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- All Subjects: Computer Science
- Genre: Masters Thesis
- Creators: Arizona State University
- Status: Published
Description
In combinatorial mathematics, a Steiner system is a type of block design. A Steiner triple system is a special case of Steiner system where all blocks contain 3 elements and each pair of points occurs in exactly one block. Independent sets in Steiner triple systems is the topic which is discussed in this thesis. Some properties related to independent sets in Steiner triple system are provided. The distribution of sizes of maximum independent sets of Steiner triple systems of specific order is also discussed in this thesis. An algorithm for constructing a Steiner triple system with maximum independent set whose size is restricted with a lower bound is provided. An alternative way to construct a Steiner triple system using an affine plane is also presented. A modified greedy algorithm for finding a maximal independent set in a Steiner triple system and a post-optimization method for improving the results yielded by this algorithm are established.
ContributorsWang, Zhaomeng (Author) / Colbourn, Charles (Thesis advisor) / Richa, Andrea (Committee member) / Jiang, Zilin (Committee member) / Arizona State University (Publisher)
Created2021
Description
In videos that contain actions performed unintentionally, agents do not achieve their desired goals. In such videos, it is challenging for computer vision systems to understand high-level concepts such as goal-directed behavior. On the other hand, from a very early age, humans are able to understand the relation between an agent and their ultimate goal even if the action gets disrupted or unintentional effects occur. Inculcating this ability in artificially intelligent agents would make them better social learners by not just learning from their own mistakes, i.e, reinforcement learning, but also learning from other's mistakes. For example, this could greatly reduce the search space for artificially intelligent agents for finding the correct action sequence when trying to achieve a new goal, since they would be able to learn from others what not to do as well as how/when actions result in undesired outcomes.To validate this ability of deep learning models to perform this task, the Weakly Augmented Oops (W-Oops) dataset is proposed, built upon the Oops dataset. W-Oops consists of 2,100 unintentional human action videos, with 44 goal-directed and 33 unintentional video-level activity labels collected through human annotations.
Inspired by previous methods on tasks such as weakly supervised action localization which show promise for achieving good localization results without ground truth segment annotations, this paper proposes a weakly supervised algorithm for localizing the goal-directed as well as the unintentional temporal region of a video using only video-level labels. In particular, an attention mechanism based strategy is employed that predicts the temporal regions which contributes the most to a classification task, leveraging solely video-level labels. Meanwhile, our designed overlap regularization allows the model to focus on distinct portions of the video for inferring the goal-directed and unintentional activity, while guaranteeing their temporal ordering. Extensive quantitative experiments verify the validity of our localization method.
ContributorsChakravarthy, Arnav (Author) / Yang, Yezhou (Thesis advisor) / Davulcu, Hasan (Committee member) / Pavlic, Theodore (Committee member) / Arizona State University (Publisher)
Created2021
Description
Blockchain technology is defined as a decentralized, distributed ledger recording the origin of a digital asset and all of its updates without the need of any governing authority. In Supply-Chain Management, Blockchain can be used very effectively, leading to a more open and reliable supply chain. In recent years, different companies have begun to use blockchain to build blockchain-based supply chain solutions. Blockchain has been shown to help provide improved transparency across the supply chain. This research focuses on the supply chain management of medical devices and supplies using blockchain technology. These devices are manufactured by the authorized device manufacturers and are supplied to the different healthcare institutions on their demand. This entire process becomes vulnerable as there is no track of individual product once it gets shipped till it gets used. Traceability of medical devices in this scenario is hardly efficient and not trustworthy. To address this issue, the paper presents a blockchain-based solution to maintain the supply chain of medical devices. The solution provides a distributed environment that can track various medical treatments from production to use. The finished product is stored in the blockchain through its digital thread. Required details are added from time to time which records the entire virtual life-cycle of the medical device forming the digital thread. This digital thread adds traceability to the existing supply chain. Keeping track of devices also helps in returning the expired devices to the manufacturer for its recycling. This blockchain-based solution is mainly composed of two phases. Blockchain-based solution design, this involves the design of the blockchain network architecture, which constitutes the required smart contract. This phase is implemented using the secure network of Hyperledger Fabric (HLF). The next phase includes the deployment of the generated network over the Kubernetes to make the system scalable and more available. To demonstrate and evaluate the performance matrix, a prototype solution of the designed platform is implemented and deployed on the Kubernetes. Finally, this research concludes with the benefits and shortcomings of the solution with future scope to make this platform perform better in all aspects.
