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Full-Text Articles in Entire DC Network
Public Acceptance Of Using Artificial Intelligence-Assisted Weight Management Apps In High-Income Southeast Asian Adults With Overweight And Obesity: A Cross-Sectional Study, Han Shi Jocelyn Chew, Palakorn Achananuparp, Palakorn Achananuparp, Nicholas W. S. Chew, Yip Han Chin, Yujia Gao, Bok Yan Jimmy So, Asim Shabbir, Ee-Peng Lim, Kee Yuan Ngiam
Public Acceptance Of Using Artificial Intelligence-Assisted Weight Management Apps In High-Income Southeast Asian Adults With Overweight And Obesity: A Cross-Sectional Study, Han Shi Jocelyn Chew, Palakorn Achananuparp, Palakorn Achananuparp, Nicholas W. S. Chew, Yip Han Chin, Yujia Gao, Bok Yan Jimmy So, Asim Shabbir, Ee-Peng Lim, Kee Yuan Ngiam
Research Collection School Of Computing and Information Systems
Introduction: With in increase in interest to incorporate artificial intelligence (AI) into weight management programs, we aimed to examine user perceptions of AI-based mobile apps for weight management in adults with overweight and obesity. Methods: 280 participants were recruited between May and November 2022. Participants completed a questionnaire on sociodemographic profiles, Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), and Self-Regulation of Eating Behavior Questionnaire. Structural equation modeling was performed using R. Model fit was tested using maximum-likelihood generalized unweighted least squares. Associations between influencing factors were analyzed using correlation and linear regression. Results: 271 participant responses were …
Delving Into Multimodal Prompting For Fine-Grained Visual Classification, Xin Jiang, Hao Tang, Junyao Gao, Xiaoyu Du, Shengfeng He, Zechao Li
Delving Into Multimodal Prompting For Fine-Grained Visual Classification, Xin Jiang, Hao Tang, Junyao Gao, Xiaoyu Du, Shengfeng He, Zechao Li
Research Collection School Of Computing and Information Systems
Fine-grained visual classification (FGVC) involves categorizing fine subdivisions within a broader category, which poses challenges due to subtle inter-class discrepancies and large intra-class variations. However, prevailing approaches primarily focus on uni-modal visual concepts. Recent advancements in pre-trained vision-language models have demonstrated remarkable performance in various high-level vision tasks, yet the applicability of such models to FGVC tasks remains uncertain. In this paper, we aim to fully exploit the capabilities of cross-modal description to tackle FGVC tasks and propose a novel multimodal prompting solution, denoted as MP-FGVC, based on the contrastive language-image pertaining (CLIP) model. Our MP-FGVC comprises a multimodal prompts …
Dl-Drl: A Double-Level Deep Reinforcement Learning Approach For Large-Scale Task Scheduling Of Multi-Uav, Xiao Mao, Guohua Wu, Mingfeng Fan, Zhiguang Cao, Witold Pedrycz
Dl-Drl: A Double-Level Deep Reinforcement Learning Approach For Large-Scale Task Scheduling Of Multi-Uav, Xiao Mao, Guohua Wu, Mingfeng Fan, Zhiguang Cao, Witold Pedrycz
Research Collection School Of Computing and Information Systems
Exploiting unmanned aerial vehicles (UAVs) to execute tasks is gaining growing popularity recently. To address the underlying task scheduling problem, conventional exact and heuristic algorithms encounter challenges such as rapidly increasing computation time and heavy reliance on domain knowledge, particularly when dealing with large-scale problems. The deep reinforcement learning (DRL) based methods that learn useful patterns from massive data demonstrate notable advantages. However, their decision space will become prohibitively huge as the problem scales up, thus deteriorating the computation efficiency. To alleviate this issue, we propose a double-level deep reinforcement learning (DL-DRL) approach based on a divide and conquer framework …
