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- Title
- A Comparison of Self-Service Technologies (SSTs) in the U.S. Restaurant Industry: An Evaluation of Consumer Perceived Value, Satisfaction, and Behavioral Intentions.
- Creator
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Zaitouni, Motaz, Murphy, Kevin, Zhang, Tingting, Wei, Wei, Severt, Kimberly, University of Central Florida
- Abstract / Description
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Innovation in technology has been growing rapidly in recent years. Many restaurants have been utilizing different types of self-service technologies (SSTs) to enhance their operations and customer satisfaction. Despite, the rapid spread of SSTs in the restaurant industry, very limited empirical research has been conducted to evaluate the influence of SSTs type on customer dining experience.Therefore, the purpose of this dissertation was to examine the SSTs values that influence restaurant...
Show moreInnovation in technology has been growing rapidly in recent years. Many restaurants have been utilizing different types of self-service technologies (SSTs) to enhance their operations and customer satisfaction. Despite, the rapid spread of SSTs in the restaurant industry, very limited empirical research has been conducted to evaluate the influence of SSTs type on customer dining experience.Therefore, the purpose of this dissertation was to examine the SSTs values that influence restaurant customers' satisfaction and their decision to continue to reuse SSTs. More specifically, this study utilized the Theory of Consumption Values (TCV) to examine consumers' perception of the SST values across different types of restaurant proprietary SSTs (kiosk, tabletop, restaurant mobile app, and web-based SSTs).In order to examine the hypothesized relationships, a quantitative research approach was utilized with the survey research method. An online self-administered questionnaire was developed in Qualtrics for each type of SSTs. The questionnaires were distributed utilizing Amazon mechanical Turk (MTurk). Data was collected in May 2019 from restaurant customers who previously used/experienced one of four SSTs. A total of 619 questionnaires were usable and retained for the data analysis procedures. PLS-SEM and PLS-MGA were utilized to evaluate the conceptual model.The results revealed that emotional values were the most significant SST values that influence customer satisfaction with the restaurant SST experience and continuance intention. SSTs customization features were positively related to customer satisfaction across all the SSTs included in this study. The theoretical and practical implications of the results were discussed as well as the limitations of the study and future research directions.
Show less - Date Issued
- 2019
- Identifier
- CFE0007744, ucf:52406
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007744
- Title
- Predicting Students' Academic Performance with Decision Tree and Neural Network.
- Creator
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Feng, Junshuai, Jha, Sumit Kumar, Zhang, Wei, Zhang, Shaojie, University of Central Florida
- Abstract / Description
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Educational Data Mining (EDM) is a developing research field that involves many techniques to explore data relating to educational background. EDM can analyze and resolve educational data with computational methods to address educational questions. Similar to EDM, neural networks have been utilized in widespread and successful data mining applications. In this paper, synthetic datasets are employed since this paper aims to explore the methodologies such as decision tree classifiers and neural...
Show moreEducational Data Mining (EDM) is a developing research field that involves many techniques to explore data relating to educational background. EDM can analyze and resolve educational data with computational methods to address educational questions. Similar to EDM, neural networks have been utilized in widespread and successful data mining applications. In this paper, synthetic datasets are employed since this paper aims to explore the methodologies such as decision tree classifiers and neural networks to predict student performance in the context of EDM. Firstly, it introduces EDM and some relative works that have been accomplished previously in this field along with their datasets and computational results. Then, it demonstrates how the synthetic student dataset is generated, analyzes some input attributes from the dataset such as gender and high school GPA, and delivers with some visualization results to determine which classification methods approaches are the most efficient. After testing the data with decision tree classifiers and neural networks methodologies, it concludes the effectiveness of both approaches in terms of the model evaluation performance as well as discussing some of the most promising future work of this research.
Show less - Date Issued
- 2019
- Identifier
- CFE0007455, ucf:52680
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007455
- Title
- Decision-making for Vehicle Path Planning.
