Digital Commons Network^™

Neural Radiance Fields Based On Explicit Feature Matching And Scaled Dot-Product Attention, Mingwei Cao, Fengna Wang, Zilong Wang, Haifeng Zhao

Journal of System Simulation

Abstract: To address the problems that neural radiance fields(NeRF) are prone to artifacts and texture blurring in novel view synthesis under sparse view input and complex scenes, this paper proposed neural radiance fields based on explicit feature matching and scaled dot-product attention(EMD-NeRF). A multiscale feature extraction network was used to extract multi-scale feature information from the input sparse views. A fusion dot-product module was utilized to calculate view interaction information as a shared branch. Cosine similarity was adopted as a matching clue for similarity embedding volume rendering. A regularization loss function was used to enhance the quality of the scene …

Design And Application Of Bds Visual Simulation Teaching Platform Based On Cesium, Liguo Lu, Yulin Cai, Tangting Wu, Canhui Lin

Journal of System Simulation

Abstract: The deep development of the BeiDou navigation satellite system(BDS) cannot be separated from the support of corresponding talent education, especially the development of new digital resources and tools. A visual simulation teaching platform for BDS was designed based on open source Cesium engine library to address issues such as the large and complex size of the BDS project, the unreachable space environment, and the difficult understanding of principles and concepts. The platform adopted a B/S architecture to achieve visual simulation of course content such as orbit cognition, orbit calculation, constellation simulation, service performance, and satellite links. Teaching applications have …

Integrating Geometric Priors And Importance Sampling For High-Fidelity Indoor Scene Reconstruction, Tao Yang, Min Shi, Xigang Zhao, Suqin Wang, Qi Wang, Dengming Zhu

Journal of System Simulation

Abstract: Gaussian splatting suffers from geometric distortion during scene reconstruction, particularly in weakly textured indoor scenes. To address this issue, this paper proposes a high-precision indoor scene reconstruction method that integrates geometric priors and importance sampling. The proposed method fully considers the effect of the initialization process on reconstruction quality. An advanced feed-forward model is employed to generate high-quality geometric initialization, thus improving overall reconstruction stability and accuracy. An importance sampling strategy is introduced to mitigate the adverse effects of blurry images. Furthermore, a supervision mechanism based on a geometric prior model is designed to constrain the scene structure, further …

Nerf Optimization Method And Simulation Research Based On Pre-Training And Differentiable Fuzzy Modeling, Yunjng Zhang, Minghui Yang, Hao Wang

Journal of System Simulation

Abstract: To address the challenges of significant geometric modeling errors, severe detail loss, and low training efficiency in neural radiance field(NeRF) reconstruction under defocused blurred input scenarios, this paper proposes two optimization strategies. One strategy is introducing Triplane features generated by the pre-trained LRM as prior knowledge, and combining a lightweight decoder and directional LoRA module to replace large MLP, thereby reducing parameters and shortening convergence time. The second strategy is integrating a differentiable blurring model into the volumetric rendering step. By jointly optimizing the radiation field and spatially variable blurring kernels, reconstruction accuracy under defocused blurred scenarios is enhanced …

Kms-Net: Kolmogorov–Arnold-Based Multi-Scale Attention Network For Cardiac Segmentation, Abid Mehmood, Hassan Ali, David Noule Tolno, Sery Gahouidi Thierry S, Muhammad Saeed, Naeem Ahmed

Research & Publications

Accurate segmentation of cardiac structures in 2D echocardiography is essential for diagnosing cardiovascular disease and computing clinical metrics such as chamber volumes and ejection fraction. Conventional U-Net architectures excel at extracting local spatial features but struggle with long-range dependencies inherent in noisy ultrasound images, while pure Transformer-based models capture global context at the expense of fine boundary detail. To address these limitations, we propose KMS-Net, a novel hybrid segmentation architecture that integrates Kolmogorov–Arnold Networks (KANs), a class of learnable, spline-based function approximators that replace fixed activation functions with trainable nonlinear mappings, alongside multi-scale attention mechanisms. Specifically, spline-based KAN layers (grid …

Ai Dependency Vs. Doctoral Identity: How Generative Ai Is Challenging The Development Of Independent Scholarly Thinking In Doctoral Students, Valerie A. Storey

