Open Access. Powered by Scholars. Published by Universities.®

Physical Sciences and Mathematics Commons™

Open Access. Powered by Scholars. Published by Universities.®

Research Collection School Of Computing and Information Systems

Discipline
Keyword
Publication Year
File Type

Articles 1081 - 1110 of 6891

Full-Text Articles in Physical Sciences and Mathematics

Topic-Guided Conversational Recommender In Multiple Domains, Lizi Liao, Ryuichi Takanobu, Yunshan Ma, Xun Yang, Minlie Huang, Tat-Seng Chua May 2022

Topic-Guided Conversational Recommender In Multiple Domains, Lizi Liao, Ryuichi Takanobu, Yunshan Ma, Xun Yang, Minlie Huang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Conversational systems have recently attracted significant attention. Both the research community and industry believe that it will exert huge impact on human-computer interaction, and specifically, the IR/RecSys community has begun to explore Conversational Recommendation. In real-life scenarios, such systems are often urgently needed in helping users accomplishing different tasks under various situations. However, existing works still face several shortcomings: (1) Most efforts are largely confined in single task setting. They fall short of hands in handling tasks across domains. (2) Aside from soliciting user preference from dialogue history, a conversational recommender naturally has access to the back-end data structure which …

Go to article

Structure-Aware Visualization Retrieval, Haotian Li, Yong Wang, Aoyu Wu, Huan Wei, Huamin. Qu May 2022

Structure-Aware Visualization Retrieval, Haotian Li, Yong Wang, Aoyu Wu, Huan Wei, Huamin. Qu

Research Collection School Of Computing and Information Systems

With the wide usage of data visualizations, a huge number of Scalable Vector Graphic (SVG)-based visualizations have been created and shared online. Accordingly, there has been an increasing interest in exploring how to retrieve perceptually similar visualizations from a large corpus, since it can benefit various downstream applications such as visualization recommendation. Existing methods mainly focus on the visual appearance of visualizations by regarding them as bitmap images. However, the structural information intrinsically existing in SVG-based visualizations is ignored. Such structural information can delineate the spatial and hierarchical relationship among visual elements, and characterize visualizations thoroughly from a new perspective. …

Go to article

Natural Attack For Pre-Trained Models Of Code, Zhou Yang, Jieke Shi, Junda He, David Lo May 2022

Natural Attack For Pre-Trained Models Of Code, Zhou Yang, Jieke Shi, Junda He, David Lo

Research Collection School Of Computing and Information Systems

Pre-trained models of code have achieved success in many important software engineering tasks. However, these powerful models are vulnerable to adversarial attacks that slightly perturb model inputs to make a victim model produce wrong outputs. Current works mainly attack models of code with examples that preserve operational program semantics but ignore a fundamental requirement for adversarial example generation: perturbations should be natural to human judges, which we refer to as naturalness requirement. In this paper, we propose ALERT (Naturalness Aware Attack), a black-box attack that adversarially transforms inputs to make victim models produce wrong outputs. Different from prior works, this …

Go to article

Who Will Support My Project? Interactive Search Of Potential Crowdfunding Investors Through Insearch., Songheng Zhang, Yong Wang, Haotian Li, Wanyu Zhang May 2022

Who Will Support My Project? Interactive Search Of Potential Crowdfunding Investors Through Insearch., Songheng Zhang, Yong Wang, Haotian Li, Wanyu Zhang

Research Collection School Of Computing and Information Systems

Crowdfunding provides project founders with a convenient way to reach online investors. However, it is challenging for founders to find the most potential investors and successfully raise money for their projects on crowdfunding platforms. A few machine learning based methods have been proposed to recommend investors’ interest in a specific crowdfunding project, but they fail to provide project founders with explanations in detail for these recommendations, thereby leading to an erosion of trust in predicted investors. To help crowdfunding founders find truly interested investors, we conducted semi-structured interviews with four crowdfunding experts and presentsinSearch, a visual analytic system. inSearch allows …

