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Articles 4681 - 4710 of 8495
Full-Text Articles in Computer Sciences
On The Selection Of Anchors And Targets For Video Hyperlinking, Zhi-Qi Cheng, Hao Zhang, Xiao Wu, Chong-Wah Ngo
On The Selection Of Anchors And Targets For Video Hyperlinking, Zhi-Qi Cheng, Hao Zhang, Xiao Wu, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
A problem not well understood in video hyperlinking is what qualifies a fragment as an anchor or target. Ideally, anchors provide good starting points for navigation, and targets supplement anchors with additional details while not distracting users with irrelevant, false and redundant information. The problem is not trivial for intertwining relationship between data characteristics and user expectation. Imagine that in a large dataset, there are clusters of fragments spreading over the feature space. The nature of each cluster can be described by its size (implying popularity) and structure (implying complexity). A principle way of hyperlinking can be carried out by …
On Self-Selection Biases In Online Product Reviews, Nan Hu, Paul A. Pavlou, Jie Zhang
On Self-Selection Biases In Online Product Reviews, Nan Hu, Paul A. Pavlou, Jie Zhang
Research Collection School Of Computing and Information Systems
Online product reviews help consumers infer product quality, and the mean (average) rating is often used as a proxy for product quality. However, two self-selection biases, acquisition bias (mostly consumers with a favorable predisposition acquire a product and hence write a product review) and underreporting bias (consumers with extreme, either positive or negative, ratings are more likely to write reviews than consumers with moderate product ratings), render the mean rating a biased estimator of product quality, and they result in the well-known J-shaped (positively skewed, asymmetric, bimodal) distribution of online product reviews. To better understand the nature and consequences of …
Inferring Motion Direction Using Commodity Wi-Fi For Interactive Exergames, Kun Qian, Chenshu Wu, Zimu Zhou, Yue Zheng, Yang Zheng, Yunhao Liu
Inferring Motion Direction Using Commodity Wi-Fi For Interactive Exergames, Kun Qian, Chenshu Wu, Zimu Zhou, Yue Zheng, Yang Zheng, Yunhao Liu
Research Collection School Of Computing and Information Systems
In-air interaction acts as a key enabler for ambient intelligence and augmented reality. As an increasing popular example, exergames, and the alike gesture recognition applications, have attracted extensive research in designing accurate, pervasive and low-cost user interfaces. Recent advances in wireless sensing show promise for a ubiquitous gesture-based interaction interface with Wi-Fi. In this work, we extract complete information of motion-induced Doppler shifts with only commodity Wi-Fi. The key insight is to harness antenna diversity to carefully eliminate random phase shifts while retaining relevant Doppler shifts. We further correlate Doppler shifts with motion directions, and propose a light-weight pipeline to …
Tum: Towards Ubiquitous Multi-Device Localization For Cross-Device Interaction, Han Xu, Zheng Yang, Zimu Zhou, Ke Yi, Chunyi Peng
Tum: Towards Ubiquitous Multi-Device Localization For Cross-Device Interaction, Han Xu, Zheng Yang, Zimu Zhou, Ke Yi, Chunyi Peng
Research Collection School Of Computing and Information Systems
Cross-device interaction is becoming an increasingly hot topic as we often have multiple devices at our immediate disposal in this era of mobile computing. Various cross-device applications such as file sharing, multi-screen display, and crossdevice authentication have been proposed and investigated. However, one of the most fundamental enablers remains unsolved: How to achieve ubiquitous multi-device localization? Though pioneer efforts have resorted to gesture-assisted or sensing-assisted localization, they either require extensive user participation or impose some strong assumptions on device sensing abilities. This introduces extra costs and constraints, and thus degrades their practicality. To overcome these limitations, we propose TUM, an …
Joint Optimization Of Resource Provisioning In Cloud Computing, Jonathan David Chase, Dusit Niyato
Joint Optimization Of Resource Provisioning In Cloud Computing, Jonathan David Chase, Dusit Niyato
Research Collection School Of Computing and Information Systems
Cloud computing exploits virtualization to provision resources efficiently. Increasingly, Virtual Machines (VMs) have high bandwidth requirements; however, previous research does not fully address the challenge of both VM and bandwidth provisioning. To efficiently provision resources, a joint approach that combines VMs and bandwidth allocation is required. Furthermore, in practice, demand is uncertain. Service providers allow the reservation of resources. However, due to the dangers of over-and under-provisioning, we employ stochastic programming to account for this risk. To improve the efficiency of the stochastic optimization, we reduce the problem space with a scenario tree reduction algorithm, that significantly increases tractability, whilst …
