Databases and Information Systems Commons^™

Is There Space For Violence?: A Data-Driven Approach To The Exploration Of Spatial-Temporal Dimensions Of Conflict, Tin Seong Kam, Vincent Zhi

Research Collection School Of Computing and Information Systems

With recent increases in incidences of political violence globally, the world has now become more uncertain and less predictable. Of particular concern is the case of violence against civilians, who are often caught in the crossfire between armed state or non-state actors. Classical methods of studying political violence and international relations need to be updated. Adopting the use of data analytic tools and techniques of studying big data would enable academics and policy makers to make sense of a rapidly changing world.

Cross-Border Interbank Payments And Settlements: Emerging Opportunities For Digital Transformation, Yi Meng Lau, Et Al

Research Collection School Of Computing and Information Systems

The report “Cross-Border Interbank Payments and Settlements” is a cross-jurisdictional industry collaboration between Canada, Singapore and the United Kingdom to examine the existing challenges and frictions that arise when undertaking crossborder payments. This report explores proposals for new and more efficient models for processing cross-border transactions.

Latent Dirichlet Allocation For Textual Student Feedback Analysis, Swapna Gottipati, Venky Shankararaman, Jeff Lin

Research Collection School Of Computing and Information Systems

Education institutions collect feedback from students upon course completion and analyse it to improve curriculum design, delivery methodology and students' learning experience. A large part of feedback comes in the form textual comments, which pose a challenge in quantifying and deriving insights. In this paper, we present a novel approach of the Latent Dirichlet Allocation (LDA) model to address this difficulty in handling textual student feedback. The analysis of quantitative part of student feedback provides generalratings and helps to identify aspects of the teaching that are successful and those that can improve. The reasons for the failure or success, however, …

Heterogeneous Embedding Propagation For Large-Scale E-Commerce User Alignment, Vincent W. Zheng, Mo Sha, Yuchen Li, Hongxia Yang, Yuan Fang, Zhenjie Zhang, Kian-Lee Tan, Kevin Chen-Chuan Chang

Research Collection School Of Computing and Information Systems

We study the important problem of user alignment in e-commerce: to predict whether two online user identities that access an e-commerce site from different devices belong to one real-world person. As input, we have a set of user activity logs from Taobao and some labeled user identity linkages. User activity logs can be modeled using a heterogeneous interaction graph (HIG), and subsequently the user alignment task can be formulated as a semi-supervised HIG embedding problem. HIG embedding is challenging for two reasons: its heterogeneous nature and the presence of edge features. To address the challenges, we propose a novel Heterogeneous …

Learning Generalized Video Memory For Automatic Video Captioning, Poo-Hee Chang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Recent video captioning methods have made great progress by deep learning approaches with convolutional neural networks (CNN) and recurrent neural networks (RNN). While there are techniques that use memory networks for sentence decoding, few work has leveraged on the memory component to learn and generalize the temporal structure in video. In this paper, we propose a new method, namely Generalized Video Memory (GVM), utilizing a memory model for enhancing video description generation. Based on a class of self-organizing neural networks, GVM’s model is able to learn new video features incrementally. The learned generalized memory is further exploited to decode the …

Imaginary People Representing Real Numbers: Generating Personas From Online Social Media Data, Jisun An, Haewoon Kwak, Soongyo Jung, Joni Salminen, M. Admad, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

We develop a methodology to automate creating imaginary people, referred to as personas, by processing complex behavioral and demographic data of social media audiences. From a popular social media account containing more than 30 million interactions by viewers from 198 countries engaging with more than 4,200 online videos produced by a global media corporation, we demonstrate that our methodology has several novel accomplishments, including: (a) identifying distinct user behavioral segments based on the user content consumption patterns; (b) identifying impactful demographics groupings; and (c) creating rich persona descriptions by automatically adding pertinent attributes, such as names, photos, and personal characteristics. …

Double Learning Or Double Blinding: An Investigation Of Vendor Private Information Acquisition And Consumer Learning Via Online Reviews, Nan Hu, Kevin E. Dow, Alain Yee Loong Chong, Ling Liu

Research Collection School Of Computing and Information Systems

In this paper, building upon information acquisition theory and using portfolio methods and system equations, we made an empirical investigation into how online vendors and consumers are learning from each other, and how online reviews, prices, and sales interact among each other. First, this study shows that vendors acquire information from both private and public channels to learn the quality of their products to make price adjustment. Second, for the more popular products and newly released products, vendors are more motivated to acquire private information that is more precise than the average precision to adjust their price. Third, we document …

