Databases and Information Systems Commons^™

Learning Multiple Maps From Conditional Ordinal Triplets, Duy Dung Le, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Ordinal embedding seeks a low-dimensional representation of objects based on relative comparisons of their similarities. This low-dimensional representation lends itself to visualization on a Euclidean map. Classical assumptions admit only one valid aspect of similarity. However, there are increasing scenarios involving ordinal comparisons that inherently reflect multiple aspects of similarity, which would be better represented by multiple maps. We formulate this problem as conditional ordinal embedding, which learns a distinct low-dimensional representation conditioned on each aspect, yet allows collaboration across aspects via a shared representation. Our geometric approach is novel in its use of a shared spherical representation and multiple …

Improving Urban Crowd Flow Prediction On Flexible Region Partition, Xu Wang, Zimu Zhou, Yi Zhao, Xinglin Zhang, Kai Xing, Fu Xiao, Zheng Yang, Yunhao Liu

Research Collection School Of Computing and Information Systems

Accurate forecast of citywide crowd flows on flexible region partition benefits urban planning, traffic management, and public safety. Previous research either fails to capture the complex spatiotemporal dependencies of crowd flows or is restricted on grid region partition that loses semantic context. In this paper, we propose DeepFlowFlex, a graph-based model to jointly predict inflows and outflows for each region of arbitrary shape and size in a city. Analysis on cellular datasets covering 2.4 million users in China reveals dependencies and distinctive patterns of crowd flows in not only the conventional space and time domains, but also the speed domain, …

Topic Enhanced Word Embedding For Toxic Content Detection In Q&A Sites, Do Yeon Kim, Xiaohang Li, Sheng Wang, Yunying Zhuo, Ka Wei, Roy Lee

Research Collection School Of Computing and Information Systems

Increasingly, users are adopting community question-and-answer (Q&A) sites to exchange information. Detecting and eliminating toxic and divisive content in these Q&A sites are paramount tasks to ensure a safe and constructive environment for the users. Insincere question, which is founded upon false premises, is one type of toxic content in Q&A sites. In this paper, we proposed a novel deep learning framework enhanced pre-trained word embeddings with topical information for insincere question classification. We evaluated our proposed framework on a large real-world dataset from Quora Q&A site and showed that the topically enhanced word embedding is able to achieve better …

Industry 4.0: Challenges And Opportunities In Different Countries, Keng Siau, Yingrui Xi, Cui Zou

Research Collection School Of Computing and Information Systems

Along with the rapid development of artificial intelligence (AI), cyber-physical systems (CPSs), big data analytics, and cloud computing, Industry 4.0 — a subset of the fourth Industrial Revolution — has started to emerge and take root in many countries. Many expect that Industry 4.0 will be transformative and revolutionary for multiple industries and countries. Its impact will be much more significant than those of Industry 1.0, 2.0, and 3.0. Most studies and papers on Industry 4.0 have examined its impact on various industries, jobs, and organizations. In this article, we investigate the impact of Industry 4.0 on countries and groups …

Shared Dynamic Data Audit Supporting Anonymous User Revocation In Cloud Storage, Yinghui Zhang, Chen Chen, Dong Zheng, Rui Guo, Shengmin Xu

Research Collection School Of Computing and Information Systems

Collusion between revoked users and cloud service providers can pose a threat to the security of cloud storage data. If the original legitimate users cannot be revoked securely, it will lead to the leakage of shared data, thus affecting the security of cloud storage. In this paper, we combine vector commitment and anonymous revocation of group signature to propose an integrity audit scheme for cloud storage data that can support data modification. The anonymity of the group signature ensures that users’ privacy information will not be snooped by the server. The proposed scheme supports the dynamic operation of stored data …

Designing The Arriving Refugee Informatics Surveillance And Epidemiology (Arive) System: A Web-Based Electronic Database For Epidemiological Surveillance, William A. Mattingly, Ruth M. Carrico, Timothy L. Wiemken, Robert R. Kelley, Rebecca A. Ford, Rahel Bosson, Kimberley A. Buckner, Julio A. Ramirez

Journal of Refugee & Global Health

Objectives: We design and implement the Arriving Refugee Informatics surVeillance and Epidemiology (ARIVE) system to improve the health of refugees undergoing resettlement and enhance existing health surveillance networks.