Leveraging Llms And Generative Models For Interactive Known-Item Video Search, Zhixin Ma, Jiaxin Wu, Chong-Wah Ngo
Leveraging Llms And Generative Models For Interactive Known-Item Video Search, Zhixin Ma, Jiaxin Wu, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
While embedding techniques such as CLIP have considerably boosted search performance, user strategies in interactive video search still largely operate on a trial-and-error basis. Users are often required to manually adjust their queries and carefully inspect the search results, which greatly rely on the users’ capability and proficiency. Recent advancements in large language models (LLMs) and generative models offer promising avenues for enhancing interactivity in video retrieval and reducing the personal bias in query interpretation, particularly in the known-item search. Specifically, LLMs can expand and diversify the semantics of the queries while avoiding grammar mistakes or the language barrier. In …
Men Are From Mars And Women Are From Venus: Dyadic Collaboration In The Metaverse, Shu Schiller, Fiona Fui-Hoon Nah, Andy Luse, Keng Siau
Men Are From Mars And Women Are From Venus: Dyadic Collaboration In The Metaverse, Shu Schiller, Fiona Fui-Hoon Nah, Andy Luse, Keng Siau
Research Collection School Of Computing and Information Systems
Purpose: The gender composition of teams remains an important yet complex element in unlocking the success of collaboration and performance in the metaverse. In this study, the authors examined the collaborations of same- and mixed-gender dyads to investigate how gender composition influences perceptions of the dyadic collaboration process and outcomes at both the individual and team levels in the metaverse. Design/methodology/approach: Drawing on expectation states theory and social role theory, the authors hypothesized differences between dyads of different gender compositions. A blocked design was utilized where 432 subjects were randomly assigned to teams of different gender compositions: 101 male dyads, …
Robust Permissioned Blockchain Consensus For Unstable Communication In Fanet, Zihao Wang, Hang Wang, Zhuowen Li, Xinghua Li, Yinbin Miao, Yanbing Ren, Yunwei Wang, Zhe Ren, Deng, Robert H.
Robust Permissioned Blockchain Consensus For Unstable Communication In Fanet, Zihao Wang, Hang Wang, Zhuowen Li, Xinghua Li, Yinbin Miao, Yanbing Ren, Yunwei Wang, Zhe Ren, Deng, Robert H.
Research Collection School Of Computing and Information Systems
The utilization of blockchain technology as a distributed information sharing system has gained widespread adoption across various domains. However, its application to Flying Ad-Hoc Network (FANET), characterized by severe packet loss, poses significant challenges. The high packet loss rates in FANETs can result in decreased consensus success rates and negatively impact information sharing consistency and efficiency. In this paper, we proposed RoUBC, a novel consensus scheme for Flying Ad-Hoc Networks (FANET), which is based on the Raft protocol and is designed to address the challenges posed by the severe packet loss network in FANET. The proposed scheme consists of two …
Pam3s: Progressive Two-Stage Auction-Based Multi-Platform Multi-User Mutual Selection Scheme In Mcs, Bin Luo, Xinghua Li, Yinbin Miao, Man Zhang, Ximeng Liu, Yanbing Ren, Xizhao Luo, Deng, Robert H.
Pam3s: Progressive Two-Stage Auction-Based Multi-Platform Multi-User Mutual Selection Scheme In Mcs, Bin Luo, Xinghua Li, Yinbin Miao, Man Zhang, Ximeng Liu, Yanbing Ren, Xizhao Luo, Deng, Robert H.
