Databases and Information Systems Commons^™

Enabling An Integrated Rate-Temporal Learning Scheme On Memristor, Wei He, Kejie Huang, Ning Ning, Kiruthika Ramanathan, Guoqi Li, Yu Jiang, Jiayin Sze, Luping Shi, Rong Zhao, Jing Pei

Research Collection School Of Computing and Information Systems

Learning scheme is the key to the utilization of spike-based computation and the emulation of neural/synaptic behaviors toward realization of cognition. The biological observations reveal an integrated spike time- and spike rate-dependent plasticity as a function of presynaptic firing frequency. However, this integrated rate-temporal learning scheme has not been realized on any nano devices. In this paper, such scheme is successfully demonstrated on a memristor. Great robustness against the spiking rate fluctuation is achieved by waveform engineering with the aid of good analog properties exhibited by the iron oxide-based memristor. The spike-time-dependence plasticity (STDP) occurs at moderate presynaptic firing frequencies …

Roundtriprank: Graph-Based Proximity With Importance And Specificity, Yuan Fang, Kevin Chen-Chuan Chang, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Graph-based proximity has many applications with different ranking needs. However, most previous works only stress the sense of importance by finding "popular” results for a query. Often times important results are overly general without being well-tailored to the query, lacking a sense of specificity— which only emerges recently. Even then, the two senses are treated independently, and only combined empirically. In this paper, we generalize the well-studied importance-based random walk into a round trip and develop RoundTripRank, seamlessly integrating specificity and importance in one coherent process. We also recognize the need for a flexible trade-off between the two senses, and …

Finding The Optimal Social Trust Path For The Selection Of Trustworthy Service Providers In Complex Social Networks, Guanfeng Liu, Yan Wang, Mehmet A. Orgun, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Online social networks have provided the infrastructure for a number of emerging applications in recent years, e.g., for the recommendation of service providers or the recommendation of files as services. In these applications, trust is one of the most important factors in decision making by a service consumer, requiring the evaluation of the trustworthiness of a service provider along the social trust paths from a service consumer to the service provider. However, there are usually many social trust paths between two participants who are unknown to one another. In addition, some social information, such as social relationships between participants and …

Beta Atomic Contacts: Identifying Critical Specific Contacts In Protein Binding Interfaces, Qian Lu, Chee Keong Kwoh, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Specific binding between proteins plays a crucial role in molecular functions and biological processes. Protein binding interfaces and their atomic contacts are typically defined by simple criteria, such as distance-based definitions that only use some threshold of spatial distance in previous studies. These definitions neglect the nearby atomic organization of contact atoms, and thus detect predominant contacts which are interrupted by other atoms. It is questionable whether such kinds of interrupted contacts are as important as other contacts in protein binding. To tackle this challenge, we propose a new definition called beta (β) atomic contacts. Our definition, founded on the …

Core Versus Peripheral Information Technology Employees And Their Impact On Firm Performance, Ling Liu, Daniel Q. Chen, Nan Hu, Indranil Bose, Garry D. Bruton

Research Collection School Of Computing and Information Systems

Scholars have widely argued, but not previously examined, that core employees with firm specific skills are critical to the firm's strategic success. This argument has led to the belief that employees whose skills are not firm specific can be readily replaced in the external market and are peripheral to the firm's strategic goals. Employing a resource based view of the firm, we find that the core information technology (IT) employees with firm specific skills are value-adding resources that aid the firm's performance whereas peripheral employees with less firm specific skills provide no value to the firm's performance. Examining the issue …

Twicube: A Real-Time Twitter Online Community Analysis Tool, Juan Du, Wei Xie, Cheng Li, Feida Zhu, Ee Peng Lim

Research Collection School Of Computing and Information Systems

As a micro-blogging service, Twitter differs from other social network services in two ways: 1) the absence of mutual consent in establishing follow links and 2) being a mixture of news media and social network. A key question to ask in better understanding Twitter user behavior is which part of a user’s Twitter network reflects one’s real-life social network. TwiCube is an online tool that employs a novel algorithm capable of identifying a user’s real-life social community, which we call the user’s off-line community, purely from examining the link structure among the user’s followers and followees. Based on the identified …

Dynamic Label Propagation In Social Networks, Juan Du, Feida Zhu, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Label propagation has been studied for many years, starting from a set of nodes with labels and then propagating to those without labels. In social networks, building complete user profiles like interests and affiliations contributes to the systems like link prediction, personalized feeding, etc. Since the labels for each user are mostly not filled, we often employ some people to label these users. And therefore, the cost of human labeling is high if the data set is large. To reduce the expense, we need to select the optimal data set for labeling, which produces the best propagation result. In this …

