Digital Commons Network^™

Anomalyclip: Object-Agnostic Prompt Learning For Zero-Shot Anomaly Detection, Qihang Zhou, Guansong Pang, Yu Tian, Shibo He, Jiming Chen

Research Collection School Of Computing and Information Systems

Zero-shot anomaly detection (ZSAD) requires detection models trained using auxiliary data to detect anomalies without any training sample in a target dataset. It is a crucial task when training data is not accessible due to various concerns, e.g., data privacy, yet it is challenging since the models need to generalize to anomalies across different domains where the appearance of foreground objects, abnormal regions, and background features, such as defects/tumors on different products/ organs, can vary significantly. Recently large pre-trained vision-language models (VLMs), such as CLIP, have demonstrated strong zero-shot recognition ability in various vision tasks, including anomaly detection. However, their …

Multiple Continuous Top-K Queries Over Data Stream, Rui Zhu, Yujin Jia, Xiaochun Yang, Baihua Zheng, Bin Wang, Chuanyu Zong

Research Collection School Of Computing and Information Systems

Continuous top-k" role="presentation" style="box-sizing: border-box; display: inline-block; line-height: 0; font-size: 18.72px; font-size-adjust: none; overflow-wrap: normal; word-spacing: normal; text-wrap-mode: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; margin: 0px; padding: 1px 0px; position: relative;">kk query over sliding window is a fundamental challenge in the domain of streaming data management. Specifically, a continuous top-k query q" role="presentation" style="box-sizing: border-box; display: inline-block; line-height: 0; font-size: 18.72px; font-size-adjust: none; overflow-wrap: normal; word-spacing: normal; text-wrap-mode: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; margin: 0px; padding: 1px 0px; position: relative;" …

Non-Vacuous Generalization Bounds For Adversarial Risk In Stochastic Neural Networks, Mustafa Waleed, Liznerski Philipp, Antoine Ledent, Wagner Dennis, Wang Puyu, Kloft Marius

Research Collection School Of Computing and Information Systems

Adversarial examples are manipulated samples used to deceive machine learning models, posing a serious threat in safety-critical applications. Existing safety certificates for machine learning models are limited to individual input examples, failing to capture generalization to unseen data. To address this limitation, we propose novel generalization bounds based on the PAC-Bayesian and randomized smoothing frameworks, providing certificates that predict the model’s performance and robustness on unseen test samples based solely on the training data. We present an effective procedure to train and compute the first non-vacuous generalization bounds for neural networks in adversarial settings. Experimental results on the widely recognized …

Towards Explainable Harmful Meme Detection Through Multimodal Debate Between Large Language Models, Hongzhan Lin, Ziyang Luo, Wei Gao, Jing Ma, Bo Wang, Ruichao Yang

Research Collection School Of Computing and Information Systems

The age of social media is flooded with Internet memes, necessitating a clear grasp and effective identification of harmful ones. This task presents a significant challenge due to the implicit meaning embedded in memes, which is not explicitly conveyed through the surface text and image. However, existing harmful meme detection methods do not present readable explanations that unveil such implicit meaning to support their detection decisions. In this paper, we propose an explainable approach to detect harmful memes, achieved through reasoning over conflicting rationales from both harmless and harmful positions. Specifically, inspired by the powerful capacity of Large Language Models …

Discovering Personalized Characteristic Communities In Attributed Graphs, Yudong Niu, Yuchen Li, Panagiotis Karras, Yanhao Wang, Zhao Li

Research Collection School Of Computing and Information Systems

What is the widest community in which a person exercises a strong impact? Although extensive attention has been devoted to searching communities containing given individuals, the problem of finding their unique communities of influence has barely been examined. In this paper, we study the novel problem of Characteristic cOmmunity Discovery (COD) in attributed graphs. Our goal is to identify the largest community, taking into account the query attribute, in which the query node has a significant impact. The key challenge of the COD problem is that it requires evaluating the influence of the query node over a large number of …

Escaping Saddle Points In Heterogeneous Federated Learning Via Distributed Sgd With Communication Compression, Sijin Chen, Zhize Li, Yuejie Chi

Research Collection School Of Computing and Information Systems

We consider the problem of finding second-order stationary points in the optimization of heterogeneous federated learning (FL). Previous works in FL mostly focus on first-order convergence guarantees, which do not rule out the scenario of unstable saddle points. Meanwhile, it is a key bottleneck of FL to achieve communication efficiency without compensating the learning accuracy, especially when local data are highly heterogeneous across different clients. Given this, we propose a novel algorithm PowerEF-SGD that only communicates compressed information via a novel error-feedback scheme. To our knowledge, PowerEF-SGD is the first distributed and compressed SGD algorithm that provably escapes saddle points …

