Artificial Intelligence and Robotics Commons^™

Predicting Student Belonging In Computing Education: A Multimodal Machine Learning Approach Using Eeg And Survey Data, Hannah Moshtaghi

Master's Theses

Measuring students’ sense of belonging, characterized by feelings of acceptance, inclusion, and encouragement from teachers, remains a significant challenge in computing education. Prior research has associated this multidimensional construct with positive academic outcomes and has identified instructors’ growth- and fixed-mindset messaging as a potential influence. However, belonging is a complex and deeply personal experience that is difficult to capture through direct observation alone. Current measurement methods rely on self-report surveys, which may not capture every aspect of an experience that can also involve emotional and cognitive responses.

This thesis investigates whether combining EEG data recorded during a belonging questionnaire with …

The Relativity Of Education: Student Perspectives On Artificial Intelligence In Community College, Ashley Greta Magana

Electronic Theses, Projects, and Dissertations

This hermeneutic phenomenological study examined how diverse community college students experience and make meaning of the integration of generative artificial intelligence (AI) into their educational contexts. Although AI is quickly transforming higher education through automated grading, personalized learning systems, and new models of assessment, the discourse surrounding its implementation remains dominated by administrators, faculty, and institutional stakeholders, while the perspectives of students, specifically community college students who are often historically underrepresented and economically marginalized, are systematically excluded. Most existing research is quantitative and centered on universities, leaving a critical gap in qualitative understanding of the most diverse population in higher …

Enabling Multi-Task Neural Network Inference On Heterogeneous Edge Devices, Redwanul Islam Arif

Master's Theses

Deep neural networks are increasingly required to run on the devices that generate the data. If such a device must perform more than one task, the standard practice is deploying one model per task, which makes memory grow linearly with task count, which is unacceptable when the entire budget is kilobytes. This thesis asks one question in three settings: how much capability can a network acquire without incurring deployment cost?

The first study takes an ImageNet-pretrained ResNet-18, sweeps the branch point across every residual stage and the classification-head depth across one, ten, and twenty layers, and deploys the resulting multi-head …

Three-Dimensional Gaussian Reconstruction Of Large-Scale Scenes Under Multi-View Geometry Constraints, Haohao Cui, Yanqiang Di, Qing Liu, Xianguo Meng

Journal of System Simulation

Abstract: To enhance the geometry reconstruction quality of the GS algorithm in large-scale scene reconstruction, an optimization method constrained by multi-view geometry reconstruction results was proposed. 2D Gaussian planes were used as geometric primitives to overcome depth anisotropy, and dense depth maps generated by DUSt3R and aligned by sparse point clouds were introduced as constraints. By designing a multi-stage optimization strategy that decouples geometry and rendering, the gradient conflict problem in multi-objective training was solved. Experiments on the MatrixCity dataset indicate that the method surpasses comparison methods in related indicators of geometry reconstruction quality and rendering quality in large-scale scenes. …

Caring With Ai: The Efficacy Of A Customised Chatgpt-Delivered Self-Compassion Intervention On College Students' Well-Being And Academic Functioning, Tracy Xi Chen, Chi-Ying Cheng, Andree Hartanto

Research Collection School of Social Sciences

College students face various challenges, including academic pressure, social stress, and the transition into adulthood, which can lead to increased anxiety and other mental health issues. By recognizing personal struggles as part of a shared human experience and responding with kindness, self-compassion serves as a powerful strategy for enhancing resilience, facilitating better well-being and performance outcomes. Although effective, Compassion-Focused Therapy often requires substantial resources and time, limiting its applicability to college students. To overcome these barriers, the current study designed and evaluated Your Self-Compassion Companion, a ChatGPT-powered AI chatbot intervention grounded in self-compassion theory and delivered over three weekly 20-min …

