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Bevers: A General, Simple, And Performant Framework For Automatic Fact Verification, Mitchell DeHaven 2022 University of Nebraska-Lincoln

Bevers: A General, Simple, And Performant Framework For Automatic Fact Verification, Mitchell Dehaven

Computer Science and Engineering: Theses, Dissertations, and Student Research

Fact verification has become an important process, primarily done manually by humans, to verify the authenticity of claims and statements made online. Increasingly, social media companies have utilized human effort to debunk false claims on their platforms, opting to either tag the content as misleading or false, or removing it entirely to combat misinformation on their sites. In tandem, the field of automatic fact verification has become a subject of focus among the natural language processing (NLP) community, spawning new datasets and research. The most popular dataset is the Fact Extraction and VERification (FEVER) dataset. In this thesis an end-to-end …


Sequence-Based Bioinformatics Approaches To Predict Virus-Host Relationships In Archaea And Eukaryotes, Yingshan Li 2022 University of Nebraska-Lincoln

Sequence-Based Bioinformatics Approaches To Predict Virus-Host Relationships In Archaea And Eukaryotes, Yingshan Li

Computer Science and Engineering: Theses, Dissertations, and Student Research

Viral metagenomics is independent of lab culturing and capable of investigating viromes of virtually any given environmental niches. While numerous sequences of viral genomes have been assembled from metagenomic studies over the past years, the natural hosts for the majority of these viral contigs have not been determined. Different computational approaches have been developed to predict hosts of bacteria phages. Nevertheless, little progress has been made in the virus-host prediction, especially for viruses that infect eukaryotes and archaea. In this study, by analyzing all documented viruses with known eukaryotic and archaeal hosts, we assessed the predictive power of four computational …


Data From: Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick 2022 Portland State University

Data From: Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick

Systems Science Faculty Datasets

This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This …


Obstacles In Learning Algorithm Run-Time Complexity Analysis, Bailey Licht 2022 University of Nebraska at Omaha

Obstacles In Learning Algorithm Run-Time Complexity Analysis, Bailey Licht

Theses/Capstones/Creative Projects

Algorithm run-time complexity analysis is an important topic in data structures and algorithms courses, but it is also a topic that many students struggle with. Commonly cited difficulties include the necessary mathematical background knowledge, the abstract nature of the topic, and the presentation style of the material. Analyzing the subject of algorithm analysis using multiple learning theories shows that course materials often leave out key steps in the learning process and neglect certain learning styles. Students can be more successful at learning algorithm run-time complexity analysis if these missing stages and learning styles are addressed.


Detecting Selfish Mining Attacks Against A Blockchain Using Machine Learing, Matthew A. Peterson 2022 University of South Alabama

Detecting Selfish Mining Attacks Against A Blockchain Using Machine Learing, Matthew A. Peterson

Theses and Dissertations

Selfish mining is an attack against a blockchain where miners hide newly discovered blocks instead of publishing them to the rest of the network. Selfish mining has been a potential issue for blockchains since it was first discovered by Eyal and Sirer. It can be used by malicious miners to earn a disproportionate share of the mining rewards or in conjunction with other attacks to steal money from network users. Several of these attacks were launched in 2018, 2019, and 2020 with the attackers stealing as much as $18 Million. Developers made several different attempts to fix this issue, but …


Reproducibility In Human-Robot Interaction: Furthering The Science Of Hri, Hatice Gunes, Frank Broz, Chris S. Crawford, Astrid Rosenthal-von der Putten, Megan K. Strait, Laurel Riek 2022 University of Cambridge

Reproducibility In Human-Robot Interaction: Furthering The Science Of Hri, Hatice Gunes, Frank Broz, Chris S. Crawford, Astrid Rosenthal-Von Der Putten, Megan K. Strait, Laurel Riek

Computer Science Faculty Publications and Presentations

Purpose of Review

To discuss the current state of reproducibility of research in human-robot interaction (HRI), challenges specific to the field, and recommendations for how the community can support reproducibility.

Recent Findings

As in related fields such as artificial intelligence, robotics, and psychology, improving research reproducibility is key to the maturation of the body of scientific knowledge in the field of HRI. The ACM/IEEE International Conference on Human-Robot Interaction introduced a theme on Reproducibility of HRI to their technical program in 2020 to solicit papers presenting reproductions of prior research or artifacts supporting research reproducibility.

