Computer Sciences Commons^™

Analysis Of The Status And Thematic Trends Of Ai For Science Research Abroad From 2015 To 2024, Fangyuan Wang, Huiting Xu, Jinghua Xue

Journal of Scientific Information Research

[Purpose/significance] This paper analyzes the relevant literature in the field of AI for Science（AI4S）in the WoS core database from 2015 to 2024, and sorts out the research status and development trends in this field, aiming to provide forward-looking insights for the application of AI technology in scientific research.

[Method/process] This paper combines bibliometric analysis with the BERTopic model to analyze the publication trends, publishing countries, core authors, and topic identification and development trends in the field of AI4S.

[Result/conclusion] Through bibliometric analysis, this paper reveals the exponential growth trend of AI4S-related literature, and finds that China ranks first in the …

Trust And Ethics In Ai-Driven E-Commerce: Persuasion Vs. Privacy, Akriti Nepal

Student Publications

This study examines how AI-driven features in e-commerce influence user satisfaction and the role of trust in these interactions. Using a survey-based dataset of 100 consumers, we investigated whether trust moderates or mediates the impact of AI persuasiveness and perceptions of bias, intrusiveness, and preference understanding on satisfaction. Results indicate that AI’s perceived ability to understand user preferences strongly predicts satisfaction, while trust partially mediates the relationship between helpful AI features and urgency messages and user satisfaction. Conversely, trust did not significantly moderate these relationships, and concerns about bias and intrusiveness had minimal impact. Findings suggest that AI-driven satisfaction is …

(Si15-113) Augmenting Cryptographic Security Through Inventive Application Of The Kharrat-Toma Transform Algorithm, Prabakaran Raghavendran, Tharmalingam Gunasekar, K. Sakthivel, Kamalendra Kumar, Shalini Gupta

Applications and Applied Mathematics: An International Journal (AAM)

This paper introduces a cryptographic technique combining the Kharrat-Toma Transform and congruence modulo operators to improve the security of message encryption. The proposed model uses the mathematical properties of the Kharrat-Toma Transform and its inverse for direct scrambling and unscrambling processes while embedding sufficient complexity to resist modern cryptanalytic attacks. The model is subjected to experimental tests, including encryption quality analysis, Shannon entropy, and NIST randomness tests, in order to prove the strength of the model. Through encryption quality analysis, symbol frequencies in the ciphertext are masked heavily from having much correlation between plaintext and ciphertext. Entropy values indicate near-theoretical …

Deep Learning For Hate Speech Detection: A Comparative Study, Jitendra Singh Malik, Hezhe Qiao, Guansong Pang, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

Automated hate speech detection is an important tool in combating the spread of hate speech, particularly in social media. Numerous methods have been developed for the task, including a recent proliferation of deep-learning based approaches. A variety of datasets have also been developed, exemplifying various manifestations of the hate-speech detection problem. We present here a largescale empirical comparison of deep and shallow hate-speech detection methods, mediated through the three most commonly used datasets. Our goal is to illuminate progress in the area, and identify strengths and weaknesses in the current state-of-the-art. We particularly focus our analysis on measures of practical …

Better Digital Contracts With Prosocial Friction-In-Design, Brett Frischmann, Moshe Y. Vardi

Faculty Publications

Contract law is supposed to enable people to reach genuine agreements and cooperate. If this ideal was ever a reality, the rise of mass market contracts and boil­erplate rendered it pure fiction. Modern consumer contracts are incomprehensible to most people. No one reads them anyway.

Digital contracting involves design features that amplify traditional boilerplate harms and create others. For example, digital contracting is too cheap; low marginal costs lead to overexpansion in scale and scope. To make matters worse, the loss of autonomy from repeat engagement with digital contracting systems is pernicious. People become increasingly predictable and programmable as digital …

Enhancing Llm Code Generation: A Systematic Evaluation Of Multi-Agent Collaboration And Runtime Debugging For Improving Accuracy, Reliability, And Latency, Nazmus Ashrafi

Theses

The use of large language models (LLMs) for automated code generation has emerged as a significant focus within AI research. As these pretrained models continue to evolve, their ability to understand and generate complex code structures has opened up new possibilities for automating intricate programming tasks with greater accuracy. Although contemporary foundational models demonstrate promising results, researchers continue to explore optimal post-training strategies to enhance code quality. These include supervised fine-tuning, retrieval-augmented generation (RAG), debugging, and many others. In this thesis, I combine two such widely used post training approaches—namely (1) multi agent collaboration and (2) runtime execution of information-based …

