Physical Sciences and Mathematics Commons^™

Electrochemical And Photoelectrochemical Properties Of Oxide And Chalcogenide Semiconductors, Farinaz Firouzan

Chemistry & Biochemistry Dissertations

Photoelectrochemical (PEC) water splitting via inorganic semiconductors has shown interest in technical applications such as harvesting sunlight as sustainable fuels. Chalcogen-based (S, Se, Te) semiconductors are important in numerous technology applications especially related to photovoltaic solar conversion, fuel cells, hydrogen generation etc. On the other hand, oxide semiconductors are great candidates due to their unique properties namely electrolyte stability, a wide range of bandgaps and easy access. In this vein, we investigated the quaternary metal chalcogenide, Ca(La1−xCex)2S4 (0 ≤ x ≤ 1) photoelectrochemical behavior in an aqueous redox electrolyte. These solid solution series were synthesized in Prof. Macaluso’s laboratory by …

A Set Theory Based Similarity Measure For Text Clustering And Classification, Ali A. Amer, Hassan I. Abdalla

All Works

© 2020, The Author(s). Similarity measures have long been utilized in information retrieval and machine learning domains for multi-purposes including text retrieval, text clustering, text summarization, plagiarism detection, and several other text-processing applications. However, the problem with these measures is that, until recently, there has never been one single measure recorded to be highly effective and efficient at the same time. Thus, the quest for an efficient and effective similarity measure is still an open-ended challenge. This study, in consequence, introduces a new highly-effective and time-efficient similarity measure for text clustering and classification. Furthermore, the study aims to provide a …

Improving Binary Classification Using Filtering Based On K-Nn Proximity Graphs, Maher Ala’Raj, Munir Majdalawieh, Maysam F. Abbod

All Works

© 2020, The Author(s). One of the ways of increasing recognition ability in classification problem is removing outlier entries as well as redundant and unnecessary features from training set. Filtering and feature selection can have large impact on classifier accuracy and area under the curve (AUC), as noisy data can confuse classifier and lead it to catch wrong patterns in training data. The common approach in data filtering is using proximity graphs. However, the problem of the optimal filtering parameters selection is still insufficiently researched. In this paper filtering procedure based on k-nearest neighbours proximity graph was used. Filtering parameters …

2020 December - Tennessee Monthly Climate Report, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Monthly Report

No abstract provided.

The Application And Development Of Metabolomics Methodologies For The Profiling Of Food And Cellular Toxicity, Jade Woods

Department of Chemistry: Dissertations, Theses, and Student Research

Metabolomics is a rapidly growing field of study. Its growth reflects advancements in technology and an improved understanding of the impact of the environment on metabolism. As a result, metabolomics is now commonly employed to investigate and characterize human and plant metabolism. The first chapter of this thesis provides an introduction to metabolomics and an overview of the protocols for sample preparation, data collection and statistical analysis. The second thesis chapter describes in explicit detail the step-by-step process of extracting and analyzing metabolites collected from mammalian cells, specifically brain tissue with a focus on Parkinson’s disease. The chapter highlights important …

Packet Delivery: An Investigation Of Educational Video Games For Computer Science Education, Robert Lafferty

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

The field of educational video games has rapidly grown since the 1970s, mostly producing video games to teach core education concepts such as mathematics, natural science, and English. Recently, various research groups have developed educational games to address elective topics such as finance and health. Educational video games often target grade school audiences and rarely target high school students, college students, or adults. Computer science topics are not a common theme among educational video games; the games that address Computer Science topics teach computer fundamentals, such as typing or basic programming, to young audiences.

Packet Delivery, an educational video …

Hyperpolarized Carbon-13 Magnetic Resonance Measurements Of Tissue Perfusion And Metabolism, Keith Michel

Dissertations & Theses (Open Access)

Hyperpolarized Magnetic Resonance Imaging (HP MRI) is an emerging modality that enables non-invasive interrogation of cells and tissues with unprecedented biochemical detail. This technology provides rapid imaging measurements of the activity of a small quantity of molecules with a strongly polarized nuclear magnetic moment. This polarization is created in a polarizer separate from the imaging magnet, and decays continuously towards a non-detectable thermal equilibrium once the imaging agent is removed from the polarizer and administered by intravenous injection. Specialized imaging strategies are therefore needed to extract as much information as possible from the HP signal during its limited lifetime.

