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关键词： Bitcoin   Predictive models   Uncertainty   Bayes methods   Online banking   Computer science   Social networking (online)   Bitcoin (digital currency)   volume of BTC   bitcoin   timestamp  

摘要： The transaction and market of bitcoin is volatile, meaning it's uncertain because it changes frequently. There have been a number of research studies that have presented bitcoin price prediction models, but none of them have looked at the controlling variables linked with bitcoin transaction timestamps. It might be that price is not the only key criteria influencing bitcoin transactions, or the available model for bitcoin price prediction is yet to consider timestamp as a determining factor in its transaction. A better and more accurate model would be required to predict how the Timestamp influences changes of bitcoin transactions. That is why this current study utilized a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the prediction of timestamp influence on Bitcoin value. Bitcoin historical datasets which are converted to a nonlinear regression into a "well-formulated" statistical problem in the manner of a ridge regression are used. Simulation analysis indicates that bitcoin digital currency's performance variation is highly influenced by its transaction timestamp with the prediction accuracy of 96%. The contributions of this research lies with the fact that specific Bitcoin transaction events repeat themselves over and over again, meaning that the Open-Price, High-Price, Low-Price, and Close-Price of Bitcoin price over timestamp developed a pattern that was predicted by NARX with less That means those involved in the transaction of bitcoin at the wrong timestamp will certainly face the uncertainty negative effect of the bitcoin market.

关键词： Computer science  

关键词： Computer Science   Connected Autonomous Vehicle   Edge   application orchestration   data format   data protocol   gRPC   k3os   k3s   k8s   kubernetes   protobuf  

摘要： Autonomous vehicles have made milestone strides within the past decade. Advances up the autonomy ladder have come lock-step with the advances in machine learning, namely deep-learning algorithms and huge, open training sets. And while advances in CPUs have slowed, GPUs have edged into the previous decade's TOP 500 supercomputer territory. This new class of GPUs include novel deep-learning hardware that has essentially side-stepped Moore's law, outpacing the doubling observation by a factor of ten. While GPUs have make record progress, networks do not follow Moore's law and are restricted by several bottlenecks, from protocol-based latency lower bounds to the very laws of physics. In a way, the bottlenecks that plague modern networks gave rise to Edge computing, a key component of the Connected Autonomous Vehicle system, as the need for low-latency in some domains eclipsed the need for massive processing farms. The Connected Autonomous Vehicle ecosystem is one of the most complicated environments in all of computing. Not only is the hardware scaled all the way from 16 and 32-bit microcontrollers, to multi-CPU Edge nodes, and multi-GPU Cloud servers, but the networking also encompasses the gamut of modern communication transports. I propose a framework for negotiating, encapsulating and transferring data between vehicles ensuring efficient bandwidth utilization and respecting real-time privacy levels.

关键词： Binary   Computer science education   Educational game   Game-based learning   Low-level   Serious games  

摘要： Game-based learning has had promising results for a variety of different topics; however, for computer science education in particular, its applications have mostly focused on high-level concepts such as program flow and the control structures that modify it. Considering that students are known to struggle with low-level topics, the goal of this thesis is to level down game-based learning by applying it to lower-level concepts—this is accomplished by designing, building, and evaluating two original games. The first game is the physical card game Mermaids and Narwhals, which is meant to teach binary manipulations such as shifts and rotations with broader applications in computational thinking; it was evaluated through a gameplay simulation that modelled the game's behaviour under different conditions. The second game is Binary Bubbles, which is a digital bubble shooter game designed to help students practice their Boolean/bitwise logic operations; this game was evaluated through a large two-group, pre-test post-test survey. Overall, this thesis contributes to a promising area of research that has been relatively unexplored in related literature.

关键词： Correlation   Measurement   Machine learning   Systematics   Biomedical measurement   Licenses   Computer science   Matthews correlation coefficient   diagnostic odds ratio   binary classification   confusion matrix   supervised machine learning   confusion tetrahedron  

摘要： To assess the quality of a binary classification, researchers often take advantage of a four-entry contingency table called confusion matrix, containing true positives, true negatives, false positives, and false negatives. To recap the four values of a confusion matrix in a unique score, researchers and statisticians have developed several rates and metrics. In the past, several scientific studies already showed why the Matthews correlation coefficient (MCC) is more informative and trustworthy than confusion-entropy error, accuracy, F-1 score, bookmaker informedness, markedness, and balanced accuracy. In this study, we compare the MCC with the diagnostic odds ratio (DOR), a statistical rate employed sometimes in biomedical sciences. After examining the properties of the MCC and of the DOR, we describe the relationships between them, by also taking advantage of an innovative geometrical plot called confusion tetrahedron, presented here for the first time. We then report some use cases where the MCC and the DOR produce discordant outcomes, and explain why the Matthews correlation coefficient is more informative and reliable between the two. Our results can have a strong impact in computer science and statistics, because they clearly explain why the trustworthiness of the information provided by the Matthews correlation coefficient is higher than the one generated by the diagnostic odds ratio.

