Matching Items (53)
156468-Thumbnail Image.png
Description
With the emergence of edge computing paradigm, many applications such as image recognition and augmented reality require to perform machine learning (ML) and artificial intelligence (AI) tasks on edge devices. Most AI and ML models are large and computational heavy, whereas edge devices are usually equipped with limited computational and

With the emergence of edge computing paradigm, many applications such as image recognition and augmented reality require to perform machine learning (ML) and artificial intelligence (AI) tasks on edge devices. Most AI and ML models are large and computational heavy, whereas edge devices are usually equipped with limited computational and storage resources. Such models can be compressed and reduced in order to be placed on edge devices, but they may loose their capability and may not generalize and perform well compared to large models. Recent works used knowledge transfer techniques to transfer information from a large network (termed teacher) to a small one (termed student) in order to improve the performance of the latter. This approach seems to be promising for learning on edge devices, but a thorough investigation on its effectiveness is lacking.

The purpose of this work is to provide an extensive study on the performance (both in terms of accuracy and convergence speed) of knowledge transfer, considering different student-teacher architectures, datasets and different techniques for transferring knowledge from teacher to student.

A good performance improvement is obtained by transferring knowledge from both the intermediate layers and last layer of the teacher to a shallower student. But other architectures and transfer techniques do not fare so well and some of them even lead to negative performance impact. For example, a smaller and shorter network, trained with knowledge transfer on Caltech 101 achieved a significant improvement of 7.36\% in the accuracy and converges 16 times faster compared to the same network trained without knowledge transfer. On the other hand, smaller network which is thinner than the teacher network performed worse with an accuracy drop of 9.48\% on Caltech 101, even with utilization of knowledge transfer.
ContributorsSistla, Ragini (Author) / Zhao, Ming (Thesis advisor, Committee member) / Li, Baoxin (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2018
155951-Thumbnail Image.png
Description
Recent trends in big data storage systems show a shift from disk centric models to memory centric models. The primary challenges faced by these systems are speed, scalability, and fault tolerance. It is interesting to investigate the performance of these two models with respect to some big data applications. This

Recent trends in big data storage systems show a shift from disk centric models to memory centric models. The primary challenges faced by these systems are speed, scalability, and fault tolerance. It is interesting to investigate the performance of these two models with respect to some big data applications. This thesis studies the performance of Ceph (a disk centric model) and Alluxio (a memory centric model) and evaluates whether a hybrid model provides any performance benefits with respect to big data applications. To this end, an application TechTalk is created that uses Ceph to store data and Alluxio to perform data analytics. The functionalities of the application include offline lecture storage, live recording of classes, content analysis and reference generation. The knowledge base of videos is constructed by analyzing the offline data using machine learning techniques. This training dataset provides knowledge to construct the index of an online stream. The indexed metadata enables the students to search, view and access the relevant content. The performance of the application is benchmarked in different use cases to demonstrate the benefits of the hybrid model.
ContributorsNAGENDRA, SHILPA (Author) / Huang, Dijiang (Thesis advisor) / Zhao, Ming (Committee member) / Maciejewski, Ross (Committee member) / Chung, Chun-Jen (Committee member) / Arizona State University (Publisher)
Created2017
156685-Thumbnail Image.png
Description
Compartmentalizing access to content, be it websites accessed in a browser or documents and applications accessed outside the browser, is an established method for protecting information integrity [12, 19, 21, 60]. Compartmentalization solutions change the user experience, introduce performance overhead and provide varying degrees of security. Striking a balance between

Compartmentalizing access to content, be it websites accessed in a browser or documents and applications accessed outside the browser, is an established method for protecting information integrity [12, 19, 21, 60]. Compartmentalization solutions change the user experience, introduce performance overhead and provide varying degrees of security. Striking a balance between usability and security is not an easy task. If the usability aspects are neglected or sacrificed in favor of more security, the resulting solution would have a hard time being adopted by end-users. The usability is affected by factors including (1) the generality of the solution in supporting various applications, (2) the type of changes required, (3) the performance overhead introduced by the solution, and (4) how much the user experience is preserved. The security is affected by factors including (1) the attack surface of the compartmentalization mechanism, and (2) the security decisions offloaded to the user. This dissertation evaluates existing solutions based on the above factors and presents two novel compartmentalization solutions that are arguably more practical than their existing counterparts.

