Computer and Systems Architecture Commons^™

Cognitive Resilience At The Edge: Hyperdimensional Computing Versus Deep Learning For Hardware-Degraded Rf Classification, Adrian B. Cisneros, Jeong Yang

Military Cyber Affairs

Autonomous Collaborative Combat Aircraft (CCA) operating in contested electromagnetic environments must classify Radio Frequency (RF) signals on edge silicon that degrades over the mission lifetime due to thermal stress, radiation, and manufacturing variation. Deep neural networks dominate RF classification on pristine hardware, but their weights are precise and interdependent, causing catastrophic accuracy collapse as the underlying chip ages. We investigate whether Hyperdimensional Computing (HDC), a brain-inspired paradigm that distributes information across thousands of dimensions, can provide a reliability floor where Deep Learning fails. Using the RadioML 2016.10A dataset filtered to five digital modulations relevant to drone command-and-control links, we trained …

Breaking The Build: Detecting Software Supply Chain Vulnerabilities In Ci/Cd Pipelines, Mercedes R. Wahl, Dr. Benjamin Yankson

Military Cyber Affairs

This study examines whether integrating structured DevSec- Ops security controls into CI/CD pipelines can reduce software supply chain risk by preventing vulnerable components from progressing through the software development lifecycle. Software supply chain attacks frequently originate from weaknesses or compromises within dependencies, build environments, and trusted development stages, making early detection essential. A controlled sandbox experiment compared two pipeline configurations: a baseline CI/CD pipeline with no automated security enforcement and a secure DevSecOps pipeline integrating automated vulnerability scanning, SBOM generation, and artifact integrity verification. A known vulnerable dependency, the Python requests package (version 2.19.0) associated with CVE-2018-18074, was intentionally introduced …

Mitigating Common Vulnerabilities And Exposures In Cobol-Based Critical Systems Using The Strangler-Fig Pattern, Lauren E. Caruso, Vincent J. Compeau, Assefaw H. Gebremedhin

Military Cyber Affairs

COBOL-based legacy systems continue to underpin critical infrastructure in banking and government sectors despite their age and associated cybersecurity risks. Originally developed through a Department of Defense–sponsored initiative to standardize business computing, COBOL remains widely used in mission-critical environments. However, these systems face increasing vulnerabilities due to outdated security architectures, workforce shortages, and rising maintenance costs. This paper examines cybersecurity and operational challenges associated with COBOL systems and evaluates the Strangler Fig pattern as a modernization strategy that enables incremental replacement while maintaining continuity. The findings highlight implications for financial institutions and public-sector organizations dependent on legacy infrastructure.

From Framework To Toolchain: Implementing Zero Trust Architecture In Cloud-Native Environments For Dow Compliance, Shelby C. Snyder

Military Cyber Affairs

Federal agencies face a fiscal year 2027 target for enterprise-wide Zero Trust deployment, but NIST SP 800-207A defines logical components without identifying the Kubernetes technologies that implement them. This paper proposes a three-tier mapping of the Policy Engine, Policy Administrator, and Policy Enforcement Point to service mesh, microsegmentation, and perimeter tooling, stating the criteria by which each component is classified. It then applies a defined rubric to six Zero Trust vendors across component alignment, Kubernetes capability, federal authorization posture, and evidence quality, finding that no single vendor covers all three tiers. The mapping is a testable architectural proposition; a Stage …

Hybrid Deep (Cnn-Bilstm) Intrusion Detection For Defense And Mission-Critical Networks, Corey A Cheng, Jermaine Anim-Addo, Asma Jakir Hussain, Zion O Smith-Fox, Sanjay Goel, Yuksel Celik

Military Cyber Affairs

This article examines how hybrid deep learning can strengthen intrusion detection for military and defense networks. Using the CSE-CIC-IDS2018 dataset, the study evaluates a CNN-BiLSTM model designed to detect benign traffic and multiple attack categories, including DDoS, DoS, botnet, brute-force, web attack, and infiltration activity. The model achieved strong multi-class detection performance, with 0.9893 accuracy and 0.9979 ROC-AUC. The findings suggest that AI-supported intrusion detection can improve cyber defense operations, analyst triage, and protection of mission-critical networks.

