Databases and Information Systems Commons^™

College Of Education Filemaker Extraction And End-User Database Development, Andrew Tran

Electronic Theses, Projects, and Dissertations

The College of Education (CoE) at the California State University San Bernardino (CSUSB) developed a system to keep track of both state and national accreditation requirements using FileMaker 5, a database system. This accreditation data is crucial for reporting and record-keeping for the CSU Chancellor’s Office as well as the State of California. However, the database system was developed several decades ago, and software support has long since been dropped, causing the CoE’s legacy accreditation data to be at risk of being lost should the software or hardware suffer permanent failure. The purpose of this project was to perform extraction …

Supervised Representation Learning For Improving Prediction Performance In Medical Decision Support Applications, Phawis Thammasorn

Graduate Theses and Dissertations

Machine learning approaches for prediction play an integral role in modern-day decision supports system. An integral part of the process is extracting interest variables or features to describe the input data. Then, the variables are utilized for training machine-learning algorithms to map from the variables to the target output. After the training, the model is validated with either validation or testing data before making predictions with a new dataset. Despite the straightforward workflow, the process relies heavily on good feature representation of data. Engineering suitable representation eases the subsequent actions and copes with many practical issues that potentially prevent the …

Arseek: Identifying Api Resource Using Code And Discussion On Stack Overflow, Gia Kien Luong, Mohammad Hadi, Thung Ferdian, Fatemeh H. Fard, David Lo

Research Collection School Of Computing and Information Systems

It is not a trivial problem to collect API-relevant examples, usages, and mentions on venues such as Stack Overflow. It requires efforts to correctly recognize whether the discussion refers to the API method that developers/tools are searching for. The content of the Stack Overflow thread, which consists of both text paragraphs describing the involvement of the API method in the discussion and the code snippets containing the API invocation, may refer to the given API method. Leveraging this observation, we develop ARSeek, a context-specific algorithm to capture the semantic and syntactic information of the paragraphs and code snippets in a …

Chinese Idiom Understanding With Transformer-Based Pretrained Language Models, Minghuan Tan

Dissertations and Theses Collection (Open Access)

In this dissertation, I study the understanding of Chinese idioms using transformer-based pretrained language models. By ``understanding", I confine the topics to word embeddings learning, contextualized word representations learning, multiple-choice cloze-test reading comprehension and conditional text generation. Chinese idioms are fixed phrases that have special meanings usually derived from an ancient story. The meanings of these idioms are oftentimes not directly related to their component characters, which makes it hard to model them compared with standard phrases whose meanings are compositional. We initiate the work with studying idiom representations derived from pretrained language models, in particular, BERT. We adopt probing-based …

Two Project On Information Systems Capabilities And Organizational Performance, Giridhar Reddy Bojja

Masters Theses & Doctoral Dissertations

Information systems (IS), as a multi-disciplinary research area, emphasizes the complementary relationship between people, organizations, and technology and has evolved dramatically over the years. IS and the underlying Information Technology (IT) application and research play a crucial role in transforming the business world and research within the management domain. Consistent with this evolution and transformation, I develop a two-project dissertation on Information systems capabilities and organizational outcomes.

Project 1 examines the role of hospital operational effectiveness on the link between information systems capabilities and hospital performance. This project examines the cross-lagged effects on a sample of 217 hospitals measured over …

Optimized Damage Assessment And Recovery Through Data Categorization In Critical Infrastructure System., Shruthi Ramakrishnan

Graduate Theses and Dissertations

Critical infrastructures (CI) play a vital role in majority of the fields and sectors worldwide. It contributes a lot towards the economy of nations and towards the wellbeing of the society. They are highly coupled, interconnected and their interdependencies make them more complex systems. Thus, when a damage occurs in a CI system, its complex interdependencies make it get subjected to cascading effects which propagates faster from one infrastructure to another resulting in wide service degradations which in turn causes economic and societal effects. The propagation of cascading effects of disruptive events could be handled efficiently if the assessment and …

A Novel Data Lineage Model For Critical Infrastructure And A Solution To A Special Case Of The Temporal Graph Reachability Problem, Ian Moncur

