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

Physical Sciences and Mathematics Commons™

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

Singapore Management University

Discipline
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 2281 - 2310 of 7454

Full-Text Articles in Physical Sciences and Mathematics

Skin-Mimo: Vibration-Based Mimo Communication Over Human Skin, Dong Ma, Yuezhong Wu, Ming Ding, Mahbub Hassan, Wen Hu Jul 2020

Skin-Mimo: Vibration-Based Mimo Communication Over Human Skin, Dong Ma, Yuezhong Wu, Ming Ding, Mahbub Hassan, Wen Hu

Research Collection School Of Computing and Information Systems

We explore the feasibility of Multiple-Input-Multiple-Output (MIMO) communication through vibrations over human skin. Using off-the-shelf motors and piezo transducers as vibration transmitters and receivers, respectively, we build a 2x2 MIMO testbed to collect and analyze vibration signals from real subjects. Our analysis reveals that there exist multiple independent vibration channels between a pair of transmitter and receiver, confirming the feasibility of MIMO. Unfortunately, the slow ramping of mechanical motors and rapidly changing skin channels make it impractical for conventional channel sounding based channel state information (CSI) acquisition, which is critical for achieving MIMO capacity gains. To solve this problem, we …

Go to article

Towards An Optimal Outdoor Advertising Placement: When A Budget Constraint Meets Moving Trajectories, Ping Zhang, Zhifeng Bao, Yuchen Li, Guoliang Li, Yipeng Zhang, Zhiyong Peng Jul 2020

Towards An Optimal Outdoor Advertising Placement: When A Budget Constraint Meets Moving Trajectories, Ping Zhang, Zhifeng Bao, Yuchen Li, Guoliang Li, Yipeng Zhang, Zhiyong Peng

Research Collection School Of Computing and Information Systems

In this article, we propose and study the problem of trajectory-driven influential billboard placement: given a set of billboards U (each with a location and a cost), a database of trajectories T, and a budget L, we find a set of billboards within the budget to influence the largest number of trajectories. One core challenge is to identify and reduce the overlap of the influence from different billboards to the same trajectories, while keeping the budget constraint into consideration. We show that this problem is NP-hard and present an enumeration based algorithm with (1-1/e) approximation ratio. However, the enumeration would …

Go to article

Optimising The Fit Of Stack Overflow Code Snippets Into Existing Code, Brittany Reid, Christoph Treude, Markus Wagner Jul 2020

Optimising The Fit Of Stack Overflow Code Snippets Into Existing Code, Brittany Reid, Christoph Treude, Markus Wagner

Research Collection School Of Computing and Information Systems

Software developers often reuse code from online sources such as Stack Overflow within their projects. However, the process of searching for code snippets and integrating them within existing source code can be tedious. In order to improve efficiency and reduce time spent on code reuse, we present an automated code reuse tool for the Eclipse IDE (Integrated Developer Environment), NLP2TestableCode. NLP2TestableCode can not only search for Java code snippets using natural language tasks, but also evaluate code snippets based on a user’s existing code, modify snippets to improve fit and correct errors, before presenting the user with the best snippet, …

Go to article

Answer Ranking For Product-Related Questions Via Multiple Semantic Relations Modeling, Wenxuan Zhang, Yang Deng, Wai Lam Jul 2020

Answer Ranking For Product-Related Questions Via Multiple Semantic Relations Modeling, Wenxuan Zhang, Yang Deng, Wai Lam

Research Collection School Of Computing and Information Systems

Many E-commerce sites now offer product-specific question answering platforms for users to communicate with each other by posting and answering questions during online shopping. However, the multiple answers provided by ordinary users usually vary diversely in their qualities and thus need to be appropriately ranked for each question to improve user satisfaction. It can be observed that product reviews usually provide useful information for a given question, and thus can assist the ranking process. In this paper, we investigate the answer ranking problem for product-related questions, with the relevant reviews treated as auxiliary information that can be exploited for facilitating …