Are Large Language Models Safe? A Vulnerability Analysis Of Generated Source Code, James Richards-Perhatch, Mitchell Milander, James M. Halvorsen, Assefaw Gebremedhin

Military Cyber Affairs

The increasing complexity of software and demands for rapid deployment have pushed the software industry to rely more on large language models (LLMs) in developing source code. However, as this technology is still relatively recent, questions can arise about the safety of the generated code. This paper presents an analysis of seven LLMs with respect to the presence of vulnerabilities within source code. Our findings show that LLMs are more likely to produce vulnerable web applications than vulnerable C programs, and that vulnerabilities are more likely to occur when program size and complexity increases.

Semantic Shields: Automating Critical Infrastructure Defense Via Nlp-Driven Ransomware Profiling, Henry Trowbridge, Ian Zalcberg, Ryan Schley, Carter Yagemann, Natasha Phan, Srikar Maduposu, Vimal Buck

Military Cyber Affairs

Ransomware poses a growing threat to critical infrastructure, where successful attacks can disrupt operational technology (OT) and industrial control systems (ICS) with significant public safety consequences. However, attributing ransomware incidents to specific threat actors remains challenging due to ransomware-as-a-service ecosystems, actor rebranding, and the obfuscation of traditional indicators of compromise. This paper presents Semantic Shields, an NLP-driven attribution framework that leverages BERT-generated semantic embeddings and DBSCAN clustering to profile ransomware actors through the linguistic characteristics of ransom notes. Using a dataset of 295 ransom notes from 189 distinct threat groups, the framework achieved an 87.2% true positive clustering rate and …

Llm-Generated Countermeasures For Iot Cyberattacks, James Alger, Michael Tu

Military Cyber Affairs

The rapid expansion of the Internet of Things (IoT) has introduced significant cybersecurity challenges, particularly for resource-constrained devices that traditional intrusion detection systems often fail to protect effectively. This paper proposes a novel, two-phase autonomous security pipeline designed to bridge the gap between probabilistic threat detection and deterministic network enforcement. The framework first utilizes a custom Time Series Transformer (TST) to classify multivariate network traffic and identify specific attack vectors, such as ransomware, SQL injections, and malicious file uploads. In the second phase, an agentic AI layer, comprising a locally hosted Large Language Model (LLM) orchestrated via LangGraph, processes the …

Foreward, Todd Arnold

Military Cyber Affairs

No abstract provided.

Letter From The Director: Mastery In Practice, Joseph Schafer

Military Cyber Affairs

No abstract provided.

Developing A Natural Language Interface For Knowledge Graphs, Ruth Assefa, Sarah Mendoza, Luke Voinov, Oyku Serap Ogut, Nurcan Yuruk

SMU Journal of Undergraduate Research

This paper proposes to solve the challenge of making databases more user-friendly by interfacing them with OpenAI's ChatGPT-3.5 model. We implemented this solution to assist researchers in easily finding others with similar research interests. Our study involves 184 researchers from 14 departments at Southern Methodist University (SMU). We collected researchers' areas of expertise and biographies and stored them in a Neo4j graph database. We used OpenAI's embedding models to create vector representations of the collected data, allowing for accurate similarity assessments via Neo4j's built-in algorithms. By integrating this system with LangChain, we enabled natural language queries. The results demonstrated high …

En-Feat: An Effective Feature Selection Method Using Ensemble Approach, Sasank Nath, Dhruba Kumar Bhattacharyya

Mansoura Engineering Journal

Feature selection is a crucial step in machine learning and data preprocessing, significantly influencing model performance and interpretability. This paper presents a comprehensive study and contributions in the domain of feature selection by integrating traditional learning techniques with ensemble-based, proposing an effective approach. We propose a Mutual Information-based feature aggregation approach applied to union sets of features, aiming to derive an optimal subset of features that maximizes accuracy. Then, we employ an ensemble method that utilizes forward selection over union sets to identify the optimal feature subsets through sequential feature selection. Our ensemble-based feature selection method called En-feat, is evaluated …

Using Ali-4 Z-Implication In Controller Design, Shamil Azer Ahmadov

Chemical Technology, Control and Management

One of the widely used theories in the information processing is Professor Zadeh's fuzzy logic theory. Fuzzy implications form the basis of this theory. When processing information using fuzzy implication, the chosen judgment method and the type of implication affect the result. Referring to the review of the relevant literature on fuzzy implications, it can be noted that there are still unresolved problems and issues. For example, fuzzy implications cannot be used in processing imperfect information or information based on probabilistic and fuzzy uncertainty. Existing fuzzy implications face limitations in practical applications. Fuzzy implications only take into account inaccuracy, but …