Lightweight Population-Based Policy Optimization For Pickup And Delivery Problems, Yizhou Liu, Li Li, Yixin Xu, Tang Liu, Rong Cheng, Die Wu, Jilin Yang, Jingwen Li

Research Collection School Of Computing and Information Systems

In recent years, applying deep models to automatically learn construction heuristics for vehicle routing problems has achieved remarkable advancements. However, they are less effective in searching solutions due to two primary limitations: relying on deterministic probability distributions and overlooking the strategic advantage of prioritizing nearby unvisited nodes during the route construction process, resulting in suboptimal policies In this paper, we propose a novel lightweight population-based policy optimization (LPPO) framework that learns a diverse population of solution strategies through the utilization of innovative perturbation factors, in order to facilitate search exploration. Moreover, we design a localized attention synthesis (LAS) network to …

Classical Shadows With Improved Median-Of-Means Estimation, Winston Fu, Dax Enshan Koh, Siong Thye Goh, Jian Feng Kong

Research Collection School Of Computing and Information Systems

The classical shadows protocol, introduced by Huang et al (2020 Nat. Phys. 16 1050), makes use of the median-of-means (MoM) estimator to efficiently estimate the expectation values of M observables with failure probability δ using only O ( log ⁡ ( M / δ ) ) measurements. In their analysis, Huang et al used loose constants in their asymptotic performance bounds for simplicity. However, the specific values of these constants can significantly affect the number of shots used in practical implementations. To address this, we studied a modified MoM estimator proposed by Minsker (2023 Proc. 36th Conf. on Learning Theory …

Probabilistic Prototype Calibration Of Vision-Language Models For Generalized Few-Shot Semantic Segmentation, Jie Liu, Jiayi Shen, Pan Zhou, Jan-Jakob Sonke, Stratis Gavves

Research Collection School Of Computing and Information Systems

Generalized Few-Shot Semantic Segmentation (GFSS) aims to extend a segmentation model to novel classes with only a few annotated examples while maintaining performance on base classes. Recently, pretrained vision-language models (VLMs) such as CLIP have been leveraged in GFSS to improve generalization on novel classes through multi-modal prototypes learning. However, existing prototype-based methods are inherently deterministic, limiting the adaptability of learned prototypes to diverse samples, particularly for novel classes with scarce annotations. To address this, we propose FewCLIP, a probabilistic prototype calibration framework over multi-modal prototypes from the pretrained CLIP, thus providing more adaptive prototype learning for GFSS. Specifically, FewCLIP …

Thesis: Comparing Functional And Effective Brain Connectivity Metrics For Eeg, Diksha Srishyla

Theses and Dissertations

Background:Brain connectivity measures have been used to study communication between brain regions using electroencephalography (EEG). Functional and effective connectivity estimate the synchronization and the flow of information between regions, respectively. However, findings from studies using different measures to investigate similar connections do not converge. To guide the selection of functional and effective connectivity measures in future studies, we systematically compared a set of measures in the context of resting state EEG. We examined four functional connectivity metrics (coherence (Coh), the imaginary part of coherence (imCoh), the corrected imaginary part of phase lagged value (ciPLV), the debiased weighted phase-locking index (dwPLI)) …

Multi-Perspective Feature Learning For Facial Expression Recognition In The Wild, Xiangyu Hu

Theses and Dissertations

With the rapid progress of deep learning, Facial Expression Recognition (FER) has seen substantial improvements in performance, particularly “in the wild” meaning real world conditions. Despite these advances, most existing methods extract features from facial images as the sole emotional cues, which limits the model’s ability to capture the full complexity of human emotional expressions.

In reality, facial expressions are composed of diverse and multi-perspective information, including appearance-based cues and geometric structural deformations due to activations of facial muscles. Depending exclusively on one type of representation may fail to exploit the complementary nature of these cues, an issue that becomes …

New Approaches On Source Coding For Quantum Stochastic Sources And Implementation Of Quantum Fanout Gate, Rabins Wosti

Theses and Dissertations

The accurate computation of advanced quantum algorithms like Shor’s integer factorization, quantum phase estimation (QPE), and the quantum Fourier transform (QFT) requires quantum circuits of considerable size and depth. It is difficult to achieve reliable computation with deep quantum circuits due to the limited coherence times of the current noisy quantum devices. The quantum fanout gate is known to be a powerful primitive for reducing the depth of many quantum circuits (Høyer and Špalek 2003; Gottesman and Chuang 1999). Shallow or constant-depth quantum circuits are desirable for both near-term and fault-tolerant quantum computations as they reduce noise and allow faster …

Computational Analogies In The Era Of Large Language Models, Amarakoon Mudiyanselage Thilini Wijesiriwardene

Theses and Dissertations

Analogical reasoning is an important part of human cognition requiring the integration of abstract reasoning, pattern recognition, and background knowledge. Despite significant advances in language modeling, the capacity of current methods to accurately identify, model, and evaluate analogies remains fundamentally underexplored.