Go to article

Search Me In The Dark: Privacy-Preserving Boolean Range Query Over Encrypted Spatial Data, Xiangyu Wang, Jianfeng Ma, Ximeng Liu, Robert H. Deng, Yinbin Miao, Dan Zhu, Zhuoran Ma Jul 2020

Search Me In The Dark: Privacy-Preserving Boolean Range Query Over Encrypted Spatial Data, Xiangyu Wang, Jianfeng Ma, Ximeng Liu, Robert H. Deng, Yinbin Miao, Dan Zhu, Zhuoran Ma

Research Collection School Of Computing and Information Systems

With the increasing popularity of geo-positioning technologies and mobile Internet, spatial keyword data services have attracted growing interest from both the industrial and academic communities in recent years. Meanwhile, a massive amount of data is increasingly being outsourced to cloud in the encrypted form for enjoying the advantages of cloud computing while without compromising data privacy. Most existing works primarily focus on the privacy-preserving schemes for either spatial or keyword queries, and they cannot be directly applied to solve the spatial keyword query problem over encrypted data. In this paper, we study the challenging problem of Privacy-preserving Boolean Range Query …

Go to article

The Prediction Of Delay Time At Intersection And Route Planning For Autonomous Vehicles, Genwang Gou, Yongxin Zhao, Jiawei Liang, Ling Shi Jul 2020

The Prediction Of Delay Time At Intersection And Route Planning For Autonomous Vehicles, Genwang Gou, Yongxin Zhao, Jiawei Liang, Ling Shi

Research Collection School Of Computing and Information Systems

Intelligent Intersections (roundabout and crossroads) management is considered as one of the challenges to significantly improve urban traffic efficiency. Recent researches in artificial intelligence suggest that autonomous vehicles have the possibility of forming intelligent intersection management, and likely to occupy the leading role in future urban traffic. If route planning method can be used for route decision of autonomous vehicle, the urban traffic efficiency can be further improved. In this paper, we propose an Intelligent Intersection Control Protocol (IICP) for controlling autonomous vehicles cross intersection, and recommend route for autonomous vehicles to reduce travel time and improve urban traffic efficiency. …

Go to article

How Are Deep Learning Models Similar? An Empirical Study On Clone Analysis Of Deep Learning Software, Xiongfei Wu, Liangyu Qin, Bing Yu, Xiaofei Xie, Lei Ma, Yinxing Xue, Yang Liu, Jianjun Zhao Jul 2020

How Are Deep Learning Models Similar? An Empirical Study On Clone Analysis Of Deep Learning Software, Xiongfei Wu, Liangyu Qin, Bing Yu, Xiaofei Xie, Lei Ma, Yinxing Xue, Yang Liu, Jianjun Zhao

Research Collection School Of Computing and Information Systems

Deep learning (DL) has been successfully applied to many cutting-edge applications, e.g., image processing, speech recognition, and natural language processing. As more and more DL software is made open-sourced, publicly available, and organized in model repositories and stores (Model Zoo, ModelDepot), there comes a need to understand the relationships of these DL models regarding their maintenance and evolution tasks. Although clone analysis has been extensively studied for traditional software, up to the present, clone analysis has not been investigated for DL software. Since DL software adopts the data-driven development paradigm, it is still not clear whether and to what extent …

Go to article

Sentiment Analysis Over Collaborative Relationships In Open Source Software Projects, Lingjia Li, Jian Cao, David Lo Jul 2020

Sentiment Analysis Over Collaborative Relationships In Open Source Software Projects, Lingjia Li, Jian Cao, David Lo

Research Collection School Of Computing and Information Systems

Sentiments and collaboration efficiency are key factors in the success of the open source software (OSS) development process. However, in the software engineering domain, no studies have been conducted to analyze the effect between collaborators' sentiments, and the role of sentiment in collaborative relationships during the development process. In this study, we apply sentiment analysis and statistical analysis on collaboration artifacts over five projects on GitHub. We use sentiment consistency to quantify the relation between sentiments in collaborative relationships. It is found that sentiment consistency is positively correlated with the closeness of collaborative relationships and collaborators' overall sentiment states. We …