Policy-Based Remote User Authentication From Multi-Biometrics, Yangguang Tian, Yingjiu Li, Robert H. Deng, Guomin Yang, Nan Li

Research Collection School Of Computing and Information Systems

In this paper, we introduce the first generic framework of policy-based remote user authentication from multiple biometrics. The proposed framework allows an authorized user to remotely authenticate herself to an authentication server using her multiple biometrics, which enhances both the security and usability of user authentications. The authentication server approves a user's authentication request if and only if the user's multiple biometrics satisfies an authentication policy. In particular, the authentication policy can be dynamically updated to satisfy different security and usability requirements in practice. We implement an instantiation of the proposed framework and report its performance under various authentication policies.

Lecture-Style Tutorial: Towards Graph Foundation Models, Chuan Shi, Cheng Yang, Yuan Fang, Lichao Sun, Philip Yu

Research Collection School Of Computing and Information Systems

Emerging as fundamental building blocks for diverse artificial intelligence applications, foundation models have achieved notable success across natural language processing and many other domains. Concurrently, graph machine learning has gradually evolved from shallow methods to deep models to leverage the abundant graph-structured data that constitute an important pillar in the data ecosystem for artificial intelligence. Naturally, the emergence and homogenization capabilities of foundation models have piqued the interest of graph machine learning researchers. This has sparked discussions about developing a next-generation graph learning paradigm, one that is pre-trained on broad graph data and can be adapted to a wide range …

Rule-Guided Counterfactual Explainable Recommendation, Yinwei Wei, Xiaoyang Qu, Xiang Wang, Yunshan Ma, Liqiang Nie, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

To empower the trust of current recommender systems, the counterfactual explanation (CE) method is adopted to generate the counterfactual instance for each input and take their changes causing the different outcomes as the explanation. Although promising results have been achieved by existing CE-based methods, we propose to generate the attribute-oriented counterfactual explanation. Different from them, we aim to generate the counterfactual instance by performing the intervention on the attributes, and then build an attribute-oriented counterfactual explainable recommender system. Considering the correlation and categorical values of attributes, how to efficiently generate the reliable counterfactual instances on the attributes challenges us. To …

Learning To Generate Explainable Stock Predictions Using Self‑Reflective Large Language Models, Kelvin J.L. Koa, Yunshan Ma, Ritchie Ng, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Explaining stock predictions is generally a difficult task for traditional non-generative deep learning models, where explanations are limited to visualizing the attention weights on important texts. Today, Large Language Models (LLMs) present a solution to this problem, given their known capabilities to generate human-readable explanations for their decision-making process. However, the task of stock prediction remains challenging for LLMs, as it requires the ability to weigh the varying impacts of chaotic social texts on stock prices. The problem gets progressively harder with the introduction of the explanation component, which requires LLMs to explain verbally why certain factors are more important …

Quantum Machine Learning For Credit Scoring, Nikolaos Schetakis, Davit Aghamalyan, Micheael Boguslavsky, Agnieszka Rees, Marc Rakotomalala, Paul Robert Griffin

Research Collection School Of Computing and Information Systems

This study investigates the integration of quantum circuits with classical neural networks for enhancing credit scoring for small- and medium-sized enterprises (SMEs). We introduce a hybrid quantum–classical model, focusing on the synergy between quantum and classical rather than comparing the performance of separate quantum and classical models. Our model incorporates a quantum layer into a traditional neural network, achieving notable reductions in training time. We apply this innovative framework to a binary classification task with a proprietary real-world classical credit default dataset for SMEs in Singapore. The results indicate that our hybrid model achieves efficient training, requiring significantly fewer epochs …

Numerical Solutions Of Linear Abel Integral Equations Via Boubaker Polynomials Method, Jalil Talab Abdullah, Haleema Swaidan Ali, Waleeda Swaidan Ali

Baghdad Science Journal

In this article, a numerical method based on Boubaker polynomials (BPs) was presented to solve the Linear Abel integral (LAI) Eqs of first and second types. The matrices were used to form the (LAI) Eq into a system of linear Eqs. To get Boubaker parameters, solve this system of Eqs using the Guess elimination method. To explain the results of this method, four examples have been provided and compared with the results of many methods mentioned in previous research. MATLAB R2018b program was used to perform all calculations and graphs.