ContributorsMhalgi, Kaushal Sanjay (Author) / Boscovic, Dragan (Thesis advisor) / Candan, Kasim Selcuk (Thesis advisor) / Grando, Adela (Committee member) / Arizona State University (Publisher)
Created2021
Description
Cyber-Physical Systems (CPS) are becoming increasingly prevalent around the world. Co-simulation of cyber and physical components has shown to be an effective way towards the development of time-sensitive and reliable CPS. Correctly combining continuous models with discrete models for co-simulation can often be challenging. In this thesis, the Functional Markup Interface (FMI) is used to develop an adapter called DEVS-FMI for the DEVS-Suite simulator. The adapter, implemented using JavaFMI 2.0, allows any Functional Mock-Up Unit (FMU) to be co-simulated with a Discrete Event System Specification (DEVS) model. This approach enables taking advantage of the parallel DEVS formalism to model cyber systems and using Modelica to model physical systems. An FMU serves as a slave simulator while the DEVS-Suite serves as a master simulator. The Four-Variable model is used as a guide to define the requirements for the inputs and outputs of actuator and sensor devices used in cyber and physical systems. The input and output data as non-functional abstractions of the sensor and actuator devices. Select cyber and physical parts of an electric scooter are chosen, modeled, simulated, and evaluated using the integrated OpenModelica and the DEVS-Suite simulators. Closely related research is briefly examined and expanding this work with support for implicit state-changes for continuous models and distributed co-simulation is noted.
ContributorsLin, Xuanli (Author) / Sarjoughian, Hessam S (Thesis advisor) / Pedrielli, Giulia (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2021
Description
Many residences from student apartment units to family homes use a range of smart devices to make the day-to-day lives of the residents safer and more convenient. The ability to remotely access these devices has further increased their convenience, but it comes with the increased risk of vulnerable devices being exploited to achieve unauthorized access or to conduct surveillance on the users. This highlights the need for an access control system to securely restrict home device access to authorized users only. Existing approaches for securing smart homes use less secure authentication methods, do not allow for data ownership or fine-grained access control, and do not reliably store credential modification records, access records, or access policy modification records. These records can be a valuable resource to have available in the case of a security incident.In this thesis, a secure and efficient remote mutual authentication system with fine-grained access control integrating blockchain and digital signatures to authenticate users, authenticate the home gateway, and provide reliable auditing of the credential modifications, access history, and access policy modifications of the devices is presented. The immutability and verifiability properties of blockchain make it useful for securely storing these records. In this approach, a smart contract is created in the blockchain to keep track of authorized users, manage the access policy, and record requests for access or control of the home devices. A private blockchain is used to provide trust and privacy, which is necessary for a smart home system. Elliptic curve digital signatures are used to verify identities because the shorter key sizes and signature times are more adapted to Internet of Things contexts. The approach presented in this thesis is better than existing approaches because it provides fine-grained access control, and reliably stores credential modification records, access records, and access policy modification records. The approach was implemented and evaluated using Hyperledger, a private open-source blockchain, and the results show that this approach has significant additional security benefits with negligible additional overhead cost.
ContributorsVuong, Anna (Author) / Yau, Stephen S (Thesis advisor) / Doupe, Adam (Committee member) / Ghayekhloo, Samira (Committee member) / Arizona State University (Publisher)
Created2021
Description
REACT is a distributed resource allocation protocol that can be used to negotiate airtime among nodes in a wireless network. In this thesis, REACT is extended to support quality of service (QoS) airtime in an updated version called REACT QoS . Nodes can request the higher airtime class to receive priority in the network. This differentiated service is provided by using the access categories (ACs) provided by 802.11, where one AC represents the best effort (BE) class of airtime and another represents the QoS class. Airtime allocations computed by REACT QoS are realized using an updated tuning algorithm and REACT QoS is updated to allow for QoS airtime along multi-hop paths. Experimentation on the w-iLab.t wireless testbed in an ad-hoc setting shows that these extensions are effective. In a single-hop setting, nodes requesting the higher class of airtime are guaranteed their allocation, with the leftover airtime being divided fairly among the remaining nodes. In the multi-hop scenario, REACT QoS is shown to perform better in each of airtime allocation and delay, jitter, and throughput, when compared to 802.11. Finally, the most influential factors and 2-way interactions are identified through the use of a locating array based screening experiment for delay, jitter, and throughput responses. The screening experiment includes a factor on how the channel is partitioned into data and control traffic, and its effect on the responses is determined.
ContributorsKulenkamp, Daniel J (Author) / Syrotiuk, Violet R (Thesis advisor) / Colbourn, Charles J (Committee member) / Tinnirello, Ilenia (Committee member) / Arizona State University (Publisher)
Created2021
Description
Apache Spark is one of the most widely adopted open-source Big Data processing engines. High performance and ease of use for a wide class of users are some of the primary reasons for the wide adoption. Although data partitioning increases the performance of the analytics workload, its application to Apache Spark is very limited due to layered data abstractions. Once data is written to a stable storage system like Hadoop Distributed File System (HDFS), the data locality information is lost, and while reading the data back into Spark’s in-memory layer, the reading process is random which incurs shuffle overhead. This report investigates the use of metadata information that is stored along with the data itself for reducing shuffle overload in the join-based workloads. It explores the Hyperspace library to mitigate the shuffle overhead for Spark SQL applications. The article also introduces the Lachesis system to solve the shuffle overhead problem. The benchmark results show that the persistent partition and co-location techniques can be beneficial for matrix multiplication using SQL (Structured Query Language) operator along with the TPC-H analytical queries benchmark. The study concludes with a discussion about the trade-offs of using integrated stable storage to layered storage abstractions. It also discusses the feasibility of integration of the Machine Learning (ML) inference phase with the SQL operators along with cross-engine compatibility for employing data locality information.
