Digital Commons Network^™

Seed Selection For Testing Deep Neural Networks, Yuhan Zhi, Xiaofei Xie, Chao Shen, Jun Sun, Xiaoyu Zhang, Xiaohong Guan

Research Collection School Of Computing and Information Systems

Deep learning (DL) has been applied in many applications. Meanwhile, the quality of DL systems is becoming a big concern. To evaluate the quality of DL systems, a number of DL testing techniques have been proposed. To generate test cases, a set of initial seed inputs are required. Existing testing techniques usually construct seed corpus by randomly selecting inputs from training or test dataset. Till now, there is no study on how initial seed inputs affect the performance of DL testing and how to construct an optimal one. To fill this gap, we conduct the first systematic study to evaluate …

Do-Good: Towards Distribution Shift Evaluation For Pre-Trained Visual Document Understanding Models, Jiabang He, Yi Hu, Lei Wang, Xing Xu, Ning Liu, Hui Liu

Research Collection School Of Computing and Information Systems

Numerous pre-training techniques for visual document understanding (VDU) have recently shown substantial improvements in performance across a wide range of document tasks. However, these pre-trained VDU models cannot guarantee continued success when the distribution of test data differs from the distribution of training data. In this paper, to investigate how robust existing pre-trained VDU models are to various distribution shifts, we first develop an out-of-distribution (OOD) benchmark termed Do-GOOD for the fine-Grained analysis on Document image-related tasks specifically. The Do-GOOD benchmark defines the underlying mechanisms that result in different distribution shifts and contains 9 OOD datasets covering 3 VDU related …

Silent Compiler Bug De-Duplication Via Three-Dimensional Analysis, Chen Yang, Junjie Chen, Xingyu Fan, Jiajun Jiang, Jun Sun

Research Collection School Of Computing and Information Systems

Compiler testing is an important task for assuring the quality of compilers, but investigating test failures is very time-consuming. This is because many test failures are caused by the same compiler bug (known as bug duplication problem). In particular, this problem becomes much more challenging on silent compiler bugs (also called wrong code bugs), since these bugs can provide little information (unlike crash bugs that can produce error messages) for bug de-duplication. In this work, we propose a novel technique (called D3) to solve the duplication problem on silent compiler bugs. Its key insight is to characterize the silent bugs …

An Efficient Hybrid Genetic Algorithm For The Quadratic Traveling Salesman Problem, Quang Anh Pham, Hoong Chuin Lau, Minh Hoang Ha, Lam Vu

Research Collection School Of Computing and Information Systems

The traveling salesman problem (TSP) is the most well-known problem in combinatorial optimization which hasbeen studied for many decades. This paper focuses on dealing with one of the most difficult TSP variants named thequadratic traveling salesman problem (QTSP) that has numerous planning applications in robotics and bioinformatics.The goal of QTSP is similar to TSP which finds a cycle visiting all nodes exactly once with minimum total costs. However, the costs in QTSP are associated with three vertices traversed in succession (instead of two like in TSP). This leadsto a quadratic objective function that is much harder to solve.To efficiently solve …

Few-Shot Event Detection: An Empirical Study And A Unified View, Yubo Ma, Zehao Wang, Yixin Cao, Aixin Sun

Research Collection School Of Computing and Information Systems

Few-shot event detection (ED) has been widely studied, while this brings noticeable discrepancies, e.g., various motivations, tasks, and experimental settings, that hinder the understanding of models for future progress. This paper presents a thorough empirical study, a unified view of ED models, and a better unified baseline. For fair evaluation, we compare 12 representative methods on three datasets, which are roughly grouped into prompt-based and prototype-based models for detailed analysis. Experiments consistently demonstrate that prompt-based methods, including ChatGPT, still significantly trail prototype-based methods in terms of overall performance. To investigate their superior performance, we break down their design elements along …

Take A Break In The Middle: Investigating Subgoals Towards Hierarhical Script Generation, Xinze Li, Yixin Cao, Muhao Chen, Aixin Sun

