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Articles 2341 - 2370 of 7454

Full-Text Articles in Physical Sciences and Mathematics

Hierarchical Reinforcement Learning With Integrated Discovery Of Salient Subgoals, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan May 2020

Hierarchical Reinforcement Learning With Integrated Discovery Of Salient Subgoals, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Hierarchical Reinforcement Learning (HRL) is a promising approach to solve more complex tasks which may be challenging for the traditional reinforcement learning. HRL achieves this by decomposing a task into shorter-horizon subgoals which are simpler to achieve. Autonomous discovery of such subgoals is an important part of HRL. Recently, end-to-end HRL methods have been used to reduce the overhead from offline subgoal discovery by seeking the useful subgoals while simultaneously learning optimal policies in a hierarchy. However, these methods may still suffer from slow learning when the search space used by a high level policy to find the subgoals is …

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Values Of Artificial Intelligence In Marketing, Yingrui Xi, Keng Siau May 2020

Values Of Artificial Intelligence In Marketing, Yingrui Xi, Keng Siau

Research Collection School Of Computing and Information Systems

Artificial Intelligence (AI) is causing radical changes in marketing and emerging as a competent assistant supporting all areas of the marketing field. The influences and impacts AI has created in various marketing segments have aroused much interest among marketing professionals and academic scholars. Comprehensive and systematic studies on the values of AI in marketing, however, are still lacking and the existing literature fragmented. This research provides a comprehensive review of the existing literature in the relevant fields as well as a series of systematic interviews using the Value-Focused Thinking approach to understand the values of AI in marketing. This research …

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Enhancing Cellular Communications For Uavs Via Intelligent Reflective Surface, Dong Ma, Ming Ding, Mahbub Hassan May 2020

Enhancing Cellular Communications For Uavs Via Intelligent Reflective Surface, Dong Ma, Ming Ding, Mahbub Hassan

Research Collection School Of Computing and Information Systems

Intelligent reflective surfaces (IRSs) capable of reconfiguring their electromagnetic absorption and reflection properties in real-time are offering unprecedented opportunities to enhance wireless communication experience in challenging environments. In this paper, we analyze the potential of IRS in enhancing cellular communications for UAVs, which currently suffers from poor signal strength due to the down-tilt of base station antennas optimized to serve ground users. We consider deployment of IRS on building walls, which can be remotely configured by cellular base stations to coherently direct the reflected radio waves towards specific UAVs in order to increase their received signal strengths. Using the recently …

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Platform Pricing With Strategic Buyers: The Impact Of Future Production Cost, Mei Lin, Xiajun Amy Pan, Quan Zheng May 2020

Platform Pricing With Strategic Buyers: The Impact Of Future Production Cost, Mei Lin, Xiajun Amy Pan, Quan Zheng

Research Collection School Of Computing and Information Systems

Two-sided platforms are often coupled with exclusive hardware products that connect two sides of users, the consumers of the hardware product (i.e., buyers) and the application developers (i.e., sellers). The hardware product in the platform business model introduces three important issues that are not yet well understood in the literature of platform pricing: potentially downward-trending production cost, product quality improvements, and consumers' strategic behaviors. Using analytical modeling, our study explicitly factors in these issues in analyzing a monopoly platform owner's two-sided pricing problem. The platform sequentially introduces and prices quality-improving hardware products, for which the costliness of quality may decrease. …

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Chaff From The Wheat: Characterizing And Determining Valid Bug Reports, Yuanrui Fan, Xin Xia, David Lo, Ahmed E. Hassan May 2020

Chaff From The Wheat: Characterizing And Determining Valid Bug Reports, Yuanrui Fan, Xin Xia, David Lo, Ahmed E. Hassan

Research Collection School Of Computing and Information Systems

Developers use bug reports to triage and fix bugs. When triaging a bug report, developers must decide whether the bug report is valid (i.e., a real bug). A large amount of bug reports are submitted every day, with many of them end up being invalid reports. Manually determining valid bug report is a difficult and tedious task. Thus, an approach that can automatically analyze the validity of a bug report and determine whether a report is valid can help developers prioritize their triaging tasks and avoid wasting time and effort on invalid bug reports. In this study, motivated by the …

