Databases and Information Systems Commons^™

Strategic Behavior And Market Inefficiency In Blockchain-Based Auctions, Ping Fan Ke, Jianqing Chen, Zhiling Guo

Research Collection School Of Computing and Information Systems

Blockchain-based auctions play a key role in decentralized finance, such as liquidation of collaterals in crypto-lending. In this research, we show that a Blockchain-based auction is subject to the threat to availability because of the characteristics of the Blockchain platform, which could lead to auction inefficiency or even market failure. Specifically, an adversary could occupy all of the transaction capacity of an auction by sending transactions with sufficiently high transaction fees, and then win the item in an auction with a nearly zero bid price as there are no competitors available. We discuss how to prevent this kind of strategic …

Towards Non-Intrusive Camera-Based Heart Rate Variability Estimation In The Car Under Naturalistic Condition, Shu Liu, Kevin Koch, Zimu Zhou, Martin Maritsch, Xiaoxi He, Elgar Fleisch, Felix Wortmann

Research Collection School Of Computing and Information Systems

Driver status monitoring systems are a vital component of smart cars in the future, especially in the era when an increasing amount of time is spent in the vehicle. The heart rate (HR) is one of the most important physiological signals of driver status. To infer HR of drivers, the mainstream of existing research focused on capturing subtle heartbeat-induced vibration of the torso or leveraged photoplethysmography (PPG) that detects cardiac cycle-related blood volume changes in the microvascular. However, existing approaches rely on dedicated sensors that are expensive and cumbersome to be integrated or are vulnerable to ambient noise. Moreover, their …

Robust Bipoly-Matching For Multi-Granular Entities, Ween Jiann Lee, Maksim Tkachenko, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Entity matching across two data sources is a prevalent need in many domains, including e-commerce. Of interest is the scenario where entities have varying granularity, e.g., a coarse product category may match multiple finer categories. Previous work in one-to-many matching generally presumes the `one' necessarily comes from a designated source and the `many' from the other source. In contrast, we propose a novel formulation that allows concurrent one-to-many bidirectional matching in any direction. Beyond flexibility, we also seek matching that is more robust to noisy similarity values arising from diverse entity descriptions, by introducing receptivity and reclusivity notions. In addition …

Vireo @ Trecvid 2021 Ad-Hoc Video Search, Jiaxin Wu, Phuong Anh Nguyen, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

In this paper, we summarize our submitted runs and results for Ad-hoc Video Search (AVS) task at TRECVid 2020

On Analysing Student Resilience In Higher Education Programs Using A Data-Driven Approach, Audrey Tedja Widjaja, Ee-Peng Lim, Aldy Gunawan

Research Collection School Of Computing and Information Systems

Analysing student resilience is important as research has shown that resilience is related to students’ academic performance and their persistence through academic setbacks. While questionnaires can be conducted to assess student resilience directly, they suffer from human recall errors and deliberate suppression of true responses. In this paper, we propose ACREA, ACademic REsilience Analytics framework which adopts a data-driven approach to analyse student resilient behavior with the use of student-course data. ACREA defines academic setbacks experienced by students and measures how well students overcome such setbacks using a quasi-experimental design. By applying ACREA on a real world student-course dataset, we …

Automated Doubt Identification From Informal Reflections Through Hybrid Sentic Patterns And Machine Learning Approach, Siaw Ling Lo, Kar Way Tan, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

Do my students understand? The question that lingers in every instructor’s mind after each lesson. With the focus on learner-centered pedagogy, is it feasible to provide timely and relevant guidance to individual learners according to their levels of understanding? One of the options available is to collect reflections from learners after each lesson to extract relevant feedback so that doubts or questions can be addressed in a timely manner. In this paper, we derived a hybrid approach that leverages a novel Doubt Sentic Pattern Detection (SPD) algorithm and a machine learning model to automate the identification of doubts from students’ …

Curriculum Complexity And Graduation Rates At Utah State University, Hayden Hoopes

Undergraduate Honors Capstone Projects

This study utilizes a curricular analytics framework developed by Heileman et al. (2018) to examine the relationship between curriculum complexity and graduation rates in academic programs at Utah State University. The goal in quantifying the complexity of curricula is to determine whether or not prerequisite courses and other factors of curricula structure impacts graduation from the university. To accomplish this goal, curriculum complexity spreadsheets were developed for 96 degree programs at the university, which facilitated the assignment of curriculum complexity scores to the 6,337 students who qualified for the quasi-experimental study. Logistic regression was then applied to the resulting data …

