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Articles 1 - 30 of 5423

Full-Text Articles in Physical Sciences and Mathematics

Opportunities And Challenges In Code Search Tools, Chao Liu, Xin Xia, David Lo, Cuiying Gao, Xiaohu Yang, John Grundy Dec 2022

Opportunities And Challenges In Code Search Tools, Chao Liu, Xin Xia, David Lo, Cuiying Gao, Xiaohu Yang, John Grundy

Research Collection School Of Computing and Information Systems

Code search is a core software engineering task. Effective code search tools can help developers substantially improve their software development efficiency and effectiveness. In recent years, many code search studies have leveraged different techniques, such as deep learning and information retrieval approaches, to retrieve expected code from a large-scale codebase. However, there is a lack of a comprehensive comparative summary of existing code search approaches. To understand the research trends in existing code search studies, we systematically reviewed 81 relevant studies. We investigated the publication trends of code search studies, analyzed key components, such as codebase, query, and modeling technique ...

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Which Neural Network Makes More Explainable Decisions? An Approach Towards Measuring Explainability, Mengdi Zhang, Jun Sun, Jingyi Wang Nov 2022

Which Neural Network Makes More Explainable Decisions? An Approach Towards Measuring Explainability, Mengdi Zhang, Jun Sun, Jingyi Wang

Research Collection School Of Computing and Information Systems

Neural networks are getting increasingly popular thanks to their exceptional performance in solving many real-world problems. At the same time, they are shown to be vulnerable to attacks, difficult to debug and subject to fairness issues. To improve people’s trust in the technology, it is often necessary to provide some human-understandable explanation of neural networks’ decisions, e.g., why is that my loan application is rejected whereas hers is approved? That is, the stakeholder would be interested to minimize the chances of not being able to explain the decision consistently and would like to know how often and how ...

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A Mean-Field Markov Decision Process Model For Spatial Temporal Subsidies In Ride-Sourcing Markets, Zheng Zhu, Jintao Ke, Hai Wang Jul 2022

A Mean-Field Markov Decision Process Model For Spatial Temporal Subsidies In Ride-Sourcing Markets, Zheng Zhu, Jintao Ke, Hai Wang

Research Collection School Of Computing and Information Systems

Ride-sourcing services are increasingly popular because of their ability to accommodate on-demand travel needs. A critical issue faced by ride-sourcing platforms is the supply-demand imbalance, as a result of which drivers may spend substantial time on idle cruising and picking up remote passengers. Some platforms attempt to mitigate the imbalance by providing relocation guidance for idle drivers who may have their own self-relocation strategies and decline to follow the suggestions. Platforms then seek to induce drivers to system-desirable locations by offering them subsidies. This paper proposes a mean-field Markov decision process (MF-MDP) model to depict the dynamics in ride-sourcing markets ...

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Time Dependent Orienteering Problem With Time Windows And Service Time Dependent Profits, M. Khodadadian, A. Divsalar, C. Verbeeck, Aldy Gunawan, P. Vansteenwegen Jul 2022

Time Dependent Orienteering Problem With Time Windows And Service Time Dependent Profits, M. Khodadadian, A. Divsalar, C. Verbeeck, Aldy Gunawan, P. Vansteenwegen

Research Collection School Of Computing and Information Systems

This paper addresses the time dependent orienteering problem with time windows and service time dependent profits (TDOPTW-STP). In the TDOPTW-STP, each vertex is assigned a minimum and a maximum service time and the profit collected at each vertex increases linearly with the service time. The goal is to maximize the total collected profit by determining a subset of vertices to be visited and assigning appropriate service time to each vertex, considering a given time budget and time windows. Moreover, travel times are dependent of the departure times. To solve this problem, a mixed integer linear model is formulated and a ...

