Country 2.0: Upgrading Cities With Smart Technologies,
2017
Singapore Management University
Country 2.0: Upgrading Cities With Smart Technologies, Steven M. Miller
Asian Management Insights
Advancements in technology are being used to transform our cities into smart cities, but the process is not without its risks.
Persona Generation From Aggregated Social Media Data,
2017
Singapore Management University
Persona Generation From Aggregated Social Media Data, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Moeed Ahmad, Lene Nielsen, Bernard J. Jansen
Research Collection School Of Computing and Information Systems
We develop a methodology for persona generation using real time social media data for the distribution of products via online platforms. From a large social media account containing more than 30 million interactions from users from 181 countries engaging with more than 4,200 digital products produced by a global media corporation, we demonstrate that our methodology can first identify both distinct and impactful user segments and then create persona descriptions by automatically adding pertinent features, such as names, photos, and personal attributes. We validate our approach by implementing the methodology into an actual working system that leverages large scale online …
The Economics Of The Right To Be Forgotten,
2017
University of Alabama
The Economics Of The Right To Be Forgotten, Byung-Cheol Kim, Jin Yeub Kim
Department of Economics: Faculty Publications
Scholars and practitioners debate whether to expand the scope of the right to be forgotten—the right to have certain links removed from search results—to encompass global search results. The debate centers on the assumption that the expansion will increase the incidence of link removal, which reinforces privacy while hampering free speech. We develop a game-theoretic model to show that the expansion of the right to be forgotten can reduce the incidence of link removal. We also show that the expansion does not necessarily enhance the welfare of individuals who request removal and that it can either improve or reduce societal …
Real-Time Prediction Of Length Of Stay Using Passive Wi-Fi Sensing,
2017
Singapore Management University
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,
2017
Singapore Management University
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 …
Continuous Top-K Monitoring On Document Streams,
2017
University of Macau
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. …
A Data-Driven Approach For Benchmarking Energy Efficiency Of Warehouse Buildings,
2017
Singapore Management University
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 …
Encrypted Data Processing With Homomorphic Re-Encryption,
2017
Xidian University
Encrypted Data Processing With Homomorphic Re-Encryption, Wenxiu Ding, Zheng Yan, Robert H. Deng
Research Collection School Of Computing and Information Systems
Cloud computing offers various services to users by re-arranging storage and computing resources. In order to preserve data privacy, cloud users may choose to upload encrypted data rather than raw data to the cloud. However, processing and analyzing encrypted data are challenging problems, which have received increasing attention in recent years. Homomorphic Encryption (HE) was proposed to support computation on encrypted data and ensure data confidentiality simultaneously. However, a limitation of HE is it is a single user system, which means it only allows the party that owns a homomorphic decryption key to decrypt processed ciphertexts. Original HE cannot support …
Collaborative Topic Regression For Online Recommender Systems: An Online And Bayesian Approach,
2017
Zhejiang University
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 …
A Neural Network Model For Semi-Supervised Review Aspect Identification,
2017
Singapore Management University
A Neural Network Model For Semi-Supervised Review Aspect Identification, Ying Ding, Changlong Yu, Jing Jiang
Research Collection School Of Computing and Information Systems
Aspect identification is an important problem in opinion mining. It is usually solved in an unsupervised manner, and topic models have been widely used for the task. In this work, we propose a neural network model to identify aspects from reviews by learning their distributional vectors. A key difference of our neural network model from topic models is that we do not use multinomial word distributions but instead embedding vectors to generate words. Furthermore, to leverage review sentences labeled with aspect words, a sequence labeler based on Recurrent Neural Networks (RNNs) is incorporated into our neural network. The resulting model …
Determining The Impact Regions Of Competing Options In Preference Space,
2017
Singapore Management University
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 …
Exploiting Semantic Distance In Linked Open Data For Recommendation,
2017
University of Arkansas, Fayetteville
Exploiting Semantic Distance In Linked Open Data For Recommendation, Sultan Dawood Alfarhood
Graduate Theses and Dissertations
The use of Linked Open Data (LOD) has been explored in recommender systems in different ways, primarily through its graphical representation. The graph structure of LOD is utilized to measure inter-resource relatedness via their semantic distance in the graph. The intuition behind this approach is that the more connected resources are to each other, the more related they are. One drawback of this approach is that it treats all inter-resource connections identically rather than prioritizing links that may be more important in semantic relatedness calculations. Another drawback of current approaches is that they only consider resources that are connected directly …
Dpweka: Achieving Differential Privacy In Weka,
2017
University of Arkansas, Fayetteville
Dpweka: Achieving Differential Privacy In Weka, Srinidhi Katla
Graduate Theses and Dissertations
Organizations belonging to the government, commercial, and non-profit industries collect and store large amounts of sensitive data, which include medical, financial, and personal information. They use data mining methods to formulate business strategies that yield high long-term and short-term financial benefits. While analyzing such data, the private information of the individuals present in the data must be protected for moral and legal reasons. Current practices such as redacting sensitive attributes, releasing only the aggregate values, and query auditing do not provide sufficient protection against an adversary armed with auxiliary information. In the presence of additional background information, the privacy protection …
The Impact Of Monetary Value Gains And Losses On Cybersecurity Behavior,
2017
Singapore Management University
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.
Lexicons In Sentiment Analytics,
2017
Singapore Management University
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 …
Robust Object Tracking Via Locality Sensitive Histograms,
2017
Singapore Management University
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 …
Joint Optimization Of Resource Provisioning In Cloud Computing,
2017
Singapore Management University
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 …
Impact Of Artificial Intelligence, Robotics, And Machine Learning On Sales And Marketing,
2017
Singapore Management University
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 …
Machine Learning Approaches To Sentiment Analytics,
2017
Singapore Management University
Machine Learning Approaches To Sentiment Analytics, W. Zhao, Keng Siau
Research Collection School Of Computing and Information Systems
One key aspect of sentiment analytics is emotion classification. This research studies the use of machine learning approaches to classify human emotion. Two different machine learning approaches were compared in an experimental study. In one approach, emotions from both genders were used to train the machine. In another approach, genders were separated and two separate machines were used to learn the emotions of the two genders. We also manipulated the training sample sizes and study the effect of training sample sizes on the two machine learning approaches. Our preliminary results show that the approach where the genders were separated produces …
Neural Correlates Of User Experience In Gaming,
2017
Singapore Management University
Neural Correlates Of User Experience In Gaming, Y. Tejaswini, F. Nah, Keng Siau, L. Chen
Research Collection School Of Computing and Information Systems
The objective of this research is to understand the neural correlates of user states of experience in human-computer interaction using electroencephalogram (EEG). Such user states include flow, boredom, and anxiety that are experienced when a user interacts with a computer-based system. We propose using a within-subjects experiment to collect EEG data to assess and compare the neural correlates of three main states of user experience (i.e., flow, boredom, and anxiety) as well as compare them with the resting state as a baseline. We expect the findings from this research to contribute to an improved understanding of psychophysiological means of assessing …
