Databases and Information Systems Commons^™

Rumor Detection On Twitter With Tree-Structured Recursive Neural Networks, Jing Ma, Wei Gao, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Sentiment expression in microblog posts can be affected by user’s personal character, opinion bias, political stance and so on. Most of existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning. We observed that microblog users have consistent individuality and opinion bias in different languages. Based on this observation, in this paper we propose a novel user-attention-based Convolutional Neural Network (CNN) model with adversarial cross-lingual learning framework. The user attention mechanism is leveraged in CNN model to capture user’s language-specific individuality from the posts. Then the attention-based CNN model is incorporated into a novel …

Is Information Systems Misuse Always Bad? A New Perspective On Is Misuse In Hospitals Under The Context Of Disasters, Dheyaaldin Alsalman

Masters Theses & Doctoral Dissertations

Although the extant literature has investigated how individuals engage in inappropriate behaviors based on the rational choice theory (RCT) (e.g., computer misconduct), the neutralization theory (e.g., IS security policies violation), and workarounds under normal situations, it has given little consideration to how individuals are involved in misuse of information systems with a good intention under the context of disasters. To fill this research gap, we propose a selfless misuse model, which offers a theoretical explanation for the concept of individuals’ selfless misuse intention under uncertainty caused by disasters. In this study, we show why employees make decisions to misuse the …

Hybrid Recommender For Online Petitions With Social Network And Psycholinguistic Features, Ahmed Elnoshokaty

Masters Theses & Doctoral Dissertations

The online petition has become one of the most important channels of civic participation. Most of the state-of-the-art online platforms, however, tend to use simple indicators (such as popularity) to rank petitions, hence creating a situation where the most popular petitions dominate the rank and attract most people’s attention. For the petitions which focus on specific issues, they are often in a disadvantageous position on the list. For example, a petition for local environment problem may not be seen by many people who are really concerned with it, simply because it takes multiple pages to reach it. Therefore, the simple …

Online Deep Learning: Learning Deep Neural Networks On The Fly, Doyen Sahoo, Hong Quang Pham, Jing Lu, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Deep Neural Networks (DNNs) are typically trained by backpropagation in a batch setting, requiring the entire training data to be made available prior to the learning task. This is not scalable for many real-world scenarios where new data arrives sequentially in a stream. We aim to address an open challenge of “Online Deep Learning” (ODL) for learning DNNs on the fly in an online setting. Unlike traditional online learning that often optimizes some convex objective function with respect to a shallow model (e.g., a linear/kernel-based hypothesis), ODL is more challenging as the optimization objective is non-convex, and regular DNN with …

Searching For The X-Factor: Exploring Corpus Subjectivity For Word Embeddings, Maksim Tkachenko, Chong Cher Chia, Hady W. Lauw

Research Collection School Of Computing and Information Systems

We explore the notion of subjectivity, and hypothesize that word embeddings learnt from input corpora of varying levels of subjectivity behave differently on natural language processing tasks such as classifying a sentence by sentiment, subjectivity, or topic. Through systematic comparative analyses, we establish this to be the case indeed. Moreover, based on the discovery of the outsized role that sentiment words play on subjectivity-sensitive tasks such as sentiment classification, we develop a novel word embedding SentiVec which is infused with sentiment information from a lexical resource, and is shown to outperform baselines on such tasks.

Striving To Earn More: A Survey Of Work Strategies And Tool Use Among Crowd Workers, Toni Kaplan, Susumu Saito, Kotaro Hara, Jeffrey P. Bigham

Research Collection School Of Computing and Information Systems

Earning money is a primary motivation for workers on Amazon Mechanical Turk, but earning a good wage is difficult because work that pays well is not easily identified and can be time-consuming to find. We explored the strategies that both low- and high-earning workers use to find and complete tasks via a survey of 360 workers. Nearly all workers surveyed had earning money as their primary goal, and workers used many of the same tools (browser extensions and scripts) and strategies in an attempt to earn more money, regardless of earning level. However, high-earning workers used more tools, were more …

Knowledge-Aware Attentive Neural Network For Ranking Question Answer Pairs, Ying Shen, Yang Deng, Min Yang, Yaliang Li, Nan Du, Wei Fan, Kai Lei