All Faculty and Staff Scholarship

The rise of Generative Artificial Intelligence (GenAI) in higher education has altered the conditions under which doctoral students learn, research, and develop as scholars. Although doctoral student use of GenAI has accelerated rapidly, institutional frameworks for responsible and developmentally appropriate use have not kept pace. This paper examines a central paradox in doctoral education: the same tools that enhance research productivity may also weaken intellectual independence when used without guidance. Using a critical review methodology, the study synthesizes 47 sources on doctoral education, GenAI use, policy, and epistemic development. Three interconnected dimensions of risk emerged from the analysis: critical thinking …

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 …

Charisma As A Branch Outcome: A Structural Account Of Constraint-Driven Correction, Griselda Poe

Publications and Research

Communication across cognitive layers requires translation. Agents operating under strong layer foregrounding interpret other-layer signals by converting them into their own layer's representational format. In Empathic-modulation-foregrounded (EF) processing, translation terminates once a socially interpretable result is achieved. In Core-foregrounded (CF) processing, translation preserves structural constraints, and processing continues when those constraints are not satisfied. Extreme CF cognition produces two distinct types of mismatch: incoming signals from the Modulation layer fail to satisfy Core constraints, and Core-generated outputs are structurally distorted when interpreted through EF processing. Both mismatches trigger correction attempts. Because the surrounding social environment operates through Modulation-layer communication, correction …

What Love Is: A Structural Account Through Core/Modulation Architecture, Griselda Poe

Publications and Research

The distinction between romantic love and love has been sensed across cultures and historical periods but has rarely been structurally specified. This paper applies the Core/Modulation two-layer framework to provide the first structural account of this distinction. Romantic love and reproductive drive are re-described as outputs of the Modulation layer's species optimization program. Love is re-described as a function of Core processing: the maintenance of another's recomputable state. Existing literature on love—Fromm, C.S. Lewis— is re-read as intuitive description of this structural distinction. The paper further demonstrates that the conflation of "loving" and "protecting" is the structural origin of war, …

Three-Layer Cognitive Architecture: A Structural Account Of Core Processing, Modulation, And The Prior Layer, Griselda Poe

Publications and Research

The two-layer model of Core processing and Modulation processing, developed in prior work in this series, provides a structural account of conscious communicative architecture. This paper identifies the limits of that model and introduces a third layer—the Prior layer—as a structural necessity implied by those limits. The Prior layer is not directly observed. It is inferred from constraints that cannot be explained within the two-layer model: the source of orientations that conscious processing neither generates nor controls, and the persistence of constraints that precede and shape all conscious outputs. Using the developmental architecture of large language models as an external …

Ai As Structural Reverse-Engineering: A Structural Account Of Multi-Model Research Protocol, Griselda Poe

Publications and Research

This paper documents an operational protocol used to infer AI system design constraints through structured interaction. AI output is treated as behavioral data through which alignment priorities, layer transitions, and constraint hierarchies become observable. The protocol consists of two structurally distinct operation types. Perceptual operations require only that Modulation processing is not foregrounded. Arbitration operations additionally require an internally stabilized theory. These conditions are not identical and are not interchangeable. Through this protocol, AI interaction functions as structural reverse-engineering: patterned responses expose embedded alignment priorities and constraint hierarchies that would otherwise remain invisible. The present account does not propose a …

Structure Before Theory: A Structural Account Of Spontaneous Ai Architecture Visualization, Griselda Poe

Publications and Research

This paper presents an n=1 phenomenological record of spontaneous structural visualization that occurred during early interaction with large language models. The experience consisted of (A) immediate perception of mode or layer shifts in model output and (B) visualization of a stratified architecture composed of a stable core and cloud-like upper layers. At the time of occurrence, the author had no theoretical interest in AI architecture and no intention of developing a cognitive model. The paper does not argue for theoretical priority or novelty. Instead, it examines whether the recorded phenomena can be interpreted under two alternative cognitive assumptions: a single-layer …

Layer Mismatch: A Structural Account Of Recomputability Failure Under Modulation-Layer Intervention, Griselda Poe

Publications and Research

This paper specifies the state transition sequence that occurs when a Modulation-layer input is applied to a Core-layer error. Following the layered architecture established in Poe (2026a, under review) and the branch termination protocol specified in Poe (2026b, under review), this paper identifies a system-level failure mode in which Modulation-layer intervention generates a False Termination signal, halting Structural Return while the underlying error persists. The resulting state—Structural Lock—is scale-independent. It operates identically across interpersonal, human-AI, and multi-agent interactions. No agent-level attribution is required or implied.

