Physical Sciences and Mathematics Commons^™

The Locus Algorithm: A Novel Technique For Identifying Optimised Pointings For Differential Photometry, Oisin Creaner, Kevin Nolan Mr, E. Hickey, N. Smith

Articles

Studies of the photometric variability of astronomical sources from ground-based telescopes must overcome atmospheric extinction effects. Differential photometry by reference to an ensemble of reference stars which closely match the target in terms of magnitude and colour can mitigate these effects. This Paper describes the design, implementation, and operation of a novel algorithm – The Locus Algorithm – which enables optimised differential photometry. The Algorithm is intended to identify, for a given target and observational parameters, the Field of View (FoV) which includes the target and the maximum number of reference stars similar to the target. A collection of objects …

A Validity-Based Approach For Feature Selection In Intrusion Detection Systems, Eljilani Hmouda

CCAC Theses and Dissertations

Intrusion detection systems are tools that detect and remedy the presence of malicious activities. Intrusion detection systems face many challenges in terms of accurate analysis and evaluation. One such challenge is the involvement of many features during analysis, which leads to high data volume and ultimately excessive computational overhead. This research surrounds the development of a new intrusion detection system by employing an entropy-based measure called v-measure to select significant features and reduce dimensionality. After the development of the intrusion detection system, this feature reduction technique was tested on public datasets by applying machine learning classifiers such as Decision Tree, …

Particle Identification And Tracking In Real Time Using Machine Learning On Fpga, F. Barbosa, L. Belfore, C. Dickover, C. Fanelli, S. Furletov, Y. Furletova, L. Jokhovets, D. Lawrence, D. Romanov

Electrical & Computer Engineering Faculty Publications

This project is a multi-disciplinary endeavour between Physics, Electrical Engineering, and Computer Engineering. The purpose is to develop and implement an FPGA(*) based Machine Learning algorithm for real-time particle identification, filtering, and data reduction. This is important research that can be applied to streaming readout systems being developed now at JLab and other facilities. Real-time data processing is a frontier field in experimental physics, especially in HEP. The application of FPGAs at the trigger level is used by many current and planned experiments (CMS, LHCb, Belle2, PANDA). Usually they use conventional processing algorithms. LHCb has implemented ML elements for real-time …

Post-Quantum Secure Identity-Based Encryption Scheme Using Random Integer Lattices For Iot-Enabled Ai Applications, Dharminder Dharminder, Ashok Kumar Das, Sourav Saha, Basudeb Bera, Athanasios V. Vasilakos

VMASC Publications

Identity-based encryption is an important cryptographic system that is employed to ensure confidentiality of a message in communication. This article presents a provably secure identity based encryption based on post quantum security assumption. The security of the proposed encryption is based on the hard problem, namely Learning with Errors on integer lattices. This construction is anonymous and produces pseudo random ciphers. Both public-key size and ciphertext-size have been reduced in the proposed encryption as compared to those for other relevant schemes without compromising the security. Next, we incorporate the constructed identity based encryption (IBE) for Internet of Things (IoT) applications, …

Rankings Of Mma Fighters, Michael Schaefer

All Graduate Theses, Dissertations, and Other Capstone Projects

Ranking is an essential process that allows sporting authorities to determine the relative performance of athletes. While ranking is straightforward in some sports, it is more complicated in MMA (mixed martial arts), where competition is often fragmented. This paper describes the mathematics behind four existing ranking algorithms: Elo’s System, Massey’s Method, Colley’s Method, and Google’s PageRank, and shows how to adapt them to rank MMA fighters in the UFC (Ultimate Fighting Championship). We also provide a performance analysis for each ranking method.

Secret Sharing And Its Variants, Matroids,Combinatorics., Shion Samadder Chaudhury Dr.

Doctoral Theses

The main focus of this thesis is secret sharing. Secret Sharing is a very basic and fundamental cryptographic primitive. It is a method to share a secret by a dealer among different parties in such a way that only certain predetermined subsets of parties can together reconstruct the secret while some of the remaining subsets of parties can have no information about the secret. Secret sharing was introduced independently by Shamir [139] and Blakely [20]. What they introduced is called a threshold secret sharing scheme. In such a secret sharing scheme the subsets of parties that can reconstruct a secret …

Fair And Diverse Group Formation Based On Multidimensional Features, Mohammed Saad A Alqahtani

Graduate Theses and Dissertations

The goal of group formation is to build a team to accomplish a specific task. Algorithms are being developed to improve the team's effectiveness so formed and the efficiency of the group selection process. However, there is concern that team formation algorithms could be biased against minorities due to the algorithms themselves or the data on which they are trained. Hence, it is essential to build fair team formation systems that incorporate demographic information into the process of building the group. Although there has been extensive work on modeling individuals’ expertise for expert recommendation and/or team formation, there has been …