Research Collection School Of Computing and Information Systems

Ranking question answer pairs has attracted increasing attention recently due to its broad applications such as information retrieval and question answering (QA). Significant progresses have been made by deep neural networks. However, background information and hidden relations beyond the context, which play crucial roles in human text comprehension, have received little attention in recent deep neural networks that achieve the state of the art in ranking QA pairs. In the paper, we propose KABLSTM, a Knowledge-aware Attentive Bidirectional Long Short-Term Memory, which leverages external knowledge from knowledge graphs (KG) to enrich the representational learning of QA sentences. Specifically, we develop …

Probabilistic Guided Exploration For Reinforcement Learning In Self-Organizing Neural Networks, Peng Wang, Weigui Jair Zhou, Di Wang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Exploration is essential in reinforcement learning, which expands the search space of potential solutions to a given problem for performance evaluations. Specifically, carefully designed exploration strategy may help the agent learn faster by taking the advantage of what it has learned previously. However, many reinforcement learning mechanisms still adopt simple exploration strategies, which select actions in a pure random manner among all the feasible actions. In this paper, we propose novel mechanisms to improve the existing knowledgebased exploration strategy based on a probabilistic guided approach to select actions. We conduct extensive experiments in a Minefield navigation simulator and the results …

Analysis Of Public Transportation Patterns In A Densely Populated City With Station-Based Shared Bikes, Di Wang, Evan Wu, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Densely populated cities face great challenges of high transportation demand and limited physical space. Thus, in these cities, the public transportation system is heavily relied on. Conventional public transportation modes such as bus, taxi and subway have been globally deployed over the past century. In the last decade, a new type of public transportation mode, shared bike, emerged in many cities. These shared bikes are deployed by either government-regulated or profit-driven companies and are either station-based or station-less. Nonetheless, all of them are designed to better solve the last-mile problem in densely populated cities as complements to the conventional public …

Autonomous Agents In Snake Game Via Deep Reinforcement Learning, Zhepei Wei, Di Wang, Ming Zhang, Ah-Hwee Tan, Chunyan Miao, You Zhou

Research Collection School Of Computing and Information Systems

Since DeepMind pioneered a deep reinforcement learning (DRL) model to play the Atari games, DRL has become a commonly adopted method to enable the agents to learn complex control policies in various video games. However, similar approaches may still need to be improved when applied to more challenging scenarios, where reward signals are sparse and delayed. In this paper, we develop a refined DRL model to enable our autonomous agent to play the classical Snake Game, whose constraint gets stricter as the game progresses. Specifically, we employ a convolutional neural network (CNN) trained with a variant of Q-learning. Moreover, we …

Role Of Social Media In Public Accounting Firms, Brenda Eschenbrenner, Fiona Fui-Hoon Nah, Zhiwei Lu

Research Collection School Of Computing and Information Systems

Social media has been widely used for both professional and personal communications. Businesses recognize the importance of social media and are using them to fulfill various business objectives. In this paper, we focus on analyzing the business objectives of public accounting firms that have both a firm-wide main page and a career page on Facebook. More specifically, we compare the business objectives they are achieving with their firm-wide main pages versus career pages. We not only find differences in the objectives that are being achieved, but also identify other objectives that are not actively being pursued on either page but …

Effect Of Gamification On Intrinsic Motivation, Edna Chan, Fiona Fui-Hoon Nah, Qizhang Liu, Zhiwei Lu

Research Collection School Of Computing and Information Systems

Gamification has been increasing in popularity in a variety of online context, including online learning. However, its impact on intrinsic motivation is still unclear. In this research, we carried out an experiment to assess the impact of providing two gamification features in an online learning system – point and leaderboard – on intrinsic motivation.

An Effectual Approach For The Development Of Novel Applications On Digital Platforms, Onkar Shamrao Malgonde

USF Tampa Graduate Theses and Dissertations

The development of novel software applications on digital platforms differs from traditional software development and provides unique challenges to the software development manager and team. Application producers must achieve application-platform match, application-market match, value propositions exceeding platform’s core value propositions, and novelty. These desired properties support a new vision of the software development team as entrepreneurs with a goal of developing novel applications on digital platforms. Digital platforms are characterized by an uncertain, risky, and resource-constrained environment, where existing approaches—plan-driven, ad-hoc, and controlled-flexible—have limited applicability. Building on the theoretical basis of the theory of effectuation from the entrepreneurship domain, this …

