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Unified Training Of Universal Time Series Forecasting Transformers, Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, Doyen Sahoo
Unified Training Of Universal Time Series Forecasting Transformers, Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, Doyen Sahoo
Research Collection School Of Computing and Information Systems
Deep learning for time series forecasting has traditionally operated within a one-model-per-dataset framework, limiting its potential to leverage the game-changing impact of large pre-trained models. The concept of universal forecasting, emerging from pre-training on a vast collection of time series datasets, envisions a single Large Time Series Model capable of addressing diverse downstream forecasting tasks. However, constructing such a model poses unique challenges specific to time series data: i) cross-frequency learning, ii) accommodating an arbitrary number of variates for multivariate time series, and iii) addressing the varying distributional properties inherent in large-scale data. To address these challenges, we present novel …
Hierarchical Damage Correlations For Old Photo Restoration, Weiwei Cai, Xuemiao Xu, Jiajia Xu, Huaidong Zhang, Haoxin Yang, Kun Zhang, Shengfeng He
Hierarchical Damage Correlations For Old Photo Restoration, Weiwei Cai, Xuemiao Xu, Jiajia Xu, Huaidong Zhang, Haoxin Yang, Kun Zhang, Shengfeng He
Research Collection School Of Computing and Information Systems
Restoring old photographs can preserve cherished memories. Previous methods handled diverse damages within the same network structure, which proved impractical. In addition, these methods cannot exploit correlations among artifacts, especially in scratches versus patch-misses issues. Hence, a tailored network is particularly crucial. In light of this, we propose a unified framework consisting of two key components: ScratchNet and PatchNet. In detail, ScratchNet employs the parallel Multi-scale Partial Convolution Module to effectively repair scratches, learning from multi-scale local receptive fields. In contrast, the patch-misses necessitate the network to emphasize global information. To this end, we incorporate a transformer-based encoder and decoder …
Double Issuer-Hiding Attribute-Based Credentials From Tag-Based Aggregatable Mercurial Signatures, Rui Shi, Yang Yang, Yingjiu Li, Huamin Feng, Guozhen Shi, Hwee Hwa Pang, Robert H. Deng
Double Issuer-Hiding Attribute-Based Credentials From Tag-Based Aggregatable Mercurial Signatures, Rui Shi, Yang Yang, Yingjiu Li, Huamin Feng, Guozhen Shi, Hwee Hwa Pang, Robert H. Deng
Research Collection School Of Computing and Information Systems
Attribute-based anonymous credentials offer users fine-grained access control in a privacy-preserving manner. However, in such schemes obtaining a user's credentials requires knowledge of the issuer's public key, which obviously reveals the issuer's identity that must be hidden from users in certain scenarios. Moreover, verifying a user's credentials also requires the knowledge of issuer's public key, which may infer the user's private information from their choice of issuer. In this article, we introduce the notion of double issuer-hiding attribute-based credentials ( DIHAC ) to tackle these two problems. In our model, a central authority can issue public-key credentials for a group …
A Feasibility-Preserved Quantum Approximate Solver For The Capacitated Vehicle Routing Problem, Ningyi Xie, Xinwei Lee, Dongsheng Cai, Yoshiyuki Saito, Nobuyoshi Asai, Hoong Chuin Lau
A Feasibility-Preserved Quantum Approximate Solver For The Capacitated Vehicle Routing Problem, Ningyi Xie, Xinwei Lee, Dongsheng Cai, Yoshiyuki Saito, Nobuyoshi Asai, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
The Capacitated Vehicle Routing Problem (CVRP) is an NP-optimization problem (NPO) that arises in various fields including transportation and logistics. The CVRP extends from the Vehicle Routing Problem (VRP), aiming to determine the most efficient plan for a fleet of vehicles to deliver goods to a set of customers, subject to the limited carrying capacity of each vehicle. As the number of possible solutions increases exponentially with the number of customers, finding high-quality solutions remains a significant challenge. Recently, the Quantum Approximate Optimization Algorithm (QAOA), a quantum–classical hybrid algorithm, has exhibited enhanced performance in certain combinatorial optimization problems, such as …