Research Collection School Of Computing and Information Systems
Mobile crowdsensing (MCS) has been applied in various fields to realize data sharing, where multiple platforms and multiple Mobile Users () have appeared recently. However, aiming at mutual selection, the existing works ignore making ' utilities with the limited resources and platforms' utilities while achieving the desired sensing data quality maximum as far as possible. Thus, they cannot motivate both and platforms to participate. To address this problem, standing on both sides of and platforms with conflicting interests, we propose a Progressive two-stage Auction-based Multi-platform Multi-user Mutual Selection scheme (). Specifically, in, we treat mutual selection as a two-stage auction …
Recommendations With Minimum Exposure Guarantees: A Post-Processing Framework, Ramon Lopes, Rodrigo Alves, Antoine Ledent, Rodrygo L. T. Santos, Marius Kloft
Recommendations With Minimum Exposure Guarantees: A Post-Processing Framework, Ramon Lopes, Rodrigo Alves, Antoine Ledent, Rodrygo L. T. Santos, Marius Kloft
Research Collection School Of Computing and Information Systems
Relevance-based ranking is a popular ingredient in recommenders, but it frequently struggles to meet fairness criteria because social and cultural norms may favor some item groups over others. For instance, some items might receive lower ratings due to some sort of bias (e.g. gender bias). A fair ranking should balance the exposure of items from advantaged and disadvantaged groups. To this end, we propose a novel post-processing framework to produce fair, exposure-aware recommendations. Our approach is based on an integer linear programming model maximizing the expected utility while satisfying a minimum exposure constraint. The model has fewer variables than previous …
Machine Learning For Refining Knowledge Graphs: A Survey, Budhitama Subagdja, D. Shanthoshigaa, Zhaoxia Wang, Ah-Hwee Tan
Machine Learning For Refining Knowledge Graphs: A Survey, Budhitama Subagdja, D. Shanthoshigaa, Zhaoxia Wang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Knowledge graph (KG) refinement refers to the process of filling in missing information, removing redundancies, and resolving inconsistencies in knowledge graphs. With the growing popularity of KG in various domains, many techniques involving machine learning have been applied, but there is no survey dedicated to machine learning-based KG refinement yet. Based on a novel framework following the KG refinement process, this paper presents a survey of machine learning approaches to KG refinement according to the kind of operations in KG refinement, the training datasets, mode of learning, and process multiplicity. Furthermore, the survey aims to provide broad practical insights into …
Handling Long And Richly Constrained Tasks Through Constrained Hierarchical Reinforcement Learning, Yuxiao Lu, Arunesh Sinha, Pradeep Varakantham
Handling Long And Richly Constrained Tasks Through Constrained Hierarchical Reinforcement Learning, Yuxiao Lu, Arunesh Sinha, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
Safety in goal directed Reinforcement Learning (RL) settings has typically been handled through constraints over trajectories and have demonstrated good performance in primarily short horizon tasks. In this paper, we are specifically interested in the problem of solving temporally extended decision making problems such as robots cleaning different areas in a house while avoiding slippery and unsafe areas (e.g., stairs) and retaining enough charge to move to a charging dock; in the presence of complex safety constraints. Our key contribution is a (safety) Constrained Search with Hierarchical Reinforcement Learning (CoSHRL) mechanism that combines an upper level constrained search agent (which …
Glop: Learning Global Partition And Local Construction For Solving Large-Scale Routing Problems In Real-Time, Haoran Ye, Jiarui Wang, Helan Liang, Zhiguang Cao, Yong Li, Fanzhang Li
Glop: Learning Global Partition And Local Construction For Solving Large-Scale Routing Problems In Real-Time, Haoran Ye, Jiarui Wang, Helan Liang, Zhiguang Cao, Yong Li, Fanzhang Li
Research Collection School Of Computing and Information Systems
The recent end-to-end neural solvers have shown promise for small-scale routing problems but suffered from limited real-time scaling-up performance. This paper proposes GLOP (Global and Local Optimization Policies), a unified hierarchical framework that efficiently scales toward large-scale routing problems. GLOP partitions large routing problems into Travelling Salesman Problems (TSPs) and TSPs into Shortest Hamiltonian Path Problems. For the first time, we hybridize non-autoregressive neural heuristics for coarse-grained problem partitions and autoregressive neural heuristics for fine-grained route constructions, leveraging the scalability of the former and the meticulousness of the latter. Experimental results show that GLOP achieves competitive and state-of-the-art real-time performance …