Determining The Impact Regions Of Competing Options In Preference Space, Bo Tang, Kyriakos Mouratidis, Man Lung. Yiu
Determining The Impact Regions Of Competing Options In Preference Space, Bo Tang, Kyriakos Mouratidis, Man Lung. Yiu
Research Collection School Of Computing and Information Systems
In rank-aware processing, user preferences are typically represented by a numeric weight per data attribute, collectively forming a weight vector. The score of an option (data record) is defined as the weighted sum of its individual attributes. The highest-scoring options across a set of alternatives (dataset) are shortlisted for the user as the recommended ones. In that setting, the user input is a vector (equivalently, a point) in a d-dimensional preference space, where d is the number of data attributes. In this paper we study the problem of determining in which regions of the preference space the weight vector should …
Android Repository Mining For Detecting Publicly Accessible Functions Missing Permission Checks, Huu Hoang Nguyen, Lingxiao Jiang, Thanh Tho Quan
Android Repository Mining For Detecting Publicly Accessible Functions Missing Permission Checks, Huu Hoang Nguyen, Lingxiao Jiang, Thanh Tho Quan
Research Collection School Of Computing and Information Systems
Android has become the most popular mobile operating system. Millions of applications, including many malware, haven been developed for it. Even though its overall system architecture and many APIs are documented, many other methods and implementation details are not, not to mention potential bugs and vulnerabilities that may be exploited. Manual documentation may also be easily outdated as Android evolves constantly with changing features and higher complexities. Techniques and tool supports are thus needed to automatically extract information from different versions of Android to facilitate whole-system analysis of undocumented code. This paper presents an approach for alleviating the challenges associated …
Collaborative Topic Regression For Online Recommender Systems: An Online And Bayesian Approach, Chenghao Liu, Tao Jin, Steven C. H. Hoi, Peilin Zhao, Jianling Sun
Collaborative Topic Regression For Online Recommender Systems: An Online And Bayesian Approach, Chenghao Liu, Tao Jin, Steven C. H. Hoi, Peilin Zhao, Jianling Sun
Research Collection School Of Computing and Information Systems
Collaborative Topic Regression (CTR) combines ideas of probabilistic matrix factorization (PMF) and topic modeling (such as LDA) for recommender systems, which has gained increasing success in many applications. Despite enjoying many advantages, the existing Batch Decoupled Inference algorithm for the CTR model has some critical limitations: First of all, it is designed to work in a batch learning manner, making it unsuitable to deal with streaming data or big data in real-world recommender systems. Secondly, in the existing algorithm, the item-specific topic proportions of LDA are fed to the downstream PMF but the rating information is not exploited in discovering …
Online/Offline Provable Data Possession, Yujue Wang, Qianhong Wu, Bo Qin, Shaohua Tang, Willy Susilo
Online/Offline Provable Data Possession, Yujue Wang, Qianhong Wu, Bo Qin, Shaohua Tang, Willy Susilo
Research Collection School Of Computing and Information Systems
Provable data possession (PDP) allows a user to outsource data with a guarantee that the integrity can be efficiently verified. Existing publicly verifiable PDP schemes require the user to perform expensive computations, such as modular exponentiations for processing data before outsourcing to the storage server, which is not desirable for weak users with limited computation resources. In this paper, we introduce and formalize an online/offline PDP (OOPDP) model, which divides the data processing procedure into offline and online phases. In OOPDP, most of the expensive computations for processing data are performed in the offline phase, and the online phase requires …
A Multi-Agent System For Coordinating Vessel Traffic, Teck-Hou Teng, Hoong Chuin Lau, Akshat Kumar
A Multi-Agent System For Coordinating Vessel Traffic, Teck-Hou Teng, Hoong Chuin Lau, Akshat Kumar
Research Collection School Of Computing and Information Systems