Efficient Test-Time Retrieval Augmented Generation, Hailong Yin, Bin Zhu, Jingjing Chen, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Although Large Language Models (LLMs) demonstrate significant capabilities, their reliance on parametric knowledge often leads to inaccuracies. Retrieval Augmented Generation (RAG) mitigates this by incorporating external knowledge, but these methods may introduce irrelevant retrieved documents, leading to inaccurate responses. While the integration methods filter out incorrect answers from multiple responses, but lack external knowledge like RAG methods, and their high costs require balancing overhead with performance gains. To address these issues, we propose an Efficient Test-Time Retrieval-Augmented Generation Framework named ET2RAG to improve the performance of LLMs while maintaining efficiency. Specifically, ET2RAG is a training-free method, that first retrieves the …

Efficient And Universal Watermarking For Llm-Generated Code Detection, Boquan Li, Zirui Fu, Mengdi Zhang, Peixin Zhang, Jun Sun, Xingmei Wang

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have significantly enhanced the usability of AI-generated code, providing effective assistance to programmers. This advancement also raises ethical and legal concerns, such as academic dishonesty and the generation of malicious code. For accountability, it is imperative to detect whether a piece of code is AI-generated. Watermarking is broadly considered a promising solution and has been successfully applied to identify LLM-generated text. However, existing efforts on code are far from ideal, suffering from limited universality and excessive time and memory consumption. In this work, we propose a plugand- play watermarking approach for AI-generated code detection, named ACW …

Continuous Query For Top-K Maximal Sum Intervals Over Streaming Data, Zhongshuai Zhang, Xiaochun Yang, Baihua Zheng, Rui Zhu, Haomin Li, Bin Wang

Research Collection School Of Computing and Information Systems

The continuous identification of top-k maximal sum intervals using a sliding window over a data stream is a critical operation for applications in IoT and beyond. A maximal sum interval is a non-overlapping, contiguous subsequence with the maximal sum in a sequence of signed values. Existing algorithms are ill-suited for streaming contexts: they either exhaustively enumerate all intervals even for small k values, or depend on indexes that require frequent and costly restructuring. We propose a novel partition-based strategy. Our core insight is a partitioning scheme that guarantees that any maximal sum interval is fully contained within a single partition, …

Dynamic Spectral Denoising With Global-Context Attention For Multi-Behavior Recommendation, Miaomiao Cai, Yunshan Ma, Fangqi Zhu, Junfeng Fang, Zhijie Zhang, Zhiyong Cheng, Xiang Wang, See-Kiong Ng

Research Collection School Of Computing and Information Systems

Multi-behavior recommendation improves target-behavior predic-tion by exploiting heterogeneous auxiliary feedback (e.g., view,collect, and cart), yet its robustness is often undermined by behavior-dependent noise and inconsistency. We argue that the key bottle-neck is not merely noisy behaviors, but a representation-level failurecaused by two coupled heterogeneities. First, intra-behavior rep-resentation entanglement arises when multi-hop propagationblends incidental signals with true preferences in the embeddingspace. This entanglement renders coarse spatial denoising inef-fective, since it cannot suppress noise without sacrificing weak-but-informative niche signals. Second, inter-behavior reliabilityheterogeneity complicates cross-behavior fusion, as the predic-tive value of auxiliary behaviors varies substantially across usersand contexts. Without reliability calibration, aggregation can …

Approximation And Learning-Based Algorithms For Influence Maximization In Multilayer Social Networks, Xueqin Chang, Ruize Liu, Qing Liu, Baihua Zheng, Yunjun Gao

Research Collection School Of Computing and Information Systems

Motivated by the observation that users in the real world often engage across multiple social networks simultaneously, we study the problem of influence maximization in multilayer social networks (Mlim), aiming to select a small set of nodes that maximizes the total influence spread across all layers. To this end, we introduce a hybrid propagation model that jointly captures layer-specific diffusion dynamics and probabilistic cross-layer propagation. Based on this model, we formally define the Mlim problem and establish its NP-hardness, monotonicity, and submodularity. To address the Mlim problem, we first propose a greedy baseline Mlim-Greedy, which achieves a (1-1/e) approximation. Since …

