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Articles 4981 - 5010 of 9025
Full-Text Articles in Computer Sciences
Proactive And Reactive Coordination Of Non-Dedicated Agent Teams Operating In Uncertain Environments, Pritee Agrawal, Pradeep Varakantham
Proactive And Reactive Coordination Of Non-Dedicated Agent Teams Operating In Uncertain Environments, Pritee Agrawal, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
Domains such as disaster rescue, security patrolling etc. often feature dynamic environments where allocations of tasks to agents become ineffective due to unforeseen conditions that may require agents to leave the team. Agents leave the team either due to arrival of high priority tasks (e.g., emergency, accident or violation) or due to some damage to the agent. Existing research in task allocation has only considered fixed number of agents and in some instances arrival of new agents on the team. However, there is little or no literature that considers situations where agents leave the team after task allocation. To that …
Mechanism Design For Strategic Project Scheduling, Pradeep Varakantham, Na Fu
Mechanism Design For Strategic Project Scheduling, Pradeep Varakantham, Na Fu
Research Collection School Of Computing and Information Systems
Organizing large scale projects (e.g., Conferences, IT Shows, F1 race) requires precise scheduling of multiple dependent tasks on common resources where multiple selfish entities are competing to execute the individual tasks. In this paper, we consider a well studied and rich scheduling model referred to as RCPSP (Resource Constrained Project Scheduling Problem). The key change to this model that we consider in this paper is the presence of selfish entities competing to perform individual tasks with the aim of maximizing their own utility. Due to the selfish entities in play, the goal of the scheduling problem is no longer only …
Fair Deposits Against Double-Spending For Bitcoin Transactions, Xingjie Yu, Shiwen M. Thang, Yingjiu Li, Robert H. Deng
Fair Deposits Against Double-Spending For Bitcoin Transactions, Xingjie Yu, Shiwen M. Thang, Yingjiu Li, Robert H. Deng
Research Collection School Of Computing and Information Systems
In Bitcoin network, the distributed storage of multiple copies of the blockchain opens up possibilities for double spending, i.e., a payer issues two separate transactions to two different payees transferring the same coins. To detect the doublespending and penalize the malicious payer, decentralized non-equivocation contracts have been proposed. The basic idea of these contracts is that the payer locks some coins in a deposit when he initiates a transaction with the payee. If the payer double spends, a cryptographic primitive called accountable assertions can be used to reveal his Bitcoin credentials for the deposit. Thus, the malicious payer could be …
Can Syntax Help? Improving An Lstm-Based Sentence Compression Model For New Domains, Liangguo Wang, Jing Jiang, Hai Leong Chieu, Chen Hui Ong, Dandan Song, Lejian Liao
Can Syntax Help? Improving An Lstm-Based Sentence Compression Model For New Domains, Liangguo Wang, Jing Jiang, Hai Leong Chieu, Chen Hui Ong, Dandan Song, Lejian Liao
Research Collection School Of Computing and Information Systems
In this paper, we study how to improve thedomain adaptability of a deletion-basedLong Short-Term Memory (LSTM) neuralnetwork model for sentence compression.We hypothesize that syntactic informationhelps in making such modelsmore robust across domains. We proposetwo major changes to the model: usingexplicit syntactic features and introducingsyntactic constraints through Integer LinearProgramming (ILP). Our evaluationshows that the proposed model works betterthan the original model as well as a traditionalnon-neural-network-based modelin a cross-domain setting.
