Computer Sciences Commons^™

Hotel Management System, Yimin Jin

All Capstone Projects

With the development of social service industries, using software to manage the hotel business requirements are gradually warming, conditional hotel before going to the relevant hotel management through, resolved depends on the original manual records management, inefficient, error-prone flaws, the hotel industry itself to provide the quality of service and the ability to have it higher requirements, hotel information management system is therefore increasingly attention.

Hotel information management system to achieve the hotel rooms management, customer information management, customer management add , modify customer management, customer management function.so the whole hotel information management system is divided into two parts, room …

An Adaptive Computational Model For Personalized Persuasion, Yilin Kang, Ah-Hwee Tan, Chunyan Miao

Research Collection School Of Computing and Information Systems

While a variety of persuasion agents have been created and applied in different domains such as marketing, military training and health industry, there is a lack of a model which can provide a unified framework for different persuasion strategies. Specifically, persuasion is not adaptable to the individuals’ personal states in different situations. Grounded in the Elaboration Likelihood Model (ELM), this paper presents a computational model called Model for Adaptive Persuasion (MAP) for virtual agents. MAP is a semi-connected network model which enables an agent to adapt its persuasion strategies through feedback. We have implemented and evaluated a MAP-based virtual nurse …

Reliability Assessment For Distributed Systems Via Communication Abstraction And Refinement, Lin Gui, Jun Sun, Yang Liu, Jin Song Dong

Research Collection School Of Computing and Information Systems

Distributed systems like cloud-based services are ever more popular. Assessing the reliability of distributed systems is highly non-trivial. Particularly, the order of executions among distributed components adds a dimension of non-determinism, which invalidates existing reliability assessment methods based on Markov chains. Probabilistic model checking based on models like Markov decision processes is designed to deal with scenarios involving both probabilistic behavior (e.g., reliabilities of system components) and non-determinism. However, its application is currently limited by state space explosion, which makes reliability assessment of distributed system particularly difficult. In this work, we improve the probabilistic model checking through a method of …

Towards City-Scale Mobile Crowdsourcing: Task Recommendations Under Trajectory Uncertainties, Chen Cen, Shih-Fen Cheng, Hoong Chuin Lau, Archan Misra

Research Collection School Of Computing and Information Systems

In this work, we investigate the problem of large-scale mobile crowdsourcing, where workers are financially motivated to perform location-based tasks physically. Unlike current industry practice that relies on workers to manually pick tasks to perform, we automatically make task recommendation based on workers’ historical trajectories and desired time budgets. The challenge of predicting workers’ trajectories is that it is faced with uncertainties, as a worker does not take same routes every day. In this work, we depart from deterministic modeling and study the stochastic task recommendation problem where each worker is associated with several predicted routine routes with probabilities. We …

Message Passing For Collective Graphical Models, Tao Sun, Daniel Sheldon, Akshat Kumar

Research Collection School Of Computing and Information Systems

Collective graphical models (CGMs) are a formalism for inference and learning about a population of independent and identically distributed individuals when only noisy aggregate data are available. We highlight a close connection between approximate MAP inference in CGMs and marginal inference in standard graphical models. The connection leads us to derive a novel Belief Propagation (BP) style algorithm for collective graphical models. Mathematically, the algorithm is a strict generalization of BP—it can be viewed as an extension to minimize the Bethe free energy plus additional energy terms that are non-linear functions of the marginals. For CGMs, the algorithm is much …

Landmark Classification With Hierarchical Multi-Modal Exemplar Feature, Lei Zhu, Jialie Shen, Hai Jin, Liang Xie, Ran Zheng

Research Collection School Of Computing and Information Systems

Landmark image classification attracts increasing research attention due to its great importance in real applications, ranging from travel guide recommendation to 3-D modelling and visualization of geolocation. While large amount of efforts have been invested, it still remains unsolved by academia and industry. One of the key reasons is the large intra-class variance rooted from the diverse visual appearance of landmark images. Distinguished from most existing methods based on scalable image search, we approach the problem from a new perspective and model landmark classification as multi-modal categorization, which enjoys advantages of low storage overhead and high classification efficiency. Toward this …