Querying Large Databases, Nathan Beneke

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

This paper investigates two approaches to improving query times on large relational databases. The first technique capitalizes on the knowledge of a database's structures and properties one typically has. This technique can execute some queries exactly in a constant, bounded amount of time. When this technique cannot be used to exactly execute a query we show how it can still be used to drastically lower the run-time on the query while getting a good approximation of the exact result. We also discuss the complexity of deciding whether a query is evaluable in this way, both theoretically and practically. The second …

Tanzanian Adolescents In The Digital Age Of Cell Phones And The Internet: Access, Use And Risks, Hezron Zacharia Onditi

Journal of Humanities and Social Sciences

This study explored cell phones and internet access, use, and potential risks among Tanzanian secondary school adolescents. A total of 778 students aged 14-18 in Form I to Form IV responded to a self-report questionnaire, and a subset of 20 participants participated in semi-structured interviews. Results revealed a remarkable uptake of cell phones and internet technologies among Tanzanian adolescents. In particular, whereas about 50% of the students reported to own cell phones (nearly 60% own simcards), 76% admitted using cell phones at home, and 86% reported to connect to the Internet. Results showed that male and older adolescents seem to …

Deaddrop: Message Passing Without Metadata Leakage, Davis Mike Arndt

Computer Science and Software Engineering

Even when network data is encrypted, observers can make inferences about content based on collected metadata. DeadDrop is an exploratory API designed to protect the metadata of a conversation from both outside observers and the facilitating server. To do so, DeadDrop servers are passed no recipient address, instead relying upon the recipient to check for messages of their own volition. In addition, the recipient downloads a copy of every encrypted message on the server to prevent even the server from knowing to whom each message is intended. To these purposes, DeadDrop is mostly successful. However, it does not obscure all …

Augmented Personalized Health: Using Semantically Integrated Multimodal Data For Patient Empowered Health Management Strategies, Amit P. Sheth, Hong Y. Yip, Utkarshani Jaimini, Dipesh Kadariya, Vaikunth Sridharan, R. Venkataramanan, Tanvi Banerjee, Krishnaprasad Thirunarayan, Maninder Kalra

Kno.e.sis Publications

Healthcare as we know it is in the process of going through a massive change from:

1. Episodic to continuous

2. Disease-focused to wellness and quality of life focused

3. Clinic-centric to anywhere a patient is

4. Clinician controlled to patient empowered

5. Being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data-driven URL: https://mhealth.md2k.org/2018-tech-showcase-home

The Effect Of Endgame Tablebases On Modern Chess Engines, Christopher D. Peterson

Computer Engineering

Modern chess engines have the ability to augment their evaluation by using massive tables containing billions of positions and their memorized solutions. This report examines the importance of these tables to better understand the circumstances under which they should be used. The analysis conducted in this paper empirically examines differences in size and speed of memorized positions and their impacts on engine strength. Using this technique, situations where memorized tables improve play (and situations where they do not) are discovered.

The Wider Impact Of A National Cryptocurrency, Dennis Ng, Paul Griffin

Research Collection Lee Kong Chian School Of Business

This study looks at the impact of a national cryptocurrency on the payment landscape in the midst of the rise of globalpublic cryptocurrencies and interest from central banks in a possible national cryptocurrency. The impacts are analysed for consumers, merchants, banks,payment providers, international money transfer operators and central banks.The study analyses the pros and cons for each player with an overall impactranking. There is a particular emphasis on central banks as they hold key regulatory oversight for economic and financial matters affecting a country.Whilst finding that there is an overall benefit, there are also significant risks. A sandbox approach is …

Region-Aware Reflection Removal With Unified Content And Gradient Priors, Renjie Wan, Boxin Shi, Ling-Yu Duan, Ah-Hwee Tan, Wen Gao, Alex C. Kot

Research Collection School Of Computing and Information Systems

Removing the undesired reflections in images taken through the glass is of broad application to various image processing and computer vision tasks. Existing single image-based solutions heavily rely on scene priors such as separable sparse gradients caused by different levels of blur, and they are fragile when such priors are not observed. In this paper, we notice that strong reflections usually dominant a limited region in the whole image, and propose a region-aware reflection removal approach by automatically detecting and heterogeneously processing regions with and without reflections. We integrate content and gradient priors to jointly achieve missing contents restoration, as …