Synthesis Of Molecularly Imprinted Polymers For Selective Extraction Followed By Solid Phase Determination Of Metformin In Pharmaceutical Preparation And In Human Serum, Rana A. Kamal Aldeen, Yehya K. Al-Bayati

Baghdad Science Journal

This paper demonstrates that the synthesising and storage of molecular-imprinted polymers (MIP) at room temperature using bulk polymerisation of Metformin (Met) is characterised by high sensitivity, reduced costs, increased stability, and extended life. The research used 0.8:4:20 mmol ratios of template, monomer and cross-linking agents for the polymerisation in order to ensure an appropriate adsorption capacity. Benzoyl peroxide BPO was employed as the initiator for the functional monomer styrene C8H8 , cross-linked with Ethylene glycol dimethacrylate EGDMA C10H14O4, thereby creating MIP for Metformin(Met-MIP) that could be characterised with UV-Visible Spectrophotometry at 236nm, for pharmaceutical drugs and human serum. Fourier-transform infrared …

The Effect Of Heat Transformation And Rotation On Sutterby Fluid Peristaltic Flow In An Inclined, Asymmetric Channel With The Porousness, Asmaa A. Mohammed, Liqaa Zeki Hummady

Baghdad Science Journal

In this paper, the effect of the rotation variable and other variables on the peristaltic flow of Sutterby fluid in an Inclined asymmetric channel containing a porous medium with heat transfer is examined. In the presence of rotation, mathematical modeling is developed using constitutive equations based on the model of Sutterby fluid. In flow analysis, assumptions such as long wavelength approximation and low Reynolds number are used. The resulting nonlinear ordinary differential equation was analytically solved using the perturbation method. The effects of the Grashof number, the Hartmann number, the Reynold number, the Froude number, the Hall parameter, the Darcy …

Optimum System Design Using Rough Interval Multi-Objective De Novo Programming, Iftikhar Hussein, Hegazy Zaher, Naglaa Ragaa Saeid, Hebaa Sayed Roshdy

Baghdad Science Journal

The Multi-objective de novo programming method is an effective tool to deal with the optimal system design by determining the optimal level of resources allocation (RA) to improve the value of the objective functions according to the price of resources (the conditions are certainty). This paper suggested a new approach for solving uncertainty of De novo programming problems (DNP) using a combination model consisting of a rough interval multi-objective programming (RIMOP) and DNP, where coefficients of decision variables of objective functions and constraints are rough intervals (RIC). Three methods are used to find the optimal system design for the proposed …

Recurrency On The Space Of Hilbert-Schmidt Operators, Mansooreh Moosapoor

Baghdad Science Journal

In this paper, it is proved that if a C0-semigroup is chaotic, hypermixing or supermixing, then the related left multiplication C0-semigroup on the space of Hilbert-Schmidt operators is recurrent if and only if it is hypercyclic. Also, it is stated that under some conditions recurrence of a C0-semigroup and the recurrency of the left multiplication C0-semigroup that is related to it, on the space of Hilbert-Schmidt operators are equivalent. Moreover, some sufficient conditions for recurrency and hypercyclicity of the left multiplication C0-semigroup are presented that are based on dense subsets

Cardiac Conduction Diseases: Understanding The Molecular Mechanisms To Uncover Targets For Future Treatments, Tingting Li, Qussay Marashly, Jitae A Kim, Na Li, Mihail G Chelu

Faculty, Staff and Students Publications

Introduction: The cardiac conduction system (CCS) is crucial for maintaining adequate cardiac frequency at rest and modulation during exercise. Furthermore, the atrioventricular node and His-Purkinje system are essential for maintaining atrioventricular and interventricular synchrony and consequently maintaining an adequate cardiac output.

Areas covered: In this review article, we examine the anatomy, physiology, and pathophysiology of the CCS. We then discuss in detail the most common genetic mutations and the molecular mechanisms of cardiac conduction disease (CCD) and provide our perspectives on future research and therapeutic opportunities in this field.

Expert opinion: Significant advancement has been made in understanding the molecular …

Fashionregen: Llm‑Empowered Fashion Report Generation, Yujuan Ding, Yunshan Ma, Wenqi Fan, Yige Yao, Tat‑Seng Chua, Qing Li

Research Collection School Of Computing and Information Systems

Fashion analysis refers to the process of examining and evaluating trends, styles, and elements within the fashion industry to understand and interpret its current state, generating fashion reports. It is traditionally performed by fashion professionals based on their expertise and experience, which requires high labour cost and may also produce biased results for relying heavily on a small group of people. In this paper, to tackle the Fashion Report Generation (FashionReGen) task, we propose an intelligent Fashion Analyzing and Reporting system based the advanced Large Language Models (LLMs), debbed as GPT-FAR. Specifically, it tries to deliver FashionReGen based on effective …

