Databases and Information Systems Commons^™

Personalized Web Image Organization, Lei Meng, Ah-Hwee Tan, Donald C. Wunsch

Research Collection School Of Computing and Information Systems

Due to the problem of semantic gap, i.e. the visual content of an image may not represent its semantics well, existing efforts on web image organization usually transform this task to clustering the surrounding text. However, because the surrounding text is usually short and the words therein usually appear only once, existing text clustering algorithms can hardly use the statistical information for image representation and may achieve downgraded performance with higher computational cost caused by learning from noisy tags. This chapter presents using the Probabilistic ART with user preference architecture, as introduced in Sects. 3.5 and 3.4, for personalized web …

Online Multimodal Co-Indexing And Retrieval Of Social Media Data, Lei Meng, Ah-Hwee Tan, Donald C. Wunsch

Research Collection School Of Computing and Information Systems

Effective indexing of social media data is key to searching for information on the social Web. However, the characteristics of social media data make it a challenging task. The large-scale and streaming nature is the first challenge, which requires the indexing algorithm to be able to efficiently update the indexing structure when receiving data streams. The second challenge is utilizing the rich meta-information of social media data for a better evaluation of the similarity between data objects and for a more semantically meaningful indexing of the data, which may allow the users to search for them using the different types …

Peerlens: Peer-Inspired Interactive Learning Path Planning In Online Question Pool, Meng Xia, Mingfei Sun, Huan Wei, Qing Chen, Yong Wang, Lei Shi, Huamin Qu, Xiaojuan Ma

Research Collection School Of Computing and Information Systems

Online question pools like LeetCode provide hands-on exercises of skills and knowledge. However, due to the large volume of questions and the intent of hiding the tested knowledge behind them, many users find it hard to decide where to start or how to proceed based on their goals and performance. To overcome these limitations, we present PeerLens, an interactive visual analysis system that enables peer-inspired learning path planning. PeerLens can recommend a customized, adaptable sequence of practice questions to individual learners, based on the exercise history of other users in a similar learning scenario. We propose a new way to …

Socially-Enriched Multimedia Data Co-Clustering, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Heterogeneous data co-clustering is a commonly used technique for tapping the rich meta-information of multimedia web documents, including category, annotation, and description, for associative discovery. However, most co-clustering methods proposed for heterogeneous data do not consider the representation problem of short and noisy text and their performance is limited by the empirical weighting of the multimodal features. This chapter explains how to use the Generalized Heterogeneous Fusion Adaptive Resonance Theory (GHF-ART) generalized heterogeneous fusion adaptive resonance theory for clustering large-scale web multimedia documents. Specifically, GHF-ART is designed to handle multimedia data with an arbitrarily rich level of meta-information. For handling …

Clustering And Its Extensions In The Social Media Domain, Lei Meng, Ah-Hwee Tan, Donald C. Wunsch

Research Collection School Of Computing and Information Systems

This chapter summarizes existing clustering and related approaches for the identified challenges as described in Sect. 1.2 and presents the key branches of social media mining applications where clustering holds a potential. Specifically, several important types of clustering algorithms are first illustrated, including clustering, semi-supervised clustering, heterogeneous data co-clustering, and online clustering. Subsequently, Sect. 2.5 presents a review on existing techniques that help decide the value of the predefined number of clusters (required by most clustering algorithms) automatically and highlights the clustering algorithms that do not require such a parameter. It better illustrates the challenge of input parameter sensitivity of …

Concluding Remarks, Lei Meng, Ah-Hwee Tan, Donald C. Wunsch

Research Collection School Of Computing and Information Systems

This chapter summarizes the major contributions in this book and discusses their possible positions and requirements in some future scenarios. Section 8.1 follows the book structure to revisit the key contributions of this book in both theories and applications. The developed algorithms, such as the VA-ARTs for hyperparameter adaptation and the GHF-ART for multimedia representation and fusion, and the four applications, such as clustering and retrieving socially enriched multimedia data, are concentrated using one paragraph and three paragraphs, respectively. In Sect. 8.2, the roles of the proposed ART-embodied algorithms in social media clustering tasks are highlighted, and their possible evolutions …