Go to article

Recovering Fitness Gradients For Interprocedural Boolean Flags In Search-Based Testing, Yun Lin, Jun Sun, Gordon Fraser, Ziheng Xiu, Ting Liu, Jin Song Dong Jul 2020

Recovering Fitness Gradients For Interprocedural Boolean Flags In Search-Based Testing, Yun Lin, Jun Sun, Gordon Fraser, Ziheng Xiu, Ting Liu, Jin Song Dong

Research Collection School Of Computing and Information Systems

In Search-based Software Testing (SBST), test generation is guided by fitness functions that estimate how close a test case is to reach an uncovered test goal (e.g., branch). A popular fitness function estimates how close conditional statements are to evaluating to true or false, i.e., the branch distance. However, when conditions read Boolean variables (e.g., if(x && y)), the branch distance provides no gradient for the search, since a Boolean can either be true or false. This flag problem can be addressed by transforming individual procedures such that Boolean flags are replaced with numeric comparisons that provide better guidance for …

Go to article

A Review Study Of Functional Autoregressive Models With Application To Energy Forecasting, Ying Chen, Thorsten Koch, Kian Guan Lim, Xiaofei Xu, Nazgul Zakiyeva Jul 2020

A Review Study Of Functional Autoregressive Models With Application To Energy Forecasting, Ying Chen, Thorsten Koch, Kian Guan Lim, Xiaofei Xu, Nazgul Zakiyeva

Research Collection Lee Kong Chian School Of Business

In this data‐rich era, it is essential to develop advanced techniques to analyze and understand large amounts of data and extract the underlying information in a flexible way. We provide a review study on the state‐of‐the‐art statistical time series models for univariate and multivariate functional data with serial dependence. In particular, we review functional autoregressive (FAR) models and their variations under different scenarios. The models include the classic FAR model under stationarity; the FARX and pFAR model dealing with multiple exogenous functional variables and large‐scale mixed‐type exogenous variables; the vector FAR model and common functional principal component technique to handle …

Go to article

Busting Myths And Dispelling Doubts About Covid-19, Mark Findlay Jul 2020

Busting Myths And Dispelling Doubts About Covid-19, Mark Findlay

Research Collection Yong Pung How School Of Law

The Centre for AI and Data Governance (CAIDG) at Singapore Management University (SMU) has embarked over past months on a programme of research designed to confront concerns about the pandemic and its control. Our interest is primarily directed to the ways in which AI-assisted technologies and mass data sharing have become a feature of pandemic control strategies. We want to know what impact these developments are having on community confidence and health safety. In developing this work, we have come across many myths that need busting.

Go to article

Privacy-Enhanced Remote Data Integrity Checking With Updatable Timestamp, Tong Wu, Guomin Yang, Yi Mu, Rongmao Chen, Shengmin Xu Jul 2020

Privacy-Enhanced Remote Data Integrity Checking With Updatable Timestamp, Tong Wu, Guomin Yang, Yi Mu, Rongmao Chen, Shengmin Xu

Research Collection School Of Computing and Information Systems

Remote data integrity checking (RDIC) enables clients to verify whether the outsourced data is intact without keeping a copy locally or downloading it. Nevertheless, the existing RDIC schemes do not support the pay-as-you-go (PAYG) payment model, where the payment is decided by the volume and duration of the outsourced data. Specifically, none of the existing works have considered the client’s control over changes in storage duration. In this paper, we propose an RDIC scheme to simultaneously check the data content and storage duration represented by an updatable timestamp via the third-party auditor (TPA). Also, our proposed scheme achieves indistinguishable privacy …

Go to article

Active Fuzzing For Testing And Securing Cyber-Physical Systems, Yuqi Chen, Bohan Xuan, Christopher M. Poskitt, Jun Sun, Fan Zhang Jul 2020

Active Fuzzing For Testing And Securing Cyber-Physical Systems, Yuqi Chen, Bohan Xuan, Christopher M. Poskitt, Jun Sun, Fan Zhang