Graduate Theses and Dissertations

Rapid and accurate damage assessment is crucial to minimize downtime in critical infrastructure. Dependency on modern technology requires fast and consistent techniques to prevent damage from spreading while also minimizing the impact of damage on system users. One technique to assist in assessment is data lineage, which involves tracing a history of dependencies for data items. The goal of this thesis is to present one novel model and an algorithm that uses data lineage with the goal of being fast and accurate. In function this model operates as a directed graph, with the vertices being data items and edges representing …

Gdefects4dl: A Dataset Of General Real-World Deep Learning Program Defects, Yunkai Liang, Yun Lin, Xuezhi Song, Jun Sun, Zhiyong Feng, Jin Song Dong

Research Collection School Of Computing and Information Systems

The development of deep learning programs, as a new programming paradigm, is observed to suffer from various defects. Emerging research works have been proposed to detect, debug, and repair deep learning bugs, which drive the need to construct the bug benchmarks. In this work, we present gDefects4DL, a dataset for general bugs of deep learning programs. Comparing to existing datasets, gDefects4DL collects bugs where the root causes and fix solutions can be well generalized to other projects. Our general bugs include deep learning program bugs such as (1) violation of deep learning API usage pattern (e.g., the standard to implement …

Learning Semantically Rich Network-Based Multi-Modal Mobile User Interface Embeddings, Meng Kiat Gary Ang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Semantically rich information from multiple modalities - text, code, images, categorical and numerical data - co-exist in the user interface (UI) design of mobile applications. Moreover, each UI design is composed of inter-linked UI entities which support different functions of an application, e.g., a UI screen comprising a UI taskbar, a menu and multiple button elements. Existing UI representation learning methods unfortunately are not designed to capture multi-modal and linkage structure between UI entities. To support effective search and recommendation applications over mobile UIs, we need UI representations that integrate latent semantics present in both multi-modal information and linkages between …

Storm The Capitol: Linking Offline Political Speech And Online Twitter Extra-Representational Participation On Qanon And The January 6 Insurrection, Claire Seungeun Lee, Juan Merizalde, John D. Colautti, Jisun An, Haewoon Kwak

Research Collection School Of Computing and Information Systems

The transfer of power stemming from the 2020 presidential election occurred during an unprecedented period in United States history. Uncertainty from the COVID-19 pandemic, ongoing societal tensions, and a fragile economy increased societal polarization, exacerbated by the outgoing president's offline rhetoric. As a result, online groups such as QAnon engaged in extra political participation beyond the traditional platforms. This research explores the link between offline political speech and online extra-representational participation by examining Twitter within the context of the January 6 insurrection. Using a mixed-methods approach of quantitative and qualitative thematic analyses, the study combines offline speech information with Twitter …

On The Transferability Of Pre-Trained Language Models For Low-Resource Programming Languages, Fuxiang Chen, Fatemeh H. Fard, David Lo, Timofey Bryksin

Research Collection School Of Computing and Information Systems

A recent study by Ahmed and Devanbu reported that using a corpus of code written in multilingual datasets to fine-tune multilingual Pre-trained Language Models (PLMs) achieves higher performance as opposed to using a corpus of code written in just one programming language. However, no analysis was made with respect to fine-tuning monolingual PLMs. Furthermore, some programming languages are inherently different and code written in one language usually cannot be interchanged with the others, i.e., Ruby and Java code possess very different structure. To better understand how monolingual and multilingual PLMs affect different programming languages, we investigate 1) the performance of …

Mapping Salt-Affected Land In The South-West Of Western Australia Using Satellite Remote Sensing, P A. Caccetta, John A. Simons, S Furby, Nicholas J. Wright, Richard J. George Dr

Natural resources published reports

Dryland salinity is a pervasive form of land degradation that has resulted from the clearing of about 17 M ha of native vegetation and the introduction of predominately cereal and pasture-based farming systems in the South-West of Western Australia. The change in water balance caused by clearing deep rooted endemic woodlands increased recharge and resulted in rising groundwater levels. After a lag period, the regolith began filling and groundwater approached the soil surface, evaporating and depositing stored salts in the rootzone of salt sensitive crops. Groundwater levels also rise and affect areas of remnant native vegetation, streams, wet-lands and rural …