Yelp Improved : Aggregating Restaurant Reviews, Kunal Sonar

Creative Activity and Research Day - CARD

In the near future, online food delivery service companies would occupy a big market share in the food industry. This project aims to provide factual information from customer reviews as part of the numerous innovations in place to drive business and demands. Natural Language Processing is used to provide a comprehensive view of individual restaurants using technologies like NLTK, SpaCy, Gensim and Sklearn. Data of one million Las Vegas restaurant customer reviews is curated from the Yelp Dataset Challenge. Reviews are pre-processed, split into chunks of phrases and mapped to attributes like food, budget, service etc. These attributes are derived …

Building Consumer Trust In The Cloud: An Experimental Analysis Of The Cloud Trust Label Approach, Lisa Van Der Werff, Grace Fox, Ieva Masevic, Vincent C. Emeakaroha, John P. Morrison, Theo Lynn

Department of Computer Science Publications

The lack of transparency surrounding cloud service provision makes it difficult for consumers to make knowledge based purchasing decisions. As a result, consumer trust has become a major impediment to cloud computing adoption. Cloud Trust Labels represent a means of communicating relevant service and security information to potential customers on the cloud service provided, thereby facilitating informed decision making. This research investigates the potential of a Cloud Trust Label system to overcome the trust barrier. Specifically, it examines the impact of a Cloud Trust Label on consumer perceptions of a service and cloud service provider trustworthiness and trust in the …

Implementation Considerations For The Digital Bronco Id, Bryan Gilginas

Honors Theses

This paper aims to discuss the conditions and preferences of students that Western Michigan University should take if they ever implement a Digital Bronco ID. These conditions are found via an anonymous survey given to random students. These students were prompted to answer questions based on their preference and possible uses of the Digital Bronco ID. It was found that the respondents were significantly diverse in their answers. However, things such as gender, major, and age range played a significant role in patterns in which students chose their preferences. Within the paper, these patterns are interpreted and discussed for the …

Cooperation In Community Colleges, Frederic S. Gore

USF Tampa Graduate Theses and Dissertations

With the mounting pressures on institutions of higher education to do more with limited resources, the opportunity to collaborate with other colleges has emerged as a viable tool to create efficiencies and obtain valuable knowledge otherwise unattainable by an institution, even if that collaboration takes place with a competing institution. Enterprise resource planning (ERP) systems are critical to managing student information and college operations, but can be challenging for colleges to implement. Consortia present a unique solution to colleges to address gaps in their expertise and skills needed to achieve a successful ERP implementation. This study explores the factors that …

Big Data And The Consumer, Seema Chokshi

MITB Thought Leadership Series

What is big data? The intuitive meaning of the phrase ‘big data’ might be “data that is huge in quantity”. But is that interpretation enough? Data of this type has existed for as long as humans have made records of their work. Some of the earliest writings, such as cuneiform, contain vast amounts of data covering areas as diverse as law, mapping and mathematical equations.

Google Trends Data As A Proxy For Interest In Leadership, Finley W. Walker

Doctor of Education (Ed.D)

The purpose of this quantitative study was to investigate the observable patterns of online search behavior in the topic of leadership using Google Trends data. Institutions have had a historically difficult time predicting good leadership candidates. Better predictions can be made by using the big data offered by groups such as Google to learn who, where, and when people are interested in leadership. The study utilized descriptive, comparative, and correlative methodologies to study Google users’ interest in leadership from 2004 to 2017. Society has placed great value into leadership throughout history, and though overall interest remains strong, it appears that …

Modeling And Economic Analysis Of A Crop–Livestock Production System Incorporating Cereal Rye As A Forage, Eric R. Coufal

Department of Agricultural Economics: Dissertations, Theses, and Student Research

This thesis consists of two chapters using agent-based modeling for a crop-livestock production system incorporating human labor. The first chapter examines the principles used to develop a fundamental simulation pertaining to grazing cereal rye (Secale cereal L.) with calves. Within the software guidelines, the base model has the ability to capture diverse system interactions between livestock/plants and land management with human labor efficiency. AnyLogic incorporates agent-based modeling while combining with discrete event modeling and system dynamics. The purpose of the model was to find the economic returns of grazing cover crops relative to the area of Mead, Nebraska. In …

Alpha Insurance: A Predictive Analytics Case To Analyze Automobile Insurance Fraud Using Sas Enterprise Miner (Tm), Richard Mccarthy, Wendy Ceccucci, Mary Mccarthy, Leila Halawi