Research Collection School Of Computing and Information Systems

Cyber-physical systems (CPSs) in critical infrastructure face a pervasive threat from attackers, motivating research into a variety of countermeasures for securing them. Assessing the effectiveness of these countermeasures is challenging, however, as realistic benchmarks of attacks are difficult to manually construct, blindly testing is ineffective due to the enormous search spaces and resource requirements, and intelligent fuzzing approaches require impractical amounts of data and network access. In this work, we propose active fuzzing, an automatic approach for finding test suites of packet-level CPS network attacks, targeting scenarios in which attackers can observe sensors and manipulate packets, but have no existing …

Go to article

Interactive Entity Linking Using Entity-Word Representations, Pei Chi Lo, Ee-Peng Lim Jul 2020

Interactive Entity Linking Using Entity-Word Representations, Pei Chi Lo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

To leverage on entity and word semantics in entity linking, embedding models have been developed to represent entities, words and their context such that candidate entities for each mention can be determined and ranked accurately using their embeddings. To leverage on entity and word semantics in entity linking, embedding models have been developed to represent entities, words and their context such that candidate entities for each mention can be determined and ranked accurately using their embeddings. In this paper, we leverage on human intelligence for embedding-based interactive entity linking. We adopt an active learning approach to select mentions for human …

Go to article

Spinfer: Inferring Semantic Patches For The Linux Kernel, Lucas Serrano, Van-Anh Nguyen, Ferdian Thung, Lingxiao Jiang, David Lo, Julia Lawall, Gilles Muller Jul 2020

Spinfer: Inferring Semantic Patches For The Linux Kernel, Lucas Serrano, Van-Anh Nguyen, Ferdian Thung, Lingxiao Jiang, David Lo, Julia Lawall, Gilles Muller

Research Collection School Of Computing and Information Systems

In a large software system such as the Linux kernel, there is a continual need for large-scale changes across many source files, triggered by new needs or refined design decisions. In this paper, we propose to ease such changes by suggesting transformation rules to developers, inferred automatically from a collection of examples. Our approach can help automate large-scale changes as well as help understand existing large-scale changes, by highlighting the various cases that the developer who performed the changes has taken into account. We have implemented our approach as a tool, Spinfer. We evaluate Spinfer on a range of challenging …

Go to article

Psc2code: Denoising Code Extraction From Programming Screencasts, Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Minghui Wu, Xiaohu Yang Jul 2020

Psc2code: Denoising Code Extraction From Programming Screencasts, Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Minghui Wu, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Programming screencasts have become a pervasive resource on the Internet, which help developers learn new programming technologies or skills. The source code in programming screencasts is an important and valuable information for developers. But the streaming nature of programming screencasts (i.e., a sequence of screen-captured images) limits the ways that developers can interact with the source code in the screencasts. Many studies use the Optical Character Recognition (OCR) technique to convert screen images (also referred to as video frames) into textual content, which can then be indexed and searched easily. However, noisy screen images significantly affect the quality of source …

Go to article

Keen2act: Activity Recommendation In Online Social Collaborative Platforms, Roy Ka-Wei Lee, Thong Hoang, Richard J. Oentaryo, David Lo Jul 2020

Keen2act: Activity Recommendation In Online Social Collaborative Platforms, Roy Ka-Wei Lee, Thong Hoang, Richard J. Oentaryo, David Lo

Research Collection School Of Computing and Information Systems

Social collaborative platforms such as GitHub and Stack Overflow have been increasingly used to improve work productivity via collaborative efforts. To improve user experiences in these platforms, it is desirable to have a recommender system that can suggest not only items (e.g., a GitHub repository) to a user, but also activities to be performed on the suggested items (e.g., forking a repository). To this end, we propose a new approach dubbed Keen2Act, which decomposes the recommendation problem into two stages: the Keen and Act steps. The Keen step identifies, for a given user, a (sub)set of items in which he/she …

