Computer Sciences Commons^™

Learning To Handle Complex Constraints For Vehicle Routing Problems, Jieyi Bi, Yining Ma, Jianan Zhou, Wen Song, Zhiguang Cao, Yaoxin Wu, Jie Zhang

Research Collection School Of Computing and Information Systems

Vehicle Routing Problems (VRPs) can model many real-world scenarios and often involve complex constraints. While recent neural methods excel in constructing solutions based on feasibility masking, they struggle with handling complex constraints, especially when obtaining the masking itself is NP-hard. In this paper, we propose a novel Proactive Infeasibility Prevention (PIP) framework to advance the capabilities of neural methods towards more complex VRPs. Our PIP integrates the Lagrangian multiplier as a basis to enhance constraint awareness and introduces preventative infeasibility masking to proactively steer the solution construction process. Moreover, we present PIP-D, which employs an auxiliary decoder and two adaptive …

Reevo: Large Language Models As Hyper-Heuristics With Reflective Evolution, Haoran Ye, Jiarui Wang, Zhiguang Cao, Federico Berto, Chuanbo Hua, Haeyeon Kim, Jinkyoo Park, Guojie Song

Research Collection School Of Computing and Information Systems

The omnipresence of NP-hard combinatorial optimization problems (COPs) compels domain experts to engage in trial-and-error heuristic design process. The long-standing endeavor of design automation has gained new momentum with the rise of large language models (LLMs). This paper introduces Language Hyper-Heuristics (LHHs), an emerging variant of Hyper-Heuristics that leverages LLMs for heuristic generation, featuring minimal manual intervention and open-ended heuristic spaces. To empower LHHs, we present Reflective Evolution (ReEvo), a generic searching framework that emulates the reflective design approach of human experts while far surpassing human capabilities with its scalable LLM inference, Internet-scale domain knowledge, and powerful evolutionary search. Evaluations …

Flexfl: Heterogeneous Federated Learning Via Apoz-Guided Flexible Pruning In Uncertain Scenarios, Zekai Chen, Chentao Jia, Ming Hu, Xiaofei Xie, Anran Li, Mingsong Chen

Research Collection School Of Computing and Information Systems

Along with the increasing popularity of Deep Learning (DL) techniques, more and more Artificial Intelligence of Things (AIoT) systems are adopting federated learning (FL) to enable privacy-aware collaborative learning among AIoT devices. However, due to the inherent data and device heterogeneity issues, existing FL-based AIoT systems suffer from the model selection problem. Although various heterogeneous FL methods have been investigated to enable collaborative training among heterogeneous models, there is still a lack of i) wise heterogeneous model generation methods for devices, ii) consideration of uncertain factors, and iii) performance guarantee for large models, thus strongly limiting the overall FL performance. …

Sprinql : Sub-Optimal Demonstrations Driven Offline Imitation Learning, Minh Huy Hoang, Tien Mai, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

We focus on offline imitation learning (IL), which aims to mimic an expert's behavior using demonstrations without any interaction with the environment. One of the main challenges in offline IL is the limited support of expert demonstrations, which typically cover only a small fraction of the state-action space. While it may not be feasible to obtain numerous expert demonstrations, it is often possible to gather a larger set of sub-optimal demonstrations. For example, in treatment optimization problems, there are varying levels of doctor treatments available for different chronic conditions. These range from treatment specialists and experienced general practitioners to less …

A Community-Driven Framework To Evaluate Factors Influencing Nft Buyers, Yi Meng Lau, Ping Fan Ke

Research Collection School Of Computing and Information Systems

This study examines the pivotal role of community-driven social dynamics in shaping non-fungible tokens (NFT) buyers’ purchasing intentions and the increasing social significance of digital assets. Building on prior research in online word-of-mouth, we propose a comprehensive framework that integrates technological, personal, social, economic, and political factors influencing NFT purchases. The framework emphasizes the importance of service providers, personalization, perceived value, and associated risks, while also providing insights into how online word-of-mouth and community dynamics impact these aspects. As a preliminary exploration, this research lays the foundation for future empirical studies to validate the framework and assess its applicability across …

Towards General Conceptual Model Editing Via Adversarial Representation Engineering, Yihao Zhang, Zeming Wei, Jun Sun, Meng Sun

Research Collection School Of Computing and Information Systems

Since the rapid development of Large Language Models (LLMs) has achieved remarkable success, understanding and rectifying their internal complex mechanisms has become an urgent issue. Recent research has attempted to interpret their behaviors through the lens of inner representation. However, developing practical and efficient methods for applying these representations for general and flexible model editing remains challenging. In this work, we explore how to leverage insights from representation engineering to guide the editing of LLMs by deploying a representation sensor as an editing oracle. We first identify the importance of a robust and reliable sensor during editing, then propose an …

