Open Access. Powered by Scholars. Published by Universities.®

Computer Sciences Commons™

Open Access. Powered by Scholars. Published by Universities.®

Research Collection School Of Computing and Information Systems

Discipline
Keyword
Publication Year
File Type

Articles 1141 - 1170 of 8458

Full-Text Articles in Computer Sciences

Lecture-Style Tutorial: Towards Graph Foundation Models, Chuan Shi, Cheng Yang, Yuan Fang, Lichao Sun, Philip Yu May 2024

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 …

Go to article

An Evaluation Of Heart Rate Monitoring With In-Ear Microphones Under Motion, Kayla-Jade Butkow, Ting Dang, Andrea Ferlini, Dong Ma, Yang Liu, Cecilia Mascolo May 2024

An Evaluation Of Heart Rate Monitoring With In-Ear Microphones Under Motion, Kayla-Jade Butkow, Ting Dang, Andrea Ferlini, Dong Ma, Yang Liu, Cecilia Mascolo

Research Collection School Of Computing and Information Systems

With the soaring adoption of in-ear wearables, the research community has started investigating suitable in-ear heart rate detection systems. Heart rate is a key physiological marker of cardiovascular health and physical fitness. Continuous and reliable heart rate monitoring with wearable devices has therefore gained increasing attention in recent years. Existing heart rate detection systems in wearables mainly rely on photoplethysmography (PPG) sensors, however, these are notorious for poor performance in the presence of human motion. In this work, leveraging the occlusion effect that enhances low-frequency bone-conducted sounds in the ear canal, we investigate for the first time in-ear audio-based motion-resilient …

Go to article

Social Balance On Networks: Local Minima And Best-Edge Dynamics, Krishnendu Chatterjee, Jakub Svoboda, Dorde Zikelic, Andreas Pavlogiannis, Josef Tkadlec May 2024

Social Balance On Networks: Local Minima And Best-Edge Dynamics, Krishnendu Chatterjee, Jakub Svoboda, Dorde Zikelic, Andreas Pavlogiannis, Josef Tkadlec

Research Collection School Of Computing and Information Systems

Structural balance theory is an established framework for studying social relationships of friendship and enmity. These relationships are modeled by a signed network whose energy potential measures the level of imbalance, while stochastic dynamics drives the network toward a state of minimum energy that captures social balance. It is known that this energy landscape has local minima that can trap socially aware dynamics, preventing it from reaching balance. Here we first study the robustness and attractor properties of these local minima. We show that a stochastic process can reach them from an abundance of initial states and that some local …

Go to article

Attribute-Hiding Fuzzy Encryption For Privacy-Preserving Data Evaluation, Zhenhua Chen, Luqi Huang, Guomin Yang, Willy Susilo, Xingbing Fu, Xingxing Jia May 2024

Attribute-Hiding Fuzzy Encryption For Privacy-Preserving Data Evaluation, Zhenhua Chen, Luqi Huang, Guomin Yang, Willy Susilo, Xingbing Fu, Xingxing Jia

Research Collection School Of Computing and Information Systems

Privacy-preserving data evaluation is one of the prominent research topics in the big data era. In many data evaluation applications that involve sensitive information, such as the medical records of patients in a medical system, protecting data privacy during the data evaluation process has become an essential requirement. Aiming at solving this problem, numerous fuzzy encryption systems for different similarity metrics have been proposed in literature. Unfortunately, the existing fuzzy encryption systems either fail to achieve attribute-hiding or achieve it, but are impractical. In this paper, we propose a new fuzzy encryption scheme for privacy-preserving data evaluation based on overlap …

Go to article

Diffusion-Based Negative Sampling On Graphs For Link Prediction, Yuan Fang, Yuan Fang May 2024

Diffusion-Based Negative Sampling On Graphs For Link Prediction, Yuan Fang, Yuan Fang

Research Collection School Of Computing and Information Systems

Link prediction is a fundamental task for graph analysis with important applications on the Web, such as social network analysis and recommendation systems, etc. Modern graph link prediction methods often employ a contrastive approach to learn robust node representations, where negative sampling is pivotal. Typical negative sampling methods aim to retrieve hard examples based on either predefined heuristics or automatic adversarial approaches, which might be inflexible or difficult to control. Furthermore, in the context of link prediction, most previous methods sample negative nodes from existing substructures of the graph, missing out on potentially more optimal samples in the latent space. …

Go to article

Multigprompt For Multi-Task Pre-Training And Prompting On Graphs, Xingtong Yu, Chang Zhou, Yuan Fang, Xinming Zhan May 2024

