Databases and Information Systems Commons^™

Crest Or Trough? How Research Libraries Used Emerging Technologies To Survive The Pandemic, So Far, Scout Calvert

University of Nebraska-Lincoln Libraries: Faculty Publications

Introduction

In the first months of the COVID-19 pandemic, it was impossible to tell if we were at the crest of a wave of new transmissions, or a trough of a much larger wave, still yet to peak. As of this writing, as colleges and universities prepare for mostly in-person fall 2021 semesters, case counts in the United States are increasing again after a decline that coincided with easier access to the COVID vaccine. Plans for a return to campus made with confidence this spring may be in doubt, as we climb the curve of what is already the second …

(2021 Revision) Chapter 4: Essential Aspects Of Physical Design And Implementation Of Relational Databases, Tatiana Malyuta, Ashwin Satyanarayana

Open Educational Resources

No abstract provided.

Measuring Data Collection Diligence For Community Healthcare, Galawala Ramesha Samurdhi Karunasena, M. S. Ambiya, Arunesh Sinha, R. Nagar, S. Dalal, Abdullah. H., D. Thakkar, D. Narayanan, M. Tambe

Research Collection School Of Computing and Information Systems

Data analytics has tremendous potential to provide targeted benefit in low-resource communities, however the availability of highquality public health data is a significant challenge in developing countries primarily due to non-diligent data collection by community health workers (CHWs). Our use of the word non-diligence here is to emphasize that poor data collection is often not a deliberate action by CHW but arises due to a myriad of factors, sometime beyond the control of the CHW. In this work, we define and test a data collection diligence score. This challenging unlabeled data problem is handled by building upon domain expert’s guidance …

Quantum Computing: Computational Excellence For Society 5.0, Paul R. Griffin, Michael Boguslavsky, Junye Huang, Robert J. Kauffman, Brian R. Tan

Research Collection School Of Computing and Information Systems

In this chapter, we consider which general business problems may be suitable for exploring the utilization of quantum computing and provide a framework for applying quantum computing. The characteristics of quantum computing systems are mapped into business problems to show the potential advantages of quantum computing. The framework shows how quantum computing can be applied in general, and a use case is offered for quantum machine learning (QML) related to the credit ratings of small and medium-size enterprises (SMEs).

Noahqa: Numerical Reasoning With Interpretable Graph Question Answering Dataset, Qiyuan Zhang, Lei Wang, Sicheng Yu, Shuohang Wang, Yang Wang, Jing Jiang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

While diverse question answering (QA) datasets have been proposed and contributed significantly to the development of deep learning models for QA tasks, the existing datasets fall short in two aspects. First, we lack QA datasets covering complex questions that involve answers as well as the reasoning processes to get the answers. As a result, the state-of-the-art QA research on numerical reasoning still focuses on simple calculations and does not provide the mathematical expressions or evidences justifying the answers. Second, the QA community has contributed much effort to improving the interpretability of QA models. However, these models fail to explicitly show …

Occluded Person Re-Identification With Single-Scale Global Representations, Cheng Yan, Guansong Pang, Jile Jiao, Xiao Bai, Xuetao Feng, Chunhua Shen

Research Collection School Of Computing and Information Systems

Occluded person re-identification (ReID) aims at re-identifying occluded pedestrians from occluded or holistic images taken across multiple cameras. Current state-of-the-art (SOTA) occluded ReID models rely on some auxiliary modules, including pose estimation, feature pyramid and graph matching modules, to learn multi-scale and/or part-level features to tackle the occlusion challenges. This unfortunately leads to complex ReID models that (i) fail to generalize to challenging occlusions of diverse appearance, shape or size, and (ii) become ineffective in handling non-occluded pedestrians. However, real-world ReID applications typically have highly diverse occlusions and involve a hybrid of occluded and non-occluded pedestrians. To address these two …

Assessing Generalizability Of Codebert, Xin Zhou, Donggyun Han, David Lo

Research Collection School Of Computing and Information Systems

Pre-trained models like BERT have achieved strong improvements on many natural language processing (NLP) tasks, showing their great generalizability. The success of pre-trained models in NLP inspires pre-trained models for programming language. Recently, CodeBERT, a model for both natural language (NL) and programming language (PL), pre-trained on code search dataset, is proposed. Although promising, CodeBERT has not been evaluated beyond its pre-trained dataset for NL-PL tasks. Also, it has only been shown effective on two tasks that are close in nature to its pre-trained data. This raises two questions: Can CodeBERT generalize beyond its pre-trained data? Can it generalize to …

(2021 Revision) Chapter 7: Essential Aspects Of Physical Design And Implementation Of Relational Databases, Tatiana Malyuta, Ashwin Satyanarayana

Open Educational Resources

No abstract provided.

