Computer Science Research Research Articles

Enhancing missing persons search strategies through technological touchpoints

ABSTRACT The search for the missing persons is a top priority for police, and technology is playing an increasingly important role in increasing the accuracy and rigour of these investigations. This study aims to increase understanding of police decision-making in the missing persons search process and to examine the desired point and level of technology participation from the user's perspective. Three empirical studies and workshops were conducted with police, UX researchers, and computer science researchers. During the workshops, participants developed diagrams to represent police work methods and difficulties and discussed how to identify optimal touch points for technology integration. The findings revealed that the uncertainty of information is the biggest challenge in missing persons search, and that closed-circuit television (CCTV) is a valuable tool. A comparative experiment was conducted to compare the CCTV utilisation investigation strategies of police experts and novices. Findings showed that experts are more efficient and effective in using CCTV to search for missing persons. Experts used their experiential knowledge to interpret information and judge the reliability of information more accurately than novices. Additionally, novices used strategies through rapid repetition of trial and error, while experts made decisions based on experience and intuition, but pursued decision-making based on explicit information. The study concludes by discussing technological approaches for information identification, analysis, and labour replacement in missing person investigations. Such approaches could help overcome the challenge of uncertainty and improve the overall efficiency and effectiveness of these investigations.

An Enhanced Computational Approach Using Multi-kernel Positive Unlabeled Learning for Predicting Drug-target Interactions.

In recent years, analyzing complex biological networks to predict future links in such networks has attracted the attention of many medical and computer science researchers. The discovery of new drugs is one of the application cases for predicting future connections in biological networks. The operation of drug-target interactions prediction (DTIP) can be considered a fundamental step in identifying potential interactions between drug and target to identify new drugs. The previous studies reveal that predictions are made based on known interactions using computational methods to solve the cost problem and avoid blind study of all interactions. But, there seem to be challenges such as the lack of confirmed negative samples and the low accuracy in some computational methods. Thus, we have proposed an efficient and hybrid approach called MKPUL-BLM to manage some of the aforementioned challenges for predicting drug-target interactions. The MKPUL-BLM combins multi-kernel and positive unlabeled learning (PUL) approaches. Our method uses more information to increase accuracy, in addition to minimizing small similarities using network information. Also, potential negative samples are produced using a PUL approach because of lacking negative laboratory samples. Finally, labels are expanded via a semi-supervised. Our method improved to 0.98 and 0.94 in the old interactions set for the ROCAUC and AUPR criteria, respectively. Also, this method enhanced ROCAUC and AUPR criteria by 0.89 and 0.77 for the new interactions set. The MKPUL-BLM can be considered an efficient alternative to achieve more reliable predictions in the field of DTIP.

Empowering computer science scholarship: the role of Computer Science and Engineering Research

In the ever-evolving landscape of computer science and engineering, staying abreast of the latest advancements is imperative for both researchers and practitioners. As technology continues to permeate every facet of our lives, the need for a dedicated platform to disseminate cutting-edge research becomes increasingly crucial. Enter Computer Science and Engineering Research (CSER), a premier international peer-reviewed journal committed to advancing the frontiers of knowledge in the field. At CSER, we recognize the dynamic nature of computer science and engineering, encompassing a wide array of topics ranging from cyber-physical systems to computational neuroscience. Our mission is clear, to serve as a catalyst for innovation by providing a platform for researchers, practitioners, and industry professionals to share their latest findings, insights, and methodologies. With a diverse scope that includes but is not limited to cyber-physical systems, artificial intelligence, blockchain technologies, and augmented reality, CSER offers a comprehensive perspective on the multifaceted realm of computer science and engineering. By covering such a broad spectrum of topics, we aim to foster interdisciplinary collaboration and drive forward the collective knowledge base of the field.