Rmm: Reinforced Memory Management For Class-Incremental Learning, Yaoyao Liu, Qianru Sun, Qianru Sun

Research Collection School Of Computing and Information Systems

Class-Incremental Learning (CIL) [38] trains classifiers under a strict memory budget: in each incremental phase, learning is done for new data, most of which is abandoned to free space for the next phase. The preserved data are exemplars used for replaying. However, existing methods use a static and ad hoc strategy for memory allocation, which is often sub-optimal. In this work, we propose a dynamic memory management strategy that is optimized for the incremental phases and different object classes. We call our method reinforced memory management (RMM), leveraging reinforcement learning. RMM training is not naturally compatible with CIL as the …

A Fine-Grained Attribute Based Data Retrieval With Proxy Re-Encryption Scheme For Data Outsourcing Systems, Hanshu Hong, Ximeng Liu, Zhixin Sun

Research Collection School Of Computing and Information Systems

Attribute based encryption is suitable for data protection in data outsourcing systems such as cloud computing. However, the leveraging of encryption technique may retrain some routine operations over the encrypted data, particularly in the field of data retrieval. This paper presents an attribute based date retrieval with proxy re-encryption (ABDR-PRE) to provide both fine-grained access control and retrieval over the ciphertexts. The proposed scheme achieves fine-grained data access management by adopting KP-ABE mechanism, a delegator can generate the re-encryption key and search indexes for the ciphertexts to be shared over the target delegatee’s attributes. Throughout the process of data sharing, …

Learning Large Neighborhood Search Policy For Integer Programming, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang

Research Collection School Of Computing and Information Systems

We propose a deep reinforcement learning (RL) method to learn large neighborhood search (LNS) policy for integer programming (IP). The RL policy is trained as the destroy operator to select a subset of variables at each step, which is reoptimized by an IP solver as the repair operator. However, the combinatorial number of variable subsets prevents direct application of typical RL algorithms. To tackle this challenge, we represent all subsets by factorizing them into binary decisions on each variable. We then design a neural network to learn policies for each variable in parallel, trained by a customized actor-critic algorithm. We …

Neurolkh: Combining Deep Learning Model With Lin-Kernighan-Helsgaun Heuristic For Solving The Traveling Salesman Problem, Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang

Research Collection School Of Computing and Information Systems

We present NeuroLKH, a novel algorithm that combines deep learning with the strong traditional heuristic Lin-Kernighan-Helsgaun (LKH) for solving Traveling Salesman Problem. Specifically, we train a Sparse Graph Network (SGN) with supervised learning for edge scores and unsupervised learning for node penalties, both of which are critical for improving the performance of LKH. Based on the output of SGN, NeuroLKH creates the edge candidate set and transforms edge distances to guide the searching process of LKH. Extensive experiments firmly demonstrate that, by training one model on a wide range of problem sizes, NeuroLKH significantly outperforms LKH and generalizes well to …

On Analysing Student Resilience In Higher Education Programs Using A Data-Driven Approach, Audrey Tedja Widjaja, Ee Peng Lim, Aldy Gunawan

Research Collection School Of Computing and Information Systems

Analysing student resilience is important as research has shown that resilience is related to students’ academic performance and their persistence through academic setbacks. While questionnaires can be conducted to assess student resilience directly, they suffer from human recall errors and deliberate suppression of true responses. In this paper, we propose ACREA, ACademic REsilience Analytics framework which adopts a datadriven approach to analyse student resilient behavior with the use of student-course data. ACREA defines academic setbacks experienced by students and measures how well students overcome such setbacks using a quasi-experimental design. By applying ACREA on a real world student-course dataset, we …

Channel Integration Services In Online Healthcare Communities, Anqi Zhao, Qian Tang

Research Collection School Of Computing and Information Systems

In online healthcare communities, channel integration services have become the bridge between online and offline channels, enabling patients to easily migrate across channels. Different from pure online services, online-to-offline (On2Off) and offline-to-online (Off2On) channel integration services involve both channels. This study examines the interrelationships between pure online services and channel integration services. Using a panel dataset composed of data from an online healthcare community, we find that pure online services decrease patients’ demand for On2Off integration services but increase their use of Off2On integration services. Our findings suggest that providing healthcare services online can reduce online patients’ needs to visit …

