Computer Sciences Commons^™

Semi-Supervised Hashing With Semantic Confidence For Large Scale Visual Search, Yingwei Pan, Ting Yao, Houqiang Li, Chong-Wah Ngo, Tao Mei

Research Collection School Of Computing and Information Systems

Similarity search is one of the fundamental problems for large scale multimedia applications. Hashing techniques, as one popular strategy, have been intensively investigated owing to the speed and memory efficiency. Recent research has shown that leveraging supervised information can lead to high quality hashing. However, most existing supervised methods learn hashing function by treating each training example equally while ignoring the different semantic degree related to the label, i.e. semantic confidence, of different examples. In this paper, we propose a novel semi-supervised hashing framework by leveraging semantic confidence. Specifically, a confidence factor is first assigned to each example by neighbor …

Efficacy Of Social Media Utilization By Public Accounting Firms: Findings And Directions For Future Research, B. Eschenbrenner, Fiona Fui-Hoon Nah, V. Telaprolu

Research Collection School Of Computing and Information Systems

Social media presents a new platform for businesses to communicate and interact with others, both internally and externally. Social media may be utilized for activities such as sharing success stories and providing industry updates. Although a plethora of opportunities to achieve business objectives with social media usage exists, the efficacy of its use by public accounting firms is unclear. This article identifies the business objectives that Big 4 and second-tier firms are pursuing with social media. Primary business objectives being fulfilled by social media include Knowledge Sharing, Branding and Marketing, and Socialization and Onboarding. The findings suggest that Big 4 …

Facilitating Image Search With A Scalable And Compact Semantic Mapping, Meng Wang, Weisheng Li, Dong Liu, Bingbing Ni, Jialie Shen, Shuicheng Yan

Research Collection School Of Computing and Information Systems

This paper introduces a novel approach to facilitating image search based on a compact semantic embedding. A novel method is developed to explicitly map concepts and image contents into a unified latent semantic space for the representation of semantic concept prototypes. Then, a linear embedding matrix is learned that maps images into the semantic space, such that each image is closer to its relevant concept prototype than other prototypes. In our approach, the semantic concepts equated with query keywords and the images mapped into the vicinity of the prototype are retrieved by our scheme. In addition, a computationally efficient method …

Towards A Robust Sparse Data Representation In Wireless Sensor Networks, Abu Alsheik Mohammad, Shaowei Lin, Hwee-Pink Tan, Dusit Niyato

Research Collection School Of Computing and Information Systems

Compressive sensing has been successfully used for optimized operations in wireless sensor networks. However, raw data collected by sensors may be neither originally sparse nor easily transformed into a sparse data representation. This paper addresses the problem of transforming source data collected by sensor nodes into sparse representation with a few nonzero elements. Our contributions that address three major issues include: 1) an effective method that extracts population sparsity of the data, 2) a sparsity ratio guarantee scheme, and 3) a customized leaerning algorithm of the sparsifying dictionary. We introduce an unsupervised neural network to extract an intrinsic sparse coding …

Cooperation In Delay-Tolerant Networks With Wireless Energy Transfer: Performance Analysis And Optimization, Dusit Niyato, Ping Wang, Hwee-Pink Tan, Walid Saad, Dong In Kim

Research Collection School Of Computing and Information Systems

We consider a delay-tolerant network (DTN) whose mobile nodes are assigned to collect packets from data sources and deliver them to a sink (i.e., a gateway). Each mobile node operates by using energy transferred wirelessly from the gateway. For such a network, two main issues are studied. First, when a mobile node is at the data source, this node must decide on whether to accept the packet received from the data source or not. In contrast, whenever a mobile node is at the gateway, it has to decide on whether to transmit the packets collected from the data sources or …

Ad-Hoc Automated Teller Machine Failure Forecast And Field Service Optimization, Michelle L. F. Cheong, Ping Shung Koo, B. Chandra Babu

