Computer Sciences Commons^™

Escaping The Cyberstorm: A Gamified Social Engineering Training Program, Noah Mcclanahan, Fadi Abu-Amara, Ali Khattab, Travis Jett, Andre Jackson

Journal of Cybersecurity Education, Research and Practice

In this research work, we explored the effectiveness of gamification in improving cybersecurity awareness and training users on targeted social engineering attacks. Traditional cybersecurity training focuses on lectures and videos. These training methods may not actively engage employees, which reduces their knowledge retention and ability to recognize social engineering attacks. This lack of involvement is a concern, as social engineering continues to be one of the most prevalent attack methods faced by end-users. A gamified training program, Escaping the Cyberstorm, was developed using the Godot game engine to address key challenges in spreading cybersecurity awareness. The game includes real-life …

Stop Blaming My Users: Illumination Of The Technocentric Mythos Bias, Ervin H. Frenzel, Richard Lightcap

Journal of Cybersecurity Education, Research and Practice

 Abstract -This conceptual essay addresses the need for systemic and systematic transdisciplinary analytical techniques within cybersecurity and technical security. This conceptual essay is contingent upon recognition that cybersecurity is not simply technical in nature, it does not need an adversary, and more importantly it is based upon systems engineering and systems thinking.  The essay contributes a socio-technical attribution chain and field-specific ontology/taxonomy which distinguish user-triggered events from root causes, latent conditions, technical debt, validation failures, governance failures, and attribution bias before assigning responsibility to end users. It systematically defines an ontology inclusive of developer technical debt, organizational debt arising from …

Artificial Intelligence Mechanisms In The Limit Of Crimes And Law Enforcement, Saad Mefleh Alsuwaileh

Journal of Police and Legal Sciences

This study explores the potential of employing technological mechanisms and modern innovations brought about by the Fourth Industrial Revolution, particularly advancements in the field of information technology, in the domains of criminal investigation, crime prevention, and law enforcement. It aims to analyze the impact of these technologies on crime control efforts and the promotion of justice.

The significance of the study lies in highlighting the power of technology in processing and analyzing massive volumes of data with greater speed and accuracy, thereby enhancing the efficiency of criminal investigations and the ability to predict and prevent crimes. The core research question …

Applying Artificial Intelligence Within Decision Support Systems And Its Role In Improving Proactive Thinking And Reducing Security Threats: The Mediating Role Of Data Quality, Hany Shaaban El Anany

Journal of Police and Legal Sciences

The study aimed to identify the impact of applying artificial intelligence within decision support systems in improving the level of proactive thinking and reducing security threats in government institutions in the Arab Republic of Egypt, as well as to examine the mediating role of data quality in this relationship, at a significance level of (α ≤ 0.05). The study sample consisted of (360) participants working in the departments of information technology, decision support, and cybersecurity within government institutions and national authorities that rely on AI-enhanced decision support systems.

The study adopted the descriptive analytical method and used a questionnaire as …

Match Made In Ml: Developing Compatibility Relationships In Evidential Reasoning Approaches With Machine Learning, Ella Jolie Thomas

Master's Theses

The presented expectation maximization informed evidential reasoning model extends the ability of the evidential reasoning calculus to support decision making by integrating an adaptive model learning capability. Compatibility relationships in Evidential Reasoning models are traditionally built by human domain experts. This process is labor-intensive, especially for large and complex models. Additionally, when new data becomes available, compatibility relationships must be reconstructed. Using machine learning and the expectation maximization algorithm, it is demonstrated that compatibility relationships can be constructed that learn relationships between domain knowledge that is used to make decisions. Using drug development as a domain of application, a traditional …

Accessibility Fairness Practices In Ai Applications For People With Disabilities, Megan Gross

