Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Physical Sciences and Mathematics (8519)
- Computer Sciences (8502)
- Databases and Information Systems (3446)
- Software Engineering (2153)
- Artificial Intelligence and Robotics (1686)
-
- Information Security (1056)
- Numerical Analysis and Scientific Computing (1023)
- Engineering (955)
- Graphics and Human Computer Interfaces (926)
- Social and Behavioral Sciences (734)
- Business (685)
- Computer Engineering (511)
- Theory and Algorithms (493)
- Operations Research, Systems Engineering and Industrial Engineering (417)
- Programming Languages and Compilers (379)
- OS and Networks (322)
- Communication (310)
- Social Media (248)
- Public Affairs, Public Policy and Public Administration (227)
- Transportation (204)
- Medicine and Health Sciences (183)
- Education (178)
- Management Information Systems (177)
- Data Storage Systems (175)
- E-Commerce (151)
- International and Area Studies (125)
- Asian Studies (124)
- Digital Communications and Networking (123)
- Technology and Innovation (114)
- Health Information Technology (110)
- Keyword
-
- Deep learning (124)
- Machine learning (124)
- Social media (80)
- Artificial intelligence (71)
- Reinforcement learning (69)
-
- Data mining (65)
- Privacy (61)
- Cloud computing (58)
- Deep Learning (56)
- Empirical study (55)
- Optimization (55)
- Security (53)
- Singapore (52)
- Visualization (52)
- Twitter (50)
- Neural networks (49)
- Online learning (49)
- Software engineering (49)
- Training (49)
- Task analysis (48)
- Anomaly detection (47)
- Collaboration (45)
- Feature extraction (45)
- Large Language Models (45)
- Blockchain (44)
- Semantics (43)
- Access control (41)
- Algorithms (40)
- Android (39)
- Classification (38)
- Publication Year
- File Type
Articles 1201 - 1230 of 8692
Full-Text Articles in Entire DC Network
Learning Dynamic Multimodal Network Slot Concepts From The Web For Forecasting Environmental, Social And Governance Ratings, Gary Ang, Ee-Peng Lim
Learning Dynamic Multimodal Network Slot Concepts From The Web For Forecasting Environmental, Social And Governance Ratings, Gary Ang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Dynamic multimodal networks are networks with node attributes from different modalities where the at- tributes and network relationships evolve across time, i.e., both networks and multimodal attributes are dynamic; for example, dynamic relationship networks between companies that evolve across time due to changes in business strategies and alliances, which are associated with dynamic company attributes from multiple modalities such as textual online news, categorical events, and numerical financial-related data. Such information can be useful in predictive tasks involving companies. Environmental, social, and gov- ernance (ESG) ratings of companies are important for assessing the sustainability risks of companies. The process of …
Improving Interpretable Embeddings For Ad-Hoc Video Search With Generative Captions And Multi-Word Concept Bank, Jiaxin Wu, Chong-Wah Ngo, Wing-Kwong Chan
Improving Interpretable Embeddings For Ad-Hoc Video Search With Generative Captions And Multi-Word Concept Bank, Jiaxin Wu, Chong-Wah Ngo, Wing-Kwong Chan
Research Collection School Of Computing and Information Systems
Aligning a user query and video clips in cross-modal latent space and that with semantic concepts are two mainstream approaches for ad-hoc video search (AVS). However, the effectiveness of existing approaches is bottlenecked by the small sizes of available video-text datasets and the low quality of concept banks, which results in the failures of unseen queries and the out-of-vocabulary problem. This paper addresses these two problems by constructing a new dataset and developing a multi-word concept bank. Specifically, capitalizing on a generative model, we construct a new dataset consisting of 7 million generated text and video pairs for pre-training. To …
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
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 …
Enhancing Visual Grounding In Vision-Language Pre-Training With Position-Guided Text Prompts, Alex Jinpeng Wang, Pan Zhou, Mike Zheng Shou, Shuicheng Yan
Enhancing Visual Grounding In Vision-Language Pre-Training With Position-Guided Text Prompts, Alex Jinpeng Wang, Pan Zhou, Mike Zheng Shou, Shuicheng Yan
Research Collection School Of Computing and Information Systems