Reducing Systemic Bias In Behavioral Targeting Using Explainable Ai: The Harmonia Complex Systems Approach, Ruchira Deokar, Preethi Nanjundan, Jossy P. George, Carlos Gershenson

Northeast Journal of Complex Systems (NEJCS)

Behavioral targeting is a key part of the modern advertising web's algorithmic engine. However, it is unclear whether optimization processes worsen bias, promote unchecked spread in filter bubbles or lower overall users' trust levels. This paper introduces HARMONIA (Holistic Adaptive Regulatory Model for Optimizing Non-transparent Intelligent Advertising), a comprehensive, data-driven Explainable Artificial Intelligence (XAI) framework aimed at transforming behavioral targeting via transparency, interpretability, and adaptive ethical regulation. This paper conducted a comprehensive Explorative Data Analysis (EDA) on the public Criteo Display Advertising Dataset, which contains over 45 million records, to identify patterns in high-dimensional user-ad interaction space. This analysis uncovered …

Toward Transparent Bureaucracy: Nlp-Based Document Classification And Power Dynamics In The Srikandi System, Zulfatun Sofiyani, Suprayitno Suprayitno, Faisal Fahmi, Mega Putri Mahadewi

Proceedings from the Document Academy

As the Indonesian government advances digital document management through the SRIKANDI system, challenges persist regarding fragmented and subjective classification practices. This study proposes the integration of Natural Language Processing (NLP)-based classification within SRIKANDI to enhance consistency, transparency, and accountability in document management. Framed by an interdisciplinary theoretical foundation, the study synthesizes Michael Buckland’s document theory, viewing documents as dynamic social evidence, with Michel Foucault’s theory of power, highlighting classification as an exercise of institutional authority, and NLP methodologies that enable automated, content-driven categorization. The study positions documents as both technological artifacts and political constructs, whose classification practices simultaneously structure meaning …

Implementation Of Zero Trust Architecture On Local Server Management In Educational Institutions, Joko Purwanto, Safar Dwi Kurniawan

Journal of Strategic and Global Studies

Educational institutions are facing increasingly complex cyber threats, particularly as they continue to rely on on-premises servers with limited cybersecurity resources. Zero Trust Architecture (ZTA) offers a security model that rejects implicit trust and requires strict verification of each access request. This study aims to examine the applicability of ZTA in managing local servers within educational institutions through a qualitative literature review approach. Relevant literature from 2014 to 2025 was analyzed using thematic synthesis to identify recurring concepts, strategies, and gaps. The results show that ZTA, when integrated with Identity and Access Management (IAM), Security Information and Event Management (SIEM), …

Pengujian High Fidelity Prototipe Media Pembelajaran Interaktif Bahasa Inggris Dengan Gamifikasi Menggunakan User Experience Questionare, Adhitya Prastyadi, Meizano Muhammad Ardhi, Daniel Rinaldi, Margaretha Karolina Sagala

Jurnal Sosial Humaniora Terapan

Abstract

Testing a Hi-Fi Prototype of Interactive English Learning Media with Gamification Using UEQ

This study aims to test and evaluate the User Experience (UX) quality of a High-Fidelity prototype for interactive English learning media integrating gamification elements. The research is motivated by the low English proficiency and motivation among Indonesian students, and the potential of digital media and gamification to enhance learning engagement. Inspired by the success of the Duolingo application, this prototype was designed to create an enjoyable and effective learning experience. A quantitative research methodology was employed, involving high school students as participants selected via purposive sampling. …

Too Warm To Win Big? Unpacking The Backer Dynamics Behind Female Crowdfunding Success Using A Warmth And Competence Perspective, Dan Liu

Journal of International Technology and Information Management

While crowdfunding is often heralded as a democratized funding avenue that empowers women with higher success rates, this study reveals a more nuanced picture of gender dynamics. The Stereotype Content Model suggests that women are often perceived as warmer but less competent. Using a large dataset from Kickstarter, we find that female-led projects can attract more backers, likely due to warmth-driven appeal, but receive smaller average contributions, potentially due to concerns about risk linked to lower perceived competence. However, the total funding raised by female-led campaigns is comparable to that of male-led ones, showing no clear advantage or disadvantage. This …

