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A Full-History Network Dataset For Btc Asset Decentralization Profiling, Ling Cheng, Qian Shao, Fengzhu Zeng, Feida Zhu
A Full-History Network Dataset For Btc Asset Decentralization Profiling, Ling Cheng, Qian Shao, Fengzhu Zeng, Feida Zhu
Research Collection School Of Computing and Information Systems
Since its advent in 2009, Bitcoin (BTC) has garnered increasing attention from both academia and industry. However, due to the massive transaction volume, no systematic study has quantitatively measured the asset decentralization degree specifically from a network perspective.In this paper, by conducting a thorough analysis of the BTC transaction network, we first address the significant gap in the availability of full-history BTC graph and network property dataset, which spans over 15 years from the genesis block (1st March, 2009) to the 845651-th block (29, May 2024). We then present the first systematic investigation to profile BTC's asset decentralization and design …
Agchain: A Blockchain-Based Gateway For Trustworthy App Delegation From Mobile App Markets, Mengjie Chen, Xiao Yi, Daoyuan Wu, Jianliang Xu, Yingjiu Li, Debin Gao
Agchain: A Blockchain-Based Gateway For Trustworthy App Delegation From Mobile App Markets, Mengjie Chen, Xiao Yi, Daoyuan Wu, Jianliang Xu, Yingjiu Li, Debin Gao
Research Collection School Of Computing and Information Systems
The popularity of smartphones has led to the growth of mobile app markets, creating a need for enhanced transparency, global access, and secure downloading. This paper introduces AGChain, a blockchain-based gateway that enables trustworthy app delegation within existing markets. AGChain ensures that markets can continue providing services while users benefit from permanent, distributed, and secure app delegation. During its development, we address two key challenges: significantly reducing smart contract gas costs and enabling fully distributed IPFS-based file storage. Additionally, we tackle three system issues related to security and sustainability. We have implemented a prototype of AGChain on Ethereum and Polygon …
Triadic Temporal-Semantic Alignment For Weakly-Supervised Video Moment Retrieval, Jin Liu, Jialong Xie, Fengyu Zhou, Shengfeng He
Triadic Temporal-Semantic Alignment For Weakly-Supervised Video Moment Retrieval, Jin Liu, Jialong Xie, Fengyu Zhou, Shengfeng He
Research Collection School Of Computing and Information Systems
Video Moment Retrieval (VMR) aims to identify specific event moments within untrimmed videos based on natural language queries. Existing VMR methods have been criticized for relying heavily on moment annotation bias rather than true multi-modal alignment reasoning. Weakly supervised VMR approaches inherently overcome this issue by training without precise temporal location information. However, they struggle with fine-grained semantic alignment and often yield multiple speculative predictions with prolonged video spans. In this paper, we take a step forward in the context of weakly supervised VMR by proposing a triadic temporalsemantic alignment model. Our proposed approach augments weak supervision by comprehensively addressing …
An Aggregate Matching And Pick-Up Model For Mobility-On-Demand Services, Xinwei Li, Jintao Ke, Hai Yang, Hai Wang, Yaqian Zhou
An Aggregate Matching And Pick-Up Model For Mobility-On-Demand Services, Xinwei Li, Jintao Ke, Hai Yang, Hai Wang, Yaqian Zhou
Research Collection School Of Computing and Information Systems
This paper presents an Aggregate Matching and Pick-up (AMP) model to delineate the matching and pick-up processes in mobility-on-demand (MoD) service markets by explicitly considering the matching mechanisms in terms of matching intervals and matching radii. With passenger demand rate, vehicle fleet size and matching strategies as inputs, the AMP model can well approximate drivers’ idle time and passengers’ waiting time for matching and pick-up by considering batch matching in a stationary state. Properties of the AMP model are then analyzed, including the relationship between passengers’ waiting time and drivers’ idle time, and their changes with market thickness, which is …
Towards Unified Multimodal Editing With Enhanced Knowledge Collaboration, Kaihang Pan, Zhaoyu Fan, Juncheng Li, Qifan Yu, Hao Fei, Siliang Tang, Richang Hong, Hanwang Zhang, Qianru Sun
Towards Unified Multimodal Editing With Enhanced Knowledge Collaboration, Kaihang Pan, Zhaoyu Fan, Juncheng Li, Qifan Yu, Hao Fei, Siliang Tang, Richang Hong, Hanwang Zhang, Qianru Sun
Research Collection School Of Computing and Information Systems