Research Collection School Of Computing and Information Systems

Goal-oriented Script Generation is a new task of generating a list of steps that can fulfill the given goal. In this paper, we propose to extend the task from the perspective of cognitive theory. Instead of a simple flat structure, the steps are typically organized hierarchically — Human often decompose a complex task into subgoals, where each subgoal can be further decomposed into steps. To establish the benchmark, we contribute a new dataset, propose several baseline methods, and set up evaluation metrics. Both automatic and human evaluation verify the high-quality of dataset, as well as the effectiveness of incorporating subgoals …

Cheer: Centrality-Aware High-Order Event Reasoning Network For Document-Level Event Causality Identification, Meiqi Chen, Yixin Cao, Yan Zhang, Zhiwei Liu

Research Collection School Of Computing and Information Systems

Document-level Event Causality Identification (DECI) aims to recognize causal relations between events within a document. Recent studies focus on building a document-level graph for cross-sentence reasoning, but ignore important causal structures — there are one or two “central” events that prevail throughout the document, with most other events serving as either their cause or consequence. In this paper, we manually annotate central events for a systematical investigation and propose a novel DECI model, CHEER, which performs high-order reasoning while considering event centrality. First, we summarize a general GNN-based DECI model and provide a unified view for better understanding. Second, we …

Balancing Privacy And Flexibility Of Cloud-Based Personal Health Records Sharing System, Yudi Zhang, Fuchun Guo, Willy Susilo, Guomin Yang

Research Collection School Of Computing and Information Systems

The Internet of Things and cloud services have been widely adopted in many applications, and personal health records (PHR) can provide tailored medical care. The PHR data is usually stored on cloud servers for sharing. Weighted attribute-based encryption (ABE) is a practical and flexible technique to protect PHR data. Under a weighted ABE policy, the data user's attributes will be “scored”, if and only if the score reaches the threshold value, he/she can access the data. However, while this approach offers a flexible access policy, the data owners have difficulty controlling their privacy, especially sharing PHR data in collaborative e-health …

Reducing Spatial Labeling Redundancy For Active Semi-Supervised Crowd Counting, Yongtuo Liu, Sucheng Ren, Liangyu Chai, Hanjie Wu, Dan Xu, Jing Qin, Shengfeng He

Research Collection School Of Computing and Information Systems

Labeling is onerous for crowd counting as it should annotate each individual in crowd images. Recently, several methods have been proposed for semi-supervised crowd counting to reduce the labeling efforts. Given a limited labeling budget, they typically select a few crowd images and densely label all individuals in each of them. Despite the promising results, we argue the None-or-All labeling strategy is suboptimal as the densely labeled individuals in each crowd image usually appear similar while the massive unlabeled crowd images may contain entirely diverse individuals. To this end, we propose to break the labeling chain of previous methods and …

Beyond "Protected" And "Private": An Empirical Security Analysis Of Custom Function Modifiers In Smart Contracts, Yuzhou Fang, Daoyuan Wu, Xiao Yi, Shuai Wang, Yufan Chen, Mengjie Chen, Yang Liu, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

A smart contract is a piece of application-layer code running on blockchain ledgers and it provides programmatic logic via transaction-based execution of pre-defined functions. Smart contract functions are by default invokable by any party. To safeguard them, the mainstream smart contract language, i.e., Solidity of the popular Ethereum blockchain, proposed a unique language-level keyword called “modifier,” which allows developers to define custom function access control policies beyond the traditional “protected” and “private” modifiers in classic programming languages.In this paper, we aim to conduct a large-scale security analysis of the modifiers used in real-world Ethereum smart contracts. To achieve this, we …

Synthesizing Speech Test Cases With Text-To-Speech? An Empirical Study On The False Alarms In Automated Speech Recognition Testing, Julia Kaiwen Lau, Kelvin Kai Wen Kong, Julian Hao Yong, Per Hoong Tan, Zhou Yang, Zi Qian Yong, Joshua Chern Wey Low, Chun Yong Chong, Mei Kuan Lim, David Lo

Research Collection School Of Computing and Information Systems

Recent studies have proposed the use of Text-To-Speech (TTS) systems to automatically synthesise speech test cases on a scale and uncover a large number of failures in ASR systems. However, the failures uncovered by synthetic test cases may not reflect the actual performance of an ASR system when it transcribes human audio, which we refer to as false alarms. Given a failed test case synthesised from TTS systems, which consists of TTS-generated audio and the corresponding ground truth text, we feed the human audio stating the same text to an ASR system. If human audio can be correctly transcribed, an …

