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Articles 4621 - 4650 of 9025
Full-Text Articles in Computer Sciences
Towards Easy Comparison Of Local Businesses Using Online Reviews, Yong Wang, Hammad Haleem, Conglei Shi, Yanhong Wu, Xun Zhao, Siwei Fu, Huamin Qu
Towards Easy Comparison Of Local Businesses Using Online Reviews, Yong Wang, Hammad Haleem, Conglei Shi, Yanhong Wu, Xun Zhao, Siwei Fu, Huamin Qu
Research Collection School Of Computing and Information Systems
With the rapid development of e-commerce, there is an increasing number of online review websites, such as Yelp, to help customers make better purchase decisions. Viewing online reviews, including the rating score and text comments by other customers, and conducting a comparison between different businesses are the key to making an optimal decision. However, due to the massive amount of online reviews, the potential difference of user rating standards, and the significant variance of review time, length, details and quality, it is difficult for customers to achieve a quick and comprehensive comparison. In this paper, we present E-Comp, a carefully-designed …
Multi-Worker-Aware Task Planning In Real-Time Spatial Crowdsourcing, Qian Tao, Yuxiang Zeng, Zimu Zhou, Yongxin Tong, Lei Chen, Ke Xu
Multi-Worker-Aware Task Planning In Real-Time Spatial Crowdsourcing, Qian Tao, Yuxiang Zeng, Zimu Zhou, Yongxin Tong, Lei Chen, Ke Xu
Research Collection School Of Computing and Information Systems
Spatial crowdsourcing emerges as a new computing paradigm with the development of mobile Internet and the ubiquity of mobile devices. The core of many real-world spatial crowdsourcing applications is to assign suitable tasks to proper workers in real time. Many works only assign a set of tasks to each worker without making the plan how to perform the assigned tasks. Others either make task plans only for a single worker or are unable to operate in real time. In this paper, we propose a new problem called the Multi-Worker-Aware Task Planning (MWATP) problem in the online scenario, in which we …
Ai: Augmentation, More So Than Automation, Steven M. Miller
Ai: Augmentation, More So Than Automation, Steven M. Miller
Asian Management Insights
The take-up of Artificial Intelligence (AI)-enabled systems in organisations is expanding rapidly. Integrating AI-enabled automation with people into workplace processes and societal systems is a complex and evolving challenge. The articles takes a managerial perspective on how firms can effectively deploy human minds and intelligent machines in the workplace.
Entity Summarization Of Reviews And Micro-Reviews, Thanh Son Nguyen
Entity Summarization Of Reviews And Micro-Reviews, Thanh Son Nguyen
Dissertations and Theses Collection (Open Access)
Along with the regular review content, there is a new type of user-generated content arising from the prevalence of mobile devices and social media, that is micro-review. Micro-reviews are bite-size reviews (usually under 200 char- acters), commonly posted on social media or check-in services, using a mobile device. They capture the immediate reaction of users, and they are rich in information, concise, and to the point. Both reviews and micro-reviews are useful for users to get to know the entity of interest, thus facilitating users in making their decision of purchasing or dining. However, the abundant number of both reviews …
Recommending Apis For Software Evolution, Ferdian Thung
Recommending Apis For Software Evolution, Ferdian Thung
Dissertations and Theses Collection (Open Access)
Softwares are constantly evolving. This evolution has been made easier through the use of Application Programming Interfaces (APIs). By leveraging APIs, developers reuse previously implemented functionalities and concentrate on writing new codes. These APIs may originate from either third parties or internally from other compo- nents of the software that are currently developed. In the first case, developers need to know how to find and use third party APIs. In the second case, developers need to be aware of internal APIs in their own software. In either case, there is often too much information to digest. For instance, finding the …
Automatic Vulnerability Detection And Repair, Siqi Ma
Automatic Vulnerability Detection And Repair, Siqi Ma
Dissertations and Theses Collection (Open Access)