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Digbug—Pre/Post-Processing Operator Selection For Accurate Bug Localization, Kisub Kim, Sankalp Ghatpande, Kui Liu, Anil Koyuncu, Dongsun Kim, Tegawendé F. Bissyande, Jacques Klein, Yves Le Traon Jul 2022

Digbug—Pre/Post-Processing Operator Selection For Accurate Bug Localization, Kisub Kim, Sankalp Ghatpande, Kui Liu, Anil Koyuncu, Dongsun Kim, Tegawendé F. Bissyande, Jacques Klein, Yves Le Traon

Research Collection School Of Computing and Information Systems

Bug localization is a recurrent maintenance task in software development. It aims at identifying relevant code locations (e.g., code files) that must be inspected to fix bugs. When such bugs are reported by users, the localization process become often overwhelming as it is mostly a manual task due to incomplete and informal information (written in natural languages) available in bug reports. The research community has then invested in automated approaches, notably using Information Retrieval techniques. Unfortunately, reported performance in the literature is still limited for practical usage. Our key observation, after empirically investigating a large dataset of bug reports ...

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Decomposing Generation Networks With Structure Prediction For Recipe Generation, Hao Wang, Guosheng Lin, Steven C. H. Hoi, Chunyan Miao Jun 2022

Decomposing Generation Networks With Structure Prediction For Recipe Generation, Hao Wang, Guosheng Lin, Steven C. H. Hoi, Chunyan Miao

Research Collection School Of Computing and Information Systems

Recipe generation from food images and ingredients is a challenging task, which requires the interpretation of the information from another modality. Different from the image captioning task, where the captions usually have one sentence, cooking instructions contain multiple sentences and have obvious structures. To help the model capture the recipe structure and avoid missing some cooking details, we propose a novel framework: Decomposing Generation Networks (DGN) with structure prediction, to get more structured and complete recipe generation outputs. Specifically, we split each cooking instruction into several phases, and assign different sub-generators to each phase. Our approach includes two novel ideas ...

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Managing The Phaseout Of Coal Power: A Comparison Of Power Decarbonization Pathways In Jilin Province, Weirong Zhang, Zhixu Meng, Jiongjun Yang, Yan Song, Yiou Zhou, Changhong Zhao, Jiahai Yuan May 2022

Managing The Phaseout Of Coal Power: A Comparison Of Power Decarbonization Pathways In Jilin Province, Weirong Zhang, Zhixu Meng, Jiongjun Yang, Yan Song, Yiou Zhou, Changhong Zhao, Jiahai Yuan

Research Collection School Of Computing and Information Systems

With the periodic goals of reaching carbon emission peak before 2030 and achieving carbon neutrality before 2060 (“dual carbon” goals), China shows its unprecedented determination to coal power phaseout. This research takes Jilin Province to showcase possible pathways of coal power units’ phaseout on provincial level. We set up four different coal power phaseout scenarios, under which their transition cost and effectiveness would be calculated, respectively. In terms of natural resource endowment and electricity demand, Jilin Province would achieve a complete coal power phaseout by 2045 or even by 2040. However, after assessing the effectiveness of power transition under the ...

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Hierarchical Value Decomposition For Effective On-Demand Ride Pooling, Hao Jiang, Pradeep Varakantham May 2022

Hierarchical Value Decomposition For Effective On-Demand Ride Pooling, Hao Jiang, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

On-demand ride-pooling (e.g., UberPool, GrabShare) services focus on serving multiple different customer requests using each vehicle, i.e., an empty or partially filled vehicle can be assigned requests from different passengers with different origins and destinations. On the other hand, in Taxi on Demand (ToD) services (e.g., UberX), one vehicle is assigned to only one request at a time. On-demand ride pooling is not only beneficial to customers (lower cost), drivers (higher revenue per trip) and aggregation companies (higher revenue), but is also of crucial importance to the environment as it reduces the number of vehicles required on ...

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Downscaling Of Physical Risks For Climate Scenario Design, Enrico Biffis, Shuai Wang Apr 2022

Downscaling Of Physical Risks For Climate Scenario Design, Enrico Biffis, Shuai Wang

Sim Kee Boon Institute for Financial Economics

Southeast Asia is arguably one of the areas most vulnerable to natural disasters due to its dense population, coastal urbanization, and rainfall variability driven by the local monsoon systems. In this report, we focus on the impact of global warming in the region along four climate dimensions: temperature, precipitation, wind speed and coastal surge. The latter represents the surge of water from the ocean in excess of astronomical tides. Our objective is to downscale the outputs of global climate models to temporal and spatial resolutions of interest to market participants wishing to quantify climate risk vulnerability via climate stress testing ...