Master's Theses

Advancements in artificial intelligence (AI) improve the lives of people every day with tools like the auto-captioning of videos, improved screen-reader capabilities, and advanced mobility control through speech. However, are all groups of people benefiting from AI or are some being overlooked and left out? Although AI tools made for people with disabilities (PWDs) have improved their lives, AI for the general population generally ignores the experiences of PWDs, making them unable to interact with and benefit from technology. This research evaluates ChatGPT and Gemini in Gmail for usability fairness and analyzes how current regulations and development processes fail to …

Can Machines Testify? Llms And The Boundaries Of Testimonial Epistemology, Michael J. Cummins

Philosophy Summer Fellows

As Large Language Models and AI chatbots become increasingly prevalent, pressing questions are raised about whether beliefs formed through LLM interactions carry the same epistemic weight as beliefs formed through human testimony. How we answer this question depends on whether LLMs can function as testifiers, a role which is typically assumed to require a human or human-like agent. This assumption has gone largely unexamined, yet its consequences are significant: if LLM outputs cannot constitute testimony, then the justificatory tools of testimonial epistemology are unavailable to any beliefs formed through LLM interaction. This paper challenges that assumption. It first argues that …

Entity Labels Are Not Entity Signals: A Framework For Observable Relevance In Document Re-Ranking, Utshab Kumar Ghosh, Shubham Chatterjee

Computer Science Faculty Research & Creative Works

Entity-aware document retrieval uses query-associated entities as ranking signals, assuming that semantically relevant entities are also useful retrieval signals. We show this assumption is insufficient - and explain why. Unlike terms, which are ground-truth observations, entity links are hypotheses produced by an imperfect linker: an entity can be topically central yet provide no discriminative signal if the linker fires indiscriminately across relevant and non-relevant documents. We formalize this as a distinction between Conceptual Entity Relevance (CER) - whether an entity is topically related to a query - and Observable Entity Relevance (OER) - whether its observed presence in a collection …

Stylometric And Formal Patterns In The Scholarly Impact Of Scientific Literature, Joshua Ange, Eric Godat, Rajani Sudan

SMU Journal of Undergraduate Research

Scientific communication is typically tied to promoting public engagement and interest in science, increasing scientific literacy, and playing an essential role in policymaking. The success of public communication of scientific findings is largely associated with secondary characteristics of research (e.g. the style of writing and presentation), rather than the primary content or research quality. But it is unclear to what extent the success of scientific literature intended for working scientists is influenced by those same secondary characteristics. Does the writing style of scientific articles impact their success in academic spheres? In this study, we explore the stylometric and formal characteristics …

Image Fusion Based On Deep Learning With Different Locations And Sizes Of Objects, Baneen Al-Kalabi, Tawfiq Al-Assadi

Journal of Intelligent Informatics, Networking, and Cybersecurity

Multi-focus image fusion combines partially focused images into a single all-in-focus composite. Existing object-based methods assume precise spatial and scale alignment across source images, an assumption that frequently fails in Misaligned Multi-Focus Dataset scenarios due to camera displacement and focal length variation. This paper proposes a novel training-free, object-aware fusion framework to address this limitation through a five-stage pipeline: YOLOv8x detection, SAM2-L segmentation, LoFTR correspondence matching, a novel Scale-Aware Area Resize Algorithm, and GLCM-guided MSB/LSB bit-level fusion. The framework was evaluated on the EDMF benchmark (20 image pairs, synthetically modified to simulate Misaligned Multi-Focus Dataset shifts) and a Misaligned Multi-Focus …

Lossless Medical Image Compression Using Integer Discrete Wavelet Transform With Adaptive Subband Differencing And Context-Adaptive Entropy Coding, Rasha F. Nadhim, Ibrahim Adel Ibrahim, Ashwaq T. Hashim

Journal of Intelligent Informatics, Networking, and Cybersecurity

From transform-domain decorrelation and adaptive entropy coding, we propose a method for efficient lossless compression of medical images in this work. We implement the Integer Discrete Wavelet Transform (IDWT) to decompose the input image into four subbands of LL, LH, HL, HH, encompassing approximation and directional detail elements, in the initial implementation. It also removes spatial redundancy in image information and decomposes image information into a less correlated and more sparsely distributed set of coefficients. To decrease redundancy further, it proposes a subband-dependent differencing scheme, which decorrelates neighbouring wavelet coefficients with directional prediction. So horizontal differencing is done on LH, …