In …

A Divide And Conquer With Semi-Global Failover For Software Defined Networks, Kasra Goravanchi

UNLV Theses, Dissertations, Professional Papers, and Capstones

Nowadays, many service providers need to provide many other functions than just a network connectivity. They also need to provide network functions such as network address translation, firewall, encryption, Domain Name Service (DNS), caching, routing and many other services. Usually these functions come with the hardware at the user or customer’s premises. This can increase the revenue of the revenue, but also can cost a lot and also be extremely difficult to maintain. Moreover, it is important to be able to configure the network and later modify the configuration to create fault tolerance and to prepare the system for future …

An Exploration Of The Numeracy Skills Required For Safe, Quality Nursing Practice, Anna Wendel

UNLV Theses, Dissertations, Professional Papers, and Capstones

The purpose of this study was to explore the numeracy skills required for safe, quality nursing practice. Using a descriptive mixed methods design, this study answered two research questions: 1) What numeracy skills do nurses perceive as important for providing safe, quality nursing care in the first three years of practice? 2) How do nurses incorporate numeracy skills into daily patient care during the first three years of practice? Early career nurses from a not-for-profit health care organization in the mid-Atlantic region of the United States (n=109) responded to an online survey tool developed by the student investigator that ranked …

In-Depth Analysis Of College Students’ Data Privacy Awareness, Vernon D. Andrews

Theses and Dissertations

While attending university, college students need to be aware of issues related to data privacy. Regardless of the age, gender, race, or education level of college students, every student can relate to the advancement of the internet within the last decades. The internet has completely transformed the way the world receives and stores messages. Positively, the internet allows messages to be sent across the globe within the fraction of a second and provides students with access to potentially unlimited information on a variety of subjects. On the downside, there are issues on the internet that can cause more harm than …

Proactive Management In Academic Libraries: Promoting Improved Communication And Inclusion Of Academic Librarians And Archivists In Cybersecurity Policy Creation, Paul J. Luft

Theses and Dissertations

Although increasing cybersecurity threats continue in libraries, not many studies are available which examine surrounding cybersecurity policies. Even less has been done on specific types of libraries such as academic and archives. When it comes to academic libraries, cybersecurity policies take a top-down approach to managing and creating policies. The problem is that both academic libraries and archives are unique areas within a university setting. Some of the general policies do not always handle specific issues dealt with in an academic library or archives. This paper investigates if an actual gap or void in policy exists which could create issues …

Synthesis, X-Ray Absorption, And X-Ray Diffraction Spectroscopy Studies Of Uranium Based Heavy Fermion Systems, Vahe Mkrtchyan

UNLV Theses, Dissertations, Professional Papers, and Capstones

Heavy fermion systems have attracted a great deal of interest since the discovery of the phenomenon in the 1970s. Over the past 35 years, the research in this field revealed a variety of unprecedented properties that the conventional theoretical framework considered conflicting or impossible. The subset of intermetallic compounds containing rare earth and actinide elements is of special interest, as these materials exhibit a range of exotic properties associated with 4f or 5f electrons. A thorough examination of the reports in the literature indicates a delicate interplay between the competing ground states, giving rise to anomalous properties including non-Fermi liquid …

Clarity On Cronbach’S Alpha Use, Jack Barbera, Nicole Naibert, Regis Komperda, Thomas C. Pentecost

Chemistry Faculty Publications and Presentations

The Cronbach’s alpha (α) statistic is regularly reported in science education studies. However, recent reviews have noted that it is not well-understood. Therefore, this commentary provides additional clarity regarding the language used when describing and interpreting alpha and other estimates of reliability.

