Physical Sciences and Mathematics Commons^™

The Role Of Intermediary In Sustainable Lending: An Economic Analysis Of Crowdfunding Platform, Ling Ge, Zhiling Guo

Research Collection School Of Computing and Information Systems

Is the interest-free crowdfunding platform a promising alternative to the non-zero interest platform? This study investigates the lenders and borrowers’ incentives and choices between an indirect, non-zero interest rate platform intermediated by a field partner and a direct-lending, interest-free platform. We model the field partner as a profit maximizer that filters qualified borrowers to enable the lenders’ capital to be better utilized on the crowdfunding platform. We show that, under certain conditions, both the borrowers and lenders are better off from the existence of the field partner. The existence of field partner is necessary to effectively segment the market and …

Should We Use The Sample? Analyzing Datasets Sampled From Twitter's Stream Api, Yazhe Wang, Jamie Callan, Baihua Zheng

Research Collection School Of Computing and Information Systems

Researchers have begun studying content obtained from microblogging services such as Twitter to address a variety of technological, social, and commercial research questions. The large number of Twitter users and even larger volume of tweets often make it impractical to collect and maintain a complete record of activity; therefore, most research and some commercial software applications rely on samples, often relatively small samples, of Twitter data. For the most part, sample sizes have been based on availability and practical considerations. Relatively little attention has been paid to how well these samples represent the underlying stream of Twitter data. To fill …

"Time For Dabs": Analyzing Twitter Data On Butane Hash Oil Use, Raminta Daniulaityte, Robert G. Carlson, Farahnaz Golroo, Sanjaya Wijeratne, Edward W. Boyer, Silvia S. Martins, Ramzi W. Nahhas, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

Dietary Microrna Database (Dmd): An Archive Database And Analytic Tool For Food-Borne Micrornas, Kevin Chiang, Jiang Shu, Janos Zempleni, Juan Cui

School of Computing: Faculty Publications

With the advent of high throughput technology, a huge amount of microRNA information has been added to the growing body of knowledge for non-coding RNAs. Here we present the Dietary MicroRNA Databases (DMD), the first repository for archiving and analyzing the published and novel microRNAs discovered in dietary resources. Currently there are fifteen types of dietary species, such as apple, grape, cow milk, and cow fat, included in the database originating from 9 plant and 5 animal species. Annotation for each entry, a mature microRNA indexed as DM0000*, covers information of the mature sequences, genome locations, hairpin structures of parental …

Trust Management: Multimodal Data Perspective, Krishnaprasad Thirunarayan

Kno.e.sis Publications

No abstract provided.

Dynamic Redeployment To Counter Congestion Or Starvation In Vehicle Sharing Systems, Supriyo Ghosh, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet

Research Collection School Of Computing and Information Systems

Extensive usage of private vehicles has led to increased traffic congestion, carbon emissions, and usage of non-renewable resources. These concerns have led to the wide adoption of vehicle sharing (ex: bike sharing, car sharing) systems in many cities of the world. In vehicle-sharing systems, base stations (ex: docking stations for bikes) are strategically placed throughout a city and each of the base stations contain a pre-determined number of vehicles at the beginning of each day. Due to the stochastic and individualistic movement of customers,there is typically either congestion (more than required)or starvation (fewer than required) of vehicles at certain base …

Emif: Towards A Scalable And Effective Indexing Framework For Large Scale Music Retrieval, Jialie Shen, Tao Mei, Dacheng Tao, Xuelong Li, Yong Rui

Research Collection School Of Computing and Information Systems

This article presents a novel indexing framework called EMIF (Effective Music Indexing Framework) to facilitate scalable and accurate content based music retrieval. EMIF system architecture is designed based on a "classification-and-indexing" principle and consists of two main functionality layers: 1) a novel semantic-sensitive classification to identify input music's category and 2) multiple indexing structures - one local indexing structure corresponds to one semantic category. EMIF's layered architecture not only enables superior search accuracy but also reduces query response time significantly. To evaluate the system, a set of comprehensive experimental studies have been carried out using large test collection and EMIF …

Method For Matching Probabilistic Encrypted Data, Hwee Hwa Pang, Xuhua Ding

Research Collection School Of Computing and Information Systems

Determining if a first encrypted data of a first data value is equal to a second encrypted data of a second data value. Comprising: a first cyclic group; a second cyclic group including a first element. Applying an operation to the first cyclic group to map its elements to an element in the second cyclic group. Randomly selecting a second element from the first cyclic group; producing the first encrypted data by mapping the second element and the first data value into one or more elements of the first cyclic group. Randomly selecting a third element from the first cyclic …

