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关键词： Robotics   Remote sensing   Computer science  

摘要： In a wide range of domains, such as pipeline inspection, surveillance in smart cities and tracking of multiple microparticles by an optical microscope, a common goal is to use mobile agents to persistently monitor a set of targets. We refer to this as the persistent monitoring problem. In this dissertation, we assume that each of these targets has an internal state that evolves with linear stochastic dynamics. The agents can observe these states when they are close to the targets, and the goal is to plan agent trajectories such that the sensed data can be used to minimize the uncertainty of the estimation process. We study scalable approaches for planning agent trajectories that minimize the long term uncertainty of the target states. We design algorithms that are computationally efficient and simple to implement, but grounded in mathematically proven performance guarantees. First we approach the problem from a continuous time perspective with the goal of finding locally optimal agent trajectories using a gradient descent scheme. We assume that trajectories are fully defined by a finite set of parameters and compute the cost gradients. Considering periodic agent trajectories and an infinite time horizon, we prove that, under some natural assumptions, the uncertainty of each target converges to a limit cycle. We also show that, in 1D environments with bounded controls, an optimal control is parametric. In multidimensional settings, we propose an efficient parameterization using Fourier curves. Simulation results show the efficiency of our approach. Next, we consider a graph-constrained, single-agent version of the problem, where agents can only move in the edges of the graph and observe the target when they are visiting the node corresponding to it. We prove that, in this scenario, an optimal policy is such that all the agent have a common peak uncertainty. Using this property of the optimal solution, we develop lightweight algorithms that, instead of directly solvin

关键词： Point defects   Energy   Algorithms   Deformation   Grain boundaries   Crystallography   Symmetry   Computer science   Geography   Mechanics   Physics  

摘要： Grain boundaries (GBs) profoundly affect the physical, chemical, and mechanical properties of polycrystalline solids. The GB properties are known to be anisotropic and can vary significantly with misorientation and boundary-plane inclination. Despite its recognized importance, the experimental and theoretical literature on GB crystallography-property relationships and the anisotropy of these properties has not been systematically quantified. Some of the challenges are the complex topology of the GB space and the time-consuming nature of both experimental and computational investigations in mapping the crystallography-property relationships. Experimental characterization on individual grain boundaries is difficult, and simulation methods are limited due to computational costs. In this thesis, we developed several tools to facilitate high-throughput simulations of grain boundaries using atomistic simulations First, we present a hybrid Monte Carlo Molecular Dynamics (MC-MD) algorithm to determine the minimum energy structures of grain boundaries. This MC-MD technique is based on atom removal or insertion in the grain boundary region, followed by an annealing process. This algorithm makes the sampling of the microscopic degrees of freedom more efficient and decreases the computational costs required to find minimum energy structures. This framework shows that this algorithm can be applied on low symmetry crystal structures to find the minimum energy structure and the meta-stable phases. This algorithm is simple and flexible enough to facilitate the simulation of GBs in low-symmetry crystal systems and those with mixed crystallography. In the second part of this thesis, we develop an algorithm to determine the Coincidence Site Lattices (CSLs) or near-CSLs and their corresponding Σ-misorientations for any kind of Bravais lattices. This algorithm is based on finding all the unique sub-lattices and eliminating the ones related to each other by the symmetry operations of the

关键词： Maps   Agreements   Cartography   Algorithms   Sensitivity analysis   Artificial intelligence   Computer science  

关键词： Blockchains   Bitcoin   Consensus algorithm   Computer science   Proof of Work   Proposals   Licenses   51% attack   bitcoin   blockchain   double-speding   ethereum   hash rate  

摘要： The applications that use blockchain are cryptocurrencies, decentralized finance applications, video games, and many others. Most of these applications trust that the blockchain will prevent issues like fraud, thanks to the built-in cryptographic mechanisms provided by the data structure and the consensus protocol. However, blockchains suffer from what is called a 51% attack or majority attack, which is considered a high risk for the integrity of these blockchains, where if a miner, or a group of them, has more than half the computing capability of the network, it can rewrite the blockchain. Even though this attack is possible in theory, it is regarded as hard-achievable in practice, due to the assumption that, with enough active members, it is very complicated to have that much computing power;however, this assumption has not been studied with enough detail. In this work, a detailed characterization of the miners in the Bitcoin and Crypto Ethereum blockchains is presented, with the aim of proving the computing distribution assumption and creating profiles that may allow the detection of anomalous behaviors and prevent 51% attacks. The results of the analysis show that, in the last years, there has been an increasing concentration of hash rate power in a very small set of miners, which generates a real risk for current blockchains. Also, that there is a pattern in mining among the main miners, which makes it possible to identify out-of-normal behavior.