Comparative Analysis Of Hate Speech Detection: Traditional Vs. Deep Learning Approaches, Haibo Pen, Nicole Anne Huiying Teo, Zhaoxia Wang
Comparative Analysis Of Hate Speech Detection: Traditional Vs. Deep Learning Approaches, Haibo Pen, Nicole Anne Huiying Teo, Zhaoxia Wang
Research Collection School Of Computing and Information Systems
Detecting hate speech on social media poses a significant challenge, especially in distinguishing it from offensive language, as learning-based models often struggle due to nuanced differences between them, which leads to frequent misclassifications of hate speech instances, with most research focusing on refining hate speech detection methods. Thus, this paper seeks to know if traditional learning-based methods should still be used, considering the perceived advantages of deep learning in this domain. This is done by investigating advancements in hate speech detection. It involves the utilization of deep learning-based models for detailed hate speech detection tasks and compares the results with …
Toward Effective Secure Code Reviews: An Empirical Study Of Security-Related Coding Weaknesses, Wachiraphan Charoenwet, Patanamon Thongtanunam, Thuan Pham, Christoph Treude
Toward Effective Secure Code Reviews: An Empirical Study Of Security-Related Coding Weaknesses, Wachiraphan Charoenwet, Patanamon Thongtanunam, Thuan Pham, Christoph Treude
Research Collection School Of Computing and Information Systems
Identifying security issues early is encouraged to reduce the latent negative impacts on software systems. Code review is a widely-used method that allows developers to manually inspect modified code, catching security issues during a software development cycle. However, existing code review studies often focus on known vulnerabilities, neglecting coding weaknesses, which can introduce real-world security issues that are more visible through code review. The practices of code reviews in identifying such coding weaknesses are not yet fully investigated. To better understand this, we conducted an empirical case study in two large open-source projects, OpenSSL and PHP. Based on 135,560 code …
Reproducibility Debt: Challenges And Future Pathways, Zara Hassan, Christoph Treude, Michael Norrish, Graham Williams, Alex Potanin
Reproducibility Debt: Challenges And Future Pathways, Zara Hassan, Christoph Treude, Michael Norrish, Graham Williams, Alex Potanin
Research Collection School Of Computing and Information Systems
Reproducibility of scientic computation is a critical factor in validating its underlying process, but it is often elusive. Complexity and continuous evolution in software systems have introduced new challenges for reproducibility across a myriad of computational sciences, resulting in growing debt. This requires a comprehensive domain-agnostic study to dene and asses Reproducibility Debt (RpD) in scientic software, thus uncovering and classifying all underlying factors attributed towards its emergence and identication i.e., causes and eects. Moreover, an organised map of prevention strategies is imperative to guide researchers for its proactive management. This vision paper highlights the challenges that hinder eective management …
Generalization Analysis Of Deep Nonlinear Matrix Completion, Antoine Ledent, Rodrigo Alves
Generalization Analysis Of Deep Nonlinear Matrix Completion, Antoine Ledent, Rodrigo Alves
Research Collection School Of Computing and Information Systems
We provide generalization bounds for matrix completion with Schatten $p$ quasi-norm constraints, which is equivalent to deep matrix factorization with Frobenius constraints. In the uniform sampling regime, the sample complexity scales like $\widetilde{O}\left( rn\right)$ where $n$ is the size of the matrix and $r$ is a constraint of the same order as the ground truth rank in the isotropic case. In the distribution-free setting, the bounds scale as $\widetilde{O}\left(r^{1-\frac{p}{2}}n^{1+\frac{p}{2}}\right)$, which reduces to the familiar $\sqrt{r}n^{\frac{3}{2}}$ for $p=1$. Furthermore, we provide an analogue of the weighted trace norm for this setting which brings the sample complexity down to $\widetilde{O}(nr)$ in all …
Secure And Flexible Wildcard Queries, Qing Wang, Donghui Hu, Meng Li, Guomin Yang
Secure And Flexible Wildcard Queries, Qing Wang, Donghui Hu, Meng Li, Guomin Yang
Research Collection School Of Computing and Information Systems