No recognised ethical standards, no broad consent: navigating the quandary in computational social science research

Recital 33 GDPR has often been interpreted as referring to ‘broad consent’. This version of informed consent was intended to allow data subjects to provide their consent for certain areas of research, or parts of research projects, conditional to the research being in line with ‘recognised ethical standards’. In this article, we argue that broad consent is applicable in the emerging field of Computational Social Science (CSS), which lies at the intersection of data science and social science. However, the lack of recognised ethical standards specific to CSS poses a practical barrier to the use of broad consent in this field and other fields that lack recognised ethical standards. Upon examining existing research ethics standards in social science and data science, we argue that they are insufficient for CSS. We further contend that the fragmentation of European Union (EU) law and research ethics sources makes it challenging to establish universally recognised ethical standards for scientific research. As a result, CSS researchers and other researchers in emerging fields that lack recognised ethical standards are left without sufficient guidance on the use of broad consent as provided for in the GDPR. We conclude that responsible EU bodies should provide additional guidance to facilitate the use of broad consent in CSS research.

Contextualization, Authenticity, and the Problem Description Effect

In recent years, computing education researchers have investigated the impact of problem context on students’ learning and programming performance. This work continues the investigation motivated, in part, by cognitive load theory and educational research in computer science and other disciplines. The results of this study could help inform computing assessment design. If the context and authenticity of a programming problem aid student performance then, instructors’ time in creating appropriately contextualized programming problem descriptions is time well-spent. On the other hand, if the context of a programming problem hinders performance, then instructors should leave it out of programming problems. Recent studies investigating the impact of programming problem context on student success have arrived at different conclusions. Presented here is a series of experiments, conducted over 3 years, investigating the impact of context on novice programmers’ success in algorithmic programming assignments using three contextualized tasks and their generic counterparts. This experiment series also looked into the possibility of “authenticity” as a factor affecting performance. Common sense would suggest that a student would perform better on a problem if they understood or cared about it. Contextualization could provide authenticity and authenticity could provide interest. Research suggests that perceiving a problem as authentic has a positive effect on engagement and learning. Alternatively, if a problem is “just an abstract set of numbers”, it may be harder to make sense of the details and the lack of context could consequently contribute additional cognitive load to an already challenging assignment. The results of this study show that assignment context and problem context authenticity have no effect on the performance of novice programmers. We think, however, that contextualization could be worth investing in to support students’ interest in computing. Additional implications of the results suggest that instructors can assign equivalent versions of the same problem in varied contexts to suit their students’ interests without worrying if the context will hinder performance.

Academic Freedom and International Students, Part 5: The gray areas

Computer science and software engineering students and researchers are busy with their courses, in their labs, working on their studies and research - for the most part not really thinking about the impact of politics on their research. However, there are increasing restrictions on the information researchers are allowed to share with their students, and who researchers are allowed to collaborate with respect to national security and increasing concerns with China. To an increasing extent researchers are made aware at grant time. Research institutions must follow federal rules in the grant and hiring process and are conducting workshops and roundtables on issues of national security. The AIP, The American Institute of Physics provides through their mailing list an informative brief on national science policy titled "FYI: Science Policy News from AIP, This Week." The issue for January 23, 2023, invited those interested to join a National Academies of Science, Engineering, Medicine workshop called a "Meeting of experts," on the topic of "Fundamental Research, Openness, and Protecting the U.S. Technological Advantage: NSDD-189 in the New Global Context."

Interdisciplinary Dynamics in COVID-19 Research: Examining the Role of Computer Science and Collaboration Patterns