Vulnerability becomes a major threat to the security of many systems, including computer systems (e.g., Windows and Linux) and mobile systems (e.g., Android and iOS). Attackers can steal private information and perform harmful actions by exploiting unpatched vulnerabilities. Vulnerabilities often remain undetected for a long time as they may not affect the typical functionalities of systems. Thus, it is important to detect and repair a vulnerability in time. However, it is often difficult for a developer to detect and repair a vulnerability correctly and timely if he/she is not a security expert. Fortunately, automatic repair approaches significantly assist developers to …
Finding All Nearest Neighbors With A Single Graph Traversal, Yixin Xu, Qi Jianzhong, Borovica‐Gajic Renata, Kulik Lars
Finding All Nearest Neighbors With A Single Graph Traversal, Yixin Xu, Qi Jianzhong, Borovica‐Gajic Renata, Kulik Lars
Research Collection School Of Computing and Information Systems
Finding the nearest neighbor is a key operation in data analysis and mining. An important variant of nearest neighbor query is the all nearest neighbor (ANN) query, which reports all nearest neighbors for a given set of query objects. Existing studies on ANN queries have focused on Euclidean space. Given the widespread occurrence of spatial networks in urban environments, we study the ANN query in spatial network settings. An example of an ANN query on spatial networks is finding the nearest car parks for all cars currently on the road. We propose VIVET, an index-based algorithm to efficiently process ANN …
Anflo: Detecting Anomalous Sensitive Information Flows In Android Apps, Biniam Fisseha Demissie, Mariano Ceccato, Lwin Khin Shar
Anflo: Detecting Anomalous Sensitive Information Flows In Android Apps, Biniam Fisseha Demissie, Mariano Ceccato, Lwin Khin Shar
Research Collection School Of Computing and Information Systems
Smartphone apps usually have access to sensitive user data such as contacts, geo-location, and account credentials and they might share such data to external entities through the Internet or with other apps. Confidentiality of user data could be breached if there are anomalies in the way sensitive data is handled by an app which is vulnerable or malicious. Existing approaches that detect anomalous sensitive data flows have limitations in terms of accuracy because the definition of anomalous flows may differ for different apps with different functionalities; it is normal for “Health” apps to share heart rate information through the Internet …
Learning From Mutants: Using Code Mutation To Learn And Monitor Invariants Of A Cyber-Physical System, Yuqi Chen, Christopher M. Poskitt, Jun Sun
Learning From Mutants: Using Code Mutation To Learn And Monitor Invariants Of A Cyber-Physical System, Yuqi Chen, Christopher M. Poskitt, Jun Sun
Research Collection School Of Computing and Information Systems
Cyber-physical systems (CPS) consist of sensors, actuators, and controllers all communicating over a network; if any subset becomes compromised, an attacker could cause significant damage. With access to data logs and a model of the CPS, the physical effects of an attack could potentially be detected before any damage is done. Manually building a model that is accurate enough in practice, however, is extremely difficult. In this paper, we propose a novel approach for constructing models of CPS automatically, by applying supervised machine learning to data traces obtained after systematically seeding their software components with faults ("mutants"). We demonstrate the …
Efficient And Expressive Keyword Search Over Encrypted Data In The Cloud, Hui Cui, Zhiguo Wan, Deng, Robert H., Guilin Wang, Yingjiu Li
Efficient And Expressive Keyword Search Over Encrypted Data In The Cloud, Hui Cui, Zhiguo Wan, Deng, Robert H., Guilin Wang, Yingjiu Li
Research Collection School Of Computing and Information Systems
Searchable encryption allows a cloud server to conduct keyword search over encrypted data on behalf of the data users without learning the underlying plaintexts. However, most existing searchable encryption schemes only support single or conjunctive keyword search, while a few other schemes that are able to perform expressive keyword search are computationally inefficient since they are built from bilinear pairings over the composite-order groups. In this paper, we propose an expressive public-key searchable encryption scheme in the prime-order groups, which allows keyword search policies (i.e., predicates, access structures) to be expressed in conjunctive, disjunctive or any monotonic Boolean formulas and …
Doas: Efficient Data Owner Authorized Search Over Encrypted Cloud Data, Yibin Miao, Jianfeng Ma, Ximeng Liu, Zhiquan Liu, Junwei Zhang, Fushan Wei
Doas: Efficient Data Owner Authorized Search Over Encrypted Cloud Data, Yibin Miao, Jianfeng Ma, Ximeng Liu, Zhiquan Liu, Junwei Zhang, Fushan Wei
Research Collection School Of Computing and Information Systems