The swift advancement in Multimodal LLMs (MLLMs) also presents significant challenges for effective knowledge editing. Current methods, including intrinsic knowledge editing and external knowledge resorting, each possess strengths and weaknesses, struggling to balance the desired properties of reliability, generality, and locality when applied to MLLMs. In this paper, we propose UniKE, a novel multimodal editing method that establishes a unified perspective and paradigm for intrinsic knowledge editing and external knowledge resorting. Both types of knowledge are conceptualized as vectorized key-value memories, with the corresponding editing processes resembling the assimilation and accommodation phases of human cognition, conducted at the same semantic …
Learning De-Biased Representations For Remote-Sensing Imagery, Zichen Tian, Zhaozheng Chen, Qianru Sun
Learning De-Biased Representations For Remote-Sensing Imagery, Zichen Tian, Zhaozheng Chen, Qianru Sun
Research Collection School Of Computing and Information Systems
Remote sensing (RS) imagery, requiring specialized satellites to collect and being difficult to annotate, suffers from data scarcity and class imbalance in certain spectrums. Due to data scarcity, training any large-scale RS models from scratch is unrealistic, and the alternative is to transfer pre-trained models by fine-tuning or a more data-efficient method LoRA. Due to class imbalance, transferred models exhibit strong bias, where features of the major class dominate over those of the minor class. In this paper, we propose debLoRA---a generic training approach that works with any LoRA variants to yield debiased features. It is an unsupervised learning approach …
Reinforcement Learning Based Online Request Scheduling Framework For Workload-Adaptive Edge Deep Learning Inference, Xinrui Tan, Hongjia Li, Xiaofei Xie, Lu Guo, Nirwan Ansari, Xueqing Huang, Liming Wang, Zhen Xu, Yang Liu
Reinforcement Learning Based Online Request Scheduling Framework For Workload-Adaptive Edge Deep Learning Inference, Xinrui Tan, Hongjia Li, Xiaofei Xie, Lu Guo, Nirwan Ansari, Xueqing Huang, Liming Wang, Zhen Xu, Yang Liu
Research Collection School Of Computing and Information Systems
The recent advances of deep learning in various mobile and Internet-of-Things applications, coupled with the emergence of edge computing, have led to a strong trend of performing deep learning inference on the edge servers located physically close to the end devices. This trend presents the challenge of how to meet the quality-of-service requirements of inference tasks at the resource-constrained network edge, especially under variable or even bursty inference workloads. Solutions to this challenge have not yet been reported in the related literature. In the present paper, we tackle this challenge by means of workload-adaptive inference request scheduling: in different workload …
Unsupervised Modality Adaptation With Text-To-Image Diffusion Models For Semantic Segmentation, Ruihao Xia, Yu Liang, Peng-Tao Jiang, Hao Zhang, Bo Li, Yang Tang, Pan Zhou
Unsupervised Modality Adaptation With Text-To-Image Diffusion Models For Semantic Segmentation, Ruihao Xia, Yu Liang, Peng-Tao Jiang, Hao Zhang, Bo Li, Yang Tang, Pan Zhou
Research Collection School Of Computing and Information Systems
Despite their success, unsupervised domain adaptation methods for semantic segmentation primarily focus on adaptation between image domains and do not utilize other abundant visual modalities like depth, infrared and event. This limitation hinders their performance and restricts their application in real-world multimodal scenarios. To address this issue, we propose Modality Adaptation with text-toimage Diffusion Models (MADM) for semantic segmentation task which utilizes text-to-image diffusion models pre-trained on extensive image-text pairs to enhance the model’s cross-modality capabilities. Specifically, MADM comprises two key complementary components to tackle major challenges. First, due to the large modality gap, using one modal data to generate …
Lova3 : Learning To Visual Question Answering, Asking And Assessment, Henry Hengyuan Zhao, Pan Zhou, Difei Gao, Bai Shou, Mike Zheng Shou
Lova3 : Learning To Visual Question Answering, Asking And Assessment, Henry Hengyuan Zhao, Pan Zhou, Difei Gao, Bai Shou, Mike Zheng Shou
Research Collection School Of Computing and Information Systems
Question answering, asking, and assessment are three innate human traits crucial for understanding the world and acquiring knowledge. By enhancing these capabilities, humans can more effectively utilize data, leading to better comprehension and learning outcomes. Current Multimodal Large Language Models (MLLMs) primarily focus on question answering, often neglecting the full potential of questioning and assessment skills. Inspired by the human learning mechanism, we introduce LOVA3 , an innovative framework named “Learning tO Visual question Answering, Asking and Assessment,” designed to equip MLLMs with these additional capabilities. Our approach involves the creation of two supplementary training tasks GenQA and EvalQA, aiming …