Simple Image-Level Classification Improves Open-Vocabulary Object Detection, Ruohuan Fang, Guansong Pang, Xiao Bai
Simple Image-Level Classification Improves Open-Vocabulary Object Detection, Ruohuan Fang, Guansong Pang, Xiao Bai
Research Collection School Of Computing and Information Systems
Open-Vocabulary Object Detection (OVOD) aims to detect novel objects beyond a given set of base categories on which the detection model is trained. Recent OVOD methods focus on adapting the image-level pre-trained vision-language models (VLMs), such as CLIP, to a region-level object detection task via, eg., region-level knowledge distillation, regional prompt learning, or region-text pre-training, to expand the detection vocabulary. These methods have demonstrated remarkable performance in recognizing regional visual concepts, but they are weak in exploiting the VLMs' powerful global scene understanding ability learned from the billion-scale image-level text descriptions. This limits their capability in detecting hard objects of …
Earnhft: Efficient Hierarchical Reinforcement Learning For High Frequency Trading, Molei Qin, Shuo Sun, Wentao Zhang, Haochong Xia, Xinrun Wang, Bo An
Earnhft: Efficient Hierarchical Reinforcement Learning For High Frequency Trading, Molei Qin, Shuo Sun, Wentao Zhang, Haochong Xia, Xinrun Wang, Bo An
Research Collection School Of Computing and Information Systems
High-frequency trading (HFT) is using computer algorithms to make trading decisions in short time scales (e.g., second-level), which is widely used in the Cryptocurrency (Crypto) market, (e.g., Bitcoin). Reinforcement learning (RL) in financial research has shown stellar performance on many quantitative trading tasks. However, most methods focus on low-frequency trading, e.g., day-level, which cannot be directly applied to HFT because of two challenges. First, RL for HFT involves dealing with extremely long trajectories (e.g., 2.4 million steps per month), which is hard to optimize and evaluate. Second, the dramatic price fluctuations and market trend changes of Crypto make existing algorithms …
Market-Gan: Adding Control To Financial Market Data Generation With Semantic Context, Haochong Xia, Shuo Sun, Xinrun Wang, Bo An
Market-Gan: Adding Control To Financial Market Data Generation With Semantic Context, Haochong Xia, Shuo Sun, Xinrun Wang, Bo An
Research Collection School Of Computing and Information Systems
Financial simulators play an important role in enhancing forecasting accuracy, managing risks, and fostering strategic financial decision-making. Despite the development of financial market simulation methodologies, existing frameworks often struggle with adapting to specialized simulation context. We pinpoint the challenges as i) current financial datasets do not contain context labels; ii) current techniques are not designed to generate financial data with context as control, which demands greater precision compared to other modalities; iii) the inherent difficulties in generating context-aligned, high-fidelity data given the non-stationary, noisy nature of financial data. To address these challenges, our contributions are: i) we proposed the Contextual …
Predicting Viral Rumors And Vulnerable Users With Graph-Based Neural Multi-Task Learning For Infodemic Surveillance, Xuan Zhang, Wei Gao
Predicting Viral Rumors And Vulnerable Users With Graph-Based Neural Multi-Task Learning For Infodemic Surveillance, Xuan Zhang, Wei Gao
Research Collection School Of Computing and Information Systems
In the age of the infodemic, it is crucial to have tools for effectively monitoring the spread of rampant rumors that can quickly go viral, as well as identifying vulnerable users who may be more susceptible to spreading such misinformation. This proactive approach allows for timely preventive measures to be taken, mitigating the negative impact of false information on society. We propose a novel approach to predict viral rumors and vulnerable users using a unified graph neural network model. We pre-train network-based user embeddings and leverage a cross-attention mechanism between users and posts, together with a community-enhanced vulnerability propagation (CVP) …
Quantifying The Competitiveness Of A Dataset In Relation To General Preferences, Kyriakos Mouratidis, Keming Li, Bo Tang
Quantifying The Competitiveness Of A Dataset In Relation To General Preferences, Kyriakos Mouratidis, Keming Li, Bo Tang
Research Collection School Of Computing and Information Systems
Typically, a specific market (e.g., of hotels, restaurants, laptops, etc.) is represented as a multi-attribute dataset of the available products. The topic of identifying and shortlisting the products of most interest to a user has been well-explored. In contrast, in this work we focus on the dataset, and aim to assess its competitiveness with regard to different possible preferences. We define measures of competitiveness, and represent them in the form of a heat-map in the domain of preferences. Our work finds application in market analysis and in business development. These applications are further enhanced when the competitiveness heat-map is used …