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Algorithm Selection For The Team Orienteering Problem, Mustafa Misir, Aldy Gunawan, Pieter Vansteenwegen Apr 2022

Algorithm Selection For The Team Orienteering Problem, Mustafa Misir, Aldy Gunawan, Pieter Vansteenwegen

Research Collection School Of Computing and Information Systems

This work utilizes Algorithm Selection for solving the Team Orienteering Problem (TOP). The TOP is an NP-hard combinatorial optimization problem in the routing domain. This problem has been modelled with various extensions to address different real-world problems like tourist trip planning. The complexity of the problem motivated to devise new algorithms. However, none of the existing algorithms came with the best performance across all the widely used benchmark instances. This fact suggests that there is a performance gap to fill. This gap can be targeted by developing more new algorithms as attempted by many researchers before. An alternative strategy is ...

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Pre-Training Graph Neural Networks For Link Prediction In Biomedical Networks, Yahui Long, Min Wu, Yong Liu, Yuan Fang, Chee Kong Kwoh, Jiawei Luo, Xiaoli Li Apr 2022

Pre-Training Graph Neural Networks For Link Prediction In Biomedical Networks, Yahui Long, Min Wu, Yong Liu, Yuan Fang, Chee Kong Kwoh, Jiawei Luo, Xiaoli Li

Research Collection School Of Computing and Information Systems

Motivation: Graphs or networks are widely utilized to model the interactions between different entities (e.g., proteins, drugs, etc) for biomedical applications. Predicting potential links in biomedical networks is important for understanding the pathological mechanisms of various complex human diseases, as well as screening compound targets for drug discovery. Graph neural networks (GNNs) have been designed for link prediction in various biomedical networks, which rely on the node features extracted from different data sources, e.g., sequence, structure and network data. However, it is challenging to effectively integrate these data sources and automatically extract features for different link prediction tasks ...

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Chatbot4qr: Interactive Query Refinement For Technical Question Retrieval, Neng Zhang, Qiao Huang, Xin Xia, Ying Zou, David Lo, Zhenchang Xing Apr 2022

Chatbot4qr: Interactive Query Refinement For Technical Question Retrieval, Neng Zhang, Qiao Huang, Xin Xia, Ying Zou, David Lo, Zhenchang Xing

Research Collection School Of Computing and Information Systems

Technical Q&A sites (e.g., Stack Overflow(SO)) are important resources for developers to search for knowledge about technical problems. Search engines provided in Q&A sites and information retrieval approaches have limited capabilities to retrieve relevant questions when queries are imprecisely specified, such as missing important technical details (e.g., the user's preferred programming languages). Although many automatic query expansion approaches have been proposed to improve the quality of queries by expanding queries with relevant terms, the information missed is not identified. Moreover, without user involvement, the existing query expansion approaches may introduce unexpected terms and lead to undesired results. In this paper, we propose an interactive query refinement approach for question retrieval, named Chatbot4QR, which assists users in recognizing and clarifying technical details missed in queries and thus retrieve more relevant questions for users. Chatbot4QR automatically detects missing technical details in a query and generates several clarification questions (CQs) to interact with the user to capture their overlooked technical details. To ensure the accuracy of CQs, we design a heuristic-based approach for CQ generation after building two kinds of technical knowledge bases: a manually categorized result of 1,841 technical tags in SO and the multiple version-frequency information of the tags. We collect 1.88 million SO questions as the repository for question retrieval. To evaluate Chatbot4QR, we conduct six user studies with 25 participants on 50 experimental queries. The results show that: (1) On average 60.8% of the CQs generated for a query are useful for helping the participants recognize missing technical details; (2) Chatbot4QR can rapidly respond to the participants after receiving a query within ~1.3 seconds; (3) The refined queries contribute to retrieving more relevant SO questions than nine baseline approaches. For more than 70% of the participants who have preferred techniques on the query tasks, Chatbot4QR significantly outperforms the state-of-the-art word embedding-based retrieval approach with an improvement of at least 54.6% in terms of Pre@k and NDCG@k; and (4)For 48%-88% of the assigned query tasks, the participants obtain more desired results after interacting with Chatbot4QR than directly searching from Web search engines ...