关键词： Computer science   Cryptography   Cybersecurity   Quantum algorithms   Quantum computing  

摘要： This work aims to demonstrate the abilities of modern quantum computers and quantum programming languages through implementations and practical usages of two of the most famous quantum algorithms: Shor's algorithm and Grover's algorithm, as well as through general quantum computing operations. These implementations will further demonstrate a proof-of-concept of relatively few-qubit quantum computers to encode and transmit data in a secure, tamper-proof manner using the Chinese Remainder Theorem. Lastly, this work discusses an implementation of Grover's algorithm requiring fewer qubits, but which may be limited in overall applicability

关键词： Measurement   Feature extraction   Extraterrestrial measurements   Task analysis   Euclidean distance   Lighting   Computer science   Person re-identification   cross-modal retrieval   metric learning  

摘要： Person re-identification across visible and near-infrared cameras (VIS-NIR Re-ID) has widespread applications. The challenge of this task lies in heterogeneous image matching. Existing methods attempt to learn discriminative features via complex feature extraction strategies. Nevertheless, the distributions of visible and near-infrared features are disparate caused by modal gap, which significantly affects feature metric and makes the performance of the existing models poor. To address this problem, we propose a novel approach from the perspective of metric learning. We conduct metric learning on a well-designed angular space. Geometrically, features are mapped from the original space to the hypersphere manifold, which eliminates the variations of feature norm and concentrates on the angle between the feature and the target category. Specifically, we propose a cyclic projection network (CPN) that transforms features into an angle-related space while identity information is preserved. Furthermore, we proposed three kinds of loss functions, AICAL, LAL and DAL, in angular space for angular metric learning. Multiple experiments on two existing public datasets, SYSU-MM01 and RegDB, show that performance of our method greatly exceeds the SOTA performance.

摘要： Next-generation RNA sequencing technology has revolutionized translational research. RNA-Seq enables researchers to accurately quantify and compare the gene expression levels within and across diverse biological samples. Today, petabytes of multidimensional, complex RNA-Seq data are available publicly. Researchers analyze this multidimensional data using dimensionality reduction algorithms and heat maps—however, many available tools are written in the R or Python language. These tools lack an easy-to-use interactive interface and are not suitable for large data sets. Interactivity is a key requirement for efficient data analysis of complex multidimensional data sets. Based on this, Singh et al. developed a Java tool, MetaOmGraph [Singh et al. (2020)], for fully interactive analysis of large omics data sets. This thesis extends the functionality of MetaOmGraph (MOG) software and adds dimensionality reduction features for easy and efficient analysis of bulk and single-cell RNA-Seq data. I have added Principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) dimensionality reduction algorithms, and heat maps to MOG to facilitate multidimensional data analysis. Researchers can interactively visualize PCA, t-SNE, and heat maps by changing the final appearance of plots using various options provided in each of these. Sample filtering in MOG is now made easy and efficient for the user by creating sample subset lists. Overall, these contributions to MOG make it more user-friendly. MOG software is available at https://***/MetNet_*** and the code is at https://***/urmi-21/MetaOmGraph.

关键词： Computer science  

ISBN: (数字)9781636391694 ISBN: (纸本)9781636391700;9781636391687

摘要： This book is the first to present the state of the art and provide technical focus on the latest advances in the foundations of blockchain systems. It is a collaborative work between specialists in cryptography, distributed systems, formal languages, and economics, and addresses hot topics in blockchains from a theoretical perspective: cryptographic primitives, consensus, formalization of blockchain properties, game theory applied to blockchains, and economical issues. This book reflects the expertise of the various authors, and is intended to benefit researchers, students, and engineers who seek an understanding of the theoretical foundations of blockchains.

关键词： Library technology   Computer science students   Library employment   Recruitment   Mentorship  

摘要： Academic libraries in the United States often have difficulty recruiting for technology-focused positions. This mixed-methods study examines what technology skills libraries are seeking in entry-level technology positions and explores ways to increase interest in library employment. Utilizing Lent’s (2013) social cognitive career theory (SCCT) framework, this study seeks to understand why students study computer science, how computer science students seek future employment, and explores how a large university in the southeastern United States can facilitate interest in applying for library technology positions. Quantitative data was determined through an examination of library technology positions to explore trends and what skills employers are seeking. Qualitative data was gathered from recorded interviews with current junior and senior level undergraduate computer science majors. Combined with an in-depth look at the literature and recruitment needs of libraries, possible solutions to the problem of practice are offered in the form of practical internships, interdisciplinary collaboration, and a potential graduate certificate with the goal of connecting computer science students to software development positions in libraries.