform future training curriculum, policy, strategic planning and to stimulate new lines of patient-oriented research.ResultsEleven challenge themes emerged related to white-colonial normativity, lack of cultural humility, inaccessibility and affordability of participating in the digital world, lack of youth and community involvement, risks of too much digital time in youth's lives, and lack of scientific evidence derived from equity-deserving communities. Nine action recommendations focused on diversifying research and development funding, policy and standards, youth and community led promotion, long-term trust-building and collaboration, and needing to callout and advocate against unsafe digital services and processes.ConclusionKey policy, training and practice implications are discussed.

Bibliometric Analysis Of Studies On The Concept Of Fear Of Missing Out With VOSsviewer

This study aims to map the studies on the concept of Fear of Missing Out. Within the scope of quantitative data in the study, it is aimed to examine the existing literature on the concept of Fear of Missing Out, which has become one of the main problems of our age, using bibliometric analysis. The unit of analysis is based on the bibliometric data of various types of studies scanned in the Web of Science database and published between 2013 and 2023. When we look at the distribution of 648 studies on Fear of Missing Out according to the years of publication, we see that there is the highest concentration in 2023 (148 studies) and 2022 (134 studies), the most cited researchers are Andrew K. Przybylski, Kou Murayama, Cody R. DeHaan and Valerie Gladwell, the type of publication is mostly journal articles (563), the fields of research are psychology (264), business economics (88), computer science (51) and social sciences (30), and there are more publications indexed in SSCI (468), SCI (185) and ESCI (114).

Weighted Asymmetry Index: A New Graph-Theoretic Measure for Network Analysis and Optimization

Graph theory is a crucial branch of mathematics in fields like network analysis, molecular chemistry, and computer science, where it models complex relationships and structures. Many indices are used to capture the specific nuances in these structures. In this paper, we propose a new index, the weighted asymmetry index, a graph-theoretic metric quantifying the asymmetry in a network using the distances of the vertices connected by an edge. This index measures how uneven the distances from each vertex to the rest of the graph are when considering the contribution of each edge. We show how the index can capture the intrinsic asymmetries in diverse networks and is an important tool for applications in network analysis, optimization problems, social networks, chemical graph theory, and modeling complex systems. We first identify its extreme values and describe the corresponding extremal trees. We also give explicit formulas for the weighted asymmetry index for path, star, complete bipartite, complete tripartite, generalized star, and wheel graphs. At the end, we propose some open problems.

Paediatric systemic inflammatory response syndrome (SIRS) and the development of patient-specific therapy: ethical perspectives through experts’ opinions

BackgroundResearch for personalised therapies concerning the Systemic Inflammatory Response Syndrome (SIRS) in children involves the utilisation of OMICS technologies and Artificial Intelligence (AI).MethodsTo identify specific ethical challenges through the perspective of healthcare professionals, we conducted 10 semi-structured interviews. The development of interview questions for the interviews was preceded by a systematic review of the scientific literature. To address the complexities of paediatric emergency research, informed consent, and data processing, experts with expertise in paediatric intensive care, computer science, and medical law were sought. After the transcription and anonymisation, the analysis followed established guidelines for qualitative content and thematic analysis.ResultsInterviewees highlighted the intricacies of managing consent in personalised SIRS research due to the large amount and complexity of information necessary for autonomous decision-making. Thus, instruments aimed at enhancing the understanding of legal guardians and to empowering the child were appreciated and the need for specific guidelines and establishing standards was expressed. Medical risks were estimated to be low, but the challenges of securing anonymisation and data protection were expected. It was emphasised that risks and benefits cannot be anticipated at this stage. Social justice issues were identified because of possible biases within the research population. Our findings were analysed using current ethical and legal frameworks for research with a focus on the particularities of the patient group and the emergency background. In this particular context, experts advocated for an enabling approach pertaining to AI in combination with OMICS technologies.ConclusionAs with every new technological development, ethical and legal challenges cannot be foreseen for SIRS-personalised treatment. Given this circumstance, experts emphasised the importance of extending the ethics-legal discourse beyond mere restrictions. The organisation of supervision should be reconsidered and not limited only to the precautionary principle, which per se was seen as impeding both the medical progress and clinical flexibility. It was noted that the establishment and monitoring of guidelines were emergent and should evolve through an interdisciplinary discourse. Therefore, it was recommended to enhance the qualifications of physicians in the field of computer science, impart ethics training to AI developers, and involve experts with expertise in medical law and data protection.