Modeling And Analysis Of Information Product Maps, Christopher Harris Heien

Theses and Dissertations

Information Product Maps are visual diagrams used to represent the inputs, processing, and outputs of data within an Information Manufacturing System. A data unit, drawn as an edge, symbolizes a grouping of raw data as it travels through this system. Processes, drawn as vertices, transform each data unit input into various forms prior to delivery to consumers. These visual representations act as documentation for all processes, and data unit transformations, leading to the final construction and delivery of an Information Product. The science of Information Quality strives to measure the fitness for use of these products, expressed by consumer satisfaction. …

Spatial Search Techniques For Mobile 3d Queries In Sensor Web Environments, Junjun Yin, James Carswell

Articles

Developing mobile geo-information systems for sensor web applications involves technologies that can access linked geographical and semantically related Internet information. Additionally, in tomorrow’s Web 4.0 world, it is envisioned that trillions of inexpensive micro-sensors placed throughout the environment will also become available for discovery based on their unique geo-referenced IP address. Exploring these enormous volumes of disparate heterogeneous data on today’s location and orientation aware smartphones requires context-aware smart applications and services that can deal with “information overload”. 3DQ (Three Dimensional Query) is our novel mobile spatial interaction (MSI) prototype that acts as a next-generation base for human interaction within …

Session F-4: Using Web Tools And Strategies To Enhance Student Engagement, Jackie Naughton, Frank Tomsic

Professional Learning Day

Web tools and strategies will be introduced and modeled. Teachers will leave this session able to immediately implement the tools and strategies in their classrooms. Participants must bring their own devices.

Predicting Parkinson's Disease Progression With Smartphone Data, Pramod Anantharam, Krishnaprasad Thirunarayan, Vahid Taslimi, Amit P. Sheth

Kno.e.sis Publications

Most of the existing approaches for detecting diseases/risk score form observations (sensor and textual) ignore the presence of any prior knowledge of the disease. In this work, we start top-down by enumerating the symptoms of Parkinson's Disease (PD) and map the symptoms to its possible manifestations in sensor observations (bottom-up). We show such manifestations and further use these manifestations as features to build classifiers to differentiate between the PD patients and the control group.

Robust Image Analysis With Sparse Representation On Quantized Visual Features, Bingkun Bao, Guangyu Zhu, Jialie Shen, Shuicheng Yan

Research Collection School Of Computing and Information Systems

Recent techniques based on Sparse Representation (SR) have demonstrated promising performance on high-level visual recognition, exemplified by the high-accuracy face recognition under occlusions and other sparse corruptions [1]. Most research in this area has focused on classification algorithms using raw image pixels, and very few have been proposed to utilize the quantized visual features, such as the popular Bagof- Words (BOW) feature abstraction. In such cases, besides the inherent quantization errors, ambiguity associated with visual word assignment and mis-detection of feature points due to factors such as visual occlusions and noises, constitutes the major causes to the dense corruptions of …

A Self-Training Framework For Automatic Identification Of Exploratory Dialogue, Zhongyu Wei, Yulan He, Simon Shum, Rebecca Ferguson, Wei Gao, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

The dramatic increase in online learning materials over the last decade has made it difficult for individuals to locate information they need. Until now, researchers in the field of Learning Analytics have had to rely on the use of manual approaches to identify exploratory dialogue. This type of dialogue is desirable in online learning environments, since training learners to use it has been shown to improve learning outcomes. In this paper, we frame the problem of exploratory dialogue detection as a binary classification task, classifying a given contribution to an online dialogue as exploratory or non-exploratory. We propose a self-training …

Data Visualization On Interactive Surfaces: A Research Agenda, Petra Isenberg, Tobias Isenberg, Tobias Hesselmann, Bongshin Lee, Ulrich Von Zadow, Anthony Tang

Research Collection School Of Computing and Information Systems

Interactive tabletops and surfaces (ITSs) provide rich opportunities for data visualization and analysis and consequently are used increasingly in such settings. A research agenda of some of the most pressing challenges related to visualization on ITSs emerged from discussions with researchers and practitioners in human-computer interaction, computer-supported collaborative work, and a variety of visualization fields at the 2011 Workshop on Data Exploration for Interactive Surfaces (Dexis 2011)

Spoons: Netflix Outage Detection Using Microtext Classification, Eriq A. Augusitne

Master's Theses

Every week there are over a billion new posts to Twitter services and many of those messages contain feedback to companies about their services. One company that recognizes this unused source of information is Netflix. That is why Netflix initiated the development of a system that lets them respond to the millions of Twitter and Netflix users that are acting as sensors and reporting all types of user visible outages. This system enhances the feedback loop between Netflix and its customers by increasing the amount of customer feedback that Netflix receives and reducing the time it takes for Netflix to …