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Cornac: A Comparative Framework For Multimodal Recommender Systems, Aghiles Salah, Quoc Tuan Truong, Hady W. Lauw May 2020

Cornac: A Comparative Framework For Multimodal Recommender Systems, Aghiles Salah, Quoc Tuan Truong, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Cornac is an open-source Python framework for multimodal recommender systems. In addition to core utilities for accessing, building, evaluating, and comparing recommender models, Cornac is distinctive in putting emphasis on recommendation models that leverage auxiliary information in the form of a social network, item textual descriptions, product images, etc. Such multimodal auxiliary data supplement user-item interactions (e.g., ratings, clicks), which tend to be sparse in practice. To facilitate broad adoption and community contribution, Cornac is publicly available at https://github.com/PreferredAI/cornac, and it can be installed via Anaconda or the Python Package Index (pip). Not only is it well-covered by unit tests …

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Beyond Physical Entrainment: Competitive And Cooperative Mental Stances During Identical Joint-Action Tasks Differently Affect Inter-Subjective Neural Synchrony And Judgments Of Agency, Philip S. Cho, Nicolas Escoffier, Yinan Mao, Christopher Green, Richard C. Davis May 2020

Beyond Physical Entrainment: Competitive And Cooperative Mental Stances During Identical Joint-Action Tasks Differently Affect Inter-Subjective Neural Synchrony And Judgments Of Agency, Philip S. Cho, Nicolas Escoffier, Yinan Mao, Christopher Green, Richard C. Davis

Research Collection School Of Computing and Information Systems

Little work has examined how mental stance alone, apart from physical entrainment, affects between-participant neural synchrony during joint social interaction. We report the first findings on how cooperative and competitive mental stances, even during identical visuomotor joint-action tasks, result in distinct neural oscillatory signatures in low beta and theta band between-participant phase synchrony. Two participants jointly controlled a cursor and were instructed to either compete or cooperate to move it to one of three targets. The visuomotor output was identical for both the compete and cooperate conditions because participants were privately given the same target for experimental trials. Cooperation enhanced …

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A 2020 Perspective On "Client Risk Informedness In Brokered Cloud Services: An Experimental Pricing Study", Di Shang, Robert J. Kauffman May 2020

A 2020 Perspective On "Client Risk Informedness In Brokered Cloud Services: An Experimental Pricing Study", Di Shang, Robert J. Kauffman

Research Collection School Of Computing and Information Systems

Cloud computing and the cloud services market have advanced in the past ten years. Cloud services now include most information technology (IT) services from fundamental computing services to more cutting- edge artificial intelligence (AI) services. Accordingly, opportunities have emerged for research on the design of new market features to improve the cloud services market to benefit providers and users. Based on our observation of the recent development of cloud services, in this short research commentary, we share our agenda for future studies of this important sector of IT services.

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Robust Graph Learning From Noisy Data, Zhao Kang, Haiqi Pan, Steven C. H. Hoi, Zenglin Xu May 2020

Robust Graph Learning From Noisy Data, Zhao Kang, Haiqi Pan, Steven C. H. Hoi, Zenglin Xu

Research Collection School Of Computing and Information Systems

Learning graphs from data automatically have shown encouraging performance on clustering and semisupervised learning tasks. However, real data are often corrupted, which may cause the learned graph to be inexact or unreliable. In this paper, we propose a novel robust graph learning scheme to learn reliable graphs from the real-world noisy data by adaptively removing noise and errors in the raw data. We show that our proposed model can also be viewed as a robust version of manifold regularized robust principle component analysis (RPCA), where the quality of the graph plays a critical role. The proposed model is able to …

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Who And When To Screen: Multi-Round Active Screening For Network Recurrent Infectious Diseases Under Uncertainty, Han-Ching Ou, Arunesh Sinha, Sze-Chuan Suen, Andrew Perrault, Alpan Raval, Milind Tambe May 2020