(2021 Revision) Chapter 6: Essential Aspects Of Physical Design And Implementation Of Relational Databases, Tatiana Malyuta, Ashwin Satyanarayana

Open Educational Resources

No abstract provided.

Appendix: Essential Aspects Of Physical Design And Implementation Of Relational Databases, Tatiana Malyuta, Ashwin Satyanarayana

Open Educational Resources

No abstract provided.

Employees Breaking Bad With Technology: An Exploratory Analysis Of Human Factors That Drive Cyberspace Insider Threats, Marcus L. Green

USF Tampa Graduate Theses and Dissertations

As implementation of computer systems has continued to grow in business contexts, employee-driven cyberspace infractions have also grown in number. Employee cyberspace behaviors have continued to have detrimental effects on company computer systems. Actions that violate company cybersecurity policies can be either malicious or unmalicious. Solutions, by and large, have been electronic and centered on hardware and software. Those proposing solutions have begun to shift their focus to human risk vulnerabilities.

This study was novel in that its focus was identification of individual, cultural, and technological risk factors that drive cyberspace insider threat activities. Identifying factors that reduce insider threat …

Integrated Discourse Analysis & Learning Skills Framework For Class Conversations, Devyn Wei Hung Tan, Gottipati Swapna, Kyong Jin Shim, Shankararaman, Venky

Research Collection School Of Computing and Information Systems

Constructive interactions through discussion forums allow students to open their horizons and thought processes to acquire more knowledge and develop skills. Thus, discussion forums play an important role in supporting learning. Additionally, the discussion forum provides the content for creating a knowledge repository. It contains discussion threads related to key course topics that are debated by the students. One approach to understanding the student learning experience is through the analysis of the discussion threads. This research proposes the application of discourse analysis and collaborative learning frameworks to discussion forums to gain further insights into the student’s learning in a classroom. …

Bv-Person: A Large-Scale Dataset For Bird-View Person Re-Identification, Cheng Yan, Guansong Pang, Lei Wang, Jile Jiao, Xuetao Feng, Chunhua Shen, Jingjing Li

Research Collection School Of Computing and Information Systems

Person Re-IDentification (ReID) aims at re-identifying persons from non-overlapping cameras. Existing person ReID studies focus on horizontal-view ReID tasks, in which the person images are captured by the cameras from a (nearly) horizontal view. In this work we introduce a new ReID task, bird-view person ReID, which aims at searching for a person in a gallery of horizontal-view images with the query images taken from a bird's-eye view, i.e., an elevated view of an object from above. The task is important because there are a large number of video surveillance cameras capturing persons from such an elevated view at public …

Towards Source-Aligned Variational Models For Cross-Domain Recommendation, Aghiles Salah, Thanh-Binh Tran, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Data sparsity is a long-standing challenge in recommender systems. Among existing approaches to alleviate this problem, cross-domain recommendation consists in leveraging knowledge from a source domain or category (e.g., Movies) to improve item recommendation in a target domain (e.g., Books). In this work, we advocate a probabilistic approach to cross-domain recommendation and rely on variational autoencoders (VAEs) as our latent variable models. More precisely, we assume that we have access to a VAE trained on the source domain that we seek to leverage to improve preference modeling in the target domain. To this end, we propose a model which learns …

