Databases and Information Systems Commons^™

Enhancing Third-Party Software Reliability Through Bug Bounty Programs, Tianlu Zhou, Dan Ma, Nan Feng

Research Collection School Of Computing and Information Systems

Bug Bounty Programs (BBPs) reward external hackers for identifying and reporting software vulnerabilities. As the number of security issues caused by third-party applications has been significantly increased recently, many digital platforms are considering launching BBPs to help enhance the reliability of third-party software. BBPs bring benefits to the platform and vendors, meanwhile impose additional costs on them as well. As a result, the overall impact of using BBP is unclear. In this paper, we present an analytical model to examine the strategic decisions of launching and participating in a BBP for the platform and the third-party vendor, respectively. We find …

Efficient Convoy Routing And Bridge Load Optimization User Interface, Brandon Lacy, Will Heller, Yonas Kassa, Brian Ricks, Robin Gandhi

Information Systems and Quantitative Analysis Faculty Proceedings & Presentations

No abstract provided.

How To Select Simple-Yet-Accurate Model Of Bridge Maintenance?, Akshay Kale, Yonas Kassa, Brian Ricks, Robin Gandhi

Information Systems and Quantitative Analysis Faculty Proceedings & Presentations

No abstract provided.

Developing Architecture For A Routing System Using Bridge Data And Adversary Avoidance, Will Heller, Brian Ricks, Yonas Kassa, Brandon Lacy, Rahul Kamar Nethakani

Information Systems and Quantitative Analysis Faculty Proceedings & Presentations

No abstract provided.

Building Explainable Machine Learning Lifecycle: Model Training, Selection, And Deployment With Explainability, Vidit Singh, Yonas Kassa, Brian Ricks, Robin Gandhi

Information Systems and Quantitative Analysis Faculty Proceedings & Presentations

No abstract provided.

Progress In A New Visualization Strategy For Ml Models, Alex Wissing, Brian Ricks, Robin Gandhi, Yonas Kassa, Akshay Kale

Information Systems and Quantitative Analysis Faculty Proceedings & Presentations

No abstract provided.

Fair Signposting Profile, Herbert Van De Sompel, Martin Klein, Shawn Jones, Michael L. Nelson, Simeon Warner, Anusuriya Devaraju, Robert Huber, Wilko Steinhoff, Vyacheslav Tykhonov, Luc Boruta, Enno Meijers, Stian Soiland-Reyes, Mark Wilkonson

Computer Science Faculty Publications

[First paragraph] This page details concrete recipes that platforms that host research outputs (e.g. data repositories, institutional repositories, publisher platforms, etc.) can follow to implement Signposting, a lightweight yet powerful approach to increase the FAIRness of scholarly objects.

Feature Selection From Clinical Surveys Using Semantic Textual Similarity, Benjamin Warner

McKelvey School of Engineering Graduate Student Theses & Dissertations

Survey data collected from human subjects can contain a high number of features while having a comparatively low quantity of examples. Machine learning models that attempt to predict outcomes from survey data under these conditions can overfit and result in poor generalizability. One remedy to this issue is feature selection, which attempts to select an optimal subset of features to learn upon. A relatively unexplored source of information in the feature selection process is the usage of textual names of features, which may be semantically indicative of which features are relevant to a target outcome. The relationships between feature names …

An Analysis And Examination Of Consensus Attacks In Blockchain Networks, Thomas R. Clark

Senior Honors Projects, 2020-current

This paper examines consensus attacks as they relate to blockchain networks. Consensus attacks are a significant threat to the security and integrity of blockchain networks, and understanding these attacks is crucial for developers and stakeholders. The primary contribution of the paper is to present blockchain and consensus attacks in a clear and accessible manner, with the aim of making these complex concepts easily understandable for a general audience. Using literature review, the paper identifies various methods to prevent consensus attacks, including multi-chain networks, proof-of-work consensus algorithms, and network auditing and monitoring. An analysis revealed that these methods for preventing consensus …

Understanding Data Mining And Its Relation To Information Systems, Malak Alammari

Publications and Research

This research project aims to enrich an Open Educational Resource (OER) textbook on Introduction to Information Systems/Technology with a focus on data mining and its relation to hardware and software components of information systems. The study will address the following research questions: (1) What is data mining? and (2) How does data relate to the hardware and software components of information systems? To answer these questions, the researcher will conduct research to ascertain the current state of data mining and its relevance in the field of information systems/technology. The results of the research will be incorporated into an existing OER …

Blockchain Security: Double-Spending Attack And Prevention, William Henry Scott Iii

Electronic Theses and Dissertations

This thesis shows that distributed consensus systems based on proof of work are vulnerable to hashrate-based double-spending attacks due to abuse of majority rule. Through building a private fork of Litecoin and executing a double-spending attack this thesis examines the mechanics and principles behind the attack. This thesis also conducts a survey of preventative measures used to deter double-spending attacks, concluding that a decentralized peer-to-peer network using proof of work is best protected by the addition of an observer system whether internal or external.