Wildcard Keyword Searchable Encryption (WKSE) enables users to search desired encrypted files with wildcard queries. Previous schemes only enabled single-character wildcard queries or restricted multi-character wildcard queries. Even if the two types of queries are supported by several schemes, they are vulnerable to correlation attacks and composition attacks. In this paper, we propose a WKSE scheme Secure Flexible Wildcard Queries (SFWQ) that supports highly flexible wildcard queries and resists correlation and composition attacks. Specifically, we adopt the interval matching method instead of traditional position matching, so that SFWQ supports a variety of queries, including single-character wildcard queries, multi-character wildcard queries, …
Evilscreen Attack: Smart Tv Hijacking Via Multi-Channel Remote Control Mimicry, Yiwei Zhang, Siqi Ma, Tiancheng Chen, Juanru Li, Robert H. Deng, Elisa Bertino
Evilscreen Attack: Smart Tv Hijacking Via Multi-Channel Remote Control Mimicry, Yiwei Zhang, Siqi Ma, Tiancheng Chen, Juanru Li, Robert H. Deng, Elisa Bertino
Research Collection School Of Computing and Information Systems
Modern smart TVs often communicate with their remote controls (including the smartphone simulated ones) using multiple wireless channels (e.g., Infrared, Bluetooth, and Wi-Fi). However, this multi-channel remote control communication introduces a new attack surface. An inherent security flaw is that remote controls of most smart TVs are designed to work in a benign environment rather than an adversarial one, and thus wireless communications between a smart TV and its remote controls are not strongly protected. Attackers can leverage such a flaw to abuse the remote control communication and compromise smart TV systems. In this paper, we propose EvilScreen, a novel …
Fine-Grained Passenger Load Prediction Inside Metro Network Via Smart Card Data, Xiancai Tian, Chen Zhang, Baihua Zheng
Fine-Grained Passenger Load Prediction Inside Metro Network Via Smart Card Data, Xiancai Tian, Chen Zhang, Baihua Zheng
Research Collection School Of Computing and Information Systems
Metro system serves as the backbone for urban public transportation. Accurate passenger load prediction for the metro system plays a crucial role in metro service quality improvement, such as helping operators schedule train timetables and passengers plan their trips. However, existing works can only predict low-grained passenger flows of origin-destination (O-D) paths or inflows/outflows of each station but cannot predict passenger load distribution over the whole metro network. To this end, this paper proposes an end-to-end inference framework, PIPE, for passenger load prediction of every metro segment between two adjacent stations, by only utilizing smart card data. In particular, PIPE …
Verifiable Outsourced Attribute-Based Encryption Scheme For Cloud-Assisted Mobile E-Health System, Yinbin Miao, Feng Li, Xinghua Li, Jianting Ning, Hongwei Li, Kim-Kwang Raymond Choo, Robert H. Deng
Verifiable Outsourced Attribute-Based Encryption Scheme For Cloud-Assisted Mobile E-Health System, Yinbin Miao, Feng Li, Xinghua Li, Jianting Ning, Hongwei Li, Kim-Kwang Raymond Choo, Robert H. Deng
Research Collection School Of Computing and Information Systems
The cloud-assisted mobile electronic health (e-health) system facilitates e-health data sharing between healthcare providers and patients, but also raises the security and privacy concerns of e-health data. Although Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has been a promising technique to achieve fine-grained access control over encrypted e-health data, it still incurs high encryption and decryption burdens on mobile users such as smartphones and sensors. In addition, malicious cloud servers may conduct incorrect operations due to various interest incentives (e.g., leaking sensitive information to illegal users, saving computation and storage costs). To solve the above issues, in this paper we first propose an …
Privacy-Preserved Data Trading Via Verifiable Data Disturbance, Man Zhang, Xinghua Li, Yanbing Ren, Bin Luo, Yinbin Miao, Ximeng Liu, Robert H. Deng
Privacy-Preserved Data Trading Via Verifiable Data Disturbance, Man Zhang, Xinghua Li, Yanbing Ren, Bin Luo, Yinbin Miao, Ximeng Liu, Robert H. Deng
Research Collection School Of Computing and Information Systems