Watch Out! Motion Is Blurring The Vision Of Your Deep Neural Networks, Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Jian Wang, Bing Yu, Wei Feng, Yang Liu

Research Collection School Of Computing and Information Systems

The state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples with additive random noise-like perturbations. While such examples are hardly found in the physical world, the image blurring effect caused by object motion, on the other hand, commonly occurs in practice, making the study of which greatly important especially for the widely adopted real-time image processing tasks (e.g., object detection, tracking). In this paper, we initiate the first step to comprehensively investigate the potential hazards of blur effect for DNN, caused by object motion. We propose a novel adversarial attack method that can generate visually natural motion-blurred adversarial examples, …

Security Analysis Of Permission Re-Delegation Vulnerabilities In Android Apps, Biniam Fisseha Demissie, Mariano Ceccato, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

The Android platform facilitates reuse of app functionalities by allowing an app to request an action from another app through inter-process communication mechanism. This feature is one of the reasons for the popularity of Android, but it also poses security risks to the end users because malicious, unprivileged apps could exploit this feature to make privileged apps perform privileged actions on behalf of them. In this paper, we investigate the hybrid use of program analysis, genetic algorithm based test generation, natural language processing, machine learning techniques for precise detection of permission re-delegation vulnerabilities in Android apps. Our approach first groups …

Sharper Generalisation Bounds For Pairwise Learning, Yunwen Lei, Antoine Ledent, Marius Kloft

Research Collection School Of Computing and Information Systems

Pairwise learning refers to learning tasks with loss functions depending on a pair of training examples, which includes ranking and metric learning as specific examples. Recently, there has been an increasing amount of attention on the generalization analysis of pairwise learning to understand its practical behavior. However, the existing stability analysis provides suboptimal high-probability generalization bounds. In this paper, we provide a refined stability analysis by developing generalization bounds which can be √nn-times faster than the existing results, where nn is the sample size. This implies excess risk bounds of the order O(n−1/2) (up to a logarithmic factor) for both …

Unmaking California’S Central Valley, Sayd Randle

Research Collection College of Integrative Studies

IN THIS YEAR of heat records and fire tornadoes, California faces another potential crisis: drought. In November 2020, more than 80 percent of the state’s land mass was classified as somewhere between “abnormally dry” and “extreme drought” by the United States Drought Monitor. The chances of the winter offering relief look slim, given what’s called a “La Niña climate pattern,” which is associated with arid conditions in much of California. The months ahead are, in general, far more likely to bring water worries than happy surprises.To longtime California residents, such fears are familiar. The state’s most recent drought began in …

Graphmp: I/O-Efficient Big Graph Analytics On A Single Commodity Machine, Peng Sun, Yonggang Wen, Nguyen Binh Duong Ta, Xiaokui Xiao

Research Collection School Of Computing and Information Systems

Recent studies showed that single-machine graph processing systems can be as highly competitive as cluster-based approaches on large-scale problems. While several out-of-core graph processing systems and computation models have been proposed, the high disk I/O overhead could significantly reduce performance in many practical cases. In this paper, we propose GraphMP to tackle big graph analytics on a single machine. GraphMP achieves low disk I/O overhead with three techniques. First, we design a vertex-centric sliding window (VSW) computation model to avoid reading and writing vertices on disk. Second, we propose a selective scheduling method to skip loading and processing unnecessary edge …

A Deep Learning Framework Supporting Model Ownership Protection And Traitor Tracing, Guowen Xu, Hongwei Li, Yuan Zhang, Xiaodong Lin, Robert H. Deng, Xuemin (Sherman) Shen

Research Collection School Of Computing and Information Systems

Cloud-based deep learning (DL) solutions have been widely used in applications ranging from image recognition to speech recognition. Meanwhile, as commercial software and services, such solutions have raised the need for intellectual property rights protection of the underlying DL models. Watermarking is the mainstream of existing solutions to address this concern, by primarily embedding pre-defined secrets in a model's training process. However, existing efforts almost exclusively focus on detecting whether a target model is pirated, without considering traitor tracing. In this paper, we present SecureMark_DL, which enables a model owner to embed a unique fingerprint for every customer within parameters …