Digital Dna: The Ethical Implications Of Big Data As The World’S New-Age Commodity, Clark H. Dotson

Honors Theses

In the emerging digital world that we find ourselves in, it becomes apparent that data collection has become a staple of daily life, whether we like it or not. This research discussion aims to bring light to just how much one’s own digital identity is valued in the technologically-infused world of today, with distinct research and local examples to bring awareness to the ethical implications of your online presence. The paper in question examines anecdotal and research evidence of the collection of data, both through true and unjust means, as well as ethical implications of what this information truly represents. …

Developing A Multi-Platform Application To Facilitate Internal Campus Hiring, Carissa Patton

Computer Science and Computer Engineering Undergraduate Honors Theses

Undergraduate research has proven to be highly beneficial to students, yet there are many students who do not know how to get involved or who are too timid to approach professors to inquire about potential research opportunities. Our hypothesis is that a cross-platform application has the potential to bridge the gap and help more students get involved in undergraduate research by providing them information about open positions and the faculty or staff members who are mentoring the projects. The key focus of this thesis is to develop an application that provides details about participating faculty or staff including their research …

Rbac Attack Exposure Auditor. Tracking User Risk Exposure Per Role-Based Access Control Permissions, Adelaide Damrau

Undergraduate Honors Theses

Access control models and implementation guidelines for determining, provisioning, and de-provisioning user permissions are challenging due to the differing approaches, unique for each organization, the lack of information provided by case studies concerning the organization’s security policies, and no standard means of implementation procedures or best practices. Although there are multiple access control models, one stands out, role-based access control (RBAC). RBAC simplifies maintenance by enabling administrators to group users with similar permissions. This approach to managing user permissions supports the principle of least privilege and separation of duties, which are needed to ensure an organization maintains acceptable user access …

Bluetooth Low Energy Indoor Positioning System, Jackson T. Diamond, Jordan Hanson

Whittier Scholars Program

Robust indoor positioning systems based on low energy bluetooth signals will service a wide range of applications. We present an example of a low energy bluetooth positioning system. First, the steps taken to locate the target with the bluetooth data will be reviewed. Next, we describe the algorithms of the set of android apps developed to utilize the bluetooth data for positioning. Similar to GPS, the algorithms use trilateration to approximate the target location by utilizing the corner devices running one of the apps. Due to the fluctuating nature of the bluetooth signal strength indicator (RSSI), we used an averaging …

Connecting The Dots For Contextual Information Retrieval, Pei-Chi Lo

Dissertations and Theses Collection (Open Access)

There are many information retrieval tasks that depend on knowledge graphs to return contextually relevant result of the query. We call them Knowledgeenriched Contextual Information Retrieval (KCIR) tasks and these tasks come in many different forms including query-based document retrieval, query answering and others. These KCIR tasks often require the input query to contextualized by additional facts from a knowledge graph, and using the context representation to perform document or knowledge graph retrieval and prediction. In this dissertation, we present a meta-framework that identifies Contextual Representation Learning (CRL) and Contextual Information Retrieval (CIR) to be the two key components in …

Compositional Prompt Tuning With Motion Cues For Open-Vocabulary Video Relation Detection, Kaifeng Gao, Long Chen, Hanwang Zhang, Jun Xiao, Qianru Sun

Research Collection School Of Computing and Information Systems

Prompt tuning with large-scale pretrained vision-language models empowers open-vocabulary prediction trained on limited base categories, e.g., object classification and detection. In this paper, we propose compositional prompt tuning with motion cues: an extended prompt tuning paradigm for compositional predictions of video data. In particular, we present Relation Prompt (RePro) for Open-vocabulary Video Visual Relation Detection (Open-VidVRD), where conventional prompt tuning is easily biased to certain subject-object combinations and motion patterns. To this end, RePro addresses the two technical challenges of Open-VidVRD: 1) the prompt tokens should respect the two different semantic roles of subject and object, and 2) the tuning …

Trace Dna Detection Using Diamond Dye: A Recovery Technique To Yield More Dna, Leah Davis

Master's Theses

This study aspires to find a new screening approach to trace DNA recovery techniques to yield a higher quantity of trace DNA from larger items of evidence. It takes the path of visualizing trace DNA on items of evidence with potential DNA so analysts can swab a more localized area rather than attempting to recover trace DNA through the general swabbing technique currently used for trace DNA recovery. The first and second parts consisted of observing trace DNA interaction with Diamond Dye on porous and non-porous surfaces.