Environmental, regulatory and resource constraints affects the safety and efficiency of vessels navigating in and out of the ports. Movement of vessels under such constraints must be coordinated for improving safety and efficiency. Thus, we frame the vessel coordination problem as a multi-agent path-finding (MAPF) problem. We solve this MAPF problem using a Coordinated Path-Finding (CPF) algorithm. Based on the local search paradigm, the CPF algorithm improves on the aggregated path quality of the vessels iteratively. Outputs of the CPF algorithm are the coordinated trajectories. The Vessel Coordination Module (VCM) described here is the module encapsulating our MAPF-based approach for …
Continuous Top-K Monitoring On Document Streams, Leong Hou U, Junjie Zhang, Kyriakos Mouratidis, Ye Li
Continuous Top-K Monitoring On Document Streams, Leong Hou U, Junjie Zhang, Kyriakos Mouratidis, Ye Li
Research Collection School Of Computing and Information Systems
The efficient processing of document streams plays an important role in many information filtering systems. Emerging applications, such as news update filtering and social network notifications, demand presenting end-users with the most relevant content to their preferences. In this work, user preferences are indicated by a set of keywords. A central server monitors the document stream and continuously reports to each user the top-k documents that are most relevant to her keywords. Our objective is to support large numbers of users and high stream rates, while refreshing the top-k results almost instantaneously. Our solution abandons the traditional frequency-ordered indexing approach. …
Robust Object Tracking Via Locality Sensitive Histograms, Shengfeng He, Rynson W.H Lau, Qingxiong Yang, Jiang Wang, Ming-Hsuan Yang
Robust Object Tracking Via Locality Sensitive Histograms, Shengfeng He, Rynson W.H Lau, Qingxiong Yang, Jiang Wang, Ming-Hsuan Yang
Research Collection School Of Computing and Information Systems
This paper presents a novel locality sensitive histogram (LSH) algorithm for visual tracking. Unlike the conventional image histogram that counts the frequency of occurrence of each intensity value by adding ones to the corresponding bin, an LSH is computed at each pixel location, and a floating-point value is added to the corresponding bin for each occurrence of an intensity value. The floating-point value exponentially reduces with respect to the distance to the pixel location where the histogram is computed. An efficient algorithm is proposed that enables the LSHs to be computed in time linear in the image size and the …
Assertion Generation Through Active Learning, Long H. Pham, Jun Sun, Jun Sun
Assertion Generation Through Active Learning, Long H. Pham, Jun Sun, Jun Sun
Research Collection School Of Computing and Information Systems
Program assertions are useful for many program analysis tasks. They are however often missing in practice. In this work, we develop a novel approach for generating likely assertions automatically based on active learning. Our target is complex Java programs which cannot be symbolically executed (yet). Our key idea is to generate candidate assertions based on test cases and then apply active learning techniques to iteratively improve them. The experiments show that active learning really helps to improve the generated assertions.
Feedback-Based Debugging, Yun Lin, Jun Sun, Yinxing Xue, Yang Liu, Jin Song Dong
Feedback-Based Debugging, Yun Lin, Jun Sun, Yinxing Xue, Yang Liu, Jin Song Dong
Research Collection School Of Computing and Information Systems
Software debugging has long been regarded as a time and effort consuming task. In the process of debugging, developers usually need to manually inspect many program steps to see whether they deviate from their intended behaviors. Given that intended behaviors usually exist nowhere but in human mind, the automation of debugging turns out to be extremely hard, if not impossible. In this work, we propose a feedback-based debugging approach, which (1) builds on light-weight human feedbacks on a buggy program and (2) regards the feedbacks as partial program specification to infer suspicious steps of the buggy execution. Given a buggy …
Parametric Model Checking Timed Automata Under Non-Zenoness Assumption, Étienne Andre, Hoang Gia Nguyen, Laure Petrucci, Jun Sun
Parametric Model Checking Timed Automata Under Non-Zenoness Assumption, Étienne Andre, Hoang Gia Nguyen, Laure Petrucci, Jun Sun
Research Collection School Of Computing and Information Systems
Real-time systems often involve hard timing constraints and concurrency, and are notoriously hard to design or verify. Given a model of a real-time system and a property, parametric model-checking aims at synthesizing timing valuations such that the model satisfies the property. However, the counter-example returned by such a procedure may be Zeno (an infinite number of discrete actions occurring in a finite time), which is unrealistic. We show here that synthesizing parameter valuations such that at least one counterexample run is non-Zeno is undecidable for parametric timed automata (PTAs). Still, we propose a semi-algorithm based on a transformation of PTAs …