Artificial Intelligence For Social Impact: Learning And Planning In The Data-To-Deployment Pipeline, Andrew Perrault, Fei Fang, Arunesh Sinha, Milind Tambe

Research Collection School Of Computing and Information Systems

With the maturing of artificial intelligence (AI) and multiagent systems research, we have a tremendous opportunity to direct these advances toward addressing complex societal problems. In pursuit of this goal of AI for social impact, we as AI researchers must go beyond improvements in computational methodology; it is important to step out in the field to demonstrate social impact. To this end, we focus on the problems of public safety and security, wildlife conservation, and public health in low-resource communities, and present research advances in multiagent systems to address one key cross-cutting challenge: how to effectively deploy our limited intervention …

The Spatial And Temporal Impact Of Agricultural Crop Residual Burning On Local Land Surface Temperature In Three Provinces Across China From 2015 To 2017, Wenting Zhang, Mengmeng Yu, Qingqing He, Tianwei Wang, Lu Lin, Kai Cao, Wei Huang, Peihong Fu, Jiaxin Chen

Research Collection School Of Computing and Information Systems

China has suffered from severe crop residue burning (CRB) for a long time. As a type of biomass burning, CRB leads to a huge alteration in climate due to the emission of greenhouse gases and particulates in the atmosphere and damages to surface characteristics on land. At present, a growing body of research focuses on the impact of biomass burning (BB) (e.g., forest fire, grass fire, and CRB) on climate change from the aspect of atmospheric process. Meanwhile, a small number of research studies have started to pay attention on the damage caused by BB (e.g. forest fire) on land …

Blockchain-Based Public Auditing And Secure Deduplication With Fair Arbitration, Haoran Yuan, Xiaofeng Chen, Jianfeng Wang, Jiaming Yuan, Hongyang Yan, Willy Susilo

Research Collection School Of Computing and Information Systems

Data auditing enables data owners to verify the integrity of their sensitive data stored at an untrusted cloud without retrieving them. This feature has been widely adopted by commercial cloud storage. However, the existing approaches still have some drawbacks. On the one hand, the existing schemes have a defect of fair arbitration, i.e., existing auditing schemes lack an effective method to punish the malicious cloud service provider (CSP) and compensate users whose data integrity is destroyed. On the other hand, a CSP may store redundant and repetitive data. These redundant data inevitably increase management overhead and computational cost during the …

Driving Cybersecurity Policy Insights From Information On The Internet, Qiu-Hong Wang, Steven Mark Miller, Robert H. Deng

Research Collection School Of Computing and Information Systems

Cybersecurity policy analytics quantitatively evaluates the effectiveness of cybersecurity protection measures consisting of both technical and managerial countermeasures and is inherently interdisciplinary work, drawing on the concepts and methods from economics, business, social science, and law.

Differential Privacy Protection Over Deep Learning: An Investigation Of Its Impacted Factors, Ying Lin, Ling-Yan Bao, Ze-Minghui Li, Shu-Sheng Si, Chao-Hsien Chu

Research Collection School Of Computing and Information Systems

Deep learning (DL) has been widely applied to achieve promising results in many fields, but it still exists various privacy concerns and issues. Applying differential privacy (DP) to DL models is an effective way to ensure privacy-preserving training and classification. In this paper, we revisit the DP stochastic gradient descent (DP-SGD) method, which has been used by several algorithms and systems and achieved good privacy protection. However, several factors, such as the sequence of adding noise, the models used etc., may impact its performance with various degrees. We empirically show that adding noise first and clipping second will not only …

Interventional Few-Shot Learning, Zhongqi Yue, Zhang Hanwang, Qianru Sun, Xian-Sheng Hua

