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2021

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Articles 241 - 270 of 436

Full-Text Articles in Databases and Information Systems

Using Oracle To Solve Zookeeper On Two-Replica Problems, Ching-Chan Lee May 2021

Using Oracle To Solve Zookeeper On Two-Replica Problems, Ching-Chan Lee

Master's Projects

The project introduces an Oracle, a failure detector, in Apache ZooKeeper and makes it fault-tolerant in a two-node system. The project demonstrates the Oracle authorizes the primary process to maintain the liveness when the majority’s rule becomes an obstacle to continue Apache ZooKeeper service. In addition to the property of accuracy and completeness from Chandra et al.’s research, the project proposes the property of see to avoid losing transactions and the property of mutual exclusion to avoid split-brain issues. The hybrid properties render not only more sounder flexibility in the implementation but also stronger guarantees on safety. Thus, the Oracle …

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Federated Learning In Gaze Recognition (Fligr), Arun Gopal Govindaswamy May 2021

Federated Learning In Gaze Recognition (Fligr), Arun Gopal Govindaswamy

College of Computing and Digital Media Dissertations

The efficiency and generalizability of a deep learning model is based on the amount and diversity of training data. Although huge amounts of data are being collected, these data are not stored in centralized servers for further data processing. It is often infeasible to collect and share data in centralized servers due to various medical data regulations. This need for diversely distributed data and infeasible storage solutions calls for Federated Learning (FL). FL is a clever way of utilizing privately stored data in model building without the need for data sharing. The idea is to train several different models locally …

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Pitcher Effectiveness: A Step Forward For In Game Analytics And Pitcher Evaluation, Christopher Watkins, Vincent Berardi, Cyril Rakovski May 2021

Pitcher Effectiveness: A Step Forward For In Game Analytics And Pitcher Evaluation, Christopher Watkins, Vincent Berardi, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

With the introduction of Statcast in 2015, baseball analytics have become more precise. Statcast allows every play to be accurately tracked and the data it generates is easily accessible through Baseball Savant, which opens the opportunity for improved performance statistics to be developed. In this paper we propose a new tool, Pitcher Effectiveness, that uses Statcast data to evaluate starting pitchers dynamically, based on the results of in-game outcomes after each pitch. Pitcher Effectiveness successfully predicts instances where starting pitchers give up several runs, which we believe make it a new and important tool for the in-game and post-game evaluation …

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Analysis Of Theoretical And Applied Machine Learning Models For Network Intrusion Detection, Jonah Baron May 2021

Analysis Of Theoretical And Applied Machine Learning Models For Network Intrusion Detection, Jonah Baron

Masters Theses & Doctoral Dissertations

Network Intrusion Detection System (IDS) devices play a crucial role in the realm of network security. These systems generate alerts for security analysts by performing signature-based and anomaly-based detection on malicious network traffic. However, there are several challenges when configuring and fine-tuning these IDS devices for high accuracy and precision. Machine learning utilizes a variety of algorithms and unique dataset input to generate models for effective classification. These machine learning techniques can be applied to IDS devices to classify and filter anomalous network traffic. This combination of machine learning and network security provides improved automated network defense by developing highly-optimized …

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Smart Contract Security: A Practitioners' Perspective, Zhiyuan Wan, Xin Xia, David Lo, Jiachi Chen, Xiapu Luo, Xiaohu Yang May 2021

Smart Contract Security: A Practitioners' Perspective, Zhiyuan Wan, Xin Xia, David Lo, Jiachi Chen, Xiapu Luo, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Smart contracts have been plagued by security incidents, which resulted in substantial financial losses. Given numerous research efforts in addressing the security issues of smart contracts, we wondered how software practitioners build security into smart contracts in practice. We performed a mixture of qualitative and quantitative studies with 13 interviewees and 156 survey respondents from 35 countries across six continents to understand practitioners' perceptions and practices on smart contract security. Our study uncovers practitioners' motivations and deterrents of smart contract security, as well as how security efforts and strategies fit into the development lifecycle. We also find that blockchain platforms …

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Approximate Difference Rewards For Scalable Multigent Reinforcement Learning, Arambam James Singh, Akshat Kumar May 2021

Approximate Difference Rewards For Scalable Multigent Reinforcement Learning, Arambam James Singh, Akshat Kumar

Research Collection School Of Computing and Information Systems

We address the problem of multiagent credit assignment in a large scale multiagent system. Difference rewards (DRs) are an effective tool to tackle this problem, but their exact computation is known to be challenging even for small number of agents. We propose a scalable method to compute difference rewards based on aggregate information in a multiagent system with large number of agents by exploiting the symmetry present in several practical applications. Empirical evaluation on two multiagent domains—air-traffic control and cooperative navigation, shows better solution quality than previous approaches.