To motivate data owner (DO) to trade data, the existing data trading allows DO to sell the disturbed data to the data consumer (DC), where the disturbance parameter and the data price are negotiated by them, and DO independently adds the disturbance noise to data (usually continuous type) following the negotiation result. However, DOs may violate the negotiated parameter and add more noise to data while obtaining the negotiated price, which damages DC's disturbed data availability. This deficiency is rooted in the absence of supervision and verifiability on DOs’ independent disturbances. Aiming at the above problem, we devise a privacy-preserved …
Microkarta: Visualising Microservice Architectures, Oscar Manglaras, Alex Farkas, Peter Fule, Christoph Treude, Markus Wagner
Microkarta: Visualising Microservice Architectures, Oscar Manglaras, Alex Farkas, Peter Fule, Christoph Treude, Markus Wagner
Research Collection School Of Computing and Information Systems
Conceptualising and debugging a microservice architecture can be a challenge for developers due to the complex topology of inter-service communication, which may only apparent when viewing the architecture as a whole. In this paper, we present MicroKarta, a dashboard containing three types of network diagram that visualise complex microservice architectures, and that are designed to address problems faced by developers of these architectures. Initial feedback from industry developers has been positive. This dashboard can be used by developers to explore and debug microservice architectures, and can be used to compare the effectiveness of different types of network visualisation for assisting …
Partial Solution Based Constraint Solving Cache In Symbolic Execution, Ziqi Shuai, Zhenbang Chen, Kelin Ma, Kunlin Liu, Yufeng Zhang, Jun Sun, Ji Wang
Partial Solution Based Constraint Solving Cache In Symbolic Execution, Ziqi Shuai, Zhenbang Chen, Kelin Ma, Kunlin Liu, Yufeng Zhang, Jun Sun, Ji Wang
Research Collection School Of Computing and Information Systems
Constraint solving is one of the main challenges for symbolic execution. Caching is an effective mechanism to reduce the number of the solver invocations in symbolic execution and is adopted by many mainstream symbolic execution engines. However, caching can not perform well on all programs. How to improve caching’s effectiveness is challenging in general. In this work, we propose a partial solution-based caching method for improving caching’s effectiveness. Our key idea is to utilize the partial solutions inside the constraint solving to generate more cache entries. A partial solution may satisfy other constraints of symbolic execution. Hence, our partial solution-based …
A Bottom-Up Multi-Disciplinary Approach For Sustainability Education: Un-Sdg 13.3, Benjamin Gan, Thomas Menkhoff, Eng Lieh Ouh, Kevin Cheong
A Bottom-Up Multi-Disciplinary Approach For Sustainability Education: Un-Sdg 13.3, Benjamin Gan, Thomas Menkhoff, Eng Lieh Ouh, Kevin Cheong
Research Collection School Of Computing and Information Systems
Teaching both information systems and business undergraduates to break the current inertia in sustainability action requires innovative teaching & learning approaches as well as inter-disciplinary knowledge inputs. This study presents a bottom-up T&L approach delivered by a group of educators from different disciplines aimed at addressing UN-SDG Goal 13 ‘Climate Action’ with a novel approach. Integrating a problem-centric community project assignment into existing courses, our students worked on different disciplinary elements such as persuasive technologies and awareness campaigns to help to address local sustainability initiatives by community partners. We collected data to measure how students’ motivation, engagement, teamwork, and community …
Exploring The Market Impact Of Web3 Identity Imitation In Ethereum Name Service, Ping Fan Ke, Yi Meng Lau
Exploring The Market Impact Of Web3 Identity Imitation In Ethereum Name Service, Ping Fan Ke, Yi Meng Lau
Research Collection School Of Computing and Information Systems
Digital identities are paramount in today’s digital landscape. However, in the Web3 ecosystem, the absence of a central governing body leaves digital identities, such as domain names, vulnerable to cybersquatting and identity imitation. This study examines the market impact of identity imitation in the Web3 ecosystem. By scrutinizing trading activities within Web3 domain names from Ethereum Name Service (ENS) and its imitator, "Ether Name Service," we found that the presence of a newly imitating domain name increases the subsequent resale value of the authentic domain name. Additionally, we find a positive correlation between the resale value of the imitating domain …
