Computer Engineering Commons^™

Understanding And Defending Large-Scale Networked Systems: From Wi-Fi Localization To Provenance- Based Intrusion Detection, Xiao Han

USF Tampa Graduate Theses and Dissertations

Modern networked systems increasingly depend on wireless sensing and large-scale telemetry to provide critical services and maintain security. However, many of these systems function as black boxes or encounter significant scalability limitations, which leave them vulnerable to sophisticated adversaries. In this dissertation, I present my research on enhancing the security and efficiency of networked systems through measurement-driven vulnerability analysis and the design of scalable defense mechanisms.

First, I present my research on the security and privacy risks of Wi-Fi–based localization services. Although location spoofing attacks against Wi-Fi positioning systems have been studied for over a decade, constructing practical attacks in …

A Data-Driven Framework For Modeling Car-Following Behavior Using Conditional Transfer Entropy And Dynamic Mode Decomposition, Poorendra Ramlall

Student Research Symposium (SRS)

Accurate modeling of car-following behavior is essential for understanding traffic dynamics and enabling predictive control in intelligent transportation systems. This study presents a novel data-driven framework that combines information-theoretic input selection via conditional transfer entropy (CTE) with dynamic mode decomposition with control (DMDc) for identifying and forecasting car-following dynamics. In the first step, CTE is employed to identify the specific vehicles that exert directional influence on a given subject vehicle, thereby systematically determining the relevant control inputs for modeling its behavior. In the second step, DMDc is applied to estimate and predict the dynamics by reconstructing the closed-form expression of …

Enhanced Antenna Selection Techniques For Energy-Efficient Code Index Modulation Aided Spatial Modulated Wireless Communication Systems, Fati̇h Çögen, Burak Ahmet Özden, Erdoğan Aydin

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes an integrated multiple-input multiple-output (MIMO) transceiver framework, termed CIM-HQAM-SM, which combines code index modulation (CIM) and spatial modulation (SM) with energy-efficient hexagonal quadrature amplitude modulation (HQAM). In the proposed bit mapping, the information bits jointly select (i) the active transmit-antenna index, (ii) the Walsh–Hadamard spreading-code indices for the in-phase and quadrature branches, and (iii) an HQAM symbol. Hence, the payload is conveyed through the constellation symbol as well as through antenna and code indices. For the considered Rayleigh-fading scenarios and matched spectral-efficiency settings, the proposed framework offers BER improvements over conventional SM and quadrature SM (QSM), while …

An Ensembled Two-Phase Deep Learning Approach For A Psychiatric Disorder Detection, Prajna Paramita Debata, Midhun Chakkaravarthy, Brojo Kishore Mishra

Turkish Journal of Electrical Engineering and Computer Sciences

Computational Psychiatry represents a burgeoning realm within scientific inquiry, delving into the intricate interplay of neurobiology within the brain. The escalating prevalence of mental illness underscores the urgency to confront this challenge. Among the prevalent disorders, Schizophrenia and Bipolar Disorder loom large, affecting a significant portion of the population at some point in their lives. However, pinpointing psychiatric disorders poses a formidable challenge. Genetic predispositions significantly influence the development of mental illnesses, with intriguing overlaps observed among certain disorders. This convergence complicates accurate diagnosis. Here, a deep learning approach is considered for significant gene biomarker identification and classification of Schizophrenia …

Sentisec: Combining Keyword Heuristics And Sentiment Modeling For Ai-Powered Threat Detection, Ridho Surya Kusuma, Erum Ashraf, Selvakumar Manickam, Shankar Karuppayah