ContributorsBarhate, Pratik Narhar (Author) / Zou, Jia (Thesis advisor) / Zhao, Ming (Committee member) / Elsayed, Mohamed Sarwat (Committee member) / Arizona State University (Publisher)
Created2021
Description
This work considers the task of vision-and-language inference (VLI): predicting whether an inputthe sentence is true for given images or videos and starts with an investigation of model robustness to
a set of 13 linguistic transformations, categorized as Semantics-Preserving or Semantics-Inverting
based on whether they change the meaning of the sentence. It is observed that existing VLI models
degenerate to close-to-random performance when tested on these linguistic transformations which
include simple phenomena such as synonyms, antonyms, negation, swap-ping of subject and object,
paraphrasing, and the substitutions of pronouns, comparatives, and numbers.
This observation is utilized to design STAT(Semantics-Transformed Adversarial Training) { a
model-agnostic and task-agnostic min-max optimization algorithm, with an inner maximization
that utilizes semantic perturbations of in-put sentences to nd adversarial samples and an outer
maximization that updates model parameters. Extensive experiments on three benchmark datasets
(NLVR2, VIOLIN, VQA \Yes-No") not only demonstrate large gains in robustness to adversarial
input sentences but also show model-agnostic performance improvements. This works also presents
the suite of linguistic transformations as a robustness benchmark that may benet future research
in vision and language robustness.
ContributorsChaudhary, Abhishek (Author) / Yang, Yezhou Dr. (Thesis advisor) / Li, Baoxin Dr. (Committee member) / Baral, Chitta Dr. (Committee member) / Arizona State University (Publisher)
Created2021
Description
The volume of scientific research is growing at an exponential rate over the past100 years. With the advent of the internet and ubiquitous access to the web, academic research search engines such as Google Scholar, Microsoft Academic, etc., have become the go-to platforms for systemic reviews and search. Although many academic search engines host lots of content, they provide minimal context about where the search terms matched. Many of these search engines also fail to provide additional tools which can help enhance a researcher’s understanding of research content outside their respective websites. An example of such a tool can be a browser extension/plugin that surfaces context-relevant information about a research article when the user reads a research article. This dissertation discusses a solution developed to bring more intrinsic characteristics of research documents such as the structure of the research document, tables in the document, the keywords associated with the document to improve search capabilities and augment the information a researcher may read. The prototype solution named Sci-Genie(https://sci-genie.com/) is a search engine over scientific articles from Computer Science ArXiv. Sci-Genie parses research papers and indexes research documents’ structure to provide context-relevant information about the matched search fragments. The same search engine also powers a browser extension to augment the information about a research article the user may be reading. The browser extension augments the user’s interface with information about tables from the cited papers, other papers by the same authors, and even the citations to and from the current article. The browser extension is further powered with access endpoints that leverage a machine learning model to filter tables comparing various entities. The dissertation further discusses these machine learning models and some baselines that help classify whether a table is comparing various entities or not. The dissertation finally concludes by discussing the current shortcomings of Sci-Genie and possible future research scope based on learnings after building Sci-Genie.
ContributorsDave, Valay (Author) / Zou, Jia (Thesis advisor) / Ben Amor, Heni (Thesis advisor) / Candan, Kasim Selcuk (Committee member) / Arizona State University (Publisher)
Created2021
Description
Autonomous Driving (AD) systems are being researched and developed actively in recent days to solve the task of controlling the vehicles safely without human intervention. One method to solve such task is through deep Reinforcement Learning (RL) approach. In deep RL, the main objective is to find an optimal control behavior, often called policy performed by an agent, which is AD system in this case. This policy is usually learned through Deep Neural Networks (DNNs) based on the observations that the agent perceives along with rewards feedback received from environment.However, recent studies demonstrated the vulnerability of such control policies learned through deep RL against adversarial attacks. This raises concerns about the application of such policies to risk-sensitive tasks like AD. Previous adversarial attacks assume that the threats can be broadly realized in two ways: First one is targeted attacks through manipu- lation of the agent’s complete observation in real time and the other is untargeted attacks through manipulation of objects in environment. The former assumes full access to the agent’s observations at almost all time, while the latter has no control over outcomes of attack. This research investigates the feasibility of targeted attacks through physical adver- sarial objects in the environment, a threat that combines the effectiveness and practicality.
Through simulations on one of the popular AD systems, it is demonstrated that a fixed optimal policy can be malfunctioned over time by an attacker e.g., performing an unintended self-parking, when an adversarial object is present. The proposed approach is formulated in such a way that the attacker can learn a dynamics of the environment and also utilizes common knowledge of agent’s dynamics to realize the attack. Further, several experiments are conducted to show the effectiveness of the proposed attack on different driving scenarios empirically. Lastly, this work also studies robustness of object location, and trade-off between the attack strength and attack length based on proposed evaluation metrics.
ContributorsBuddareddygari, Prasanth (Author) / Yang, Yezhou (Thesis advisor) / Ren, Yi (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Created2021