The third part involved applying the Diamond Dye solution by spraying it onto …

A Study Of Various Data Sizes Using Machine Learning, Sochaeta Koeum

Electronic Theses, Projects, and Dissertations

Social media is a great domain for news consumption; however, it is referred to as a double-edged sword. While it is user-friendly and low-cost, social media is the reason why fake news can spread rapidly, which is detrimental to society, businesses, and many consumers. Therefore, fake news detection is an emerging field. However, some challenges have restricted other researchers from developing a universal machine learning model that is fast, efficient, and reliable to stop the proliferation because of the lack of resources available, such as large-sized datasets. The goal of this culminating experience project is to explore how varying datasets …

What Do Users Ask In Open-Source Ai Repositories? An Empirical Study Of Github Issues, Zhou Yang, Chenyu Wang, Jieke Shi, Thong Hoang, Pavneet Singh Kochhar, Qinghua Lu, Zhenchang Xing, David Lo

Research Collection School Of Computing and Information Systems

Artificial Intelligence (AI) systems, which benefit from the availability of large-scale datasets and increasing computational power, have become effective solutions to various critical tasks, such as natural language understanding, speech recognition, and image processing. The advancement of these AI systems is inseparable from open-source software (OSS). Specifically, many benchmarks, implementations, and frameworks for constructing AI systems are made open source and accessible to the public, allowing researchers and practitioners to reproduce the reported results and broaden the application of AI systems. The development of AI systems follows a data-driven paradigm and is sensitive to hyperparameter settings and data separation. Developers …

Mando-Hgt: Heterogeneous Graph Transformers For Smart Contract Vulnerability Detection, Huu Hoang Nguyen, Nhat Minh Nguyen, Chunyao Xie, Zahra Ahmadi, Daniel Kudendo, Thanh-Nam Doan, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Smart contracts in blockchains have been increasingly used for high-value business applications. It is essential to check smart contracts' reliability before and after deployment. Although various program analysis and deep learning techniques have been proposed to detect vulnerabilities in either Ethereum smart contract source code or bytecode, their detection accuracy and scalability are still limited. This paper presents a novel framework named MANDO-HGT for detecting smart contract vulnerabilities. Given Ethereum smart contracts, either in source code or bytecode form, and vulnerable or clean, MANDO-HGT custom-builds heterogeneous contract graphs (HCGs) to represent control-flow and/or function-call information of the code. It then …

Rattus Norvegicus As A Biological Detector Of Clandestine Remains And The Use Of Ultrasonic Vocalizations As A Locating Mechanism, Gabrielle M. Johnston

Master's Theses

In investigations, locating missing persons and clandestine remains are imperative. One way that first responder and police agencies can search for the remains is by using cadaver dogs as biological detectors. Cadaver dogs are typically used due to their olfactory sensitivity and ability to detect low concentrations of volatile organic compounds produced by biological remains. Cadaver dogs are typically chosen for their stamina, agility, and olfactory sensitivity. However, what is not taken into account often is the size of the animal and the expense of maintaining and training the animal. Cadaver dogs are typically large breeds that cannot fit in …

Generation-Based Code Review Automation: How Far Are We?, Xin Zhou, Kisub Kim, Bowen Xu, Donggyun Han, Junda He, David Lo

Research Collection School Of Computing and Information Systems

Code review is an effective software quality assurance activity; however, it is labor-intensive and time-consuming. Thus, a number of generation-based automatic code review (ACR) approaches have been proposed recently, which leverage deep learning techniques to automate various activities in the code review process (e.g., code revision generation and review comment generation).We find the previous works carry three main limitations. First, the ACR approaches have been shown to be beneficial in each work, but those methods are not comprehensively compared with each other to show their superiority over their peer ACR approaches. Second, general-purpose pre-trained models such as CodeT5 are proven …