Vision-Language Pre-Training (VLP) has demonstrated remarkable potential in aligning image and text pairs, paving the way for a wide range of cross-modal learning tasks. Nevertheless, we have observed that VLP models often fall short in terms of visual grounding and localization capabilities, which are crucial for many downstream tasks, such as visual reasoning. In response, we introduce a novel Position-guided Text Prompt ( PTP ) paradigm to bolster the visual grounding abilities of cross-modal models trained with VLP. In the VLP phase, PTP divides an image into N x N blocks and employs a widely-used object detector to identify objects …
Vibmilk: Non-Intrusive Milk Spoilage Detection Via Smartphone Vibration, Yuezhong Wu, Wei Song, Yanxiang Wang, Dong Ma, Weitao Xu, Mahbub Hassan, Wen Hu
Vibmilk: Non-Intrusive Milk Spoilage Detection Via Smartphone Vibration, Yuezhong Wu, Wei Song, Yanxiang Wang, Dong Ma, Weitao Xu, Mahbub Hassan, Wen Hu
Research Collection School Of Computing and Information Systems
Quantifying the chemical process of milk spoilage is challenging due to the need for bulky, expensive equipment that is not user-friendly for milk producers or customers. This lack of a convenient and accurate milk spoilage detection system can cause two significant issues. First, people who consume spoiled milk may experience serious health problems. Secondly, milk manufacturers typically provide a “best before” date to indicate freshness, but this date only shows the highest quality of the milk, not the last day it can be safely consumed, leading to significant milk waste. A practical and efficient solution to this problem is proposed …
Large Language Models For Qualitative Research In Software Engineering: Exploring Opportunities And Challenges, Muneera Bano, Rashina Hoda, Didar Zowghi, Christoph Treude
Large Language Models For Qualitative Research In Software Engineering: Exploring Opportunities And Challenges, Muneera Bano, Rashina Hoda, Didar Zowghi, Christoph Treude
Research Collection School Of Computing and Information Systems
The recent surge in the integration of Large Language Models (LLMs) like ChatGPT into qualitative research in software engineering, much like in other professional domains, demands a closer inspection. This vision paper seeks to explore the opportunities of using LLMs in qualitative research to address many of its legacy challenges as well as potential new concerns and pitfalls arising from the use of LLMs. We share our vision for the evolving role of the qualitative researcher in the age of LLMs and contemplate how they may utilize LLMs at various stages of their research experience.
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
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 …
Cornac-Ab : An Open-Source Recommendation Framework With Native A/B Testing Integration, Rong Sheng Ong, Quoc Tuan Truong, Hady Wirawan Lauw
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 …
Unveiling Code Pre-Trained Models: Investigating Syntax And Semantics Capacities, Wei Ma, Shangqing Liu, Mengjie Zhao, Xiaofei Xie, Wenhang Wang, Qiang Hu, Jie Zhang, Liu Yang
Unveiling Code Pre-Trained Models: Investigating Syntax And Semantics Capacities, Wei Ma, Shangqing Liu, Mengjie Zhao, Xiaofei Xie, Wenhang Wang, Qiang Hu, Jie Zhang, Liu Yang
Research Collection School Of Computing and Information Systems
Code models have made significant advancements in code intelligence by encoding knowledge about programming languages. While previous studies have explored the capabilities of these models in learning code syntax, there has been limited investigation on their ability to understand code semantics. Additionally, existing analyses assume the number of edges between nodes at the abstract syntax tree (AST) is related to syntax distance, and also often require transforming the high-dimensional space of deep learning models to a low-dimensional one, which may introduce inaccuracies. To study how code models represent code syntax and semantics, we conduct a comprehensive analysis of 7 code …
Navigating Real-World Challenges: A Quadruped Robot Guiding System For Visually Impaired People In Diverse Environments, Shaojun Cai, Ashwin Ram, Zhengtai Gou, Mohd Alqama Wasim Shaikh, Yu-An Chen, Yingjia Wan, Kotaro Hara, Shengdong Zhao, David Hsu
Navigating Real-World Challenges: A Quadruped Robot Guiding System For Visually Impaired People In Diverse Environments, Shaojun Cai, Ashwin Ram, Zhengtai Gou, Mohd Alqama Wasim Shaikh, Yu-An Chen, Yingjia Wan, Kotaro Hara, Shengdong Zhao, David Hsu
Research Collection School Of Computing and Information Systems