- Creator
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Xu, Jun, Turgut, Damla, Zhang, Shaojie, Zhang, Wei, Hasan, Samiul, University of Central Florida
- Abstract / Description
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This dissertation presents novel algorithms for vehicle path planning in scenarios where the environment changes. In these dynamic scenarios the path of the vehicle needs to adapt to changes in the real world. In these scenarios, higher performance paths can be achieved if we are able to predict the future state of the world, by learning the way it evolves from historical data. We are relying on recent advances in the field of deep learning and reinforcement learning to learn appropriate...
Show moreThis dissertation presents novel algorithms for vehicle path planning in scenarios where the environment changes. In these dynamic scenarios the path of the vehicle needs to adapt to changes in the real world. In these scenarios, higher performance paths can be achieved if we are able to predict the future state of the world, by learning the way it evolves from historical data. We are relying on recent advances in the field of deep learning and reinforcement learning to learn appropriate world models and path planning behaviors.There are many different practical applications that map to this model. In this dissertation we propose algorithms for two applications that are very different in domain but share important formal similarities: the scheduling of taxi services in a large city and tracking wild animals with an unmanned aerial vehicle.The first application models a centralized taxi dispatch center in a big city. It is a multivariate optimization problem for taxi time scheduling and path planning. The first goal here is to balance the taxi service demand and supply ratio in the city. The second goal is to minimize passenger waiting time and taxi idle driving distance. We design different learning models that capture taxi demand and destination distribution patterns from historical taxi data. The predictions are evaluated with real-world taxi trip records. The predicted taxi demand and destination is used to build a taxi dispatch model. The taxi assignment and re-balance is optimized by solving a Mixed Integer Programming (MIP) problem.The second application concerns animal monitoring using an unmanned aerial vehicle (UAV) to search and track wild animals in a large geographic area. We propose two different path planing approaches for the UAV. The first one is based on the UAV controller solving Markov decision process (MDP). The second algorithms relies on the past recorded animal appearances. We designed a learning model that captures animal appearance patterns and predicts the distribution of future animal appearances. We compare the proposed path planning approaches with traditional methods and evaluated them in terms of collected value of information (VoI), message delay and percentage of events collected.
Show less - Date Issued
- 2019
- Identifier
- CFE0007557, ucf:52606
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007557
- Title
- detecting anomalies from big data system logs.
- Creator
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Lu, Siyang, Wang, Liqiang, Zhang, Shaojie, Zhang, Wei, Wu, Dazhong, University of Central Florida
- Abstract / Description
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Nowadays, big data systems (e.g., Hadoop and Spark) are being widely adopted by many domains for offering effective data solutions, such as manufacturing, healthcare, education, and media. A common problem about big data systems is called anomaly, e.g., a status deviated from normal execution, which decreases the performance of computation or kills running programs. It is becoming a necessity to detect anomalies and analyze their causes. An effective and economical approach is to analyze...
Show moreNowadays, big data systems (e.g., Hadoop and Spark) are being widely adopted by many domains for offering effective data solutions, such as manufacturing, healthcare, education, and media. A common problem about big data systems is called anomaly, e.g., a status deviated from normal execution, which decreases the performance of computation or kills running programs. It is becoming a necessity to detect anomalies and analyze their causes. An effective and economical approach is to analyze system logs. Big data systems produce numerous unstructured logs that contain buried valuable information. However manually detecting anomalies from system logs is a tedious and daunting task.This dissertation proposes four approaches that can accurately and automatically analyze anomalies from big data system logs without extra monitoring overhead. Moreover, to detect abnormal tasks in Spark logs and analyze root causes, we design a utility to conduct fault injection and collect logs from multiple compute nodes. (1) Our first method is a statistical-based approach that can locate those abnormal tasks and calculate the weights of factors for analyzing the root causes. In the experiment, four potential root causes are considered, i.e., CPU, memory, network, and disk I/O. The experimental results show that the proposed approach is accurate in detecting abnormal tasks as well as finding the root causes. (2) To give a more reasonable probability result and avoid ad-hoc factor weights calculating, we propose a neural network approach to analyze root causes of abnormal tasks. We leverage General Regression Neural Network (GRNN) to identify root causes for abnormal tasks. The likelihood of reported root causes is presented to users according to the weighted factors by GRNN. (3) To further improve anomaly detection by avoiding feature extraction, we propose a novel approach by leveraging Convolutional Neural Networks (CNN). Our proposed model can automatically learn event relationships in system logs and detect anomaly with high accuracy. Our deep neural network consists of logkey2vec embeddings, three 1D convolutional layers, a dropout layer, and max pooling. According to our experiment, our CNN-based approach has better accuracy compared to other approaches using Long Short-Term Memory (LSTM) and Multilayer Perceptron (MLP) on detecting anomaly in Hadoop DistributedFile System (HDFS) logs. (4) To analyze system logs more accurately, we extend our CNN-based approach with two attention schemes to detect anomalies in system logs. The proposed two attention schemes focus on different features from CNN's output. We evaluate our approaches with several benchmarks, and the attention-based CNN model shows the best performance among all state-of-the-art methods.