Who And When To Screen: Multi-Round Active Screening For Network Recurrent Infectious Diseases Under Uncertainty, Han-Ching Ou, Arunesh Sinha, Sze-Chuan Suen, Andrew Perrault, Alpan Raval, Milind Tambe

Research Collection School Of Computing and Information Systems

Controlling recurrent infectious diseases is a vital yet complicated problem in global health. During the long period of time from patients becoming infected to finally seeking treatment, their close contacts are exposed and vulnerable to the disease they carry. Active screening (or case finding) methods seek to actively discover undiagnosed cases by screening contacts of known infected people to reduce the spread of the disease. Existing practice of active screening methods often screen all contacts of an infected person, requiring a large budget. In cooperation with a research institute in India, we develop a model of the active screening problem …

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Jplink: On Linking Jobs To Vocational Interest Types, Amila Silva, Pei Chi Lo, Ee-Peng Lim May 2020

Jplink: On Linking Jobs To Vocational Interest Types, Amila Silva, Pei Chi Lo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Linking job seekers with relevant jobs requires matching based on not only skills, but also personality types. Although the Holland Code also known as RIASEC has frequently been used to group people by their suitability for six different categories of occupations, the RIASEC category labels of individual jobs are often not found in job posts. This is attributed to significant manual efforts required for assigning job posts with RIASEC labels. To cope with assigning massive number of jobs with RIASEC labels, we propose JPLink, a machine learning approach using the text content in job titles and job descriptions. JPLink exploits …

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Posit: Simultaneously Tagging Natural And Programming Languages, Profir-Petru Pârțachi, Santanu Dash, Christoph Treude, Earl T. Barr May 2020

Posit: Simultaneously Tagging Natural And Programming Languages, Profir-Petru Pârțachi, Santanu Dash, Christoph Treude, Earl T. Barr

Research Collection School Of Computing and Information Systems

Software developers use a mix of source code and natural language text to communicate with each other: Stack Overflow and Developer mailing lists abound with this mixed text. Tagging this mixed text is essential for making progress on two seminal software engineering problems — traceability, and reuse via precise extraction of code snippets from mixed text. In this paper, we borrow code-switching techniques from Natural Language Processing and adapt them to apply to mixed text to solve two problems: language identification and token tagging. Our technique, POSIT, simultaneously provides abstract syntax tree tags for source code tokens, part-of-speech tags for …

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Symbolic Verification Of Message Passing Interface Programs, Hengbiao Yu, Zhenbang Chen, Xianjin Fu, Ji Wang, Zhendong Su, Jun Sun, Chun Huang, Wei Dong May 2020

Symbolic Verification Of Message Passing Interface Programs, Hengbiao Yu, Zhenbang Chen, Xianjin Fu, Ji Wang, Zhendong Su, Jun Sun, Chun Huang, Wei Dong

Research Collection School Of Computing and Information Systems

Message passing is the standard paradigm of programming in high-performance computing. However, verifying Message Passing Interface (MPI) programs is challenging, due to the complex program features (such as non-determinism and non-blocking operations). In this work, we present MPI symbolic verifier (MPI-SV), the first symbolic execution based tool for automatically verifying MPI programs with non-blocking operations. MPI-SV combines symbolic execution and model checking in a synergistic way to tackle the challenges in MPI program verification. The synergy improves the scalability and enlarges the scope of verifiable properties. We have implemented MPI-SV and evaluated it with 111 real-world MPI verification tasks. The …

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The Future Of Work Now: Cyber Threat Attribution At Fireeye, Thomas H. Davenport, Steven M. Miller May 2020

The Future Of Work Now: Cyber Threat Attribution At Fireeye, Thomas H. Davenport, Steven M. Miller

Research Collection School Of Computing and Information Systems

One of the most frequently-used phrases at business events these days is “the future of work.” It’s increasingly clear that artificial intelligence and other new technologies will bring substantial changes in work tasks and business processes. But while these changes are predicted for the future, they’re already present in many organizations for many different jobs. The job and incumbent described below is an example of this phenomenon. It’s a clear example of an existing job that’s been transformed by AI and related tools.

