Databases and Information Systems Commons^™

Lifetime Lexical Variation In Social Media, Lizi Liao, Jing Jiang, Ying Ding, Heyan Huang, Ee Peng Lim

Research Collection School Of Computing and Information Systems

As the rapid growth of online social media attracts a large number of Internet users, the large volume of content generated by these users also provides us with an opportunity to study the lexical variation of people of different ages. In this paper, we present a latent variable model that jointly models the lexical content of tweets and Twitter users’ ages. Our model inherently assumes that a topic has not only a word distribution but also an age distribution. We propose a Gibbs-EM algorithm to perform inference on our model. Empirical evaluation shows that our model can learn meaningful age-specific …

Fsph: Fitted Spectral Hashing For Efficient Similarity Search, Yong-Dong Zhang, Yu Wang, Sheng Tang, Steven C. H. Hoi, Jin-Tao Li

Research Collection School Of Computing and Information Systems

Spectral hashing (SpH) is an efficient and simple binary hashing method, which assumes that data are sampled from a multidimensional uniform distribution. However, this assumption is too restrictive in practice. In this paper we propose an improved method, fitted spectral hashing (FSpH), to relax this distribution assumption. Our work is based on the fact that one-dimensional data of any distribution could be mapped to a uniform distribution without changing the local neighbor relations among data items. We have found that this mapping on each PCA direction has certain regular pattern, and could be fitted well by S-curve function (Sigmoid function). …

Fsph: Fitted Spectral Hashing For Efficient Similarity Search, Yong-Dong Zhang, Yu Wang, Sheng Tang, Steven C. H. Hoi, Jin-Tao Li

Research Collection School Of Computing and Information Systems

Spectral hashing (SpH) is an efficient and simple binary hashing method, which assumes that data are sampled from a multidimensional uniform distribution. However, this assumption is too restrictive in practice. In this paper we propose an improved method, fitted spectral hashing (FSpH), to relax this distribution assumption. Our work is based on the fact that one-dimensional data of any distribution could be mapped to a uniform distribution without changing the local neighbor relations among data items. We have found that this mapping on each PCA direction has certain regular pattern, and could be fitted well by S-curve function (Sigmoid function). …

On Predicting Religion Labels In Microblogging Networks, Minh Thap Nguyen, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Religious belief plays an important role in how people behave, influencing how they form preferences, interpret events around them, and develop relationships with others. Traditionally, the religion labels of user population are obtained by conducting a large scale census study. Such an approach is both high cost and time consuming. In this paper, we study the problem of predicting users' religion labels using their microblogging data. We formulate religion label prediction as a classification task, and identify content, structure and aggregate features considering their self and social variants for representing a user. We introduce the notion of representative user to …

Board Interlock Networks And The Use Of Relative Performance Evaluation, Qian Hao, Nan Hu, Ling Liu, Lee J. Yao

Research Collection School Of Computing and Information Systems

Purpose - The purpose of this paper is to explore how networks of boards of directors affect relative performance evaluation (RPE) in chief executive officer (CEO) compensation. Design/methodology/approach - In this study, the authors propose that an interlocking network is an important inter-corporate setting, which has a bearing on whether boards decide to use RPE in CEO compensation. They adopt four typical graph measures to depict the centrality/position of each board in the interlock network: degree, betweenness, eigenvector and closeness, and study their impacts on RPE use. Findings - The authors find that firms that have more connected board members …

Integrating Self-Organizing Neural Network And Motivated Learning For Coordinated Multi-Agent Reinforcement Learning In Multi-Stage Stochastic Game, Teck-Hou Teng, Ah-Hwee Tan, Janusz A. Starzyk, Yuan-Sin Tan, Loo-Nin Teow

Research Collection School Of Computing and Information Systems

Most non-trivial problems require the coordinated performance of multiple goal-oriented and time-critical tasks. Coordinating the performance of the tasks is required due to the dependencies among the tasks and the sharing of resources. In this work, an agent learns to perform a task using reinforcement learning with a self-organizing neural network as the function approximator. We propose a novel coordination strategy integrating Motivated Learning (ML) and a self-organizing neural network for multi-agent reinforcement learning (MARL). Specifically, we adapt the ML idea of using pain signal to overcome the resource competition issue. Dependency among the agents is resolved using domain knowledge …

Cenknn: A Scalable And Effective Text Classifier, Guansong Pang, Huidong Jin, Shengyi Jiang