Multigprompt For Multi-Task Pre-Training And Prompting On Graphs, Xingtong Yu, Chang Zhou, Yuan Fang, Xinming Zhan

Research Collection School Of Computing and Information Systems

Graph Neural Networks (GNNs) have emerged as a mainstream technique for graph representation learning. However, their efficacy within an end-to-end supervised framework is significantly tied to the availability of task-specific labels. To mitigate labeling costs and enhance robustness in few-shot settings, pre-training on self-supervised tasks has emerged as a promising method, while prompting has been proposed to further narrow the objective gap between pretext and downstream tasks. Although there has been some initial exploration of prompt-based learning on graphs, they primarily leverage a single pretext task, resulting in a limited subset of general knowledge that could be learned from the …

Go to article

Unraveling The ‘Anomaly’ In Time Series Anomaly Detection: A Self-Supervised Tri-Domain Solution, Yuting Sun, Guansong Pang, Guanhua Ye, Tong Chen, Xia Hu, Hongzhi Yin May 2024

Unraveling The ‘Anomaly’ In Time Series Anomaly Detection: A Self-Supervised Tri-Domain Solution, Yuting Sun, Guansong Pang, Guanhua Ye, Tong Chen, Xia Hu, Hongzhi Yin

Research Collection School Of Computing and Information Systems

The ongoing challenges in time series anomaly detection (TSAD), including the scarcity of anomaly labels and the variability in anomaly lengths and shapes, have led to the need for a more robust and efficient solution. As limited anomaly labels hinder traditional supervised models in anomaly detection, various state-of-the-art (SOTA) deep learning (DL) techniques (e.g., self-supervised learning) are introduced to tackle this issue. However, they encounter difficulties handling variations in anomaly lengths and shapes, limiting their adaptability to diverse anomalies. Additionally, many benchmark datasets suffer from the problem of having explicit anomalies that even random functions can detect. This problem is …

Go to article

Explaining Sequences Of Actions In Multi-Agent Deep Reinforcement Learning Models, Phyo Wai Khaing, Minghong Geng, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan May 2024

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 …

Go to article

Term Importance For Transformer-Based Qa Retrieval : A Case Study Of Stackexchange, Bryan Zhi Yang Tan, Hady W. Lauw May 2024

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 …

Go to article

Dlvs4audio2sheet: Deep Learning-Based Vocal Separation For Audio Into Music Sheet Conversion, Nicole Teo, Zhaoxia Wang, Ezekiel Ghe, Yee Sen Tan, Kevan Oktavio, Alexander Vincent Lewi, Allyne Zhang, Seng-Beng Ho May 2024

Dlvs4audio2sheet: Deep Learning-Based Vocal Separation For Audio Into Music Sheet Conversion, Nicole Teo, Zhaoxia Wang, Ezekiel Ghe, Yee Sen Tan, Kevan Oktavio, Alexander Vincent Lewi, Allyne Zhang, Seng-Beng Ho

Research Collection School Of Computing and Information Systems

While manual transcription tools exist, music enthusiasts, including amateur singers, still encounter challenges when transcribing performances into sheet music. This paper addresses the complex task of translating music audio into music sheets, particularly challenging in the intricate field of choral arrangements where multiple voices intertwine. We propose DLVS4Audio2Sheet, a novel method leveraging advanced deep learning models, Open-Unmix and Band-Split Recurrent Neural Networks (BSRNN), for vocal separation. DLVS4Audio2Sheet segments choral audio into individual vocal sections and selects the optimal model for further processing, aiming towards audio into music sheet conversion. We evaluate DLVS4Audio2Sheet’s performance using these deep learning algorithms and assess …

Go to article

Cmd: Co-Analyzed Iot Malware Detection And Forensics Via Network And Hardware Domains, Ziming Zhao, Zhaoxuan Li, Jiongchi Yu, Fan Zhang, Xiaofei Xie, Haitao Xu, Binbin Chen May 2024

Cmd: Co-Analyzed Iot Malware Detection And Forensics Via Network And Hardware Domains, Ziming Zhao, Zhaoxuan Li, Jiongchi Yu, Fan Zhang, Xiaofei Xie, Haitao Xu, Binbin Chen

Research Collection School Of Computing and Information Systems

With the widespread use of Internet of Things (IoT) devices, malware detection has become a hot spot for both academic and industrial communities. Existing approaches can be roughly categorized into network-side and host-side. However, existing network-side methods are difficult to capture contextual semantics from cross-source traffic, and previous host-side methods could be adversary-perceived and expose risks for tampering. More importantly, a single perspective cannot comprehensively track the multi-stage lifecycle of IoT malware. In this paper, we present CMD, a co-analyzed IoT malware detection and forensics system by combining hardware and network domains. For the network part, CMD proposes a tailored …