The first solution, called FlexICon, is an attractive alternative in the design space of compartmentalization solutions on the desktop. FlexICon allows for the creation of a large number of containers with small memory footprint and low disk overhead. This is achieved by using lightweight virtualization based on Linux namespaces. FlexICon uses two mechanisms to reduce user mistakes: 1) a trusted file dialog for selecting files for opening and launching it in the appropriate containers, and 2) a secure URL redirection mechanism that detects the user’s intent and opens the URL in the proper container. FlexICon also provides a language to specify the access constraints that should be enforced by various containers.

The second solution called Auto-FBI, deals with web-based attacks by creating multiple instances of the browser and providing mechanisms for switching between the browser instances. The prototype implementation for Firefox and Chrome uses system call interposition to control the browser’s network access. Auto-FBI can be ported to other platforms easily due to simple design and the ubiquity of system call interposition methods on all major desktop platforms.
ContributorsZohrevandi, Mohsen (Author) / Bazzi, Rida A (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Doupe, Adam (Committee member) / Zhao, Ming (Committee member) / Arizona State University (Publisher)
Created2018
156948-Thumbnail Image.png
Description
The Internet of Things ecosystem has spawned a wide variety of embedded real-time systems that complicate the identification and resolution of bugs in software. The methods of concurrent checkpoint provide a means to monitor the application state with the ability to replay the execution on like hardware and software,

The Internet of Things ecosystem has spawned a wide variety of embedded real-time systems that complicate the identification and resolution of bugs in software. The methods of concurrent checkpoint provide a means to monitor the application state with the ability to replay the execution on like hardware and software, without holding off and delaying the execution of application threads. In this thesis, it is accomplished by monitoring physical memory of the application using a soft-dirty page tracker and measuring the various types of overhead when employing concurrent checkpointing. The solution presented is an advancement of the Checkpoint and Replay In Userspace (CRIU) thereby eliminating the large stalls and parasitic operation for each successive checkpoint. Impact and performance is measured using the Parsec 3.0 Benchmark suite and 4.11.12-rt16+ Linux kernel on a MinnowBoard Turbot Quad-Core board.
ContributorsPrinke, Michael L (Author) / Lee, Yann-Hang (Thesis advisor) / Shrivastava, Aviral (Committee member) / Zhao, Ming (Committee member) / Arizona State University (Publisher)
Created2018
156945-Thumbnail Image.png
Description
Blockchain scalability is one of the issues that concerns its current adopters. The current popular blockchains have initially been designed with imperfections that in- troduce fundamental bottlenecks which limit their ability to have a higher throughput and a lower latency.

One of the major bottlenecks for existing blockchain technologies is fast

Blockchain scalability is one of the issues that concerns its current adopters. The current popular blockchains have initially been designed with imperfections that in- troduce fundamental bottlenecks which limit their ability to have a higher throughput and a lower latency.

One of the major bottlenecks for existing blockchain technologies is fast block propagation. A faster block propagation enables a miner to reach a majority of the network within a time constraint and therefore leading to a lower orphan rate and better profitability. In order to attain a throughput that could compete with the current state of the art transaction processing, while also keeping the block intervals same as today, a 24.3 Gigabyte block will be required every 10 minutes with an average transaction size of 500 bytes, which translates to 48600000 transactions every 10 minutes or about 81000 transactions per second.