Go to article

Rumorlens: Interactive Analysis And Validation Of Suspected Rumors On Social Media, Ran Wang, Kehan Du, Qianhe Chen, Yifei Zhao, Mojie Tang, Hongxi Tao, Shipan Wang, Yiyao Li, Yong Wang May 2022

Rumorlens: Interactive Analysis And Validation Of Suspected Rumors On Social Media, Ran Wang, Kehan Du, Qianhe Chen, Yifei Zhao, Mojie Tang, Hongxi Tao, Shipan Wang, Yiyao Li, Yong Wang

Research Collection School Of Computing and Information Systems

With the development of social media, various rumors can be easily spread on the Internet and such rumors can have serious negative effects on society. Thus, it has become a critical task for social media platforms to deal with suspected rumors. However, due to the lack of effective tools, it is often difficult for platform administrators to analyze and validate rumors from a large volume of information on a social media platform efficiently. We have worked closely with social media platform administrators for four months to summarize their requirements of identifying and analyzing rumors, and further proposed an interactive visual …

Go to article

Ptm4tag: Sharpening Tag Recommendation Of Stack Overflow Posts With Pre-Trained Models, Junda He, Bowen Xu, Zhou Yang, Donggyun Han, Chengran Yang, David Lo May 2022

Ptm4tag: Sharpening Tag Recommendation Of Stack Overflow Posts With Pre-Trained Models, Junda He, Bowen Xu, Zhou Yang, Donggyun Han, Chengran Yang, David Lo

Research Collection School Of Computing and Information Systems

Stack Overflow is often viewed as one of the most influential Software Question & Answer (SQA) websites, containing millions of programming-related questions and answers. Tags play a critical role in efficiently structuring the contents in Stack Overflow and are vital to support a range of site operations, e.g., querying relevant contents. Poorly selected tags often introduce extra noise and redundancy, which raises problems like tag synonym and tag explosion. Thus, an automated tag recommendation technique that can accurately recommend high-quality tags is desired to alleviate the problems mentioned above.

Go to article

Simple Or Complex? Together For A More Accurate Just-In-Time Defect Predictor, Xin Zhou, Donggyun Han, David Lo May 2022

Simple Or Complex? Together For A More Accurate Just-In-Time Defect Predictor, Xin Zhou, Donggyun Han, David Lo

Research Collection School Of Computing and Information Systems

Just-In-Time (JIT) defect prediction aims to automatically predict whether a commit is defective or not, and has been widely studied in recent years. In general, most studies can be classified into two categories: 1) simple models using traditional machine learning classifiers with hand-crafted features, and 2) complex models using deep learning techniques to automatically extract features. Hand-crafted features used by simple models are based on expert knowledge but may not fully represent the semantic meaning of the commits. On the other hand, deep learning-based features used by complex models represent the semantic meaning of commits but may not reflect useful …

Go to article

Arseek: Identifying Api Resource Using Code And Discussion On Stack Overflow, Gia Kien Luong, Mohammad Hadi, Thung Ferdian, Fatemeh H. Fard, David Lo May 2022

Arseek: Identifying Api Resource Using Code And Discussion On Stack Overflow, Gia Kien Luong, Mohammad Hadi, Thung Ferdian, Fatemeh H. Fard, David Lo

Research Collection School Of Computing and Information Systems

It is not a trivial problem to collect API-relevant examples, usages, and mentions on venues such as Stack Overflow. It requires efforts to correctly recognize whether the discussion refers to the API method that developers/tools are searching for. The content of the Stack Overflow thread, which consists of both text paragraphs describing the involvement of the API method in the discussion and the code snippets containing the API invocation, may refer to the given API method. Leveraging this observation, we develop ARSeek, a context-specific algorithm to capture the semantic and syntactic information of the paragraphs and code snippets in a …

Go to article

On The Transferability Of Pre-Trained Language Models For Low-Resource Programming Languages, Fuxiang Chen, Fatemeh H. Fard, David Lo, Timofey Bryksin May 2022

On The Transferability Of Pre-Trained Language Models For Low-Resource Programming Languages, Fuxiang Chen, Fatemeh H. Fard, David Lo, Timofey Bryksin