Materials and Methods: Using the REDCap electronic data capture software as a basis we create a refugee health database incorporating data from the Center for Disease Control and Prevention’s Electronic Disease Notification (EDN) system and domestic screening data from refugee health care providers.

Results: Domestic screening and EDN refugee health data have been integrated for 13,824 refugees resettled from 35 different countries into the state of Kentucky from the years 2013-2016.

Discussion: …

The Rise Of Citizen Science In Health And Biomedical Research, Andrea Wiggins, John Wilbanks

Information Systems and Quantitative Analysis Faculty Publications

Citizen science models of public participation in scientific research represent a growing area of opportunity for health and biomedical research, as well as new impetus for more collaborative forms of engagement in large-scale research. However, this also surfaces a variety of ethical issues that both fall outside of and build upon the standard human subjects concerns in bioethics. This article provides background on citizen science, examples of current projects in the field, and discussion of established and emerging ethical issues for citizen science in health and biomedical research.

Mathematical And Computer Simulation Of The Processes Of Two-Phase Joint Gas Filtration And Water In A Porous Environment, Elmira Nazirova

Bulletin of TUIT: Management and Communication Technologies

A mathematical model, methods and algorithms for the numerical solution of problems of joint gas-water filtration in porous media are considered. The mathematical model of the process of non-stationary joint gas-water filtration in a porous medium is described by a system of nonlinear differential equations of parabolic type. In the numerical solution of the boundary value problem of gas displacement by water in a porous medium, the differential sweeping method is used for systems of differential-difference equations. The system of differential-difference equations with respect to the gas pressure function is nonlinear, therefore, an iterative method is used for it, based …

Iamhappy: Towards An Iot Knowledge-Based Cross-Domain Well-Being Recommendation System For Everyday Happiness, Amelia Gyrard, Amit Sheth

Kno.e.sis Publications

Nowadays, healthy lifestyle, fitness, and diet habits have become central applications in our daily life. Positive psychology such as well-being and happiness is the ultimate dream of everyday people’s feelings (even without being aware of it). Wearable devices are being increasingly employed to support well-being and fitness. Those devices produce physiological signals that are analyzed by machines to understand emotions and physical state. The Internetof Things (IoT) technology connects (wearable) devices to the Internet to easily access and process data, even using Web technologies (aka Web of Things).

We design IAMHAPPY, an innovative IoT-based well-being recommendation system to encourage every …

An Architecture For Blockchain-Based Collaborative Signature-Based Intrusion Detection System, Daniel Laufenberg

Master of Science in Information Technology Theses

Collaborative intrusion detection system (CIDS), where IDS hosts work with each other and share resources, have been proposed to cope with the increasingly sophisticated cyberattacks. Despite the promising benefits such as expanded signature databases and alert data from multiple sites, trust management and consensus building remain as challenges for a CIDS to work effectively. The blockchain technology with built-in immutability and consensus building capability provides a viable solution to the issues of CIDS. In this paper, we introduce an architecture for a blockchain-enabled signature-based collaborative IDS, discuss the implementation strategy of the proposed architecture and developed a prototype using Hyperledger …

An Exploration Of Turkish Kindergarten Early Career Stage Teachers’ Technology Beliefs And Practices, Ozge Ozel

USF Tampa Graduate Theses and Dissertations

The purpose of this study was to explore Turkish kindergarten early career stage teachers’ self-efficacy beliefs towards technology and their technology integration practices in their classrooms by answering: What are self-efficacy beliefs of Turkish kindergarten early career stage teachers towards technology? How do Turkish kindergarten early career teachers integrate technology into their classrooms’ instructions? The study was designed as a qualitative multiple case study and guided by Bandura’s (1986) social cognitive theory and Mishra and Koehler’s (2006) TPACK conceptual framework. I conducted this study in Istanbul, where is the most crowded and metropolitan city in Turkey. The schools were chosen …

Social Media Text Mining Framework For Drug Abuse: An Opioid Crisis Case Analysis, Tareq Nasralah

Masters Theses & Doctoral Dissertations

Social media is considered as a promising and viable source of data for gaining insights into various disease conditions, patients’ attitudes and behaviors, and medications. The daily use of social media provides new opportunities for analyzing several aspects of communication. Social media as a big data source can be used to recognize communication and behavioral themes of problematic use of prescription drugs. Mining and analyzing such media have challenges and limitations with respect to topic deduction and data quality. There is a need for a structured approach to efficiently and effectively analyze social media content related to drug abuse in …