Accelerating Dynamic Graph Analytics On Gpus, Mo Shan, Yuchen Li, Bingsheng He, Kian-Lee Tan
Accelerating Dynamic Graph Analytics On Gpus, Mo Shan, Yuchen Li, Bingsheng He, Kian-Lee Tan
Research Collection School Of Computing and Information Systems
As graph analytics often involves compute-intensive operations,GPUs have been extensively used to accelerate the processing. However, in many applications such as social networks, cyber security, and fraud detection, their representative graphs evolve frequently and one has to perform are build of the graph structure on GPUs to incorporate the updates. Hence, rebuilding the graphs becomes the bottleneck of processing high-speed graph streams. In this paper,we propose a GPU-based dynamic graph storage scheme to support existing graph algorithms easily. Furthermore,we propose parallel update algorithms to support efficient stream updates so that the maintained graph is immediately available for high-speed analytic processing …
Projection-Free Distributed Online Learning In Networks, Wenpeng Zhang, Peilin Zhao, Wenwu Zhu, Steven C. H. Hoi, Tong Zhang
Projection-Free Distributed Online Learning In Networks, Wenpeng Zhang, Peilin Zhao, Wenwu Zhu, Steven C. H. Hoi, Tong Zhang
Research Collection School Of Computing and Information Systems
The conditional gradient algorithm has regained a surge of research interest in recent years due to its high efficiency in handling large-scale machine learning problems. However, none of existing studies has explored it in the distributed online learning setting, where locally light computation is assumed. In this paper, we fill this gap by proposing the distributed online conditional gradient algorithm, which eschews the expensive projection operation needed in its counterpart algorithms by exploiting much simpler linear optimization steps. We give a regret bound for the proposed algorithm as a function of the network size and topology, which will be smaller …
On Return Oriented Programming Threats In Android Runtime, Akshaya Venkateswara Raja, Jehyun Lee, Debin Gao
On Return Oriented Programming Threats In Android Runtime, Akshaya Venkateswara Raja, Jehyun Lee, Debin Gao
Research Collection School Of Computing and Information Systems
Android has taken a large share of operating systems forsmart devices including smartphones, and has been an attractive target to theattackers. The arms race between attackers and defenders typically occurs ontwo front lines — the latest attacking technology and the latest updates to theoperating system (including defense mechanisms deployed). In terms of attackingtechnology, Return-Oriented Programming (ROP) is one of the most sophisticatedattack methods on Android devices. In terms of the operating system updates,Android Runtime (ART) was the latest and biggest change to the Android family.In this paper, we investigate the extent to which Android Runtime (ART) makesReturn-Oriented Programming (ROP) attacks …
Real-Time Influence Maximization On Dynamic Social Streams, Yanhao Wang, Qi Fan, Yuchen Li, Kian-Lee Tan
Real-Time Influence Maximization On Dynamic Social Streams, Yanhao Wang, Qi Fan, Yuchen Li, Kian-Lee Tan
Research Collection School Of Computing and Information Systems
Influence maximization (IM), which selects a set of k users(called seeds) to maximize the influence spread over a social network, is a fundamental problem in a wide range of applications such as viral marketing and network monitoring.Existing IM solutions fail to consider the highly dynamic nature of social influence, which results in either poor seed qualities or long processing time when the network evolves.To address this problem, we define a novel IM query named Stream Influence Maximization (SIM) on social streams.Technically, SIM adopts the sliding window model and maintains a set of k seeds with the largest influence value over …
Object Detection Meets Knowledge Graphs, Yuan Fang, Kingsley Kuan, Jie Lin, Cheston Tan, Vijay Chandrasekhar
Object Detection Meets Knowledge Graphs, Yuan Fang, Kingsley Kuan, Jie Lin, Cheston Tan, Vijay Chandrasekhar
Research Collection School Of Computing and Information Systems
Object detection in images is a crucial task in computer vision, with important applications ranging from security surveillance to autonomous vehicles. Existing state-of-the-art algorithms, including deep neural networks, only focus on utilizing features within an image itself, largely neglecting the vast amount of background knowledge about the real world. In this paper, we propose a novel framework of knowledge-aware object detection, which enables the integration of external knowledge such as knowledge graphs into any object detection algorithm. The framework employs the notion of semantic consistency to quantify and generalize knowledge, which improves object detection through a re-optimization process to achieve …