Mitigating Adversarial Attacks On Data-Driven Invariant Checkers For Cyber-Physical Systems, Rajib Ranjan Maiti, Cheah Huei Yoong, Venkata Reddy Palleti, Arlindo Silva, Christopher M. Poskitt

Research Collection School Of Computing and Information Systems

The use of invariants in developing security mechanisms has become an attractive research area because of their potential to both prevent attacks and detect attacks in Cyber-Physical Systems (CPS). In general, an invariant is a property that is expressed using design parameters along with Boolean operators and which always holds in normal operation of a system, in particular, a CPS. Invariants can be derived by analysing operational data of various design parameters in a running CPS, or by analysing the system's requirements/design documents, with both of the approaches demonstrating significant potential to detect and prevent cyber-attacks on a CPS. While …

A Data-Driven Approach For Scheduling Bus Services Subject To Demand Constraints, Brahmanage Janaka Chathuranga Thilakarathna, Thivya Kandappu, Baihua Zheng

Research Collection School Of Computing and Information Systems

Passenger satisfaction is extremely important for the success of a public transportation system. Many studies have shown that passenger satisfaction strongly depends on the time they have to wait at the bus stop (waiting time) to get on a bus. To be specific, user satisfaction drops faster as the waiting time increases. Therefore, service providers want to provide a bus to the waiting passengers within a threshold to keep them satisfied. It is a two-pronged problem: (a) to satisfy more passengers the transport planner may increase the frequency of the buses, and (b) in turn, the increased frequency may impact …

Singapore's Hospital To Home Program: Raising Patient Engagement Through Ai, John Abisheganaden, Kheng Hock Lee, Lian Leng Low, Eugene Shum, Han Leong Goh, Christine Gian Lee Ang, Andy Wee An Ta, Steven M. Miller

Research Collection School Of Computing and Information Systems

Because of their complex care needs, many elderly patients are discharged from hospitals only to be readmitted for multiple stays within the following twelve months. John Abisheganaden and his fellow authors describe Singapore’s Hospital to Home program, a community care initiative fueled by artificial intelligence.

Impact Of Difficult Negatives On Twitter Crisis Detection, Yuhao Zhang, Siaw Ling Lo, Phyo Yi Win Myint

Research Collection School Of Computing and Information Systems

Twitter has become an alternative information source during a crisis. However, the short, noisy nature of tweets hinders information extraction. While models trained with standard Twitter crisis datasets accomplished decent performance, it remained a challenge to generalize to unseen crisis events. Thus, we proposed adding “difficult” negative examples during training to improve model generalization for Twitter crisis detection. Although adding random noise is a common practice, the impact of difficult negatives, i.e., negative data semantically similar to true examples, was never examined in NLP. Most of existing research focuses on the classification task, without considering the primary information need of …

Imitation Improvement Learning For Large-Scale Capacitated Vehicle Routing Problems, The Viet Bui, Tien Mai

Research Collection School Of Computing and Information Systems

Recent works using deep reinforcement learning (RL) to solve routing problems such as the capacitated vehicle routing problem (CVRP) have focused on improvement learning-based methods, which involve improving a given solution until it becomes near-optimal. Although adequate solutions can be achieved for small problem instances, their efficiency degrades for large-scale ones. In this work, we propose a newimprovement learning-based framework based on imitation learning where classical heuristics serve as experts to encourage the policy model to mimic and produce similar or better solutions. Moreover, to improve scalability, we propose Clockwise Clustering, a novel augmented framework for decomposing large-scale CVRP into …

Human Resource Management (Hrm) Practices And Female Mechanical Engineer Turnover Intentions: The Mediating Role Of Social Capital, Tyrone Walston Gorrick

Theses and Dissertations

The objective of the present study was to investigate the effect of human resource management (HRM) practices on female mechanical engineer turnover intentions while examining the mediating role of social capital. This study drew on insights from the HRM, social capital, and turnover theories and related models in the literature to formulate a set of hypothesized relationships and predictions that HRM practices positively influence social capital and social capital negatively affects female mechanical engineers' turnover intention. Turnover remains a challenging issue for all organizations, driving the need for measures that reduce employee quit intentions and, ultimately, turnover. After all, the …