Summary

This review provides an …


A Survey On Security Analysis Of Amazon Echo Devices, Surendra Pathak, Sheikh Ariful Islam, Honglu Jiang, Lei Xu, Emmett Tomai 2022 The University of Texas Rio Grande Valley

A Survey On Security Analysis Of Amazon Echo Devices, Surendra Pathak, Sheikh Ariful Islam, Honglu Jiang, Lei Xu, Emmett Tomai

Computer Science Faculty Publications and Presentations

Since its launch in 2014, Amazon Echo family of devices has seen a considerable increase in adaptation in consumer homes and offices. With a market worth millions of dollars, Echo is used for diverse tasks such as accessing online information, making phone calls, purchasing items, and controlling the smart home. Echo offers user-friendly voice interaction to automate everyday tasks making it a massive success. Though many people view Amazon Echo as a helpful assistant at home or office, few know its underlying security and privacy implications. In this paper, we present the findings of our research on Amazon Echo’s security …


Denoising And Deconvolving Sperm Whale Data In The Northern Gulf Of Mexico Using Fourier And Wavelet Techniques, Kendal McCain Leftwich 2022 University of New Orleans, New Orleans

Denoising And Deconvolving Sperm Whale Data In The Northern Gulf Of Mexico Using Fourier And Wavelet Techniques, Kendal Mccain Leftwich

University of New Orleans Theses and Dissertations

The use of underwater acoustics can be an important component in obtaining information from the oceans of the world. It is desirable (but difficult) to compile an acoustic catalog of sounds emitted by various underwater objects to complement optical catalogs. For example, the current visual catalog for whale tail flukes of large marine mammals (whales) can identify even individual whales from their individual fluke characteristics. However, since sperm whales, Physeter microcephalus, do not fluke up when they dive, they cannot be identified in this manner. A corresponding acoustic catalog for sperm whale clicks could be compiled to identify individual …


Computer Ethics In Curriculum, Tiya Williams 2022 CUNY New York City College of Technology

Computer Ethics In Curriculum, Tiya Williams

Publications and Research

Ethics specifically in Computer Curriculum is a growing problem that has yet to be widely addressed. Although, start of computer ethics being taught has been traced back to the early 1940’s it has not been standardized or implemented in all computer curriculum. The objective of this research is to diagnose the reasons why ethics is so crucial in computer curriculum at all levels. I used surveys to investigate whether students were taught ethics in their computer curriculum. I also conducted surveys for professors at universities and colleges if they were taught ethics while obtaining their degree, as well as if …


Investigating 9-1-1 Call Experience For Medical Emergencies For Future Design, Leanna Machado 2022 Pace University

Investigating 9-1-1 Call Experience For Medical Emergencies For Future Design, Leanna Machado

Honors College Theses

Emergency calling services have continued to use the 9-1-1 phone number to share information about a medical emergency for around 60 years. However, there are limitations to the current emergency call services that can have improved communication, speed, and accuracy with the implementation of advancing technology. By examining the experience and opinions of 9-1-1 medical emergency callers, we are able to investigate the design of 9-1-1 services to enhance the call experience. The major challenges found in the study are difficulty in verbally explaining the emergency situation and difficulty in describing the location of the emergency. Technologies such as location …


Opportunities And Challenges In Code Search Tools, Chao LIU, Xin XIA, David LO, Cuiying GAO, Xiaohu YANG, John GRUNDY 2022 Zhejiang University

Opportunities And Challenges In Code Search Tools, Chao Liu, Xin Xia, David Lo, Cuiying Gao, Xiaohu Yang, John Grundy

Research Collection School Of Computing and Information Systems

Code search is a core software engineering task. Effective code search tools can help developers substantially improve their software development efficiency and effectiveness. In recent years, many code search studies have leveraged different techniques, such as deep learning and information retrieval approaches, to retrieve expected code from a large-scale codebase. However, there is a lack of a comprehensive comparative summary of existing code search approaches. To understand the research trends in existing code search studies, we systematically reviewed 81 relevant studies. We investigated the publication trends of code search studies, analyzed key components, such as codebase, query, and modeling technique …