Research Collection School Of Computing and Information Systems

A big challenge in text classification is to perform classification on a large-scale and high-dimensional text corpus in the presence of imbalanced class distributions and a large number of irrelevant or noisy term features. A number of techniques have been proposed to handle this challenge with varying degrees of success. In this paper, by combining the strengths of two widely used text classification techniques, K-Nearest-Neighbor (KNN) and centroid based (Centroid) classifiers, we propose a scalable and effective flat classifier, called CenKNN, to cope with this challenge. CenKNN projects high-dimensional (often hundreds of thousands) documents into a low-dimensional (normally a few …

A Retail Bank's Bpm Experience, Shankararaman, Venky, Gottipati Swapna, Randall E. Duran

Research Collection School Of Computing and Information Systems

This real-life case study, which was undertaken by a leading financial services group in the Asia-Pacific region, is used to demonstrate the innovative use of BPM (Business Process Management) technology in a competitive business area. It describes how a BPM project, within the Application Verification and Capture (AVC), was conceived, designed and implemented in order to deliver strategic value to the organization. Hereafter, the financial services group will be referred to as “the bank”. The AVC project was targeted at one of the bank's processes called the Application Verification and Capture (AVC) process for unit trust products. This process involved …

Assisting Coordination During Crisis: A Domain Ontology Based Approach To Infer Resource Needs From Tweets, Shreyansh Bhatt, Hemant Purohit, Andrew J. Hampton, Valerie L. Shalin, Amit P. Sheth, John Flach

Kno.e.sis Publications

Ubiquitous social media during crises provides citizen reports on the situation, needs and supplies. Previous research extracts resource needs directly from the text (e.g. "Power cut to Coney Island and Brighton beach" indicates a power need). This approach assumes that citizens derive and write about specific needs from their observations, properly specified for the emergency response system, an assumption that is not consistent with general conversational behavior. In our study, Twitter messages (tweets) from Hurricane Sandy in 2012 clearly indicate power blackouts, but not their probable implications (e.g. loss of power to hospital life support systems). We use a domain …

From Media Reporting To International Relations: A Case Study Of Asia-Pacific Economic Cooperation (Apec), Chun-Hua Tsai, Yu-Ru Lin

Information Systems and Quantitative Analysis Faculty Proceedings & Presentations

This paper reviews logical approaches and challenges raised for explaining AI. We discuss the issues of presenting explanations as accurate computational models that users cannot understand or use. Then, we introduce pragmatic approaches that consider explanation a sort of speech act that commits to felicity conditions, including intelligibility, trustworthiness, and usefulness to the users. We argue Explainable AI (XAI) is more than a matter of accurate and complete computational explanation, that it requires pragmatics to address the issues it seeks to address. At the end of this paper, we draw a historical analogy to usability. This term was understood logically …

The Use And Effectiveness Of Online Social Media In Volunteer Organizations, Amy J. Connolly

USF Tampa Graduate Theses and Dissertations

Volunteer organizations face two challenges not found in non-volunteer organizations: recruiting and retaining volunteers. While social media use is increasing amongst individuals, its use and effectiveness for volunteer recruitment and retention by volunteer organizations is unknown. The dissertation reports the results of three studies to investigate this important question. Using a mixed-methods approach, it addressed the dual nature of social media and its effectiveness by including volunteer organizations and social media users.

This dissertation found that although volunteer organizations are not using social media effectively, they could virtualize requirements of the recruitment process by focusing on relatable events instead of …

The Lived Experience Of Young Adult Burn Survivors' Use Of Social Media, Marie S. Giordano

Dissertations, Theses, and Capstone Projects

The purpose of this phenomenological study was to illuminate the meaning of social media use by young adult burn survivors. Five females and four males, aged 20-25, who sustained burns > 25%, were interviewed. Van Manen's (1999) phenomenological methodology provided the framework for this study. The meaning of the context of the lived experience is described in the five essential themes of identity, connectivity, social support, making meaning, and privacy. These young adult burn survivors, having experienced the traumatic effects of a burn during adolescence, use social media as a way of expressing their identity, while being cautious about privacy. Part …

Bass In Your Face: A Case-Study Exploration Of Networked Culture, Samantha Phyllis Kretmar

Dissertations, Theses, and Capstone Projects

Using dubstep DJ Bassnectar as a case-study example, this thesis explores the impact of social networks and mobile connectivity. As evidenced by Bassnectar's digitally based approach to experiencing, distributing, and consuming music, these developments have contributed to the shift to a new model I describe as Networked Culture.