Efficient Privacy-Preserving Spatial Data Query In Cloud Computing, Yinbin Miao, Yutao Yang, Xinghua Li, Linfeng Wei, Zhiquan Liu, Robert H. Deng
Efficient Privacy-Preserving Spatial Data Query In Cloud Computing, Yinbin Miao, Yutao Yang, Xinghua Li, Linfeng Wei, Zhiquan Liu, Robert H. Deng
Research Collection School Of Computing and Information Systems
With the rapid development of geographic location technology and the explosive growth of data, a large amount of spatial data is outsourced to the cloud server for reducing the local high storage and computing burdens, but at the same time causes security issues. Thus, extensive privacy-preserving spatial data query schemes have been proposed. Most of the existing schemes use Asymmetric Scalar-Product-Preserving Encryption (ASPE) to encrypt data, but ASPE has proven to be insecure against known plaintext attack. And the existing schemes require users to provide more information about query range and thus generate a large amount of ciphertexts, which causes …
Conversational Localization: Indoor Human Localization Through Intelligent Conversation, Sheshadri Smitha, Kotaro Hara
Conversational Localization: Indoor Human Localization Through Intelligent Conversation, Sheshadri Smitha, Kotaro Hara
Research Collection School Of Computing and Information Systems
We propose a novel sensorless approach to indoor localization by leveraging natural language conversations with users, which we call conversational localization. To show the feasibility of conversational localization, we develop a proof-of-concept system that guides users to describe their surroundings in a chat and estimates their position based on the information they provide. We devised a modular architecture for our system with four modules. First, we construct an entity database with available image-based floor maps. Second, we enable the dynamic identification and scoring of information provided by users through our utterance processing module. Then, we implement a conversational agent that …
Conceptthread: Visualizing Threaded Concepts In Mooc Videos, Zhiguang Zhou, Li Ye, Lihong Cai, Lei Wang, Yigang Wang, Yongheng Wang, Wei Chen, Yong Wang
Conceptthread: Visualizing Threaded Concepts In Mooc Videos, Zhiguang Zhou, Li Ye, Lihong Cai, Lei Wang, Yigang Wang, Yongheng Wang, Wei Chen, Yong Wang
Research Collection School Of Computing and Information Systems
Massive Open Online Courses (MOOCs) platforms are becoming increasingly popular in recent years. Online learners need to watch the whole course video on MOOC platforms to learn the underlying new knowledge, which is often tedious and time-consuming due to the lack of a quick overview of the covered knowledge and their structures. In this paper, we propose ConceptThread , a visual analytics approach to effectively show the concepts and the relations among them to facilitate effective online learning. Specifically, given that the majority of MOOC videos contain slides, we first leverage video processing and speech analysis techniques, including shot recognition, …
Tracking People Across Ultra Populated Indoor Spaces By Matching Unreliable Wi-Fi Signals With Disconnected Video Feeds, Quang Hai Truong, Dheryta Jaisinghani, Shubham Jain, Arunesh Sinha, Jeong Gil Ko, Rajesh Krishna Balan
Tracking People Across Ultra Populated Indoor Spaces By Matching Unreliable Wi-Fi Signals With Disconnected Video Feeds, Quang Hai Truong, Dheryta Jaisinghani, Shubham Jain, Arunesh Sinha, Jeong Gil Ko, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
Tracking in dense indoor environments where several thousands of people move around is an extremely challenging problem. In this paper, we present a system — DenseTrack for tracking people in such environments. DenseTrack leverages data from the sensing modalities that are already present in these environments — Wi-Fi (from enterprise network deployments) and Video (from surveillance cameras). We combine Wi-Fi information with video data to overcome the individual errors induced by these modalities. More precisely, the locations derived from video are used to overcome the localization errors inherent in using Wi-Fi signals where precise Wi-Fi MAC IDs are used to …
Soci+: An Enhanced Toolkit For Secure Outsourced Computation On Integers, Bowen Zhao, Weiquan Deng, Xiaoguo Li, Ximeng Liu, Qingqi Pei, Robert H. Deng
Soci+: An Enhanced Toolkit For Secure Outsourced Computation On Integers, Bowen Zhao, Weiquan Deng, Xiaoguo Li, Ximeng Liu, Qingqi Pei, Robert H. Deng
Research Collection School Of Computing and Information Systems