Development and application of digital technology in craniomaxillofacial surgery

The treatment of craniomaxillofacial (CMF) defects and deformities is highly challenging due to the complex anatomical structures, significant individual variations and high demands for personalized reconstruction. In recent decades, the integration of cutting-edge technologies from fields such as computer science, mechanical engineering, and material science into medicine has greatly facilitated the transformation of CMF surgery from traditional, experience-based approaches to digitalized and intelligent methods. This review thoroughly analyzes the development and application areas of digital technology in CMF surgery including virtual surgical planning and facial prediction, three-dimensional printing, computer-assisted navigation and robotic surgery, virtual and mixed reality, as well as big data and artificial intelligence technologies. The aim is to offer comprehensive and in-depth reference information for the clinical practice in digital CMF surgery.

A Comprehensive Study of Datasets for Research Works in IoT Networks

Internet of Things is a trending subject in the field of Computer Science and Information Technology. When a research scholar tries to find a problem statement in the arena of IoT, he or she probably Google the phrase “Security Issues in Internet of Things”. On this day August 22, 2024, when we searched the Google with this phrase, we got about 48,40,00,000 results in 0.46 seconds. When we searched Google Scholar with the same phrase, we got about 40,70,000 results (0.52 sec). This shows that lakhs of researchers are toiling to find solutions for Security Issues in IoT. There is a new and trending way of research that is finding solutions for issues in IoT using Machine Learning and Deep Learning Models. ML and DL algorithms need large sets of Data to train and test the algorithms. This made us to take up a study on available Datasets for this research topic. When we made review of literature, in this arena of study, we found that researchers are working on only a few datasets, though numerous datasets are available freely on the internet for academic purposes. Hence in this paper we made a comprehensive study of Datasets available for research works in Internet of Things.

Public attitudes towards social media field experiments.

The growing use of social media field experiments demands a rethink of current research ethics in computational social science and psychological research. Here, we provide an exploratory empirical account of key user concerns and outline a number of critical discussions that need to take place to protect participants and help researchers to make use of the novel opportunities of digital data collection and field studies. Our primary contention is that we need to elicit public perceptions to devise more up-to-date guidelines for review boards whilst also allowing and encouraging researchers to arrive at more ethical individual study design choices themselves. To ground our discussion in real-world examples of online experiments, we focus on recent social media studies in the field of misinformation, polarization, and hate speech research. We conclude by discussing how we can better strike a balance between meeting ethical guidelines and the concerns of social media users alongside maximizing scientific impact and credibility.

Evolutionary biology as a frontier for research on misinformation.

The field of misinformation studies has experienced a boom of scholarship in recent years. Buoyed by the emergence of information operations surrounding the 2016 election and the rise of so-called "fake news," researchers hailing from fields ranging from philosophy to computer science have taken up the challenge of detecting, analyzing, and theorizing false and misleading information online. In an attempt to understand the spread of misinformation online, researchers have adapted concepts from different disciplines. Concepts from epidemiology, for example, have opened doors to thinking about spread, contagion, and resistance. The life sciences offer concepts and theories to further extend what we know about how misinformation adapts; by viewing information as an organism within a complex ecosystem, we can better understand why some narratives succeed and others fail. Collaborations between misinformation researchers and life scientists to develop responsible adaptations of fitness models can bolster misinformation research.