A Blockchain-Integrated Federated Learning Model And Autoencoder-Based Feature Reduction For Improving Iot Intrusion Detection, Tahseen A. Wotaifi

Journal of Intelligent Informatics, Networking, and Cybersecurity

The rapid growth of Internet of Things (IoT) environments has brought forth a wealth of security challenges in detecting network intrusions in diverse and resource-restricted systems. Privacy, scalability, and single point of failure issues plague traditional centralized intrusion detection solutions. To address these challenges, the study proposes a secure and adaptive intrusion detection model using Federated Learning (FL) and Blockchain, augmented with autoencoder-based feature reduction. The ToN-IoT dataset is pre-processed, and then an unsupervised autoencoder is used to build informative low-dimensional feature representations. The processed data is deployed to various clients to mimic a real federated situation. Every client will …

A Dual- Task Hierarchical Graph Attention Network For Protein-Protein Interaction Sites Prediction, Oras A. Hussein, Eman S. Al-Shamery

Journal of Intelligent Informatics, Networking, and Cybersecurity

In computational structural biology, it is still very hard to accurately find protein-protein interaction (PPI) sites and estimate how strong the interaction would be. In this research, we provide an innovative two-stage deep learning framework that combines residue-level graph representation learning with protein-level regression to achieve a thorough modeling of protein interactions. Protein structures first encoded as residue graphs, with nodes that stand for amino acids and edges that show how close they are to each other in space. To find binding residues, a deep residual Graph Attention Network v2 (GATv2) uses multi-head attention, residual connections, and Jumping Knowledge aggregation …

The Climasentar Dataset For Evaluating Cross-Domain Transferability Of Large Language Models In Climate Sentiment Analysis, Sanaa Kaddoura, Mariam Alzaabi, Reem Nassar

All Works

Arabic research on climate sentiment analysis remains limited due to linguistic diversity, limited resources, and the scarcity of annotated datasets. Existing approaches primarily rely on questionnaires or general-purpose sentiment analysis models, which often fail to capture domain-specific sentiment expressions. To address this gap, this paper introduces ClimaSentAR , the first large-scale Arabic climate-focused sentiment dataset comprising 23,957 samples from the X platform. Native Arabic speakers annotate the dataset with a moderate inter-annotator agreement of 0.45. It contains 5510 positive, 8140 negative, and 10,307 neutral instances. To validate the importance and robustness of ClimaSentAR and address the gap in Arabic climate …

Tile-Based Knot Assembly With Celtic!, Divya Bajaj, Ryan Knobel, Juan Manuel Perez, Rene Reyes, Ramiro Santos, Tim Wylie

Computer Science Faculty Publications

In this paper we focus on the intersection of tile assembling systems, edge-matching puzzles, combinatorial games, and knot construction and identity. As a basis, we utilize the game Celtic!, which is a 2-player board game where the goal of the game is to construct knots where one knot uses more of a player’s pieces than the other player over all knots. All pieces must build off an existing knot and a valid knot must be closed. We consider three variations: a 0-player self-assembly variation that deterministically places pieces to form a closed knot of some length, a 1-player puzzle variation …

Cytotoxicity Of Bioactive Peptides From Cowpea (Vigna Unguiculata L. Walp) Against Breast Cancer Cells (Mcf-7), Dian R. Ningsih, Zusfahair Zusfahair, Ely Setiawan, Purwati Purwati, Kasta Gurning, Fitri A. Puspita Sari, Elya Widiawati