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A Framework To Detect The Susceptibility Of Employees To Social Engineering Attacks, Hashim H. Alneami May 2021

A Framework To Detect The Susceptibility Of Employees To Social Engineering Attacks, Hashim H. Alneami

Doctoral Dissertations and Master's Theses

Social engineering attacks (SE-attacks) in enterprises are hastily growing and are becoming increasingly sophisticated. Generally, SE-attacks involve the psychological manipulation of employees into revealing confidential and valuable company data to cybercriminals. The ramifications could bring devastating financial and irreparable reputation loss to the companies. Because SE-attacks involve a human element, preventing these attacks can be tricky and challenging and has become a topic of interest for many researchers and security experts. While methods exist for detecting SE-attacks, our literature review of existing methods identified many crucial factors such as the national cultural, organizational, and personality traits of employees that enable …

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Achieving Differential Privacy And Fairness In Machine Learning, Depeng Xu May 2021

Achieving Differential Privacy And Fairness In Machine Learning, Depeng Xu

Graduate Theses and Dissertations

Machine learning algorithms are used to make decisions in various applications, such as recruiting, lending and policing. These algorithms rely on large amounts of sensitive individual information to work properly. Hence, there are sociological concerns about machine learning algorithms on matters like privacy and fairness. Currently, many studies only focus on protecting individual privacy or ensuring fairness of algorithms separately without taking consideration of their connection. However, there are new challenges arising in privacy preserving and fairness-aware machine learning. On one hand, there is fairness within the private model, i.e., how to meet both privacy and fairness requirements simultaneously in …

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Network-Based Detection And Prevention System Against Dns-Based Attacks, Yasir Faraj Mohammed May 2021

Network-Based Detection And Prevention System Against Dns-Based Attacks, Yasir Faraj Mohammed

Graduate Theses and Dissertations

Individuals and organizations rely on the Internet as an essential environment for personal or business transactions. However, individuals and organizations have been primary targets for attacks that steal sensitive data. Adversaries can use different approaches to hide their activities inside the compromised network and communicate covertly between the malicious servers and the victims. The domain name system (DNS) protocol is one of these approaches that adversaries use to transfer stolen data outside the organization's network using various forms of DNS tunneling attacks. The main reason for targeting the DNS protocol is because DNS is available in almost every network, ignored, …

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Prediction, Recommendation And Group Analytics Models In The Domain Of Mashup Services And Cyber-Argumentation Platform, Md Mahfuzer Rahman May 2021

Prediction, Recommendation And Group Analytics Models In The Domain Of Mashup Services And Cyber-Argumentation Platform, Md Mahfuzer Rahman

Graduate Theses and Dissertations

Mashup application development is becoming a widespread software development practice due to its appeal for a shorter application development period. Application developers usually use web APIs from different sources to create a new streamlined service and provide various features to end-users. This kind of practice saves time, ensures reliability, accuracy, and security in the developed applications. Mashup application developers integrate these available APIs into their applications. Still, they have to go through thousands of available web APIs and chose only a few appropriate ones for their application. Recommending relevant web APIs might help application developers in this situation. However, very …

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How Does Land Cover Classification In Google Earth Engine Compare With Traditional Methods Of Land Cover Classification? What Are The Tradeoffs?, Carlos Sebastian Reyes May 2021

How Does Land Cover Classification In Google Earth Engine Compare With Traditional Methods Of Land Cover Classification? What Are The Tradeoffs?, Carlos Sebastian Reyes