Go to article

Time-Controllable Keyword Search Scheme With Efficient Revocation In Mobile E-Health Cloud, Yinbin Miao, Feng Li, Xinghua Li, Zhiquan Liu, Jianting Ning, Hongwei Li, Kim-Kwang Raymond Choo, Deng, Robert H. May 2024

Time-Controllable Keyword Search Scheme With Efficient Revocation In Mobile E-Health Cloud, Yinbin Miao, Feng Li, Xinghua Li, Zhiquan Liu, Jianting Ning, Hongwei Li, Kim-Kwang Raymond Choo, Deng, Robert H.

Research Collection School Of Computing and Information Systems

Electronic health (e-health) systems may outsource data such as patient e-health records to mobile cloud servers for efficiency gains (e.g., minimizing local storage and computation costs). However, such a move may result in privacy implications in the presence of semi-honest cloud servers. Searchable Encryption (SE) can potentially facilitate privacy-preserving searches based on keywords for encrypted data stored in the mobile cloud, but most existing SE solutions do not support temporal access control (i.e., a mechanism that grants access permissions to users for specified time ranges). Hence, in this paper we design a time-controllable keyword search scheme by using an attribute-based …

Go to article

Breathpro: Monitoring Breathing Mode During Running With Earables, Changshuo Hu, Thivya Kandappu, Yang Liu, Cecilia Mascolo, Dong Ma May 2024

Breathpro: Monitoring Breathing Mode During Running With Earables, Changshuo Hu, Thivya Kandappu, Yang Liu, Cecilia Mascolo, Dong Ma

Research Collection School Of Computing and Information Systems

Running is a popular and accessible form of aerobic exercise, significantly benefiting our health and wellness. By monitoring a range of running parameters with wearable devices, runners can gain a deep understanding of their running behavior, facilitating performance improvement in future runs. Among these parameters, breathing, which fuels our bodies with oxygen and expels carbon dioxide, is crucial to improving the efficiency of running. While previous studies have made substantial progress in measuring breathing rate, exploration of additional breathing monitoring during running is still lacking. In this work, we fill this gap by presenting BreathPro, the first breathing mode monitoring …

Go to article

The Impact Of Avatar Completeness On Embodiment And The Detectability Of Hand Redirection In Virtual Reality, Martin Feick, Andre Zenner, Simon Seibert, Anthony Tang, Antonio Krüger May 2024

The Impact Of Avatar Completeness On Embodiment And The Detectability Of Hand Redirection In Virtual Reality, Martin Feick, Andre Zenner, Simon Seibert, Anthony Tang, Antonio Krüger

Research Collection School Of Computing and Information Systems

To enhance interactions in VR, many techniques introduce offsets between the virtual and real-world position of users’ hands. Nevertheless, such hand redirection (HR) techniques are only effective as long as they go unnoticed by users—not disrupting the VR experience. While several studies consider how much unnoticeable redirection can be applied, these focus on mid-air floating hands that are disconnected from users’ bodies. Increasingly, VR avatars are embodied as being directly connected with the user’s body, which provide more visual cue anchoring, and may therefore reduce the unnoticeable redirection threshold. In this work, we studied more complete avatars and their effect …

Go to article

Knowledge Enhanced Multi-Intent Transformer Network For Recommendation, Ding Zou, Wei Wei, Feida Zhu, Chuanyu Xu, Tao Zhang, Chengfu Huo May 2024

Knowledge Enhanced Multi-Intent Transformer Network For Recommendation, Ding Zou, Wei Wei, Feida Zhu, Chuanyu Xu, Tao Zhang, Chengfu Huo

Research Collection School Of Computing and Information Systems

Incorporating Knowledge Graphs (KGs) into Recommendation has attracted growing attention in industry, due to the great potential of KG in providing abundant supplementary information and interpretability for the underlying models. However, simply integrating KG into recommendation usually brings in negative feedback in industry, mainly due to the ignorance of the following two factors: i) users' multiple intents, which involve diverse nodes in KG. For example, in e-commerce scenarios, users may exhibit preferences for specific styles, brands, or colors. ii) knowledge noise, which is a prevalent issue in Knowledge Enhanced Recommendation (KGR) and even more severe in industry scenarios. The irrelevant …