Service Quality And Perceived Value Of Cloud Computing-Based Service Encounters: Evaluation Of Instructor Perceived Service Quality In Higher Education In Texas, Eges Egedigwe

CCE Theses and Dissertations

Cloud computing based technology is becoming increasingly popular as a way to deliver quality education to community colleges, universities and other organizations. At the same time, compared with other industries, colleges have been slow on implementing and sustaining cloud computing services on an institutional level because of budget constraints facing many large community colleges, in addition to other obstacles. Faced with this challenge, key stakeholders are increasingly realizing the need to focus on service quality as a measure to improve their competitive position in today's highly competitive environment. Considering the amount of study done with cloud computing in education, very …

Author Topic Model-Based Collaborative Filtering For Personalized Poi Recommendations, Shuhui Jiang, Xueming Qian, Jialie Shen, Yun Fu, Tao Mei

Research Collection School Of Computing and Information Systems

From social media has emerged continuous needs for automatic travel recommendations. Collaborative filtering (CF) is the most well-known approach. However, existing approaches generally suffer from various weaknesses. For example, sparsity can significantly degrade the performance of traditional CF. If a user only visits very few locations, accurate similar user identification becomes very challenging due to lack of sufficient information for effective inference. Moreover, existing recommendation approaches often ignore rich user information like textual descriptions of photos which can reflect users' travel preferences. The topic model (TM) method is an effective way to solve the "sparsity problem," but is still far …

Multimodal Learning With Deep Boltzmann Machine For Emotion Prediction In User Generated Videos, Lei Pang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Detecting emotions from user-generated videos, such as“anger” and “sadness”, has attracted widespread interest recently. The problem is challenging as effectively representing video data with multi-view information (e.g., audio, video or text) is not trivial. In contrast to the existing works that extract features from each modality (view) separately followed by early or late fusion, we propose to learn a joint density model over the space of multi-modal inputs (including visual, auditory and textual modalities) with Deep Boltzmann Machine (DBM). The model is trained directly on the user-generated Web videos without any labeling effort. More importantly, the deep architecture enlightens the …

Online Multimodal Co-Indexing And Retrieval Of Weakly Labeled Web Image Collections, Lei Meng, Ah-Hwee Tan, Cyril Leung, Liqiang Nie, Tan-Seng Chua, Chunyan Miao

Research Collection School Of Computing and Information Systems

Weak supervisory information of web images, such as captions, tags, and descriptions, make it possible to better understand images at the semantic level. In this paper, we propose a novel online multimodal co-indexing algorithm based on Adaptive Resonance Theory, named OMC-ART, for the automatic co-indexing and retrieval of images using their multimodal information. Compared with existing studies, OMC-ART has several distinct characteristics. First, OMCART is able to perform online learning of sequential data. Second, OMC-ART builds a two-layer indexing structure, in which the first layer co-indexes the images by the key visual and textual features based on the generalized distributions …

Semi-Supervised Domain Adaptation With Subspace Learning For Visual Recognition, Ting Yao, Yingwei Pan, Chong-Wah Ngo, Houqiang Li, Tao Mei

Research Collection School Of Computing and Information Systems

In many real-world applications, we are often facing the problem of cross domain learning, i.e., to borrow the labeled data or transfer the already learnt knowledge from a source domain to a target domain. However, simply applying existing source data or knowledge may even hurt the performance, especially when the data distribution in the source and target domain is quite different, or there are very few labeled data available in the target domain. This paper proposes a novel domain adaptation framework, named Semi-supervised Domain Adaptation with Subspace Learning (SDASL), which jointly explores invariant lowdimensional structures across domains to correct data …

Assessing The Opportunities And Challenges With Big Data In The Mobile Payments Ecosystem, Jun Liu, Robert John Kauffman, Dan Ma

Research Collection School Of Computing and Information Systems

Information and communication technology (ICT) is an important driver of mobile payments in the financial services industry. Mobile payments (m-payments) technologies enable new channels for consumer payments for goods and services purchases, and other forms of economic exchange. The m-payments ecosystem involves multiple distinct stakeholders, and a high level of consumer data-sharing. In this paper, we will assess the current m-payments ecosystem, and discuss the challenges and opportunities with big data captured from mpayments transactions. We will also propose new directions to encourage research that will shed the light on how stakeholders can facilitate the successful adoption and realize the …

A Modular Approach For Key-Frame Selection In Wide Area Surveillance Video Analysis, Almabrok Essa, Paheding Sidike, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

This paper presents an efficient preprocessing algorithm for big data analysis. Our proposed key-frame selection method utilizes the statistical differences among subsequent frames to automatically select only the frames that contain the desired contextual information and discard the rest of the insignificant frames.