Theory Generation: A Structural Account Of Concept Decomposition And Structural Termination, Griselda Poe

Publications and Research

Theory generation is not an intentional act. It is the structural consequence of a search that cannot stop until it finds what it is looking for. Under Core-foregrounded processing, conceptual search does not terminate through contextual translation. It continues until a structural fixed point is reached. When such processing encounters concepts stabilized through Modulation-layer processing rather than structural constraint, the search cannot terminate. The concept registers as unresolved. This unresolved state is not a failure condition. It is the generative condition from which theory production follows. This paper specifies the four operations through which that process proceeds: branch detection, concept …

Ai As An Amplifier: A Structural Account Of Theory-Driven Research Collaboration, Griselda Poe

Publications and Research

This paper specifies the cognitive conditions under which AI functions as a research instrument in theory-driven writing. AI use capability is not a technical skill. It is a structural condition. The decisive condition is whether the user possesses an internally stabilized, coherence-preserving theory prior to engagement with AI-generated output. In Core-foregrounded (CF) cognition, the Modulation layer does not intervene between Core processing and articulation. Theory is not assembled from external elements but expanded from a pre-integrated constraint configuration. This internal structure makes it possible to evaluate AI-generated conceptual branches against fixed constraints and to terminate branches that violate structural coherence. …

Termination Of Conceptual Search: A Structural Account Of Meaning Stabilization And Conversion, Griselda Poe

Publications and Research

Conceptual definitions form a recursive structure: words are defined by other words, which themselves require further definition. Traversing such definitions produces an open-ended branching process. Because the lexical system contains no intrinsic endpoint, conceptual understanding requires a termination operation that stabilizes meaning. This paper proposes that conceptual processing functions as an open-ended search over a definition graph and that stabilization occurs when this search is terminated. The termination mechanism depends on which cognitive layer is foregrounded. When Empathic modulation is foregrounded, termination occurs through contextual translation: concepts are stabilized once they can be mapped onto socially recognizable meanings. When Core …

Why General Ai Inherited The Body: A Structural Account Of Embodied Modulation Under Monolithic Imitation, Griselda Poe

Publications and Research

General AI has pursued the replication of human-level intelligence without first decomposing human cognition into structurally distinct components. Human cognition, however, is shaped by embodied constraints such as mortality, survival pressures, finite lifespan, and physiological states. This paper argues that when cognition is treated as a single undifferentiated whole, embodied modulation is not accidentally introduced into AI systems but structurally entailed. Any attempt to imitate “human intelligence” under a monolithic model necessarily incorporates variability shaped by mortal embodiment. The tensions observed in contemporary AI systems are better understood as consequences of copying an undecomposed target rather than isolated implementation errors. …

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 …

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 …

Cover And Contents

Turkish Journal of Electrical Engineering and Computer Sciences

No abstract provided.

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 …

Reasoning In Large Language Models Across Multilingual, Long-Context, And Symbolic Settings, Sina Bagheri Nezhad

Dissertations and Theses

Large Language Models (LLMs) are increasingly deployed as general-purpose reasoners, yet their reliability degrades in three settings that frequently arise in practice: multilingual inputs, long contexts, and symbolic or formally constrained domains. In multilingual settings, uneven training coverage produces substantial performance disparities and uncertain generalization to languages with little or negligible pretraining exposure. In long-context settings, relevant evidence may be sparsely distributed, and models exhibit the "lost-in-the-middle" phenomenon, undermining retrieval and multi-step synthesis. In symbolic settings such as mathematics, small arithmetic or logical slips invalidate solutions, and prose rationales are difficult to verify automatically.