Go to article

Make Revocation Cheaper: Hardware-Based Revocable Attribute-Based Encryption, Xiaoguo Li, Guomin Yang, Tao Xiang, Shengmin Xu, Bowen Zhao, Robert H. Deng, Hwee Hwa Pang May 2024

Make Revocation Cheaper: Hardware-Based Revocable Attribute-Based Encryption, Xiaoguo Li, Guomin Yang, Tao Xiang, Shengmin Xu, Bowen Zhao, Robert H. Deng, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

As an advanced one-to-many public key encryption system, attribute-based encryption (ABE) is widely believed to be a promising technology for achieving flexible and fine-grained access control of encrypted data on untrusted storage servers (e.g., public cloud servers). However, user revocation in ABE is a critical but challenging problem, and designing efficient revocable ABE has been an active research topic in the past decade. Almost all the existing revocable ABE schemes incorporate a timestamp in the encryption algorithm such that revoked users cannot decrypt ciphertexts generated in future time intervals. To prevent revoked users from decrypting past ciphertexts, the storage server …

Go to article

Scaling Up Cooperative Multi-Agent Reinforcement Learning Systems, Minghong Geng May 2024

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 …

Go to article

Factored Mdp Based Moving Target Defense With Dynamic Threat Modeling, Megha Bose, Praveen Paruchuri, Akshat Kumar May 2024

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 …

Go to article

Algorithms For Canvas-Based Attention Scheduling With Resizing, Yigong Hu, Ila Gokarn, Shengzhong Liu, Archan Misra, Tarek Adbelzaher May 2024

Algorithms For Canvas-Based Attention Scheduling With Resizing, Yigong Hu, Ila Gokarn, Shengzhong Liu, Archan Misra, Tarek Adbelzaher

Research Collection School Of Computing and Information Systems

Canvas-based attention scheduling was recently pro-posed to improve the efficiency of real-time machine perception systems. This framework introduces a notion of focus locales, referring to those areas where the attention of the inference system should “allocate its attention”. Data from these locales (e.g., parts of the input video frames containing objects of interest) are packed together into a smaller canvas frame which is processed by the downstream machine learning algorithm. Compared with processing the entire input data frame, this practice saves resources while maintaining inference quality. Previous work was limited to a simplified solution where the focus locales are quantized …

Go to article

Regret-Based Defense In Adversarial Reinforcement Learning, Roman Belaire, Pradeep Varakantham, Thanh Hong Nguyen, David Lo May 2024

Regret-Based Defense In Adversarial Reinforcement Learning, Roman Belaire, Pradeep Varakantham, Thanh Hong Nguyen, David Lo

Research Collection School Of Computing and Information Systems

Deep Reinforcement Learning (DRL) policies are vulnerable to adversarial noise in observations, which can have disastrous consequences in safety-critical environments. For instance, a self-driving car receiving adversarially perturbed sensory observations about traffic signs (e.g., a stop sign physically altered to be perceived as a speed limit sign) can be fatal. Leading existing approaches for making RL algorithms robust to an observation-perturbing adversary have focused on (a) regularization approaches that make expected value objectives robust by adding adversarial loss terms; or (b) employing "maximin'' (i.e., maximizing the minimum value) notions of robustness. While regularization approaches are adept at reducing the probability …

Go to article

Hjg: An Effective Hierarchical Joint Graph For Anns In Multi-Metric Spaces, Yifan Zhu, Lu Chen, Yunjun Gao, Ruiyao Ma, Baihua Zheng, Jingwen Zhao May 2024

Hjg: An Effective Hierarchical Joint Graph For Anns In Multi-Metric Spaces, Yifan Zhu, Lu Chen, Yunjun Gao, Ruiyao Ma, Baihua Zheng, Jingwen Zhao

Research Collection School Of Computing and Information Systems

Owing to the widespread deployment of smartphones and networked devices, massive amount of data in different types are generated every day, including numeric data, locations, text data, images, etc. Nearest neighbour search in multi-metric spaces has attracted much attention, as it can accommodate any type of data and support search on flexible combinations of multiple metrics. However, most existing methods focus on single metric queries, failing to answer multi-metric queries efficiently due to the complex metric combinations. In this paper, for the first time, we study the approximate nearest neighbour search (ANNS) in multi-metric spaces, and propose HJG, a hierarchical …

Go to article

Evaluation Of Orca 2 Against Other Llms For Retrieval Augmented Generation, Donghao Huang, Zhaoxia Wang May 2024

Evaluation Of Orca 2 Against Other Llms For Retrieval Augmented Generation, Donghao Huang, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