Question-Attentive Review-Level Explanation For Neural Rating Regression, Trung Hoang Le, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Recommendation explanations help to improve their acceptance by end users. Explanations come in many different forms. One that is of interest here is presenting an existing review of the recommended item as the explanation. The challenge is in selecting a suitable review, which is customarily addressed by assessing the relative importance or “attention” of each review to the recommendation objective. Our focus is improving review-level explanation by leveraging additional information in the form of questions and answers (QA). The proposed framework employs QA in an attention mechanism that aligns reviews to various QAs of an item and assesses their contribution …

Self-Supervised Fine-Tuning For Neural Expert Finding, Budhitama Subagdja, Dan Sanchari, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Expert finding systems allow ones to find individuals who have expertise in specific fields or domains. Traditional expert finding are mostly based on topic modeling or keyword search methods that are limited in their capability to encode contextual knowledge from natural language. To address the limitation, this paper presents Neural Expert Finder (NEF), a novel method that takes a transfer learning approach based on transformer encoder networks to leverage the rich seman-tic and syntactic patterns of language encoded in pre-trained language models (PLMs). We propose a self-supervised learning approach utilizing contrastive training using both positive and automatically generated negative samples …

Replay-And-Forget-Free Graph Class-Incremental Learning: A Task Profiling And Prompting Approach, Chaoxi Niu, Guansong Pang, Ling Chen, Bing Liu

Research Collection School Of Computing and Information Systems

Class-incremental learning (CIL) aims to continually learn a sequence of tasks, with each task consisting of a set of unique classes. Graph CIL (GCIL) follows the same setting but needs to deal with graph tasks (e.g., node classification in a graph). The key characteristic of CIL lies in the absence of task identifiers (IDs) during inference, which causes a significant challenge in separating classes from different tasks (i.e., inter-task class separation). Being able to accurately predict the task IDs can help address this issue, but it is a challenging problem. In this paper, we show theoretically that accurate task ID …

Safety Through Feedback In Constrained Rl, Shashank Reddy Chirra, Pradeep Varakantham, Praveen Paruchuri

Research Collection School Of Computing and Information Systems

In safety-critical RL settings, the inclusion of an additional cost function is often favoured over the arduous task of modifying the reward function to ensure the agent's safe behaviour. However, designing or evaluating such a cost function can be prohibitively expensive. For instance, in the domain of self-driving, designing a cost function that encompasses all unsafe behaviours (e.g., aggressive lane changes, risky overtakes) is inherently complex, it must also consider all the actors present in the scene making it expensive to evaluate. In such scenarios, the cost function can be learned from feedback collected offline in between training rounds. This …

User Acceptance Of Advice By Ai Agents: Expectation-System Fit Perspective, Jingyuan Cai, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Algorithms have increasing influence on our daily decisions, especially when the recommendations are presented by human-like AI agents. This study applies the Theory of Effective Use to investigate how the fit between the user’s role expectation for an AI agent and the agent’s interaction style impacts AI advice adoption. We proposed a new concept termed Perceived Expectation-System Fit (PESF) and empirically examined its impact on user perceptions and advice acceptance. We found that low PESF reduces advice acceptance by diminishing cognitive and affective trust in the AI agent. Furthermore, increased algorithm transparency increases PESF's impact on decision-making. Our findings provide …

Delidar: Decoupling Lidars For Pervasive Spatial Computing, Kanatta Gamage Ramesh Darshana Rathnayake, Razat Sutradhar, Abbaas A. M. Nishar, Weerakoon Dulaj S., Ashwin Ashok, Archan Misra

Research Collection School Of Computing and Information Systems

Unbounded proliferation of LiDAR-equipped pervasive devices generates two challenges: (a) mutual interference among emitters and (b) significantly higher sensing energy overhead. We propose a fundamentally different approach for LiDAR sensing, in indoor spaces, that decouples the sensor’s emitter and receiver components. Our proposed approach, called DeLiDAR, centralizes the emitter functionality in one or more stationary nodes that continually emit pulses; this decoupling allows each mobile LiDAR sensor to be an ultra-low power, pure receiver unit consisting solely of passive multiple photodiodes. We explain how the emitter can utilize VLC-based encoding of its pulses to convey parameter settings that allow a …

A Full-History Network Dataset For Btc Asset Decentralization Profiling, Ling Cheng, Qian Shao, Fengzhu Zeng, Feida Zhu