Online Community Dynamics: An Analysis Using Louvain During Major Sporting Events, Anushka Jaint, Yashodhan Karulkar, Kashish Jindal, Sri Sai Harshita Gadavarthi, Sanya Gulati

Journal of International Technology and Information Management

With the power of social media transforming the way people connect and interact with each other, the dynamics of community formation on platforms such as X during major events are of crucial importance. While social media is an increasingly key driver in determining interactions, little is known about the online influence forming and developing fan communities in high-stakes events. This study looks into the development of user communities for datasets drawn from Kaggle on two of the world’s largest sporting events: the FIFA World Cup 2022, or football, and the T20 World Cup 2022, or cricket, with the aim of …

Blockchain As A Digital Coordination Infrastructure For Project Management: A Systematic Review And Integrative Framework, Cherie Bakker Noteboom, Sai Neelima Seru, Aravindh Sekar

Journal of International Technology and Information Management

Blockchain technology has gained increasing attention as a digital infrastructure capable of improving transparency, trust, and coordination in complex, multi-organizational project environments. However, existing research on blockchain-enabled project management remains fragmented and industry-focused, providing limited guidance for organizational adoption and integration. This study addresses this gap through a systematic literature review of 29 peer-reviewed studies, following PRISMA guidelines, to examine how blockchain capabilities are incorporated into project management practices across industries and maturity stages.

Grounded in Resource-Based View and Coordination Theory, the analysis employs a feature-to-process mapping approach to link six core blockchain capabilities—decentralization, transparency, immutability, smart contracts, traceability, and …

The Value Of Personal Data Ecosystems: A Flemish Media Sector Case Study, Maarten De Mildt, Melanie Verstraete, Sofie Verbrugge, Didier Colle

Journal of International Technology and Information Management

Personal Data Stores (PDSs) have been proposed as a privacy-preserving approach to data sharing that increases individual control over personal data while enabling new forms of cross-organizational collaboration. This collaboration leads to the emergence of Personal Data Ecosystems (PDEs). Despite growing interest in PDEs, limited research has examined how the organizational and economic barriers identified in prior studies manifest in practice. This paper investigates these challenges through a case study of the Flemish media sector within the Solid4Media project, which explores the use of PDSs to support data sharing and personalization across media organizations. Using a qualitative research design, data …

Responsible People Analytics For Remote-Work Decisions: A Machine-Learning Benchmark For Classifying Perceived Productivity, Ruth Menjivar, Nima Molavi, Narges Mashhadi Nejad

Journal of International Technology and Information Management

This study examines whether employee-perception survey data can support responsible people-analytics decisions about remote-work productivity. Using the public New South Wales (NSW) Remote Working Survey 2021 (N=1,512), the study benchmarks statistical and machine-learning classifiers for self-reported perceived productivity classes (same, less, or more productive when working remotely relative to onsite work), not objective output, under default, class-weighted, and resampling protocols. Main evidence comes from 5×5 repeated stratified cross-validation using macro F1 and balanced accuracy with fixed model specifications. Class-balanced separability is modest. Random Forest, CatBoost, and LightGBM form a leading cluster with overlapping confidence intervals (macro F1 ≈0.51–0.52). Affective/well-being items, …

Towards Developing A Career Technology Fit Framework And Analyzing Its Influence On Work-Related Outcomes Among It Professionals, Gunjan Tomer

Journal of International Technology and Information Management

With growing attrition rate and significant demand for skilled IT professionals, the importance of studying their behaviour has become important for both academia and industry. Despite ample amount of research, there is still a gap between theory and practice. Based on our qualitative study conducted on Indian IT professionals we propose that technology allocation might contribute in understanding the behaviour of IT professionals. We found that IT professionals evaluate the technology allocated to them based on their individual career motives. This evaluation, either positive or negative, influences their job outcomes. Further, we explored the factors that make a technology preferable …

High Technology And The Developing State: The Arrival Of Supercomputers In India, Ramesh Subramanian

Journal of International Technology and Information Management

While India has made vast strides in information technology in the last few decades, its success is mainly attributed to its software, rather than its hardware sector. In fact, India’s attempts at developing computer hardware that can match international standards have largely been unsuccessful. A notable exception is its development of a series of supercomputers that match and exceed many international standards. This paper looks at an interesting period in India’s computing history – namely the 1980s and 1990s – focusing on its development of an indigenous supercomputer. During that period, supercomputers were thought to be the sole privy of …