Research Collection School Of Computing and Information Systems

A recent study by Ahmed and Devanbu reported that using a corpus of code written in multilingual datasets to fine-tune multilingual Pre-trained Language Models (PLMs) achieves higher performance as opposed to using a corpus of code written in just one programming language. However, no analysis was made with respect to fine-tuning monolingual PLMs. Furthermore, some programming languages are inherently different and code written in one language usually cannot be interchanged with the others, i.e., Ruby and Java code possess very different structure. To better understand how monolingual and multilingual PLMs affect different programming languages, we investigate 1) the performance of …

Go to article

Exais: Executable Ai Semantics, Richard Schumi, Jun Sun May 2022

Exais: Executable Ai Semantics, Richard Schumi, Jun Sun

Research Collection School Of Computing and Information Systems

Neural networks can be regarded as a new programming paradigm, i.e., instead of building ever-more complex programs through (often informal) logical reasoning in the programmers' mind, complex 'AI' systems are built by optimising generic neural network models with big data. In this new paradigm, AI frameworks such as TensorFlow and PyTorch play a key role, which is as essential as the compiler for traditional programs. It is known that the lack of a proper semantics for programming languages (such as C), i.e., a correctness specification for compilers, has contributed to many problematic program behaviours and security issues. While it is …

Go to article

Designing Visuo-Haptic Illusions With Proxies In Virtual Reality: Exploration Of Grasp, Movement Trajectory And Object Mass, Martin Feick, Kora Persephone Regitz, Anthony Tang, Antonio Kruger May 2022

Designing Visuo-Haptic Illusions With Proxies In Virtual Reality: Exploration Of Grasp, Movement Trajectory And Object Mass, Martin Feick, Kora Persephone Regitz, Anthony Tang, Antonio Kruger

Research Collection School Of Computing and Information Systems

Visuo-haptic illusions are a method to expand proxy-based interactions in VR by introducing unnoticeable discrepancies between the virtual and real world. Yet how different design variables affect the illusions with proxies is still unclear. To unpack a subset of variables, we conducted two user studies with 48 participants to explore the impact of (1) different grasping types and movement trajectories, and (2) different grasping types and object masses on the discrepancy which may be introduced. Our Bayes analysis suggests that grasping types and object masses (≤ 500 g) did not noticeably affect the discrepancy, but for movement trajectory, results were …

Go to article

Watch Your Flavors: Augmenting People's Flavor Perceptions And Associated Emotions Based On Videos Watched While Eating, Meetha Nesam James, Nimesha Ranasinghe, Anthony Tang, Lora Oehlberg May 2022

Watch Your Flavors: Augmenting People's Flavor Perceptions And Associated Emotions Based On Videos Watched While Eating, Meetha Nesam James, Nimesha Ranasinghe, Anthony Tang, Lora Oehlberg

Research Collection School Of Computing and Information Systems

People engage in different activities while eating alone, such as watching television or scrolling through social media on their phones. However, the impacts of these visual contents on human cognitive processes, particularly related to flavor perception and its attributes, are still not thoroughly explored. This paper presents a user study to evaluate the influence of six different types of video content (including nature, cooking, and a new food video genre known as mukbang) on people’s flavor perceptions in terms of taste sensations, liking, and emotions while eating plain white rice. Our findings revealed that the participants’ flavor perceptions are augmented …

Go to article

Probabilistic Path Prioritization For Hybrid Fuzzing, Lei Zhao, Pengcheng Cao, Yue Duan, Heng Yin, Jifeng Xuan May 2022

Probabilistic Path Prioritization For Hybrid Fuzzing, Lei Zhao, Pengcheng Cao, Yue Duan, Heng Yin, Jifeng Xuan

