Physical Sciences and Mathematics Commons^™

Aspect Discovery From Product Reviews, Ying Ding

Dissertations and Theses Collection

With the rapid development of online shopping sites and social media, product reviews are accumulating. These reviews contain information that is valuable to both businesses and customers. To businesses, companies can easily get a large number of feedback of their products, which is difficult to achieve by doing customer survey in the traditional way. To customers, they can know the products they are interested in better by reading reviews, which may be uneasy without online reviews. However, the accumulation has caused consuming all reviews impossible. It is necessary to develop automated techniques to efficiently process them. One of the most …

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 …

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.

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 …

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 …

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 …

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 …

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

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 …

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 …

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 …

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 …

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 …

Dynamic Nearest Neighbor Queries In Euclidean Space, Sarana Nutanong, Mohammed Eunus Ali, Egemen Tanin, Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

Given a query point q and a set D of data points, a nearest neighbor (NN) query returns the data point p in D that minimizes the distance DIST(q,p), where the distance function DIST(,) is the L₂norm. One important variant of this query type is kNN query, which returns k data points with the minimum distances. When taking the temporal dimension into account, the k NN query result may change over a period of time due to changes in locations of the query point and/or data points.

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. …

Exploiting Contextual Information For Fine-Grained Tweet Geolocation, Wen Haw Chong, Ee Peng Lim

Research Collection School Of Computing and Information Systems

The problem of fine-grained tweet geolocation is to link tweets to their posting venues. We solve this in a learning to rank framework by ranking candidate venues given a test tweet. The problem is challenging as tweets are short and the vast majority are non-geocoded, meaning information is sparse for building models. Nonetheless, although only a small fraction of tweets are geocoded, we find that they are posted by a substantial proportion of users. Essentially, such users have location history data. Along with tweet posting time, these serve as additional contextual information for geolocation. In designing our geolocation models, we …

Data-Driven Approach To Measuring The Level Of Press Freedom Using Media Attention Diversity From Unfiltered News, Jisun An, Haewoon Kwak

Research Collection School Of Computing and Information Systems

Published by Reporters Without Borders every year, the Press Freedom Index (PFI) reflects the fear and tension in the newsroom pushed by the government and private sectors. While the PFI is invaluable in monitoring media environ- ments worldwide, the current survey-based method has in- herent limitations to updates in terms of cost and time. In this work, we introduce an alternative way to measure the level of press freedom using media attention diversity compiled from Unfiltered News.

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 …

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 …

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, 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 …

Improving Discovery And Patron Experience Through Data Mining, Boyuan Guan, Jamie Rogers

Works of the FIU Libraries

As information professionals, we know simple database searches are imperfect. With rich and expansive digital collections, patrons may not find content that is buried in a long list of results. So, how do we improve discovery of pertinent materials and offer serendipitous experience? Following the example of recommendation functionality in online applications like Netflix, we have developed a recommendation function for our digital library system that provides relevant content beyond the narrow scope of patrons' original search parameters. This session will outline the reasoning, methodology, and design of the recommendation system as well as preliminary results from implementation.

The Creation Of A Building Map Application For A University Setting, William T. Whitesell

Senior Honors Theses

The use of navigational technology in mobile and web devices has sharply increased in recent years. With the capability to create interactive maps now available, navigating in real time between locations has become possible. This is especially essential in areas and organizations experiencing rapid expansion like Liberty University (LU). Therefore, the author proposes a project to create an interactive map application (IMA) for LU’s academic buildings that is scalable and usable through both the university’s website and with a mobile application. There are several considerations that must be taken into account when creating the LU map application, such as development …

Mapping Community Space And Place In Mto Wa Mbu, Tanzania Through Surveys And Gis, Jessica Craigg

Georgia College Student Research Events

Cities throughout the African continent have been developing at an unprecedented pace, many of them due to the influence of the tourism industry. This is particularly true in Tanzania, a country famous for its national parks and their draw to tourists who help provide money for development. However, the only way to get the whole story on how to spend this money is through the experiences and needs of the people themselves. This study focuses on a small town in northeastern Tanzania, Mto wa Mbu, situated near Lake Manyara National Park, and its people’s perceptions of the park and community. …