Structure-Aware Visualization Retrieval, Haotian Li, Yong Wang, Aoyu Wu, Huan Wei, Huamin. Qu

Research Collection School Of Computing and Information Systems

With the wide usage of data visualizations, a huge number of Scalable Vector Graphic (SVG)-based visualizations have been created and shared online. Accordingly, there has been an increasing interest in exploring how to retrieve perceptually similar visualizations from a large corpus, since it can benefit various downstream applications such as visualization recommendation. Existing methods mainly focus on the visual appearance of visualizations by regarding them as bitmap images. However, the structural information intrinsically existing in SVG-based visualizations is ignored. Such structural information can delineate the spatial and hierarchical relationship among visual elements, and characterize visualizations thoroughly from a new perspective. …

Topic-Guided Conversational Recommender In Multiple Domains, Lizi Liao, Ryuichi Takanobu, Yunshan Ma, Xun Yang, Minlie Huang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Conversational systems have recently attracted significant attention. Both the research community and industry believe that it will exert huge impact on human-computer interaction, and specifically, the IR/RecSys community has begun to explore Conversational Recommendation. In real-life scenarios, such systems are often urgently needed in helping users accomplishing different tasks under various situations. However, existing works still face several shortcomings: (1) Most efforts are largely confined in single task setting. They fall short of hands in handling tasks across domains. (2) Aside from soliciting user preference from dialogue history, a conversational recommender naturally has access to the back-end data structure which …

Natural Attack For Pre-Trained Models Of Code, Zhou Yang, Jieke Shi, Junda He, David Lo

Research Collection School Of Computing and Information Systems

Pre-trained models of code have achieved success in many important software engineering tasks. However, these powerful models are vulnerable to adversarial attacks that slightly perturb model inputs to make a victim model produce wrong outputs. Current works mainly attack models of code with examples that preserve operational program semantics but ignore a fundamental requirement for adversarial example generation: perturbations should be natural to human judges, which we refer to as naturalness requirement. In this paper, we propose ALERT (Naturalness Aware Attack), a black-box attack that adversarially transforms inputs to make victim models produce wrong outputs. Different from prior works, this …

Who Will Support My Project? Interactive Search Of Potential Crowdfunding Investors Through Insearch., Songheng Zhang, Yong Wang, Haotian Li, Wanyu Zhang

Research Collection School Of Computing and Information Systems

Crowdfunding provides project founders with a convenient way to reach online investors. However, it is challenging for founders to find the most potential investors and successfully raise money for their projects on crowdfunding platforms. A few machine learning based methods have been proposed to recommend investors’ interest in a specific crowdfunding project, but they fail to provide project founders with explanations in detail for these recommendations, thereby leading to an erosion of trust in predicted investors. To help crowdfunding founders find truly interested investors, we conducted semi-structured interviews with four crowdfunding experts and presentsinSearch, a visual analytic system. inSearch allows …

Rumorlens: Interactive Analysis And Validation Of Suspected Rumors On Social Media, Ran Wang, Kehan Du, Qianhe Chen, Yifei Zhao, Mojie Tang, Hongxi Tao, Shipan Wang, Yiyao Li, Yong Wang

Research Collection School Of Computing and Information Systems

With the development of social media, various rumors can be easily spread on the Internet and such rumors can have serious negative effects on society. Thus, it has become a critical task for social media platforms to deal with suspected rumors. However, due to the lack of effective tools, it is often difficult for platform administrators to analyze and validate rumors from a large volume of information on a social media platform efficiently. We have worked closely with social media platform administrators for four months to summarize their requirements of identifying and analyzing rumors, and further proposed an interactive visual …

Detecting False Alarms From Automatic Static Analysis Tools: How Far Are We?, Hong Jin Kang, Khai Loong Aw, David Lo

Research Collection School Of Computing and Information Systems

Automatic static analysis tools (ASATs), such as Findbugs, have a high false alarm rate. The large number of false alarms produced poses a barrier to adoption. Researchers have proposed the use of machine learning to prune false alarms and present only actionable warnings to developers. The state-of-the-art study has identified a set of “Golden Features” based on metrics computed over the characteristics and history of the file, code, and warning. Recent studies show that machine learning using these features is extremely effective and that they achieve almost perfect performance. We perform a detailed analysis to better understand the strong performance …