Show less - Date Issued
- 2019
- Identifier
- CFE0007673, ucf:52499
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007673
- Title
- Student Community Detection and Recommendation of Customized Paths to Reinforce Academic Success.
- Creator
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Shao, Yuan, Jha, Sumit Kumar, Zhang, Wei, Zhang, Shaojie, University of Central Florida
- Abstract / Description
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Educational Data Mining (EDM) is a research area that analyzes educational data and extracts interesting and unique information to address education issues. EDM implements computational methods to explore data for the purpose of studying questions related to educational achievements. A common task in an educational environment is the grouping of students and the identification of communities that have common features. Then, these communities of students may be studied by a course developer to...
Show moreEducational Data Mining (EDM) is a research area that analyzes educational data and extracts interesting and unique information to address education issues. EDM implements computational methods to explore data for the purpose of studying questions related to educational achievements. A common task in an educational environment is the grouping of students and the identification of communities that have common features. Then, these communities of students may be studied by a course developer to build a personalized learning system, promote effective group learning, provide adaptive contents, etc. The objective of this thesis is to find an approach to detect student communities and analyze students who do well academically with particular sequences of classes in each community. Then, we compute one or more sequences of courses that a student in a community may pursue to higher their chances of obtaining good academic performance.
Show less - Date Issued
- 2019
- Identifier
- CFE0007529, ucf:52623
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007529
- Title
- Exploring FPGA Implementation for Binarized Neural Network Inference.
- Creator
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Yang, Li, Fan, Deliang, Zhang, Wei, Lin, Mingjie, University of Central Florida
- Abstract / Description
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Deep convolutional neural network has taken an important role in machine learning algorithm. It is widely used in different areas such as computer vision, robotics, and biology. However, the models of deep neural networks become larger and more computation complexity which is a big obstacle for such huge model to implement on embedded systems. Recent works have shown the binarized neural networks (BNN), utilizing binarized (i.e. +1 and -1) convolution kernel and binarized activation function,...
Show moreDeep convolutional neural network has taken an important role in machine learning algorithm. It is widely used in different areas such as computer vision, robotics, and biology. However, the models of deep neural networks become larger and more computation complexity which is a big obstacle for such huge model to implement on embedded systems. Recent works have shown the binarized neural networks (BNN), utilizing binarized (i.e. +1 and -1) convolution kernel and binarized activation function, can significantly reduce the parameter size and computation cost, which makes it hardware-friendly for Field-Programmable Gate Arrays (FPGAs) implementation with efficient energy cost. This thesis proposes to implement a new parallel convolutional binarized neural network (i.e. PC-BNN) on FPGA with accurate inference. The embedded PC-BNN is designed for image classification on CIFAR-10 dataset and explores the hardware architecture and optimization of customized CNN topology.The parallel-convolution binarized neural network has two parallel binarized convolution layers which replaces the original single binarized convolution layer. It achieves around 86% on CIFAR-10 dataset and owns 2.3Mb parameter size. We implement our PC-BNN inference into the Xilinx PYNQ Z1 FPGA board which only has 4.9Mb on-chip Block RAM. Since the ultra-small network parameter, the whole model parameters can be stored on on-chip memory which can greatly reduce energy consumption and computation latency. Meanwhile, we design a new pipeline streaming architecture for PC-BNN hardware inference which can further increase the performance. The experiment results show that our PC-BNN inference on FPGA achieves 930 frames per second and 387.5 FPS/Watt, which are among the best throughput and energy efficiency compared to most recent works.