Publications

Automobile Insurance fraud costs the insurance industry billions of dollars annually. This case study addresses claim fraud based on data extracted from Alpha Insurance’s automobile claim database. Students are provided the business problem and data sets. Initially, the students are required to develop their hypotheses and analyze the data. This includes identification of any missing or inaccurate data values and outliers as well as evaluation of the 22 variables. Next students will develop and optimize their predictive models using five techniques: regression, decision tree, neural network, gradient boosting, and ensemble. Then students will determine which model is the best fit …

Question Answering With Textual Sequence Matching, Shuohang Wang

Dissertations and Theses Collection (Open Access)

Question answering (QA) is one of the most important applications in natural language processing. With the explosive text data from the Internet, intelligently getting answers of questions will help humans more efficiently collect useful information. My research in this thesis mainly focuses on solving question answering problem with textual sequence matching model which is to build vectorized representations for pairs of text sequences to enable better reasoning. And our thesis consists of three major parts.

In Part I, we propose two general models for building vectorized representations over a pair of sentences, which can be directly used to solve the …

Modeling Sequential And Basket-Oriented Associations For Top-K Recommendation, Duc-Trong Le Duc Trong

Dissertations and Theses Collection (Open Access)

Top-K recommendation is a typical task in Recommender Systems. In traditional approaches, it mainly relies on the modeling of user-item associations, which emphasizes the user-specific factor or personalization. Here, we investigate another direction that models item-item associations, especially with the notions of sequence-aware and basket-level adoptions . Sequences are created by sorting item adoptions chronologically. The associations between items along sequences, referred to as “sequential associations”, indicate the influence of the preceding adoptions on the following adoptions. Considering a basket of items consumed at the same time step (e.g., a session, a day), “basket-oriented associations” imply correlative dependencies among these …

Efficient Algorithms For Solving Aggregate Keyword Routing Problems, Qize Jiang, Weiwei Sun, Baihua Zheng, Kunjie Chen

Research Collection School Of Computing and Information Systems

With the emergence of smart phones and the popularity of GPS, the number of point of interest (POIs) is growing rapidly and spatial keyword search based on POIs has attracted significant attention. In this paper, we study a more sophistic type of spatial keyword searches that considers multiple query points and multiple query keywords, namely Aggregate Keyword Routing (AKR). AKR looks for an aggregate point m together with routes from each query point to m. The aggregate point has to satisfy the aggregate keywords, the routes from query points to the aggregate point have to pass POIs in order to …

Maximizing Multifaceted Network Influence, Yuchen Li, Ju Fan, George V. Ovchinnikov, Panagiotis Karras

Research Collection School Of Computing and Information Systems

An information dissemination campaign is often multifaceted, involving several facets or pieces of information disseminating from different sources. The question then arises, how should we assign such pieces to eligible sources so as to achieve the best viral dissemination results? Past research has studied the problem of Influence Maximization (IM), which is to select a set of k promoters that maximizes the expected reach of a message over a network. However, in this classical IM problem, each promoter spreads out the same unitary piece of information. In this paper, we propose the Optimal Influential Pieces Assignment (OIPA) problem, which is …

Online Collaborative Filtering With Implicit Feedback, Jianwen Yin, Chenghao Liu, Jundong Li, Bing Tian Dai, Yun-Chen Chen, Min Wu, Jianling Sun

Research Collection School Of Computing and Information Systems

Studying recommender systems with implicit feedback has become increasingly important. However, most existing works are designed in an offline setting while online recommendation is quite challenging due to the one-class nature of implicit feedback. In this paper, we propose an online collaborative filtering method for implicit feedback. We highlight three critical issues of existing works. First, when positive feedback arrives sequentially, if we treat all the other missing items for this given user as the negative samples, the mis-classified items will incur a large deviation since some items might appear as the positive feedback in the subsequent rounds. Second, the …

Automatic Short Answer Grading Using Siamese Bidirectional Lstm Based Regression, Arya Prabhudesai, Nguyen Binh Duong Ta

Research Collection School Of Computing and Information Systems

Automatic student assessment plays an important role in education - it provides instant feedback to learners, and at the same time reduces tedious grading workload for instructors. In this paper, we investigate new machine learning techniques for automatic short answer grading (ASAG). The ASAG problem mainly involves assessing short, natural language responses to given questions automatically. While current research in the field has focused either on feature engineering or deep learning, we propose a new approach which combines the advantages of both. More specifically, we propose a Siamese Bidirectional LSTM Neural Network based Regressor in conjunction with handcrafted features for …