Picaso: Enhancing Api Recommendations With Relevant Stack Overflow Posts, Ivana Clairine Irsan, Ting Zhang, Ferdian Thung, Kisub Kim, David Lo

Research Collection School Of Computing and Information Systems

While having options could be liberating, too many options could lead to the sub-optimal solution being chosen. This is not an exception in the software engineering domain. Nowadays, API has become imperative in making software developers' life easier. APIs help developers implement a function faster and more efficiently. However, given the large number of open-source libraries to choose from, choosing the right APIs is not a simple task. Previous studies on API recommendation leverage natural language (query) to identify which API would be suitable for the given task. However, these studies only consider one source of input, i.e., GitHub or …

Graph Neural Point Process For Temporal Interaction Prediction, Wenwen Xia, Yuchen Li, Shengdong Li

Research Collection School Of Computing and Information Systems

Temporal graphs are ubiquitous data structures in many scenarios, including social networks, user-item interaction networks, etc. In this paper, we focus on predicting the exact time of the next interaction, given a node pair on a temporal graph. This novel problem can support interesting applications, such as time-sensitive items recommendation, congestion prediction on road networks, and many others. We present Graph Neural Point Process (GNPP) to tackle this problem. GNPP relies on the graph neural message passing and the temporal point process framework. Most previous graph neural models only utilize the chronological order of observed events and ignore exact timestamps. …

Chronos: Time-Aware Zero-Shot Identification Of Libraries From Vulnerability Reports, Yunbo Lyu, Thanh Le Cong, Hong Jin Kang, Ratnadira Widyasari, Zhipeng Zhao, Xuan-Bach Dinh Le, Ming Li, David Lo

Research Collection School Of Computing and Information Systems

Tools that alert developers about library vulnerabilities depend on accurate, up-to-date vulnerability databases which are maintained by security researchers. These databases record the libraries related to each vulnerability. However, the vulnerability reports may not explicitly list every library and human analysis is required to determine all the relevant libraries. Human analysis may be slow and expensive, which motivates the need for automated approaches. Researchers and practitioners have proposed to automatically identify libraries from vulnerability reports using extreme multi-label learning (XML). While state-of-the-art XML techniques showed promising performance, their experimental settings do not practically fit what happens in reality. Previous studies …

Reinforced Adaptation Network For Partial Domain Adaptation, Keyu Wu, Min Wu, Zhenghua Chen, Ruibing Jin, Wei Cui, Zhiguang Cao, Xiaoli Li

Research Collection School Of Computing and Information Systems

Domain adaptation enables generalized learning in new environments by transferring knowledge from label-rich source domains to label-scarce target domains. As a more realistic extension, partial domain adaptation (PDA) relaxes the assumption of fully shared label space, and instead deals with the scenario where the target label space is a subset of the source label space. In this paper, we propose a Reinforced Adaptation Network (RAN) to address the challenging PDA problem. Specifically, a deep reinforcement learning model is proposed to learn source data selection policies. Meanwhile, a domain adaptation model is presented to simultaneously determine rewards and learn domain-invariant feature …

Link Prediction On Latent Heterogeneous Graphs, Trung Kien Nguyen, Zemin Liu, Yuan Fang

Research Collection School Of Computing and Information Systems

On graph data, the multitude of node or edge types gives rise to heterogeneous information networks (HINs). To preserve the heterogeneous semantics on HINs, the rich node/edge types become a cornerstone of HIN representation learning. However, in real-world scenarios, type information is often noisy, missing or inaccessible. Assuming no type information is given, we define a so-called latent heterogeneous graph (LHG), which carries latent heterogeneous semantics as the node/edge types cannot be observed. In this paper, we study the challenging and unexplored problem of link prediction on an LHG. As existing approaches depend heavily on type-based information, they are suboptimal …

Multi-Lingual Multi-Partite Product Title Matching, Huan Lin Tay, Wei Jie Tay, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

In a globalized marketplace, one could access products or services from almost anywhere. However, resolving which product in one language corresponds to another product in a different language remains an under-explored problem. We explore this from two perspectives. First, given two products of different languages, how to assess their similarity that could signal a potential match. Second, given products from various languages, how to arrive at a multi-partite clustering that respects cardinality constraints efficiently. We describe algorithms for each perspective and integrate them into a promising solution validated on real-world datasets.

Resale Hdb Price Prediction Considering Covid-19 Through Sentiment Analysis, Srinaath Anbu Durai, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or …