Show less - Date Issued
- 2018
- Identifier
- CFE0007384, ucf:52067
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007384
- Title
- Blockchain-Driven Secure and Transparent Audit Logs.
- Creator
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Ahmad, Ashar, Mohaisen, David, Awad, Amro, Zhang, Wei, Posey, Clay, University of Central Florida
- Abstract / Description
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In enterprise business applications, large volumes of data are generated daily, encoding business logic and transactions. Those applications are governed by various compliance requirements, making it essential to provide audit logs to store, track, and attribute data changes. In traditional audit log systems, logs are collected and stored in a centralized medium, making them prone to various forms of attacks and manipulations, including physical access and remote vulnerability exploitation...
Show moreIn enterprise business applications, large volumes of data are generated daily, encoding business logic and transactions. Those applications are governed by various compliance requirements, making it essential to provide audit logs to store, track, and attribute data changes. In traditional audit log systems, logs are collected and stored in a centralized medium, making them prone to various forms of attacks and manipulations, including physical access and remote vulnerability exploitation attacks, and eventually allowing for unauthorized data modification, threatening the guarantees of audit logs. Moreover, such systems, and given their centralized nature, are characterized by a single point of failure. To harden the security of audit logs in enterprise business applications, in this work we explore the design space of blockchain-driven secure and transparent audit logs. We highlight the possibility of ensuring stronger security and functional properties by a generic blockchain system for audit logs, realize this generic design through BlockAudit, which addresses both security and functional requirements, optimize BlockAudit through multi-layered design in BlockTrail, and explore the design space further by assessing the functional and security properties the consensus algorithms through comprehensive evaluations. The first component of this work is BlockAudit, a design blueprint that enumerates structural, functional, and security requirements for blockchain-based audit logs. BlockAudit uses a consensus-driven approach to replicate audit logs across multiple application peers to prevent the single-point-of-failure. BlockAudit also uses the Practical Byzantine Fault Tolerance (PBFT) protocol to achieve consensus over the state of the audit log data. We evaluate the performance of BlockAudit using event-driven simulations, abstracted from IBM Hyperledger. Through the performance evaluation of BlockAudit, we pinpoint a need for high scalability and high throughput. We achieve those requirements by exploring various design optimizations to the flat structure of BlockAudit inspired by real-world application characteristics. Namely, enterprise business applications often operate across non-overlapping geographical hierarchies including cities, counties, states, and federations. Leveraging that, we applied a similar transformation to BlockAudit to fragment the flat blockchain system into layers of codependent hierarchies, capable of processing transactions in parallel. Our hierarchical design, called BlockTrail, reduced the storage and search complexity for blockchains substantially while increasing the throughput and scalability of the audit log system. We prototyped BlockTrail on a custom-built blockchain simulator and analyzed its performance under varying transactions and network sizes demonstrating its advantages over BlockAudit. A recurring limitation in both BlockAudit and BlockTrail is the use of the PBFT consensus protocol, which has high complexity and low scalability features. Moreover, the performance of our proposed designs was only evaluated in computer simulations, which sidestepped the complexities of the real-world blockchain system. To address those shortcomings, we created a generic cloud-based blockchain testbed capable of executing five well-known consensus algorithms including Proof-of-Work, Proof-of-Stake, Proof-of-Elapsed Time, Clique, and PBFT. For each consensus protocol, we instrumented our auditing system with various benchmarks to measure the latency, throughput, and scalability, highlighting the trade-off between the different protocols.
Show less - Date Issued
- 2019
- Identifier
- CFE0007773, ucf:52375
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007773
- Title
- Managing IO Resource for Co-running Data Intensive Applications in Virtual Clusters.
- Creator
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Huang, Dan, Wang, Jun, Zhou, Qun, Sun, Wei, Zhang, Shaojie, Wang, Liqiang, University of Central Florida
- Abstract / Description
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Today Big Data computer platforms employ resource management systems such as Yarn, Torque, Mesos, and Google Borg to enable sharing the physical computing among many users or applications. Given virtualization and resource management systems, users are able to launch their applications on the same node with low mutual interference and management overhead on CPU and memory. However, there are still challenges to be addressed before these systems can be fully adopted to manage the IO resources...