4-Bit Shampoo For Memory-Efficient Network Training, Sike Wang, Pan Zhou, Jia Li, Hua Huang
4-Bit Shampoo For Memory-Efficient Network Training, Sike Wang, Pan Zhou, Jia Li, Hua Huang
Research Collection School Of Computing and Information Systems
Second-order optimizers, maintaining a matrix termed a preconditioner, are superior to first-order optimizers in both theory and practice. The states forming the preconditioner and its inverse root restrict the maximum size of models trained by second-order optimizers. To address this, compressing 32-bit optimizer states to lower bitwidths has shown promise in reducing memory usage. However, current approaches only pertain to first-order optimizers. In this paper, we propose the first 4-bit second-order optimizers, exemplified by 4-bit Shampoo, maintaining performance similar to that of 32-bit ones. We show that quantizing the eigenvector matrix of the preconditioner in 4-bit Shampoo is remarkably better …
3d Snapshot: Invertible Embedding Of 3d Neural Representations In A Single Image, Yuqin Lu, Bailin Deng, Zhixuan Zhong, Tianle Zhang, Yuhui Quan, Hongmin Cai, Shengfeng He
3d Snapshot: Invertible Embedding Of 3d Neural Representations In A Single Image, Yuqin Lu, Bailin Deng, Zhixuan Zhong, Tianle Zhang, Yuhui Quan, Hongmin Cai, Shengfeng He
Research Collection School Of Computing and Information Systems
3D neural rendering enables photo-realistic reconstruction of a specific scene by encoding discontinuous inputs into a neural representation. Despite the remarkable rendering results, the storage of network parameters is not transmission-friendly and not extendable to metaverse applications. In this paper, we propose an invertible neural rendering approach that enables generating an interactive 3D model from a single image (i.e., 3D Snapshot). Our idea is to distill a pre-trained neural rendering model (e.g., NeRF) into a visualizable image form that can then be easily inverted back to a neural network. To this end, we first present a neural image distillation method …
Mimicking To Dominate: Imitation Learning Strategies For Success In Multiagent Competitive Games, The Viet Bui, Tien Mai, Hong Thanh Nguyen
Mimicking To Dominate: Imitation Learning Strategies For Success In Multiagent Competitive Games, The Viet Bui, Tien Mai, Hong Thanh Nguyen
Research Collection School Of Computing and Information Systems
Training agents in multi-agent games presents significant challenges due to their intricate nature. These challenges are exacerbated by dynamics influenced not only by the environment but also by strategies of opponents. Existing methods often struggle with slow convergence and instability. To address these challenges, we harness the potential of imitation learning (IL) to comprehend and anticipate actions of the opponents, aiming to mitigate uncertainties with respect to the game dynamics. Our key contributions include: (i) a new multi-agent IL model for predicting next moves of the opponents --- our model works with hidden actions of opponents and local observations; (ii) …
Sampdetox : Black-Box Backdoor Defense Via Perturbation-Based Sample Detoxification, Yanxin Yang, Chentao Jia, Dengke Yan, Ming Hu, Tianlin Li, Xiaofei Xie, Xian Wei, Mingsong Chen
Sampdetox : Black-Box Backdoor Defense Via Perturbation-Based Sample Detoxification, Yanxin Yang, Chentao Jia, Dengke Yan, Ming Hu, Tianlin Li, Xiaofei Xie, Xian Wei, Mingsong Chen
Research Collection School Of Computing and Information Systems
The advancement of Machine Learning has enabled the widespread deployment of Machine Learning as a Service (MLaaS) applications. However, the untrustworthy nature of third-party ML services poses backdoor threats. Existing defenses in MLaaS are limited by their reliance on training samples or white-box model analysis, highlighting the need for a black-box backdoor purification method. In our paper, we attempt to use diffusion models for purification by introducing noise in a forward diffusion process to destroy backdoors and recover clean samples through a reverse generative process. However, since a higher noise also destroys the semantics of the original samples, it still …
Learning To Handle Complex Constraints For Vehicle Routing Problems, Jieyi Bi, Yining Ma, Jianan Zhou, Wen Song, Zhiguang Cao, Yaoxin Wu, Jie Zhang