No Thermal Anomalies In The Mantle Transition Zone Beneath An Incipient Continental Rift: Evidence From The First Receiver Function Study Across The Okavango Rift Zone, Botswana, Youqiang Yu, Kelly H. Liu, M. Moidaki, Cory A. Reed, Stephen S. Gao

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Mechanisms leading to the initiation and early-stage development of continental rifts remain enigmatic, in spite of numerous studies. Among the various rifting models, which were developed mostly based on studies of mature rifts, far-field stresses originating from plate interactions (passive rifting) and nearby active mantle upwelling (active rifting) are commonly used to explain rift dynamics. Situated atop of the hypothesized African Superplume, the incipient Okavango Rift Zone (ORZ) of northern Botswana is ideal to investigate the role of mantle plumes in rift initiation and development, as well as the interaction between the upper and lower mantle. The ORZ developed within …

State Preserving Extreme Learning Machine For Face Recognition, Md. Zahangir Alom, Paheding Sidike, Vijayan K. Asari, Tarek M. Taha

Electrical and Computer Engineering Faculty Publications

Extreme Learning Machine (ELM) has been introduced as a new algorithm for training single hidden layer feed-forward neural networks (SLFNs) instead of the classical gradient-based algorithms. Based on the consistency property of data, which enforce similar samples to share similar properties, ELM is a biologically inspired learning algorithm with SLFNs that learns much faster with good generalization and performs well in classification applications. However, the random generation of the weight matrix in current ELM based techniques leads to the possibility of unstable outputs in the learning and testing phases. Therefore, we present a novel approach for computing the weight matrix …

Accuracy Comparison Of Numerical Integration Algorithms For Real-Time Hybrid Simulations, Ganesh Anant Reddy

Civil & Environmental Engineering Theses & Dissertations

The use of accurate numerical integration algorithms is one of the key factors for a successful real-time hybrid simulation (RTHS). In RTHSs, explicit integration algorithms are preferred more than implicit methods since all calculations need to be completed within a given time step during simulation. Explicit methods require the use of effective stiffness and damping for experimental substructures, which are incorporated into the calculation of the integration parameters. In general, those values that are greater than the expected stiffness and damping of the experimental substructure are used to ensure the stability of simulation. If a rate-dependent and nonlinear experimental substructure …

Automatic Video Self Modeling For Voice Disorder, Ju Shen, Changpeng Ti, Anusha Raghunathan, Sen-Ching S. Cheung, Rita Patel

Computer Science Faculty Publications

Video self modeling (VSM) is a behavioral intervention technique in which a learner models a target behavior by watching a video of him- or herself. In the field of speech language pathology, the approach of VSM has been successfully used for treatment of language in children with Autism and in individuals with fluency disorder of stuttering. Technical challenges remain in creating VSM contents that depict previously unseen behaviors. In this paper, we propose a novel system that synthesizes new video sequences for VSM treatment of patients with voice disorders. Starting with a video recording of a voice-disorder patient, the proposed …

Salient Object Detection Via Augmented Hypotheses, Tam Nguyen, Jose Sepulveda

Computer Science Faculty Publications

In this paper, we propose using augmented hypotheses which consider objectness, foreground, and compactness for salient object detection. Our algorithm consists of four basic steps. First, our method generates the objectness map via objectness hypotheses. Based on the objectness map, we estimate the foreground margin and compute the corresponding foreground map which prefers the foreground objects. From the objectness map and the foreground map, the compactness map is formed to favor the compact objects. We then derive a saliency measure that produces a pixel-accurate saliency map which uniformly covers the objects of interest and consistently separates foreground and background.