Figure 1 is a video highlighting the Bassnectar concert experience. Figure 2 is an audio clip illustrating the "drop" in dubstep. Figure 3 is another audio clip demonstrating the dubstep sound. Figure 4 is an image of an Ableton Live sound library. Figure 5 is an image of Ableton Live's functionality. Figure …

Active Learning With Efficient Feature Weighting Methods For Improving Data Quality And Classification Accuracy, Justin Martineau, Lu Chen, Doreen Cheng, Amit P. Sheth

Kno.e.sis Publications

Many machine learning datasets are noisy with a substantial number of mislabeled instances. This noise yields sub-optimal classification performance. In this paper we study a large, low quality annotated dataset, created quickly and cheaply using Amazon Mechanical Turk to crowdsource annotations. We describe computationally cheap feature weighting techniques and a novel non-linear distribution spreading algorithm that can be used to iteratively and interactively correcting mislabeled instances to significantly improve annotation quality at low cost. Eight different emotion extraction experiments on Twitter data demonstrate that our approach is just as effective as more computationally expensive techniques. Our techniques save a considerable …

Semantic Modelling Of Smart City Data, Stefan Bischof, Athanasios Karapantelakis, Cosmin-Septimiu Nechifor, Amit P. Sheth, Alessandra Mileo, Payam Barnaghi

Kno.e.sis Publications

Cities present an opportunity for rendering Web of Things-enabled services. According to the World Health Organization, population in cities will double by the middle of this century, while cities deal with increasingly pressing issues such as environmental sustainability, economic growth and citizen mobility. In this paper, we propose a discussion around the need for common semantic descriptions for smart city data to facilitate future services in "smart cities". We present examples of data that can be collected from cities, discuss issues around this data and put forward some preliminary thoughts for creating a semantic description model to describe and help …

Pre-R: Making Health Care Healthier, Grant Ramil, Mark Corpuz, Eliot Mestre, Akshay Rangnekar, Alex Lin

Computer Engineering

The Pre-R project provides a central database of medical service fees offered by hospitals across the United States. The database is crowdsourced and all data points are provided by end users or by hospital providers themselves. The fees are searchable through a website (www.pre-r.com) and iOS and Android applications. The mobile applications also provide a way for users to capture a picture of any medical bill and upload it for analysis and entry to our database.

Contextual Anomaly Detection In Big Sensor Data, Michael Hayes, Miriam A M Capretz

Electrical and Computer Engineering Publications

Performing predictive modelling, such as anomaly detection, in Big Data is a difficult task. This problem is compounded as more and more sources of Big Data are generated from environmental sensors, logging applications, and the Internet of Things. Further, most current techniques for anomaly detection only consider the content of the data source, i.e. the data itself, without concern for the context of the data. As data becomes more complex it is increasingly important to bias anomaly detection techniques for the context, whether it is spatial, temporal, or semantic. The work proposed in this paper outlines a contextual anomaly detection …

Recommendation Support For Multi-Attribute Databases, Jilian Zhang

Dissertations and Theses Collection (Open Access)

This dissertation studies the subject of providing recommendation support for multi-attribute databases. Recommendation is an important and very useful information evaluation mechanism that explores a database of huge volume, and retrieves from it the interesting data items (tuples) for users based on their preferences.

Semantics-Enhanced Geoscience Interoperability, Analytics, And Applications, Krishnaprasad Thirunarayan, Amit P. Sheth

Kno.e.sis Publications

We present our research ideas for developing cyberinfrastructure for Geoscience applications developed in the context of the EarthCube initiative, and our NSF-sponsored work on incorporating spatial-temporal-thematic semantics for enhanced querying and feature extraction from sensor data streams.

Ar-Miner: Mining Informative Reviews For Developers From Mobile App Marketplace, Ning Chen, Jialiu Lin, Steven C. H. Hoi, Xiaokui Xiao, Boshen Zhang

Research Collection School Of Computing and Information Systems

With the popularity of smartphones and mobile devices, mobile application (a.k.a. “app”) markets have been growing exponentially in terms of number of users and downloads. App developers spend considerable effort on collecting and exploiting user feedback to improve user satisfaction, but suffer from the absence of effective user review analytics tools. To facilitate mobile app developers discover the most “informative” user reviews from a large and rapidly increasing pool of user reviews, we present “AR-Miner” — a novel computational framework for App Review Mining, which performs comprehensive analytics from raw user reviews by (i) first extracting informative user reviews by …