Hvi-Cidnet+: Beyond Extreme Darkness For Low-Light Image Enhancement, Kangbiao Shi, Xiaowen Ma, Yixu Feng, Tao Hu, Peng Wu, Guansong Pang, Qingsen Yan

Research Collection School Of Computing and Information Systems

Low-Light Image Enhancement (LLIE) aims to recover visually pleasing content and details from degraded low-light images. However, existing RGB-based methods often suffer from color bias and brightness artifacts due to inherent high color sensitivity. Although the HSV color space can decouple brightness and color, it introduces noticeable red and black noise artifacts. To address these challenges, we adopt the Horizontal/Vertical-Intensity (HVI) color space for LLIE, which is defined by the HV color map and learnable intensity. The former enforces small distances for red coordinates to alleviate red noise artifacts, while the latter adaptively compresses low-light regions to suppress black noise …

Audeter: A Large-Scale Dataset For Deepfake Audio Detection In Open Worlds, Qizhou Wang, Hanxun Huang, Guansong Pang, Sarah Erfani, Christopher Leckie

Research Collection School Of Computing and Information Systems

Speech synthesis systems can now produce highly realistic vocalisations that pose significant authenticity challenges. Despite substantial progress in deepfake detection models, their real-world effectiveness is often undermined by evolving distribution shifts between training and test data, driven by the complexity of human speech and the rapid evolution of synthesis systems. Existing datasets suffer from limited real speech diversity, insufficient coverage of recent synthesis systems, and heterogeneous mixtures of deepfake sources, which hinder systematic evaluation and open-world model training. To address these issues, we introduce AUDETER (AUdio DEepfake TEst Range), a large-scale and highly diverse deepfake audio dataset comprising over 4,500 …

Left: Learnable Fusion Of Tri-View Tokens For Unsupervised Time Series Anomaly Detection, Dezheng Wang, Tong Chen, Guansong Pang, Congyan Chen, Shihua Li, Hongzhi Yin

Research Collection School Of Computing and Information Systems

As a fundamental data mining task, unsupervised time series anomaly detection (TSAD) aims to build a model for identifying abnormal timestamps without assuming the availability of annotations. A key challenge in unsupervised TSAD is that many anomalies are too subtle to exhibit detectable deviation in any single view (e.g., time domain), and instead manifest as inconsistencies across multiple views like time, frequency, and a mixture of resolutions. However, most cross-view methods rely on feature or score fusion and do not enforce analysis–synthesis consistency, meaning the frequency branch is not required to reconstruct the time signal through an inverse transform, and …

Timeradar: A Domain-Rotatable Foundation Model For Time Series Anomaly Detection, Hui He, Hezhe Qiao, Yutong Chen, Kun Yi, Guansong Pang

Research Collection School Of Computing and Information Systems

Current time series foundation models (TSFMs) primarily focus on learning prevalent and regular patterns within a predefined time or frequency domain to enable supervised downstream tasks (\eg, forecasting). Consequently, they are often ineffective for inherently unsupervised downstream tasks—such as time series anomaly detection (TSAD), which aims to identify rare, irregular patterns. This limitation arises because such abnormal patterns can closely resemble the regular patterns when presented in the same time/frequency domain. To address this issue, we introduce TimeRadar, an innovative TSFM built in a fractional time–frequency domain to support generalist TSAD across diverse unseen datasets. Our key insight is that …

Task-Aligned Haze Removal With Semantic-Aware Fusion And Contrast Self-Correction, Jinbin Wang, Aiping Yang, Guosong Jiang, Wenlong Yu, Dongwei Ren, Qinghua Hu

Research Collection School Of Computing and Information Systems

Adverse haze conditions introduce complex degradations that obscure scene details and distort structural cues critical for object detection, posing persistent challenges for vision‐based sensing systems. Although existing haze removal methods have achieved notable improvements in visual clarity, their optimisation objectives are often misaligned with downstream detection requirements, leading to limited detection performance in real‐world scenarios. To address this issue, this work proposes a task‐aligned weakly supervised haze removal framework, termed Dehaze4Detection, which explicitly aligns low‐level restoration with high‐level detection objectives. The framework incorporates a Semantic‐Aware Multi‐Scale Fusion Module (SMFM) that embeds pixel‐level semantic knowledge into the dehazing process, enabling selective …