Obscuration Analysis Of Camera Imagery For Aviation Applications, Patrick James Roelant

Theses and Dissertations

Image feature detection is a potent tool with many applications, such as fog identification, roadway conditions, etc. As part of the recent surge in machine learning applications, cloud detection has also become an increasingly engaged area of research. Identifying low clouds is especially useful with respect to aviation, particularly in regions of complex topography prone to visibility-related hazards such as haze or fog. To address this issue, a threshold-based semi-automated algorithm was developed and tested to determine whether or not an image is obscured by fog or haze. Images were obtained from a ground-based camera network in Southern California, the …

Socialz: Multi-Feature Social Fuzz Testing, Francisco Zanartu, Christoph Treude, Markus Wagner

Research Collection School Of Computing and Information Systems

Online social networks have become an integral aspect of our daily lives and play a crucial role in shaping our relationships with others. However, bugs and glitches, even minor ones, can cause anything from frustrating problems to serious data leaks that can have farreaching impacts on millions of users. To mitigate these risks, fuzz testing, a method of testing with randomised inputs, can provide increased confidence in the correct functioning of a social network. However, implementing traditional fuzz testing methods can be prohibitively difficult or impractical for programmers outside of the network’s development team. To tackle this challenge, we present …

Mdps As Distribution Transformers: Affine Invariant Synthesis For Safety Objectives, S. Akshay, Krishnendu Chatterjee, Tobias Meggendorfer, Dorde Zikelic

Research Collection School Of Computing and Information Systems

Markov decision processes can be viewed as transformers of probability distributions. While this view is useful from a practical standpoint to reason about trajectories of distributions, basic reachability and safety problems are known to be computationally intractable (i.e., Skolem-hard) to solve in such models. Further, we show that even for simple examples of MDPs, strategies for safety objectives over distributions can require infinite memory and randomization.In light of this, we present a novel overapproximation approach to synthesize strategies in an MDP, such that a safety objective over the distributions is met. More precisely, we develop a new framework for template-based …

A Unified Multi-Task Learning Framework For Multi-Goal Conversational Recommender Systems, Yang Deng, Wenxuan Zhang, Weiwen Xu, Wenqiang Lei, Tat-Seng Chua, Wai Lam

Research Collection School Of Computing and Information Systems

Question generation (QG) aims to automatically generate fluent and relevant questions, where the two most mainstream directions are generating questions from unstructured contextual texts (CQG), such as news articles, and generating questions from structured factoid texts (FQG), such as knowledge graphs or tables. Existing methods for these two tasks mainly face challenges of limited internal structural information as well as scarce background information, while these two tasks can benefit each other for alleviating these issues. For example, when meeting the entity mention “United Kingdom” in CQG, it can be inferred that it is a country in European continent based on …

Peerda: Data Augmentation Via Modeling Peer Relation For Span Identification Tasks, Weiwen Xu, Xin Li, Yang Deng, Wai Lam, Lidong Bing

Research Collection School Of Computing and Information Systems

Span identification aims at identifying specific text spans from text input and classifying them into pre-defined categories. Different from previous works that merely leverage the Subordinate (SUB) relation (i.e. if a span is an instance of a certain category) to train models, this paper for the first time explores the Peer (PR) relation, which indicates that two spans are instances of the same category and share similar features. Specifically, a novel Peer Data Augmentation (PeerDA) approach is proposed which employs span pairs with the PR relation as the augmentation data for training. PeerDA has two unique advantages: (1) There are …

Characterization And Estimation Of Musculoskeletal Pain Using Machine Learning, Boluwatife Faremi

Master's Theses

Traditional scales utilized for recording pain are known to be highly subjective and biased due to inaccuracies in recollecting actual pain intensities. As a result, machine learning (ML) models that are trained using these scores as ground truth are reported to have low performance for objective pain classification because of the huge disparity between what was felt in moments of pain and the scores recorded afterward.