Data outsourcing service can shift the local data storage and maintenance to cloud service provider (CSP) to ease the burden from data owner, but it brings the data security threats as CSP is always considered to honest-but-curious. Therefore, searchable encryption (SE) technique which allows cloud clients (including data owner and data user) to securely search over ciphertext through keywords and selectively retrieve files of interest is of prime importance. However, in practice, data user’s access permission always dynamically varies with data owner’s preferences. Moreover, existing SE schemes which are based on attribute-based encryption (ABE) incur heavy computational burden through attribution …
Exploring Relationship Between Indistinguishability-Based And Unpredictability-Based Rfid Privacy Models, Anjia Yang, Yunhui Zhuang, Jian Weng, Gerhard Hancke, Duncan S. Wong, Guomin Yang
Exploring Relationship Between Indistinguishability-Based And Unpredictability-Based Rfid Privacy Models, Anjia Yang, Yunhui Zhuang, Jian Weng, Gerhard Hancke, Duncan S. Wong, Guomin Yang
Research Collection School Of Computing and Information Systems
A comprehensive privacy model plays a vital role in the design of privacy-preserving RFID authentication protocols. Among various existing RFID privacy models, indistinguishability-based (ind-privacy) and unpredictability-based (unp-privacy) privacy models are the two main categories. Unp*-privacy, a variant of unp-privacy has been claimed to be stronger than ind-privacy. In this paper, we focus on studying RFID privacy models and have three-fold contributions. We start with revisiting unp*-privacy model and figure out a limitation of it by giving a new practical traceability attack which can be proved secure under unp*-privacy model. To capture this kind of attack, we improve unp*-privacy model to …
Expressive Query Over Outsourced Encrypted Data, Yang Yang, Ximeng Liu, Robert H. Deng
Expressive Query Over Outsourced Encrypted Data, Yang Yang, Ximeng Liu, Robert H. Deng
Research Collection School Of Computing and Information Systems
Data security and privacy concerns in cloud storage services compel data owners to encrypt their sensitive data before outsourcing. Standard encryption systems, however, hinder users from issuing search queries on encrypted data. Though various systems for search over encrypted data have been proposed in the literature, existing systems use different encrypted index structures to conduct search on different search query patterns and hence are not compatible with each other. In this paper, we propose a query over encrypted data system which supports expressive search query patterns, such as single/conjunctive keyword query, range query, boolean query and mixed boolean query, all …
Understanding The Effects Of Taxi Ride-Sharing: A Case Study Of Singapore, Yazhe Wang, Baihua Zheng, Ee Peng Lim
Understanding The Effects Of Taxi Ride-Sharing: A Case Study Of Singapore, Yazhe Wang, Baihua Zheng, Ee Peng Lim
Research Collection School Of Computing and Information Systems
This paper studies the effects of ride-sharing among those calling on taxis in Singapore for similar origin and destination pairs at nearly the same time of day. It proposes a simple yet practical framework for taxi ride-sharing and scheduling, to reduce waiting times and travel times during peak demand periods. The solution method helps taxi users save money while helping taxi drivers serve multiple requests per day, thus increasing their earnings. A comprehensive simulation study is conducted, based on real taxi booking data for the city of Singapore, to evaluate the effect of various factors of the ride-sharing practice, e.g., …
Finding Small-Bowel Lesions: Challenges In Endoscopy-Image-Based Learning Systems, Jungmo Ahn, Loc Nguyen Huynh, Rajesh Krishna Balan, Youngki Lee, Jeonggil Ko
Finding Small-Bowel Lesions: Challenges In Endoscopy-Image-Based Learning Systems, Jungmo Ahn, Loc Nguyen Huynh, Rajesh Krishna Balan, Youngki Lee, Jeonggil Ko
Research Collection School Of Computing and Information Systems
Capsule endoscopy identifies damaged areas in a patient's small intestine but often outputs poor-quality images or misses lesions, leading to either misdiagnosis or repetition of the lengthy procedure. The authors propose applying deep-learning models to automatically process the captured images and identify lesions in real time, enabling the capsule to take additional images of a specific location, adjust its focus level, or improve image quality. The authors also describe the technical challenges in realizing a viable automated capsule-endoscopy system.