Semi-Supervised Heterogeneous Fusion For Multimedia Data Co-Clustering, Lei Meng, Ah-Hwee Tan, Dong Xu

Research Collection School Of Computing and Information Systems

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 multi-modal features. In this paper, we propose a generalized form of Heterogeneous Fusion Adaptive Resonance Theory, called GHF-ART, for co-clustering of large-scale web multimedia documents. By extending the two-channel Heterogeneous Fusion ART (HF-ART) to multiple channels, GHF-ART is designed to handle multimedia data with an arbitrarily rich …

Confidence Weighted Mean Reversion Strategy For Online Portfolio Selection, Bin Li, Steven C. H. Hoi, Peilin Zhao, Vivekanand Gopalkrishnan

Research Collection School Of Computing and Information Systems

Online portfolio selection has been attracting increasing attention from the data mining and machine learning communities. All existing online portfolio selection strategies focus on the first order information of a portfolio vector, though the second order information may also be beneficial to a strategy. Moreover, empirical evidence shows that relative stock prices may follow the mean reversion property, which has not been fully exploited by existing strategies. This article proposes a novel online portfolio selection strategy named Confidence Weighted Mean Reversion (CWMR). Inspired by the mean reversion principle in finance and confidence weighted online learning technique in machine learning, CWMR …

Image Collection Summarization Via Dictionary Learning For Sparse Representation, Chunlei Yang, Jialie Shen, Jinye Peng, Jianping Fan

Research Collection School Of Computing and Information Systems

In this paper, a novel approach is developed to achieve automatic image collection summarization. The effectiveness of the summary is reflected by its ability to reconstruct the original set or each individual image in the set. We have leveraged the dictionary learning for sparse representation model to construct the summary and to represent the image. Specifically we reformulate the summarization problem into a dictionary learning problem by selecting bases which can be sparsely combined to represent the original image and achieve a minimum global reconstruction error, such as MSE (Mean Square Error). The resulting “Sparse Least Square” problem is NP-hard, …

Data Mining The Functional Characterizations Of Proteins To Predict Their Cancer-Relatedness, Peter Revesz, Christopher Assi

School of Computing: Faculty Publications

This paper considers two types of protein data. First, data about protein function described in a number of ways, such as, GO terms and PFAM families. Second, data about whether individual proteins are experimentally associated with cancer by an anomalous elevation or lowering of their expressions within cancerous cells. We combine these two types of protein data and test whether the first type of data, that is, the functional descriptors, can predict the second type of data, that is, cancer-relatedness. By using data mining and machine learning, we derive a classifier algorithm that using only GO term and PFAM family …

Guest Editorial: Selected Papers From Icimcs 2011, Chong-Wah Ngo, Changsheng Xu, Xiao Wu, Abdulmotaleb El Saddik

Research Collection School Of Computing and Information Systems

International Conference on Internet Multimedia Computing and Services (ICIMCS) is an annual conference sponsored by ACM SIGMM China Chapter. The conference is especially interested in the latest technologies and applications that deal with the web-scale processing and management of heterogeneous data from the Internet for multimedia computing and service. ICIMCS 2011 held in Chengdu, China— the ancient hometown of lovely panda. The conference has attracted around 80 participants, including researchers from academia and industries across ten countries/regions, for sharing their recent works in the topics ranging from visual information analysis and mining, query processing and search, multimedia privacy and security.

Synthetic Controllable Turbulence Using Robust Second Vorticity Confinement, Shengfeng He, Rynson W. H. Lau

Research Collection School Of Computing and Information Systems

Capturing fine details of turbulence on a coarse grid is one of the main tasks in real-time fluid simulation. Existing methods for doing this have various limitations. In this paper, we propose a new turbulence method that uses a refined second vorticity confinement method, referred to as robust second vorticity confinement, and a synthesis scheme to create highly turbulent effects from coarse grid. The new technique is sufficiently stable to efficiently produce highly turbulent flows, while allowing intuitive control of vortical structures. Second vorticity confinement captures and defines the vortical features of turbulence on a coarse grid. However, due to …

K-Pop Live: Social Networking & Language Learning Platform, Thomas Chua, Chin Leng Ong, Kian Ming Png, Aloysius Lau, Houston Toh, Feida Zhu, Kyong Jin Shim, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

K-Pop live is a social networking and language learning platform developed by an undergraduate student team from Singapore Management University. K-Pop live aims to combine social media together with gamification to promote Korean culture. It consolidates all relevant Tweets from Twitter as well as videos from YouTube. The platform allows the user to connect with his friends who share similar interests in terms of K-pop artists and music.