Turkish Journal of Electrical Engineering and Computer Sciences

This work presents SENTISEC, a hybrid LLM-based threat detection framework designed to classify security logs by integrating keyword heuristics, domain-adapted sentiment scoring, and Retrieval-Augmented Generation (RAG). The system achieves an overall accuracy of 93.67%, with 91.46% macro recall, 89.07% macro F1, and 95.15% threat recall, while maintaining a low false-positive rate of 1.68%. Its methodology incorporates strict keyword and IOC matching, a domain-tuned DistilBERT sentiment module, hybrid BM25–MiniLM retrieval enhanced with BGE reranking, adaptive quantile-based threshold calibration, and SHAP-based explainability. Comparative evaluations against keyword-only, sentiment-only, classical machine-learning models, and DistilBERT-only baselines show that SENTISEC consistently improves both true-positive and true-negative …

Optimal Network Reconfiguration Based On Discrete Metaheuristic Techniques For Reduction Of Power Loss And Carbon Emission In Distribution Networks, Asad Ali, Hazlie Mokhlis, Nurulafiqah Nadzirah Mansor, Hussain Shareef, Hasmaini Mohamad, Munir Azam Muhammad

Turkish Journal of Electrical Engineering and Computer Sciences

Power distribution systems play a crucial role in transmitting electrical power from generation sources to end users. During transmission, significant power losses occur in the form of heat as the current flowing along the lines/cables has resistance. To minimize power losses, distribution network reconfiguration (DNR) has been widely adopted. This paper proposes optimal DNR based on metaheuristic techniques with discrete mutation feature targeting active power loss reduction, which subsequently lowers carbon emissions and operational costs. Through the discrete mutation feature, computational time to find optimal solution has been reduced significantly with fewer iterations compared to conventional mutation techniques. The proposed …

Sgsc-Kko-Lstm: A Deeplearning Classifier Model For Smart Grid, Dushmanta Kumar Das, Samaniba Imchen

Turkish Journal of Electrical Engineering and Computer Sciences

Maintaining smart grid stability is crucial for the reliable operation of decentralized electricity networks, especially as the energy sector becomes more complex. The process of ensuring grid stability begins with collecting consumer data and comparing it to power supply requirements. Ultimately, consumers receive a report showing their energy use and pricing details. However, this process is time-consuming and can be improved by leveraging artificial intelligence to predict smart grid stability more efficiently. Specifically, an optimized Long Short-Term Memory (LSTM) network is proposed to predict smart grid stability, addressing the challenges associated with traditional data collection and evaluation methods. Simulations from …

Reducing Complexity In Versatile Video Coding Intra-Coding Through Machine Learning-Based Optimization Of Partitioning And Prediction, Amina Kessentini, Amna Maraoui, Imen Werda, Fatma Ezahra Sayadi

Turkish Journal of Electrical Engineering and Computer Sciences

The escalating demand for high-resolution multimedia content has necessitated more efficient video compression solutions. The Versatile Video Coding (VVC) standard, despite achieving remarkable compression gains, introduces significant computational complexity, primarily due to its exhaustive Rate-Distortion Optimization (RDO) process. To address this, we propose an intelligent approach leveraging supervised machine learning techniques to streamline the VVC encoding process. Specifically, we introduce a Lightweight Neural Network (LNN) for efficient coding unit partitioning decisions and a Decision Tree (DT) classifier for optimizing the intra prediction process. This dual-method framework, tailored for All Intra coding configuration, significantly reduces encoder complexity while maintaining compression performance …

Designing Risk-Aware Mixed-Mode Evacuation Strategies For Tsunamis: Insights From İstanbul, Vedat Bayram, Doruk Ergez, Ada Arikanoğlu

Turkish Journal of Electrical Engineering and Computer Sciences

​Tsunamis pose severe and time-critical risks to densely populated coastal cities, where limited warning times and infrastructure constraints demand carefully coordinated evacuation strategies. This study develops an integrated, risk-aware optimization framework that jointly considers vertical and horizontal sheltering options together with mixed pedestrian-vehicular evacuation dynamics. The proposed mixed-integer second-order cone programming (MISOCP) model simultaneously determines vertical shelter location, evacuee assignment, road-use designation for pedestrians and vehicles, and route selection under congestion, capacity, and budget constraints. Vehicle travel times incorporate congestion effects through a convex flow-dependent function, while pedestrian routing ensures convergent and conflict free evacuation paths. A risk-minimization objective accounts …

Cover And Contents

Turkish Journal of Electrical Engineering and Computer Sciences

No abstract provided.