Open Access Theses & Dissertations

The project focuses on comparing land cover classification of traditional methods such as ArcGIS with newer ones such as Google Earth Engine (GEE) as well as discussing any potential tradeoffs. Two studies were performed in both platforms, the first involved analyzing land cover change in the Middle Rio Grande (MRG) region of southern New Mexico, far west Texas, and northern Chihuahua, Mexico. The MRG study focused on urban and agricultural change in the region using two different classification methods. The second study focused on creating a post-hurricane damage assessment (PDA) with the goal of developing an automated method of estimating …

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Effect Of Augmented Reality On Consumer Behavior In E-Commerce, Chibuke Uzoechina, Fiona Fui-Hoon Nah May 2021

Effect Of Augmented Reality On Consumer Behavior In E-Commerce, Chibuke Uzoechina, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

AR technology has been referred to as the future of e-commerce. In this paper, we propose to investigate the effect of AR on consumer behavior in e-commerce. Based on the theory of telepresence, we generated a set of hypotheses on their relationships. An experimental study is proposed to test the hypotheses. Our findings will be of interest to e-commerce companies that are looking at incorporating AR into their e-commerce platforms.

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Machine Learning Approaches To Dribble Hand-Off Action Classification With Sportvu Nba Player Coordinate Data, Dembe Stephanos May 2021

Machine Learning Approaches To Dribble Hand-Off Action Classification With Sportvu Nba Player Coordinate Data, Dembe Stephanos

Electronic Theses and Dissertations

Recently, strategies of National Basketball Association teams have evolved with the skillsets of players and the emergence of advanced analytics. One of the most effective actions in dynamic offensive strategies in basketball is the dribble hand-off (DHO). This thesis proposes an architecture for a classification pipeline for detecting DHOs in an accurate and automated manner. This pipeline consists of a combination of player tracking data and event labels, a rule set to identify candidate actions, manually reviewing game recordings to label the candidates, and embedding player trajectories into hexbin cell paths before passing the completed training set to the classification …

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Learning Index Policies For Restless Bandits With Application To Maternal Healthcare, Arpita Biswas, Gaurav Aggarwal, Pradeep Varakantham, Milind Tambe May 2021

Learning Index Policies For Restless Bandits With Application To Maternal Healthcare, Arpita Biswas, Gaurav Aggarwal, Pradeep Varakantham, Milind Tambe

Research Collection School Of Computing and Information Systems

In many community health settings, it is crucial to have a systematic monitoring and intervention process to ensure that the patients adhere to healthcare programs, such as periodic health checks or taking medications. When these interventions are expensive, they can be provided to only a fixed small fraction of the patients at any period of time. Hence, it is important to carefully choose the beneficiaries who should be provided with interventions and when. We model this scenario as a restless multi-armed bandit (RMAB) problem, where each beneficiary is assumed to transition from one state to another depending on the intervention …

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On Decentralization Of Bitcoin: An Asset Perspective, Ling Cheng, Feida Zhu, Huiwen Liu, Chunyan Miao May 2021

On Decentralization Of Bitcoin: An Asset Perspective, Ling Cheng, Feida Zhu, Huiwen Liu, Chunyan Miao

Research Collection School Of Computing and Information Systems

Since its advent in 2009, Bitcoin, a cryptography-enabled peer-to-peer digital payment system, has been gaining increasing attention from both academia and industry. An effort designed to overcome a cluster of bottlenecks inherent in existing centralized financial systems, Bitcoin has always been championed by the crypto community as an example of the spirit of decentralization. While the decentralized nature of Bitcoin's Proof-of-Work consensus algorithm has often been discussed in great detail, no systematic study has so far been conducted to quantitatively measure the degree of decentralization of Bitcoin from an asset perspective -- How decentralized is Bitcoin as a financial asset? …

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Solving 3d Bin Packing Problem Via Multimodal Deep Reinforcement Learning, Yuan Jiang, Zhiguang Cao, Jie Zhang May 2021

Solving 3d Bin Packing Problem Via Multimodal Deep Reinforcement Learning, Yuan Jiang, Zhiguang Cao, Jie Zhang

Research Collection School Of Computing and Information Systems

Recently, there is growing attention on applying deep reinforcement learning (DRL) to solve the 3D bin packing problem (3D BPP), given its favorable generalization and independence of ground-truth label. However, due to the relatively less informative yet computationally heavy encoder, and considerably large action space inherent to the 3D BPP, existing methods are only able to handle up to 50 boxes. In this paper, we propose to alleviate this issue via an end-to-end multimodal DRL agent, which sequentially addresses three sub-tasks of sequence, orientation and position, respectively. The resulting architecture enables the agent to solve large-scale instances of 100 boxes …