Joint Representation Learning Of Cross-Lingual Words And Entities Via Attentive Distant Supervision, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Chengjiang Li, Xu Chen, Tiansi Dong

Research Collection School Of Computing and Information Systems

Joint representation learning of words and entities benefits many NLP tasks, but has not been well explored in cross-lingual settings. In this paper, we propose a novel method for joint representation learning of cross-lingual words and entities. It captures mutually complementary knowledge, and enables cross-lingual inferences among knowledge bases and texts. Our method does not require parallel corpora, and automatically generates comparable data via distant supervision using multi-lingual knowledge bases. We utilize two types of regularizers to align cross-lingual words and entities, and design knowledge attention and crosslingual attention to further reduce noises. We conducted a series of experiments on …

Comparing Elm With Svm In The Field Of Sentiment Classification Of Social Media Text Data, Zhihuan Chen, Zhaoxia Wang, Zhiping Lin, Ting Yang

Research Collection School Of Computing and Information Systems

Machine learning has been used in various fields with thousands of applications. Extreme learning machine (ELM), which is the most recently developed machine learning algorithm, has become increasingly popular for its good generalization ability. However, it has been relatively less applied to the domain of social media. Support Vector Machine (SVM), another popular learning-based algorithm, has been applied for sentiment classification of social media text data and has obtained good results. This paper investigates and compares the capabilities of these two learning-based methods in the field of sentiment classification of social media. The results indicate that SVM can obtain good …

Vulnerability Assessment & Penetration Testing: Case Study On Web Application Security, Gazmend Krasniqi, Veton Bejtullahu

UBT International Conference

Complexity of information systems are increasing day by day. The security of information systems that are connected to public networks can be compromised by unauthorized, and usually anonymous, attempts to access them. By using public networks businesses and other institutions are exposed to numerous risks. This leads to more and more vulnerabilities in Information Systems. This situation calls for test methods that are devised from the attacker’s perspective to ensure that test conditions are as realistic as possible. In this paper we will describe complete stages of Vulnerability Assessment and Penetration Testing on some systems in UBT and proactive action …

Effective Visualization Approaches For Ultra-High Dimensional Datasets, Gurminder Kaur

LSU Doctoral Dissertations

Multivariate informational data, which are abstract as well as complex, are becoming increasingly common in many areas such as scientific, medical, social, business, and so on. Displaying and analyzing large amounts of multivariate data with more than three variables of different types is quite challenging. Visualization of such multivariate data suffers from a high degree of clutter when the numbers of dimensions/variables and data observations become too large. We propose multiple approaches to effectively visualize large datasets of ultrahigh number of dimensions by generalizing two standard multivariate visualization methods, namely star plot and parallel coordinates plot. We refine three variants …

Building Iot Based Applications For Smart Cities: How Can Ontology Catalogs Help?, Amelia Gyrard, Antoine Zimmermann, Amit P. Sheth

Kno.e.sis Publications

The Internet of Things (IoT) plays an ever-increasing role in enabling smart city applications. An ontology-based semantic approach can help improve interoperability between a variety of IoT-generated as well as complementary data needed to drive these applications. While multiple ontology catalogs exist, using them for IoT and smart city applications require significant amount of work. In this paper, we demonstrate how can ontology catalogs be more effectively used to design and develop smart city applications? We consider four ontology catalogs that are relevant for IoT and smart cities: 1) READY4SmartCities; 2) linked open vocabulary (LOV); 3) OpenSensingCity (OSC); and 4) …

Using Electronic Health Records To Characterize Prescription Patterns: Focus On Antidepressants In Nonpsychiatric Outpatient Settings, Joseph J. Deferio, Tomer T. Levin, Judith Cukor, Samprit Banerjee, Rozan Abdulrahman, Amit P. Sheth, Neel Mehta, Jyotishman Pathak

Kno.e.sis Publications

Objective

To characterize nonpsychiatric prescription patterns of antidepressants according to drug labels and evidence assessments (on-label, evidence-based, and off-label) using structured outpatient electronic health record (EHR) data. Methods

A retrospective analysis was conducted using deidentified EHR data from an outpatient practice at a New York City-based academic medical center. Structured “medication–diagnosis” pairs for antidepressants from 35 325 patients between January 2010 and December 2015 were compared to the latest drug product labels and evidence assessments. Results

Of 140 929 antidepressant prescriptions prescribed by primary care providers (PCPs) and nonpsychiatry specialists, 69% were characterized as “on-label/evidence-based uses.” Depression diagnoses were associated …

Data Communication And Networking (Ksu), Meng Han, Lei Li, Zhigang Li, Svetana Peltsverger, Ming Yang, Guangzhi Zheng

Computer Science and Information Technology Grants Collections

This Grants Collection for Data Communication and Networking was created under a Round Ten ALG Textbook Transformation Grant.