Learning To Handle Complex Constraints For Vehicle Routing Problems, Jieyi Bi, Yining Ma, Jianan Zhou, Wen Song, Zhiguang Cao, Yaoxin Wu, Jie Zhang
Research Collection School Of Computing and Information Systems
Vehicle Routing Problems (VRPs) can model many real-world scenarios and often involve complex constraints. While recent neural methods excel in constructing solutions based on feasibility masking, they struggle with handling complex constraints, especially when obtaining the masking itself is NP-hard. In this paper, we propose a novel Proactive Infeasibility Prevention (PIP) framework to advance the capabilities of neural methods towards more complex VRPs. Our PIP integrates the Lagrangian multiplier as a basis to enhance constraint awareness and introduces preventative infeasibility masking to proactively steer the solution construction process. Moreover, we present PIP-D, which employs an auxiliary decoder and two adaptive …
Self-Supervised Fine-Tuning For Neural Expert Finding, Budhitama Subagdja, Dan Sanchari, Ah-Hwee Tan
Self-Supervised Fine-Tuning For Neural Expert Finding, Budhitama Subagdja, Dan Sanchari, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Expert finding systems allow ones to find individuals who have expertise in specific fields or domains. Traditional expert finding are mostly based on topic modeling or keyword search methods that are limited in their capability to encode contextual knowledge from natural language. To address the limitation, this paper presents Neural Expert Finder (NEF), a novel method that takes a transfer learning approach based on transformer encoder networks to leverage the rich seman-tic and syntactic patterns of language encoded in pre-trained language models (PLMs). We propose a self-supervised learning approach utilizing contrastive training using both positive and automatically generated negative samples …
Improving Environment Novelty Quantification For Effective Unsupervised Environment Design, Jayden Teoh, Wenjun Li, Pradeep Varakantham
Improving Environment Novelty Quantification For Effective Unsupervised Environment Design, Jayden Teoh, Wenjun Li, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
Unsupervised Environment Design (UED) formalizes the problem of autocurricula through interactive training between a teacher agent and a student agent. The teacher generates new training environments with high learning potential, curating an adaptive curriculum that strengthens the student’s ability to handle unseen scenarios. Existing UED methods mainly rely on regret, a metric that measures the difference between the agent’s optimal and actual performance, to guide curriculum design. Regret-driven methods generate curricula that progressively increase environment complexity for the student but overlook environment novelty–a critical element for enhancing an agent’s generalizability. Measuring environment novelty is especially challenging due to the underspecified …
Meta-Entrepreneurship: An Analysis Theory On Integrating Generative Ai, Agentic Ai, And Metaverse For Entrepreneurship, Yingying Zhang, Keng Siau
Meta-Entrepreneurship: An Analysis Theory On Integrating Generative Ai, Agentic Ai, And Metaverse For Entrepreneurship, Yingying Zhang, Keng Siau
Research Collection School Of Computing and Information Systems
Metaverse entrepreneurship has emerged as an innovative topic alongside the development of generative AI, agentic AI and metaverse. This study conceptualizes meta-entrepreneurship as a novel form of entrepreneurial activity that enables value creation within virtual and physical realms and proposes an analytical theoretical framework based on a systematic literature review, observations, and focus group study. Our framework is structured around three layers (infrastructure, content, and experience) and two domains (metaverse-based operational domain and AI-based production domain), aims to conceptualize “what is meta-entrepreneurship” and identify new possibilities. The research highlights the multifaceted impact of meta-entrepreneurship on individuals, corporations, industries, societies, and …
Editorial For The Special Issue Of The Metaverse, Fiona Fui-Hoon Nah, Gert-Jan De Vreede, Lakshmi Goel, Eric Lim, Shu Schiller, Chee-Wee Tan
Editorial For The Special Issue Of The Metaverse, Fiona Fui-Hoon Nah, Gert-Jan De Vreede, Lakshmi Goel, Eric Lim, Shu Schiller, Chee-Wee Tan
Research Collection School Of Computing and Information Systems