We …

Between Worlds: Securing Mixed Javascript/Actionscript Multi-Party Web Content, Phu Huu Phung, Maliheh Monshizadeh, Meera Sridhar, Kevin W. Hamlen, V. N. Venkatakrishnan

Computer Science Faculty Publications

Mixed Flash and JavaScript content has become increasingly prevalent; its purveyance of dynamic features unique to each platform has popularized it for myriad Web development projects. Although Flash and JavaScript security has been examined extensively, the security of untrusted content that combines both has received considerably less attention. This article considers this fusion in detail, outlining several practical scenarios that threaten the security of Web applications. The severity of these attacks warrants the development of new techniques that address the security of Flash-JavaScript content considered as a whole, in contrast to prior solutions that have examined Flash or JavaScript security …

Domain Specific Document Retrieval Framework For Real-Time Social Health Data, Swapnil Soni

Kno.e.sis Publications

With the advent of the web search and microblogging, the percentage of Online Health Information Seekers (OHIS) using these online services to share and seek health real-time information has in- creased exponentially. OHIS use web search engines or microblogging search services to seek out latest, relevant as well as reliable health in- formation. When OHIS turn to microblogging search services to search real-time content, trends and breaking news, etc. the search results are not promising. Two major challenges exist in the current microblogging search engines are keyword based techniques and results do not contain real-time information. To address these challenges, …

Characterizations Of Levy Distribution Via Sub-Independence Of The Random Variables And Truncated Moments, Gholamhossein G. Hamedani, M. Ahsanullah, Seyed Morteza Najibi

Mathematics, Statistics and Computer Science Faculty Research and Publications

The concept of sub-independence is based on the convolution of the distributions of the random variables. It is much weaker than that of independence, but is shown to be sufficient to yield the conclusions of important theorems and results in probability and statistics. It also provides a measure of dissociation between two random variables which is much stronger than uncorrelatedness. Following Ahsanullah and Nevzorov (2014), we present certain characterizations of Levy distribution based on: (i) the sub-independence of the random variables; (ii) a simple relationship between two truncated moments; (iii) conditional expectation of certain function of the random variable. In …

Interference Effects For Intermediate Energy Electron-Impact Ionization Of H₂ And N₂ Molecules, Zehra Nur Ozer, Hari Chaluvadi, Don H. Madison, Mevlut Dogan

Physics Faculty Research & Creative Works

We have studied electron impact ionization of H₂ and N₂ molecules at intermediate energies to look for possible two center interference effects experimentally and theoretically. Here we report a study of the interference factor I for 250 eV electron-impact ionization. The experimental measurements are performed using a crossed-beam-type electron-electron coincidence spectrometer and theoretical calculations are obtained using the Molecular Three Body Distorted Wave Approximation (M3DW). We found that the I-factor demonstrated strong evidence for two-center interference effects for both H₂ and N₂. We also found that the I-factor is more sensitive to projectile angular scans …

Tools For Outreach Presentations, Ronald I. Greenberg, Dale Reed

Computer Science: Faculty Publications and Other Works

We present resources we have constructed and culled from the internet that can be used in computing outreach visits in K–12 classrooms, especially high schools. We have used such tools at about 100 schools, reaching several thousand students, and achieving positive attitudinal responses in surveys of several hundred of these students.

Missing Photos, Suffering Withdrawal, Or Finding Freedom? How Experiences Of Social Media Non-Use Influence The Likelihood Of Reversion, Eric P.S. Baumer, Shion Guha, Emily Quan, David Mimno, Geri K. Gay

Mathematics, Statistics and Computer Science Faculty Research and Publications

This article examines social media reversion, when a user intentionally ceases using a social media site but then later resumes use of the site. We analyze a convenience sample of survey data from people who volunteered to stay off Facebook for 99 days but, in some cases, returned before that time. We conduct three separate analyses to triangulate on the phenomenon of reversion: simple quantitative predictors of reversion, factor analysis of adjectives used by respondents to describe their experiences of not using Facebook, and statistical topic analysis of free-text responses. Significant factors predicting either increased or decreased likelihood of reversion …

An Improved Classifier Chain Ensemble For Multi-Dimensionalclassification With Conditional Dependence, Joseph Ethan Heydorn