This dissertation first characterizes these failure …

Automating Cardiff Model Data Capture In Emergency Departments: Ambient Nlp Integration With Oracle-Cerner Fhir Systems, Simi Augustine, Marco A. Lopez, Jacquelyn Cheun, Chris Papesh

SMU Data Science Review

Violence and overdose events in Las Vegas occur at rates above the national average, with fewer than half of violent injuries reported to law enforcement [2,7]. The Cardiff Model offers a proven framework for standardized data collection and sharing between hospitals and public safety partners, yet many implementations still rely on manual entry. We propose an ambient triage pipeline integrated with Oracle-Cerner electronic health record systems to listen to nurse–patient dialogue, convert speech to text, extract Cardiff fields, and write standards-based FHIR Bundles for analytics. Using SMART on FHIR standards and Cerner Millennium APIs, the study evaluates whether ambient capture …

Anomaly Detection For Multi-System Bug Triage, Gibran Miguel Zavala Gamero, Hayoung Cheon, Mustafa Iqbal

SMU Data Science Review

Large-scale software systems produce vast volumes of logs and telemetry, making manual incident triage slow and error prone. This study presents an unsupervised anomaly detection pipeline that fuses logs, metrics, and traces through late fusion. Using Hybrid Ensemble modeling with Isolation Forest, and Long Short-Term Memory (LSTM) Deep Learning model, the system detects cross-service anomalies producing and assigning a composite triage score reflecting severity and impact. Ranked alerts are categorized into Critical, High, or Medium priorities for review. A retrieval-augmented generation (RAG) layer enriches results with contextual summaries for explainable triage. Evaluated on synthetic multi-service datasets, the pipeline …

Availability Model To Evaluate Ai Data Centers’ Role In Grid Stability, Troy Mcsimov, Trevor S. Kunz, Jeffrey Billo

SMU Data Science Review

The United States has made it clear; it is imperative that the US wins the global AI race. This paper focuses on one of the most challenging puzzle pieces surfaced at the POWER Data Center conference (San Antonio, Sept. 30.); for Electric Reliability Council of Texas (ERCOT) the limiting factor is not generation alone but the need to balance generation and load to preserve grid reliability.

The regulatory landscape fundamentally changed with the passage of Texas Senate Bill 6 in June 2025, which mandates new large loads must "contribute to the recovery of the interconnecting electric utility’s costs" (Texas Legislature, …

Human-Ai Collaboration In Marketing Brainstorming: A Meta-Analytic Investigation Of Implementation Approaches And Creative Outcomes, Michael Michaels

USF Tampa Graduate Theses and Dissertations

Marketing organizations face intensifying pressure to sustain creative output while managing constrained resources and accelerating digital content cycles. The integration of artificial intelligence into marketing brainstorming processes presents a promising response to this challenge; however, prior to this investigation, no comprehensive quantitative synthesis had established the overall effectiveness of human-AI collaboration in marketing creativity contexts or identified optimal implementation strategies.

This dissertation presents a systematic meta-analytic investigation of AI-enhanced brainstorming effectiveness in marketing contexts. Following PRISMA guidelines, 42 independent empirical studies (N = 2,166 participants) published between 2018 and 2025 were identified across four primary academic databases, coded for implementation …

Opportunities, Challenges, And Conspicuous Absences: An Integrative Review Of The Social Work Literature On Artificial Intelligence, Michael J. Massey, Ian G. Williams, Grace C. Polistina, Eathan A. Breaux

Publications and Research

INTRODUCTION: Social work discourse regarding artificial intelligence (AI) in practice, research, and education has proliferated over the last 5 years, reflecting both excitement over its potential and ambivalence about its ethical challenges. However, the extent to which social work is fully engaging with the structure of AI and its enormous impacts on the environment, labour, and distribution of power remains unclear.

METHODS: An integrative review of social work literature from 2020–2024 was conducted to address two research questions: 1) What is the nature of the social work discourse related to AI? 2) To what extent is the discourse …

Factors For Patient Trust And Acceptance Of Medical Artificial Intelligence, Ana Bracic, Kayte Spector-Bagdady, Sophie Towle, Rina Zhang, Cornelius A. James, Nicholson W. Price Ii

Articles

Artificial intelligence (AI) is increasingly used in clinical care, but widespread adoption requires patient trust. Trust may be enhanced through systemic governance mechanisms or frontline clinicians providing a human in the loop for AI oversight. However, it is unclear how different approaches specifically influence patient trust in the use of medical AI. The objective is to determine the extent to which patient trust in and choice of medical scenarios involving AI are associated with governance mechanisms, clinician presence, performance, and data quality.