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A Survey On Modern Deep Neural Network For Traffic Prediction: Trends, Methods And Challenges, David Alexander Tedjopumomo, Zhifeng Bao, Baihua Zheng, Farhana Murtaza Choudhury, Kai Qin Apr 2022

A Survey On Modern Deep Neural Network For Traffic Prediction: Trends, Methods And Challenges, David Alexander Tedjopumomo, Zhifeng Bao, Baihua Zheng, Farhana Murtaza Choudhury, Kai Qin

Research Collection School Of Computing and Information Systems

In this modern era, traffic congestion has become a major source of negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate traffic congestion is through future traffic prediction. The field of traffic prediction has evolved greatly ever since its inception in the late 70s. Earlier studies mainly use classical statistical models such as ARIMA and its variants. Then, researchers started to focus on machine learning models due to their power and flexibility. As theoretical and technological advances emerge, we enter the era of deep neural network, which gained popularity due to its ...

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Sibnet: Food Instance Counting And Segmentation, Huu-Thanh. Nguyen, Chong-Wah Ngo, Wing-Kwong Chan Apr 2022

Sibnet: Food Instance Counting And Segmentation, Huu-Thanh. Nguyen, Chong-Wah Ngo, Wing-Kwong Chan

Research Collection School Of Computing and Information Systems

Food computing has recently attracted considerable research attention due to its significance for health risk analysis. In the literature, the majority of research efforts are dedicated to food recognition. Relatively few works are conducted for food counting and segmentation, which are essential for portion size estimation. This paper presents a deep neural network, named SibNet, for simultaneous counting and extraction of food instances from an image. The problem is challenging due to varying size and shape of food as well as arbitrary viewing angle of camera, not to mention that food instances often occlude each other. SibNet is novel for ...

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Estimating Stranded Coal Assets In China's Power Sector, Weirong Zhang, Mengjia Ren, Junjie Kang, Yiou Zhou, Jiahai Yuan Apr 2022

Estimating Stranded Coal Assets In China's Power Sector, Weirong Zhang, Mengjia Ren, Junjie Kang, Yiou Zhou, Jiahai Yuan

Research Collection School Of Computing and Information Systems

China has suffered overcapacity in coal power since 2016. With growing electricity demand and an economic crisis due to the Covid-19 pandemic, China faces a dilemma between easing restrictive policies for short-term growth in coal-fired power production and keeping restrictions in place for long-term sustainability. In this paper, we measure the risks faced by China's coal power units to become stranded in the next decade and estimate the associated economic costs for different shareholders. By implementing restrictive policies on coal power expansion, China can avoid 90% of stranded coal assets by 2025.

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Learning For Amalgamation: A Multi-Source Transfer Learning Framework For Sentiment Classification, Cuong V. Nguyen, Khiem H. Le, Hong Quang Pham, Quang H. Pham, Binh T. Nguyen Apr 2022

Learning For Amalgamation: A Multi-Source Transfer Learning Framework For Sentiment Classification, Cuong V. Nguyen, Khiem H. Le, Hong Quang Pham, Quang H. Pham, Binh T. Nguyen

Research Collection School Of Computing and Information Systems

Transfer learning plays an essential role in Deep Learning, which can remarkably improve the performance of the target domain, whose training data is not sufficient. Our work explores beyond the common practice of transfer learning with a single pre-trained model. We focus on the task of Vietnamese sentiment classification and propose LIFA, a framework to learn a unified embedding from several pre-trained models. We further propose two more LIFA variants that encourage the pre-trained models to either cooperate or compete with one another. Studying these variants sheds light on the success of LIFA by showing that sharing knowledge among the ...

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Fine-Grained Detection Of Academic Emotions With Spatial Temporal Graph Attention Networks Using Facial Landmarks, Hua Leong Fwa Apr 2022

Fine-Grained Detection Of Academic Emotions With Spatial Temporal Graph Attention Networks Using Facial Landmarks, Hua Leong Fwa

Research Collection School Of Computing and Information Systems

With the incidence of the Covid-19 pandemic, institutions have adopted online learning as the main lessondelivery channel. A common criticism of online learning is that sensing of learners’ affective states such asengagement is lacking which degrades the quality of teaching. In this study, we propose automatic sensing of learners’ affective states in an online setting with web cameras capturing their facial landmarks and head poses. We postulate that the sparsely connected facial landmarks can be modelled using a Graph Neural Network. Using the publicly available in the wild DAiSEE dataset, we modelled both the spatial and temporal dimensions of the ...