The Crowdfunding Paradox In Crisis: Rising Funder Demand Vs. Declining Entrepreneur Supply, Dan Liu, Guangzhi Shang, Cynthia Fan Yang

Journal of International Technology and Information Management

This study investigates how the crowdfunding marketplace responds to major crises, focusing on behavioral shifts among funders and entrepreneurs. Results show a dual impact on platform dynamics. On the demand side, funders become more engaged, with notable increases in the number of backers, average contributions, and total pledge amounts. This heightened activity suggests stronger altruistic motivations, as individuals view crowdfunding as a way to support others during difficult times. On the supply side, however, entrepreneurs act more cautiously, leading to a decline in new project launches. This drop likely reflects increased risk aversion and uncertainty as creators navigate volatile conditions. …

Dual-Interface Wifi Packet Sniffer System Using Esp32-Cam With Real-Time Pcap Generation For Iot Network Analysis, Boy Setiawan Boy, Maghfiroh Maulani, Zico Pratama Putra, Muhammad Senoyodha Brennaf

Makara Journal of Technology

This study focuses on designing and implementing a cost-effective and energy-efficient WiFi packet sniffer system using the ESP32. The ESP32-CAM module, which combines WiFi, Bluetooth, and microSD support, is used to capture IEEE 802.11 frames in real-time via promiscuous mode. Packets are stored in packet capture format, which is compatible with tools such as Wireshark and Scapy. Developed using the official ESP-IDF, it offers low-level control and high performance. Two user interfaces were implemented: a UART-based text menu and a web-based HTTPS menu hosted on the ESP32 itself. Functional and performance evaluations were conducted with a focus on capturing broadcast …

The Emergence Of Ai Chatbots In Education, Trek Martin

Journal of Graduate Education Research

The recent advent of popular AI applications in educational contexts has sparked renewed interest in the question of AI and guided learning platforms as teaching tools. What are the possibilities for learning? In the attempt to answer that question, limitations of the field must be brought to full attention, as well an understanding of whether or not those limitations will continue into the immediate future; this research examines the technical evolution of artificial intelligence in education, from early symbolic reasoning systems to modern machine-learning-based chatbots. It then examines that evolution in terms of the key challenges it faced throughout, and …

Exploring Arabic Large Language Models: A Comprehensive Review, Lamar Aljahdali, Joud Kaki

Effat Undergraduate Research Journal

This paper presents a comprehensive review of Arabic large language models (LLMs), exploring their capabilities, limitations, and potential impact on the Arabic NLP landscape. We analyze the performance of prominent LLMs, including JAIS, AraBERT, and BLOOM, highlighting their strengths and weaknesses on various NLP tasks. The review delves into critical challenges faced by Arabic LLMs, such as domain adaptation, cross-lingual capabilities, and ethical considerations. Additionally, the paper emphasizes the importance of responsible development and deployment practices for LLMs, ensuring fairness, transparency, and cultural sensitivity.

Intelligent Control Of Reducing Energy Consumption And Water Loss In Smart Home Systems, Hakimjon Nasriddinovich Zaynidinov, Damira Farxodovna Hodjaeva, Dhananjay Singh

Technical science and innovation

The article presents an innovative approach to developing an intelligent control system for managing temperature and water level in smart home systems. The proposed method integrates an adaptive PID controller with fuzzy logic algorithms, enabling dynamic adjustment of the PID controller coefficients in real time. The mathematical model of the system incorporates heat balance equations, differential heat transfer relations, and nonlinear models of fluid loss. The adaptive control algorithms developed within the study allow the system to effectively respond to changes in input data.

A comprehensive analysis of the membership functions is conducted, along with adaptive tuning of the PID …

Syntax-Enhanced Boundary-Aware Named Entity Recognition Model, Chuanming Yu, Bin Deng, Zhengang Zhang

Journal of Scientific Information Research

[Purpose/significance] This study addresses the issue of inadequate perception of entity boundaries in traditional character-level modeling-based named entity recognition models by integrating syntax information containing entity boundary features into the task using a multi-head graph attention network with dense connections. This integration enhances the effectiveness of named entity recognition.

[Method/process] This study proposes a Syntax-enhanced Boundary-aware Named Entity Recognition Model (SynBNER), which utilizes BERT for text semantic representation and integrates syntax information using a dense-connected graph attention network. This integration incorporates implicit entity boundary information from syntax information into word representations, thereby enhancing the model's entity boundary perception capability.

[Result/conclusion] …