Show moreToday Big Data computer platforms employ resource management systems such as Yarn, Torque, Mesos, and Google Borg to enable sharing the physical computing among many users or applications. Given virtualization and resource management systems, users are able to launch their applications on the same node with low mutual interference and management overhead on CPU and memory. However, there are still challenges to be addressed before these systems can be fully adopted to manage the IO resources in Big Data File Systems (BDFS) and shared network facilities. In this study, we mainly study on three IO management problems systematically, in terms of the proportional sharing of block IO in container-based virtualization, the network IO contention in MPI-based HPC applications and the data migration overhead in HPC workflows. To improve the proportional sharing, we develop a prototype system called BDFS-Container, by containerizing BDFS at Linux block IO level. Central to BDFS-Container, we propose and design a proactive IOPS throttling based mechanism named IOPS Regulator, which improves proportional IO sharing under the BDFS IO pattern by 74.4% on an average. In the aspect of network IO resource management, we exploit using virtual switches to facilitate network traffic manipulation and reduce mutual interference on the network for in-situ applications. In order to dynamically allocate the network bandwidth when it is needed, we adopt SARIMA-based techniques to analyze and predict MPI traffic issued from simulations. Third, to solve the data migration problem in small-medium sized HPC clusters, we propose to construct a sided IO path, named as SideIO, to explicitly direct analysis data to BDFS that co-locates computation with data. By experimenting with two real-world scientific workflows, SideIO completely avoids the most expensive data movement overhead and achieves up to 3x speedups compared with current solutions.
Show less - Date Issued
- 2018
- Identifier
- CFE0007195, ucf:52268
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007195
- Title
- Rethinking Routing and Peering in the era of Vertical Integration of Network Functions.
- Creator
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Dey, Prasun, Yuksel, Murat, Wang, Jun, Ewetz, Rickard, Zhang, Wei, Hasan, Samiul, University of Central Florida
- Abstract / Description
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Content providers typically control the digital content consumption services and are getting the most revenue by implementing an (")all-you-can-eat(") model via subscription or hyper-targeted advertisements. Revamping the existing Internet architecture and design, a vertical integration where a content provider and access ISP will act as unibody in a sugarcane form seems to be the recent trend. As this vertical integration trend is emerging in the ISP market, it is questionable if existing...
Show moreContent providers typically control the digital content consumption services and are getting the most revenue by implementing an (")all-you-can-eat(") model via subscription or hyper-targeted advertisements. Revamping the existing Internet architecture and design, a vertical integration where a content provider and access ISP will act as unibody in a sugarcane form seems to be the recent trend. As this vertical integration trend is emerging in the ISP market, it is questionable if existing routing architecture will suffice in terms of sustainable economics, peering, and scalability. It is expected that the current routing will need careful modifications and smart innovations to ensure effective and reliable end-to-end packet delivery. This involves new feature developments for handling traffic with reduced latency to tackle routing scalability issues in a more secure way and to offer new services at cheaper costs. Considering the fact that prices of DRAM or TCAM in legacy routers are not necessarily decreasing at the desired pace, cloud computing can be a great solution to manage the increasing computation and memory complexity of routing functions in a centralized manner with optimized expenses. Focusing on the attributes associated with existing routing cost models and by exploring a hybrid approach to SDN, we also compare recent trends in cloud pricing (for both storage and service) to evaluate whether it would be economically beneficial to integrate cloud services with legacy routing for improved cost-efficiency. In terms of peering, using the US as a case study, we show the overlaps between access ISPs and content providers to explore the viability of a future in terms of peering between the new emerging content-dominated sugarcane ISPs and the healthiness of Internet economics. To this end, we introduce meta-peering, a term that encompasses automation efforts related to peering (-) from identifying a list of ISPs likely to peer, to injecting control-plane rules, to continuous monitoring and notifying any violation (-) one of the many outcroppings of vertical integration procedure which could be offered to the ISPs as a standalone service.
Show less - Date Issued
- 2019
- Identifier
- CFE0007797, ucf:52351
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007797