Practitioners' Expectations On Automated Code Comment Generation, Xing Hu, Xin Xia, David Lo, Zhiyuan Wan, Qiuyuan Chen, Thomas Zimmermann

Research Collection School Of Computing and Information Systems

Good comments are invaluable assets to software projects, as they help developers understand and maintain projects. However, due to some poor commenting practices, comments are often missing or inconsistent with the source code. Software engineering practitioners often spend a significant amount of time and effort reading and understanding programs without or with poor comments. To counter this, researchers have proposed various techniques to automatically generate code comments in recent years, which can not only save developers time writing comments but also help them better understand existing software projects. However, it is unclear whether these techniques can alleviate comment issues and …

Static Inference Meets Deep Learning: A Hybrid Type Inference Approach For Python, Yun Peng, Cuiyun Gao, Zongjie Li, Bowei Gao, David Lo, Qirun Zhang, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Type inference for dynamic programming languages such as Python is an important yet challenging task. Static type inference techniques can precisely infer variables with enough static constraints but are unable to handle variables with dynamic features. Deep learning (DL) based approaches are feature-agnostic, but they cannot guarantee the correctness of the predicted types. Their performance significantly depends on the quality of the training data (i.e., DL models perform poorly on some common types that rarely appear in the training dataset). It is interesting to note that the static and DL-based approaches offer complementary benefits. Unfortunately, to our knowledge, precise type …

Automated Identification Of Libraries From Vulnerability Data: Can We Do Better?, Stefanus A. Haryono, Hong Jin Kang, Abhishek Sharma, Asankhaya Sharma, Andrew E. Santosa, Ming Yi Ang, David Lo

Research Collection School Of Computing and Information Systems

Software engineers depend heavily on software libraries and have to update their dependencies once vulnerabilities are found in them. Software Composition Analysis (SCA) helps developers identify vulnerable libraries used by an application. A key challenge is the identification of libraries related to a given reported vulnerability in the National Vulnerability Database (NVD), which may not explicitly indicate the affected libraries. Recently, researchers have tried to address the problem of identifying the libraries from an NVD report by treating it as an extreme multi-label learning (XML) problem, characterized by its large number of possible labels and severe data sparsity. As input, …

Simple Or Complex? Together For A More Accurate Just-In-Time Defect Predictor, Xin Zhou, Donggyun Han, David Lo

Research Collection School Of Computing and Information Systems

Just-In-Time (JIT) defect prediction aims to automatically predict whether a commit is defective or not, and has been widely studied in recent years. In general, most studies can be classified into two categories: 1) simple models using traditional machine learning classifiers with hand-crafted features, and 2) complex models using deep learning techniques to automatically extract features. Hand-crafted features used by simple models are based on expert knowledge but may not fully represent the semantic meaning of the commits. On the other hand, deep learning-based features used by complex models represent the semantic meaning of commits but may not reflect useful …

An Exploratory Study On Code Attention In Bert, Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard, David Lo

Research Collection School Of Computing and Information Systems

Many recent models in software engineering introduced deep neural models based on the Transformer architecture or use transformerbased Pre-trained Language Models (PLM) trained on code. Although these models achieve the state of the arts results in many downstream tasks such as code summarization and bug detection, they are based on Transformer and PLM, which are mainly studied in the Natural Language Processing (NLP) field. The current studies rely on the reasoning and practices from NLP for these models in code, despite the differences between natural languages and programming languages. There is also limited literature on explaining how code is modeled. …

Arsearch: Searching For Api Related Resources From Stack Overflow And Github, Kien Luong, Ferdian Thung, David Lo

Research Collection School Of Computing and Information Systems

Stack Overflow and GitHub are two popular platforms containing API-related resources for developers to learn how to use APIs. The platforms are good sources for information about API such as code examples, usages, sentiment, bug reports, etc. However, it is difficult to collect the correct resources regarding a particular API due to the ambiguity of an API method name. An API method name mentioned in the text would only refer to one API, but the method name could match with different APIs. To help people in finding the correct resources for a particular API, we introduce ARSearch. ARSearch finds Stack …