The metaverse is laying the groundwork for more accessible and immersive experiences by blending the physical and virtual worlds into a unified space where people can interact, create, and connect in entirely new ways. It holds the potential to revolutionize how we work, socialize, and learn, which in turn gives rise to unprecedented opportunities for innovation. In this special issue, we present four articles that depict the current state of research in metaverse, the key themes and theoretical underpinnings within this space, as well as emerging directions for future work. This special issue delivers valuable insights for both researchers and …
Virtual Conferencing Fatigue: Look‑Alike Avatar And Facial Attractiveness, Yuxin Liu, Keng Siau, Xueqing Wang, Yang Yang
Virtual Conferencing Fatigue: Look‑Alike Avatar And Facial Attractiveness, Yuxin Liu, Keng Siau, Xueqing Wang, Yang Yang
Research Collection School Of Computing and Information Systems
The rapid evolution of avatar-related technologies provides extensive opportunities for diverse avatar applications in various areas. This study aims to investigate the innovative use of avatars to mitigate virtual conferencing fatigue, which refers to the physical and mental exhaustion from the inappropriate use of virtual conferencing applications. Grounded in Self-Awareness Theory, the research compares the impact of using real faces and user-look-alike avatars on virtual conferencing fatigue, delving into its underlying factors. In addition, the study examines the role of facial attractiveness enhancement on virtual conferencing fatigue. Laboratory experiments with a 2-by-2 between-subject design are employed to test hypotheses. The …
Dc-Instruct : An Effective Framework For Generative Multi-Intent Spoken Language Understanding, Bowen Xing, Lizi Liao, Minlie Huang
Dc-Instruct : An Effective Framework For Generative Multi-Intent Spoken Language Understanding, Bowen Xing, Lizi Liao, Minlie Huang
Research Collection School Of Computing and Information Systems
In the realm of multi-intent spoken language understanding, recent advancements have leveraged the potential of prompt learning frameworks. However, critical gaps exist in these frameworks: the lack of explicit modeling of dual-task dependencies and the oversight of task-specific semantic differences among utterances. To address these shortcomings, we propose DC-Instruct, a novel generative framework based on Dual-task Inter-dependent Instructions (DII) and Supervised Contrastive Instructions (SCI). Specifically, DII guides large language models (LLMs) to generate labels for one task based on the other task’s labels, thereby explicitly capturing dual-task inter-dependencies. Moreover, SCI leverages utterance semantics differences by guiding LLMs to determine whether …
Revisiting Conversation Discourse For Dialogue Disentanglement, Bobo Li, Hao Fei, Fei Li, Shengqiong Wu, Lizi Liao, Yinwei Wei, Tat-Seng Chua, Donghong Ji
Revisiting Conversation Discourse For Dialogue Disentanglement, Bobo Li, Hao Fei, Fei Li, Shengqiong Wu, Lizi Liao, Yinwei Wei, Tat-Seng Chua, Donghong Ji
Research Collection School Of Computing and Information Systems
Dialogue disentanglement aims to detach the chronologically ordered utterances into several independent sessions. Conversation utterances are essentially organized and described by the underlying discourse, and thus dialogue disentanglement requires the full understanding and harnessing of the intrinsic discourse attribute. In this article, we propose enhancing dialogue disentanglement by taking full advantage of the dialogue discourse characteristics. First of all, in feature encoding stage, we construct the heterogeneous graph representations to model the various dialogue-specific discourse structural features, including the static speaker-role structures (i.e., speaker-utterance and speaker-mentioning structure) and the dynamic contextual structures (i.e., the utterance-distance and partial-replying structure). We then …
Improving Conversational Recommender System Via Contextual And Time-Aware Modeling With Less Domain-Specific Knowledge, Lingzhi Wang, Shafiq Joty, Wei Gao, Xingshan Zeng, Kam-Fai Wong
Improving Conversational Recommender System Via Contextual And Time-Aware Modeling With Less Domain-Specific Knowledge, Lingzhi Wang, Shafiq Joty, Wei Gao, Xingshan Zeng, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
Conversational Recommender Systems (CRS) has become an emerging research topic seeking to perform recommendations through interactive conversations, which generally consist of generation and recommendation modules. Prior work on CRS tends to incorporate more external and domain-specific knowledge like item reviews to enhance performance. Despite the fact that the collection and annotation of the external domain-specific information needs much human effort and degenerates the generalizability, too much extra knowledge introduces more difficulty to balance among them. Therefore, we propose to fully discover and extract the internal knowledge from the context. We capture both entity-level and contextual-level representations to jointly model user …