In academia, it is rare for an event or issue to foster the extensive participation of multiple disciplines. Research related to COVID-19 has undeniably yielded a wealth of valuable insights and impetus for the progress of interdisciplinary research, encompassing concepts, methodologies, intellectual approaches, theories, frameworks, data integration and analysis, and pertinent considerations. In the academic community, there is a widespread expectation that as science and technology continue to progress, the convergence of medicine with various other fields will gain momentum. Fields like computer science are anticipated to see expanded applications in domains such as medicine, vaccine research, disease diagnosis, and more. This study aims to examine interdisciplinary approaches in health-related research, particularly in the context of COVID-19. The goal is to analyze and comprehend the involvement and collaboration patterns of various disciplines in pandemic research, with a specific emphasis on the role and integration level of computer science. This study analyzed 240,509 COVID-19 related articles published from December 2019 to September 2022 using methods such as chord diagrams, modularity analysis, and eigenvector centrality analysis in Social Networking Analysis (SNA). The findings revealed an emerging trend of integration trend between Humanities & Social Sciences and Natural Sciences. Expectations that computer science would prominently feature in pandemic research during this technology-driven era haven’t materialized. While it maintains links with engineering, it hasn’t formed strong connections with medicine. This indicates a gap between computer science and core medical research in large-scale health crises, where COVID-19 research remains centered on medicine with varying interdisciplinary collaboration, and high-tech disciplines like computer science have not achieved their expected influence in these studies.

Identifying knowledge evolution in computer science from the perspective of academic genealogy

Academic genealogy (AG) provides valuable insights into the transmission of knowledge from mentors to mentees, revealing the evolution of knowledge within the academic community. This study explores the intricate dynamics of knowledge evolution within academic genealogies, utilizing on a dataset comprising 16,852 computer science researchers, 613,277 papers, and 11,988 mentorship relationships. By focusing on small-scale knowledge units, our analysis aims to uncover patterns of knowledge inheritance and mutation across different subfields of computer science and highlights several aspects of knowledge evolution in computer science. Firstly, computer science is characterized by strong mentorship ties, indicating the significance of knowledge transmission within the field. Additionally, there is a mix of foundational and developing areas, suggesting a field that is growing and diversifying rather than declining, as indicated by linear regression outcomes. Secondly, our research reveals a surge in collaborative knowledge exchange in computer science since 2000, with fields such as Computer-Communication Networks and Software Engineering leading in terms of output and impact. Furthermore, areas like Computer Graphics and Artificial Intelligence stand out for their depth and novelty. Thirdly, we categorize researchers into three types: roots, branches, and leaves, reflecting their role in knowledge transmission. Branch researchers tend to innovate, while leaf researchers show a combination of traditional knowledge uptake and new contributions, illustrating the dynamic flow of ideas within the field. Future research endeavors are encouraged to embrace larger datasets and further fortify our understanding of the topic.

Fandom meets artificial intelligence: Rethinking participatory culture as human–community–machine interactions

Fandom as an interpretive community is the frontier for rich insights into the multi-layered interactions and practices between individual users, communities and intelligent machines. To understand the challenges and opportunities Artificial Intelligence (AI) presents to contemporary media and culture, we discuss how fans engage with AI, as fandom provides meaningful insights into interactions between individual technology users, the collective and machines. We propose rethinking participatory culture as human–community–machine interactions to understand the wider implications of AI to society and culture. We outline three emerging phenomena and potential consequences of fans’ engagement with AI: (1) fan labour made (un)easy, (2) parasocial interactions (un)familiarised and (3) realities (un)settled. Our conceptualisation transcends existing computer science research, which assumes technology users as isolated individuals and also fan studies, which largely focus on text-audience relationships and tend to take digital technologies for granted as the preconditions of fans’ creative and playful practices.