Breathing-Based Authentication On Resource-Constrained Iot Devices Using Recurrent Neural Networks, Jagmohan Chauhan, Suranga Seneviratne, Yining Hu, Archan Misra, Aruna Seneviratne, Youngki Lee
Breathing-Based Authentication On Resource-Constrained Iot Devices Using Recurrent Neural Networks, Jagmohan Chauhan, Suranga Seneviratne, Yining Hu, Archan Misra, Aruna Seneviratne, Youngki Lee
Research Collection School Of Computing and Information Systems
Recurrent neural networks (RNNs) have shown promising resultsin audio and speech-processing applications. The increasingpopularity of Internet of Things (IoT) devices makes a strongcase for implementing RNN-based inferences for applicationssuch as acoustics-based authentication and voice commandsfor smart homes. However, the feasibility and performance ofthese inferences on resource-constrained devices remain largelyunexplored. The authors compare traditional machine-learningmodels with deep-learning RNN models for an end-to-endauthentication system based on breathing acoustics.
Discovering Hidden Topical Hubs And Authorities In Online Social Networks, Roy Ka-Wei Lee, Tuan-Anh Hoang, Ee-Peng Lim
Discovering Hidden Topical Hubs And Authorities In Online Social Networks, Roy Ka-Wei Lee, Tuan-Anh Hoang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Finding influential users in online social networks is an important problem with many possible useful applications. HITS and other link analysis methods, in particular, have been often used to identify hub and authority users in web graphs and online social networks. These works, however, have not considered topical aspect of links in their analysis. A straightforward approach to overcome this limitation is to first apply topic models to learn the user topics before applying the HITS algorithm. In this paper, we instead propose a novel topic model known as Hub and Authority Topic (HAT) model to combines the two process …
Hierarchical Learning Of Cross-Language Mappings Through Distributed Vector Representations For Code, Nghi D. Q. Bui, Lingxiao Jiang
Hierarchical Learning Of Cross-Language Mappings Through Distributed Vector Representations For Code, Nghi D. Q. Bui, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Translating a program written in one programming language to another can be useful for software development tasks that need functionality implementations in different languages. Although past studies have considered this problem, they may be either specific to the language grammars, or specific to certain kinds of code elements (e.g., tokens, phrases, API uses). This paper proposes a new approach to automatically learn cross-language representations for various kinds of structural code elements that may be used for program translation. Our key idea is two folded: First, we normalize and enrich code token streams with additional structural and semantic information, and train …
Wisdom In Sum Of Parts: Multi-Platform Activity Prediction In Social Collaborative Sites, Roy Ka-Wei Lee, David Lo
Wisdom In Sum Of Parts: Multi-Platform Activity Prediction In Social Collaborative Sites, Roy Ka-Wei Lee, David Lo
Research Collection School Of Computing and Information Systems
In this paper, we proposed a novel framework which uses user interests inferred from activities (a.k.a., activity interests) in multiple social collaborative platforms to predict users’ platform activities. Included in the framework are two prediction approaches: (i) direct platform activity prediction, which predicts a user’s activities in a platform using his or her activity interests from the same platform (e.g., predict if a user answers a given Stack Overflow question using the user’s interests inferred from his or her prior answer and favorite activities in Stack Overflow), and (ii) cross-platform activity prediction, which predicts a user’s activities in a platform …
Evidence Aggregation For Answer Re-Ranking In Open-Domain Question Answering, Shuohang Wang, Mo Yu, Jing Jiang, Wei Zhang, Xiaoxiao Guo, Shiyu Chang, Zhiguo Wang, Tim Klinger, Gerald Tesauro, Murray Campbell
Evidence Aggregation For Answer Re-Ranking In Open-Domain Question Answering, Shuohang Wang, Mo Yu, Jing Jiang, Wei Zhang, Xiaoxiao Guo, Shiyu Chang, Zhiguo Wang, Tim Klinger, Gerald Tesauro, Murray Campbell
Research Collection School Of Computing and Information Systems
A popular recent approach to answering open-domain questions is to first search for question-related passages and then apply reading comprehension models to extract answers. Existing methods usually extract answers from single passages independently. But some questions require a combination of evidence from across different sources to answer correctly. In this paper, we propose two models which make use of multiple passages to generate their answers. Both use an answer-reranking approach which reorders the answer candidates generated by an existing state-of-the-art QA model. We propose two methods, namely, strength-based re-ranking and coverage-based re-ranking, to make use of the aggregated evidence from …