Karbala International Journal of Modern Science

Cancer is characterized by dysregulated metabolic signaling pathways, leading to uncontrolled cellular proliferation. Current cancer treatments, which involve surgery, radiotherapy, and chemotherapy, still cause adverse systemic toxicities. Accordingly, one possible effort that can be undertaken is to explore safe anticancer substances from protein sources such as peptides. Bioactive peptides can be isolated by hydrolyzing cowpea (Vigna unguiculata L. Walp) protein with trypsin. The objectives of this study were to isolate and fractionate bioactive peptides from cowpea, to determine the anticancer activity of peptide fractions, to identify active peptide fractions as anticancer agents using Liquid Chromatography High Resolution Mass Spectrometry …

Exogenous Ribosome Incorporation Induces Lineage Transdifferentiation In Intestinal And Liver Cancer Cells, Haoxuan Gu, Nadia Wahyuningsih, Keisuke Yamashita, Shota Inoue, Fuchao Tan, Mani Sharaki, Muhaimin Rifa’I, Kunimasa Ohta

Karbala International Journal of Modern Science

It has previously been shown that exogenous bacterial ribosomes added to somatic cells and various cancer cell lines generate ribosome-induced cell clusters (RICs) capable of transdifferentiating into multiple cellular lineages. However, the scope of ribosome-induced transdifferentiation in cancer cells remains poorly understood. This study aimed to analyze the effects of exogenous ribosome incorporation on human cancer cell lines, specifically the intestinal epithelial cell line Caco-2 and the hepatocellular carcinoma cell line HepG2. Caco-2 and HepG2 cells were cultured with purified bacterial ribosomes in human ES/iPS cell medium after trypsinization, resulting in the formation of ribosome-induced Caco-2 (RICs-Ca) and HepG2 (RICs-He) …

Mathematically Driven Enhancement In Information Security, Suhaib Badran, Asmaa Alqassab

Karbala International Journal of Modern Science

A new mathematically based cryptographic method has been proposed to improve information security; it involves transforming data into a matrix and performing bit-level operations. This decryption then follows the basic mechanism of a deterministic, fully invertible algorithm, with this invertible algorithm having the same principle as the existing ones: the process of decryption is symmetrical, where the process is reversed based on the input of the ciphertext. The plaintext is transformed into a square matrix, which includes matrix rotation, permutation of rows, circular bit shifting, and finally an affine linear transformation followed by an additional Base62-like encoding layer to further …

Tapping Into The Ocean’S Hidden Energy: Feasibility Of A 5 Mw Otec Installation In North Bali, Widodo Setiyo Pranowo, Yani Permanawati, Gisela Malya Asoka Anindita, Agung Kurniawan, Albertus Sulaiman, Johar Setiyadi, Safri Burhanuddin, Ivonne Milichristi Radjawane, Hansan Park, Endro Sigit Kurniawan

Karbala International Journal of Modern Science

Indonesia is an archipelagic country surrounded by water and faces energy challenges due to the low use of renewable energy. Among the potential renewable options, marine energy is a particularly suitable resource given the country’s geographical nature. Based on this discussion, seawater temperature can be used as an alternative ocean thermal en-ergy known as ocean thermal energy conversion (OTEC) by using the difference in sea surface and deep-sea water temperatures. Therefore, this study aimed to examine OTEC installations in North Bali waters using closed-cycle OTEC calculations for a 5 MW system. Following this objective, we examined the water conditions by …

Assessment And Evidence Practices In Cybersecurity Education: A Systematic Review (2015–2025), James K. Mayberry

Journal of Cybersecurity Education, Research and Practice

This study presents a PRISMA-based systematic review of 412 cybersecurity education intervention studies, coding assessment methods, evidence types, claimed outcomes, use of established assessment instruments, and artifact availability. Despite frequent claims of skill development and workforce preparation, 45.4% of studies reported no identifiable assessment. Knowledge tests appeared in 11.4% of studies, while performance assessments appeared in 10.2%. From 2015 to 2025, assessment practices remained dominated by post-only designs or no assessment, with no statistically detectable increase in pre/post-capable designs. Use of established assessment instruments was rare, with 94.2% of assessed studies using ad hoc measures or not identifying an established …