A Joint Optimization-Based Novel Attack For Genomic Beacon Reconstruction, Kousar Saleem, Si̇nem Sav

Turkish Journal of Electrical Engineering and Computer Sciences

Genomic data sharing has become an essential component of biomedical research, enabling large-scale collaborations and accelerating discoveries in human genetics. To balance the need for accessibility with privacy concerns, several controlled-access mechanisms have been proposed, including genomic beacons. Genomic beacons answer simple presence/absence queries about specific genetic variants. However, prior work has demonstrated that beacons remain vulnerable to genome reconstruction attacks, where an adversary can recover large portions of participants’ genomes using summary statistics. Building on insights from prior reconstruction attacks, we introduce an approach that unifies SNP correlation and allele frequency alignment objectives within a single-stage joint optimization framework. …

Automated Software Size Measurement Using Multilingual Domain-Adapted Language Models, Samet Tenekeci̇, Hüseyi̇n Ünlü, Burak Keçeci̇, Muhammed Efe İnci̇r, Onur Demi̇rörs

Turkish Journal of Electrical Engineering and Computer Sciences

Software Size Measurement (SSM) is crucial for estimating required project effort as well as budget and schedule. However, many small and medium-sized companies struggle to apply objective SSM due to limited resources and lack of expertise. This often leads to inaccurate estimates and project overruns. There is a need for practical, low-resource solutions that support these tasks without requiring expert involvement. Motivated by this challenge, this study proposes an automated software size measurement approach that formulates the measurement task as supervised regression over natural language requirements, using domain-adapted transformer models. We construct large-scale Turkish and English software engineering corpora to …

Time-Robust Evaluation For Multi-Dataset Intrusion Detection Reveals Temporal Shortcuts And Strong Baselines, Kyle A. Mccleary

LSU Master's Theses

Pooled multi-dataset benchmarks are an attractive way to evaluate intrusion detection systems (IDS) across heterogeneous public corpora, but they can quietly reward shortcut features tied to capture schedules and dataset identity. This work introduces TRACER, an auditable benchmark specification that standardizes seven public IDS corpora into a shared transaction-window prediction unit and a shared label ontology, enabling controlled comparisons between compact sequence backbones and strong tabular baselines under matched splits, training budgets, and scoring rules.

Under this protocol, absolute clock time is a strong shortcut under pooled random splits. Enforcing time-robust controls (timestamp rebasing, circular shifts, and schedule-token masking) reduces …

Corrigendum Notice: Motion Fusion: A Robust Ensemble Learning Framework For Accurate Sensor-Based Human Activity Recognition, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF CORRIGENDUM FOR: Almulla, Hussein K.; Mohammed, Hussam J.; Al-Waisy, Alaa S.; Al-Fahdawi, Shumoos; Had, Ahmed Adnan; and AL-Attar, Bourair (2025) ``MotionFusion: A Robust Ensemble Learning Framework for Accurate Sensor-Based Human Activity Recognition,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 3, Article 14.
DOI: https://doi.org/10.52866/2788-7421.1289.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/14.
Reason for Corrigendum: In the published version of the above article, a number of typographical and formatting errors were identified. These corrections do not affect the study design, results, or conclusions, and are provided below to ensure accuracy and consistency. 1) Feature subset sizes …

Corrigendum Notice: Machine Learning Algorithms To Detect Cyber-Attack In The Internet Of Things Platform, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF CORRIGENDUM FOR: Al-anni, Maad Kamal; Almuttairi, Rafah M.; Al-Hamadani, Ammar A.; Zidan, Khamis A.; Alsaadi, Husam Ibrahiem Husain; and Al-Sultany, Ghaidaa A (2025) ``Machine Learning Algorithms to Detect Cyber-Attack in the Internet of Things Platform,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 2, Article 26.
DOI: https://doi.org/10.52866/2788-7421.1268.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss2/26.
Reason for Corrigendum: In the published version of this article, the authors and publisher wish to correct the following items:

1. DOI correction: The DOI printed in the article PDF as ``10.52866/ijcsm.0000'' is incorrect and should be ``10.52866/2788-7421.1268.''
2. Dataset split correction: …

Corrigendum Notice: Finding General Mathematical Formulas For Extraction The Minimal Path Sets Of Complex Parallel-Series Networks, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF CORRIGENDUM FOR: Lafta, Mariem Hassan and Hassan, Zahir Abdul Haddi (2025) ``Finding General Mathematical Formulas for Extraction the Minimal Path Sets of Complex Parallel-Series Networks,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 1, Article 11.
DOI: https://doi.org/10.52866/2788-7421.1237.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss1/11.
Reason for Corrigendum: In the published article, the definition of minimal path set is stated incorrectly. The text defines a minimal path set as ``a set of components whose failure leads to failure of the whole CPSN,'' which corresponds to a minimal cut set, not a minimal path set. Correction: A …

Corrigendum Notice: Federated Learning-Driven Iot And Edge Cloud Networks For Smart Wheelchair Applications, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF CORRIGENDUM FOR: Mohammed, Mazin Abed; Abd Ghani, Mohd Khanapi; Lakhan, Abdullah; AL-Attar, Bourair; and Khaled, Waleed (2025) ``Federated Learning-Driven IoT and Edge Cloud Networks for Smart Wheelchair Applications,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 1, Article 9.
DOI: https://doi.org/10.52866/2788-7421.1241.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss1/9.
Reason for Corrigendum: In the published version of this article, the authors and publisher wish to correct the following items:

1. Table 5 (Task Status entry) — data cell error
In Table 5, the entry for Device ID 3, Iteration 5 contains an incorrect/misaligned value in the Task Status …

Corrigendum Notice: A New Approach For Multiprocessor System-On-Chip Application Scheduling In Hybrid Flow Shop, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF CORRIGENDUM FOR: Khraibet, Tahani Jabbar; Kalaf, Bayda Atiya; and Jasim, Ahmed Abbas (2025) ``A New Approach for Multiprocessor System-On-Chip Application Scheduling in Hybrid Flow Shop,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 3, Article 45.
DOI: https://doi.org/10.52866/2788-7421.1318.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/45.
Reason for Corrigendum: This note corrects (i) author-affiliation metadata, (ii) minor textual duplications/typographical errors, (iii) a naming inconsistency of the proposed algorithm, and (iv) duplicated sentences in the dataset description. These corrections do not change the experimental results or conclusions; they improve clarity and metadata accuracy. 1) Correction to author affiliation metadata …

Retraction Notice: Reliability-Based Design Optimization Using Differential-Algebraic Equations, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: Abed, Saad Abbas; Ghassan, Mona; Latef, Shaimaa Qais; and Hassan, Hind S. (2025) ``Reliability-Based Design Optimization Using Differential-Algebraic Equations,'' Iraqi Journal for Computer Science and Mathematics: vol. 6, Iss. 3, Article 8. DOI: https://doi.org/10.52866/2788-7421.1280.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/8.

Beyond The Sayable: Wittgenstein, Theory Of Mind And Affective Simulation Of Llms, Haomiaomiao Wang, Lili Zhang, Tomás E. Ward

Women+ in Early Career Research Symposium

Wittgenstein’s distinction between what can be said and what can only be shown frames a limit of propositional language. Affect and aesthetic are not primarily matters of correct description but of how expressions function within shared forms of life. Large language models (LLMs), however, increasingly produce fluent language that resembles such understanding by reproducing the patterns through which people ordinarily talk about emotion and perspective.

This paper argues that apparent Theory of Mind (ToM) in LLMs is understood as competence in the publicly shared patterns of mental-state language, rather than as grounded understanding. Using abstract artworks, we show that LLMs …