Online Multiple Kernel Classification, Steven C. H. Hoi, Rong Jin, Peilin Zhao, Tianbao Yang

Research Collection School Of Computing and Information Systems

Although both online learning and kernel learning have been studied extensively in machine learning, there is limited effort in addressing the intersecting research problems of these two important topics. As an attempt to fill the gap, we address a new research problem, termed Online Multiple Kernel Classification (OMKC), which learns a kernel-based prediction function by selecting a subset of predefined kernel functions in an online learning fashion. OMKC is in general more challenging than typical online learning because both the kernel classifiers and the subset of selected kernels are unknown, and more importantly the solutions to the kernel classifiers and …

Online Multi-Modal Distance Learning For Scalable Multimedia Retrieval, Hao Xia, Pengcheng Wu, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

In many real-word scenarios, e.g., multimedia applications, data often originates from multiple heterogeneous sources or are represented by diverse types of representation, which is often referred to as "multi-modal data". The definition of distance between any two objects/items on multi-modal data is a key challenge encountered by many real-world applications, including multimedia retrieval. In this paper, we present a novel online learning framework for learning distance functions on multi-modal data through the combination of multiple kernels. In order to attack large-scale multimedia applications, we propose Online Multi-modal Distance Learning (OMDL) algorithms, which are significantly more efficient and scalable than the …

What Kind Of #Conversation Is Twitter? Mining #Psycholinguistic Cues For Emergency Coordination, Hemant Purohit, Andrew Hampton, Valerie L. Shalin, Amit P. Sheth, John M. Flach, Shreyansh Bhatt

Kno.e.sis Publications

The information overload created by social media messages in emergency situations challenges response organizations to find targeted content and users. We aim to select useful messages by detecting the presence of conversation as an indicator of coordinated citizen action. Using simple linguistic indicators associated with conversation analysis in social science, we model the presence of conversation in the communication landscape of Twitter in a large corpus of 1.5M tweets for various disaster and non-disaster events spanning different periods, lengths of time and varied social significance. Within Replies, Retweets and tweets that mention other Twitter users, we found that domain-independent, linguistic …

Stock Market Prediction Without Sentiment Analysis: Using A Web-Traffic Based Classifier And User-Level Analysis, Pierpaolo Dondio

Conference papers

This paper provides further evidence on the predictive power of online community traffic with regard to stock prices. Using the largest dataset to date, spanning 8 years and almost the complete set of SP500 stocks, we train a classifier using a set of features entirely extracted from web-traffic data of financial online communities. The classifier is shown to outperform the predictive power of a baseline classifier solely based on price time-series, and to have similar performances as the classifier built considering price and traffic features together. The best predictive performances are achieved when information about stock capitalization is coupled with …

Detecting Mobility Patterns In Mobile Phone Data From The Ivory Coast, Matthew Dixon, Spencer P. Aiello, Funmi Fapohunda, William Goldstein

Business Analytics and Information Systems

This paper investigates the Data for Development (D4D) challenge [3], an open challenge set by the French mobile phone company, Orange, who have provided anonymized records of their customers in the Ivory Coast. This data spans a 5 month (150 day) horizon spread across 4 different sets containing antenna-to-antenna traffic, trace data for 50,000 customers at varying spatial resolution, and social graphs for 5,000 customers. By leveraging cloud-based and open-source analytics infrastructure to (1) merge the D4D datasets with Geographic Information System (GIS) data and (2) apply data mining algorithms, this paper presents a number of techniques for detecting mobility …

Multiple Bounding Boxes Algorithm In Collision Detection And Its Performances In Sequential Vs Cuda Parallel Processing, Min Qi

Electronic Theses and Dissertations

The traditional method for detecting collisions in a 2D computer game uses a axisaligned bounding box around each sprite, and checks to determine if the bounding boxes overlap periodically. Using this single bounding box method may result in a large amount of pixel intersection tests, since a sprite may be composed of areas where the pixels are empty and the intersecting bounding box test results in false positives.

Our algorithm analysis shows that the optimal two or three bounding boxes is the best partition we can get for a reasonable time complexity. The results further show significantly diminishing returns for …

California State University, San Bernardino Managing Cloud Computing Resources With Cloudstack Using Kvm Hypervisor, Ian Buckner Jacobs

Theses Digitization Project

The purpose of this project is to present a detailed description of the Enterprise Cloud Computing implementation of CloudStack using KVM. It will explain the purpose and features of the system, the interfaces of the system, what the system will do, and the constraints or limitations implementing CloudStack with the hardware available for this study.

Online Examination System, Yuvesh Kumar Singh

Theses Digitization Project

The purpose of this project focused on how to implement a secure environment for online-examination in an academic and business environment without the need of any special network topologies and hardware devices. It will not only reduce the instructor's load, but will also enhance the system flexibility to fit every instructor's needs and from the examinees point of view. Contains source code.