This study presents a comprehensive evaluation of Microsoft Research’s Orca 2, a small yet potent language model, in the context of Retrieval Augmented Generation (RAG). The research involved comparing Orca 2 with other significant models such as Llama-2, GPT-3.5-Turbo, and GPT-4, particularly focusing on its application in RAG. Key metrics, included faithfulness, answer relevance, overall score, and inference speed, were assessed. Experiments conducted on high-specification PCs revealed Orca 2’s exceptional performance in generating high quality responses and its efficiency on consumer-grade GPUs, underscoring its potential for scalable RAG applications. This study highlights the pivotal role of smaller, efficient models like …

Go to article

Large Language Model Powered Agents In The Web, Yang Deng, An Zhang, Yankai Lin, Xu Chen, Ji-Rong Wen, Tat-Seng Chua May 2024

Large Language Model Powered Agents In The Web, Yang Deng, An Zhang, Yankai Lin, Xu Chen, Ji-Rong Wen, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Web applications serve as vital interfaces for users to access information, perform various tasks, and engage with content. Traditional web designs have predominantly focused on user interfaces and static experiences. With the advent of large language models (LLMs), there’s a paradigm shift as we integrate LLM-powered agents into these platforms. These agents bring forth crucial human capabilities like memory and planning to make them behave like humans in completing various tasks, effectively enhancing user engagement and offering tailored interactions in web applications. In this tutorial, we delve into the cutting-edge techniques of LLM-powered agents across various web applications, such as …

Go to article

Difference Of Convex Functions Programming For Policy Optimization In Reinforcement Learning, Akshat Kumar May 2024

Difference Of Convex Functions Programming For Policy Optimization In Reinforcement Learning, Akshat Kumar

Research Collection School Of Computing and Information Systems

We formulate the problem of optimizing an agent's policy within the Markov decision process (MDP) model as a difference-of-convex functions (DC) program. The DC perspective enables optimizing the policy iteratively where each iteration constructs an easier-to-optimize lower bound on the value function using the well known concave-convex procedure. We show that several popular policy gradient based deep RL algorithms (both for discrete and continuous state, action spaces, and stochastic/deterministic policies) such as actor-critic, deterministic policy gradient (DPG), and soft actor critic (SAC) can be derived from the DC perspective. Additionally, the DC formulation enables more sample efficient learning approaches by …

Go to article

Non-Vacuous Generalization Bounds For Adversarial Risk In Stochastic Neural Networks, Mustafa Waleed, Liznerski Philipp, Antoine Ledent, Wagner Dennis, Wang Puyu, Kloft Marius May 2024

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 …

Go to article

Q-Learning Based Framework For Solving The Stochastic E-Waste Collection Problem, Dang Viet Anh Nguyen, Aldy Gunawan, Mustafa Misir, Pieter Vansteenwegen May 2024

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 …

Go to article

Benchmarking Marl On Long Horizon Sequential Multi-Objective Tasks, Minghong Geng, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan May 2024

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 …

Go to article

Multiple Continuous Top-K Queries Over Data Stream, Rui Zhu, Yujin Jia, Xiaochun Yang, Baihua Zheng, Bin Wang, Chuanyu Zong May 2024

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;" …

Go to article

Collaborative Deep Reinforcement Learning For Solving Multi-Objective Vehicle Routing Problems, Yaoxin Wu, Mingfeng Fan, Zhiguang Cao, Ruobin Gao, Yaqing Hou, Guillaume Sartoretti May 2024

Collaborative Deep Reinforcement Learning For Solving Multi-Objective Vehicle Routing Problems, Yaoxin Wu, Mingfeng Fan, Zhiguang Cao, Ruobin Gao, Yaqing Hou, Guillaume Sartoretti

Research Collection School Of Computing and Information Systems

Existing deep reinforcement learning (DRL) methods for multi-objective vehicle routing problems (MOVRPs) typically decompose an MOVRP into subproblems with respective preferences and then train policies to solve corresponding subproblems. However, such a paradigm is still less effective in tackling the intricate interactions among subproblems, thus holding back the quality of the Pareto solutions. To counteract this limitation, we introduce a collaborative deep reinforcement learning method. We first propose a preference-based attention network (PAN) that allows the DRL agents to reason out solutions to subproblems in parallel, where a shared encoder learns the instance embedding and a decoder is tailored for …

Go to article

Rule-Guided Counterfactual Explainable Recommendation, Yinwei Wei, Xiaoyang Qu, Xiang Wang, Yunshan Ma, Liqiang Nie, Tat‑Seng Chua May 2024

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 …

Go to article

First Prev Next Last