In the present study, two devices were designed for gathering real-time, continuous in-session subjective pain scores and the recording of the autonomic nervous system (ANS) altered endodermal (EDA) activity. 24 participants were recruited to …

Low-Incidence Special Education Teachers Lived Experience Teaching During Covid-19, Christina Samone Hatcher

Graduate Student Dissertations, Theses, Capstones, and Portfolios

This study's purpose is to identify how the shift has impacted low-incidence special education teachers to remote learning during the COVID-19 pandemic. There is minimal research regarding the lived experiences of how low-incidence special education teachers teach remotely during the COVID-19 pandemic. This study will consist of evaluating how prepared low-incidence special education teachers felt they were for the demands of remote learning, how they mitigated the demands, what resources and support were received that were helpful, as well as what resources and support low-incidence special education teachers feel they still need to continue to teach remotely, and how their …

Teacher Use Of Computer Generated Feedback Through Online Programs, Sheena Wehr

Graduate Student Dissertations, Theses, Capstones, and Portfolios

Since the COVID-19 pandemic, the use of computer-based supplemental programs has grown in popularity (Li & Zhao, 2022). These programs provide both teachers and students with varying amounts of feedback and data for each standard addressed (Shin et al., 2021). However, since many schools implemented these programs during the pandemic there was a lack of training or professional development, therefore teachers relied on learn-as-you-go methods. The goal of this research was to determine how teachers in Florida were currently using computer-generated feedback to drive their instruction and improve overall student achievement. The study concentrated on secondary mathematics teachers who utilize …

Parametric Delineation Uncertainties Contouring (Pduc) Modeling On Ct Scans Of Prostate Cancer Patients, Vi Ly, Lizhong Liu, Carlos Cardenas, Sean Maroongroge, Brian De, Daniel El Basha, Laurence Court, Xi Luo

Faculty, Staff and Student Publications

PURPOSE: Variability in contouring contributes to large variations in radiation therapy planning and treatment outcomes. The development and testing of tools to automatically detect contouring errors require a source of contours that includes well-understood and realistic errors. The purpose of this work was to develop a simulation algorithm that intentionally injects errors of varying magnitudes into clinically accepted contours and produces realistic contours with different levels of variability.

METHODS: We used a dataset of CT scans from 14 prostate cancer patients with clinician-drawn contours of the regions of interest (ROI) of the prostate, bladder, and rectum. Using our newly developed …

The Schools, Saint Mary's College Of California

Undergraduate Academic Catalog

No abstract provided.

Reinforcement Learning For Sequential Decision Making With Constraints, Jiajing Ling

Dissertations and Theses Collection (Open Access)

Reinforcement learning is a widely used approach to tackle problems in sequential decision making where an agent learns from rewards or penalties. However, in decision-making problems that involve safety or limited resources, the agent's exploration is often limited by constraints. To model such problems, constrained Markov decision processes and constrained decentralized partially observable Markov decision processes have been proposed for single-agent and multi-agent settings, respectively. A significant challenge in solving constrained Dec-POMDP is determining the contribution of each agent to the primary objective and constraint violations. To address this issue, we propose a fictitious play-based method that uses Lagrangian Relaxation …

Article Processing Charges For Open Access Journal Publishing: A Review, Ángel Borrego

Copyright, Fair Use, Scholarly Communication, etc.

Abstract

Some open access (OA) publishers charge authors fees to make their articles freely available online. This paper reviews literature on article processing charges (APCs) that has been published since 2000. Despite praise for diamond OA journals, which charge no fees, most OA articles are published by commercial publishers that charge APCs. Publishers fix APCs depending on the reputation assigned to journals by peers. Evidence shows a relationship between high impact metrics and higher, faster rising APCs. Authors express reluctance about APCs, although this varies by discipline depending on previous experience of paying publication fees and the availability of research …

The Impact Of Morning Meetings On Student Teacher Relationships, Peer Relationships, And Student Achievement, Breanne Gnann

Master's Theses & Capstone Projects

Many students today need social-emotional supports in place to have academic success. Morning Meetings are one way to provide social-emotional support. This elementary school improvement plan demonstrates a need for teachers to implement daily Morning Meetings with fidelity in their classrooms. These Morning Meetings will help create a positive learning environment for students to build trusting relationships with their teacher and peers. Students are likely to feel increased levels of safety, personal value, and support in their classrooms. Creating a classroom community using Morning Meetings will cause students to be more motivated to learn, experience more academic success, and have …