Evaluating Machine Learning Models On Classification Of Novel Cyber Attacks In The Healthcare Domain, Promise Ehimen

Dissertations, Theses, and Projects

The increasing adoption of the Internet of Medical Things (IoMT) has improved healthcare delivery through connected medical devices while simultaneously expanding the cybersecurity risks facing healthcare organizations. Although machine learning based intrusion detection systems have demonstrated high detection accuracy, their ability to respond reliably to previously unseen cyberattacks remains uncertain. This study investigated how a Neural Network model and a Logistic Regression model classified novel cyberattacks within the IoMT environment. The Neural Network and Logistic Regression models were both trained and tested using a subset of the CICIoMT2024 benchmark dataset. The Neural Network achieved 99.82% test accuracy and a 0.94 …

Vision Transformers And Convolutional Neural Networks For Land Use Scene Classification, Arun D. Kulkarni

Computer Science Faculty Publications and Presentations

Land use scene classification (LUSC) from remote sensing imagery plays a critical role in environmental monitoring, urban planning, and sustainable resource management. In recent years, deep learning methods have significantly advanced the state-of-the-art, with Convolutional Neural Networks (CNNs) dominating the field because of their strong ability to capture local spatial features. However, the emergence of Vision Transformers (ViTs) has introduced a new paradigm that models long-range dependencies through self attention mechanisms, potentially enabling improved global context understanding. This study presents a comparative assessment of Vision Transformers and CNN-based architectures for remote sensing land use scene classification. Representative CNN models, such …

A Data-Driven Framework For Mitigating Breast Cancer Overdiagnosis: From Estimation To Risk-Adjusted Computer-Aided Diagnosis, William M. Brown Jr.

LSU Doctoral Dissertations

In Computer-Aided Diagnosis (CAD) of cancer, standard cost metrics (false-positives and false-negatives) fundamentally fail to account for overdiagnosis. Overdiagnosis is a critical scenario where a disease is correctly detected (true-positive) but is biologically indolent and would never have caused the patient harm or symptoms. While widely recognized in the medical community as a major healthcare crisis driving stressful and invasive overtreatment, overdiagnosis remains severely under-researched within computer science and engineering. This dissertation addresses this interdisciplinary gap by defining the three key computational challenges of overdiagnosis: (i) accurate estimation, (ii) harm quantification, and (iii) algorithmic mitigation. To overcome the estimation challenge, …

Ai-Powered Resume Screening, Sang Suh, Numery Zaber

Faculty Publications

Traditional resume screening is manual, slow, and susceptible to bias, and it struggles to keep pace with today’s application volumes. This paper presents a dual-engine, AI-powered resume screening system designed for transparency and reproducibility. The primary (classical) pipeline encodes resumes and job descriptions using Sentence-BERT (SBERT), computes a resume–job match score via cosine similarity, classifies candidates into 25 job categories using XGBoost, and provides model interpretability through SHAP. In parallel, a prompted large language model (LLM) baseline (GPT-4o/4o-mini) outputs a match score and predicted category for comparative analysis. A Streamlit-based interface integrates both engines to support recruiter workflows and human-in-the-loop …

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 …

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 …

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 …

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 …

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 …

Compressed Cinema As A Study In Llm Latent Spaces, Mallen Clifton

ELO (un)supervised 2026

In his article “Spec Acts” (2021), Matthew Kirschenbaum analyzes the AI-generated novel 1 the Road to develop his titular concept of the spec act, “the future in its multitudes collapsing into an actionable present.” With the proliferation of texts produced by generative AI and subsequent critical analyses of them, one element in particular calls for further theorization: “the future in its multitudes,” or more directly, the latent space. This echoes arguments by critics such as Antonio Somaini, who offered his own “Theory of Latent Spaces” last year. However, where Somaini’s attention is towards visual culture, I turn mine to the …