Research Collection School Of Computing and Information Systems

Hybrid fuzzing that combines fuzzing and concolic execution has become an advanced technique for software vulnerability detection. Based on the observation that fuzzing and concolic execution are complementary in nature, state-of-the-art hybrid fuzzing systems deploy “optimal concolic testing” and “demand launch” strategies. Although these ideas sound intriguing, we point out several fundamental limitations in them, due to unrealistic or oversimplified assumptions. Further, we propose a novel “discriminative dispatch” strategy and design a probabilistic hybrid fuzzing system to better utilize the capability of concolic execution. Specifically, we design a Monte Carlo-based probabilistic path prioritization model to quantify each path’s difficulty, and …

Go to article

Active Warden Attack: On The (In)Effectiveness Of Android App Repackage-Proofing, Haoyu Ma, Shijia Li, Debin Gao, Daoyuan Wu, Qiaowen Jia, Chunfu Jia May 2022

Active Warden Attack: On The (In)Effectiveness Of Android App Repackage-Proofing, Haoyu Ma, Shijia Li, Debin Gao, Daoyuan Wu, Qiaowen Jia, Chunfu Jia

Research Collection School Of Computing and Information Systems

App repackaging has raised serious concerns to the Android ecosystem with the repackage-proofing technology attracting attention in the Android research community. In this paper, we first show that existing repackage-proofing schemes rely on a flawed security assumption, and then propose a new class of active warden attack that intercepts and falsifies the metrics used by repackage-proofing for detecting the integrity violations during repackaging. We develop a proof-of-concept toolkit to demonstrate that all the existing repackage-proofing schemes can be bypassed by our attack toolkit. On the positive side, our analysis further identifies a new integrity metric in the Android ART runtime …

Go to article

Undiscounted Recursive Path Choice Models: Convergence Properties And Algorithms, Tien Mai, Emma Frejinger May 2022

Undiscounted Recursive Path Choice Models: Convergence Properties And Algorithms, Tien Mai, Emma Frejinger

Research Collection School Of Computing and Information Systems

Traffic flow predictions are central to a wealth of problems in transportation. Path choice models can be used for this purpose, and in state-of-the-art models—so-called recursive path choice (RPC) models—the choice of a path is formulated as a sequential arc choice process using undiscounted Markov decision process (MDP) with an absorbing state. The MDP has a utility maximization objective with unknown parameters that are estimated based on data. The estimation and prediction using RPC models require repeatedly solving value functions that are solutions to the Bellman equation. Although there are several examples of successful applications of RPC models in the …

Go to article

Unified Route Planning For Shared Mobility: An Insertion-Based Framework, Yongxin Tong, Yuxiang Zeng, Zimu Zhou, Lei Chen, Ke. Xu May 2022

Unified Route Planning For Shared Mobility: An Insertion-Based Framework, Yongxin Tong, Yuxiang Zeng, Zimu Zhou, Lei Chen, Ke. Xu

Research Collection School Of Computing and Information Systems

There has been a dramatic growth of shared mobility applications such as ride-sharing, food delivery, and crowdsourced parcel delivery. Shared mobility refers to transportation services that are shared among users, where a central issue is route planning. Given a set of workers and requests, route planning finds for each worker a route, i.e., a sequence of locations to pick up and drop off passengers/parcels that arrive from time to time, with different optimization objectives. Previous studies lack practicability due to their conflicted objectives and inefficiency in inserting a new request into a route, a basic operation called insertion. In addition, …

Go to article

Guided Attention Multimodal Multitask Financial Forecasting With Inter-Company Relationships And Global And Local News, Meng Kiat Gary Ang, Ee-Peng Lim May 2022

Guided Attention Multimodal Multitask Financial Forecasting With Inter-Company Relationships And Global And Local News, Meng Kiat Gary Ang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Most works on financial forecasting use information directly associated with individual companies (e.g., stock prices, news on the company) to predict stock returns for trading. We refer to such company-specific information as local information. Stock returns may also be influenced by global information (e.g., news on the economy in general), and inter-company relationships. Capturing such diverse information is challenging due to the low signal-to-noise ratios, different time-scales, sparsity and distributions of global and local information from different modalities. In this paper, we propose a model that captures both global and local multimodal information for investment and risk management-related forecasting tasks. …