Coordinating Meaning With Ai System Cards: A Thematic Analysis, Jennifer Rene French Cyrek

Doctoral Dissertations and Projects

As the meaning of AI risk remains unsettled across sociotechnical and public discourse, AI system cards are an emergent, yet understudied, genre of technical documentation through which AI technology developers publicly frame new AI system capabilities including risks. This thematic content analysis study examines how AI technology developers coordinate meaning regarding risk and responsible development in stewardship of AI. Guided by a constitutive view of communication and systems theory, second-order cybernetics, and the cybernetic tradition, this study analyzes a purposive corpus of AI system cards collected from 2023-2025 using thematic content analysis and the hierarchy of meaning heuristic from coordinated …

Ai-Powered Knowledge Engines As Research Infrastructure For Systematic Knowledge Discovery, Gary Welz

Publications and Research

This paper proposes knowledge engines as a framework for understanding how intelligent systems — both human and artificial — systematically discover, integrate, and generate knowledge. We argue that history’s greatest scientific minds functioned as knowledge engines, processing information through iterative cycles of ingestion, analysis, synthesis, and communication, guided by curiosity and willingness to challenge established beliefs.

We propose a taxonomy of nine integrated capabilities — ingestion, digestion, analysis, calculation, comparison, connection, association, analogy, and multimodal communication — that any serious knowledge engine must combine systematically. The argument is deliberately integrative: achieving ambitious research goals requires orchestrating all nine capabilities within …

Building Ai-Native Innovation System To Drive Transformation And Innovation In Research Organization And Management Models, Hong Xuehai

Bulletin of Chinese Academy of Sciences (Chinese Version)

Artificial intelligence (AI) is profoundly reshaping research paradigms. This study aims to analyze the intrinsic mechanisms through which AI empowers scientific research and its impact on the organizational management models of research. By summarizing what AI can and cannot do in empowering research, it reveals the current effectiveness and capability boundaries of AI in this domain. Based on the extraction of common core conditions for AI-empowered research and the deconstruction of typical cases of AI-enabled research organizational models, this study analyzes the differences between the organizational management model of AI-empowered research and traditional research organizational models. Furthermore, it proposes three …

Understanding And Foresight On Construction Of Foundation Model Industry Innovation Ecosystem, Liu Liu, Chunhui Jia, Yangfan Han, Qiqi Zhang, Jialin Li, Dayuan Li, Junpu Wang

Bulletin of Chinese Academy of Sciences (Chinese Version)

Foundation models are a key vehicle driving artificial intelligence toward general intelligence, and their industrialization urgently requires support from a systematic and collaborative innovation ecosystem. This study focuses on the construction of the foundation model industry innovation ecosystem. It first reviews the frontier progress and identifies its essence as a complex innovation network featuring the three-dimensional synergy of technological, organizational, and industrial architectures, and then analyzes the architecture along the upstream, midstream, and downstream of the industrial chain: the upstream supports computing power and data, the midstream undertakes algorithmic innovation and platform services, and the downstream realizes multi-scenario value transformation. …

Insights And Implications Of Ai For Science Strategies Of Major Science And Technology Powers, Zhang Zhiqiang, Yawei Shao

Bulletin of Chinese Academy of Sciences (Chinese Version)

Artificial intelligence (AI) is transitioning from a research aid to a scientific discovery agent. The new paradigm of “AI + Science” (AI for Science, AI4S) – the intelligent science paradigm (or the fifth paradigm of science) – characterized by the deep integration of artificial intelligence into the entire process of scientific discovery, is rapidly emerging and becoming a “new agent” for intelligent and autonomous execution of scientific discovery and technological invention as well as a key force in reshaping the human knowledge production system and the global landscape of technological competition. The intervention of AI in the field of knowledge …

New Paradigm Of Forestry And Grassland Research Driven By Artificial Intelligence, Zhang Huaiqing, Jiaojun Zhu, Yang Liu, Tingdong Yang, Tian Gao, Jing Zhang, Xueyan Zhu, Yan Chen, Xian Jiang, Zeyu Cui, Jingwei Tan, Kexin Lei