Measuring Commonality in Recommendation of Cultural Content to Strengthen Cultural Citizenship

Recommender systems have become the dominant means of curating cultural content, significantly influencing the nature of individual cultural experience. While the majority of academic and industrial research on recommender systems optimizes for personalized user experience, this paradigm does not capture the ways that recommender systems impact cultural experience in the aggregate, across populations of users. Although existing novelty, diversity, and fairness studies probe how recommender systems relate to the broader social role of cultural content, they do not adequately center culture as a core concept and challenge. In this work, we introduce commonality as a new measure of recommender systems that reflects the degree to which recommendations familiarize a given user population with specified categories of cultural content. Our proposed commonality metric responds to a set of arguments developed through an interdisciplinary dialogue between researchers in computer science and the social sciences and humanities. With reference to principles underpinning public service media (PSM) systems in democratic societies, we identify universality of address and content diversity in the service of strengthening cultural citizenship as particularly relevant goals for recommender systems delivering cultural content. We develop commonality as a measure of recommender system alignment with the promotion of a shared cultural experience of, and exposure to, diverse cultural content across a population of users. Moreover, we advocate for the involvement of human editors accountable to a larger value community as a fundamental part of defining categories in the service of cultural citizenship. We empirically compare the performance of recommendation algorithms using commonality with existing utility, diversity, novelty, and fairness metrics using three different domains. Our results demonstrate that commonality captures a property of system behavior complementary to existing metrics and suggests the need for alternative, non-personalized interventions in recommender systems oriented to strengthening cultural citizenship across populations of users. Moreover, commonality demonstrates both consistent results under different editorial policies and robustness to missing labels and users. Alongside existing fairness and diversity metrics, commonality contributes to a growing body of scholarship developing “public good” rationales for digital media and machine learning systems.

Quali-quanti visual methods and political bots

Computational social science research on automated social media accounts, colloquially dubbed “bots”, has tended to rely on binary verification methods to detect bot operations on social media. Typically focused on textual data from Twitter (now rebranded as "X"), these inference-based methods are prone to finding false positives and failing to understand the subtler ways in which bots operate over time, through visual content and in particular contexts. This research brings methodological contributions to such studies, focusing on what it calls “bolsobots” in Brazilian social media. Named after former Brazilian President Jair Bolsonaro, the bolsobots refer to the extensive and skilful usage of partial or fully automated accounts by marketing teams, hackers, activists or campaign supporters. These accounts leverage online political culture to sway public opinion for or against public policies, opposition figures, or Bolsonaro himself. Drawing on empirical case studies, this paper implements quali-quanti visual methods to operationalise specific techniques for interpreting bot-associated image collections and textual content across Instagram, TikTok and Twitter/X. To unveil the modus operandi of bolsobots, we map the networks of users they follow (“following networks”), explore the visual-textual content they post, and observe the strategies they deploy to adapt to platform content moderation. Such analyses tackle methodological challenges inherent in bot studies by employing three key strategies: 1) designing context-sensitive queries and curating datasets with platforms’ interfaces and search engines to mitigate the limitations of bot scoring detectors, 2) engaging qualitatively with data visualisations to understand the vernaculars of bots, and 3) adopting a non-binary analysis framework that contextualises bots within their socio-technical environments. By acknowledging the intricate interplay between bots, user and platform cultures, this paper contributes to method innovation on bot studies and emerging quali-quanti visual methods literature.

Numerical study of two operator splitting localized radial basis function method for Allen–Cahn problem

In this article, we will explore the numerical simulation of the Allen–Cahn equation and provide effective combination methods to efficiently solve it. The Allen–Cahn equation, an equation of mathematical physics, represents a singularly perturbed reaction–diffusion phenomenon that elucidates the phase separation mechanism occurring in multi-component alloy systems. Finding a numerical solution for the Allen–Cahn equation presents a difficult challenge for computational science and engineering researchers. To solve the Allen–Cahn equation, we will use Lie–Trotter’s and Strang’s splitting techniques combined with the radial basis function partition of unity method. We will discretize the problem’s spatial domain using the classical and direct RBF-PU methods. We will also provide suitable theorems for analytical support of the presented numerical methods. We will provide several numerical examples which include examples with exact solutions to check the accuracy and efficiency of the method and examples in the field of phase transition to demonstrate the adaptability, performance, and efficiency of the proposed method.