Imon: Appearance-Based Gaze Tracking System On Mobile Devices, Sinh Huynh, Rajesh Krishna Balan, Jeonggil Ko

Research Collection School Of Computing and Information Systems

Gaze tracking is a key building block used in many mobile applications including entertainment, personal productivity, accessibility, medical diagnosis, and visual attention monitoring. In this paper, we present iMon, an appearance-based gaze tracking system that is both designed for use on mobile phones and has significantly greater accuracy compared to prior state-of-the-art solutions. iMon achieves this by comprehensively considering the gaze estimation pipeline and then overcoming three different sources of errors. First, instead of assuming that the user's gaze is fixed to a single 2D coordinate, we construct each gaze label using a probabilistic 2D heatmap gaze representation input to …

Spurring Digital Transformation In Singapore's Legal Industry, Xin Juan Chua, Steven M. Miller

Research Collection School Of Computing and Information Systems

COVID-19 has transformed the way we live and work. It has caused the processes and operations of businesses and organisations to be restructured, as well as transformed business models. A 2020 McKinsey Global survey reported that companies all over the world claim they have accelerated the digitalisation of their customer and supply-chain interactions, as well as their internal operations, by three to four years. They also said they thought the share of digital or digitally enabled products in their portfolios has advanced by seven years. While technology transformation is not new to the legal profession, COVID-19 has cemented the importance …

Context-Aware Graph Convolutional Network For Dynamic Origin-Destination Prediction, Juan Nathaniel, Baihua Zheng

Research Collection School Of Computing and Information Systems

A robust Origin-Destination (OD) prediction is key to urban mobility. A good forecasting model can reduce operational risks and improve service availability, among many other upsides. Here, we examine the use of Graph Convolutional Net-work (GCN) and its hybrid Markov-Chain (GCN-MC) variant to perform a context-aware OD prediction based on a large-scale public transportation dataset in Singapore. Compared with the baseline Markov-Chain algorithm and GCN, the proposed hybrid GCN-MC model improves the prediction accuracy by 37% and 12% respectively. Lastly, the addition of temporal and historical contextual information further improves the performance of the proposed hybrid model by 4 –12%.

Canita: Faster Rates For Distributed Convex Optimization With Communication Compression, Zhize Li, Peter Richtarik

Research Collection School Of Computing and Information Systems

Due to the high communication cost in distributed and federated learning, methods relying on compressed communication are becoming increasingly popular. Besides, the best theoretically and practically performing gradient-type methods invariably rely on some form of acceleration/momentum to reduce the number of communications (faster convergence), e.g., Nesterov's accelerated gradient descent (Nesterov, 1983, 2004) and Adam (Kingma and Ba, 2014). In order to combine the benefits of communication compression and convergence acceleration, we propose a \emph{compressed and accelerated} gradient method based on ANITA (Li, 2021) for distributed optimization, which we call CANITA. Our CANITA achieves the \emph{first accelerated rate} $O\bigg(\sqrt{\Big(1+\sqrt{\frac{\omega^3}{n}}\Big)\frac{L}{\epsilon}} + \omega\big(\frac{1}{\epsilon}\big)^{\frac{1}{3}}\bigg)$, …

Self-Supervised Learning Disentangled Group Representation As Feature, Tan Wang, Zhongqi Yue, Jianqiang Huang, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

A good visual representation is an inference map from observations (images) to features (vectors) that faithfully reflects the hidden modularized generative factors (semantics). In this paper, we formulate the notion of “good” representation from a group-theoretic view using Higgins’ definition of disentangled representation [38], and show that existing Self-Supervised Learning (SSL) only disentangles simple augmentation features such as rotation and colorization, thus unable to modularize the remaining semantics. To break the limitation, we propose an iterative SSL algorithm: Iterative Partition-based Invariant Risk Minimization (IP-IRM), which successfully grounds the abstract semantics and the group acting on them into concrete contrastive learning. …

Integration Of Blockchain Technology Into Automobiles To Prevent And Study The Causes Of Accidents, John Kim