Analogies enable individuals to perceive deep similarities between superficially different situations. Effective analogy-making requires integrating knowledge about the external world with abstract reasoning and pattern recognition capabilities. While current language models (LMs), trained on massive textual corpora using autoregressive or masked objectives, achieve impressive performance across Natural Language Processing (NLP) tasks such as text generation, summarization, and classification, their …

Visual-Enhanced Multimodal Framework For Flexible Job Shop Scheduling Problem, Peng Zhao, Zhiguang Cao, Di Wang, Wen Song, Wei Pang, You Zhou, Yuan Jiang

Research Collection School Of Computing and Information Systems

Multimodal models leverage complementary information across modalities to enrich feature representations. While visual information shows potential in representing structure for some combinatorial optimization problems (COPs), its application to complex scheduling like the Flexible Job Shop Scheduling Problem (FJSP) remains underexplored. Current learning-based FJSP solvers predominantly rely on handcrafted state features. This dependence can lead to inconsistencies and may not fully capture the problem's intricate dynamics. Crucially, these methods overlook visual modalities. Visual representations offer a distinct advantage by inherently capturing the global topological structure and complex resource interactions within the FJSP state. Unlike localized handcrafted features, this holistic, structural view …

Genwardrobe: A Fully Generative System For Travel Fashion Wardrobe Construction, Peng Jin, Yilin Wen, Mingzhe Yu, Yunshan Ma, Rong Zheng, Jin‑Tu Fan, Chong Wah Ngo

Research Collection School Of Computing and Information Systems

With the increasing demand for outfit planning in real-world travel scenarios, the need for constructing a travel fashion wardrobe, a series of outfits tailored to a user's personalization and destination-specific context over a short travel period, has grown significantly. However, existing systems or works often focus on isolated factors and rely on retrieval-based methods, with insufficient utilization of generative models, limiting their adaptability to real-world travel scenarios. To address this issue, this study introduces GenWardrobe, a fully generative system for travel fashion wardrobe construction. GenWardrobe consists of three key modules: user query analysis, fashion knowledge retrieval via retrieval-augmented generation and …

Contrastrepair: Enhancing Conversation-Based Automated Program Repair Via Contrastive Test Case Pairs, Jiaolong Kong, Xiaofei Xie, Mingfei Cheng, Shangqing Liu, Xiaoning Du, Qi Guo

Research Collection School Of Computing and Information Systems

Automated Program Repair (APR) aims to automatically generate patches for rectifying software bugs. Recentstrides in Large Language Models (LLM), such as ChatGPT, have yielded encouraging outcomes in APR,especially within the conversation-driven APR framework. Nevertheless, the efficacy of conversation-drivenAPR is contingent on the quality of the feedback information. In this article, we propose ContrastRepair, anovel conversation-based APR approach that augments conversation-driven APR by providing LLMs withcontrastive test pairs. A test pair consists of a failing test and a passing test, which offer contrastive feedback tothe LLM. Our key insight is to minimize the difference between the generated passing test and the …

A Data-Driven Framework For Optimal Retail Store Location, Ming Hui Tan

Dissertations and Theses Collection (Open Access)

This study develops a data-driven framework for optimal retail store location planning that integrates road network analysis, mobility data and optimization techniques. By addressing the limitations of traditional approaches that rely on outdated census data and manual site selection, this research offers a scalable and adaptable solution for retail expansion in diverse urban environments. Chapters 1 and 2 establish the foundational context and theoretical underpinnings of this research. Chapter 1 introduces the research problem and motivation, highlighting the limitations of existing approaches and defining three key research objectives: automating candidate site identification, improving footfall estimation, and developing a scalable multi-site …

Harnessing Se Community Knowledge For Developer-Centric Code Intelligence, Chengran Yang

Dissertations and Theses Collection (Open Access)

The integration of Large Language Models (LLMs), particularly those tailored for programming tasks—referred to as code LLMs—has created novel opportunities to enhance developer productivity. These advanced models automate routine and repetitive coding tasks, such as code generation and debugging, and enable faster prototyping and more efficient problem-solving. Despite these remarkable advantages, the current generation of code LLMs exhibits notable limitations that impact their practical effectiveness in real-world software engineering scenarios. These models frequently produce code that is inefficient or suboptimal in runtime performance, demonstrate opaque reasoning processes, and struggle to adapt effectively to diverse developer contexts and specific requirements. Moreover, …

Improving Universities Through The Use Of Ai & Transformative Technology: A Case Study Analysis At The University Of South Carolina, Cameron A. Caulk

Senior Theses

This thesis aims to give university leaders a practical guide to implementing AI, using lessons learned from the University of South Carolina’s experience as a case study. The project started with a review of literature and industry practices for the Finance & Administration division, which led to key deliverables like AI usage guidelines, DoIT’s position paper on AI systems, and the ParkUSC parking app. One ongoing project, Project Shuttlecock, even sets the stage for future AI initiatives at the university.