Data science essentials in business administration: A multidisciplinary perspective

This study presents an overview of data science, exploring the role of data scientists and the methods they employ to investigate analytical problems within business administration. It discusses the responsibilities of data scientists, emphasizing their multidisciplinary nature and the importance of technical skills, domain knowledge, and effective communication. The paper outlines commonly utilized methods by data scientists, drawing from computer science, statistics, and operations research. It illustrates the practical application of these methods in addressing analytical problems, including descriptive analytics, predictive analytics, and prescriptive analytics. The paper also highlights potential pitfalls and challenges encountered in the analytic process, such as problem formulation errors and results interpretation.

The Science of Implicit Race Bias: Evidence from the Implicit Association Test

Abstract Beginning in the mid-1980s, scientific psychology underwent a revolution – the implicit revolution – that led to the development of methods to capture implicit bias: attitudes, stereotypes, and identities that operate without full conscious awareness or conscious control. This essay focuses on a single notable thread of discoveries from the Race Attitude Implicit Association Test (RA-IAT) by providing 1) the historical origins of the research, 2) signature and replicated empirical results for construct validation, 3) further validation from research in sociocognitive development, neuroscience, and computer science, 4) new validation from robust association between regional levels of race bias and socially significant outcomes, and 5) evidence for both short- and long-term attitude change. As such, the essay provides the first comprehensive repository of research on implicit race bias using the RA-IAT. Together, the evidence lays bare the hollowness of current-day actions to rectify disadvantage experienced by Black Americans at individual, institutional, and societal levels.

“More than Words”: A Legal Approach to the Risks of Commercial Chatbots Powered by Generative Artificial Intelligence

Abstract The recent commercial release of a new generation of chatbot systems, particularly those leveraging Transformer-based large language models (LLMs) such as ChatGPT, has caught the world by surprise and sparked debate about their potential consequences for society. While concerns about the existential threat posed by these technologies are often discussed, it is crucial to shift our focus towards the more immediate risks associated with their deployment. Such risks are further compounded by the lack of proactive measures addressing users’ literacy and the for-profit model via which these chatbots are distributed. Drawing on research in computer science and other fields, this paper looks at the immediate risks triggered by these products and reflects on the role of law within a broader policy directed at steering generative artificial intelligence technology towards the common good. It also reviews the relevant amendments proposed by the European Parliament to the European Commission’s proposal for an AI Act.

Entangled collaborations: tensions in cross-disciplinary user experience studies in cyberinfrastructure projects

ABSTRACT Cyberinfrastructure research develops and deploys solutions that benefit cyberinfrastructure and the broader scientific community. Designing novel cyberinfrastructure solutions is inherently complex and requires collaborative relations between heterogeneous scientific stakeholders, governing bodies, and organisations. This study investigated the problems and pitfalls experienced in the cross-disciplinary collaboration of three groups: computer scientists (Group 1), User Experience (UX) researchers (Group 2), and domain scientists (Group 3 – bioinformatics, and health informatics researchers) who worked together to build cyberinfrastructure applications. Using participatory action research (PAR), we studied the dynamics of conducting UX research. The main results indicate four tensions that impacted the collaborative practices of cross-disciplinary groups using UX studies in cyberinfrastructure projects: (1) contradictory views on the quality of prototype development, (2) mental models of the work processes (know how) varied among the different groups (3) clarity of feedback was lacking, and (4) the usability problem was perceived to be with the users. Our results highlight the significance of aligning UX and computer science research goals and actively engaging involved cyberinfrastructure research members to meet user expectations. Findings reveal the nuanced ways in which computer science and UX work processes become entangled during the research process and shape cyberinfrastructure development.