Interpretable Fashion Matching With Rich Attributes, Xun Yang, Xiangnan He, Xiang Wang, Yunshan Ma, Fuli Feng, Meng Wang, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Understanding the mix-and-match relationships of fashion items receives increasing attention in fashion industry. Existing methods have primarily utilized the visual content to learn the visual compatibility and performed matching in a latent space. Despite their effectiveness, these methods work like a black box and cannot reveal the reasons that two items match well. The rich attributes associated with fashion items, e.g.,off-shoulder dress and black skinny jean, which describe the semantics of items in a human-interpretable way, have largely been ignored.This work tackles the interpretable fashion matching task, aiming to inject interpretability into the compatibility modeling of items. Specifically, given a …

Searching Activity Trajectories With Semantics, Li-Hua Yin, Huiwen Liu

PhD Student’s Publications Collection

With the widespread use of smart phones and mobile Internet, social network users have generated massive geo-tagged tweets, photos and videos to form lots of informative trajectories which reveal not only their spatio-temporal dynamics, but also their activities in the physical world. Existing spatial trajectory query studies mainly focus on analyzing the spatio-temporal properties of the users’ trajectories, while leaving the understanding of their activities largely untouched. In this paper, we incorporate the semantics of the activity information embedded in trajectories into query modelling and processing, with the aim of providing end users more informative and meaningful results. To this …

Early Information Access To Alleviate Emergency Department Congestion, Anjee Gorkhali

Theses and Dissertations in Business Administration

Alleviating Emergency Department (ED) congestion results in shorter hospital stay which not only reduces the cost of medical procedure but also increase the hospital performance. Length of patient stay is used to determine the hospital performance. Organization Information Processing (OIPT) Theory is used to explain the impact of information access and availability on the information processing need and ability of a hospital. Technical devices such as RFID that works as “Auto Identification tags” is suggested to increase the information availability as well as the information processing capability of the hospitals. This study suggests that the OIPT needs to be further …

Data-Driven Database Education: A Quantitative Study Of Sql Learning In An Introductory Database Course, Andrew C. Von Dollen

Master's Theses

The Structured Query Language (SQL) is widely used and challenging to master. Within the context of lab exercises in an introductory database course, this thesis analyzes the student learning process and seeks to answer the question: ``Which SQL concepts, or concept combinations, trouble students the most?'' We provide comprehensive taxonomies of SQL concepts and errors, identify common areas of student misunderstanding, and investigate the student problem-solving process. We present an interactive web application used by students to complete SQL lab exercises. In addition, we analyze data collected by this application and we offer suggestions for improvement to database lab activities.

Compositional Coding For Collaborative Filtering, Chenghao Liu, Tao Lu, Xin Wang, Zhiyong Cheng, Jianling Sun, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Efficiency is crucial to the online recommender systems, especially for the ones which needs to deal with tens of millions of users and items. Because representing users and items as binary vectors for Collaborative Filtering (CF) can achieve fast user-item affinity computation in the Hamming space, in recent years, we have witnessed an emerging research effort in exploiting binary hashing techniques for CF methods. However, CF with binary codes naturally suffers from low accuracy due to limited representation capability in each bit, which impedes it from modeling complex structure of the data. In this work, we attempt to improve the …

Securing Messaging Services Through Efficient Signcryption With Designated Equality Test, Yujue Wang, Hwee Hwa Pang, Robert H. Deng, Yong Ding, Qianhong Wu, Bo Qin

Research Collection School Of Computing and Information Systems

To address security and privacy issues in messaging services, we present a public key signcryption scheme with designated equality test on ciphertexts (PKS-DET) in this paper. The scheme enables a sender to simultaneously encrypt and sign (signcrypt) messages, and to designate a tester to perform equality test on ciphertexts, i.e., to determine whether two ciphertexts signcrypt the same underlying plaintext message. We introduce the PKS-DET framework, present a concrete construction and formally prove its security against three types of adversaries, representing two security requirements on message confidentiality against outsiders and the designated tester, respectively, and a requirement on message unforgeability …

Redpc: A Residual Error-Based Density Peak Clustering Algorithm, Milan Parmar, Di Wang, Xiaofeng Zhang, Ah-Hwee Tan, Chunyan Miao, You Zhou