Go to article

Message-Locked Searchable Encryption: A New Versatile Tool For Secure Cloud Storage, Xueqiao Liu, Guomin Yang, Willy Susilo, Joseph Tonien, Rongmao Chen, Xixiang Lv May 2022

Message-Locked Searchable Encryption: A New Versatile Tool For Secure Cloud Storage, Xueqiao Liu, Guomin Yang, Willy Susilo, Joseph Tonien, Rongmao Chen, Xixiang Lv

Research Collection School Of Computing and Information Systems

Message-Locked Encryption (MLE) is a useful tool to enable deduplication over encrypted data in cloud storage. It can significantly improve the cloud service quality by eliminating redundancy to save storage resources, and hence user cost, and also providing defense against different types of attacks, such as duplicate faking attack and brute-force attack. A typical MLE scheme only focuses on deduplication. On the other hand, supporting search operations on stored content is another essential requirement for cloud storage. In this article, we present a message-locked searchable encryption (MLSE) scheme in a dual-server setting, which achieves simultaneously the desirable features of supporting …

Go to article

Uipdroid: Unrooted Dynamic Monitor Of Android App Uis For Fine-Grained Permission Control, Mulin Duan, Lingxiao Jiang, Lwin Khin Shar, Debin Gao May 2022

Uipdroid: Unrooted Dynamic Monitor Of Android App Uis For Fine-Grained Permission Control, Mulin Duan, Lingxiao Jiang, Lwin Khin Shar, Debin Gao

Research Collection School Of Computing and Information Systems

Proper permission controls in Android systems are important for protecting users' private data when running applications installed on the devices. Currently Android systems require apps to obtain authorization from users at the first time when they try to access users' sensitive data, but every permission is only managed at the application level, allowing apps to (mis)use permissions granted by users at the beginning for different purposes subsequently without informing users. Based on privacy-by-design principles, this paper develops a new permission manager, named UIPDroid, that (1) enforces the users' basic right-to-know through user interfaces whenever an app uses permissions, and (2) …

Go to article

Feasibility Studies In Indoor Localization Through Intelligent Conversation, Sheshadri Smitha, Linus Cheng, Kotaro Hara May 2022

Feasibility Studies In Indoor Localization Through Intelligent Conversation, Sheshadri Smitha, Linus Cheng, Kotaro Hara

Research Collection School Of Computing and Information Systems

We propose a model to achieve human localization in indoor environments through intelligent conversation between users and an agent. We investigated the feasibility of conversational localization by conducting two studies. First, we conducted a Wizard-of-Oz study with N = 7 participants and studied the feasibility of localizing users through conversation. We identified challenges posed by users’ language and behavior. Second, we collected N = 800 user descriptions of virtual indoor locations from N = 80 Amazon Mechanical Turk participants to analyze user language. We explored the effects of conversational agent behavior and observed that people describe indoor locations differently based …

Go to article

Do Pre-Trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation And A Reasonable Approach, Xin Lv, Yankai Lin, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Peng Li, Jie Zhou May 2022

Do Pre-Trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation And A Reasonable Approach, Xin Lv, Yankai Lin, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Peng Li, Jie Zhou

Research Collection School Of Computing and Information Systems

In recent years, pre-trained language models (PLMs) have been shown to capture factual knowledge from massive texts, which encourages the proposal of PLM-based knowledge graph completion (KGC) models. However, these models are still quite behind the SOTA KGC models in terms of performance. In this work, we find two main reasons for the weak performance: (1) Inaccurate evaluation setting. The evaluation setting under the closed-world assumption (CWA) may underestimate the PLM-based KGC models since they introduce more external knowledge; (2) Inappropriate utilization of PLMs. Most PLM-based KGC models simply splice the labels of entities and relations as inputs, leading to …

Go to article

Translate-Train Embracing Translationese Artifacts, Sicheng Yu, Qianru Sun, Hao Zhang, Jing Jiang May 2022

Translate-Train Embracing Translationese Artifacts, Sicheng Yu, Qianru Sun, Hao Zhang, Jing Jiang