Blind and Visually Impaired (BVI) people find challenges in navigating unfamiliar environments, even using assistive tools such as white canes or smart devices. Increasingly affordable quadruped robots offer us opportunities to design autonomous guides that could improve how BVI people find ways around unfamiliar environments and maneuver therein. In this work, we designed RDog, a quadruped robot guiding system that supports BVI individuals’ navigation and obstacle avoidance in indoor and outdoor environments. RDog combines an advanced mapping and navigation system to guide users with force feedback and preemptive voice feedback. Using this robot as an evaluation apparatus, we conducted experiments …
Algorithms For Canvas-Based Attention Scheduling With Resizing, Yigong Hu, Ila Gokarn, Shengzhong Liu, Archan Misra, Tarek Adbelzaher
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 …
Non-Vacuous Generalization Bounds For Adversarial Risk In Stochastic Neural Networks, Mustafa Waleed, Liznerski Philipp, Antoine Ledent, Wagner Dennis, Wang Puyu, Kloft Marius
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 …
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
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 …
Attribute-Hiding Fuzzy Encryption For Privacy-Preserving Data Evaluation, Zhenhua Chen, Luqi Huang, Guomin Yang, Willy Susilo, Xingbing Fu, Xingxing Jia
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 …
Lecture-Style Tutorial: Towards Graph Foundation Models, Chuan Shi, Cheng Yang, Yuan Fang, Lichao Sun, Philip Yu
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 …
Policy-Based Remote User Authentication From Multi-Biometrics, Yangguang Tian, Yingjiu Li, Robert H. Deng, Guomin Yang, Nan Li
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.
Vaid: Indexing View Designs In Visual Analytics System, Lu Ying, Aoyu Wu, Haotian Li, Zikun Deng, Ji Lan, Jiang Wu, Yong Wang, Huamin Qu, Dazhen Deng, Yingcai Wu
Vaid: Indexing View Designs In Visual Analytics System, Lu Ying, Aoyu Wu, Haotian Li, Zikun Deng, Ji Lan, Jiang Wu, Yong Wang, Huamin Qu, Dazhen Deng, Yingcai Wu
Research Collection School Of Computing and Information Systems
Visual analytics (VA) systems have been widely used in various application domains. However, VA systems are complex in design, which imposes a serious problem: although the academic community constantly designs and implements new designs, the designs are difficult to query, understand, and refer to by subsequent designers. To mark a major step forward in tackling this problem, we index VA designs in an expressive and accessible way, transforming the designs into a structured format. We first conducted a workshop study with VA designers to learn user requirements for understanding and retrieving professional designs in VA systems. Thereafter, we came up …
Compositional Policy Learning In Stochastic Control Systems With Formal Guarantees, Dorde Zikelic, Mathias Lechner, Abhinav Verma, Krishnendu Chatterjee, Thomas A. Henzinger
Compositional Policy Learning In Stochastic Control Systems With Formal Guarantees, Dorde Zikelic, Mathias Lechner, Abhinav Verma, Krishnendu Chatterjee, Thomas A. Henzinger
Research Collection School Of Computing and Information Systems
Reinforcement learning has shown promising results in learning neural network policies for complicated control tasks. However, the lack of formal guarantees about the behavior of such policies remains an impediment to their deployment. We propose a novel method for learning a composition of neural network policies in stochastic environments, along with a formal certificate which guarantees that a specification over the policy's behavior is satisfied with the desired probability. Unlike prior work on verifiable RL, our approach leverages the compositional nature of logical specifications provided in SPECTRL, to learn over graphs of probabilistic reach-avoid specifications. The formal guarantees are provided …
Social Balance On Networks: Local Minima And Best-Edge Dynamics, Krishnendu Chatterjee, Jakub Svoboda, Dorde Zikelic, Andreas Pavlogiannis, Josef Tkadlec
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 …
Grasper: A Generalist Pursuer For Pursuit-Evasion Problems, Pengdeng Li, Shuxin Li, Xinrun Wang, Jakub Cerny, Youzhi Zhang, Stephen Mcaleer, Hau Chan, Bo An
Grasper: A Generalist Pursuer For Pursuit-Evasion Problems, Pengdeng Li, Shuxin Li, Xinrun Wang, Jakub Cerny, Youzhi Zhang, Stephen Mcaleer, Hau Chan, Bo An
Research Collection School Of Computing and Information Systems
Pursuit-evasion games (PEGs) model interactions between a team of pursuers and an evader in graph-based environments such as urban street networks. Recent advancements have demonstrated the effectiveness of the pre-training and fine-tuning paradigm in Policy-Space Response Oracles (PSRO) to improve scalability in solving large-scale PEGs. However, these methods primarily focus on specific PEGs with fixed initial conditions that may vary substantially in real-world scenarios, which significantly hinders the applicability of the traditional methods. To address this issue, we introduce Grasper, a GeneRAlist purSuer for Pursuit-Evasion pRoblems, capable of efficiently generating pursuer policies tailored to specific PEGs. Our contributions are threefold: …