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Hybrid Tabu Search Algorithm For Unrelated Parallel Machine Scheduling In Semiconductor Fabs With Setup Times, Job Release, And Expired Times, Changyu Chen, Madhi Fathi, Marzieh Khakifirooz, Kan Wu Mar 2022

Hybrid Tabu Search Algorithm For Unrelated Parallel Machine Scheduling In Semiconductor Fabs With Setup Times, Job Release, And Expired Times, Changyu Chen, Madhi Fathi, Marzieh Khakifirooz, Kan Wu

Research Collection School Of Computing and Information Systems

This research is motivated by a scheduling problem arising in the ion implantation process of wafer fabrication. The ion implementation scheduling problem is modeled as an unrelated parallel machine scheduling (UPMS) problem with sequence-dependent setup times that are subject to job release time and expiration time of allowing a job to be processed on a specific machine, defined as: R|rj,eij,STsd|Cmax. The objective is first to maximize the number of processed jobs, then minimize the maximum completion time (makespan), and finally minimize the maximum completion times of the non-bottleneck machines. A mixed-integer programming (MIP) model is proposed ...

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Jscsp: A Novel Policy-Based Xss Defense Mechanism For Browsers, Guangquan Xu, Xiaofei Xie, Shuhan Huang, Jun Zhang, Lei Pan, Wei Lou, Kaitai Liang Mar 2022

Jscsp: A Novel Policy-Based Xss Defense Mechanism For Browsers, Guangquan Xu, Xiaofei Xie, Shuhan Huang, Jun Zhang, Lei Pan, Wei Lou, Kaitai Liang

Research Collection School Of Computing and Information Systems

To mitigate cross-site scripting attacks (XSS), the W3C group recommends web service providers to employ a computer security standard called Content Security Policy (CSP). However, less than 3.7 percent of real-world websites are equipped with CSP according to Google’s survey. The low scalability of CSP is incurred by the difficulty of deployment and non-compatibility for state-of-art browsers. To explore the scalability of CSP, in this article, we propose JavaScript based CSP (JSCSP), which is able to support most of real-world browsers but also to generate security policies automatically. Specifically, JSCSP offers a novel self-defined security policy which enforces ...

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Wifitrace: Network-Based Contact Tracing For Infectious Diseases Using Passive Wifi Sensing, Amee Trivedi, Camellia Zakaria, Rajesh Krishna Balan, Ann Becker, George Corey, Prashant Shenoy Mar 2022

Wifitrace: Network-Based Contact Tracing For Infectious Diseases Using Passive Wifi Sensing, Amee Trivedi, Camellia Zakaria, Rajesh Krishna Balan, Ann Becker, George Corey, Prashant Shenoy

Research Collection School Of Computing and Information Systems

Contact tracing is a well-established and effective approach for the containment of the spread of infectious diseases. While Bluetooth-based contact tracing method using phones has become popular recently, these approaches suffer from the need for a critical mass adoption to be effective. In this paper, we present WiFiTrace, a network-centric approach for contact tracing that relies on passive WiFi sensing with no client-side involvement. Our approach exploits WiFi network logs gathered by enterprise networks for performance and security monitoring, and utilizes them for reconstructing device trajectories for contact tracing. Our approach is specifically designed to enhance the efficacy of traditional ...

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Coordinated Delivery To Shopping Malls With Limited Docking Capacity, Ruidian Song, Hoong Chuin Lau, Xue Luo, Lei Zhao Mar 2022

Coordinated Delivery To Shopping Malls With Limited Docking Capacity, Ruidian Song, Hoong Chuin Lau, Xue Luo, Lei Zhao

Research Collection School Of Computing and Information Systems

Shopping malls are densely located in major cities such as Singapore and Hong Kong. Tenants in these shopping malls generate a large number of freight orders to their contracted logistics service providers, who independently plan their own delivery schedules. These uncoordinated deliveries and limited docking capacity jointly cause congestion at the shopping malls. A delivery coordination platform centrally plans the vehicle routes for the logistics service providers and simultaneously schedules the dock time slots at the shopping malls for the delivery orders. Vehicle routing and dock scheduling decisions need to be made jointly against the backdrop of travel time and ...