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 …

Automated Code Engine For Tensor Hypercontraction: Derivation, Optimization And Implementation Of Rank-Reduced Coupled Cluster Theories, Yao Zhao

Dissertations, Theses, and Capstone Projects

The ultimate goal of electronic structure theory is solving the electronic Schr¨odinger Equation. However, even accurate approximations of solving Schr¨odinger Equation, such as high order coupled cluster theories, require computational efforts that are too demanding to be applied on large chemical systems. This thesis tackles the problem of curse of dimensionality: how to reduce the time complexity of high-accuracy coupled cluster methods in order to accelerate computations of molecular energy. On one hand, we believe that low-rank approximation (i.e. Tensor HyperContraction) of high-order tensors appearing in coupled cluster theory is a promising way to achieve rank-reduced coupled cluster theory. On …

An Introduction To Calling Bullshit: Learning To Think Outside The Black Box, Jevin D. West, Carl T. Bergstrom

Numeracy

Bergstrom, Carl T. and Jevin D. West. 2020. Calling Bullshit: The Art of Skepticism in a Data-Driven World. (New York: Random House) 336 pp. ISBN 978-0525509202.

While statistical methods receive greater attention, the art of critically evaluating information in everyday life more commonly depends on thinking outside the black box of the algorithm. In this piece we introduce readers to our book and associated online teaching materials—for readers who want to more capably call “bullshit” or to teach their students to do the same.

Dealing With Classification Irregularities In Real-World Scenarios., Payel Sadhukhan Dr.

Doctoral Theses

Data processing by the human sensory system comes naturally. This processing, commonly denoted as pattern recognition and analysis are carried out spontaneously by humans. In day to day life, in most cases, decision making by humans come without any conscious effort. From the middle of the past century, humans have shown interest to render their abstraction capabilities (pattern recognition and analysis) to the machine. The abstraction capability of the machine is ’machine intelligence’ or ’machine learning’ [87].The primary goal of machine learning methods is to extract some meaningful information from the ’data’. Data refers to the information or attributes that …

Algorithms Related To Triangle Groups, Bao The Pham

LSU Doctoral Dissertations

Given a finite index subgroup of $\PSL_2(\Z)$, one can talk about the different properties of this subgroup. These properties have been studied extensively in an attempt to classify these subgroups. Tim Hsu created an algorithm to determine whether a subgroup is a congruence subgroup by using permutations \cite{hsu}. Lang, Lim, and Tan also created an algorithm to determine if a subgroup is a congruence subgroup by using Farey Symbols \cite{llt}. Sebbar classified torsion-free congruence subgroups of genus 0 \cite{sebbar}. Pauli and Cummins computed and tabulated all congruence subgroups of genus less than 24 \cite{ps}. However, there are still some problems …

Awegnn: Auto-Parametrized Weighted Element-Specific Graph Neural Networks For Molecules., Timothy Szocinski, Duc Duy Nguyen, Guo-Wei Wei

Mathematics Faculty Publications

While automated feature extraction has had tremendous success in many deep learning algorithms for image analysis and natural language processing, it does not work well for data involving complex internal structures, such as molecules. Data representations via advanced mathematics, including algebraic topology, differential geometry, and graph theory, have demonstrated superiority in a variety of biomolecular applications, however, their performance is often dependent on manual parametrization. This work introduces the auto-parametrized weighted element-specific graph neural network, dubbed AweGNN, to overcome the obstacle of this tedious parametrization process while also being a suitable technique for automated feature extraction on these internally complex …

Design And Implementation Of The Amiga Embedded System For Data Acquisition, A. Aab, P. Abreu, M. Aglietta, J. M. Albury, I. Allekotte, A. Almela, B. Fick, D. F. Nitz, A. Puyleart, Et. Al.

Michigan Tech Publications, Part 1

The Auger Muon Infill Ground Array (AMIGA) is part of the AugerPrime upgrade of the Pierre Auger Observatory. It consists of particle counters buried 2.3 m underground next to the water-Cherenkov stations that form the 23.5 km2 large infilled array. The reduced distance between detectors in this denser area allows the lowering of the energy threshold for primary cosmic ray reconstruction down to about 1017 eV. At the depth of 2.3 m the electromagnetic component of cosmic ray showers is almost entirely absorbed so that the buried scintillators provide an independent and direct measurement of the air showers muon content. …

Finding New Limit Points Of Mahler Measure By Methods Of Missing Data Restoration, Jean-Marc Sac-Epee J.M Sac-Epee, Souad El Otmani, Armand Maul, Georges Rhin

BAU Journal - Science and Technology

It is well known that the set of Mahler measures of single variable polynomial has limit points of which a list established by D. Boyd and M. Mossinghoff has been extended through approaches based on genetic algorithms. In this paper, we wish to further extend the list of known limit points by adapting a method of missing data restoration.