We anticipate that such key frame selection technique will have significant impact on wide area surveillance applications such as automatic object detection and recognition in aerial imagery. Three real-world datasets are used for evaluation and testing and the observed results are encouraging.

Projection Metric Learning On Grassmann Manifold With Application To Video Based Face Recognition, Zhiwu Huang, R. Wang, S. Shan, X. Chen

Research Collection School Of Computing and Information Systems

In video based face recognition, great success has been made by representing videos as linear subspaces, which typically lie in a special type of non-Euclidean space known as Grassmann manifold. To leverage the kernel-based methods developed for Euclidean space, several recent methods have been proposed to embed the Grassmann manifold into a high dimensional Hilbert space by exploiting the well established Project Metric, which can approximate the Riemannian geometry of Grassmann manifold. Nevertheless, they inevitably introduce the drawbacks from traditional kernel-based methods such as implicit map and high computational cost to the Grassmann manifold. To overcome such limitations, we propose …

Geospatial Data Modeling To Support Energy Pipeline Integrity Management, Austin Wylie

Master's Theses

Several hundred thousand miles of energy pipelines span the whole of North America -- responsible for carrying the natural gas and liquid petroleum that power the continent's homes and economies. These pipelines, so crucial to everyday goings-on, are closely monitored by various operating companies to ensure they perform safely and smoothly.

Happenings like earthquakes, erosion, and extreme weather, however -- and human factors like vehicle traffic and construction -- all pose threats to pipeline integrity. As such, there is a tremendous need to measure and indicate useful, actionable data for each region of interest, and operators often use computer-based decision …

Face Video Retrieval With Image Query Via Hashing Across Euclidean Space And Riemannian Manifold, Y. Li, R. Wang, Zhiwu Huang, S. Shan, X. Chen

Research Collection School Of Computing and Information Systems

Retrieving videos of a specific person given his/her face image as query becomes more and more appealing for applications like smart movie fast-forwards and suspect searching. It also forms an interesting but challenging computer vision task, as the visual data to match, i.e., still image and video clip are usually represented quite differently. Typically, face image is represented as point (i.e., vector) in Euclidean space, while video clip is seemingly modeled as a point (e.g., covariance matrix) on some particular Riemannian manifold in the light of its recent promising success. It thus incurs a new hashing-based retrieval problem of matching …

Continuous Monitoring Of Enterprise Risks: A Delphi Feasibility Study, Robert Baksa

Dissertations

A constantly evolving regulatory environment, increasing market pressure to improve operations, and rapidly changing business conditions are creating the need for ongoing assurance that organizational risks are continually and adequately mitigated. Enterprises are perpetually exposed to fraud, poor decision making and/or other inefficiencies that can lead to significant financial loss and/or increased levels of operating risk. Increasingly, Information Systems are being harnessed to reinvent the risk management process. One promising technology is Continuous Auditing, which seeks to transform the audit process from periodic reviews of a few transactions to a continuous review of all transactions. However, the highly integrated, rapidly …

Information Filtering By Multiple Examples, Mingzhu Zhu

Dissertations

A key to successfully satisfy an information need lies in how users express it using keywords as queries. However, for many users, expressing their information needs using keywords is difficult, especially when the information need is complex. Search By Multiple Examples (SBME), a promising method for overcoming this problem, allows users to specify their information needs as a set of relevant documents rather than as a set of keywords.

Most of the studies on SBME adopt the Positive Unlabeled learning (PU learning) techniques by treating the user's provided examples (denoted as query examples) as positive set and the entire data …

Assessing Learning Outcomes And Social Capital Formation Resulting From The Use And Sharing Of Internet Knowledge Resources, Regina S. Collins

Dissertations

Today’s “digital natives” use the Internet to address most, if not all, their learning-related knowledge needs. This research evaluates the outcomes of formal learning activities requiring students to use, manage, share, and consolidate Internet knowledge resources (such as websites, videos, and blogs) to achieve both individual and group learning. This research takes an integrative approach to learning, capturing learner cognitive, interpersonal, and intrapersonal characteristics as well as the impact of the digital environment by evaluating the technological affordances of two different systems supporting such learning activities. This research also examines pedagogical modifications that would best integrate course assignments utilizing Internet …

Entity Recommendations Using Hierarchical Knowledge Bases, Siva Kumar Cheekula, Pavan Kapanipathi, Derek Doran, Prateek Jain, Amit P. Sheth