Managing Sensor Systems For Early Detection Of Mild Cognitive Impairment In Community Elderly: Lessons Learned And Future Work, Boon Thai Ng, Hwee-Pink Tan, Hwee Xian Tan
Managing Sensor Systems For Early Detection Of Mild Cognitive Impairment In Community Elderly: Lessons Learned And Future Work, Boon Thai Ng, Hwee-Pink Tan, Hwee Xian Tan
Research Collection School Of Computing and Information Systems
The aging population is a pertinent issue faced by governments globally. One of the most common and costly health issues associated with the aging population is cognitive decline, leading up to dementia. In this paper, we describe a non-intrusive, continuous and scalable system for early detection of Mild Cognitive Impairment (MCI) in the elderly, which enables early medical interventions to be provided. We focus on the system design and feature extraction of the sensor system, to validate our hypothesis of the use of sensor systems for early detection of MCI. Lessons learned from deploying the sensor system is presented, together …
Seeing Through The Same Lens: Introspecting Guest Address Space At Native Speed, Siqi Zhao, Xuhua Ding, Wen Xu, Dawu Gu
Seeing Through The Same Lens: Introspecting Guest Address Space At Native Speed, Siqi Zhao, Xuhua Ding, Wen Xu, Dawu Gu
Research Collection School Of Computing and Information Systems
Software-based MMU emulation lies at the heart of out-of-VM live memory introspection, an important technique in the cloud setting that applications such as live forensics and intrusion detection depend on. Due to the emulation, the software-based approach is much slower compared to native memory access by the guest VM. The slowness not only results in undetected transient malicious behavior, but also inconsistent memory view with the guest; both undermine the effectiveness of introspection. We propose the immersive execution environment (ImEE) with which the guest memory is accessed at native speed without any emulation. Meanwhile, the address mappings used within the …
Flexible Online Task Assignment In Real-Time Spatial Data, Yongxin Tong, Libin Wang, Zimu Zhou, Bolin Ding, Lei Chen, Jieping Ye, Ke Xu
Flexible Online Task Assignment In Real-Time Spatial Data, Yongxin Tong, Libin Wang, Zimu Zhou, Bolin Ding, Lei Chen, Jieping Ye, Ke Xu
Research Collection School Of Computing and Information Systems
The popularity of Online To Offline (O2O) service platforms has spurred the need for online task assignment in real-time spatial data, where streams of spatially distributed tasks and workers are matched in real time such that the total number of assigned pairs is maximized. Existing online task assignment models assume that each worker is either assigned a task immediately or waits for a subsequent task at a fixed location once she/he appears on the platform. Yet in practice a worker may actively move around rather than passively wait in place if no task is assigned. In this paper, we define …
Recommendation Vs Sentiment Analysis: A Text-Driven Latent Factor Model For Rating Prediction With Cold-Start Awareness, Kaisong Song, Wei Gao, Shi Feng Feng, Daling Wang, Kam-Fai Wong, Chengqi Zhang
Recommendation Vs Sentiment Analysis: A Text-Driven Latent Factor Model For Rating Prediction With Cold-Start Awareness, Kaisong Song, Wei Gao, Shi Feng Feng, Daling Wang, Kam-Fai Wong, Chengqi Zhang
Research Collection School Of Computing and Information Systems
Review rating prediction is an important research topic. The problem was approached from either the perspective of recommender systems (RS) or that of sentiment analysis (SA). Recent SA research using deep neural networks (DNNs) has realized the importance of user and product interaction for better interpreting the sentiment of reviews. However, the complexity of DNN models in terms of the scale of parameters is very high, and the performance is not always satisfying especially when user-product interaction is sparse. In this paper, we propose a simple, extensible RS-based model, called Text-driven Latent Factor Model (TLFM), to capture the semantics of …
The Role Of Knowledge Sharing Via Organizational Social Media In The Workplace, Murad A. Moqbel, Fiona Fui-Hoon Nah
The Role Of Knowledge Sharing Via Organizational Social Media In The Workplace, Murad A. Moqbel, Fiona Fui-Hoon Nah
Research Collection School Of Computing and Information Systems
Although a plethora of studies have been recently conducted on organizational social media, little research has specifically examined how organizational social media can contribute to organizational workers’ performance through knowledge sharing. The objective of this research is to fill this gap by investigating the role of knowledge sharing in organizational social media use and its effect on in-role and innovative performance through the lens of social capital and social cognitive theories. Hypotheses were developed and a survey study is proposed.