Research Collection School Of Computing and Information Systems

Since its advent in 2009, Bitcoin (BTC) has garnered increasing attention from both academia and industry. However, due to the massive transaction volume, no systematic study has quantitatively measured the asset decentralization degree specifically from a network perspective.In this paper, by conducting a thorough analysis of the BTC transaction network, we first address the significant gap in the availability of full-history BTC graph and network property dataset, which spans over 15 years from the genesis block (1st March, 2009) to the 845651-th block (29, May 2024). We then present the first systematic investigation to profile BTC's asset decentralization and design …

Ohss: Optimizing Homomorphic Secret Sharing To Support Fast Matrix Multiplication, Shuguang Zhang, Jianli Bai, Kun Tu, Ziyue Yin, Chan Liu

Research Collection School Of Computing and Information Systems

Homomorphic Secret Sharing (HSS) has evolved as a state-of-the-art methodology for achieving secure two-party computation, synthesizing the advantages of secret sharing and homomorphic encryption. This amalgamation ensures minimal computational and communicational overhead, making it particularly adept at arithmetic operations. However, HSS faces challenges in scalability and efficiency when confronted with extensive matrix operations, including both matrix-vector and matrix-matrix multiplications, which are fundamental in numerous privacy-preserving computations, notably within the realm of privacy-preserving machine learning. In this research, we introduce Optimized Homomorphic Secret Sharing (OHSS), a refined version of HSS, crafted to address these limitations. Our contributions include enhancements to the …

Gtree: Gpu-Friendly Privacy-Preserving Decision Tree Training And Inference, Qifan Wang, Shujie Cui, Lei Zhou, Ye Dong, Jianli Bai, Yun Sing Koh, Giovanni Russello

Research Collection School Of Computing and Information Systems

Outsourcing Decision tree (DT) training and inference to cloud platforms raises privacy concerns. Recent Secure Multi-Party Computation (MPC)-based methods are hindered by heavy overhead. Few recent studies explored GPUs to improve MPC-protected deep learning, yet integrating GPUs into MPC-protected DT with massive data-dependent operations remains challenging, raising question: can MPC-protected DT training and inference fully leverage GPUs for optimal performance?We present GTree, the first scheme that exploits GPU to accelerate MPC-protected secure DT training and inference. GTree is built across 3 parties who jointly perform DT training and inference with GPUs. GTree is secure against semi-honest adversaries, ensuring that no …

Intent Visualization In Human-Agent Teams, Rahul Tushar Mehta

Theses and Dissertations

Despite advances in autonomous systems, effective collaboration between humans and intelligent agents remains a significant challenge, particularly in shared-control scenarios. This study investigates how intent visualization affects human-agent collaboration in telecollaboration scenarios, examining its impact on team performance, operator trust, and workload. Using a custom simulation environment and Wizard-of-Oz methodology, we conducted an experiment with 13 participants who completed exploration tasks under two conditions: a baseline interface and an enhanced interface with intent visualization. Results showed that while intent visualization did not significantly improve objective performance metrics, it led to a 22.6\% increase in explicit disagreements between operators and the …

Decoding Neural Networks: An Information-Theoretic Guide To Interpretability, Error Analysis And Efficiency, Mackenzie J. Meni

Theses and Dissertations

This dissertation addresses critical challenges in neural network design by leveraging entropy-based techniques to improve model efficiency, interpretability, and bias reduction. Focusing on the unique demands of computer vision applications, particularly object detection and classification for real-time systems, this work introduces a series of innovative methods centered on information theory. At the core of these methods is the Probabilistic Explanations of Entropic Knowledge (PEEK) framework, a tool developed to analyze and visualize entropy distributions across feature maps. PEEK offers insights into information flow within neural networks, making it possible to pinpoint layers that contribute meaningfully to decision-making or identify those …

Chain Of Preference Optimization: Improving Chain-Of-Thought Reasoning In Llms, Xuan Zhang, Chao Du, Tianyu Pang, Qian Liu, Wei Gao, Min Lin

Research Collection School Of Computing and Information Systems

The recent development of chain-of-thought (CoT) decoding has enabled large language models (LLMs) to generate explicit logical reasoning paths for complex problem-solving. However, research indicates that these paths are not always deliberate and optimal. The tree-of-thought (ToT) method employs tree-searching to extensively explore the reasoning space and find better reasoning paths that CoT decoding might overlook. This deliberation, however, comes at the cost of significantly increased inference complexity. In this work, we demonstrate that fine-tuning LLMs leveraging the search tree constructed by ToT allows CoT to achieve similar or better performance, thereby avoiding the substantial inference burden. This is achieved …