Theses and Dissertations

We focus on multi-dimensional classification (MDC) problems with conditional dependence, which we call multiple output dependence (MOD) problems. MDC is the task of predicting a vector of categorical outputs for each input. Conditional dependence in MDC means that the choice for one output value affects the choice for others, so it is not desirable to predict outputs independently. We show that conditional dependence in MDC implies that a single input can map to multiple correct output vectors. This means it is desirable to find multiple correct output vectors per input. Current solutions for MOD problems are not sufficient because they …

Meta-Raps Hybridization With Machine Learning Algorithms, Fatemah Al-Duoli

Engineering Management & Systems Engineering Theses & Dissertations

This dissertation focuses on advancing the Metaheuristic for Randomized Priority Search algorithm, known as Meta-RaPS, by integrating it with machine learning algorithms. Introducing a new metaheuristic algorithm starts with demonstrating its performance. This is accomplished by using the new algorithm to solve various combinatorial optimization problems in their basic form. The next stage focuses on advancing the new algorithm by strengthening its relatively weaker characteristics. In the third traditional stage, the algorithms are exercised in solving more complex optimization problems. In the case of effective algorithms, the second and third stages can occur in parallel as researchers are eager to …

Estimating Effectiveness Of Twitter Messages With A Personalized Machine Learning Approach, Xunhu Sun

Theses and Dissertations

In Twitter, many aspects of retweeting behavior, which is the most effective indicator of spreading effectiveness of the tweet, have been researched, such as whether a reader will retweet a certain tweet or not. However, the total number of retweets of the tweet, which is the quantitative measure of quality, has not been well addressed by existing work. To estimate the number of retweets and associated factors, this paper proposes a procedure to develop a personalized model for one author. The training data comes from the author’s past tweets. We propose 3 types of new features based on the contents …

Enhanced Presentation Of Tomographic Data, Carmen Watts Clayton, Bernice Mills, George Beffleben, Thien Vu-Nguyen

STAR Program Research Presentations

X-ray tomography yields a very large amount of data in three dimensions. Effectively displaying this data to a broad audience is a challenge. Techniques are discussed to improve presentation of movies of both 2D and 3D tomographic data using commercially available softwares.

Privacy-Preserving Offloading Of Mobile App To The Public Cloud, Yue Duan, Mu Zhang, Heng Yin, Yuzhe Tang

Research Collection School Of Computing and Information Systems

To support intensive computations on resource-restricting mobile devices, studies have been made to enable the offloading of a part of a mobile program to the cloud. However, none of the existing approaches considers user privacy when transmitting code and data off the device, resulting in potential privacy breach. In this paper, we present the design and implementation of a system that automatically performs fine-grained privacy-preserving Android app offloading. It utilizes static analysis and bytecode instrumentation techniques to ensure transparent and efficient Android app offloading while preserving user privacy. We evaluate the effectiveness and performance of our system using two Android …

A New Public Remote Integrity Checking Scheme With User Privacy, Yiteng Feng, Yi Mu, Guomin Yang, Joseph Liu

Research Collection School Of Computing and Information Systems

With a cloud storage, users can store their data files on a remote cloud server with a high quality on-demand cloud service and are able to share their data with other users. Since cloud servers are not usually regarded as fully trusted and the cloud data can be shared amongst users, the integrity checking of the remote files has become an important issue. A number of remote data integrity checking protocols have been proposed in the literature to allow public auditing of cloud data by a third party auditor (TPA). However, user privacy is not taken into account in most …

An Automatic Approach To Detect Unusual Events In Software Repositories, Larissa Leite, Christoph Treude, Fernando Figueira Filho

Research Collection School Of Computing and Information Systems

This work presents an automatic approach to detect unusual events in software repositories. The approach collects data from source code repositories and analyzes new commits based on historical data in order to detect unusual events that are displayed to developers and managers in an awareness tool.