Big Data For Climate Change Actions And The Paradox Of Citizen Informedness, Kustini Lim-Wavde, Robert J. Kauffman
Big Data For Climate Change Actions And The Paradox Of Citizen Informedness, Kustini Lim-Wavde, Robert J. Kauffman
Research Collection School Of Computing and Information Systems
Advanced sensor technology, social media, and other information technologies have provided us with “big data” on climate change. Due to the World Meteorological Organization’s Global Climate Observing System, climate observations and records, as well as discussions on climate-related concerns such as measurement of air temperature, are widely available now. The United Nations’ Global Pulse visualises public engagement on climate change globally, with data such as the volume of climate-related tweets. Big data, data analytics, and the sharing of scientific results in the popular press have created, as a result, an unprecedented level of citizen informedness—the degree to which citizens have …
Analyzing Requirements And Traceability Information To Improve Bug Localization, Michael Rath, David Lo, Patrick Mader
Analyzing Requirements And Traceability Information To Improve Bug Localization, Michael Rath, David Lo, Patrick Mader
Research Collection School Of Computing and Information Systems
Locating bugs in industry-size software systems is time consuming and challenging. An automated approach for assisting the process of tracing from bug descriptions to relevant source code benefits developers. A large body of previous work aims to address this problem and demonstrates considerable achievements. Most existing approaches focus on the key challenge of improving techniques based on textual similarity to identify relevant files. However, there exists a lexical gap between the natural language used to formulate bug reports and the formal source code and its comments. To bridge this gap, state-of-the-art approaches contain a component for analyzing bug history information …
Recommending Frequently Encountered Bugs, Yun Zhang, David Lo, Xin Xia, Jing Jiang, Jianling Sun
Recommending Frequently Encountered Bugs, Yun Zhang, David Lo, Xin Xia, Jing Jiang, Jianling Sun
Research Collection School Of Computing and Information Systems
Developers introduce bugs during software development which reduce software reliability. Many of these bugs are commonly occurring and have been experienced by many other developers. Informingdevelopers, especially novice ones, about commonly occurring bugsin a domain of interest (e.g., Java), can help developers comprehendprogram and avoid similar bugs in the future. Unfortunately, information about commonly occurring bugs are not readily available. Toaddress this need, we propose a novel approach named RFEB whichrecommends frequently encountered bugs (FEBugs) that may affectmany other developers. RFEB analyzes Stack Overflow which is thelargest software engineering-specific Q&A communities. Amongthe plenty of questions posted in Stack Overflow, many …
Deep Code Comment Generation, Xing Hu, Ge Li, Xin Xia, David Lo, Zhi Jin
Deep Code Comment Generation, Xing Hu, Ge Li, Xin Xia, David Lo, Zhi Jin
Research Collection School Of Computing and Information Systems
During software maintenance, code comments help developerscomprehend programs and reduce additional time spent on readingand navigating source code. Unfortunately, these comments areoften mismatched, missing or outdated in the software projects.Developers have to infer the functionality from the source code.This paper proposes a new approach named DeepCom to automatically generate code comments for Java methods. The generatedcomments aim to help developers understand the functionalityof Java methods. DeepCom applies Natural Language Processing(NLP) techniques to learn from a large code corpus and generatescomments from learned features. We use a deep neural networkthat analyzes structural information of Java methods for bettercomments generation. We conduct …
Empirical Risk Landscape Analysis For Understanding Deep Neural Networks, Pan Zhou, Jiashi Feng
Empirical Risk Landscape Analysis For Understanding Deep Neural Networks, Pan Zhou, Jiashi Feng
Research Collection School Of Computing and Information Systems