Go to article

Deep Learning For Real-World Object Detection, Xiongwei Wu Jul 2020

Deep Learning For Real-World Object Detection, Xiongwei Wu

Dissertations and Theses Collection (Open Access)

Despite achieving significant progresses, most existing detectors are designed to detect objects in academic contexts but consider little in real-world scenarios. In real-world applications, the scale variance of objects can be significantly higher than objects in academic contexts; In addition, existing methods are designed for achieving localization with relatively low precision, however more precise localization is demanded in real-world scenarios; Existing methods are optimized with huge amount of annotated data, but in certain real-world scenarios, only a few samples are available. In this dissertation, we aim to explore novel techniques to address these research challenges to make object detection algorithms …

Go to article

Context-Aware And Scale-Insensitive Temporal Repetition Counting, Huaidong Zhang, Xuemiao Xu, Guoqiang Han, Shengfeng He Jun 2020

Context-Aware And Scale-Insensitive Temporal Repetition Counting, Huaidong Zhang, Xuemiao Xu, Guoqiang Han, Shengfeng He

Research Collection School Of Computing and Information Systems

Temporal repetition counting aims to estimate the number of cycles of a given repetitive action. Existing deep learning methods assume repetitive actions are performed in a fixed time-scale, which is invalid for the complex repetitive actions in real life. In this paper, we tailor a context-aware and scale-insensitive framework, to tackle the challenges in repetition counting caused by the unknown and diverse cycle-lengths. Our approach combines two key insights: (1) Cycle lengths from different actions are unpredictable that require large-scale searching, but, once a coarse cycle length is determined, the variety between repetitions can be overcome by regression. (2) Determining …

Go to article

The Global Sustainability Footprint Of Sovereign Wealth Funds, Hao Liang, Luc Renneboog Jun 2020

The Global Sustainability Footprint Of Sovereign Wealth Funds, Hao Liang, Luc Renneboog

Research Collection Lee Kong Chian School Of Business

With the emergence of sovereign wealth funds (SWFs) around the world managing equity of over $8 trillion, their impact on the corporate landscape and social welfare is being scrutinized. This study investigates whether and how SWFs incorporate environmental, social, and governance (ESG) considerations in their investment decisions in publicly listed corporations, as well as the subsequent evolution of target firms' ESG performance. We find that SWF funds do consider the level of past ESG performance as well as recent ESG score improvement when taking ownership stakes in listed companies. These results are driven by the SWF funds that do have …

Go to article

Revisiting Supervised And Unsupervised Methods For Effort-Aware Cross-Project Defect Prediction, Chao Ni, Xin Xia, David Lo, Xiang Chen, Qing Gu Jun 2020

Revisiting Supervised And Unsupervised Methods For Effort-Aware Cross-Project Defect Prediction, Chao Ni, Xin Xia, David Lo, Xiang Chen, Qing Gu

Research Collection School Of Computing and Information Systems

Cross-project defect prediction (CPDP), aiming to apply defect prediction models built on source projects to a target project, has been an active research topic. A variety of supervised CPDP methods and some simple unsupervised CPDP methods have been proposed. In a recent study, Zhou et al. found that simple unsupervised CPDP methods (i.e., ManualDown and ManualUp) have a prediction performance comparable or even superior to complex supervised CPDP methods. Therefore, they suggested that the ManualDown should be treated as the baseline when considering non-effort-aware performance measures (NPMs) and the ManualUp should be treated as the baseline when considering effort-aware performance …

Go to article

A Machine Learning Approach For Vulnerability Curation, Yang Chen, Andrew E. Santosa, Ming Yi Ang, Abhishek Sharma, Asankhaya Sharma, David Lo Jun 2020

A Machine Learning Approach For Vulnerability Curation, Yang Chen, Andrew E. Santosa, Ming Yi Ang, Abhishek Sharma, Asankhaya Sharma, David Lo