ISBN: (纸本)9781496832986

摘要： In Black to Nature: Pastoral Return and African American Culture, author Stefanie K. Dunning considers both popular and literary texts that range from Beyoncé’s Lemonade to Jesmyn Ward’s Salvage the Bones . These key works restage Black women in relation to nature. Dunning argues that depictions of protagonists who return to pastoral settings contest the violent and racist history that incentivized Black disavowal of the natural world. Dunning offers an original theoretical paradigm for thinking through race and nature by showing that diverse constructions of nature in these texts are deployed as a means of rescrambling the teleology of the Western progress narrative. In a series of fascinating close readings of contemporary Black texts, she reveals how a range of artists evoke nature to suggest that interbeing with nature signals a call for what Jared Sexton calls “the dream of Black Studies”—abolition. Black to Nature thus offers nuanced readings that advance an emerging body of critical and creative work at the nexus of Blackness, gender, and nature. Written in a clear, approachable, and multilayered style that aims to be as poignant as nature itself, the volume offers a unique combination of theoretical breadth, narrative beauty, and broader perspective that suggests it will be a foundational text in a new critical turn towards framing nature within a cultural studies context.

关键词： Artificial intelligence   Unemployment   Employment   Service robots   Computer science   Technological innovation   Software engineering   Artificial intelligence   competence   knowledge   university students   Vietnam  

摘要： Artificial Intelligence (AI) and related technologies have been employed to simulate human decision-making processes to improve people's lives. Accordingly, AI knowledge and related competence are crucial, especially for students pursuing information technology (IT) and computer-related degrees, since they will eventually be the next generation of AI designers or users. While the importance of AI technology and its applications have been widely discussed and explored, AI technology in Vietnam is in a nascent stage due to the shortage of skilled experts and the reluctance to adopt AI applications by businesses. Thus, the primary objective of this study was to explore the knowledge of and competence in AI among Vietnamese university IT students. A total of 206 university students from software engineering and computer science programs participated in this study. The results indicate the need for a focused effort to establish a strong foundation for a comprehensive and accessible AI mandatory course plan for IT students.

关键词： Bibliometrics   countries   computer science   research productivity   ranking   Web of Science  

摘要： This paper presents a current overview of the main productive and influential countries around the world in the computer science field. Research in the computer science field has experienced significant growth in recent years. This study develops a bibliometric overview of all journals that have been indexed in the Web of Science (WoS) database over the past 25 years (1995-2019), according to several bibliometric indicators in the seven categories of computer science research. The study shows that United States is the leading country in the computer science field. Other countries, such as the United Kingdom, China, Canada and Germany, also obtain high positions in the ranking. The average country that performs research in computer science is European, has English-speaking researchers, is highly developed and has a high income. However, there is a wide range of countries that perform research in computer science, including South American and Arabic countries, meaning that computer science traverses many countries and cultures.

关键词： Computer science   Computer engineering   Information technology   Artificial intelligence  

摘要： As modern cloud provider growth continues, the need to optimize physical resources to contain and reduce energy consumption is a predominant problem. Prior research has shown solutions for the virtual machine placement problem (VMPP) that can optimize physical resources and reduce energy consumption. However, as the VMPP is an NP-hard type problem, research continues for more optimized approximations. The research presented in this dissertation has demonstrated a unique approximation as a VMPP solution. The use of a convolutional neural network (CNN) and K-Means combinational algorithm-based time-series clustering artifact to identify the initial placement with predicted future workload physical host cohesion as a VMPP solution. The research has demonstrated a rejection of the null hypothesis, in which the energy consumption for the artifact VMPP solution, as measured in the number of activated physical servers, was shown to be less than the energy consumption of a random distribution placement algorithm as a VMPP solution (p < 0.001). The reduction was shown in seven simulation scenarios in which the initial activation of hosts was sustained during a simulated 24 hours. The reduction average of 7.5 physical hosts also matched the performance of prior research VMPP solution algorithms. Finally, the use of CNN and K-Means algorithms combined for time-series clustering can now be considered a valid methodology for VMPP solution research.

关键词： Artificial intelligence   Computer science  

关键词： Feature extraction   Principal component analysis   Optimization   Transforms   Genetic algorithms   Computer science   Support vector machines   Data transformation   fast independent component analysis   feature selection   principal component analysis   salp swarm optimizer  

摘要： Feature selection is a technique commonly used in Data Mining and Machine Learning. Traditional feature selection methods, when applied to large datasets, generate a large number of feature subsets. Selecting optimal features within this high dimensional data space is time-consuming and negatively affects the system's performance. This paper proposes a new binary Salp Swarm Algorithm (bSSA) for selecting the best feature set from transformed datasets. The proposed feature selection method first transforms the original data-set using Principal Component Analysis (PCA) and fast Independent Component Analysis (fastICA) based hybrid data transformation methods;next, a binary Salp Swarm optimizer is used for finding the best features. The proposed feature selection approach improves accuracy and eliminates the selection of irrelevant features. We validate our technique on fifteen different benchmark data sets. We conduct an extensive study to measure the performance and feature selection accuracy of the proposed technique. The proposed bSSA is compared to Binary Genetic Algorithm (bGA), Binary Binomial Cuckoo Search (bBCS), Binary Grey Wolf Optimizer (bGWO), Binary Competitive Swarm Optimizer (bCSO), and Binary Crow Search Algorithm (bCSA). The proposed method attains a mean accuracy of 95.26% with 7.78% features on PCA-fastICA transformed datasets. The results show that bSSA outperforms the existing methods for the majority of the performance measures.