Improving Environment Novelty Quantification For Effective Unsupervised Environment Design, Jayden Teoh, Wenjun Li, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Unsupervised Environment Design (UED) formalizes the problem of autocurricula through interactive training between a teacher agent and a student agent. The teacher generates new training environments with high learning potential, curating an adaptive curriculum that strengthens the student’s ability to handle unseen scenarios. Existing UED methods mainly rely on regret, a metric that measures the difference between the agent’s optimal and actual performance, to guide curriculum design. Regret-driven methods generate curricula that progressively increase environment complexity for the student but overlook environment novelty–a critical element for enhancing an agent’s generalizability. Measuring environment novelty is especially challenging due to the underspecified …

A Comprehensive Study On Static Application Security Testing (Sast) Tools For Android, Jingyun Zhu, Kaixuan Li, Sen Chen, Lingling Fan, Junjie Wang, Xiaofei Xie

Research Collection School Of Computing and Information Systems

To identify security vulnerabilities in Android applications, numerous static application security testing (SAST) tools have been proposed. However, it poses significant challenges to assess their overall performance on diverse vulnerability types. The task is non-trivial and poses considerable challenges. Firstly, the absence of a unified evaluation platform for defining and describing tools’ supported vulnerability types, coupled with the lack of normalization for the intricate and varied reports generated by different tools, significantly adds to the complexity. Secondly, there is a scarcity of adequate benchmarks, particularly those derived from real-world scenarios. To address these problems, we are the first to propose …

Converting Vocal Performances Into Sheet Music Leveraging Large Language Models, Jinjing Jiang, Nicole Teo, Haibo Pen, Seng-Beng Ho, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Advanced natural language processing (NLP) models are increasingly applied in music composition and performance, particularly for generating vocal melodies and simulating singing voices. While NLP techniques have been effective in analyzing vocal performance data to assess quality and style, the automatic transcription of vocal performances into sheet music remains a significant challenge. Manual transcription tools often fall short due to the intricate dynamics of vocal expression. This study tackles the automation of vocal performance transcription into sheet music using innovative techniques, including large language models (LLMs). We propose a method to translate vocal audio input into display-ready sheet music effectively. …

Unsupervised Modality Adaptation With Text-To-Image Diffusion Models For Semantic Segmentation, Ruihao Xia, Yu Liang, Peng-Tao Jiang, Hao Zhang, Bo Li, Yang Tang, Pan Zhou

Research Collection School Of Computing and Information Systems

Despite their success, unsupervised domain adaptation methods for semantic segmentation primarily focus on adaptation between image domains and do not utilize other abundant visual modalities like depth, infrared and event. This limitation hinders their performance and restricts their application in real-world multimodal scenarios. To address this issue, we propose Modality Adaptation with text-toimage Diffusion Models (MADM) for semantic segmentation task which utilizes text-to-image diffusion models pre-trained on extensive image-text pairs to enhance the model’s cross-modality capabilities. Specifically, MADM comprises two key complementary components to tackle major challenges. First, due to the large modality gap, using one modal data to generate …

Unified Generative And Discriminative Training For Multi-Modal Large Language Models, Wei Chow, Juncheng Li, Kaihang Pan, Qifan Yu, Hao Fei, Zhiqi Ge, Shuai Yang, Siliang Teng, Hanwang Zhang, Qianru Sun

Research Collection School Of Computing and Information Systems

In recent times, Vision-Language Models (VLMs) have been trained under two predominant paradigms. Generative training has enabled Multimodal Large Language Models (MLLMs) to tackle various complex tasks, yet issues such as hallucinations and weak object discrimination persist. Discriminative training, exemplified by models like CLIP, excels in zero-shot image-text classification and retrieval, yet struggles with complex scenarios requiring fine-grained semantic differentiation. This paper addresses these challenges by proposing a unified approach that integrates the strengths of both paradigms. Considering interleaved image-text sequences as the general format of input samples, we introduce a structure-induced training strategy that imposes semantic relationships between input …

Generative Semi-Supervised Graph Anomaly Detection, Hezhe Qiao, Qingsong Wen, Xiaoli Li, Ee-Peng Lim, Guansong Pang

Research Collection School Of Computing and Information Systems

This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal, contrasting to the extensively explored unsupervised setting with a fully unlabeled graph. We reveal that having access to the normal nodes, even just a small percentage of normal nodes, helps enhance the detection performance of existing unsupervised GAD methods when they are adapted to the semi-supervised setting. However, their utilization of these normal nodes is limited. In this paper we propose a novel Generative GAD approach (namely GGAD) for the semi-supervised scenario to better exploit the …