Research Collection School Of Computing and Information Systems

We uncover an ever-overlooked deficiency in the prevailing Few-Shot Learning (FSL) methods: the pre-trained knowledge is indeed a confounder that limits the performance. This finding is rooted from our causal assumption: a Structural Causal Model (SCM) for the causalities among the pre-trained knowledge, sample features, and labels. Thanks to it, we propose a novel FSL paradigm: Interventional Few-Shot Learning (IFSL). Specifically, we develop three effective IFSL algorithmic implementations based on the backdoor adjustment, which is essentially a causal intervention towards the SCM of many-shot learning: the upper-bound of FSL in a causal view. It is worth noting that the contribution …

Debunking Rumors On Twitter With Tree Transformer, Jing Ma, Wei Gao

Research Collection School Of Computing and Information Systems

Rumors are manufactured with no respect for accuracy, but can circulate quickly and widely by "word-of-post" through social media conversations. Conversation tree encodes important information indicative of the credibility of rumor. Existing conversation-based techniques for rumor detection either just strictly follow tree edges or treat all the posts fully-connected during feature learning. In this paper, we propose a novel detection model based on tree transformer to better utilize user interactions in the dialogue where post-level self-attention plays the key role for aggregating the intra-/inter-subtree stances. Experimental results on the TWITTER and PHEME datasets show that the proposed approach consistently improves …

Digital Social Listening On Conversations About Sexual Harassment, Xuesi Sim, Ern Rae Chang, Yu Xiang Ong, Jie Ying Yeo, Christine Bai Shuang Yan, Eugene Wen Jia Choy, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

In light of the #MeToo movement and publicized sexual harassment incidents in Singapore in recent years, we built an analytics pipeline for performing digital social listening on conversations about sexual harassment for AWARE (Association of Women for Action and Research). Our social network analysis results identified key influencers that AWARE can engage for sexual harassment awareness campaigns. Further, our analysis results suggest new hashtags that AWARE can use to run social media campaigns and achieve greater reach.

Co-Embedding Attributed Networks With External Knowledge, Pei-Chi Lo, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Attributed network embedding aims to learn representations of nodes and their attributes in a low-dimensional space that preserves their semantics. The existing embedding models, however, consider node connectivity and node attributes only while ignoring external knowledge that can enhance node representations for downstream applications. In this paper, we propose a set of new VAE-based embedding models called External Knowledge-Aware Co-Embedding Attributed Network (ECAN) Embeddings to incorporate associations among attributes from relevant external knowledge. Such external knowledge can be extracted from text corpus and knowledge graphs. We use multi-VAE structures to model the attribute associations. To cope with joint encoding of …

Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam In Deep Learning, Pan Zhou, Jiashi Feng, Chao Ma, Caiming Xiong, Steven C. H. Hoi, Weinan E

Research Collection School Of Computing and Information Systems

It is not clear yet why ADAM-alike adaptive gradient algorithms suffer from worse generalization performance than SGD despite their faster training speed. This work aims to provide understandings on this generalization gap by analyzing their local convergence behaviors. Specifically, we observe the heavy tails of gradient noise in these algorithms. This motivates us to analyze these algorithms through their Lévy-driven stochastic differential equations (SDEs) because of the similar convergence behaviors of an algorithm and its SDE. Then we establish the escaping time of these SDEs from a local basin. The result shows that (1) the escaping time of both SGD …

Algorithms And Hardness Results For Computing Cores Of Markov Chains, Ali Ahmadi, Krishnendu Chatterjee, Amir Kafshdar Goharshady, Tobias Meggendorfer, Roodabeh Safavi, Dorde Zikelic

Research Collection School Of Computing and Information Systems

Given a Markov chain M = (V,v0,δ), with state space V and a starting state v0, and a probability threshold ϵ, an ϵ-core is a subset C of states that is left with probability at most ϵ. More formally, C ⊆V is an ϵ-core, iff P reach(V\C) ≤ ϵ. Cores have been applied in a wide variety of verification problems over Markov chains, Markov decision processes, and probabilistic programs, as a means of discarding uninteresting and low-probability parts of a probabilistic system and instead being able to focus on the states that are likely to be encountered in a real-world …