Violet: Visual Analytics For Explainable Quantum Neural Networks, Shaolun Ruan, Zhiding Liang, Qiang Guan, Paul Robert Griffin, Xiaolin Wen, Yanna Lin, Yong Wang
Violet: Visual Analytics For Explainable Quantum Neural Networks, Shaolun Ruan, Zhiding Liang, Qiang Guan, Paul Robert Griffin, Xiaolin Wen, Yanna Lin, Yong Wang
Research Collection School Of Computing and Information Systems
With the rapid development of Quantum Machine Learning, quantum neural networks (QNN) have experienced great advancement in the past few years, harnessing the advantages of quantum computing to significantly speed up classical machine learning tasks. Despite their increasing popularity, the quantum neural network is quite counter-intuitive and difficult to understand, due to their unique quantum-specific layers (e.g., data encoding and measurement) in their architecture. It prevents QNN users and researchers from effectively understanding its inner workings and exploring the model training status. To fill the research gap, we propose VIOLET , a novel visual analytics approach to improve the explainability …
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 …
Locality-Aware Tail Node Embeddings On Homogeneous And Heterogeneous Networks, Zemin Liu, Yuan Fang, Wentao Zhang, Xinming Zhang, Steven C. H. Hoi
Locality-Aware Tail Node Embeddings On Homogeneous And Heterogeneous Networks, Zemin Liu, Yuan Fang, Wentao Zhang, Xinming Zhang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
While the state-of-the-art network embedding approaches often learn high-quality embeddings for high-degree nodes with abundant structural connectivity, the quality of the embeddings for low-degree or nodes is often suboptimal due to their limited structural connectivity. While many real-world networks are long-tailed, to date little effort has been devoted to tail node embeddings. In this article, we formulate the goal of learning tail node embeddings as a problem, given the few links on each tail node. In particular, since each node resides in its own local context, we personalize the regression model for each tail node. To reduce overfitting in the …
To Protect Or To Hide: An Investigation On Corporate Redacted Disclosure Motives Under New Fast Act Regulation, Yan Ma, Qian Mao, Nan Hu
To Protect Or To Hide: An Investigation On Corporate Redacted Disclosure Motives Under New Fast Act Regulation, Yan Ma, Qian Mao, Nan Hu
Research Collection School Of Computing and Information Systems
China adopted amendments allowing companies to redact filings without prior approval in 2016. Leveraging this change as a quasi-nature experiment, we explore whether managers utilize redacted information to withhold bad information in the more lenient regulatory environment. Our investigation uncovers a significant shift in managerial behavior: Since 2016, managers incline to employ redactions to obscure negative news rather than safeguarding proprietary data. Furthermore, we find that the poorer firm performance and a higher cost of equity are associated with the redacted disclosures after 2016, suggesting that investors perceive an increase in firm-specific risk attributed to withholding bad news through redactions.
Dappscan: Building Large-Scale Datasets For Smart Contract Weaknesses In Dapp Projects, Zibin Zheng, Jianzhong Su, Jiachi Chen, David Lo, Zhijie Zhong, Mingxi Ye
Dappscan: Building Large-Scale Datasets For Smart Contract Weaknesses In Dapp Projects, Zibin Zheng, Jianzhong Su, Jiachi Chen, David Lo, Zhijie Zhong, Mingxi Ye
Research Collection School Of Computing and Information Systems
The Smart Contract Weakness Classification Registry (SWC Registry) is a widely recognized list of smart contract weaknesses specific to the Ethereum platform. Despite the SWC Registry not being updated with new entries since 2020, the sustained development of smart contract analysis tools for detecting SWC-listed weaknesses highlights their ongoing significance in the field. However, evaluating these tools has proven challenging due to the absence of a large, unbiased, real-world dataset. To address this problem, we aim to build a large-scale SWC weakness dataset from real-world DApp projects. We recruited 22 participants and spent 44 person-months analyzing 1,199 open-source audit reports …