Design And Implementation Of An Rfid-Based Customer Shopping Behavior Mining System, Zimu Zhou, Longfei Shangguan, Xiaolong Zheng, Lei Yang, Yunhao Liu

Research Collection School Of Computing and Information Systems

Shopping behavior data is of great importance in understanding the effectiveness of marketing and merchandising campaigns. Online clothing stores are capable of capturing customer shopping behavior by analyzing the click streams and customer shopping carts. Retailers with physical clothing stores, however, still lack effective methods to comprehensively identify shopping behaviors. In this paper, we show that backscatter signals of passive RFID tags can be exploited to detect and record how customers browse stores, which garments they pay attention to, and which garments they usually pair up. The intuition is that the phase readings of tags attached to items will demonstrate …

Factored Similarity Models With Social Trust For Top-N Item Recommendation, Guibing Guo, Jie Zhang, Feida Zhu, Xingwei Wang

Research Collection School of Computing and Information Systems

Trust-aware recommender systems have attracted much attention recently due to the prevalence of social networks. However, most existing trust-based approaches are designed for the recommendation task of rating prediction. Only few trust-aware methods have attempted to recommend users an ordered list of interesting items, i.e., item recommendation. In this article, we propose three factored similarity models with the incorporation of social trust for item recommendation based on implicit user feedback. Specifically, we introduce a matrix factorization technique to recover user preferences between rated items and unrated ones in the light of both user-user and item-item similarities. In addition, we claim …

Viewability Prediction For Display Advertising, Chong Wang

Dissertations

As a massive industry, display advertising delivers advertisers’ marketing messages to attract customers through graphic banners on webpages. Display advertising is also the most essential revenue source of online publishers. Currently, advertisers are charged by user response or ad serving. However, recent studies show that users barely click or convert display ads. Moreover, about half of the ads are actually never seen by users. In this case, advertisers cannot enhance their brand awareness and increase return on investment. Publishers also lose much revenue. Therefore, the ad pricing standards are shifting to a new model: ad impressions are paid if they …

Eassistant: Cognitive Assistance For Identification And Auto-Triage Of Actionable Conversations, Hamid R. Motahari Nezhad, Kalpa Gunaratna, Juan Cappi

Kno.e.sis Publications

The browser and screen have been the main user interfaces of the Web and mobile apps. The notification mechanism is an evolution in the user interaction paradigm by keeping users updated without checking applications. Conversational agents are posed to be the next revolution in user interaction paradigms. However, without intelligence on the triage of content served by the interaction and content differentiation in applications, interaction paradigms may still place the burden of information overload on users. In this paper, we focus on the problem of intelligent identification of actionable information in the content served by applications, and in particular in …

What Are People Tweeting About Zika? An Exploratory Study Concerning Its Symptoms, Treatment, Transmission, And Prevention, Michele Miller, Tanvi Banerjee, Roopteja Muppalla, William L. Romine, Amit Sheth

Kno.e.sis Publications

Background: In order to harness what people are tweeting about Zika, there needs to be a computational framework that leverages machine learning techniques to recognize relevant Zika tweets and, further, categorize these into disease-specific categories to address specific societal concerns related to the prevention, transmission, symptoms, and treatment of Zika virus.

Objective: The purpose of this study was to determine the relevancy of the tweets and what people were tweeting about the 4 disease characteristics of Zika: symptoms, transmission, prevention, and treatment.

Methods: A combination of natural language processing and machine learning techniques was used to determine what people were …

Now You See It, Now You Don't! A Study Of Content Modification Behavior In Facebook, Fuxiang Chen, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Social media, as a major platform to disseminate information, has changed the way users and communities contribute content. In this paper, we aim to study content modifications on public Facebook pages operated by news media, community groups, and bloggers. We also study the possible reasons behind them, and their effects on user interaction. We conducted a detailed study of Content Censorship (CC) and Content Edit (CE) in Facebook using a detailed longitudinal dataset consisting of 57 public Facebook pages over 3 weeks covering 145,955 posts and 9,379,200 comments. We detected many CC and CE activities between 28% and 56% of …