Research Collection School Of Computing and Information Systems

As part of its overall effort to maintain good customer service while managing operational efficiency and reducing cost, a bank in Singapore has embarked on using data and decision analytics methodologies to perform better ad-hoc ATM failure forecasting and plan the field service engineers to repair the machines. We propose using a combined Data and Decision Analytics Framework which helps the analyst to first understand the business problem by collecting, preparing and exploring data to gain business insights, before proposing what objectives and solutions can and should be done to solve the problem. This paper reports the work in analyzing …

Fusing Heterogeneous Data For Alzheimer's Disease Classification, P. S. Pillai, Tze-Yun Leong

Research Collection School Of Computing and Information Systems

In multi-view learning, multimodal representations of a real world object or situation are integrated to learn its overall picture. Feature sets from distinct data sources carry different, yet complementary, information which, if analysed together, usually yield better insights and more accurate results. Neuro-degenerative disorders such as dementia are characterized by changes in multiple biomarkers. This work combines the features from neuroimaging and cerebrospinal fluid studies to distinguish Alzheimer's disease patients from healthy subjects. We apply statistical data fusion techniques on 101 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We examine whether fusion of biomarkers helps to improve diagnostic …

The Impact Of Meaningful High School Computer Science Experiences In The Chicago Public Schools, Lucia Dettori, Ronald I. Greenberg, Steven Mcgee, Dale Reed

Computer Science: Faculty Publications and Other Works

We report on initial outcomes of the Taste of Computing project, under which a meaningful computer science course has been initiated in many high schools of the Chicago Public Schools system. Surveys of students have shown that they attribute high value to the course and have experienced increases in their understanding and interest regarding the computing field. Data was also collected from teachers participating in professional development regarding their preparation and confidence in teaching the new course. We report on the strengths of various survey responses and their relationships, and we compare student responses by race and gender. The data …

Constructing Bfs Trees Using Tokens To Balance Speed And Network Traffic, Michael Spencer

UNLV Theses, Dissertations, Professional Papers, and Capstones

Constructing BFS trees rooted at each node of a network helps solve many problems. Reliable communication to other nodes is easily managed and metrics such as the network diameter, shortest path between any two nodes, the center, the radius, and others can be easily computed. A traditional way to form a BFS tree from each node is for all nodes to construct their trees in parallel. While this is the fastest way to accomplish this task, it also requires a large amount of network traffic. In this thesis, we present a way to use a token passing algorithm to form …

Long-Term Study Of Crowdfunding Platform: Predicting Project Success And Fundraising Amount, Jinwook Chung

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Crowdfunding that is the combination word of crowdsourcing and funding makes people can start a business easily. Legislating JOBS act in US played a major role in removing restricted barriers of crowdfunding on public offerings of fence and private funds for small business. The growth speed of crowdfunding takes some beating. Through Kickstarter that is a popular crowdfunding platform and being considered the typical case of crowdfunding, 480 million dollars and more than half a billion dollars were invested in about 19 thousand and 22 projects for 2013 and 2014 respectively. But in spite of the rapid growth, the successful …

Image Blur Detection With Two-Dimensional Haar Wavelet Transform, Sarat Kiran Andhavarapu

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Efficient detection of image blur and its extent is an open research problem in computer vision. Image blur has a negative impact on image quality. Blur is introduced into images due to various factors including limited contrast, improper exposure time or unstable device handling. Toward this end, an algorithm is presented for image blur detection with the use of Two-Dimensional Haar Wavelet transform (2D HWT). The algorithm is experimentally compared with two other image blur detection algorithms frequently cited in the literature. When evaluated over a sample of images, the algorithm performed on par or better than the two other …

New Product Development Flexibility In A Competitive Environment, Janne Kettunen, Yael Gruksha-Cockayne, Zeger Degraeve, Bert De Reyck

Research Collection Lee Kong Chian School Of Business

Managerial flexibility can have a significant impact on the value of new product development projects. We investigate how the market environment in which a firm operates influences the value and use of development flexibility. We characterize the market environment according to two dimensions, namely (i) its intensity, and (ii) its degree of innovation. We show that these two market characteristics can have a different effect on the value of flexibility. In particular, we show that more intense or innovative environments may increase or decrease the value of flexibility. For instance, we demonstrate that the option to defer a product launch …

Face Recognition Under Varying Illuminations, Mohammadreza Faraji

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Face recognition under illumination is really challenging. This dissertation proposes four effective methods to produce illumination-invariant features for images with various levels of illuminations. The proposed methods are called logarithmic fractal dimension (LFD), eight local directional patterns (ELDP), adaptive homomorphic eight local directional pat- terns (AH-ELDP), and complete eight local directional patterns (CELDP), respectively.