Towards Distributed Machine Learning In Shared Clusters: A Dynamically-Partitioned Approach, Peng Sun, Yonggang Wen, Nguyen Binh Duong Ta, Shengen Yan
Towards Distributed Machine Learning In Shared Clusters: A Dynamically-Partitioned Approach, Peng Sun, Yonggang Wen, Nguyen Binh Duong Ta, Shengen Yan
Research Collection School Of Computing and Information Systems
Many cluster management systems (CMSs) have been proposed to share a single cluster with multiple distributed computing systems. However, none of the existing approaches can handle distributed machine learning (ML) workloads given the following criteria: high resource utilization, fair resource allocation and low sharing overhead. To solve this problem, we propose a new CMS named Dorm, incorporating a dynamicallypartitioned cluster management mechanism and an utilizationfairness optimizer. Specifically, Dorm uses the container-based virtualization technique to partition a cluster, runs one application per partition, and can dynamically resize each partition at application runtime for resource efficiency and fairness. Each application directly launches …
Search-Driven String Constraint Solving For Vulnerability Detection, Julian Thome, Lwin Khin Shar, Domenico Bianculli, Lionel Briand
Search-Driven String Constraint Solving For Vulnerability Detection, Julian Thome, Lwin Khin Shar, Domenico Bianculli, Lionel Briand
Research Collection School Of Computing and Information Systems
—Constraint solving is an essential technique for detecting vulnerabilities in programs, since it can reason about input sanitization and validation operations performed on user inputs. However, real-world programs typically contain complex string operations that challenge vulnerability detection. State-ofthe-art string constraint solvers support only a limited set of string operations and fail when they encounter an unsupported one; this leads to limited effectiveness in finding vulnerabilities. In this paper we propose a search-driven constraint solving technique that complements the support for complex string operations provided by any existing string constraint solver. Our technique uses a hybrid constraint solving procedure based on …
A Data-Driven Approach For Benchmarking Energy Efficiency Of Warehouse Buildings, Wee Leong Lee, Kar Way Tan, Zui Young Lim
A Data-Driven Approach For Benchmarking Energy Efficiency Of Warehouse Buildings, Wee Leong Lee, Kar Way Tan, Zui Young Lim
Research Collection School Of Computing and Information Systems
This study proposes adata-driven approach for benchmarking energy efficiency of warehouse buildings.Our proposed approach provides an alternative to the limitation of existingbenchmarking approaches where a theoretical energy-efficient warehouse was usedas a reference. Our approach starts by defining the questions needed to capturethe characteristics of warehouses relating to energy consumption. Using an existingdata set of warehouse building containing various attributes, we first cluster theminto groups by their characteristics. The warehouses characteristics derivedfrom the cluster assignments along with their past annual energy consumptionare subsequently used to train a decision tree model. The decision tree providesa classification of what factors contribute to different …
Who Will Leave The Company?: A Large-Scale Industry Study Of Developer Turnover By Mining Monthly Work Report, Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Shanping Li
Who Will Leave The Company?: A Large-Scale Industry Study Of Developer Turnover By Mining Monthly Work Report, Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Shanping Li
Research Collection School Of Computing and Information Systems
Software developer turnover has become a big challenge for information technology (IT) companies. The departure of key software developers might cause big loss to an IT company since they also depart with important business knowledge and critical technical skills. Understanding developer turnover is very important for IT companies to retain talented developers and reduce the loss due to developers' departure. Previous studies mainly perform qualitative observations or simple statistical analysis of developers' activity data to understand developer turnover. In this paper, we investigate whether we can predict the turnover of software developers in non-open source companies by automatically analyzing monthly …
Cryptography And Data Security In Cloud Computing, Zheng Yan, Robert H. Deng, Vijay Varadharajan
Cryptography And Data Security In Cloud Computing, Zheng Yan, Robert H. Deng, Vijay Varadharajan
Research Collection School Of Computing and Information Systems