In order to synchronize such large blocks faster across the network while maintain- ing consensus by keeping the orphan rate below 50%, the thesis proposes to aggregate partial block data from multiple nodes using digital fountain codes. The advantages of using a fountain code is that all connected peers can send part of data in an encoded form. When the receiving peer has enough data, it then decodes the information to reconstruct the block. Along with them sending only part information, the data can be relayed over UDP, instead of TCP, improving upon the speed of propagation in the current blockchains. Fountain codes applied in this research are Raptor codes, which allow construction of infinite decoding symbols. The research, when applied to blockchains, increases success rate of block delivery on decode failures.
ContributorsChawla, Nakul (Author) / Boscovic, Dragan (Thesis advisor) / Candan, Kasim S (Thesis advisor) / Zhao, Ming (Committee member) / Arizona State University (Publisher)
Created2018
131504-Thumbnail Image.png
Description
In the last few years, billion-dollar companies like Yahoo and Equifax have had data breaches causing millions of people’s personal information to be leaked online. Other billion-dollar companies like Google and Facebook have gotten in trouble for abusing people’s personal information for financial gain as well. In this new age

In the last few years, billion-dollar companies like Yahoo and Equifax have had data breaches causing millions of people’s personal information to be leaked online. Other billion-dollar companies like Google and Facebook have gotten in trouble for abusing people’s personal information for financial gain as well. In this new age of technology where everything is being digitalized and stored online, people all over the world are concerned about what is happening to their personal information and how they can trust it is being kept safe. This paper describes, first, the importance of protecting user data, second, one easy tool that companies and developers can use to help ensure that their user’s information (credit card information specifically) is kept safe, how to implement that tool, and finally, future work and research that needs to be done. The solution I propose is a software tool that will keep credit card data secured. It is only a small step towards achieving a completely secure data anonymized system, but when implemented correctly, it can reduce the risk of credit card data from being exposed to the public. The software tool is a script that can scan every viable file in any given system, server, or other file-structured Linux system and detect if there any visible credit card numbers that should be hidden.
ContributorsPappas, Alexander (Author) / Zhao, Ming (Thesis director) / Kuznetsov, Eugene (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
134328-Thumbnail Image.png
Description
As mobile devices have risen to prominence over the last decade, their importance has been increasingly recognized. Workloads for mobile devices are often very different from those on desktop and server computers, and solutions that worked in the past are not always the best fit for the resource- and energy-constrained

As mobile devices have risen to prominence over the last decade, their importance has been increasingly recognized. Workloads for mobile devices are often very different from those on desktop and server computers, and solutions that worked in the past are not always the best fit for the resource- and energy-constrained computing that characterizes mobile devices. While this is most commonly seen in CPU and graphics workloads, this device class difference extends to I/O as well. However, while a few tools exist to help analyze mobile storage solutions, there exists a gap in the available software that prevents quality analysis of certain research initiatives, such as I/O deduplication on mobile devices. This honors thesis will demonstrate a new tool that is capable of capturing I/O on the filesystem layer of mobile devices running the Android operating system, in support of new mobile storage research. Uniquely, it is able to capture both metadata of writes as well as the actual written data, transparently to the apps running on the devices. Based on a modification of the strace program, fstrace and its companion tool fstrace-replay can record and replay filesystem I/O of actual Android apps. Using this new tracing tool, several traces from popular Android apps such as Facebook and Twitter were collected and analyzed.
ContributorsMor, Omri (Author) / Zhao, Ming (Thesis director) / Zhao, Ziming (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
The Coffee Hutch project is derived from the field of Computer Science and consists of a website, a database, and a mobile application for Android devices. This three-tiered scheme is designed to support a point-of-sale payment system to be integrated with a standalone product dispensing machine. The website contains landing