Impact Of Artificial Intelligence, Robotics, And Automation On Higher Education, Keng Siau
Impact Of Artificial Intelligence, Robotics, And Automation On Higher Education, Keng Siau
Research Collection School Of Computing and Information Systems
What is the future of higher education in the AI age? Higher education is expected to be challenged by AI, Robotics, and Automation on multiple fronts. First and foremost, AI, robotics, and automation are replacing and will continue to replace jobs and revolutionalize every nation’s economy and disrupt economic development in the world. Millions of job are expected to be replaced by machines (Zhao & Siau, 2017). Many manufacturing jobs have already been replaced by robots and middle class jobs may be taken over by AI in the near future (Siau & Yang, 2017).The short and long term impact on …
A Research Stream On Sentiment Analysis, B. Yuan, Keng Siau
A Research Stream On Sentiment Analysis, B. Yuan, Keng Siau
Research Collection School Of Computing and Information Systems
Sentiment analysis (SA) is a powerful mining technique to study online reviews and comments (Lee & Siau, 2001; Adeborna & Siau, 2014; Zhao & Siau, 2017). It is an advanced text mining technique and the goal of SA is to recognize and extract meaningful information from data using natural language processing (NLP) and computational linguistics.SA has been applied to areas such as marketing, online social media, customer service, education, and even energy fields (Yuan & Siau, 2017). For example, SA can be used to identify the attitude of customers according to polarity of the reviews and comments that they left …
Using Cognitive Maps Of Mental Models To Evaluate Learning Challenges: A Case Study, Z. Shen, Keng Siau
Using Cognitive Maps Of Mental Models To Evaluate Learning Challenges: A Case Study, Z. Shen, Keng Siau
Research Collection School Of Computing and Information Systems
Mental models are organized knowledge structures that individuals form to make sense of the world around them. Cognitive maps are the externalized portrayals of mental models in graphical format. Mental models and cognitive maps have been used as an instructional design method, an assessment tool, and a learning strategy in college education. In this paper, we propose a novel use of mental models and cognitive maps as a device to elicit students’ challenges in learning course materials. Our case study in an Information Systems class illustrates how cognitive maps are constructed from students’ mental models, how learning challenges are identified …
Embedding-Based Representation Of Categorical Data By Hierarchical Value Coupling Learning, Songlei Jian, Longbing Cao, Guansong Pang, Kai Lu, Hang Gao
Embedding-Based Representation Of Categorical Data By Hierarchical Value Coupling Learning, Songlei Jian, Longbing Cao, Guansong Pang, Kai Lu, Hang Gao
Research Collection School Of Computing and Information Systems
Learning the representation of categorical data with hierarchical value coupling relationships is very challenging but critical for the effective analysis and learning of such data. This paper proposes a novel coupled unsupervised categorical data representation (CURE) framework and its instantiation, i.e., a coupled data embedding (CDE) method, for representing categorical data by hierarchical value-to-value cluster coupling learning. Unlike existing embedding- and similarity-based representation methods which can capture only a part or none of these complex couplings, CDE explicitly incorporates the hierarchical couplings into its embedding representation. CDE first learns two complementary feature value couplings which are then used to cluster …
Learning Homophily Couplings From Non-Iid Data For Joint Feature Selection And Noise-Resilient Outlier Detection, Guansong Pang, Longbing Cao, Ling Chen, Huan Liu
Learning Homophily Couplings From Non-Iid Data For Joint Feature Selection And Noise-Resilient Outlier Detection, Guansong Pang, Longbing Cao, Ling Chen, Huan Liu
Research Collection School Of Computing and Information Systems
This paper introduces a novel wrapper-based outlier detection framework (WrapperOD) and its instance (HOUR) for identifying outliers in noisy data (i.e., data with noisy features) with strong couplings between outlying behaviors. Existing subspace or feature selection-based methods are significantly challenged by such data, as their search of feature subset(s) is independent of outlier scoring and thus can be misled by noisy features. In contrast, HOUR takes a wrapper approach to iteratively optimize the feature subset selection and outlier scoring using a top-k outlier ranking evaluation measure as its objective function. HOUR learns homophily couplings between outlying behaviors (i.e., abnormal behaviors …
Personas For Content Creators Via Decomposed Aggregate Audience Statistics, Jisun An, Haewoon Kwak, Bernard J. Jansen
Personas For Content Creators Via Decomposed Aggregate Audience Statistics, Jisun An, Haewoon Kwak, Bernard J. Jansen
Research Collection School Of Computing and Information Systems
We propose a novel method for generating personas based on online user data for the increasingly common situation of content creators distributing products via online platforms. We use non-negative matrix factorization to identify user segments and develop personas by adding personality such as names and photos. Our approach can develop accurate personas representing real groups of people using online user data, versus relying on manually gathered data.