Research Collection School Of Computing and Information Systems

Translate-train is a general training approach to multilingual tasks. The key idea is to use the translator of the target language to generate training data to mitigate the gap between the source and target languages. However, its performance is often hampered by the artifacts in the translated texts (translationese). We discover that such artifacts have common patterns in different languages and can be modeled by deep learning, and subsequently propose an approach to conduct translate-train using Translationese Embracing the effect of Artifacts (TEA). TEA learns to mitigate such effect on the training data of a source language (whose original and …

Go to article

Graphcode2vec: Generic Code Embedding Via Lexical And Program Dependence Analyses, Wei Ma, Mengjie Zhao, Ezekiel Soremekun, Qiang Hu, Jie M. Zhang, Mike Papadakis, Maxime Cordy, Xiaofei Xie, Yves Le Traon May 2022

Graphcode2vec: Generic Code Embedding Via Lexical And Program Dependence Analyses, Wei Ma, Mengjie Zhao, Ezekiel Soremekun, Qiang Hu, Jie M. Zhang, Mike Papadakis, Maxime Cordy, Xiaofei Xie, Yves Le Traon

Research Collection School Of Computing and Information Systems

Code embedding is a keystone in the application of machine learning on several Software Engineering (SE) tasks. To effectively support a plethora of SE tasks, the embedding needs to capture program syntax and semantics in a way that is generic. To this end, we propose the first self-supervised pre-training approach (called Graphcode2vec) which produces task-agnostic embedding of lexical and program dependence features. Graphcode2vec achieves this via a synergistic combination of code analysis and Graph Neural Networks. Graphcode2vec is generic, it allows pre-training, and it is applicable to several SE downstream tasks. We evaluate the effectiveness of Graphcode2vec on four (4) …

Go to article

Optimal In‐Place Suffix Sorting, Zhize Li, Jian Li, Hongwei Huo May 2022

Optimal In‐Place Suffix Sorting, Zhize Li, Jian Li, Hongwei Huo

Research Collection School Of Computing and Information Systems

The suffix array is a fundamental data structure for many applications that involve string searching and data compression. Designing time/space-efficient suffix array construction algorithms has attracted significant attention and considerable advances have been made for the past 20 years. We obtain the \emph{first} in-place suffix array construction algorithms that are optimal both in time and space for (read-only) integer alphabets. Concretely, we make the following contributions: 1. For integer alphabets, we obtain the first suffix sorting algorithm which takes linear time and uses only $O(1)$ workspace (the workspace is the total space needed beyond the input string and the output …

Go to article

Mmekg: Multi-Modal Event Knowledge Graph Towards Universal Representation Across Modalities, Yubo Ma, Zehao Wang, Mukai Li, Yixin Cao, Meiqi Chen, Xinze Li, Wenqi Sun, Kunquan Deng, Kun Wang, Aixin Sun, Jing Shao May 2022

Mmekg: Multi-Modal Event Knowledge Graph Towards Universal Representation Across Modalities, Yubo Ma, Zehao Wang, Mukai Li, Yixin Cao, Meiqi Chen, Xinze Li, Wenqi Sun, Kunquan Deng, Kun Wang, Aixin Sun, Jing Shao

Research Collection School Of Computing and Information Systems

Events are fundamental building blocks of realworld happenings. In this paper, we present a large-scale, multi-modal event knowledge graph named MMEKG. MMEKG unifies different modalities of knowledge via events, which complement and disambiguate each other. Specifically, MMEKG incorporates (i) over 990 thousand concept events with 644 relation types to cover most types of happenings, and (ii) over 863 million instance events connected through 934 million relations, which provide rich contextual information in texts and/or images. To collect billion-scale instance events and relations among them, we additionally develop an efficient yet effective pipeline for textual/visual knowledge extraction system. We also develop …

Go to article

Github Sponsors: Exploring A New Way To Contribute To Open Source, Naomichi Shimada, Tao Xiao, Hideaki Hata, Christoph Treude, Kenichi Matsumoto May 2022