Research Collection School Of Computing and Information Systems

The density peak clustering (DPC) algorithm was designed to identify arbitrary-shaped clusters by finding density peaks in the underlying dataset. Due to its aptitudes of relatively low computational complexity and a small number of control parameters in use, DPC soon became widely adopted. However, because DPC takes the entire data space into consideration during the computation of local density, which is then used to generate a decision graph for the identification of cluster centroids, DPC may face difficulty in differentiating overlapping clusters and in dealing with low-density data points. In this paper, we propose a residual error-based density peak clustering …

Multi-Channel Graph Neural Network For Entity Alignment, Yixin Cao, Zhiyuan Liu, Chengjiang Li, Zhiyuan Liu, Juanzi Li, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Entity alignment typically suffers from the issues of structural heterogeneity and limited seed alignments. In this paper, we propose a novel Multi-channel Graph Neural Network model (MuGNN) to learn alignment-oriented knowledge graph (KG) embeddings by robustly encoding two KGs via multiple channels. Each channel encodes KGs via different relation weighting schemes with respect to self-attention towards KG completion and cross-KG attention for pruning exclusive entities respectively, which are further combined via pooling techniques. Moreover, we also infer and transfer rule knowledge for completing two KGs consistently. MuGNN is expected to reconcile the structural differences of two KGs, and thus make …

Model And Analysis Of Labor Supply For Ride-Sharing Platforms In The Presence Of Sample Self-Selection And Endogeneity, Hao Sun, Hai Wang, Zhixi Wan

Research Collection School Of Computing and Information Systems

With the popularization of ride-sharing services, drivers working as freelancers on ride-sharing platforms can design their schedules flexibly. They make daily decisions regard- ing whether to participate in work, and if so, how many hours to work. Factors such as hourly income rate affect both the participation decision and working-hour decision, and evaluation of the impacts of hourly income rate on labor supply becomes important. In this paper, we propose an econometric framework with closed-form measures to estimate both the participation elasticity (i.e., extensive margin elasticity) and working-hour elasticity (i.e., intensive margin elasticity) of labor supply. We model the sample …

Stochastic Gradient Hamiltonian Monte Carlo With Variance Reduction For Bayesian Inference, Zhize Li, Tianyi Zhang, Shuyu Cheng, Jun Zhu, Jian Li

Research Collection School Of Computing and Information Systems

Gradient-based Monte Carlo sampling algorithms, like Langevin dynamics and Hamiltonian Monte Carlo, are important methods for Bayesian inference. In large-scale settings, full-gradients are not affordable and thus stochastic gradients evaluated on mini-batches are used as a replacement. In order to reduce the high variance of noisy stochastic gradients, Dubey et al. (in: Advances in neural information processing systems, pp 1154–1162, 2016) applied the standard variance reduction technique on stochastic gradient Langevin dynamics and obtained both theoretical and experimental improvements. In this paper, we apply the variance reduction tricks on Hamiltonian Monte Carlo and achieve better theoretical convergence results compared with …

One-Class Order Embedding For Dependency Relation Prediction, Meng-Fen Chiang, Ee-Peng Lim, Wang-Chien Lee, Xavier Jayaraj Siddarth Ashok, Philips Kokoh Prasetyo

Research Collection School Of Computing and Information Systems

Learning the dependency relations among entities and the hierarchy formed by these relations by mapping entities into some order embedding space can effectively enable several important applications, including knowledge base completion and prerequisite relations prediction. Nevertheless, it is very challenging to learn a good order embedding due to the existence of partial ordering and missing relations in the observed data. Moreover, most application scenarios do not provide non-trivial negative dependency relation instances. We therefore propose a framework that performs dependency relation prediction by exploring both rich semantic and hierarchical structure information in the data. In particular, we propose several negative …

An Intelligent Platform With Automatic Assessment And Engagement Features For Active Online Discussions, Michelle L. F. Cheong, Yun-Chen Chen, Bing Tian Dai

Research Collection School Of Computing and Information Systems

In a universitycontext, discussion forums are mostly available in Learning and ManagementSystems (LMS) but are often ineffective in encouraging participation due topoorly designed user interface and the lack of motivating factors toparticipate. Our integrated platform with the Telegram mobile app and aweb-based forum, is capable of automatic thoughtfulness assessment of questionsand answers posted, using text mining and Natural Language Processing (NLP)methodologies. We trained and applied the Random Forest algorithm to provideinstant thoughtfulness score prediction for the new posts contributed by thestudents, and prompted the students to improve on their posts, thereby invokingdeeper thinking resulting in better quality contributions. In addition, …