Electronic Theses, Projects, and Dissertations

Automobile collisions occur daily. We now live in an information-driven world, one where technology is quickly evolving. Blockchain technology can change the automotive industry, the safety of the motoring public and its surrounding environment by incorporating this vast array of information. It can place safety and efficiency at the forefront to pedestrians, public establishments, and provide public agencies with pertinent information securely and efficiently. Other industries where Blockchain technology has been effective in are as follows: supply chain management, logistics, and banking. This paper reviews some statistical information regarding automobile collisions, Blockchain technology, Smart Contracts, Smart Cities; assesses the feasibility …

Integration Of Internet Of Things And Health Recommender Systems, Moonkyung Yang

Electronic Theses, Projects, and Dissertations

The Internet of Things (IoT) has become a part of our lives and has provided many enhancements to day-to-day living. In this project, IoT in healthcare is reviewed. IoT-based healthcare is utilized in remote health monitoring, observing chronic diseases, individual fitness programs, helping the elderly, and many other healthcare fields. There are three main architectures of smart IoT healthcare: Three-Layer Architecture, Service-Oriented Based Architecture (SoA), and The Middleware-Based IoT Architecture. Depending on the required services, different IoT architecture are being used. In addition, IoT healthcare services, IoT healthcare service enablers, IoT healthcare applications, and IoT healthcare services focusing on Smartwatch …

The Development Of Qmms: A Case Study For Reliable Online Quiz Maker And Management System, Mohamed Abdelmoneim Elshafey Dr., Tarek Said Ghoniemy Dr.

Future Computing and Informatics Journal

The e-learning and assessment systems became a dominant technology nowadays and distribute across the globe. With severe consequences of COVID19-like crises, the key importance of such technology appeared in which courses, quizzes and questionnaires have to be conducted remotely. Moreover, the use of Learning Management Systems (LMSs), such as blackboard, eCollege, and Moodle, has been sanctioned in all respects of education. This paper presents an open-source interactive Quiz Maker and Management System (QMMS) that suits the research, education (under-grad, grad, or post-grad), and industrial organizations to perform distant quizzes, training and questionnaires with an integration facility with other LMS tools …

Teaching And Learning Under Covid-19 Public Health Edicts: The Role Of Household Lockdowns And Prior Technology Usage, Neil Guppy, David Boud, Tania Heap, Dominique Verpoorten, Uwe Matzat, Joanna Tai, Louise Lutze-Mann, Mary Roth, Patsie Polly, Jamie-Lee Burgess, Jenilyn L. Agapito, Silvia K. Bartolic

Department of Information Systems & Computer Science Faculty Publications

Public health edicts necessitated by COVID-19 prompted a rapid pivot to remote online teaching and learning. Two major consequences followed: households became students' main learning space, and technology became the sole medium of instructional delivery. We use the ideas of "digital disconnect" and "digital divide" to examine, for students and faculty, their prior experience with, and proficiency in using, learning technology. We also explore, for students, how household lockdowns and digital capacity impacted learning. Our findings are drawn from 3806 students and 283 faculty instructors from nine higher education institutions across Asia, Australia, Europe, and North America. For instructors, we …

Managing Incomplete Data In The Patient Discharge Summary To Support Correct Hospital Reimbursements, Fadi Naser Eddin

USF Tampa Graduate Theses and Dissertations

The patient discharge summary is a document that conveys the patient's story to other healthcare practitioners, external users, and, most importantly from a financial perspective, health insurers. A defect or incompleteness in the patient's discharge summary will result in delays in the collection process through denial of the entire or partial reimbursement claim or, in the best-case scenario, delay until the discharge summary issue is resolved. The purpose of this project is to address the issue of the incompleteness of discharge summary from the perspective of healthcare providers, with the goal of understanding, diagnosing, and intervening in the research problem. …

Pre-Earthquake Ionospheric Perturbation Identification Using Cses Data Via Transfer Learning, Pan Xiong, Cheng Long, Huiyu Zhou, Roberto Battiston, Angelo De Santis, Dimitar Ouzounov, Xuemin Zhang, Xuhui Shen

Mathematics, Physics, and Computer Science Faculty Articles and Research

During the lithospheric buildup to an earthquake, complex physical changes occur within the earthquake hypocenter. Data pertaining to the changes in the ionosphere may be obtained by satellites, and the analysis of data anomalies can help identify earthquake precursors. In this paper, we present a deep-learning model, SeqNetQuake, that uses data from the first China Seismo-Electromagnetic Satellite (CSES) to identify ionospheric perturbations prior to earthquakes. SeqNetQuake achieves the best performance [F-measure (F1) = 0.6792 and Matthews correlation coefficient (MCC) = 0.427] when directly trained on the CSES dataset with a spatial window centered on the earthquake epicenter with the Dobrovolsky …