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Tripdecoder: Study Travel Time Attributes And Route Preferences Of Metro Systems From Smart Card Data, Xiancai Tian, Baihua Zheng, Yazhe Wang, Hsao-Ting Huang, Chih-Cheng Hung May 2021

Tripdecoder: Study Travel Time Attributes And Route Preferences Of Metro Systems From Smart Card Data, Xiancai Tian, Baihua Zheng, Yazhe Wang, Hsao-Ting Huang, Chih-Cheng Hung

Research Collection School Of Computing and Information Systems

In this paper, we target at recovering the exact routes taken by commuters inside a metro system that are not captured by an Automated Fare Collection (AFC) system and hence remain unknown. We strategically propose two inference tasks to handle the recovering, one to infer the travel time of each travel link that contributes to the total duration of any trip inside a metro network and the other to infer the route preferences based on historical trip records and the travel time of each travel link inferred in the previous inference task. As these two inference tasks have interrelationship, most …

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Securing Fog Federation From Behavior Of Rogue Nodes, Mohammed Saleh H. Alshehri May 2021

Securing Fog Federation From Behavior Of Rogue Nodes, Mohammed Saleh H. Alshehri

Graduate Theses and Dissertations

As the technological revolution advanced information security evolved with an increased need for confidential data protection on the internet. Individuals and organizations typically prefer outsourcing their confidential data to the cloud for processing and storage. As promising as the cloud computing paradigm is, it creates challenges; everything from data security to time latency issues with data computation and delivery to end-users. In response to these challenges CISCO introduced the fog computing paradigm in 2012. The intent was to overcome issues such as time latency and communication overhead and to bring computing and storage resources close to the ground and the …

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Characteristic Reassignment For Hardware Trojan Detection, Noah Waller May 2021

Characteristic Reassignment For Hardware Trojan Detection, Noah Waller

Graduate Theses and Dissertations

With the current business model and increasing complexity of hardware designs, third-party Intellectual Properties (IPs) are prevalently incorporated into first-party designs. However, the use of third-party IPs increases security concerns related to hardware Trojans inserted by attackers. A core threat posed by Hardware Trojans is the difficulty in detecting such malicious insertions/alternations in order to prevent the damage. This thesis work provides major improvements on a soft IP analysis methodology and tool known as the Structural Checking tool, which analyzes Register-Transfer Level (RTL) soft IPs for determining their functionalities and screening for hardware Trojans. This is done by breaking down …

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Tensor Low-Rank Representation For Data Recovery And Clustering, Pan Zhou, Canyi Lu, Jiashi Feng, Zhouchen Lin, Shuicheng Yan May 2021

Tensor Low-Rank Representation For Data Recovery And Clustering, Pan Zhou, Canyi Lu, Jiashi Feng, Zhouchen Lin, Shuicheng Yan

Research Collection School Of Computing and Information Systems

Multi-way or tensor data analysis has attracted increasing attention recently, with many important applications in practice. This article develops a tensor low-rank representation (TLRR) method, which is the first approach that can exactly recover the clean data of intrinsic low-rank structure and accurately cluster them as well, with provable performance guarantees. In particular, for tensor data with arbitrary sparse corruptions, TLRR can exactly recover the clean data under mild conditions; meanwhile TLRR can exactly verify their true origin tensor subspaces and hence cluster them accurately. TLRR objective function can be optimized via efficient convex programing with convergence guarantees. Besides, we …

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Digital Banking Accelerator: A Service-Oriented Architecture Starter Kit For Banks, Alan @ Ali Madjelisi Megargel, Shankararaman, Venky May 2021

Digital Banking Accelerator: A Service-Oriented Architecture Starter Kit For Banks, Alan @ Ali Madjelisi Megargel, Shankararaman, Venky

Research Collection School Of Computing and Information Systems

Digital banking refers to the delivery of interactive financial services through online mechanisms which include web and mobile apps. The main barrier to digital banking for traditional banks, is the presence of legacy core banking systems. Service Oriented Architecture (SOA) is a key enabler to overcome this barrier, and a bank’s level of SOA maturity influences its time-to-market capability of delivering new innovative digital banking solutions. However, most traditional banks struggle with implementing an SOA due to a number of technology and organizational challenges, and the overall steep learning curve. This paper proposes a Digital Banking Accelerator, a “starter kit” …