Cloud computing offers a new way of services by re-arranging various resources and providing them to users based on their demands. It also plays an important role in the next generation mobile networks and services (5G) and Cyber-Physical and Social Computing (CPSC). Storing data in the cloud greatly reduces storage burden of users and brings them access convenience, thus it has become one of the most important cloud services. However, cloud data security, privacy and trust become a crucial issue that impacts the success of cloud computing and may impede the development of 5G and CPSC. First, storing data at …
Provably Secure Attribute Based Signcryption With Delegated Computation And Efficient Key Updating, Hanshu Hong, Yunhao Xia, Zhixin Sun, Ximeng Liu
Provably Secure Attribute Based Signcryption With Delegated Computation And Efficient Key Updating, Hanshu Hong, Yunhao Xia, Zhixin Sun, Ximeng Liu
Research Collection School Of Computing and Information Systems
Equipped with the advantages of flexible access control and fine-grained authentication, attribute based signcryption is diffusely designed for security preservation in many scenarios. However, realizing efficient key evolution and reducing the calculation costs are two challenges which should be given full consideration in attribute based cryptosystem. In this paper, we present a key-policy attribute based signcryption scheme (KP-ABSC) with delegated computation and efficient key updating. In our scheme, an access structure is embedded into user’s private key, while ciphertexts corresponds a target attribute set. Only the two are matched can a user decrypt and verify the ciphertexts. When the access …
Exploiting Anonymity And Homogeneity In Factored Dec-Mdps Through Pre-Computed Binomial Distributions, Rajiv Ranjan Kumar, Pradeep Varakantham
Exploiting Anonymity And Homogeneity In Factored Dec-Mdps Through Pre-Computed Binomial Distributions, Rajiv Ranjan Kumar, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
Recent work in decentralized stochastic planning for cooperative agents has focussed on exploiting omogeneity of agents and anonymity in interactions to solve problems with large numbers of agents. Due to a linear optimization formulation that computes joint policy and an objective that indirectly approximates joint expected reward with reward for expected number of agents in all state, action pairs, these approaches have ensured improved scalability. Such an objective closely approximates joint expected reward when there are many agents, due to law of large numbers. However, the performance deteriorates in problems with fewer agents. In this paper, we improve on the …
Real-Time Prediction Of Length Of Stay Using Passive Wi-Fi Sensing, Truc Viet Le, Baoyang Song, Laura Wynter
Real-Time Prediction Of Length Of Stay Using Passive Wi-Fi Sensing, Truc Viet Le, Baoyang Song, Laura Wynter
Research Collection School Of Computing and Information Systems
The proliferation of wireless technologies in today's everyday life is one of the key drivers of the Internet of Things (IoT). In addition to being an enabler of connectivity, the vast penetration of wireless devices today gives rise to a secondary functionality as a means of tracking and localization of the devices themselves. Indeed, in order to discover and automatically connect to known Wi-Fi networks, mobile devices have to scan and broadcast the so-called probe requests on all available channels, which can be captured and analyzed in a non-intrusive manner. Thus, one of the key applications of this feature is …
Discovering Your Selling Points: Personalized Social Influential Tags Exploration, Yuchen Li, Kian-Lee Tan, Ju Fan, Dongxiang Zhang
Discovering Your Selling Points: Personalized Social Influential Tags Exploration, Yuchen Li, Kian-Lee Tan, Ju Fan, Dongxiang Zhang
Research Collection School Of Computing and Information Systems
Social influence has attracted significant attention owing to the prevalence of social networks (SNs). In this paper, we study a new social influence problem, called personalized social influential tags exploration (PITEX), to help any user in the SN explore how she influences the network. Given a target user, it finds a size-k tag set that maximizes this user’s social influence. We prove the problem is NP-hard to be approximated within any constant ratio. To solve it, we introduce a sampling-based framework, which has an approximation ratio of 1−ǫ 1+ǫ with high probabilistic guarantee. To speedup the computation, we devise more …
Watching 360° Videos Together, Anthony Tang, Omid Fakourfar
Watching 360° Videos Together, Anthony Tang, Omid Fakourfar
Research Collection School Of Computing and Information Systems