Unmasking The Lurking: Malicious Behavior Detection For Iot Malware With Multi-Label Classification, Ruitao Feng, Sen Li, Sen Chen, Mengmeng Ge, Xuewei Li, Xiaohong Li
Unmasking The Lurking: Malicious Behavior Detection For Iot Malware With Multi-Label Classification, Ruitao Feng, Sen Li, Sen Chen, Mengmeng Ge, Xuewei Li, Xiaohong Li
Research Collection School Of Computing and Information Systems
Current methods for classifying IoT malware predominantly utilize binary and family classifications. However, these outcomes lack the detailed granularity to describe malicious behavior comprehensively. This limitation poses challenges for security analysts, failing to support further analysis and timely preventive actions. To achieve fine-grained malicious behavior identification in the lurking stage of IoT malware, we propose MaGraMal. This approach, leveraging masked graph representation, supplements traditional classification methodology, empowering analysts with critical insights for rapid responses. Through the empirical study, which took three person-months, we identify and summarize four fine-grained malicious behaviors during the lurking stage, constructing an annotated dataset. Our evaluation …
Analyzing Swimming Performance Using Drone Captured Aerial Videos, Ngoc Doan Thu Tran, Kenny Tsu Wei Choo, Shaohui Foong, Hitesh Bhardwaj, Shane Kyi Hla Win, Wei Jun Ang, Kenneth T. Goh, Rajesh Krishna Balan
Analyzing Swimming Performance Using Drone Captured Aerial Videos, Ngoc Doan Thu Tran, Kenny Tsu Wei Choo, Shaohui Foong, Hitesh Bhardwaj, Shane Kyi Hla Win, Wei Jun Ang, Kenneth T. Goh, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
Monitoring swimmer performance is crucial for improving training and enhancing athletic techniques. Traditional methods for tracking swimmers, such as above-water and underwater cameras, face limitations due to the need for multiple cameras and obstructions from water splashes. This paper presents a novel approach for tracking swimmers using a moving UAV. The proposed system employs a UAV equipped with a high-resolution camera to capture aerial footage of the swimmers. The footage is then processed using computer vision algorithms to extract the swimmers' positions and movements. This approach offers several advantages, including single camera use and comprehensive coverage. The system's accuracy is …
Efficient Cross-Modal Video Retrieval With Meta-Optimized Frames, Ning Han, Xun Yang, Ee-Peng Lim, Hao Chen, Qianru Sun
Efficient Cross-Modal Video Retrieval With Meta-Optimized Frames, Ning Han, Xun Yang, Ee-Peng Lim, Hao Chen, Qianru Sun
Research Collection School Of Computing and Information Systems
Cross-modal video retrieval aims to retrieve semantically relevant videos when given a textual query, and is one of the fundamental multimedia tasks. Most top-performing methods primarily leverage Vision Transformer (ViT) to extract video features [1]-[3]. However, they suffer from the high computational complexity of ViT, especially when encoding long videos. A common and simple solution is to uniformly sample a small number (e.g., 4 or 8) of frames from the target video (instead of using the whole video) as ViT inputs. The number of frames has a strong influence on the performance of ViT, e.g., using 8 frames yields better …
Applicability And Challenges Of Indoor Localization Using One-Sided Round Trip Time Measurements, Quang Hai Truong, Xi Kai Justin Lam, Guru Anand Anish, Rajesh Krishna Balan
Applicability And Challenges Of Indoor Localization Using One-Sided Round Trip Time Measurements, Quang Hai Truong, Xi Kai Justin Lam, Guru Anand Anish, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
Radio Frequency fingerprinting, based on WiFi or cellular signals, has been a popular approach for localization. However, adoptions in real-world applications have confronted with challenges due to low accuracy, especially in crowded environments. The received signal strength (RSS) could be easily interfered by a large number of other devices or strictly depends on physical surrounding environments, which may cause localization errors of a few meters. On the other hand, the fine time measurement (FTM) round-trip time (RTT) has shown compelling improvement in indoor localization with ~1-2 meter accuracy in both 2D and 3D environments [13]. This method relies on the …