Bulletin of Chinese Academy of Sciences (Chinese Version)

Addressing the current limitations in forestry and grassland research, particularly in cross-scale system cognition, complex process mechanism representation, and multi-scenario simulation, this study proposes an artificial intelligence-driven paradigm reconstruction framework, to shift the research model from experience-oriented approaches toward data- and intelligence-driven integration. On this basis, the study systematically establishes a multidimensional mapping between artificial intelligence and forestry and grassland research across research objects, processes, and objectives, and clarifies their intrinsic coupling mechanisms and technical pathways. Furthermore, it develops a foundational capability system to support the new paradigm from four key dimensions: data, computing power, models, and applications. By examining …

Open Sharing And Collaborative Governance: Practices And Implications Of National Institutes Of Health’S Digital Transformation, Long Yuntao, Bingzhi Wang, Zheping Xu, Yang Wang

Bulletin of Chinese Academy of Sciences (Chinese Version)

With the rapid development of information technology, research projects and tasks of research institutes are increasing rapidly. Scientific and technological resources are the core and foundation of scientific research work, and scientific research institutes are faced with the needs of processing, management and coordination of a large number of scientific and technological resources. Digital transformation has become an inevitable choice for the high-quality development of scientific research institutes. The U.S. National Institutes of Health (NIH), as the world’s top scientific research institute, has continuously introduced a number of initiatives in the field of digital transformation, such as institutional reform policies, …

Can An Ai System Be Creative? A Critical Perspective From Art And Engineering, Ivan Magrin-Chagnolleau

Presidential Fellows Articles and Research

This paper examines the question of whether artificial intelligence (AI) systems can be creative, approached from the dual perspective of a researcher trained in electrical engineering, pattern recognition, machine learning, and neural networks, who has also spent most of his life engaged in the arts as actor, stage and film director, writer, composer, and visual artist, and in philosophy. Drawing on Margaret Boden’s foundational framework — both her three properties of creativity (novelty, surprise, and value) and her three types of creative processes (combinatorial, exploratory, and transformational) — the paper argues that AI systems are structurally incapable of creativity in …

Reproduction Beyond Benchmarks: Constbert And Colbert-V2 Across Backends And Query Distributions, Utshab Kumar Ghosh, Ashish David, Shubham Chatterjee

Computer Science Faculty Research & Creative Works

Reproducibility must validate architectural robustness, not just numerical accuracy. We evaluate ColBERT-v2 and ConstBERT across five dimensions, finding that while ConstBERT reproduces within 0.05% MRR@10 on MS-MARCO, both models show a drop of 86-97% on long, narrative queries (TREC ToT 2025). Ablations prove this failure is architectural: performance plateaus at 20 words because the MaxSim operator's uniform token weighting cannot distinguish signal from filler noise. Furthermore, undocumented backend parameters create an 8-point gap due to ConstBERT's sparse centroid coverage, and fine-tuning with 3x more data actually degrades performance by up to 29%. We conclude that architectural constraints in multi-vector retrieval …

One-For-All Community Search On Unseen Graphs, Mo Li, Zhaosong Zhao, Linlin Ding, Renata Borovica-Gajic, Zhongming Yao, Jianxin Li

Research outputs 2022 to 2026

Community search is a fundamental graph-based retrieval problem that aims to identify a query-dependent subgraph whose nodes exhibit strong internal connectivity. While recent learning-based methods improve retrieval effectiveness via graph representation learning, they follow a ''one-use-one-train'' paradigm that requires retraining or fine-tuning for each target graph, leading to high data dependency, high training costs, and limited generalization. To handle this, we propose OFA-CS, a ''one-for-all'' community search framework trained once on source datasets and directly deployed to arbitrary unseen graphs without retraining or fine-tuning, while preserving strong performance. Specifically, we introduce a Spectral-Aware Feature Alignment module to unify feature dimensionality …