An Efficient Annealing-Assisted Differential Evolution For Multi-Parameter Adaptive Latent Factor Analysis, Qing LI, Guansong PANG, Mingsheng SHANG 2022 Chongqing University of Post and Telecommunications

An Efficient Annealing-Assisted Differential Evolution For Multi-Parameter Adaptive Latent Factor Analysis, Qing Li, Guansong Pang, Mingsheng Shang

Research Collection School Of Computing and Information Systems

A high-dimensional and incomplete (HDI) matrix is a typical representation of big data. However, advanced HDI data analysis models tend to have many extra parameters. Manual tuning of these parameters, generally adopting the empirical knowledge, unavoidably leads to additional overhead. Although variable adaptive mechanisms have been proposed, they cannot balance the exploration and exploitation with early convergence. Moreover, learning such multi-parameters brings high computational time, thereby suffering gross accuracy especially when solving a bilinear problem like conducting the commonly used latent factor analysis (LFA) on an HDI matrix. Herein, an efficient annealing-assisted differential evolution for multi-parameter adaptive latent factor analysis …


Segment-Wise Time-Varying Dynamic Bayesian Network With Graph Regularization, Xing YANG, Chen ZHANG, Baihua ZHENG 2022 Singapore Management University

Segment-Wise Time-Varying Dynamic Bayesian Network With Graph Regularization, Xing Yang, Chen Zhang, Baihua Zheng

Research Collection School Of Computing and Information Systems

Time-varying dynamic Bayesian network (TVDBN) is essential for describing time-evolving directed conditional dependence structures in complex multivariate systems. In this article, we construct a TVDBN model, together with a score-based method for its structure learning. The model adopts a vector autoregressive (VAR) model to describe inter-slice and intra-slice relations between variables. By allowing VAR parameters to change segment-wisely over time, the time-varying dynamics of the network structure can be described. Furthermore, considering some external information can provide additional similarity information of variables. Graph Laplacian is further imposed to regularize similar nodes to have similar network structures. The regularized maximum a …


A Logistic Regression And Linear Programming Approach For Multi-Skill Staffing Optimization In Call Centers, Thuy Anh TA, Tien MAI, Fabian BASTIN, Pierre l'ECUYER 2022 Singapore Management University

A Logistic Regression And Linear Programming Approach For Multi-Skill Staffing Optimization In Call Centers, Thuy Anh Ta, Tien Mai, Fabian Bastin, Pierre L'Ecuyer

Research Collection School Of Computing and Information Systems

We study a staffing optimization problem in multi-skill call centers. The objective is to minimize the total cost of agents under some quality of service (QoS) constraints. The key challenge lies in the fact that the QoS functions have no closed-form and need to be approximated by simulation. In this paper we propose a new way to approximate the QoS functions by logistic functions and design a new algorithm that combines logistic regression, cut generations and logistic-based local search to efficiently find good staffing solutions. We report computational results using examples up to 65 call types and 89 agent groups …


Scalable Distributional Robustness In A Class Of Non Convex Optimization With Guarantees, Avinandan BOSE, Arunesh SINHA, Tien MAI 2022 Singapore Management University

Scalable Distributional Robustness In A Class Of Non Convex Optimization With Guarantees, Avinandan Bose, Arunesh Sinha, Tien Mai

Research Collection School Of Computing and Information Systems

Distributionally robust optimization (DRO) has shown lot of promise in providing robustness in learning as well as sample based optimization problems. We endeavor to provide DRO solutions for a class of sum of fractionals, non-convex optimization which is used for decision making in prominent areas such as facility location and security games. In contrast to previous work, we find it more tractable to optimize the equivalent variance regularized form of DRO rather than the minimax form. We transform the variance regularized form to a mixed-integer second order cone program (MISOCP), which, while guaranteeing near global optimality, does not scale enough …


Dronlomaly: Runtime Detection Of Anomalous Drone Behaviors Via Log Analysis And Deep Learning, Lwin Khin SHAR, Wei MINN, Nguyen Binh Duong TA, Lingxiao JIANG, Daniel Wai Kiat LIM, Wai Kiat David LIM 2022 Singapore Management University