A Scalable Approach To Joint Cyber Insurance And Security-As-A-Service Provisioning In Cloud Computing, Jonathan David Chase, Dusit Niyato, Ping Wang, Sivadon Chaisiri, Ryan K. L. Ko

Research Collection School Of Computing and Information Systems

As computing services are increasingly cloud-based, corporations are investing in cloud-based security measures. The Security-as-a-Service (SECaaS) paradigm allows customers to outsource security to the cloud, through the payment of a subscription fee. However, no security system is bulletproof, and even one successful attack can result in the loss of data and revenue worth millions of dollars. To guard against this eventuality, customers may also purchase cyber insurance to receive recompense in the case of loss. To achieve cost effectiveness, it is necessary to balance provisioning of security and insurance, even when future costs and risks are uncertain. To this end, …

Personalized Fashion Recommendation With Visual Explanations Based On Multimodal Attention Network: Towards Visually Explainable Recommendation, Xu Chen, Hanxiong Chen, Hongteng Xu, Yongfeng Zhang, Yixin Cao, Zheng Qin, Hongyuan Zha

Research Collection School Of Computing and Information Systems

Fashion recommendation has attracted increasing attention from both industry and academic communities. This paper proposes a novel neural architecture for fashion recommendation based on both image region-level features and user review information. Our basic intuition is that: for a fashion image, not all the regions are equally important for the users, i.e., people usually care about a few parts of the fashion image. To model such human sense, we learn an attention model over many pre-segmented image regions, based on which we can understand where a user is really interested in on the image, and correspondingly, represent the image in …

Seven Hci Grand Challenges, C. Stephanidis, G. Salvendy, M. Antona, J. Chen, J. Dong, V. Duffy, X. Fang, C. Fidopiastis, G. Fragomeni, L. Fu, Y. Guo, D. Harris, A. Ioannou, K. Jeong, Keng Siau, H. Krömker, M. Kurosu, J.R. Lewis, A. Marcus, G. Meiselwitz

Research Collection School Of Computing and Information Systems

This article aims to investigate the Grand Challenges which arise in the current and emerging landscape of rapid technological evolution towards more intelligent interactive technologies, coupled with increased and widened societal needs, as well as individual and collective expectations that HCI, as a discipline, is called upon to address. A perspective oriented to humane and social values is adopted, formulating the challenges in terms of the impact of emerging intelligent interactive technologies on human life both at the individual and societal levels. Seven Grand Challenges are identified and presented in this article: Human-Technology Symbiosis; Human-Environment Interactions; Ethics, Privacy and Security; …

Use Of Mental Models And Cognitive Maps To Understand Students’ Learning Challenges, Zixing Shen, Songxin Tan, Keng Siau

Research Collection School Of Computing and Information Systems

Mental models and cognitive maps have been used in college business education as an instructional design technique, assessment tool, and learning strategy. The authors propose a novel use of mental models and cognitive maps as a device to elicit students’ challenges in learning the domain knowledge of a course. Such usage is illustrated in a management information systems course. This student-focused approach can help instructor to better understand students’ learning challenges and enhance teaching effectiveness.

Outcasts – In Search Of Identity, Syed Hasan Haider

MSJ Capstone Projects

The idea for this documentary came from a story published in the express tribune which talked about the people who are unable to vote in 2018 elections due to having Computerized National Identity Cards (CNICs) in the Ibrahim Hyderi locality in Karachi.

Not having a CNIC in Pakistan means that you are not able to participate in civic life and also not subscribe to basic facilitates like housing, water, gas and employment.

This documentary film looks at different cases and through the experience of some journalists what it is like to live as an undocumented citizen. The film also explores …

Research Methodology Adopted In Developing Tansse-L System, Ellen Kalinga

Tanzania Journal of Engineering and Technology (TJET)

Research methodology is among the very important part in a research work. It is the heart which describe the activities necessary for the completion of the research work. Research methodology provides a plan of investigation considered to obtain answers to research problems and it depends on the context of application. This paper presents the research methodology adopted when developing Tanzania Secondary Schools e-Learning (TanSSe-L) system, a learning management system (LMS) which was successfully developed through customization of Moodle open source LMS. TanSSe-L system is a context centered platform for secondary schools in Tanzania. TanSSe-L system was developed using a number …