LFD, employing the log function and the fractal analysis (FA), produces a logarithmic fractal dimension (LFD) image that is illumination-invariant. The proposed FA feature-based method is an effective edge enhancer technique to extract and enhance facial features such as eyes, eyebrows, nose, and mouth.

The proposed …

Tweet Sentiment: From Classification To Quantification, Wei Gao, Fabrizio Sebastiani

Research Collection School Of Computing and Information Systems

Sentiment classification has become a ubiquitous enabling technology in the Twittersphere, since classifying tweets according to the sentiment they convey towards a given entity (be it a product, a person, a political party, or a policy) has many applications in political science, social science, market research, and many others. In this paper we contend that most previous studies dealing with tweet sentiment classification (TSC) use a suboptimal approach. The reason is that the final goal of most such studies is not estimating the class label (e.g., Positive, Negative, or Neutral) of individual tweets, but estimating the relative frequency (a.k.a. "prevalence") …

Faitcrowd: Fine Grained Truth Discovery For Crowdsourced Data Aggregation, Fenglong Ma, Yaliang Li, Qi Li, Minghui Qiu, Jing Gao, Shi Zhi, Lu Su, Bo Zhao, Jiawei Han

Research Collection School Of Computing and Information Systems

In crowdsourced data aggregation task, there exist conflicts in the answers provided by large numbers of sources on the same set of questions. The most important challenge for this task is to estimate source reliability and select answers that are provided by high-quality sources. Existing work solves this problem by simultaneously estimating sources' reliability and inferring questions' true answers (i.e., the truths). However, these methods assume that a source has the same reliability degree on all the questions, but ignore the fact that sources' reliability may vary significantly among different topics. To capture various expertise levels on different topics, we …

Sails: Hybrid Algorithm For The Team Orienteering Problem With Time Windows, Aldy Gunawan, Hoong Chuin Lau, Kun Lu

Research Collection School Of Computing and Information Systems

The Team Orienteering Problem with Time Windows (TOPTW) is the extended version of the Orienteering Problem where each node is limited by a given time window. The objective is to maximize the total collected score from a certain number of paths. In this paper, a hybridization of Simulated Annealing and Iterated Local Search, namely SAILS, is proposed to solve the TOPTW. The efficacy of the proposed algorithm is tested using benchmark instances. The results show that the proposed algorithm is competitive with the state-of-the-art algorithms in the literature. SAILS is able to improve the best known solutions for 19 benchmark …

Memes As Building Blocks: A Case Study On Evolutionary Optimization + Transfer Learning For Routing Problems, Liang Feng, Yew-Soon Ong, Ah-Hwee Tan, Ivor W. Tsang

Research Collection School Of Computing and Information Systems

A significantly under-explored area of evolutionary optimization in the literature is the study of optimization methodologies that can evolve along with the problems solved. Particularly, present evolutionary optimization approaches generally start their search from scratch or the ground-zero state of knowledge, independent of how similar the given new problem of interest is to those optimized previously. There has thus been the apparent lack of automated knowledge transfers and reuse across problems. Taking this cue, this paper presents a Memetic Computational Paradigm based on Evolutionary Optimization + Transfer Learning for search, one that models how human solves problems, and embarks on …

Apparatus And Method For Determining The Location Of A Mobile Device Using Multiple Wireless Access Points, Kyle Jamieson, Jie Xiong