Refining Chatgpt-Generated Code: Characterizing And Mitigating Code Quality Issues, Yue Liu, Thanh Le-Cong, Ratnadira Widyasari, Chakkrit Kla Tantihamthavorn, Li Li, Xuan-Bach Dinh Le, David Lo
Refining Chatgpt-Generated Code: Characterizing And Mitigating Code Quality Issues, Yue Liu, Thanh Le-Cong, Ratnadira Widyasari, Chakkrit Kla Tantihamthavorn, Li Li, Xuan-Bach Dinh Le, David Lo
Research Collection School Of Computing and Information Systems
Since its introduction in November 2022, ChatGPT has rapidly gained popularity due to its remarkable ability in language understanding and human-like responses. ChatGPT, based on GPT-3.5 architecture, has shown great promise for revolutionizing various research fields, including code generation. However, the reliability and quality of code generated by ChatGPT remain unexplored, raising concerns about potential risks associated with the widespread use of ChatGPT-driven code generation.In this article, we systematically study the quality of 4,066 ChatGPT-generated programs of code implemented in two popular programming languages, i.e., Java and Python, for 2,033 programming tasks. The goal of this work is threefold. First, …
The Low-Carbon Vehicle Routing Problem With Dynamic Speed On Steep Roads, Jianhua Xiao, Xiaoyang Liu, Huixian Zhang, Zhiguang Cao, Liujiang Kang, Yunyun Niu
The Low-Carbon Vehicle Routing Problem With Dynamic Speed On Steep Roads, Jianhua Xiao, Xiaoyang Liu, Huixian Zhang, Zhiguang Cao, Liujiang Kang, Yunyun Niu
Research Collection School Of Computing and Information Systems
The low-carbon vehicle routing problem with dynamic speeds on steep roads (LCVRPDS-SR) considers the combined effects of dynamic speeds, steep roads, and loads on carbon emissions. Earlier low-carbon vehicle routing problems typically assumed that vehicles travel at a constant speed on flat roads. However, such models do not apply in urban or rural areas with steep roads. Although the subsequent studies further explored the effect of steep roads, their performance are still suboptimal since they fail to take into account the varying speeds on the terrain. This paper proposes an extended LCVRPDS-SR model that tackles dynamic speed decisions on steep …
Posmlp-Video: Spatial And Temporal Relative Position Encoding For Efficient Video Recognition, Yanbin Hao, Diansong Zhou, Zhicai Wang, Chong-Wah Ngo, Xiangnan He, Meng Wang
Posmlp-Video: Spatial And Temporal Relative Position Encoding For Efficient Video Recognition, Yanbin Hao, Diansong Zhou, Zhicai Wang, Chong-Wah Ngo, Xiangnan He, Meng Wang
Research Collection School Of Computing and Information Systems
In recent years, vision Transformers and MLPs have demonstrated remarkable performance in image understanding tasks. However, their inherently dense computational operators, such as self-attention and token-mixing layers, pose significant challenges when applied to spatio-temporal video data. To address this gap, we propose PosMLP-Video, a lightweight yet powerful MLP-like backbone for video recognition. Instead of dense operators, we use efficient relative positional encoding (RPE) to build pairwise token relations, leveraging small-sized parameterized relative position biases to obtain each relation score. Specifically, to enable spatio-temporal modeling, we extend the image PosMLP’s positional gating unit to temporal, spatial, and spatio-temporal variants, namely PoTGU, …
Physician-Patient Interactions In Online Healthcare Communities: The Effects Of Preconsultation On Service Delivery And Patient Satisfaction, Qian Tang, Anqi Zhao
Physician-Patient Interactions In Online Healthcare Communities: The Effects Of Preconsultation On Service Delivery And Patient Satisfaction, Qian Tang, Anqi Zhao
Research Collection School Of Computing and Information Systems
Preconsultation by medical professionals is a common practice in offline healthcare services to improve consultation efficiency but is rarely adopted for online healthcare services. In a noteworthy departure from this trend, a Chinese online healthcare community (OHC) has instituted preconsultation by assistant physicians prior to online consultations. Using comprehensive service data from this OHC, this study scrutinizes the effects of preconsultation on online healthcare services from both the physician and patient perspectives. The findings reveal that preconsultation by the assistant physician can significantly increase the attending physician’s response speed, length, and provision of informational support, while maintaining a consistent level …