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Mg2vec: Learning Relationship-Preserving Heterogeneous Graph Representations Via Metagraph Embedding, Wentao Zhang, Yuan Fang, Zemin Liu, Min Wu, Xinming Zhang Mar 2022

Mg2vec: Learning Relationship-Preserving Heterogeneous Graph Representations Via Metagraph Embedding, Wentao Zhang, Yuan Fang, Zemin Liu, Min Wu, Xinming Zhang

Research Collection School Of Computing and Information Systems

Given that heterogeneous information networks (HIN) encompass nodes and edges belonging to different semantic types, they can model complex data in real-world scenarios. Thus, HIN embedding has received increasing attention, which aims to learn node representations in a low-dimensional space, in order to preserve the structural and semantic information on the HIN. In this regard, metagraphs, which model common and recurring patterns on HINs, emerge as a powerful tool to capture semantic-rich and often latent relationships on HINs. Although metagraphs have been employed to address several specific data mining tasks, they have not been thoroughly explored for the more general ...

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Learning User Interface Semantics From Heterogeneous Networks With Multi-Modal And Positional Attributes, Gary Ang, Ee-Peng Lim Mar 2022

Learning User Interface Semantics From Heterogeneous Networks With Multi-Modal And Positional Attributes, Gary Ang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

User interfaces (UI) of desktop, web, and mobile applications involve a hierarchy of objects (e.g. applications, screens, view class, and other types of design objects) with multimodal (e.g. textual, visual) and positional (e.g. spatial location, sequence order and hierarchy level) attributes. We can therefore represent a set of application UIs as a heterogeneous network with multimodal and positional attributes. Such a network not only represents how users understand the visual layout of UIs, but also influences how users would interact with applications through these UIs. To model the UI semantics well for different UI annotation, search, and ...

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Strangan: Adversarially-Learnt Spatial Transformer For Scalable Human Activity Recognition, Abu Zaher Md Faridee, Avijoy Chakma, Archan Misra, Nirmalya Roy Mar 2022

Strangan: Adversarially-Learnt Spatial Transformer For Scalable Human Activity Recognition, Abu Zaher Md Faridee, Avijoy Chakma, Archan Misra, Nirmalya Roy

Research Collection School Of Computing and Information Systems

We tackle the problem of domain adaptation for inertial sensing-based human activity recognition (HAR) applications -i.e., in developing mechanisms that allow a classifier trained on sensor samples collected under a certain narrow context to continue to achieve high activity recognition accuracy even when applied to other contexts. This is a problem of high practical importance as the current requirement of labeled training data for adapting such classifiers to every new individual, device, or on-body location is a major roadblock to community-scale adoption of HAR-based applications. We particularly investigate the possibility of ensuring robust classifier operation, without requiring any new ...

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Estimating Financial Information Asymmetry In Real Estate Transactions In China: An Application Of Two-Tier Frontier Model, Ganlin Pu, Ying Zhang, Li-Chen Chou Mar 2022

Estimating Financial Information Asymmetry In Real Estate Transactions In China: An Application Of Two-Tier Frontier Model, Ganlin Pu, Ying Zhang, Li-Chen Chou

Research Collection School Of Computing and Information Systems

This study applies the two-tier stochastic frontier model to estimate the distribution of housing transaction information in Hangzhou, Wenzhou, Ningbo, and Jinhua (four cities in Zhejiang Province, China) during the year 2018, to analyze the difference in the price information acquired by the buyers and sellers in the transaction, and the effect of information asymmetry on the transaction price. The empirical results show that in each city, during the housing transaction process, the supplier has more complete information about house prices than consumers, and can therefore implement price discrimination strategies in setting service prices. Due to the disadvantage in acquired ...