Research Collection School Of Computing and Information Systems

Software composition analysis depends on database of open-source library vulerabilities, curated by security researchers using various sources, such as bug tracking systems, commits, and mailing lists. We report the design and implementation of a machine learning system to help the curation by by automatically predicting the vulnerability-relatedness of each data item. It supports a complete pipeline from data collection, model training and prediction, to the validation of new models before deployment. It is executed iteratively to generate better models as new input data become available. We use self-training to significantly and automatically increase the size of the training dataset, opportunistically …

Go to article

Adaptive Loss-Aware Quantization For Multi-Bit Networks, Zhongnan Qu, Zimu Zhou, Yun Cheng, Lothar Thiele Jun 2020

Adaptive Loss-Aware Quantization For Multi-Bit Networks, Zhongnan Qu, Zimu Zhou, Yun Cheng, Lothar Thiele

Research Collection School Of Computing and Information Systems

We investigate the compression of deep neural networks by quantizing their weights and activations into multiple binary bases, known as multi-bit networks (MBNs), which accelerate the inference and reduce the storage for the deployment on low-resource mobile and embedded platforms. We propose Adaptive Loss-aware Quantization (ALQ), a new MBN quantization pipeline that is able to achieve an average bitwidth below one-bit without notable loss in inference accuracy. Unlike previous MBN quantization solutions that train a quantizer by minimizing the error to reconstruct full precision weights, ALQ directly minimizes the quantizationinduced error on the loss function involving neither gradient approximation nor …

Go to article

Knowledge Enhanced Neural Fashion Trend Forecasting, Yunshan Ma, Yujuan Ding, Xun Yang, Lizi Liao, Wai Keung Wong, Tat-Seng Chua Jun 2020

Knowledge Enhanced Neural Fashion Trend Forecasting, Yunshan Ma, Yujuan Ding, Xun Yang, Lizi Liao, Wai Keung Wong, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Fashion trend forecasting is a crucial task for both academia andindustry. Although some efforts have been devoted to tackling this challenging task, they only studied limited fashion elements with highly seasonal or simple patterns, which could hardly reveal thereal fashion trends. Towards insightful fashion trend forecasting,this work focuses on investigating fine-grained fashion element trends for specific user groups. We first contribute a large-scale fashion trend dataset (FIT) collected from Instagram with extracted time series fashion element records and user information. Furthermore, to effectively model the time series data of fashion elements with rather complex patterns, we propose a Knowledge Enhanced …

Go to article

Learning Transferable Deep Convolutional Neural Networks For The Classification Of Bacterial Virulence Factors, Dandan Zheng, Guansong Pang, Bo Liu, Lihong Chen, Jian Yang Jun 2020

Learning Transferable Deep Convolutional Neural Networks For The Classification Of Bacterial Virulence Factors, Dandan Zheng, Guansong Pang, Bo Liu, Lihong Chen, Jian Yang

Research Collection School Of Computing and Information Systems

Motivation: Identification of virulence factors (VFs) is critical to the elucidation of bacterial pathogenesis and prevention of related infectious diseases. Current computational methods for VF prediction focus on binary classification or involve only several class(es) of VFs with sufficient samples. However, thousands of VF classes are present in real-world scenarios, and many of them only have a very limited number of samples available.Results: We first construct a large VF dataset, covering 3446 VF classes with 160 495 sequences, and then propose deep convolutional neural network models for VF classification. We show that (i) for common VF classes with sufficient samples, …

Go to article

Maximum A Posteriori Estimation For Information Source Detection, Biao Chang, Enhong Chen, Feida Zhu, Qi Liu, Tong Xu Jun 2020

Maximum A Posteriori Estimation For Information Source Detection, Biao Chang, Enhong Chen, Feida Zhu, Qi Liu, Tong Xu

Research Collection School Of Computing and Information Systems

Information source detection is to identify nodes initiating the diffusion process in a network, which has a wide range of applications including epidemic outbreak prevention, Internet virus source identification, and rumor source tracing in social networks. Although it has attracted ever-increasing attention from research community in recent years, existing solutions still suffer from high time complexity and inadequate effectiveness, due to high dynamics of information diffusion and observing just a snapshot of the whole process. To this end, we present a comprehensive study for single information source detection in weighted graphs. Specifically, we first propose a maximum a posteriori (MAP) …