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Angrybert: Joint Learning Target And Emotion For Hate Speech Detection, Md Rabiul Awal, Rui Cao, Roy Ka-Wei Lee, Sandra Mitrović May 2021

Angrybert: Joint Learning Target And Emotion For Hate Speech Detection, Md Rabiul Awal, Rui Cao, Roy Ka-Wei Lee, Sandra Mitrović

Research Collection School Of Computing and Information Systems

Automated hate speech detection in social media is a challenging task that has recently gained significant traction in the data mining and Natural Language Processing community. However, most of the existing methods adopt a supervised approach that depended heavily on the annotated hate speech datasets, which are imbalanced and often lack training samples for hateful content. This paper addresses the research gaps by proposing a novel multitask learning-based model, AngryBERT, which jointly learns hate speech detection with sentiment classification and target identification as secondary relevant tasks. We conduct extensive experiments to augment three commonly-used hate speech detection datasets. Our experiment …

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Contextual Transformation Networks For Online Continual Learning, Quang Pham, Chenghao Liu, Doyen Sahoo, Steve C. H. Hoi May 2021

Contextual Transformation Networks For Online Continual Learning, Quang Pham, Chenghao Liu, Doyen Sahoo, Steve C. H. Hoi

Research Collection School Of Computing and Information Systems

Continual learning methods with fixed architectures rely on a single network to learn models that can perform well on all tasks. As a result, they often only accommodate common features of those tasks but neglect each task's specific features. On the other hand, dynamic architecture methods can have a separate network for each task, but they are too expensive to train and not scalable in practice, especially in online settings. To address this problem, we propose a novel online continual learning method named ``Contextual Transformation Networks” (CTN) to efficiently model the \emph{task-specific features} while enjoying neglectable complexity overhead compared to …

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Automatic Solution Summarization For Crash Bugs, Haoye Wang, Xin Xia, David Lo, John C. Grundy, Xinyu Wang May 2021

Automatic Solution Summarization For Crash Bugs, Haoye Wang, Xin Xia, David Lo, John C. Grundy, Xinyu Wang

Research Collection School Of Computing and Information Systems

The causes of software crashes can be hidden anywhere in the source code and development environment. When encountering software crashes, recurring bugs that are discussed on Q&A sites could provide developers with solutions to their crashing problems. However, it is difficult for developers to accurately search for relevant content on search engines, and developers have to spend a lot of manual effort to find the right solution from the returned results. In this paper, we present CRASOLVER, an approach that takes into account both the structural information of crash traces and the knowledge of crash-causing bugs to automatically summarize solutions …

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On The Root Of Trust Identification Problem, Ivan De Oliveira Nunes, Xuhua Ding, Gene Tsudik May 2021

On The Root Of Trust Identification Problem, Ivan De Oliveira Nunes, Xuhua Ding, Gene Tsudik

Research Collection School Of Computing and Information Systems

Trusted Execution Environments (TEEs) are becoming ubiquitous and are currently used in many security applications: from personal IoT gadgets to banking and databases. Prominent examples of such architectures are Intel SGX, ARM TrustZone, and Trusted Platform Modules (TPMs). A typical TEE relies on a dynamic Root of Trust (RoT) to provide security services such as code/data confidentiality and integrity, isolated secure software execution, remote attestation, and sensor auditing. Despite their usefulness, there is currently no secure means to determine whether a given security service or task is being performed by the particular RoT within a specific physical device. We refer …

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An Empirical Study Of The Landscape Of Open Source Projects In Baidu, Alibaba, And Tencent, Junxiao Han, Shuiguang Deng, David Lo, Chen Zhi, Jianwei Yin, Xin Xia May 2021

An Empirical Study Of The Landscape Of Open Source Projects In Baidu, Alibaba, And Tencent, Junxiao Han, Shuiguang Deng, David Lo, Chen Zhi, Jianwei Yin, Xin Xia