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The Impact Of Ride-Hail Surge Factors On Taxi Bookings, Sumit Agarwal, Ben Charoenwong, Shih-Fen Cheng, Jussi Keppo Mar 2022

The Impact Of Ride-Hail Surge Factors On Taxi Bookings, Sumit Agarwal, Ben Charoenwong, Shih-Fen Cheng, Jussi Keppo

Research Collection School Of Computing and Information Systems

We study the role of ride-hailing surge factors on the allocative efficiency of taxis by combining a reduced-form estimation with structural analyses using machine-learning-based demand predictions. Where other research study the effect of entry on incumbent taxis, we use higher frequency granular data to study how location-time-specific surge factors affect taxi bookings to bound the effect of customer decisions while accounting for various confounding variables. We find that even in a unique market like Singapore, where incumbent taxi companies have app-based booking systems similar to those from ride-hailing companies like Uber, the estimated upper bound on the cross-platform substitution between ...

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Deep Learning For Anomaly Detection: A Review, Guansong Pang, Chunhua Shen, Longbing Cao, Anton Van Den Hengel Mar 2022

Deep Learning For Anomaly Detection: A Review, Guansong Pang, Chunhua Shen, Longbing Cao, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e., deep anomaly detection, has emerged as a critical direction. This article surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in 3 high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages, and disadvantages and discuss how they address ...

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Perceived Cultural Impacts Of Climate Change Motivate Climate Action And Support For Climate Policy, Kim-Pong Tam, Angela K. Y. Leung, Brandon Koh Mar 2022

Perceived Cultural Impacts Of Climate Change Motivate Climate Action And Support For Climate Policy, Kim-Pong Tam, Angela K. Y. Leung, Brandon Koh

Research Collection School of Social Sciences

The impacts of climate change on human cultures have received increasing attention in recent years. However, the extent to which people are aware of these impacts, whether such awareness motivates climate action, and what kinds of people show stronger awareness are rarely understood. The present investigation provides the very first set of answers to these questions. In two studies (with a student sample with N = 199 from Singapore and a demographically representative sample with N = 625 from the USA), we observed a generally high level of awareness among our participants. Most importantly, perceived cultural impacts of climate change robustly predicted ...

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Viral Pneumonia Screening On Chest X-Rays Using Confidence-Aware Anomaly Detection, Jianpeng Zhang, Yutong Xie, Guansong Pang, Zhibin Liao, Johan Verjans, Wenxing Li, Zongji Sun, Jian He, Yi Li, Chunhua Shen, Yong Xia Mar 2022

Viral Pneumonia Screening On Chest X-Rays Using Confidence-Aware Anomaly Detection, Jianpeng Zhang, Yutong Xie, Guansong Pang, Zhibin Liao, Johan Verjans, Wenxing Li, Zongji Sun, Jian He, Yi Li, Chunhua Shen, Yong Xia

Research Collection School Of Computing and Information Systems

Clusters of viral pneumonia occurrences over a short period may be a harbinger of an outbreak or pandemic. Rapid and accurate detection of viral pneumonia using chest X-rays can be of significant value for large-scale screening and epidemic prevention, particularly when other more sophisticated imaging modalities are not readily accessible. However, the emergence of novel mutated viruses causes a substantial dataset shift, which can greatly limit the performance of classification-based approaches. In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into a one-class classification-based anomaly detection problem. We therefore propose the confidence-aware ...

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Androevolve: Automated Android Api Update With Data Flow Analysis And Variable Denormalization, Stefanus A. Haryono, Ferdian Thung, David Lo, Lingxiao Jiang, Julia Lawall, Hong Jin Kang, Lucas Serrano, Gilles Muller Mar 2022

Androevolve: Automated Android Api Update With Data Flow Analysis And Variable Denormalization, Stefanus A. Haryono, Ferdian Thung, David Lo, Lingxiao Jiang, Julia Lawall, Hong Jin Kang, Lucas Serrano, Gilles Muller

Research Collection School Of Computing and Information Systems

The Android operating system is frequently updated, with each version bringing a new set of APIs. New versions may involve API deprecation; Android apps using deprecated APIs need to be updated to ensure the apps’ compatibility with old and new Android versions. Updating deprecated APIs is a time-consuming endeavor. Hence, automating the updates of Android APIs can be beneficial for developers. CocciEvolve is the state-of-the-art approach for this automation. However, it has several limitations, including its inability to resolve out-of-method variables and the low code readability of its updates due to the addition of temporary variables. In an attempt to ...

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