Github Sponsors: Exploring A New Way To Contribute To Open Source, Naomichi Shimada, Tao Xiao, Hideaki Hata, Christoph Treude, Kenichi Matsumoto

Research Collection School Of Computing and Information Systems

GitHub Sponsors, launched in 2019, enables donations to individual open source software (OSS) developers. Financial support for OSS maintainers and developers is a major issue in terms of sustaining OSS projects, and the ability to donate to individuals is expected to support the sustainability of developers, projects, and community. In this work, we conducted a mixed-methods study of GitHub Sponsors, including quantitative and qualitative analyses, to understand the characteristics of developers who are likely to receive donations and what developers think about donations to individuals. We found that: (1) sponsored developers are more active than non-sponsored developers, (2) the possibility …

Go to article

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 the roads. Since …

Go to article

Deep Depression Prediction On Longitudinal Data Via Joint Anomaly Ranking And Classification, Guansong Pang, Ngoc Thien Anh Pham, Emma Baker, Rebecca Bentley, Anton Van Den Hengel May 2022

Deep Depression Prediction On Longitudinal Data Via Joint Anomaly Ranking And Classification, Guansong Pang, Ngoc Thien Anh Pham, Emma Baker, Rebecca Bentley, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

A wide variety of methods have been developed for identifying depression, but they focus primarily on measuring the degree to which individuals are suffering from depression currently. In this work we explore the possibility of predicting future depression using machine learning applied to longitudinal socio-demographic data. In doing so we show that data such as housing status, and the details of the family environment, can provide cues for predicting future psychiatric disorders. To this end, we introduce a novel deep multi-task recurrent neural network to learn time-dependent depression cues. The depression prediction task is jointly optimized with two auxiliary anomaly …

Go to article

Who Are The 'Silent Spreaders'?: Contact Tracing In Spatio-Temporal Memory Models, Yue Hu, Budhitama Subagdja, Ah-Hwee Tan, Chai Quek, Quanjun Yin May 2022

Who Are The 'Silent Spreaders'?: Contact Tracing In Spatio-Temporal Memory Models, Yue Hu, Budhitama Subagdja, Ah-Hwee Tan, Chai Quek, Quanjun Yin

Research Collection School Of Computing and Information Systems

The COVID-19 epidemic has swept the world for over two years. However, a large number of infectious asymptomatic COVID-19 cases (ACCs) are still making the breaking up of the transmission chains very difficult. Efforts by epidemiological researchers in many countries have thrown light on the clinical features of ACCs, but there is still a lack of practical approaches to detect ACCs so as to help contain the pandemic. To address the issue of ACCs, this paper presents a neural network model called Spatio-Temporal Episodic Memory for COVID-19 (STEM-COVID) to identify ACCs from contact tracing data. Based on the fusion Adaptive …

Go to article

Tatl: Task Agnostic Transfer Learning For Skin Attributes Detection, Duy M.H. Nguyen, Thu T. Nguyen, Huong Vu, Hong Quang Pham, Manh-Duy Nguyen, Binh T. Nguyen, Daniel Sonntag May 2022

Tatl: Task Agnostic Transfer Learning For Skin Attributes Detection, Duy M.H. Nguyen, Thu T. Nguyen, Huong Vu, Hong Quang Pham, Manh-Duy Nguyen, Binh T. Nguyen, Daniel Sonntag

Research Collection School Of Computing and Information Systems

Existing skin attributes detection methods usually initialize with a pre-trained Imagenet network and then fine-tune on a medical target task. However, we argue that such approaches are suboptimal because medical datasets are largely different from ImageNet and often contain limited training samples. In this work, we propose Task Agnostic Transfer Learning (TATL), a novel framework motivated by dermatologists’ behaviors in the skincare context. TATL learns an attribute-agnostic segmenter that detects lesion skin regions and then transfers this knowledge to a set of attribute-specific classifiers to detect each particular attribute. Since TATL’s attribute-agnostic segmenter only detects skin attribute regions, it enjoys …

Go to article

First Prev Next Last