Affordable Learning Georgia Grants Collections are intended to provide faculty with the frameworks to quickly implement or revise the same materials as a Textbook Transformation Grants team, along with the aims and lessons learned from project teams during the implementation process.

Documents are in .pdf format, with a separate .docx (Word) version available for download. Each collection contains the following materials:

• Linked Syllabus
• Initial Proposal
• Final Report

Poster: Privacy-Preserving Boosting With Random Linear Classifiers, Sagar Sharma, Keke Chen

Kno.e.sis Publications

We propose SecureBoost, a privacy-preserving predictive modeling framework, that allows service providers (SPs) to build powerful boosting models over encrypted or randomly masked user submit- ted data. SecureBoost uses random linear classifiers (RLCs) as the base classifiers. A Cryptographic Service Provider (CSP) manages keys and assists the SP’s processing to reduce the complexity of the protocol constructions. The SP learns only the base models (i.e., RLCs) and the CSP learns only the weights of the base models and a limited leakage function. This separated parameter holding avoids any party from abusing the final model or conducting model-based attacks. We evaluate …

Knowledge-Aware Multimodal Dialogue Systems, Lizi Liao, Yunshan Ma, Xiangnan He, Richang Hong, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

By offering a natural way for information seeking, multimodal dialogue systems are attracting increasing attention in several domains such as retail, travel etc. However, most existing dialogue systems are limited to textual modality, which cannot be easily extended to capture the rich semantics in visual modality such as product images. For example, in fashion domain, the visual appearance of clothes and matching styles play a crucial role in understanding the user's intention. Without considering these, the dialogue agent may fail to generate desirable responses for users. In this paper, we present a Knowledge-aware Multimodal Dialogue (KMD) model to address the …

Deep Understanding Of Cooking Procedure For Cross-Modal Recipe Retrieval, Jingjing Chen, Chong-Wah Ngo, Fu-Li Feng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Finding a right recipe that describes the cooking procedure for a dish from just one picture is inherently a difficult problem. Food preparation undergoes a complex process involving raw ingredients, utensils, cutting and cooking operations. This process gives clues to the multimedia presentation of a dish (e.g., taste, colour, shape). However, the description of the process is implicit, implying only the cause of dish presentation rather than the visual effect that can be vividly observed on a picture. Therefore, different from other cross-modal retrieval problems in the literature, recipe search requires the understanding of textually described procedure to predict its …

A Learning And Masking Approach To Secure Learning, Linh Nguyen, Sky Wang, Arunesh Sinha

Research Collection School Of Computing and Information Systems

Deep Neural Networks (DNNs) have been shown to be vulnerable against adversarial examples, which are data points cleverly constructed to fool the classifier. Such attacks can be devastating in practice, especially as DNNs are being applied to ever increasing critical tasks like image recognition in autonomous driving. In this paper, we introduce a new perspective on the problem. We do so by first defining robustness of a classifier to adversarial exploitation. Next, we show that the problem of adversarial example generation can be posed as learning problem. We also categorize attacks in literature into high and low perturbation attacks; well-known …

Influence Maximization On Social Graphs: A Survey, Yuchen Li, Ju Fan, Yanhao Wang, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

Influence Maximization (IM), which selects a set of k users (called seed set) from a social network to maximize the expected number of influenced users (called influence spread), is a key algorithmic problem in social influence analysis. Due to its immense application potential and enormous technical challenges, IM has been extensively studied in the past decade. In this paper, we survey and synthesize a wide spectrum of existing studies on IM from an algorithmic perspective, with a special focus on the following key aspects (1) a review of well-accepted diffusion models that capture information diffusion process and build the foundation …

Mini-Case Study Pedagogy: Experience From A Technical Course In An Information Systems Program, Venky Shankararaman, Swapna Gottipati, Alan Megargel

Research Collection School Of Computing and Information Systems

Case method is one approach that has been widely adopted in teaching many professions including law, medicine and business. The general practice is to ask students to read the case, usually four to eight pages long, prior to the session, and then spend the whole session discussing the case. However, for technology courses, the students are required to learn concepts and apply the musing hands-on software tools through lab sessions. Hence, rather than use long cases, that cover the entire session, we need to use mini-cases that are usually one to two pages long. This pedagogy approach allows the class …