Scaling Up Cooperative Multi-Agent Reinforcement Learning Systems, Minghong Geng

Research Collection School Of Computing and Information Systems

Cooperative multi-agent reinforcement learning methods aim to learn effective collaborative behaviours of multiple agents performing complex tasks. However, existing MARL methods are commonly proposed for fairly small-scale multi-agent benchmark problems, wherein both the number of agents and the length of the time horizons are typically restricted. My initial work investigates hierarchical controls of multi-agent systems, where a unified overarching framework coordinates multiple smaller multi-agent subsystems, tackling complex, long-horizon tasks that involve multiple objectives. Addressing another critical need in the field, my research introduces a comprehensive benchmark for evaluating MARL methods in long-horizon, multi-agent, and multi-objective scenarios. This benchmark aims to …

Q-Learning Based Framework For Solving The Stochastic E-Waste Collection Problem, Dang Viet Anh Nguyen, Aldy Gunawan, Mustafa Misir, Pieter Vansteenwegen

Research Collection School Of Computing and Information Systems

Electrical and Electronic Equipment (EEE) has evolved into a gateway for accessing technological innovations. However, EEE imposes substantial pressure on the environment due to the shortened life cycles. E-waste encompasses discarded EEE and its components which are no longer in use. This study focuses on the e-waste collection problem and models it as a Vehicle Routing Problem with a heterogeneous fleet and a multi-period planning problem with time windows as well as stochastic travel times. Two different Q-learning-based methods are designed to enhance the search procedure for finding solutions. The first method involves utilizing the state-action value to determine the …

Ublade: Efficient Batch Processing For Uncertainty Graph Queries, Siyuan Yao, Yuchen Li, Shixuan Sun, Jiaxin Jiang, Bingsheng He

Research Collection School Of Computing and Information Systems

The study of uncertain graphs is crucial in diverse fields, including but not limited to protein interaction analysis, viral marketing, and network reliability. Processing queries on uncertain graphs presents formidable challenges due to the vast probabilistic space they encapsulate. While existing systems employ batch processing to address these challenges, their performance is often compromised by the suboptimal selection of parallel graph traversal methods, the excessive costs in random number generation, and additional sampling-loads intrinsic to batch processing. In this paper, we introduce uBlade, an efficient batch-processing framework for uncertain graph queries on multi-core CPUs. uBlade utilizes the work-efficient graph traversal, …

Explaining Sequences Of Actions In Multi-Agent Deep Reinforcement Learning Models, Phyo Wai Khaing, Minghong Geng, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

This paper introduces a method to explain MADRL agents’ behaviors by abstracting their actions into high-level strategies. Particularly, a spatio-temporal neural network model is applied to encode the agents’ sequences of actions as memory episodes wherein an aggregating memory retrieval can generalize them into a concise abstract representation of collective strategies. To assess the effectiveness of our method, we applied it to explain the actions of QMIX MADRL agents playing a StarCraft Multi-agent Challenge (SMAC) video game. A user study on the perceived explainability of the extracted strategies indicates that our method can provide comprehensible explanations at various levels of …

Benchmarking Marl On Long Horizon Sequential Multi-Objective Tasks, Minghong Geng, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Current MARL benchmarks fall short in simulating realistic scenarios, particularly those involving long action sequences with sequential tasks and multiple conflicting objectives. Addressing this gap, we introduce Multi-Objective SMAC (MOSMAC), a novel MARL benchmark tailored to assess MARL methods on tasks with varying time horizons and multiple objectives. Each MOSMAC task contains one or multiple sequential subtasks. Agents are required to simultaneously balance between two objectives - combat and navigation - to successfully complete each subtask. Our evaluation of nine state-of-the-art MARL algorithms reveals that MOSMAC presents substantial challenges to many state-of-the-art MARL methods and effectively fills a critical gap …

Enabling Roll-Up And Drill-Down Operations In News Exploration With Knowledge Graphs For Due Diligence And Risk Management, Sha Wang, Yuchen Li, Hanhua Xiao, Zhifeng Bao, Yanfei Dong

Research Collection School Of Computing and Information Systems

Efficient news exploration is crucial in real-world applications, particularly within the financial sector, where numerous control and risk assessment tasks rely on the analysis of public news reports. The current processes in this domain predominantly rely on manual efforts, often involving keyword-based searches and the compilation of extensive keyword lists. In this paper, we introduce NCEXPLORER, a framework designed with OLAP-like operations to enhance the news exploration experience. NCEXPLORER empowers users to use roll-up operations for a broader content overview and drill-down operations for detailed insights. These operations are achieved through integration with external knowledge graphs (KGs), encompassing both fact-based …