Generating Cultural Personas From Social Data: A Perspective Of Middle Eastern Users, Salminen Joni, Sercan Sengün, Haewoon Kwak, Bernard Jansen, Jisun An, Soon-Gyo Jung, Sarah Vieweg, D. Fox Harrell
Generating Cultural Personas From Social Data: A Perspective Of Middle Eastern Users, Salminen Joni, Sercan Sengün, Haewoon Kwak, Bernard Jansen, Jisun An, Soon-Gyo Jung, Sarah Vieweg, D. Fox Harrell
Research Collection School Of Computing and Information Systems
We conduct a mixed-method study to better understand the content consumption patterns of Middle Eastern social media users and to explore new ways to present online data by using automatic persona generation. First, we analyze millions of content interactions on YouTube to dynamically generate personas describing behavioral patterns of different demographic groups. Second, we analyze interview data on social media users in the Middle Eastern region to generate additional insights into the dynamically generated personas. Our findings provide insights into social media users in the Middle East, as well as present a novel methodology of using computational analysis and qualitative …
Testing And Debugging: A Reality Check, Pavneet Singh Kochhar
Testing And Debugging: A Reality Check, Pavneet Singh Kochhar
Dissertations and Theses Collection
Testing and debugging are important activities during software development and maintenance. Testing is performed to check if the code contains errors whereas debugging is done to locate and fix these errors. Testing can be manual or automated and can be of different types such as unit, integration, system, stress etc. Debugging can also be manual or automated. These two activities have drawn attention of researchers in the recent years. Past studies have proposed many testing techniques such as automated test generation, test minimization, test case selection etc. Studies related to debugging have proposed new techniques to find bugs using various …
Hybrid Based Approaches For Software Fault Localization And Specification Mining, Bui Tien Duy Le
Hybrid Based Approaches For Software Fault Localization And Specification Mining, Bui Tien Duy Le
Dissertations and Theses Collection
Debugging programs and writing formal specifications are essential but expensive processes to maintain quality and reliability of software systems. Developers often have to debug and create specifications manually, which take a lot of their time and effort. Recently, several automated solutions have been proposed to help developers alleviate the cost of manual labor in the two processes. In particular, fault localization techniques help developer debug by accepting textual information in bug reports or program spectra (i.e., a record of which program elements are executed for each test case). Their output is a ranked list of program elements that are likely …
How Technology Is Reshaping Financial Services: Essays On Consumer Behavior In Card, Channel And Cryptocurrency Services, Dan Geng
Dissertations and Theses Collection
The financial services sector has seen dramatic technological innovations in the last several years associated with the “fintech revolution.” Major changes have taken place in channel management, credit card rewards marketing, cryptocurren-cy, and wealth management, and have influenced consumers’ banking behavior in different ways. As a consequence, there has been a growing demand for banks to rethink their business models and operations to adapt to changing consumer be-havior and counter the competitive pressure from other banks and non-bank play-ers. In this dissertation, I study consumer behavior related to different aspects of financial innovation by addressing research questions that are motivated …
Mining Diverse Consumer Preferences For Bundling And Recommendation, Ha Loc Do
Mining Diverse Consumer Preferences For Bundling And Recommendation, Ha Loc Do
Dissertations and Theses Collection
That consumers share similar tastes on some products does not guarantee their agreement on other products. Therefore, both similarity and dierence should be taken into account for a more rounded view on consumer preferences. This manuscript focuses on mining this diversity of consumer preferences from two perspectives, namely 1) between consumers and 2) between products. Diversity of preferences between consumers is studied in the context of recommendation systems. In some preference models, measuring similarities in preferences between two consumers plays the key role. These approaches assume two consumers would share certain degree of similarity on any products, ignoring the fact …
Real-Time Bursty Topic Detection And Virality Forecasting In Microblogs, Wei Xie
Real-Time Bursty Topic Detection And Virality Forecasting In Microblogs, Wei Xie
Dissertations and Theses Collection
Microblogs such as Twitter have become the largest social platforms for users around the world to share anything happening around them with friends and beyond. A bursty topic in microblogs is one that triggers a surge of relevant tweets within a short period of time, which often reflects important events of mass interest. How to leverage microblogs for early detection and further impact analysis of bursty topics has, therefore, become an important research problem with immense practical value.