Structure-Aware Visualization Retrieval, Haotian Li, Yong Wang, Wu Aoyu, Huan Wei, Huamin Qu

Research Collection School Of Computing and Information Systems

With the wide usage of data visualizations, a huge number of Scalable Vector Graphic (SVG)-based visualizations have been created and shared online. Accordingly, there has been an increasing interest in exploring how to retrieve perceptually similar visualizations from a large corpus, since it can beneft various downstream applications such as visualization recommendation. Existing methods mainly focus on the visual appearance of visualizations by regarding them as bitmap images. However, the structural information intrinsically existing in SVG-based visualizations is ignored. Such structural information can delineate the spatial and hierarchical relationship among visual elements, and characterize visualizations thoroughly from a new perspective. …

Data Pricing In Machine Learning Pipelines, Zicun Cong, Xuan Luo, Jian Pei, Feida Zhu, Yong Zhang

Research Collection School Of Computing and Information Systems

Machine learning is disruptive. At the same time, machine learning can only succeed by collaboration among many parties in multiple steps naturally as pipelines in an eco-system, such as collecting data for possible machine learning applications, collaboratively training models by multiple parties and delivering machine learning services to end users. Data are critical and penetrating in the whole machine learning pipelines. As machine learning pipelines involve many parties and, in order to be successful, have to form a constructive and dynamic eco-system, marketplaces and data pricing are fundamental in connecting and facilitating those many parties. In this article, we survey …

Learning Transferable Perturbations For Image Captioning, Hanjie Wu, Yongtuo Liu, Hongmin Cai, Shengfeng He

Research Collection School Of Computing and Information Systems

Present studies have discovered that state-of-the-art deep learning models can be attacked by small but well-designed perturbations. Existing attack algorithms for the image captioning task is time-consuming, and their generated adversarial examples cannot transfer well to other models. To generate adversarial examples faster and stronger, we propose to learn the perturbations by a generative model that is governed by three novel loss functions. Image feature distortion loss is designed to maximize the encoded image feature distance between original images and the corresponding adversarial examples at the image domain, and local-global mismatching loss is introduced to separate the mapping encoding representation …

Causality-Based Neural Network Repair, Bing Sun, Jun Sun, Long H. Pham, Jie Shi

Research Collection School Of Computing and Information Systems

Neural networks have had discernible achievements in a wide range of applications. The wide-spread adoption also raises the concern of their dependability and reliability. Similar to traditional decision-making programs, neural networks can have defects that need to be repaired. The defects may cause unsafe behaviors, raise security concerns or unjust societal impacts. In this work, we address the problem of repairing a neural network for desirable properties such as fairness and the absence of backdoor. The goal is to construct a neural network that satisfies the property by (minimally) adjusting the given neural network's parameters (i.e., weights). Specifically, we propose …

Unified Route Planning For Shared Mobility: An Insertion-Based Framework, Yongxin Tong, Yuxiang Zeng, Zimu Zhou, Lei Chen, Ke. Xu

Research Collection School Of Computing and Information Systems

There has been a dramatic growth of shared mobility applications such as ride-sharing, food delivery, and crowdsourced parcel delivery. Shared mobility refers to transportation services that are shared among users, where a central issue is route planning. Given a set of workers and requests, route planning finds for each worker a route, i.e., a sequence of locations to pick up and drop off passengers/parcels that arrive from time to time, with different optimization objectives. Previous studies lack practicability due to their conflicted objectives and inefficiency in inserting a new request into a route, a basic operation called insertion. In addition, …

Adaptive Task Planning For Large-Scale Robotized Warehouses, Dingyuan Shi, Yongxin Tong, Zimu Zhou, Ke Xu, Wenzhe Tan, Hongbo Li

Research Collection School Of Computing and Information Systems

Robotized warehouses are deployed to automatically distribute millions of items brought by the massive logistic orders from e-commerce. A key to automated item distribution is to plan paths for robots, also known as task planning, where each task is to deliver racks with items to pickers for processing and then return the rack back. Prior solutions are unfit for large-scale robotized warehouses due to the inflexibility to time-varying item arrivals and the low efficiency for high throughput. In this paper, we propose a new task planning problem called TPRW, which aims to minimize the end-to-end makespan that incorporates the entire …