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Retrofitting Embeddings For Unsupervised User Identity Linkage, Tao Zhou, Ee-Peng Lim, Roy Ka-Wei Lee, Feida Zhu, Jiuxin Cao May 2020

Retrofitting Embeddings For Unsupervised User Identity Linkage, Tao Zhou, Ee-Peng Lim, Roy Ka-Wei Lee, Feida Zhu, Jiuxin Cao

Research Collection School Of Computing and Information Systems

User Identity Linkage (UIL) is the problem of matching user identities across multiple online social networks (OSNs) which belong to the same person. The solutions to UIL problem facilitate cross-platform research on OSN users and enable many useful applications such as user profiling and recommendation. As the UIL labeled data are often lacking and costly to obtain, learning user embeddings for matching user identities using an unsupervised approach is therefore highly desired. In this paper, we propose a novel unsupervised UIL framework for enhancing existing user embedding-based UIL methods. Our proposed framework incorporates two key ideas, user-discriminative features and retrofitting …

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Starhopper: A Touch Interface For Remote Object-Centric Drone Navigation, Jiannan Li, Ravin Balakrishnan, Tovi Grossman May 2020

Starhopper: A Touch Interface For Remote Object-Centric Drone Navigation, Jiannan Li, Ravin Balakrishnan, Tovi Grossman

Research Collection School Of Computing and Information Systems

Camera drones, a rapidly emerging technology, offer people the ability to remotely inspect an environment with a high degree of mobility and agility. However, manual remote piloting of a drone is prone to errors. In contrast, autopilot systems can require a significant degree of environmental knowledge and are not necessarily designed to support flexible visual inspections. Inspired by camera manipulation techniques in interactive graphics, we designed StarHopper, a novel touch screen interface for efficient object-centric camera drone navigation, in which a user directly specifies the navigation of a drone camera relative to a specified object of interest. The system relies …

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“Trust Me, I Have A Ph.D.”: A Propensity Score Analysis On The Halo Effect Of Disclosing One's Offline Social Status In Online Communities, Kunwoo Park, Haewoon Kwak, Hyunho Song, Meeyoung. Cha May 2020

“Trust Me, I Have A Ph.D.”: A Propensity Score Analysis On The Halo Effect Of Disclosing One's Offline Social Status In Online Communities, Kunwoo Park, Haewoon Kwak, Hyunho Song, Meeyoung. Cha

Research Collection School Of Computing and Information Systems

Online communities adopt various reputation schemes to measure content quality. This study analyzes the effect of a new reputation scheme that exposes one's offline social status, such as an education degree, within an online community. We study two Reddit communities that adopted this scheme, whereby posts include tags identifying education status referred to as flairs, and we examine how the “transferred” social status affects the interactions among the users. We computed propensity scores to test whether flairs give ad-hoc authority to the adopters while minimizing the effects of confounding variables such as topics of content. The results show that exposing …

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Short-Term Repositioning For Empty Vehicles On Ride-Sourcing Platforms, Hai Wang, Zhengli Wang May 2020

Short-Term Repositioning For Empty Vehicles On Ride-Sourcing Platforms, Hai Wang, Zhengli Wang

Research Collection School Of Computing and Information Systems

Motivation Ride sourcing companies, such as Uber, Lyft, and Didi, have been able to leverage on internet-based platforms to connect passengers and drivers. These platforms facilitate passengers and drivers’ mobility data on smartphones in real time, which enables a convenient matching between demand and supply. The imbalance of demand (i.e., passenger requests) and supply (i.e., drivers) on the platforms causes many unserved passenger requests and empty vehicles with idle drivers to exist at the same time, which poses a challenging problem for the platform. To address these challenges, some platforms display heat maps of surge-pricing multipliers or real-time demand to …

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Memlock: Memory Usage Guided Fuzzing, Cheng Wen, Haijun Wang, Yuekang Li, Shengchao Qin, Yang Liu, Zhiwu Xu, Hongxu Chen, Xiaofei Xie, Geguang Pu, Ting Liu May 2020