Aixfood'21: 3rd Workshop On Aixfood, Ricardo Guerrero, Michael Spranger, Shuqiang Jiang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Food and cooking analysis present exciting research and application challenges for modern AI systems, particularly in the context of multimodal data such as images or video. A meal that appears in a food image is a product of a complex progression of cooking stages, often described in the accompanying textual recipe form. In the cooking process, individual ingredients change their physical properties, become combined with other food components, all to produce a final, yet highly variable, appearance of the meal. Recognizing food items or meals on a plate from images or videos, their physical properties such as the amount, nutritional …

Mining Informal & Short Student Self-Reflections For Detecting Challenging Topics: A Learning Outcomes Insight Dashboard, De Lin Ong, Gottipati Swapna, Siaw Ling Lo, Venky Shankararaman

Research Collection School Of Computing and Information Systems

Having students write short self-reflections at the end of each weekly session enables them to reflect on what they have learnt in the session and topics they find challenging. Analysing these self-reflections provides instructors with insights on how to address the missing conceptions and misconceptions of the students and appropriately plan and deliver the next session. Currently, manual methods adopted to analyse these student reflections are time consuming and tedious. This paper proposes a solution model that uses content mining and NLP techniques to automate the analysis of short self-reflections. We evaluate the solution model by studying its implementation in …

Cloudnplay: Resource Optimization For A Cloud-Native Gaming System, Angelus Wibowo, Nguyen Binh Duong Ta

Research Collection School Of Computing and Information Systems

Cloud gaming enables people playing graphically intensive games from their less powerful, or even outdated computing devices. It is challenging to realize cloud gaming as it requires minimal latency in server-side processing, rendering and streaming, which are expensive in terms of resource requirements, e.g., powerful GPU servers. Commercial gaming providers, e.g., Google Stadia, Amazon Luna, etc., hardly disclose any information on how they optimize gaming performance and cloud cost. In this work, we aim to investigate resource cost optimization for such cloud gaming systems. In contrast to previous work which have been focusing more on theoretical approaches, we deliver a …

Latent Class Analysis For Identifying Subclasses Of Depression Using Jmp Pro 16, Karishma Yadav, Fei Fei Sue-Ann Seet, Tin Seong Kam, Tin Seong Kam

Research Collection School Of Computing and Information Systems

According to WHO, “Depression is a leading cause of disability worldwide and is a major contributor to the overall global burden of disease”. A major stumbling block in the care of depressed patients remains the accurate diagnosis of the severity of depression. Patient Health Questionnaire (PHQ-9), a 9-question instrument is widely used for diagnosing and determining the severity of depression. However, the popularly used 5-Category of depression severity based on the sum of responses to the 9 questions was overly subjective. In view of this limitation, our paper aims to demonstrate how Latent Class Analysis of JMP Pro can be …

Can Differential Testing Improve Automatic Speech Recognition Systems?, Muhammad Hilmi Asyrofi, Zhou Yang, Jieke Shi, Chu Wei Quan, David Lo

Research Collection School Of Computing and Information Systems

Due to the widespread adoption of Automatic Speech Recognition (ASR) systems in many critical domains, ensuring the quality of recognized transcriptions is of great importance. A recent work, CrossASR++, can automatically uncover many failures in ASR systems by taking advantage of the differential testing technique. It employs a Text-To-Speech (TTS) system to synthesize audios from texts and then reveals failed test cases by feeding them to multiple ASR systems for cross-referencing. However, no prior work tries to utilize the generated test cases to enhance the quality of ASR systems. In this paper, we explore the subsequent improvements brought by leveraging …

Weakly-Supervised Video Anomaly Detection With Contrastive Learning Of Long And Short-Range Temporal Features, Yu Tian, Guansong Pang, Yuanhong Chen, Rajvinder Singh, Johan W. Verjans, Gustavo Carneiro

Research Collection School Of Computing and Information Systems

Anomaly detection with weakly supervised video-level labels is typically formulated as a multiple instance learning (MIL) problem, in which we aim to identify snippets containing abnormal events, with each video represented as a bag of video snippets. Although current methods show effective detection performance, their recognition of the positive instances, i.e., rare abnormal snippets in the abnormal videos, is largely biased by the dominant negative instances, especially when the abnormal events are subtle anomalies that exhibit only small differences compared with normal events. This issue is exacerbated in many methods that ignore important video temporal dependencies. To address this issue, …