Transfer-Learned Pruned Deep Convolutional Neural Networks For Efficient Plant Classification In Resource-Constrained Environments, Martinson Ofori

Masters Theses & Doctoral Dissertations

Traditional means of on-farm weed control mostly rely on manual labor. This process is time-consuming, costly, and contributes to major yield losses. Further, the conventional application of chemical weed control can be economically and environmentally inefficient. Site-specific weed management (SSWM) counteracts this by reducing the amount of chemical application with localized spraying of weed species. To solve this using computer vision, precision agriculture researchers have used remote sensing weed maps, but this has been largely ineffective for early season weed control due to problems such as solar reflectance and cloud cover in satellite imagery. With the current advances in artificial …

Incbl: Incremental Bug Localization, Zhou Yang, Jieke Shi, Wang Shaowei, David Lo

Research Collection School Of Computing and Information Systems

Numerous efforts have been invested in improving the effectiveness of bug localization techniques, whereas little attention is paid to making these tools run more efficiently in continuously evolving software repositories. This paper first analyzes the information retrieval model behind a classic bug localization tool, BugLocator, and builds a mathematical foundation illustrating that the model can be updated incrementally when codebase or bug reports evolve. Then, we present IncBL, a tool for Incremental Bug Localization in evolving software repositories. IncBL is evaluated on the Bugzbook dataset, and the results show that IncBL can significantly reduce the running time by 77.79% on …

Information Extraction And Classification On Journal Papers, Lei Yu

School of Computing: Dissertations, Theses, and Student Research

The importance of journals for diffusing the results of scientific research has increased considerably. In the digital era, Portable Document Format (PDF) became the established format of electronic journal articles. This structured form, combined with a regular and wide dissemination, spread scientific advancements easily and quickly. However, the rapidly increasing numbers of published scientific articles requires more time and effort on systematic literature reviews, searches and screens. The comprehension and extraction of useful information from the digital documents is also a challenging task, due to the complex structure of PDF.

To help a soil science team from the United States …

Mapping E-Commerce Locally And Beyond: Citt K12 Special Investigation Project, Thomas O’Brien, Deanna Matsumoto

Mineta Transportation Institute

As all aspects of the American workplace become automated or digitally enhanced to some degree, K12 educators have an increasing responsibility to help their students acquire the technical skills necessary to organize and interpret information. Increasingly, this is done through Geographic Information Systems (GIS), especially in careers related to transportation and logistics. The Center for International Trade & Transportation (CITT) at CSU Long Beach has developed this K12 Special Investigation Project to introduce ArcGIS StoryMaps, an engaging, accessible and sophisticated web-based GIS application. The lessons center on e-commerce and its accompanying environmental and economic impact. Still, the activities can be …

Transforming Businesses With E-Commerce Intelligence, Yuanto Kusnadi, Gary Pan

Research Collection School Of Accountancy

2020 had been an extraordinary year as the Covid-19 pandemic struck almost all countries in the world and created an extraordinary impact on businesses worldwide. Singapore and many other Southeast Asian countries were not spared and had to implement lockdowns swiftly. To cope with physical store closures and the increased volume of online transactions, most businesses tried to revamp their business models and set up online stores to capitalise on the rise of the e-commerce wave. With the growing trend of online transactions, it has become imperative for companies operating in the Fast Moving Consumer Goods (FMCG) industry to track …

Factual Consistency Evaluation For Text Summarization Via Counterfactual Estimation, Yuexiang Xie, Fei Sun, Yang Deng, Yaliang Li, Bolin Ding

Research Collection School Of Computing and Information Systems

Despite significant progress has been achieved in text summarization, factual inconsistency in generated summaries still severely limits its practical applications. Among the key factors to ensure factual consistency, a reliable automatic evaluation metric is the first and the most crucial one. However, existing metrics either neglect the intrinsic cause of the factual inconsistency or rely on auxiliary tasks, leading to an unsatisfied correlation with human judgments or increasing the inconvenience of usage in practice. In light of these challenges, we propose a novel metric to evaluate the factual consistency in text summarization via counterfactual estimation, which formulates the causal relationship …