Research Collection School Of Computing and Information Systems

A method and apparatus are provided for determining the location of a mobile device using multiple wireless access points, each wireless access point comprising multiple antennas. The method comprises receiving a communication signal from the mobile device at said multiple antennas of said multiple wireless access points. For each wireless access point, angle-of-arrival information of the received communication signal at the wireless access point is determined, based on a difference in phase of the received signal between different antennas. The determined angle-of-arrival information for the received communication signal from the mobile device is then collected from each of the multiple …

On Mining Lifestyles From User Trip Data, Meng-Fen Chiang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Large cities today are facing major challenges in planning and policy formulation to keep their growth sustainable. In this paper, we aim to gain useful insights about people living in a city by developing novel models to mine user lifestyles represented by the users' activity centers. Two models, namely ACMM and ACHMM, have been developed to learn the activity centers of each user using a large dataset of bus and subway train trips performed by passengers in Singapore. We show that ACHMM and ACMM yield similar accuracies in location prediction task. We also propose methods to automatically predict "home", "work" …

Deep Learning For Just-In-Time Defect Prediction, Xinli Yang, David Lo, Xin Xia, Yun Zhang, Jianling Sun

Research Collection School Of Computing and Information Systems

Defect prediction is a very meaningful topic, particularly at change-level. Change-level defect prediction, which is also referred as just-in-time defect prediction, could not only ensure software quality in the development process, but also make the developers check and fix the defects in time. Nowadays, deep learning is a hot topic in the machine learning literature. Whether deep learning can be used to improve the performance of just-in-time defect prediction is still uninvestigated. In this paper, to bridge this research gap, we propose an approach Deeper which leverages deep learning techniques to predict defect-prone changes. We first build a set of …

Topological Spatial Verification For Instance Search, Wei Zhang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

This paper proposes an elastic spatial verification method for Instance Search, particularly for dealing with non-planar and non-rigid queries exhibiting complex spatial transformations. Different from existing models that map keypoints between images based on a linear transformation (e.g., affine, homography), our model exploits the topological arrangement of keypoints to address the non-linear spatial transformations that are extremely common in real life situations. In particular, we propose a novel technique to elastically verify the topological spatial consistency with the triangulated graph through a "sketch-and-match" scheme. The spatial topology configuration, emphasizing relative positioning rather than absolute coordinates, is first sketched by a …

A Study On The Geographical Distribution Of Brazil’S Prestigious Software Developers, Fernando Figueira Filho, Marcelo Gattermann Perin, Christoph Treude, Sabrina Marczak, Leandro De Almeida Melo, Igor Marques Da Silva, Lucas Bibiano Dos Santos

Research Collection School Of Computing and Information Systems

Brazil is an emerging economy with many IT initiatives from public and private sectors. To evaluate the progress of such initiatives, we study the geographical distribution of software developers in Brazil, in particular which of the Brazilian states succeed the most in attracting and nurturing them. We compare the prestige of developers with socio-economic data and find that (i) prestigious developers tend to be located in the most economically developed regions of Brazil, (ii) they are likely to follow others in the same state they are located in, (iii) they are likely to follow other prestigious developers, and (iv) they …

Identity-Based Lossy Encryption From Learning With Errors, Jingnan He, Bao Li, Xianhui Lu, Dingding Jia, Haiyang Xue, Xiaochao Sun

Research Collection School Of Computing and Information Systems

We extend the notion of lossy encryption to the scenario of identity-based encryption (IBE), and propose a new primitive called identity-based lossy encryption (IBLE). Similar as the case of lossy encryption, we show that IBLE can also achieve selective opening security. Finally, we present a construction of IBLE from the assumption of learning with errors.

Effects Of The Use Of Points, Leaderboards And Badges On In-Game Purchases Of Virtual Goods, Fiona Fui-Hoon Nah, Lakshmi Sushma Daggubati, Amith Tarigonda, Raghu Vinay Nuvvula, Ofir Turel

Research Collection School Of Computing and Information Systems

Game design elements are major factors in gamification. In this study, we seek to examine the impact of game design elements on users’ in-game purchases of virtual goods. The purchase of virtual goods due to players’ intrinsic motivation has been studied but little is known about the purchase of virtual goods due to the use of game design elements (i.e., Points, Leaderboards and Badges) built into the games. Extending our knowledge to this realm can help researchers to better understand gamers’ behaviors, and game designers and marketers to better promote and sell virtual goods in online games.