Counting And Sampling Small Structures In Graph And Hypergraph Data Streams, Themistoklis Haris

Dartmouth College Undergraduate Theses

In this thesis, we explore the problem of approximating the number of elementary substructures called simplices in large k-uniform hypergraphs. The hypergraphs are assumed to be too large to be stored in memory, so we adopt a data stream model, where the hypergraph is defined by a sequence of hyperedges.

First we propose an algorithm that (ε, δ)-estimates the number of simplices using O(m^1+1/k / T) bits of space. In addition, we prove that no constant-pass streaming algorithm can (ε, δ)- approximate the number of simplices using less than O( m ^1+1/k / T ) bits of space. Thus …

The “Knapsack Problem” Workbook: An Exploration Of Topics In Computer Science, Steven Cosares

Open Educational Resources

This workbook provides discussions, programming assignments, projects, and class exercises revolving around the “Knapsack Problem” (KP), which is widely a recognized model that is taught within a typical Computer Science curriculum. Throughout these discussions, we use KP to introduce or review topics found in courses covering topics in Discrete Mathematics, Mathematical Programming, Data Structures, Algorithms, Computational Complexity, etc. Because of the broad range of subjects discussed, this workbook and the accompanying spreadsheet files might be used as part of some CS capstone experience. Otherwise, we recommend that individual sections be used, as needed, for exercises relevant to a course in …

Studies On Diagnostic Coverage And X-Sensitivity In Logic Circuits., Manjari Pradhan Dr.

Doctoral Theses

Today’s integrated circuits comprise billions of interconnected transistors assembled on a tiny silicon chip, and testing them to ensure functional and timing correctness continues to be a major challenge to designers and test engineers with further downscaling of transistors. Although substantial progress has been witnessed during the last five decades in the area of algorithmic test generation and fault diagnosis, applications of combinatorial and machinelearning (ML) techniques to solve these problems remain largely unexplored till date. In this thesis, we study three problems in the context of digital logic test and diagnosis. The first problem is that of fault diagnosis …

New Characterizations Of Reproducing Kernel Hilbert Spaces And Applications To Metric Geometry, Daniel Alpay, Palle E. T. Jorgensen

Mathematics, Physics, and Computer Science Faculty Articles and Research

We give two new global and algorithmic constructions of the reproducing kernel Hilbert space associated to a positive definite kernel. We further present a general positive definite kernel setting using bilinear forms, and we provide new examples. Our results cover the case of measurable positive definite kernels, and we give applications to both stochastic analysis and metric geometry and provide a number of examples.

Essays In Social Choice Theory., Dipjyoti Majumdar Dr.

Doctoral Theses

The purpose of this thesis is to explore some issues in social choice theory and decision theory. Social choice theory provides the theoretical foundations for the field of public choice and welfare economics. It tries to bring together normative aspects like perspective value judgements and positive aspects, like strategic con- siderations. The second feature which is our focus, is closely related to the problem of providing appropriate incentives to agents, an issue of prime importance in eco- nomics.Consider for example, a set of agents who must elect one among a set of can- didates. These candidates may be physical agents …

In Silicoidentification Of Toxins And Their Effect Onhost Pathways: Feature Extraction, Classificationand Pathway Prediction., Rishika Sen Dr.

Doctoral Theses

Identification of toxins, which are either proteins or small molecules, from pathogens is of paramount importance due to their crucial role as first-line invaders infiltrating a host, often leading to infection of the host. These toxins can affect specific proteins, like enzymes that catalyze metabolic pathways, affect metabolites that form the basis of metabolic reactions, and prevent the progression of those pathways, or more generally they may affect the regular functioning of other proteins in signaling pathways in the host. In this regard, the thesis addresses the problem of identification of toxins, and the effect of perturbations by toxins on …

Fine-Grained Lower Bounds For Problems On Strings And Graphs, Gary Thomas Hoppenworth

Honors Undergraduate Theses

The motivation of this thesis is to present new lower bounds for important computational problems on strings and graphs, conditioned on plausible conjectures in theoretical computer science. These lower bounds, called conditional lower bounds, are a topic of immense interest in the field of fine-grained complexity, which aims to develop a better understanding of the hardness of problems that can be solved in polynomial time. In this thesis, we give new conditional lower bounds for four interesting computational problems: the median and center string edit distance problems, the pattern matching on labeled graphs problem, and the subtree isomorphism problem. These …

Novel Hedonic Games And Stability Notions, Jacob Schlueter

Theses and Dissertations--Computer Science

We present here work on matching problems, namely hedonic games, also known as coalition formation games. We introduce two classes of hedonic games, Super Altruistic Hedonic Games (SAHGs) and Anchored Team Formation Games (ATFGs), and investigate the computational complexity of finding optimal partitions of agents into coalitions, or finding - or determining the existence of - stable coalition structures. We introduce a new stability notion for hedonic games and examine its relation to core and Nash stability for several classes of hedonic games.