360° videos are made using omnidirectional cameras that capture a sphere around the camera. Viewers get an immersive experience by freely changing their field of view around the sphere. The problem is that current interfaces are designed for a single user, and we do not know what challenges groups of people will have when viewing these videos together. We report on the findings of a study where 16 pairs of participants watched 360° videos together in a "guided tour" scenario. Our findings indicate that while participants enjoyed the ability to view the scene independently, this caused challenges establishing joint references, …
A Preliminary Evaluation Of A Gamification Framework To Jump Start Collaboration Behavior Change, Flavio Steffens, Sabrina Marczak, Fernando Figueira Filho, Christoph Treude, Cleidson R. B. Da Souza
A Preliminary Evaluation Of A Gamification Framework To Jump Start Collaboration Behavior Change, Flavio Steffens, Sabrina Marczak, Fernando Figueira Filho, Christoph Treude, Cleidson R. B. Da Souza
Research Collection School Of Computing and Information Systems
In this notes paper we report on a preliminary qualitative evaluation of a gamification framework to address collaboration issues in software engineering. Findings suggest that the use of game elements indeed is prone to motivate software developers to foster the resolution of collaboration issues in their teams. Our preliminary results motivated us to design large scale, in-depth, and longitudinal studies to further evaluate the framework. In a long run, we expect that our findings will be informative for project managers and tool designers and anyone else who is interested in helping software teams to overcome collaboration barriers and succeed on …
Effects Of The Use Of Leaderboards In Education, Yu-Hsien Chiu, Fiona Fui-Hoon Nah
Effects Of The Use Of Leaderboards In Education, Yu-Hsien Chiu, Fiona Fui-Hoon Nah
Research Collection School Of Computing and Information Systems
Gamification has been used in education to increase student motivation and performance. In this research, we are interested to examine the effect of leaderboards on student motivation by assessing the interest of students to complete optional practice questions provided to them in a course. Based on goal setting theory and cognitive evaluation theory, we hypothesize that the use of leaderboards will lead to increased student motivation. We designed a within-subject experiment where leaderboards were not provided in the first half of the semester for the optional assignments comprising practice questions but were provided in the second half of the semester …
The Impact Of Monetary Value Gains And Losses On Cybersecurity Behavior, Samuel Noah Smith, Fiona Fui-Hoon Nah, Maggie Cheng, Santosh Kuma Ravindran
The Impact Of Monetary Value Gains And Losses On Cybersecurity Behavior, Samuel Noah Smith, Fiona Fui-Hoon Nah, Maggie Cheng, Santosh Kuma Ravindran
Research Collection School Of Computing and Information Systems
This research examines if users take more risky cybersecurity actions when presented with the possibility of losing monetary value rather than gaining monetary value. Prospect theory provides the theoretical foundation for the research. An experimental design is proposed to test the hypothesis for the research.
Impact Of Artificial Intelligence, Robotics, And Machine Learning On Sales And Marketing, Keng Siau, Y. Yang
Impact Of Artificial Intelligence, Robotics, And Machine Learning On Sales And Marketing, Keng Siau, Y. Yang
Research Collection School Of Computing and Information Systems
AI, robotics, and machine learning are impacting the field of sales and marketing in an unprecedented way. A perfect storm is brewing! On one hand, online retail stores like Amazon are crushing the bricks and mortar stores. Sales and marketing professionals in bricks and mortar stores are facing a grim future. On the other hand, AI, robotics, and machine learning are replacing sales and marketing professionals in online stores. In fact, salespersons and marketers are predicted to be among the first to be replaced by robots. In a face-to-face environment, human may still prefer to interact with another human. In …
Lexicons In Sentiment Analytics, B. Yuan, Keng Siau
Lexicons In Sentiment Analytics, B. Yuan, Keng Siau
Research Collection School Of Computing and Information Systems
With the increasing amount of text data, sentiment analytics (SA) is becoming an important tool for text miners. An automated approach is needed to parse the online reviews and comments, and analyze their sentiments. Since lexicon is the most important component in SA, enhancing the quality of lexicons will improve the efficiency and accuracy of sentiment analysis. In this research, we study the effect of coupling a general lexicon with a specialized lexicon (for a specific domain) and its impact on sentiment analysis. Two special domains and one general domain were used. The two special domains are the petroleum domain …