Memlock: Memory Usage Guided Fuzzing, Cheng Wen, Haijun Wang, Yuekang Li, Shengchao Qin, Yang Liu, Zhiwu Xu, Hongxu Chen, Xiaofei Xie, Geguang Pu, Ting Liu

Research Collection School Of Computing and Information Systems

Uncontrolled memory consumption is a kind of critical software security weaknesses. It can also become a security-critical vulnerability when attackers can take control of the input to consume a large amount of memory and launch a Denial-of-Service attack. However, detecting such vulnerability is challenging, as the state-of-the-art fuzzing techniques focus on the code coverage but not memory consumption. To this end, we propose a memory usage guided fuzzing technique, named MemLock, to generate the excessive memory consumption inputs and trigger uncontrolled memory consumption bugs. The fuzzing process is guided with memory consumption information so that our approach is general and …

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Code Duplication On Stack Overflow, Sebastian Baltes, Christoph Treude May 2020

Code Duplication On Stack Overflow, Sebastian Baltes, Christoph Treude

Research Collection School Of Computing and Information Systems

Despite the unarguable importance of Stack Overflow (SO) for the daily work of many software developers and despite existing knowledge about the impact of code duplication on software maintainability, the prevalence and implications of code clones on SO have not yet received the attention they deserve. In this paper, we motivate why studies on code duplication within SO are needed and how existing studies on code reuse differ from this new research direction. We present similarities and differences between code clones in general and code clones on SO and point to open questions that need to be addressed to be …

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Semantic Understanding Of Smart Contracts: Executable Operational Semantics Of Solidity, Jiao Jiao, Shuanglong Kan, Shang Wei Lin, David Sanan, Yang Liu, Jun Sun May 2020

Semantic Understanding Of Smart Contracts: Executable Operational Semantics Of Solidity, Jiao Jiao, Shuanglong Kan, Shang Wei Lin, David Sanan, Yang Liu, Jun Sun

Research Collection School Of Computing and Information Systems

Bitcoin has been a popular research topic recently. Ethereum (ETH), a second generation of cryptocurrency, extends Bitcoin's design by offering a Turing-complete programming language called Solidity to develop smart contracts. Smart contracts allow creditable execution of contracts on EVM (Ethereum Virtual Machine) without third parties. Developing correct and secure smart contracts is challenging due to the decentralized computation nature of the blockchain. Buggy smart contracts may lead to huge financial loss. Furthermore, smart contracts are very hard, if not impossible, to patch once they are deployed. Thus, there is a recent surge of interest in analyzing and verifying smart contracts. …

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Assistance For Target Selection In Mobile Augmented Reality, Vinod Asokan, Scott Bateman, Anthony Tang May 2020

Assistance For Target Selection In Mobile Augmented Reality, Vinod Asokan, Scott Bateman, Anthony Tang

Research Collection School Of Computing and Information Systems

Mobile augmented reality - where a mobile device is used to view and interact with virtual objects displayed in the real world - is becoming more common. Target selection is the main method of interaction in mobile AR, but is particularly difficult because targets in AR can have challenging characteristics such as being moving or occluded (by digital or real world objects). Because target selection is particularly difficult and error prone in mobile AR, we conduct a comparative study of target assistance techniques. We compared four different cursor-based selection techniques against the standard touch-to-select interaction, finding that a newly adapted …

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Learning Discriminative Neural Sentiment Units For Semi-Supervised Target-Level Sentiment Classification, Jingjing Zhao, Yao Yang, Guansong Pang, Lei Lv, Hong Shang, Zhongqian Sun, Wei Yang May 2020

Learning Discriminative Neural Sentiment Units For Semi-Supervised Target-Level Sentiment Classification, Jingjing Zhao, Yao Yang, Guansong Pang, Lei Lv, Hong Shang, Zhongqian Sun, Wei Yang