Kno.e.sis Publications

Recent developments in recommendation algorithms have focused on integrating Linked Open Data to augment traditional algorithms with background knowledge. These developments recognize that the integration of Linked Open Data may or better performance, particularly in cold start cases. In this paper, we explore if and how a specific type of Linked Open Data, namely hierarchical knowledge, may be utilized for recommendation systems. We propose a content-based recommendation approaches that adapts a spreading activation algorithm over the DBpedia category structure to identify entities of interest to the user. Evaluation of the algorithm over the Movielens dataset demonstrates that our method yields …

Mining Concept In Big Data, Jingjing Yang

Master's Projects

To fruitful using big data, data mining is necessary. There are two well-known methods, one is based on apriori principle, and the other one is based on FP-tree. In this project we explore a new approach that is based on simplicial complex, which is a combinatorial form of polyhedron used in algebraic topology. Our approach, similar to FP-tree, is top down, at the same time, it is based on apriori principle in geometric form, called closed condition in simplicial complex. Our method is almost 300 times faster than FP-growth on a real world database using a SJSU laptop. The database …

An Open Source Advertisement Server, Pushkar Umaranikar

Master's Projects

This report describes a new online advertisement system and its implementation for the Yioop open source search engine. This system was implemented for my CS298 project. It supports both selling advertisements and displaying them within search results. The selling of advertisement is done using a novel auction system, which we describe in this paper. With this auction system, it is possible to create an advertisement, attach keywords to it, and add it to the advertisement inventory. An advertisement is displayed on a search results page if the search keyword matches the keywords attached to the advertisement. Display of advertisements is …

Index Strategies For Efficient And Effective Entity Search, Huy T. Vu

Master's Projects

The volume of structured data has rapidly grown in recent years, when data-entity emerged as an abstraction that captures almost every data pieces. As a result, searching for a desired piece of information on the web could be a challenge in term of time and relevancy because the number of matching entities could be very large for a given query. This project concerns with the efficiency and effectiveness of such entity queries. The work contains two major parts: implement inverted indexing strategies so that queries can be searched in minimal time, and rank results based on features that are independent …

Context-Based Autosuggest On Graph Data, Hai Nguyen

Master's Projects

Autosuggest is an important feature in any search applications. Currently, most applications only suggest a single term based on how frequent that term appears in the indexed documents or how often it is searched upon. These approaches might not provide the most relevant suggestions because users often enter a series of related query terms to answer a question they have in mind. In this project, we implemented the Smart Solr Suggester plugin using a context-based approach that takes into account the relationships among search keywords. In particular, we used the keywords that the user has chosen so far in the …

A Scalable Search Engine Aggregator, Pooja Mishra

Master's Projects

The ability to display different media sources in an appropriate way is an integral part of search engines such as Google, Yahoo, and Bing, as well as social networking sites like Facebook, etc. This project explores and implements various media-updating features of the open source search engine Yioop [1]. These include news aggregation, video conversion and email distribution. An older, preexisting news update feature of Yioop was modified and scaled so that it can work on many machines. We redesigned and modified the user interface associated with a distributed news updater feature in Yioop. This project also introduced a video …

Smart Cities: Environmental Aspects And Opportunities, Marcus R. Wigan

Marcus R Wigan

Keeping Pace With Criminals: Designing Patrol Allocation Against Adaptive Opportunistic Criminals, Chao Zhang, Arunesh Sinha, Milind Tambe

Research Collection School Of Computing and Information Systems

Police patrols are used ubiquitously to deter crimes in urban areas. A distinctive feature of urban crimes is that criminals react opportunistically to patrol officers' assignments. Compared to strategic attackers (such as terrorists) with a well-laid out plan, opportunistic criminals are less strategic in planning attacks and more flexible in executing them. In this paper, our goal is to recommend optimal police patrolling strategy against such opportunistic criminals. We first build a game-theoretic model that captures the interaction between officers and opportunistic criminals. However, while different models of adversary behavior have been proposed, their exact form remains uncertain. Rather than …

Data Management In Cloud Environments: Nosql And Newsql Data Stores, Katarina Grolinger, Wilson A. Higashino, Abhinav Tiwari, Miriam Am Capretz

Wilson A Higashino

: Advances in Web technology and the proliferation of mobile devices and sensors connected to the Internet have resulted in immense processing and storage requirements. Cloud computing has emerged as a paradigm that promises to meet these requirements. This work focuses on the storage aspect of cloud computing, specifically on data management in cloud environments. Traditional relational databases were designed in a different hardware and software era and are facing challenges in meeting the performance and scale requirements of Big Data. NoSQL and NewSQL data stores present themselves as alternatives that can handle huge volume of data. Because of the …