Improving Patient Flow With Data-Driven Patient Prioritization Method In The Emergency Department, Kar Way Tan, Sean Shao Wei Lam

Research Collection School Of Computing and Information Systems

We aim to improve the length-of-stay (LOS) of patients in the Emergency Department (ED) ambulatory care area. We propose the use of real-time computerized physician order entry data and ED patient flow management system to estimate the consultation time of patients re-entering the queue to consult a doctor again after receiving treatment or results of tests. The estimation allows decision-makers to apply dynamic prioritization strategies that help the ED to identify patients who can complete their ED treatment process quickly, freeing up resources in the ED and lowering overall LOS.

Efficient Simultaneous Task And Motion Planning For Multiple Mobile Robots Using Task Reachability Graphs, Brad Woosley

Student Work

In this thesis, we consider the problem of efficient navigation by robots in initially unknown environments while performing tasks at certain locations. In initially unknown environments, the path plans might change dynamically as the robot discovers obstacles along its route. Because robots have limited energy, adaptations to the task schedule of the robot in conjunction with updates to its path plan are required so that the robot can perform its tasks while reducing time and energy expended. However, most existing techniques consider robot path planning and task planning as separate problems. This thesis plans to bridge this gap by developing …

A Comparative Study Between Motivated Learning And Reinforcement Learning, James T. Graham, Janusz A. Starzyk, Zhen Ni, Haibo He, T.-H. Teng, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

This paper analyzes advanced reinforcement learning techniques and compares some of them to motivated learning. Motivated learning is briefly discussed indicating its relation to reinforcement learning. A black box scenario for comparative analysis of learning efficiency in autonomous agents is developed and described. This is used to analyze selected algorithms. Reported results demonstrate that in the selected category of problems, motivated learning outperformed all reinforcement learning algorithms we compared with.

A New General Framework For Secure Public Key Encryption With Keyword Search, Rongmao Chen, Yi Mu, Guomin Yang, Fuchun Guo, Xiaofen Wang

Research Collection School Of Computing and Information Systems

Public Key Encryption with Keyword Search (PEKS), introduced by Boneh et al. in Eurocrypt’04, allows users to search encrypted documents on an untrusted server without revealing any information. This notion is very useful in many applications and has attracted a lot of attention by the cryptographic research community. However, one limitation of all the existing PEKS schemes is that they cannot resist the Keyword Guessing Attack (KGA) launched by a malicious server. In this paper, we propose a new PEKS framework named Dual-Server Public Key Encryption with Keyword Search (DS-PEKS). This new framework can withstand all the attacks, including the …

Attribute-Based Encryption With Efficient Verifiable Outsourced Decryption, Baodong Qin, Robert H. Deng, Shengli Liu, Siqi Ma

Research Collection School Of Computing and Information Systems

Attribute-based encryption (ABE) with outsourced decryption not only enables fine-grained sharing of encrypted data, but also overcomes the efficiency drawback (in terms of ciphertext size and decryption cost) of the standard ABE schemes. In particular, an ABE scheme with outsourced decryption allows a third party (e.g., a cloud server) to transform an ABE ciphertext into a (short) El Gamal-type ciphertext using a public transformation key provided by a user so that the latter can be decrypted much more efficiently than the former by the user. However, a shortcoming of the original outsourced ABE scheme is that the correctness of the …

Fast Optimal Aggregate Point Search For A Merged Set On Road Networks, Weiwei Sun, Chong Chen, Baihua Zheng, Chunan Chen, Liang Zhu, Weimo Liu, Yan Huang

Research Collection School Of Computing and Information Systems

Aggregate nearest neighbor query, which returns an optimal target point that minimizes the aggregate distance for a given query point set, is one of the most important operations in spatial databases and their application domains. This paper addresses the problem of finding the aggregate nearest neighbor for a merged set that consists of the given query point set and multiple points needed to be selected from a candidate set, which we name as merged aggregate nearest neighbor(MANN) query. This paper proposes two algorithms to process MANN query on road networks when aggregate function is max. Then, we extend the algorithms …