FACIAL EXPRESSION RECOGNITION

Facial Expression Recognition is a system which provides an interface for computer and human interaction. With the advancement of technology and need of the hour such systems have earned the interest of researchers in psychology, medicine, computer science and similar other fields as its applications are identified in such fields. Facial expression recognizer is an application which uses live data coming from the camera or even the existing videos to capture the expressions of the person in the video and is represented on the screen in the form of attractive emojis. Expressions form the basis of human communication and interaction. Expressions are used as a crucial tool to study the behaviour in the medicine and psychological fields to understand the state of mind of the people. The main objective to develop such a system was to be able to classify facial expressions using CNN algorithm which is responsible for the expression detection and then providing a corresponding emoticon relevant to detected facial expression as the output.

Optimizing Cybersecurity Attack Detection in Computer Networks: A Comparative Analysis of Bio-Inspired Optimization Algorithms Using the CSE-CIC-IDS 2018 Dataset

In computer network security, the escalating use of computer networks and the corresponding increase in cyberattacks have propelled Intrusion Detection Systems (IDSs) to the forefront of research in computer science. IDSs are a crucial security technology that diligently monitor network traffic and host activities to identify unauthorized or malicious behavior. This study develops highly accurate models for detecting a diverse range of cyberattacks using the fewest possible features, achieved via a meticulous selection of features. We chose 5, 9, and 10 features, respectively, using the Artificial Bee Colony (ABC), Flower Pollination Algorithm (FPA), and Ant Colony Optimization (ACO) feature-selection techniques. We successfully constructed different models with a remarkable detection accuracy of over 98.8% (approximately 99.0%) with Ant Colony Optimization (ACO), an accuracy of 98.7% with the Flower Pollination Algorithm (FPA), and an accuracy of 98.6% with the Artificial Bee Colony (ABC). Another achievement of this study is the minimum model building time achieved in intrusion detection, which was equal to 1 s using the Flower Pollination Algorithm (FPA), 2 s using the Artificial Bee Colony (ABC), and 3 s using Ant Colony Optimization (ACO). Our research leverages the comprehensive and up-to-date CSE-CIC-IDS2018 dataset and uses the preprocessing Discretize technique to discretize data. Furthermore, our research provides valuable recommendations to network administrators, aiding them in selecting appropriate machine learning algorithms tailored to specific requirements.

Enhancing Research Quality: Emphasizing the Scientific Method, Design Guidelines, and Evidence-Based Assessment

Research quality forms the backbone of research across the disciplines of Science, Technology, Engineering, and Mathematics (STEM), providing a systematic approach to inquiry, experimentation, and discovery. In the fields of Information Technology (IT) and Computer Science, the application of research quality is particularly crucial for ensuring the rigor, validity, and reproducibility of research findings. This paper explores the importance of research quality in STEM research, with a focus on IT and Computer Science, where the rapid pace of technological advancement demands precise and reliable methodologies. The study examines how research quality facilitates hypothesis testing, data analysis, and theory development, enabling researchers to address complex problems, innovate responsibly, and contribute to the advancement of knowledge. By highlighting key examples from IT and Computer Science research, the paper underscores the essential role of the research quality in driving progress, ensuring ethical standards, and fostering interdisciplinary collaboration in the pursuit of scientific excellence

Advancements in Learning-Based Techniques for Automated Floor Plan Analysis on Raster Images: A Comparative Study

Automated floor plan analysis and recognition have long been focal points in computer science research. Recently, there has been a notable increase in the use of learning-based techniques to automatically reorganize floor plans from raster images. This advancement aims to extract valuable insights from architectural drawings, which are essential for understanding building layouts and their intended functions. These drawings often feature a variety of notations and constraints, and the lack of standardized notation leads to significant variability in both style and semantics across different floor plans. Addressing this challenge is a key focus of this review. This paper provides an extensive literature survey to tackle the issue of variability in floor plans. The review concentrates on methodologies that treat floor plans as raster images, with particular attention to learning-based approaches. By offering concise summaries of datasets, research scopes, and specific tasks, this review aims to guide future research and development in the fields of construction and design. The in-depth examination of automatic floor plan analysis and recognition methods presented here contributes to the evolving field of computer-assisted architectural understanding and design.