AI holds immense promise, but universities often hesitate due to concerns about ethics, costs, and the learning curve for staff …

Application Of Machine Learning For Vascular System Analysis, Alireza Bagheri Rajeoni

Theses and Dissertations

The analysis of vascular structures is critical for diagnosing, monitoring, and treating vascular diseases such as aneurysms, stenosis, and vascular calcification. Traditional methods often rely on manual interpretation of imaging data, which is time-consuming, subjective, and not scalable. This work explores the application of advanced machine learning techniques to automate and enhance vascular system analysis. Our contributions include achieving state-of-the-art accuracy in vascular segmentation, developing a machine learning pipeline to automatically quantify vascular calcification in peripheral arterial disease, and designing a multi-stage machine learning system for abdominal aortic aneurysm analysis that identifies aneurysm boundaries and estimates aneurysm volume in a …

Implicit Neural Representation For Image Reconstruction, Canyu Zhang

Theses and Dissertations

Image reconstruction seeks to restore corrupted images and recover visual content that has been lost or degraded. Such degradation may result from low resolution, occlusion, masking, or shadow interference. This problem has become an increasingly significant research topic, as visual information plays a central role in almost every aspect of modern life. Neural network based approaches have recently emerged as highly effective solutions for this task. In particular, convolutional neural networks and transformer based architectures have demonstrated remarkable success in producing visually convincing reconstructions. However, these models remain constrained in several important ways, one of the most critical being that …

Governance In The Absence Of Government, Tracy Hresko Pearl

Faculty Articles

Artificial intelligence (AI) is advancing at an unprecedented pace, with generative systems exerting growing influence over social, economic, and political life. While Al offers opportunities for innovation and efficiency, it also poses risks ranging from misinformation and job displacement to existential threats if highly autonomous systems evade human control. Across industry, government, and civil society, there is broad consensus that Al requires oversight.

Yet traditional U.S. regulatory approaches face six significant barriers: (1) technology outpacing legislation, (2) limited Al expertise among policymakers, (3) regulatory capture, (4) political gridlock, (5) outdated governance structures, and (6) the inherent complexity of Al. Combined …

Information Provision And Search Frictions: Evidence From The Taxi Industry In Singapore, Sumit Agarwal, Shih-Fen Cheng, Jussi Keppo, Long Wang, Yang Yang

Research Collection School Of Computing and Information Systems

Search frictions and misallocation are common in decentralized transportation markets. Using novel trip-level data of taxis in Singapore, this paper examines the impactof real-time demand information at airport terminals on search frictions. The information reduces taxi supply misallocation, increasing deadheading speed by 16.3% and decreasing deadheading time by 10.77%, benefiting both passengers and drivers. It raises daily earnings by $3.70 USD and adds 6.2 minutes of operational time per airport-trip taxi. Spatial spillovers are primarily observed among drivers in adjacentdistricts. Taxis from the Budget Terminal and drivers with fewer prior airport pickups benefit more from this information.

A System Framework To Symbolically Explore Intel Tdx Module Execution, Pansilu Pitigalaarachchillage, Xuhua Ding

Research Collection School Of Computing and Information Systems

We present TDXplorer, the first dynamic symbolic analysis system for Intel's TDX Module, the software trusted computing base of TDX. Without using TDX hardware, an analyzer function on top of TDXplorer can not only apply dynamic analysis to control and instrument the TDX Module's execution, but also carry out symbolic execution for path exploration as well as security and functionality reasoning. The two types of analysis are seamlessly integrated in a way that symbolic execution is conducted directly upon the TDX Module's binary code and runtime states, which are shaped by using dynamic analysis techniques. We implement TDXplorer on Linux …