Reviewer Acknowledgements for Computer and Information Science, Vol. 17, No. 2

Reviewer Acknowledgements for Computer and Information Science, Vol. 17, No. 2, 2024

Deep Learning and Human-machine Integration in Smart Prosthetic Control: Process and Perspectives

With the rapid development of science and technology and the maturity of deep learning technology, intelligent prosthetic limb control has made significant progress in human-machine integration. While traditional prosthetic technology has seen improvements in form and function, it still faces challenges in user experience and physiological adaptability. Deep learning, with its powerful pattern recognition and decision-making capabilities, offers new possibilities to address these issues. This paper reviews the current status and future development of the application of deep learning in intelligent prosthetic limb control, emphasizing its close relationship with the concept of human-machine integration. Intelligent prosthetic control involves not only mechanical engineering and bioengineering, but also interdisciplinary integration of the fields of neural engineering, electrical engineering, motion control and computer science. This paper reviews the limitations in traditional prosthetic technology, such as accuracy and user adaptability, introduces the successful cases and latest research results of deep learning in the medical field, and explores the potential of deep reinforcement learning in optimizing complex movements and adapting to different environments. Finally, the future direction of deep learning technology in smart prosthetic limb control is envisioned, and research challenges and recommendations are presented in the hope of providing theoretical support for the innovative development of smart prosthetic limb technology and improving the quality of life of people with disabilities.

Dirichlet Integral and Its Generalization by Different Methods

Dirichlet integral, as the integration of sine function over x from positive infinity to 0, or negative infinity, is known as one of the most important concepts in mathematical analysis. In probability and statistics, Dirichlet integral is used in calculating expectation and variance. In physics, Dirichlet integral is used in describing the movement of a charged particle in specific electromagnetic fields, and the wave function of particles in quantum physics. In computer science, Dirichlet integral is used in the optimization of neural network. In economics and management, Dirichlet integral is used in the optimization of production and distribution, the estimation of the risk and return in investment. In this paper, Dirichlet Integral and its generalization are studied. This paper provides several methods including residue theorem and Feynman’s trick to determine the value of Dirichlet integral. Meanwhile, this paper extends Dirichlet integral to the case of n -th power and deduces a general solution by gamma function for the integral.

Exploring Twitter discourse with BERTopic: topic modeling of tweets related to the major German parties during the 2021 German federal election

AbstractWe present a study in the context of computational social science that explores the topics debated in the context of the 2021 German Federal Election by using the topic modeling technique BERTopic. The corpus consists of German language tweets posted by political party accounts of the major German parties, as well as tweets by the general public mentioning the party accounts. We examined the textual content of the tweets but also included the text in images that were posted into the analysis by extracting the text using optical character recognition (OCR). Our results show that the most frequently discussed topics are party-oriented policies (including call-to-action content), climate policy and financial policy, with these topics being discussed in tweets by both, the political party accounts and tweets by accounts mentioning them. In addition, we observed that some topics were discussed consistently throughout the year, such as the COVID-19 pandemic, climate policy or digitization, while other topics, such as the return to power of the Taliban in Afghanistan or Israel were debated to a greater extent at limited time frames during the election year.

Political Communication Study: A Review

Political communication is one of the most fundamental and crucial concepts in politics and communication studies. This article systematically reviews existing relevant literature to the definition of political communication and theoretical and empirical study of political communication. It hopes to provide some guidance for this field, with the aim of promoting the development of political communication study. This study finds that digital technology is quietly and powerfully shaping a new political communication landscape and the political communication study is showing an increasingly strong trend of interdisciplinary intersection and integration, with computational and experimental social science methods continuing to empower and become new research directions.

String diagrams for Strictification and Coherence

Whereas string diagrams for strict monoidal categories are well understood, and have found application in several fields of Computer Science, graphical formalisms for non-strict monoidal categories are far less studied. In this paper, we provide a presentation by generators and relations of string diagrams for non-strict monoidal categories, and show how this construction can handle applications in domains such as digital circuits and programming languages. We prove the correctness of our construction, which yields a novel proof of Mac Lane's strictness theorem. This in turn leads to an elementary graphical proof of Mac Lane's coherence theorem, and in particular allows for the inductive construction of the canonical isomorphisms in a monoidal category.