A Comprehensive Survey On Relation Extraction: Recent Advances And New Frontiers, Xiaoyan Zhao, Yang Deng, Min Yang, Lingzhi Wang, Rui Zhang, Hong Cheng, Wai Lam, Ying Shen, Ruifeng Xu
A Comprehensive Survey On Relation Extraction: Recent Advances And New Frontiers, Xiaoyan Zhao, Yang Deng, Min Yang, Lingzhi Wang, Rui Zhang, Hong Cheng, Wai Lam, Ying Shen, Ruifeng Xu
Research Collection School Of Computing and Information Systems
Relation extraction (RE) involves identifying the relations between entities from underlying content. RE serves as the foundation for many natural language processing (NLP) and information retrieval applications, such as knowledge graph completion and question answering. In recent years, deep neural networks have dominated the field of RE and made noticeable progress. Subsequently, the large pre-trained language models (PLMs) have taken the state-of-the-art RE to a new level. This survey provides a comprehensive review of existing deep learning techniques for RE. First, we introduce RE resources, including datasets and evaluation metrics. Second, we propose a new taxonomy to categorize existing works …
Large Language Models For Software Engineering: A Systematic Literature Review, Xinyi Hou, Yanjie Zhao, Yue Liu, Zhou Yang, Kailong Wang, Li Li, Xiapu Luo, David Lo, John Grundy, Haoyu Wang
Large Language Models For Software Engineering: A Systematic Literature Review, Xinyi Hou, Yanjie Zhao, Yue Liu, Zhou Yang, Kailong Wang, Li Li, Xiapu Luo, David Lo, John Grundy, Haoyu Wang
Research Collection School Of Computing and Information Systems
Large Language Models (LLMs) have significantly impacted numerous domains, including Software Engineering (SE). Many recent publications have explored LLMs applied to various SE tasks. Nevertheless, a comprehensive understanding of the application, effects, and possible limitations of LLMs on SE is still in its early stages. To bridge this gap, we conducted a Systematic Literature Review (SLR) on LLM4SE, with a particular focus on understanding how LLMs can be exploited to optimize processes and outcomes. We selected and analyzed 395 research articles from January 2017 to January 2024 to answer four key Research Questions (RQs). In RQ1, we categorize different LLMs …
Eyetraes : Fine-Grained, Low-Latency Eye Tracking Via Adaptive Event Slicing, Argha Sen, Panahetipola Mudiyanselage Nuwan Bandara, Ila Gokarn, Thivya Kandappu, Archan Misra
Eyetraes : Fine-Grained, Low-Latency Eye Tracking Via Adaptive Event Slicing, Argha Sen, Panahetipola Mudiyanselage Nuwan Bandara, Ila Gokarn, Thivya Kandappu, Archan Misra
Research Collection School Of Computing and Information Systems
Eye-tracking technology has gained significant attention in recent years due to its wide range of applications in humancomputer interaction, virtual and augmented reality, and wearable health. Traditional RGB camera-based eye-tracking systems often struggle with poor temporal resolution and computational constraints, limiting their effectiveness in capturing rapid eye movements. To address these limitations, we propose EyeTrAES, a novel approach using neuromorphic event cameras for high-fidelity tracking of natural pupillary movement that shows significant kinematic variance. One of EyeTrAES’s highlights is the use of a novel adaptive windowing/slicing algorithm that ensures just the right amount of descriptive asynchronous event data accumulation within …
Strength Lies In Differences! Improving Strategy Planning For Non-Collaborative Dialogues Via Diversified User Simulation, Tong Zhang, Chen Huang, Yang Deng, Hongru Liang, Jia Liu, Zujie Wen, Wenqiang Lei, Tat-Seng Chua
Strength Lies In Differences! Improving Strategy Planning For Non-Collaborative Dialogues Via Diversified User Simulation, Tong Zhang, Chen Huang, Yang Deng, Hongru Liang, Jia Liu, Zujie Wen, Wenqiang Lei, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
We investigate non-collaborative dialogue agents, which are expected to engage in strategic conversations with diverse users, for securing a mutual agreement that leans favorably towards the system’s objectives. This poses two main challenges for existing dialogue agents: 1) The inability to integrate user-specific characteristics into the strategic planning, and 2) The difficulty of training strategic planners that can be generalized to diverse users. To address these challenges, we propose TRIP to enhance the capability in tailored strategic planning, incorporating a user-aware strategic planning module and a population-based training paradigm. Through experiments on benchmark non-collaborative dialogue tasks, we demonstrate the effectiveness …