This work aims to provide comprehensive landscape analysis of empirical risk in deep neural networks (DNNs), including the convergence behavior of its gradient, its stationary points and the empirical risk itself to their corresponding population counterparts, which reveals how various network parameters determine the convergence performance. In particular, for an l-layer linear neural network consisting of di neurons in the i-th layer, we prove the gradient of its empirical risk uniformly converges to the one of its population risk, at the rate of O(r 2l p l √ maxi dis log(d/l)/n). Here d is the total weight dimension, s is …
Assessing Classical And Expressive Aesthetics Of Web Pages Using Machine Learning, Ang Chen, Fiona Fui-Hoon Nah, Langtao Chen
Assessing Classical And Expressive Aesthetics Of Web Pages Using Machine Learning, Ang Chen, Fiona Fui-Hoon Nah, Langtao Chen
Research Collection School Of Computing and Information Systems
Aesthetics plays a key role in web design. However, most websites are developed based on designers’ "inspirations" or "educated guesses" (Liu, 2003). While perceptions of aesthetics are intuitive abilities of humankind, the underlying principles for assessing aesthetics are not well understood. In this research, we propose using machine learning techniques to explore and more fully understand the patterns and underlying principles of aesthetics. We propose using machine learning techniques to develop predictive models for two aesthetic dimensions – classical aesthetics and expressive aesthetics – as well as for overall aesthetics of web pages in order to evaluate the aesthetic quality …
A Qualitative Research On Marketing And Sales In The Artificial Intelligence Age, Yin Yang, Keng Siau
A Qualitative Research On Marketing And Sales In The Artificial Intelligence Age, Yin Yang, Keng Siau
Research Collection School Of Computing and Information Systems
The age of artificial intelligence is here! Artificial Intelligence, robotics, machine learning, and automation are impacting the field of marketing and sales in an unprecedented way. In this study, the qualitative research methodology will be used to better understand the revolution and evolution of marketing and sales field in the AI age. Multiple case studies will be performed in various marketing and sales units in different organizations. This research is of value to both academics and practitioners as it aims to provide a detailed analysis and documentation of the changes in marketing and sales functionalities and job markets as AI …
Neural Correlates Of States Of User Experience In Gaming Using Eeg And Predictive Analytics, Chandana Mallapragada, Fiona Fui-Hoon Nah, Keng Siau, Langtao Chen, Tejaswini Yelamanchili
Neural Correlates Of States Of User Experience In Gaming Using Eeg And Predictive Analytics, Chandana Mallapragada, Fiona Fui-Hoon Nah, Keng Siau, Langtao Chen, Tejaswini Yelamanchili
Research Collection School Of Computing and Information Systems
In this research, we will analyze EEG signals to obtain neural correlate classifications of user experience by applying predictive analytics. Boredom, flow, and anxiety are three states experienced by users interacting with a computer-based system. A within-subjects experiment was used to collect EEG data for these three states and a baseline. We will apply predictive analytics including linear regression, support vector machine, and neural networks to analyze and classify the EEG data for these three states of user experience.
Effect Of Probable And Guaranteed Monetary Value Gains And Losses On Cybersecurity Behavior Of Users, S. Ravindran, Fiona Fui-Hoon Nah, M. Cheng
Effect Of Probable And Guaranteed Monetary Value Gains And Losses On Cybersecurity Behavior Of Users, S. Ravindran, Fiona Fui-Hoon Nah, M. Cheng
Research Collection School Of Computing and Information Systems
The objective of this research is to examine users’ cybersecurity behavior in monetary gain and loss scenarios. Using Prospect Theory, we hypothesize that users are more likely to engage in risky cybersecurity behavior to avoid monetary losses than to benefit from monetary gains. We also hypothesize that guaranteed gains have a greater effect on a user’s risk-taking behavior than potential gains, and potential losses have a greater effect on a user’s risk-taking behavior than guaranteed losses. An experimental study is proposed to test the research hypotheses.
Trade-Offs Between Monetary Gain And Risk Taking In Cybersecurity Behavior, X. Zhan, Fiona Fui-Hoon Nah, M. Cheng
Trade-Offs Between Monetary Gain And Risk Taking In Cybersecurity Behavior, X. Zhan, Fiona Fui-Hoon Nah, M. Cheng
Research Collection School Of Computing and Information Systems
Phishers and hackers exploit users’ susceptibility to deception by providing incentives. This research focuses on studying the risk-taking behavior of users in downloading software from the Internet. We proposed an experimental study to assess the degree of risks that people are willing to take for monetary gains when they download software from uncertified sources.