Dronlomaly: Runtime Detection Of Anomalous Drone Behaviors Via Log Analysis And Deep Learning, Lwin Khin Shar, Wei Minn, Nguyen Binh Duong Ta, Lingxiao Jiang, Daniel Wai Kiat Lim, Wai Kiat David Lim

Research Collection School Of Computing and Information Systems

Drones are increasingly popular and getting used in a variety of missions such as area surveillance, pipeline inspection, cinematography, etc. While the drone is conducting a mission, anomalies such as sensor fault, actuator fault, configuration errors, bugs in controller program, remote cyber- attack, etc., may affect the drone’s physical stability and cause serious safety violations such as crashing into the public. During a flight mission, drones typically log flight status and state units such as GPS coordinates, actuator outputs, accelerator readings, gyroscopic readings, etc. These log data may reflect the above-mentioned anomalies. In this paper, we propose a novel, deep …


Conversation Disentanglement With Bi-Level Contrastive Learning, Chengyu HUANG, Hao FEI, Lizi LIAO, Lizi LIAO 2022 Singapore Management University

Conversation Disentanglement With Bi-Level Contrastive Learning, Chengyu Huang, Hao Fei, Lizi Liao, Lizi Liao

Research Collection School Of Computing and Information Systems

Conversation disentanglement aims to group utterances into detached sessions, which is a fundamental task in processing multi-party conversations. Existing methods have two main drawbacks. First, they overemphasize pairwise utterance relations but pay inadequate attention to the utterance-to-context relation modeling. Second, a huge amount of human annotated data is required for training, which is expensive to obtain in practice. To address these issues, we propose a general disentangle model based on bi-level contrastive learning. It brings closer utterances in the same session while encourages each utterance to be near its clustered session prototypes in the representation space. Unlike existing approaches, our …


Vr Computing Lab: An Immersive Classroom For Computing Learning, Huan Shan Shawn PANG, Kyong Jin SHIM, Yi Meng LAU, GOTTIPATI Swapna 2022 Singapore Management University

Vr Computing Lab: An Immersive Classroom For Computing Learning, Huan Shan Shawn Pang, Kyong Jin Shim, Yi Meng Lau, Gottipati Swapna

Research Collection School Of Computing and Information Systems

In recent years, virtual reality (VR) is gaining popularity amongst educators and learners. If a picture is worth a thousand words, a VR session is worth a trillion words. VR technology completely immerses users with an experience that transports them into a simulated world. Universities across the United States, United Kingdom, and other countries have already started using VR for higher education in areas such as medicine, business, architecture, vocational training, social work, virtual field trips, virtual campuses, helping students with special needs, and many more. In this paper, we propose a novel VR platform learning framework which maps elements …


Gamified Online Industry Learning Platform For Teaching Of Foundational Computing Skills, Yi Meng LAU, Rafael Jose BARROS BARRIOS, GOTTIPATI Swapna, Kyong Jin SHIM 2022 Singapore Management University

Gamified Online Industry Learning Platform For Teaching Of Foundational Computing Skills, Yi Meng Lau, Rafael Jose Barros Barrios, Gottipati Swapna, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

Online industry learning platforms are widely used by organizations for employee training and upskilling. Courses or lessons offered by these platforms can be generic or specific to an enterprise application. The increased demand of new hires to learn these platforms or who are already certified in some of these courses has led universities to look at the opportunities for integrating online industry learning platforms into their curricula. Universities hope to use these platforms to aid students in their learning of concepts and theories. At the same time, these platforms can equip students with industryrecognized certifications or digital badges. This paper …


Towards Reinterpreting Neural Topic Models Via Composite Activations, Jia Peng LIM, Hady Wirawan LAUW 2022 Singapore Management University

Towards Reinterpreting Neural Topic Models Via Composite Activations, Jia Peng Lim, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Most Neural Topic Models (NTM) use a variational auto-encoder framework producing K topics limited to the size of the encoder’s output. These topics are interpreted through the selection of the top activated words via the weights or reconstructed vector of the decoder that are directly connected to each neuron. In this paper, we present a model-free two-stage process to reinterpret NTM and derive further insights on the state of the trained model. Firstly, building on the original information from a trained NTM, we generate a pool of potential candidate “composite topics” by exploiting possible co-occurrences within the original set of …


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