Parameter-Efficient Variational Autoencoder For Multimodal Multi-Interest Recommendation, Nhu Thuat Tran, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Learning user preferences in recommendation systems is enriched by multimodal features, such as textual and visual content, and amplified by multi-interest modeling with Variational AutoEncoders (VAEs). However, prior efforts are limited by single modality focus and cumbersome, parameter-heavy architecture designs. To address these limitations, we introduce an innovative solution that blends the semantic richness of multimodal data with the representational power of multi-representation VAEs. Drawing inspiration from Mixture of Experts (MoE), we cast each VAE as an expert tailored to a specific modality, then fuse them via a novel parameter-merging function into a lean, unified model. This approach efficiently captures …

Unsupervised Visual Chain-Of-Thought Reasoning Via Preference Optimization, Kesen Zhao, Beier Zhu, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Chain-of-thought (CoT) reasoning greatly improves the interpretability and problem-solving abilities of multimodal large language models (MLLMs). However, existing ap proaches focus on text CoT, limiting their ability to lever age visual cues. Visual CoT remains underexplored, and the only work [35] is based on supervised fine-tuning that relies on extensive labeled bounding-box data and is hard to generalize to unseen cases. In this paper, we introduce Unsupervised Visual CoT (UV-CoT), a novel framework for image-level CoT reasoning via preference optimization. UV-CoTperforms preference comparisons between model generated bounding boxes (one is preferred and the other is dis-preferred), eliminating the need for …

Ivycross: A Privacy-Preserving And Concurrency Control Framework For Blockchain Interoperability, Ming Li, Jian Weng, Jia-Si Weng, Yi Li, Yongdong Wu, Dingcheng Li, Guowen Xu, Deng, Robert H.

Research Collection School Of Computing and Information Systems

Interoperability is a fundamental challenge for long-envisioned blockchain applications. A mainstream approach is using Trusted Execution Environment (TEE) to support interoperable off-chain execution. However, this incurs multiple TEE configured with non-trivial storage capabilities running on fragile concurrent processing environments, rendering current strategies based on TEE far from being practical. This paper aims to fill this gap and design a practical interoperability mechanism with simplified TEE as the underlying architecture. Specifically, we present IvyCross, a TEE-based framework that achieves low-cost, privacy-preserving, and race-free blockchain interoperability. IvyCross allows running arbitrary smart contracts across heterogeneous blockchains atop two distributed TEE-powered hosts. We design …

Reproducibility Debt In Scientific Software, Zara Hassan, Christoph Treude, Graham Williams, Michael Norrish, Alex Potanin

Research Collection School Of Computing and Information Systems

Reproducibility Debt (RpD) refers to accumulated technical and organisational issues in scientific software that hinder the ability to reproduce research results. While reproducibility is essential to scientific integrity, RpD remains poorly defined and under-addressed. This study introduces a formal definition of RpD and investigates its causes, effects, and mitigation strategies using a mixed-methods approach involving a systematic literature review (214 papers), interviews (23 practitioners), and a global survey (59 participants). We identify seven categories of contributing issues, 75 causes, 110 effects, and 61 mitigation strategies. Findings are synthesised into a cause-effect model and supported by taxonomies of team roles and …

Cookingdiffusion: Cooking Procedural Image Generation With Stable Diffusion, Yuan Wang, Bin Zhu, Yanbin Hao, Chong-Wah Ngo, Yi Tan, Xiang Wang

Research Collection School Of Computing and Information Systems

Recent advancements in text-to-image generation models have excelled in creating diverse and realistic images. This success extends to food imagery, where various conditional inputs like cooking styles, ingredients, and recipes are utilized. However, a yet-unexplored challenge is generating a sequence of procedural images based on cooking steps from a recipe. This could enhance the cooking experience with visual guidance and possibly lead to an intelligent cooking simulation system. To fill this gap, we introduce a novel task called cooking procedural image generation. This task is inherently demanding, as it strives to create photo-realistic images that align with cooking steps while …

From Holistic To Localized: Local Enhanced Adapters For Efficient Visual Instruction Fine-Tuning, Pengkun Jiao, Bin Zhu, Jingjing Chen, Chong-Wah Ngo, Yugang Jiang

Research Collection School Of Computing and Information Systems

Efficient Visual Instruction Fine-Tuning (EVIT) seeks to adapt Multimodal Large Language Models (MLLMs) to downstream tasks with minimal computational overhead. However, as task diversity and complexity increase, EVIT faces significant challenges in resolving data conflicts. To address this limitation, we propose the Dual Low-Rank Adaptation (Dual-LoRA), a holistic-to-local framework that enhances the adapter’s capacity to address data conflict through dual structural optimization. Specifically, we utilize two subspaces: a skill space for stable, holistic knowledge retention, and a rank-rectified task space that locally activates the holistic knowledge. Additionally, we introduce Visual Cue Enhancement (VCE), a multi-level local feature aggregation module designed …