Mobile Cloud Computing Based Non Rigid Registration For Image Guided Surgery, Arun Brahmavar Vishwanatha

Computer Science Theses & Dissertations

In this thesis we present the design and implementation of a Mobile Cloud computing platform for non-rigid registration required in Image Guided Surgery (MCIGS). MCIGS contributes in flexible, portable and accurate alignment of pre-operative brain data with intra-operative MRI, for image guided diagnosis and therapy and endoscopic skull base surgery. Improved precision of image guided therapy and specifically neurosurgery procedures is known to result in the improved prognosis for brain tumor patients. MCI GS system is tested with Physics Based Non-Rigid Registration method form ITK. Our preliminary results for brain images indicate that the proposed system over Wi-Fi can be …

Developing An Efficient Failure Prediction Model In Support Of A Distributed Operating System For Autonomic Networks, Jondi Hesham Abed

Dissertations

Network fault management has been a vibrant research area in computer networks because of the immediate benefits that it can deliver to network operators and service providers. Unfortunately, most existing fault management systems (to date) target a specific domain. Ideally, collateral damage due to network failures could be mitigated if they could be predicted in advance of their occurrence. Homeostasis could then be more easily maintained by taking corrective measures to avoid imminent failures before they occurred.

Here, we set out with precisely the objective of designing an automated system that is capable of predicting the imminent occurrence of system …

Maintaining Consistency Between A Design Model And Its Implementation, Hector M. Chavez

Dissertations

Software design models are increasingly being used as part of the software development process as analysis and design artifacts and to automatically generate code that developers can further modify or extend, greatly expediting the software development process. This, however, has introduced the challenge of maintaining consistency between the design models and their implementation as they evolve during the development process. Traditional software testing and verification techniques have been well studied in the past, and they are an integral part of many software development projects, however, they are not well suited for consistency checking between a design model and its implementation. …

Automatic Detection And Denoising Of Signals In Large Geophysical Datasets, Gabriel O. Trisca

Boise State University Theses and Dissertations

To fully understand the complex interactions of various phenomena in the natural world, scientific disciplines such as geology and seismology increasingly rely upon analyzing large amounts of observations. However, data collection is growing at a faster rate than what is currently possible to analyze through traditional approaches. These datasets, supplied by the increasing use of sensors and remote sensing, require specialized computer programs to effectively analyze complex and expansive volumes of data.

Elaborating on existing geophysical data processing approaches for infrasound data collected from an avalanche-prone area, this project proposes new techniques for processing large geophysical datasets. These improved techniques …

Formal Specification Driven Development, Titus Fofung

Dissertations, Theses and Capstone Projects (Full IR Collection)

This paper researches a quantitative metric of investigating Formal Specification-Driven Development (FSDD). Formal specification is needed at the beginning of the development process to prevent ambiguity and to improve the quality through corrections of errors found in the late phases of a traditional design process, Software Development Life Cycle (SDLC). The research is conducted with capstone students using both the FSDD and the SDLC (traditional) models and a quantitative analysis is presented to evaluate the internal quality of the software. The tool used to measure the internal quality is the .NET 2013 analysis tool. Formal Specification-Driven Development (FSDD) is a …

Data Analysis With Map Reduce Programming Paradigm, Mandana Bozorgi

UNLV Theses, Dissertations, Professional Papers, and Capstones

Abstract In this thesis, we present a summary of our activities associated with the storage and query processing of Google 1T 5-gram data set. We rst give a brief introduction to some of the implementation techniques for the relational algebra followed by a Map Reduce implementation of the same operators. We then implement a database schema in Hive for the Google 1T 5-gram data set.

The thesis will further look into the query processing with Hive and Pig in the Hadoop setting.

More specially, we report statistics for our queries in this environment.