Learning Nighttime Semantic Segmentation The Hard Way, Wenxi Liu, Jiaxin Cai, Qi Li, Chenyang Liao, Jingjing Cao, Shengfeng He, Yuanlong Yu

Research Collection School Of Computing and Information Systems

Nighttime semantic segmentation is an important but challenging research problem for autonomous driving. The major challenges lie in the small objects or regions from the under-/over-exposed areas or suffer from motion blur caused by the camera deployed on moving vehicles. To resolve this, we propose a novel hard- class-aware module that bridges the main network for full-class segmentation and the hard-class network for segmenting aforementioned hard-class objects. In specific, it exploits the shared focus of hard-class objects from the dual-stream network, enabling the contextual information flow to guide the model to concentrate on the pixels that are hard to classify. …

Text-Attributed Graph Representation Learning : Methods, Applications, And Challenges, Ce Zhang, Menglin Yang, Rex Ying, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Text documents are usually connected in a graph structure, resulting in an important class of data named text-attributed graph, e.g., paper citation graph and Web page hyperlink graph. On the one hand, Graph Neural Networks (GNNs) consider text in each document as general vertex attribute and do not specifically deal with text data. On the other hand, Pre-trained Language Models (PLMs) and Topic Models (TMs) learn effective document embeddings. However, most models focus on text content in each single document only, ignoring link adjacency across documents. The above two challenges motivate the development of text-attributed graph representation learning, combining GNNs …

Cornac-Ab : An Open-Source Recommendation Framework With Native A/B Testing Integration, Rong Sheng Ong, Quoc Tuan Truong, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Recommender systems significantly impact user experience across diverse domains, yet existing frameworks often prioritize offline evaluation metrics, neglecting the crucial integration of A/B testing for forward-looking assessments. In response, this paper introduces a new framework seamlessly incorporating A/B testing into the Cornac recommendation library. Leveraging a diverse collection of model implementations in Cornac, our framework enables effortless A/B testing experiment setup from offline trained models. We introduce a carefully designed dashboard and a robust backend for efficient logging and analysis of user feedback. This not only streamlines the A/B testing process but also enhances the evaluation of recommendation models in …

Term Importance For Transformer-Based Qa Retrieval : A Case Study Of Stackexchange, Bryan Zhi Yang Tan, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Question-answering (QA) retrieval is the task of retrieving the most relevant answer to a given question from a collection of answers. Various approaches to QA retrieval have been developed recently. One successful and popular model is Contextualized Late Interaction over BERT (ColBERT), a transformer-based approach that adopts a query-document scoring mechanism that retains the granularity of transformer matching, whilst improving on efficiency. However, one key limitation is that it requires further fine-tuning for new query or collection types. In this work, we explore and propose several non-parametric retrieval augmentation methods based on explicit signals of term importance that improve over …

Baffle : Hiding Backdoors In Offline Reinforcement Learning Datasets, Chen Gong, Zhou Yang, Yunpeng Bai, Junda He, Jieke Shi, Kecen Li, Arunesh Sinha, Bowen Xu, Xinwen Hou, David Lo, Tianhao Wang

Research Collection School Of Computing and Information Systems

Reinforcement learning (RL) makes an agent learn from trial-and-error experiences gathered during the interaction with the environment. Recently, offline RL has become a popular RL paradigm because it saves the interactions with environments. In offline RL, data providers share large pre-collected datasets, and others can train high-quality agents without interacting with the environments. This paradigm has demonstrated effectiveness in critical tasks like robot control, autonomous driving, etc. However, less attention is paid to investigating the security threats to the offline RL system. This paper focuses on backdoor attacks, where some perturbations are added to the data (observations) such that given …

Factored Mdp Based Moving Target Defense With Dynamic Threat Modeling, Megha Bose, Praveen Paruchuri, Akshat Kumar

Research Collection School Of Computing and Information Systems

Moving Target Defense (MTD) has emerged as a proactive defense framework to counteract ever-changing cyber threats. Existing approaches often make assumptions about attacker-side knowledge and behavior, potentially resulting in suboptimal defense. This paper introduces a novel MTD approach, leveraging a Markov Decision Process (MDP) model that eliminates the need for prior knowledge about attacker intentions or payoffs. Our framework seamlessly integrates real-time attacker responses into the defender's MDP using a dynamic Bayesian network. We use a factored MDP model to enable a more comprehensive and realistic representation of the system having multiple switchable aspects and also accommodate incremental updates of …