Secure outsourced computation is critical for cloud computing to safeguard data confidentiality and ensure data usability. Recently, secure outsourced computation schemes following a twin-server architecture based on partially homomorphic cryptosystems have received increasing attention. The Secure Outsourced Computation on Integers (SOCI) [1] toolkit is the state-of-the-art among these schemes which can perform secure computation on integers without requiring the costly bootstrapping operation as in fully homomorphic encryption; however, SOCI suffers from relatively large computation and communication overhead. In this paper, we propose SOCI+ which significantly improves the performance of SOCI. Specifically, SOCI+ employs a novel (2,2)-threshold Paillier cryptosystem with fast …
A Unified Framework For Contextual And Factoid Question Generation, Chenhe Dong, Ying Shen, Shiyang Lin, Zhenzhou Lin, Yang Deng
A Unified Framework For Contextual And Factoid Question Generation, Chenhe Dong, Ying Shen, Shiyang Lin, Zhenzhou Lin, Yang Deng
Research Collection School Of Computing and Information Systems
Question generation (QG) aims to automatically generate fluent and relevant questions, where the two most mainstream directions are generating questions from unstructured contextual texts (CQG), such as news articles, and generating questions from structured factoid texts (FQG), such as knowledge graphs or tables. Existing methods for these two tasks mainly face challenges of limited internal structural information as well as scarce background information, while these two tasks can benefit each other for alleviating these issues. For example, when meeting the entity mention “United Kingdom” in CQG, it can be inferred that it is a country in European continent based on …
Societal Impacts Of Artificial Intelligence: Ethics, Legal, And Governance Issues, Yuzhou Qian, Keng Siau, Fiona Fui-Hoon Nah
Societal Impacts Of Artificial Intelligence: Ethics, Legal, And Governance Issues, Yuzhou Qian, Keng Siau, Fiona Fui-Hoon Nah
Research Collection School Of Computing and Information Systems
Artificial intelligence (AI) is quickly changing the way we work and the way we live. The emergence of ChatGPT has thrust AI, especially Generative AI, into the spotlight. The societal impact of AI is on most people's minds. This article presents several research projects on how AI impacts work and society. Three research works are discussed in this article. The first study develops a theoretical framework structuring the legal and ethical objectives that are needed and the means to achieve them. The second study concentrates on bias and discrimination issues embedded in AI applications. It focuses on enhancing the collaboration …
Big Code Search: A Bibliography, Kisub Kim, Sankalp Ghatpande, Dongsun Kim, Xin Zhou, Kui Liu, Tegawende F. Bissyande, Jacques Klein, Traon Yves Le
Big Code Search: A Bibliography, Kisub Kim, Sankalp Ghatpande, Dongsun Kim, Xin Zhou, Kui Liu, Tegawende F. Bissyande, Jacques Klein, Traon Yves Le
Research Collection School Of Computing and Information Systems
Code search is an essential task in software development. Developers often search the internet and other code databases for necessary source code snippets to ease the development efforts. Code search techniques also help learn programming as novice programmers or students can quickly retrieve (hopefully good) examples already used in actual software projects. Given the recurrence of the code search activity in software development, there is an increasing interest in the research community. To improve the code search experience, the research community suggests many code search tools and techniques. These tools and techniques leverage several different ideas and claim a better …
Clearspeech: Improving Voice Quality Of Earbuds Using Both In-Ear And Out-Ear Microphones, Dong Ma, Ting Dang, Ming Ding, Rajesh Krishna Balan
Clearspeech: Improving Voice Quality Of Earbuds Using Both In-Ear And Out-Ear Microphones, Dong Ma, Ting Dang, Ming Ding, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
Wireless earbuds have been gaining increasing popularity and using them to make phone calls or issue voice commands requires the earbud microphones to pick up human speech. When the speaker is in a noisy environment, speech quality degrades significantly and requires speech enhancement (SE). In this paper, we present ClearSpeech, a novel deep-learningbased SE system designed for wireless earbuds. Specifically, by jointly using the earbud’s in-ear and out-ear microphones, we devised a suite of techniques to effectively fuse the two signals and enhance the magnitude and phase of the speech spectrogram. We built an earbud prototype to evaluate ClearSpeech under …
Hardware-Assisted Live Kernel Function Updating On Intel Platforms, Lei Zhou, Fengwei Zhang, Kevin Leach, Xuhua Ding, Zhenyu Ning, Guojun Wang, Jidong Xiao
Hardware-Assisted Live Kernel Function Updating On Intel Platforms, Lei Zhou, Fengwei Zhang, Kevin Leach, Xuhua Ding, Zhenyu Ning, Guojun Wang, Jidong Xiao
Research Collection School Of Computing and Information Systems