Research Collection School Of Computing and Information Systems

Target-level sentiment classification aims at assigning sentiment polarities to opinion targets in a sentence, for which it is significantly more challenging to obtain large-scale labeled data than sentence/document-level sentiment classification due to the intricate contexts and relations of the target words. To address this challenge, we propose a novel semi-supervised approach to learn sentiment-aware representations from easily accessible unlabeled data specifically for the finegrained sentiment learning. This is very different from current popular semi-supervised solutions that use the unlabeled data via pretraining to generate generic representations for various types of downstream tasks. Particularly, we show for the first time that …

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Towards Characterizing Adversarial Defects Of Deep Learning Software From The Lens Of Uncertainty, Xiyue Zhang, Xiaofei Xie, Lei Ma, Xiaoning Du, Qiang Hu, Yang Liu, Jianjun Zhao, Meng Sun May 2020

Towards Characterizing Adversarial Defects Of Deep Learning Software From The Lens Of Uncertainty, Xiyue Zhang, Xiaofei Xie, Lei Ma, Xiaoning Du, Qiang Hu, Yang Liu, Jianjun Zhao, Meng Sun

Research Collection School Of Computing and Information Systems

Over the past decade, deep learning (DL) has been successfully applied to many industrial domain-specific tasks. However, the current state-of-the-art DL software still suffers from quality issues, which raises great concern especially in the context of safety- and security-critical scenarios. Adversarial examples (AEs) represent a typical and important type of defects needed to be urgently addressed, on which a DL software makes incorrect decisions. Such defects occur through either intentional attack or physical-world noise perceived by input sensors, potentially hindering further industry deployment. The intrinsic uncertainty nature of deep learning decisions can be a fundamental reason for its incorrect behavior. …

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A Lightweight Privacy-Preserving Cnn Feature Extraction Framework For Mobile Sensing, Kai Huang, Ximeng Liu, Shaojing Fu, Deke Guo, Ming Xu May 2020

A Lightweight Privacy-Preserving Cnn Feature Extraction Framework For Mobile Sensing, Kai Huang, Ximeng Liu, Shaojing Fu, Deke Guo, Ming Xu

Research Collection School Of Computing and Information Systems

The proliferation of various mobile devices equipped with cameras results in an exponential growth of the amount of images. Recent advances in the deep learning with convolutional neural networks (CNN) have made CNN feature extraction become an effective way to process these images. However, it is still a challenging task to deploy the CNN model on the mobile sensors, which are typically resource-constrained in terms of the storage space, the computing capacity, and the battery life. Although cloud computing has become a popular solution, data security and response latency are always the key issues. Therefore, in this paper, we propose …

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Learning Expensive Coordination: An Event-Based Deep Rl Approach, Runsheng Yu, Xinrun Wang, Rundong Wang, Youzhi Zhang, Bo An, Zhen Yu Shi, Hanjiang Lai May 2020

Learning Expensive Coordination: An Event-Based Deep Rl Approach, Runsheng Yu, Xinrun Wang, Rundong Wang, Youzhi Zhang, Bo An, Zhen Yu Shi, Hanjiang Lai

Research Collection School Of Computing and Information Systems

Existing works in deep Multi-Agent Reinforcement Learning (MARL) mainly focus on coordinating cooperative agents to complete certain tasks jointly. However, in many cases of the real world, agents are self-interested such as employees in a company and clubs in a league. Therefore, the leader, i.e., the manager of the company or the league, needs to provide bonuses to followers for efficient coordination, which we call expensive coordination. The main difficulties of expensive coordination are that i) the leader has to consider the long-term effect and predict the followers’ behaviors when assigning bonuses, and ii) the complex interactions between followers make …

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Pmkt: Privacy-Preserving Multi-Party Knowledge Transfer For Financial Market Forecasting, Zhuoran Ma, Jianfeng Ma, Yinbin Miao, Kim-Kwang Raymond Choo, Ximeng Liu, Xiangyu Wang, Tengfei Yang May 2020

Pmkt: Privacy-Preserving Multi-Party Knowledge Transfer For Financial Market Forecasting, Zhuoran Ma, Jianfeng Ma, Yinbin Miao, Kim-Kwang Raymond Choo, Ximeng Liu, Xiangyu Wang, Tengfei Yang