Go to article

Hyperbolic Visual Embedding Learning For Zero-Shot Recognition, Shaoteng Liu, Jingjing Chen, Liangming Pan, Chong-Wah Ngo, Tat-Seng Chua, Yu-Gang Jiang Jun 2020

Hyperbolic Visual Embedding Learning For Zero-Shot Recognition, Shaoteng Liu, Jingjing Chen, Liangming Pan, Chong-Wah Ngo, Tat-Seng Chua, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

This paper proposes a Hyperbolic Visual Embedding Learning Network for zero-shot recognition. The network learns image embeddings in hyperbolic space, which is capable of preserving the hierarchical structure of semantic classes in low dimensions. Comparing with existing zeroshot learning approaches, the network is more robust because the embedding feature in hyperbolic space better represents class hierarchy and thereby avoid misleading resulted from unrelated siblings. Our network outperforms exiting baselines under hierarchical evaluation with an extremely challenging setting, i.e., learning only from 1,000 categories to recognize 20,841 unseen categories. While under flat evaluation, it has competitive performance as state-of-the-art methods but …

Go to article

Cookgan: Causality Based Text-To-Image Synthesis, Bin Zhu, Chong-Wah Ngo Jun 2020

Cookgan: Causality Based Text-To-Image Synthesis, Bin Zhu, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

This paper addresses the problem of text-to-image synthesis from a new perspective, i.e., the cause-and-effect chain in image generation. Causality is a common phenomenon in cooking. The dish appearance changes depending on the cooking actions and ingredients. The challenge of synthesis is that a generated image should depict the visual result of action-on-object. This paper presents a new network architecture, CookGAN, that mimics visual effect in causality chain, preserves fine-grained details and progressively upsamples image. Particularly, a cooking simulator sub-network is proposed to incrementally make changes to food images based on the interaction between ingredients and cooking methods over a …

Go to article

Secure Server-Aided Data Sharing Clique With Attestation, Yujue Wang, Hwee Hwa Pang, Robert H. Deng, Yong Ding, Qianhong Wu, Bo Qin, Kefeng Fan Jun 2020

Secure Server-Aided Data Sharing Clique With Attestation, Yujue Wang, Hwee Hwa Pang, Robert H. Deng, Yong Ding, Qianhong Wu, Bo Qin, Kefeng Fan

Research Collection School Of Computing and Information Systems

In this paper, we consider the security issues in data sharing cliques via remote server. We present a public key re-encryption scheme with delegated equality test on ciphertexts (PRE-DET). The scheme allows users to share outsourced data on the server without performing decryption-then-encryption procedures, allows new users to dynamically join the clique, allows clique users to attest the message underlying a ciphertext, and enables the server to partition outsourced user data without any further help of users after being delegated. We introduce the PRE-DET framework, propose a concrete construction and formally prove its security against five types of adversaries regarding …

Go to article

Goods Consumed During Transit In Split Delivery Vehicle Routing Problems: Modeling And Solution, Wenzhe Yang, Di Wang, Wei Pang, Ah-Hwee Tan, You Zhou Jun 2020

Goods Consumed During Transit In Split Delivery Vehicle Routing Problems: Modeling And Solution, Wenzhe Yang, Di Wang, Wei Pang, Ah-Hwee Tan, You Zhou

Research Collection School Of Computing and Information Systems

This article presents the modeling and solution of an extended type of split delivery vehicle routing problem (SDVRP). In SDVRP, the demands of customers need to be met by efficiently routing a given number of capacitated vehicles, wherein each customer may be served multiple times by more than one vehicle. Furthermore, in many real-world scenarios, consumption of vehicles en route is the same as the goods being delivered to customers, such as food, water and fuel in rescue or replenishment missions in harsh environments. Moreover, the consumption may also be in virtual forms, such as time spent in constrained tasks. …

Go to article

First Prev Next Last