Inverse Factorized Soft Q-Learning For Cooperative Multi-Agent Imitation Learning, The Viet Bui, Tien Mai, Thanh Nguyen

Research Collection School Of Computing and Information Systems

This paper concerns imitation learning (IL) in cooperative multi-agent systems.The learning problem under consideration poses several challenges, characterized by high-dimensional state and action spaces and intricate inter-agent dependencies. In a single-agent setting, IL was shown to be done efficiently via an inverse soft-Q learning process. However, extending this framework to a multi-agent context introduces the need to simultaneously learn both local value functions to capture local observations and individual actions, and a joint value function for exploiting centralized learning.In this work, we introduce a new multi-agent IL algorithm designed to address these challenges. Our approach enables thecentralized learning by leveraging …

Trustworthy Web3 Domains: A Framework For Digital Identity Verification, Yi Meng Lau, Ping Fan Ke

Research Collection School Of Computing and Information Systems

As decentralized applications evolve, digital identities represented through Web3 domain names gained prominence. This study addresses the challenges of establishing trust in Web3 domain names. The decentralized nature of Web3 introduces complexities in verifying domain name authenticity, making them targets for malicious activities such as cybersquatting and phishing. We propose a comprehensive framework that enhances traditional identification, authentication, and authorization processes by incorporating technological and social trust elements. This framework enables organizations and users to systematically assess the trustworthiness of Web3 domain names, offering a structured approach to managing digital identities in decentralized environments.

Equitable Community-Based Participatory Research Engagement With Communities Of Color Drives All Of Us Wisconsin Genomic Research Priorities, Sheikh Iqbal Ahamed, Praveen Madiraju

Computer Science Faculty Research and Publications

Objective

The NIH All of Us Research Program aims to advance personalized medicine by not only linking patient records, surveys, and genomic data but also engaging with participants, particularly from groups traditionally underrepresented in biomedical research (UBR). This study details how the dialogue between scientists and community members, including many from communities of color, shaped local research priorities.

Materials and Methods

We recruited area quantitative, basic, and clinical scientists as well as community members from our Community and Participant Advisory Boards with a predetermined interest in All of Us research as members of a Special Interest Group (SIG). An expert …

Neural Network Architecture Search Enabled Wide-Deep Learning (Nas-Wd) For Spatially Heterogenous Property Awared Chicken Woody Breast Classification And Hardness Regression, Chaitanya Pallerla, Yihong Feng, Casey M. Owens, Ramesh Bahadur Bist, Siavash Mahmoudi, Pouya Sohrabipour, Amirreza Davar, Dongyi Wang

Poultry Science Faculty Publications and Presentations

Due to intensive genetic selection for rapid growth rates and high broiler yields in recent years, the global poultry industry has faced a challenging problem in the form of woody breast (WB) conditions. This condition has caused significant economic losses as high as $200 million annually, and the root cause of WB has yet to be identified. Human palpation is the most common method of distinguishing a WB from others. However, this method is time-consuming and subjective. Hyperspectral imaging (HSI) combined with machine learning algorithms can evaluate the WB conditions of fillets in a non-invasive, objective, and high-throughput manner. In …

Temporal Metadata Analysis: A Learning Classifier System Approach, Michael C. Todd, Gilbert L. Peterson

Faculty Publications

Digital forensics is a complex field that requires expert knowledge (EK) and specialized tools to collect, analyze, and report on digital evidence. Temporal metadata analysis is particularly challenging, requiring expert knowledge to understand and interpret underlying traces and associate them with their source. This paper introduces Digital Trace Inspector (DTI), a Learning Classifier System (LCS)-based decision support tool for temporal metadata analysis. DTI leverages a binary Michigan-style LCS to locate and group corroborating temporal digital traces of targeted user activity. Rules are built from expert-created atomics encoded as feature vectors using patterns defined in a structured EK rule framework. The …

Real-Time Network Simulations For Ml/Dl Ddos Detection Using Docker, Luis D. Garcia

Master's Theses

As the integration of artificial intelligence (AI) within cybersecurity continues to

grow, machine learning (ML) and deep learning (DL) models are increasingly used to

detect cyber attacks. However, these models are rarely evaluated in real-time attack

scenarios to see how subtle changes from the real networking environment can affect

their predictions. To address this issue, we propose a scalable, platform-independent

Docker testbed specifically designed for simulating real-time Distributed Denial of

Service (DDoS) attack scenarios that allows researchers to deploy and evaluate their

pre-trained, ML and DL detection models. Our framework is simple to configure

and can run across Intel and …