Angels Or Demons: Investigating And Detecting Decentralized Financial Traps On Ethereum Smart Contracts, Jiachi Chen, Jiang Hu, Xin Xia, David Lo, John Grundy, Zhipeng Gao, Ting Chen
Angels Or Demons: Investigating And Detecting Decentralized Financial Traps On Ethereum Smart Contracts, Jiachi Chen, Jiang Hu, Xin Xia, David Lo, John Grundy, Zhipeng Gao, Ting Chen
Research Collection School Of Computing and Information Systems
Decentralized Finance (DeFi) uses blockchain technologies to transform traditional financial activities into decentralized platforms that run without intermediaries and centralized institutions. Smart contracts are programs that run on the blockchain, and by utilizing smart contracts, developers can more easily develop DeFi applications. Some key features of smart contracts—self-executed and immutability—ensure the trustworthiness, transparency and efficiency of DeFi applications and have led to a fast-growing DeFi market. However, misbehaving developers can add traps or backdoor code snippets to a smart contract, which are hard for contract users to discover. We call these code snippets in a DeFi smart contract as “DeFi …
Cas: Fusing Dnn Optimization & Adaptive Sensing For Energy-Efficient Multi-Modal Inference, Dulanga Weerakoon, Vigneshwaran Subbaraju, Joo Hwee Lim, Archan Misra
Cas: Fusing Dnn Optimization & Adaptive Sensing For Energy-Efficient Multi-Modal Inference, Dulanga Weerakoon, Vigneshwaran Subbaraju, Joo Hwee Lim, Archan Misra
Research Collection School Of Computing and Information Systems
Intelligent virtual agents are used to accomplish complex multi-modal tasks such as human instruction comprehension in mixed-reality environments by increasingly adopting richer, energy-intensive sensors and processing pipelines. In such applications, the context for activating sensors and processing blocks required to accomplish a given task instance is usually manifested via multiple sensing modes. Based on this observation, we introduce a novel Commit-and-Switch ( CAS ) paradigm that simultaneously seeks to reduce both sensing and processing energy. In CAS , we first commit to a low-energy computational pipeline with a subset of available sensors. Then, the task context estimated by this pipeline …
From Data To Application: Harnessing Big Spatial Data And Spatially Explicit Machine Learning Model For Landslide Susceptibility Prediction And Mapping, Min Naing Khant, Mei Yi Victoria Grace Ann, Tin Seong Kam
From Data To Application: Harnessing Big Spatial Data And Spatially Explicit Machine Learning Model For Landslide Susceptibility Prediction And Mapping, Min Naing Khant, Mei Yi Victoria Grace Ann, Tin Seong Kam
Research Collection School Of Computing and Information Systems
Recent advancements in information and communication technology have significantly enhanced access to extensive geospatial data, presenting a valuable opportunity to leverage big spatial data for improved modeling and predictive capabilities in natural disaster risk assessment. This paper explores the integration of a comprehensive dataset comprising historical landslide events and various geo-environmental variables within a spatially explicit machine learning framework. The study empirically demonstrates that incorporating big spatial data allows a more nuanced understanding of local variations and spatial dependencies. Ultimately, this empirical assessment produces more accurate landslide risk predictions than traditional baseline models. Using Italy’s expansive Valtellina Valley as a …
Multimodal Misinformation Detection By Learning From Synthetic Data With Multimodal Llms, Fengzhu Zeng, Wenqian Li, Wei Gao, Yan Pang
Multimodal Misinformation Detection By Learning From Synthetic Data With Multimodal Llms, Fengzhu Zeng, Wenqian Li, Wei Gao, Yan Pang
Research Collection School Of Computing and Information Systems
Detecting multimodal misinformation, especially in the form of image-text pairs, is crucial. Obtaining large-scale, high-quality real-world fact-checking datasets for training detectors is costly, leading researchers to use synthetic datasets generated by AI technologies. However, the generalizability of detectors trained on synthetic data to real-world scenarios remains unclear due to the distribution gap. To address this, we propose learning from synthetic data for detecting real-world multimodal misinformation through two model-agnostic data selection methods that match synthetic and real-world data distributions. Experiments show that our method enhances the performance of a small MLLM (13B) on real-world fact-checking datasets, enabling it to even …