Research Collection School Of Computing and Information Systems

Open source software has drawn more and more attention from researchers, developers and companies nowadays. Meanwhile, many Chinese technology companies are embracing open source and choosing to open source their projects. Nevertheless, most previous studies are concentrated on international companies such as Microsoft or Google, while the practical values of open source projects of Chinese technology companies remain unclear. To address this issue, we conduct a mixed-method study to investigate the landscape of projects open sourced by three large Chinese technology companies, namely Baidu, Alibaba, and Tencent (BAT). We study the categories and characteristics of open source projects, the developer's …

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A Differential Testing Approach For Evaluating Abstract Syntax Tree Mapping Algorithms, Yuanrui Fan, Xin Xia, David Lo, Ahmed E. Hassan, Yuan Wang, Shanping Li May 2021

A Differential Testing Approach For Evaluating Abstract Syntax Tree Mapping Algorithms, Yuanrui Fan, Xin Xia, David Lo, Ahmed E. Hassan, Yuan Wang, Shanping Li

Research Collection School Of Computing and Information Systems

Abstract syntax tree (AST) mapping algorithms are widely used to analyze changes in source code. Despite the foundational role of AST mapping algorithms, little effort has been made to evaluate the accuracy of AST mapping algorithms, i.e., the extent to which an algorithm captures the evolution of code. We observe that a program element often has only one best-mapped program element. Based on this observation, we propose a hierarchical approach to automatically compare the similarity of mapped statements and tokens by different algorithms. By performing the comparison, we determine if eachof the compared algorithms generates inaccurate mappings for a statement …

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Unveiling The Mystery Of Api Evolution In Deep Learning Frameworks: A Case Study Of Tensorflow 2, Zejun Zhang, Yanming Yang, Xin Xia, David Lo, Xiaoxue Ren, John C. Grundy May 2021

Unveiling The Mystery Of Api Evolution In Deep Learning Frameworks: A Case Study Of Tensorflow 2, Zejun Zhang, Yanming Yang, Xin Xia, David Lo, Xiaoxue Ren, John C. Grundy

Research Collection School Of Computing and Information Systems

API developers have been working hard to evolve APIs to provide more simple, powerful, and robust API libraries. Although API evolution has been studied for multiple domains, such as Web and Android development, API evolution for deep learning frameworks has not yet been studied. It is not very clear how and why APIs evolve in deep learning frameworks, and yet these are being more and more heavily used in industry. To fill this gap, we conduct a large-scale and in-depth study on the API evolution of Tensorflow 2, which is currently the most popular deep learning framework. We first extract …

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Action Selection For Composable Modular Deep Reinforcement Learning, Vaibhav Gupta, Daksh Anand, Praveen Paruchuri, Akshat Kumar May 2021

Action Selection For Composable Modular Deep Reinforcement Learning, Vaibhav Gupta, Daksh Anand, Praveen Paruchuri, Akshat Kumar

Research Collection School Of Computing and Information Systems

In modular reinforcement learning (MRL), a complex decision making problem is decomposed into multiple simpler subproblems each solved by a separate module. Often, these subproblems have conflicting goals, and incomparable reward scales. A composable decision making architecture requires that even the modules authored separately with possibly misaligned reward scales can be combined coherently. An arbitrator should consider different module's action preferences to learn effective global action selection. We present a novel framework called GRACIAS that assigns fine-grained importance to the different modules based on their relevance in a given state, and enables composable decision making based on modern deep RL …

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Guest Editorial: Non-Iid Outlier Detection In Complex Contexts, Guansong Pang, Fabrizio Angiulli, Mihai Cucuringu, Huan Liu May 2021

Guest Editorial: Non-Iid Outlier Detection In Complex Contexts, Guansong Pang, Fabrizio Angiulli, Mihai Cucuringu, Huan Liu

Research Collection School Of Computing and Information Systems

Outlier detection, also known as anomaly detection, aims at identifying data instances that are rare or significantly different from the majority of instances. Due to its significance in many critical domains like cybersecurity, fintech, healthcare, public security, and AI safety, outlier detection has been one of the most active research areas in various communities, such as machine learning, data mining, computer vision, and statistics. Traditional outlier-detection techniques generally assume that data are independent and identically distributed (IID), which are significantly challenged in complex contexts where data are actually non-IID. These contexts are ubiquitous in not only graph data, sequence data, …

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