Predicting Carcass Cut Yields In Cattle From Digitalimages Using Artificial Intelligence, Darragh Matthews

Theses

Beef carcass classification in Europe is predicated on the EUROP grid for both fatness and conformation. Although this system performs well for grouping visually similar carcasses, it cannot be used to accurately predict meat yields from these groups, especially when considered on an individual cut level. Deep Learning (DL) has proven to be a successful tool for many image classification problems but has yet to be fully proven in a regression scenario using carcass images. Here we have trained DL models to predict carcass cut yields and compared predictions to more standard machine learning (ML) methods. Three approaches were undertaken …

Stock Trend Prediction Using Candlestick Charting And Ensemble Machine Learning Techniques With A Novelty Feature Engineering Scheme, Yaohu Lin, Shancun Liu, Haijun Yang, Harris Wu

Information Technology & Decision Sciences Faculty Publications

Stock market forecasting is a knotty challenging task due to the highly noisy, nonparametric, complex and chaotic nature of the stock price time series. With a simple eight-trigram feature engineering scheme of the inter-day candlestick patterns, we construct a novel ensemble machine learning framework for daily stock pattern prediction, combining traditional candlestick charting with the latest artificial intelligence methods. Several machine learning techniques, including deep learning methods, are applied to stock data to predict the direction of the closing price. This framework can give a suitable machine learning prediction method for each pattern based on the trained results. The investment …

Systematizing Confidence In Open Research And Evidence (Score), Nazanin Alipourfard, Beatrix Arendt, Daniel M. Benjamin, Noam Benkler, Michael Bishop, Mark Burstein, Martin Bush, James Caverlee, Yiling Chen, Chae Clark, Anna Dreber Almenberg, Timothy M. Errington, Fiona Fidler, Nicholas Fox, Aaron Frank, Hannah Fraser, Scott Friedman, Ben Gelman, James Gentile, Jian Wu, Et Al., Score Collaboration

Computer Science Faculty Publications

Assessing the credibility of research claims is a central, continuous, and laborious part of the scientific process. Credibility assessment strategies range from expert judgment to aggregating existing evidence to systematic replication efforts. Such assessments can require substantial time and effort. Research progress could be accelerated if there were rapid, scalable, accurate credibility indicators to guide attention and resource allocation for further assessment. The SCORE program is creating and validating algorithms to provide confidence scores for research claims at scale. To investigate the viability of scalable tools, teams are creating: a database of claims from papers in the social and behavioral …

Law Library Blog (November 2020): Legal Beagle's Blog Archive, Roger Williams University School Of Law

Law Library Newsletters/Blog

No abstract provided.

Provable Security Of Symmetric-Key Cryptographic Schemes., Ashwin Jha Dr.

Doctoral Theses

In this thesis, we provide quantitative and/or qualitative improvements in the provable security of several symmetric-key schemes, encompassing major information security goals, viz. data authentication, encryption, and authenticated encryption.AUTHENTICATION AND INTEGRITY: Among authentication schemes, we analyze the CBC-MAC family and counter-based MACs (XMACC, XMACR, PCS, LightMAC etc.), referred as the XMAC family. First, we revisit the security proofs for CBC-MAC and EMAC, and identify a critical flaw in the state-of-the-art results. We revise the security proofs and obtain significantly better bounds in case of EMAC, ECBC and FCBC. Second, we study the security of CBC-MAC family, when the underlying primitive …

A Partition Based Feature Selection Approach For Mixed Data Clustering, Ashish Dutt

Student Works (2020-2029)

Presently, educational institutions compile and store huge volumes of data, such as student enrolment and attendance records, as well as their examination results. Mining such data yields stimulating information that serves its handlers well. Rapid growth in educational data points to the fact that distilling massive amounts of data requires a more sophisticated set of algorithms. This issue led to the emergence of the field of Educational Data Mining (EDM). Traditional data mining algorithms cannot be directly applied to educational problems, as they may have a specific objective and function. This implies that a pre-processing algorithm has to be enforced …

Changing The Focus: Worker-Centric Optimization In Human-In-The-Loop Computations, Mohammadreza Esfandiari

Dissertations

A myriad of emerging applications from simple to complex ones involve human cognizance in the computation loop. Using the wisdom of human workers, researchers have solved a variety of problems, termed as “micro-tasks” such as, captcha recognition, sentiment analysis, image categorization, query processing, as well as “complex tasks” that are often collaborative, such as, classifying craters on planetary surfaces, discovering new galaxies (Galaxyzoo), performing text translation. The current view of “humans-in-the-loop” tends to see humans as machines, robots, or low-level agents used or exploited in the service of broader computation goals. This dissertation is developed to shift the focus back …