The Coffee Hutch project is derived from the field of Computer Science and consists of a website, a database, and a mobile application for Android devices. This three-tiered scheme is designed to support a point-of-sale payment system to be integrated with a standalone product dispensing machine. The website contains landing pages which provide navigation and functional capabilities for users. The site also features a variety of PHP web services which communicate with the database using SQL commands. The application, programmed in the Java language, makes use of these services in a simple, utilitarian design aimed at modification of user data stored in the database. This database, developed with MySQL and managed with the phpMyAdmin application, contains limited information in order to maximize speed of read and write accesses from the website and Android app. Together, these three components comprise an effective payment management system model with mobile capabilities. All of the components of this project were built at no cost. The website hosting service is free and the third-party services required (such as Paypal payment services) are simulated. These simulations allowed me to demonstrate the functionality of the three-tiered product without the necessity for monetary supplication. This thesis features every aspect of the development and testing of The Coffee Hutch software components. Requirements for each function of the software are specified in one section, and they are aligned with various pieces of the code in the source documentation. Test cases which address each requirement are outlined in another section of the thesis.
ContributorsHutchison, Caleb Ryan (Author) / Burger, Kevin (Thesis director) / Zhao, Ming (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
133782-Thumbnail Image.png
Description
As we already know, fresh water is essential to human life as it sustains and replenishes our bodies. Water sustainability is clearly an important issue that need to be addressed in our world of growing demand and shrinking resources. The ASU Future H2O program seeks to make a difference in

As we already know, fresh water is essential to human life as it sustains and replenishes our bodies. Water sustainability is clearly an important issue that need to be addressed in our world of growing demand and shrinking resources. The ASU Future H2O program seeks to make a difference in the development of water sustainability programs by performing experiments that convert urine into reusable water. The goal is to make reusable water processes become inexpensive and easily accessible to local businesses. This promises a significant environmental impact. In order to make the process of development more efficient we can combine engineering technology with scientific experimentation. As an engineering student and an advocate of water sustainability, I have a chance to design the front-end platform that will use IoT to make the experimental process more accessible and effective. In this paper, I will document the entire process involved in the designing process and what I have learned.
ContributorsTran, Phung Thien (Author) / Boscovic, Dragan (Thesis director) / Boyer, Treavor (Committee member) / School of Earth and Space Exploration (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
168716-Thumbnail Image.png
Description
Stress is one of the critical factors in daily lives, as it has a profound impact onperformance at work and decision-making processes. With the development of IoT technology, smart wearables can handle diverse operations, including networking and recording biometric signals. Also, it has become easier for individual users to selfdetect stress with

Stress is one of the critical factors in daily lives, as it has a profound impact onperformance at work and decision-making processes. With the development of IoT technology, smart wearables can handle diverse operations, including networking and recording biometric signals. Also, it has become easier for individual users to selfdetect stress with recorded data since these wearables as well as their accompanying smartphones now have data processing capability. Edge computing on such devices enables real-time feedback and in turn preemptive identification of reactions to stress. This can provide an opportunity to prevent more severe consequences that might result if stress is unaddressed. From a system perspective, leveraging edge computing allows saving energy such as network bandwidth and latency since it processes data in proximity to the data source. It can also strengthen privacy by implementing stress prediction at local devices without transferring personal information to the public cloud. This thesis presents a framework for real-time stress prediction using Fitbit and machine learning with the support from cloud computing. Fitbit is a wearable tracker that records biometric measurements using optical sensors on the wrist. It also provides developers with platforms to design custom applications. I developed an application for the Fitbit and the user’s accompanying mobile device to collect heart rate fluctuations and corresponding stress levels entered by users. I also established the dataset collected from police cadets during their academy training program. Machine learning classifiers for stress prediction are built using classic models and TensorFlow in the cloud. Lastly, the classifiers are optimized using model compression techniques for deploying them on the smartphones and analyzed how efficiently stress prediction can be performed on the edge.
ContributorsSim, Sang-Hun (Author) / Zhao, Ming (Thesis advisor) / Roberts, Nicole (Committee member) / Zou, Jia (Committee member) / Arizona State University (Publisher)
Created2022