Traditional kernel updates such as perfective maintenance and vulnerability patching requires shutting the system down, disrupting continuous execution of applications. Enterprises and researchers have proposed various live updating techniques to patch the kernel with lower downtime to reduce the loss of useful uptime. However, existing kernel live update techniques either rely on specific support from the target OS, or are deployed in virtualized environments (i.e., systems running in virtual machines). In this article we present KShot , a hardware-assisted live and secure kernel function update mechanism for native operating systems. By leveraging x86 SMM and Intel SGX, KShot runs in …
Effects Of Mindfulness And Emotion Regulation On Aesthetics: A Theoretical Model From Hedonic Perspective Of Processing Fluency, Geng-Bao Lin, Fiona Fui-Hoon Nah, Choon Ling Sia
Effects Of Mindfulness And Emotion Regulation On Aesthetics: A Theoretical Model From Hedonic Perspective Of Processing Fluency, Geng-Bao Lin, Fiona Fui-Hoon Nah, Choon Ling Sia
Research Collection School Of Computing and Information Systems
Research has shown that processing fluency positively impacts perceived aesthetics, with pleasure mediating the relationship. Considering the important role of pleasure, we propose studying the role of emotion regulation in moderating the mediated relationship from processing fluency to perceived aesthetics. Based on our hypotheses, individuals’ emotion regulation strategies are expected to have moderating effects on the relationship between processing fluency and perceived aesthetics such that cognitive reappraisal positively moderates the relationship from processing fluency to pleasure, and expressive suppression negatively moderates the relationship from pleasure to perceived aesthetics. Trait mindfulness is also expected to influence perceived aesthetics through emotion regulation …
Continual Learning, Fast And Slow, Quang Anh Pham, Chenghao Liu, Steven C. H. Hoi
Continual Learning, Fast And Slow, Quang Anh Pham, Chenghao Liu, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
According to the Complementary Learning Systems (CLS) theory (McClelland et al. 1995) in neuroscience, humans do effective continual learning through two complementary systems: a fast learning system centered on the hippocampus for rapid learning of the specifics, individual experiences; and a slow learning system located in the neocortex for the gradual acquisition of structured knowledge about the environment. Motivated by this theory, we propose DualNets (for Dual Networks), a general continual learning framework comprising a fast learning system for supervised learning of pattern-separated representation from specific tasks and a slow learning system for representation learning of task-agnostic general representation via …
Privobfnet: A Weakly Supervised Semantic Segmentation Model For Data Protection, Chiat Pin Tay, Vigneshwaran Subbaraju, Thivya Kandappu
Privobfnet: A Weakly Supervised Semantic Segmentation Model For Data Protection, Chiat Pin Tay, Vigneshwaran Subbaraju, Thivya Kandappu
Research Collection School Of Computing and Information Systems
The use of social media has made it easy to communicate and share information over the internet. However, it also brings issues such as data privacy leakage, which can be exploited by recipients with malicious intentions to harm the sender. In this paper, we propose a deep neural network that analyzes user’s image for privacy sensitive content and automatically locates sensitive regions for obfuscation. Our approach relies solely on image level annotations and learns to (a) predict an overall privacy score, (b) detect sensitive attributes and (c) demarcate the sensitive regions for obfuscation, in a given input image. We validated …
A Secure And Robust Knowledge Transfer Framework Via Stratified-Causality Distribution Adjustment In Intelligent Collaborative Services, Ju Jia, Siqi Ma, Lina Wang, Yang Liu, Robert H. Deng
A Secure And Robust Knowledge Transfer Framework Via Stratified-Causality Distribution Adjustment In Intelligent Collaborative Services, Ju Jia, Siqi Ma, Lina Wang, Yang Liu, Robert H. Deng
Research Collection School Of Computing and Information Systems
The rapid development of device-edge-cloud collaborative computing techniques has actively contributed to the popularization and application of intelligent service models. The intensity of knowledge transfer plays a vital role in enhancing the performance of intelligent services. However, the existing knowledge transfer methods are mainly implemented through data fine-tuning and model distillation, which may cause the leakage of data privacy or model copyright in intelligent collaborative systems. To address this issue, we propose a secure and robust knowledge transfer framework through stratified-causality distribution adjustment (SCDA) for device-edge-cloud collaborative services. Specifically, a simple yet effective density-based estimation is first employed to obtain …