Flipped Classroom For Linear Algebra At Undergraduate Level, M. Thulasidas
Flipped Classroom For Linear Algebra At Undergraduate Level, M. Thulasidas
Research Collection School Of Computing and Information Systems
In this article, we describe our experience in developing an undergraduate Linear Algebra course tailored to highlight its relevance and applicability in Computer Science. Over the course of three years, the course transitioned from a traditional direct-instruction format to a flipped-classroom design, resulting in positive student learning outcomes. This article covers the course design philosophy, its syllabus, learning objectives, and the incorporation of both quantitative and qualitative student feedback in shaping the course. Furthermore, the article shares the insights gleaned from our experience, which can serve as best practices for instructors aiming to deliver a successful Linear Algebra course for …
The Grader: A Grading Assistant For Lab Tests And A Teaching Tool, M. Thulasidas, David Lo
The Grader: A Grading Assistant For Lab Tests And A Teaching Tool, M. Thulasidas, David Lo
Research Collection School Of Computing and Information Systems
This article presents the design and implementation of the Grader, a grading assistant application deployed for a Web Application Development course at our school. The Grader is equipped to handle various logistical aspects of lab tests, including file management, consistent application of rubrics, and auto-grading of questions with test cases. Additionally, it incorporates heuristic rules to detect cheating attempts. We anticipate that the Grader will find widespread utility in programming courses where lab tests serve as summative assessments. Developed within the same programming environment taught in the class, the Grader also serves as a pedagogical tool, demonstrating to students a …
Exploring Diffusion Time-Steps For Unsupervised Representation Learning, Zhongqi Yue, Jiankun Wang, Qianru Sun, Lei Ji, Eric I-Chao Chang, Hanwang Zhang
Exploring Diffusion Time-Steps For Unsupervised Representation Learning, Zhongqi Yue, Jiankun Wang, Qianru Sun, Lei Ji, Eric I-Chao Chang, Hanwang Zhang
Research Collection School Of Computing and Information Systems
Representation learning is all about discovering the hidden modular attributes that generate the data faithfully. We explore the potential of Denoising Diffusion Probabilistic Model (DM) in unsupervised learning of the modular attributes. We build a theoretical framework that connects the diffusion time-steps and the hidden attributes, which serves as an effective inductive bias for unsupervised learning. Specifically, the forward diffusion process incrementally adds Gaussian noise to samples at each time-step, which essentially collapses different samples into similar ones by losing attributes, e.g., fine-grained attributes such as texture are lost with less noise added (i.e., early time-steps), while coarse-grained ones such …
Regret-Based Defense In Adversarial Reinforcement Learning, Roman Belaire, Pradeep Varakantham, Thanh Hong Nguyen, David Lo
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 …
Multiple Continuous Top-K Queries Over Data Stream, Rui Zhu, Yujin Jia, Xiaochun Yang, Baihua Zheng, Bin Wang, Chuanyu Zong
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;" …
Hjg: An Effective Hierarchical Joint Graph For Anns In Multi-Metric Spaces, Yifan Zhu, Lu Chen, Yunjun Gao, Ruiyao Ma, Baihua Zheng, Jingwen Zhao
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 …
Towards Explainable Harmful Meme Detection Through Multimodal Debate Between Large Language Models, Hongzhan Lin, Ziyang Luo, Wei Gao, Jing Ma, Bo Wang, Ruichao Yang
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 …
Scaling Up Cooperative Multi-Agent Reinforcement Learning Systems, Minghong Geng
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 …
Explaining Sequences Of Actions In Multi-Agent Deep Reinforcement Learning Models, Phyo Wai Khaing, Minghong Geng, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan
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 …
Learning Nighttime Semantic Segmentation The Hard Way, Wenxi Liu, Jiaxin Cai, Qi Li, Chenyang Liao, Jingjing Cao, Shengfeng He, Yuanlong Yu
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. …