How Artificial Intelligence Is Impacting Manufacturing Industry, Deepak Srinivasan, Maitreyi Ramesh Swaroop, Balaji Rajaram, Sri Krishan Iyer
How Artificial Intelligence Is Impacting Manufacturing Industry, Deepak Srinivasan, Maitreyi Ramesh Swaroop, Balaji Rajaram, Sri Krishan Iyer
Research Collection School Of Computing and Information Systems
In this survey, we study the impact of Artificial Intelligence (AI) on manufacturing sector. AI methods can be utilized to make new thoughts several ways: by delivering novel mixes of wellknown thoughts; by investigating the capability of theoretical spaces; and by making changes that empower the era of unexplored thoughts. AI will have less trouble in displaying the era of new thoughts than in automating their assessment. We describe the advances that have been made on AI in manufacturing industry. We close with how to overcome the issues in this area.
Ehealthportal: A Social Support Hub For The Active Living Of The Elderly, Di Wang, Ah-Hwee Tan
Ehealthportal: A Social Support Hub For The Active Living Of The Elderly, Di Wang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
The absolute and relative increases in the number of elderly are evident worldwide, from the most developed countries to the lowest-income regions. The fast demographic transition poses great challenges to the healthcare system and introduces a significant burden to the elderly and their family. To meet the unprecedented challenges of global aging, various aging-in-place (AIP) solutions have been proposed to enable the elderly to live in their own home and community safely, independently and comfortably. Elderly need support in various aspects, such as physical, cognitive, emotional, and social, in their daily life. However, most existing AIP solutions provide support in …
Elderly Friendliness Evaluation Of Mobile Assistants, Di Wang, Xinjia Yu, Simon Fauvel, Ah-Hwee Tan, Chunyan Miao
Elderly Friendliness Evaluation Of Mobile Assistants, Di Wang, Xinjia Yu, Simon Fauvel, Ah-Hwee Tan, Chunyan Miao
Research Collection School Of Computing and Information Systems
The rapidly increasing elderly population in many developed and developing countries poses great challenges to elderly care systems. To alleviate the problem of a shrinking workforce to deliver elderly care, using mobile intelligent assistants to lessen the caregivers' workload becomes a promising solution. However, the friendliness of such mobile assistants, which is seldom measured in a quantitative manner, may hinder their acceptance by the elderly users. In this paper, we propose a formalized systematic approach named Elderly Friendliness Evaluation Methodology (EFEM) to measure the elderly friendliness of any product, service or system. Furthermore, we apply EFEM to evaluate the elderly …
Demographics Of News Sharing In The U.S. Twittersphere, Julio C.S. Reis, Haewoon Kwak, Jisun An, Johnnatan Messias, Benevenuto Fabrıcio.
Demographics Of News Sharing In The U.S. Twittersphere, Julio C.S. Reis, Haewoon Kwak, Jisun An, Johnnatan Messias, Benevenuto Fabrıcio.
Research Collection School Of Computing and Information Systems
The widespread adoption and dissemination of online news through social media systems have been revolutionizing many segments of our society and ultimately our daily lives. In these systems, users can play a central role as they share content to their friends. Despite that, little is known about news spreaders in social media. In this paper, we provide the first of its kind in-depth characterization of news spreaders in social media. In particular, we investigate their demographics, what kind of content they share, and the audience they reach. Among our main findings, we show that males and white users tend to …