Research Collection School Of Computing and Information Systems

While decision-making task is critical in knowledge transfer, particularly from multi-source domains, existing knowledge transfer approaches are not generally designed to be privacy preserving. This has potential legal and financial implications, particularly in sensitive applications such as financial market forecasting. Therefore, in this paper, we propose a Privacy-preserving Multi-party Knowledge Transfer system (PMKT), based on decision trees, for financial market forecasting. Specifically, in PMKT, we leverage a cryptographic-based model sharing technique to securely outsource knowledge reflected in decision trees of multiple parties, and design a secure computation mechanism to facilitate privacy-preserving knowledge transfer. An encrypted user-submitted request from the target …

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Early Detection Of Mild Cognitive Impairment With In-Home Sensors To Monitor Behavior Patterns In Community-Dwelling Senior Citizens In Singapore: Cross-Sectional Feasibility Study, Iris Rawtaer, Rathi Mahendran, Ee Heok Kua, Hwee-Pink Tan, Hwee Xian Tan, Tih-Shih Lee, Tze Pin Ng May 2020

Early Detection Of Mild Cognitive Impairment With In-Home Sensors To Monitor Behavior Patterns In Community-Dwelling Senior Citizens In Singapore: Cross-Sectional Feasibility Study, Iris Rawtaer, Rathi Mahendran, Ee Heok Kua, Hwee-Pink Tan, Hwee Xian Tan, Tih-Shih Lee, Tze Pin Ng

Research Collection School Of Computing and Information Systems

Background: Dementia is a global epidemic and incurs substantial burden on the affected families and the health care system. A window of opportunity for intervention is the predementia stage known as mild cognitive impairment (MCI). Individuals often present to services late in the course of their disease and more needs to be done for early detection; sensor technology is a potential method for detection.Objective: The aim of this cross-sectional study was to establish the feasibility and acceptability of utilizing sensors in the homes of senior citizens to detect changes in behaviors unobtrusively.Methods: We recruited 59 community-dwelling seniors (aged >65 years …

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A Fully Distributed Hierarchical Attribute-Based Encryption Scheme, Ali Mohammad, Javad Mohajeri, Ximeng Liu, Ximeng Liu May 2020

A Fully Distributed Hierarchical Attribute-Based Encryption Scheme, Ali Mohammad, Javad Mohajeri, Ximeng Liu, Ximeng Liu

Research Collection School Of Computing and Information Systems

With the development of cloud computing, many enterprises have been interested in outsourcing their data to cloud servers to decrease IT costs and rise capabilities of provided services. To afford confidentiality and fine-grained data access control, attribute-based encryption (ABE) was proposed and used in several cloud storage systems. However, scalability and flexibility in key delegation and user revocation mechanisms are primary issues in ABE systems. In this paper, we introduce the concept of a fully distributed revocable ciphertext-policy hierarchical ABE (FDR-CP-HABE) and design the first FDR-CP-HABE scheme. Our scheme offers a high level of flexibility and scalability in the key …

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Route Choice Behaviour And Travel Information In A Congested Network: Static And Dynamic Recursive Models, Giselle De Moraes Ramos, Tien Mai, Winnie Daamen, Emma Frejinger May 2020

Route Choice Behaviour And Travel Information In A Congested Network: Static And Dynamic Recursive Models, Giselle De Moraes Ramos, Tien Mai, Winnie Daamen, Emma Frejinger

Research Collection School Of Computing and Information Systems

Travel information has the potential to influence travellers choices, in order to steer travellers to less congested routes and alleviate congestion. This paper investigates, on the one hand, how travel information affects route choice behaviour, and on the other hand, the impact of the travel time representation on the interpretation of parameter estimates and prediction accuracy. To this end, we estimate recursive models using data from an innovative data collection effort consisting of route choice observation data from GPS trackers, travel diaries and link travel times on the overall network. Though such combined data sets exist, these have not yet …

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