17,157 research outputs found
Sensitivity analysis for ReaxFF reparameterization using the Hilbert-Schmidt independence criterion
We apply a global sensitivity method, the Hilbert-Schmidt independence
criterion (HSIC), to the reparameterization of a Zn/S/H ReaxFF force field to
identify the most appropriate parameters for reparameterization. Parameter
selection remains a challenge in this context as high dimensional optimizations
are prone to overfitting and take a long time, but selecting too few parameters
leads to poor quality force fields. We show that the HSIC correctly and quickly
identifies the most sensitive parameters, and that optimizations done using a
small number of sensitive parameters outperform those done using a higher
dimensional reasonable-user parameter selection. Optimizations using only
sensitive parameters: 1) converge faster, 2) have loss values comparable to
those found with the naive selection, 3) have similar accuracy in validation
tests, and 4) do not suffer from problems of overfitting. We demonstrate that
an HSIC global sensitivity is a cheap optimization pre-processing step that has
both qualitative and quantitative benefits which can substantially simplify and
speedup ReaxFF reparameterizations.Comment: author accepted manuscrip
Thread-safe lattice Boltzmann for high-performance computing on GPUs
We present thread-safe, highly-optimized lattice Boltzmann implementations,
specifically aimed at exploiting the high memory bandwidth of GPU-based
architectures. At variance with standard approaches to LB coding, the proposed
strategy, based on the reconstruction of the post-collision distribution via
Hermite projection, enforces data locality and avoids the onset of memory
dependencies, which may arise during the propagation step, with no need to
resort to more complex streaming strategies. The thread-safe lattice Boltzmann
achieves peak performances, both in two and three dimensions and it allows to
sensibly reduce the allocated memory ( tens of GigaBytes for order billions
lattice nodes simulations) by retaining the algorithmic simplicity of standard
LB computing. Our findings open attractive prospects for high-performance
simulations of complex flows on GPU-based architectures
Database for validation of thermo-hydro-chemo-mechanical behaviour in bentonites
This paper presents a database of thermo-hydro-chemo-mechanical tests on bentonites, which has been named “Bento_DB4THCM”. After a comprehensive literature review, a set of experimental tests have been compiled. The experimental data are used to perform validation exercises for numerical codes to simulate the coupled thermo-hydro-mechanical and geochemical behaviour of bentonites. The database contains the information required for the simulation of each experimental test solving a boundary value problem. The validation exercises cover a wide range of clays, including the best-known bentonites (MX-80, FEBEX, GMZ) as well as others. The results collected in this database are from free swelling, swelling under load, swelling pressure and squeezing tests. The database is attached as Supplementary material.En este artículo se presenta una base de datos de ensayos termo-hidro-quimio-mecánicos sobre bentonitas, a la que se ha denominado “Bento_DB4THCM”. Después de una revisión exhaustiva de la literatura, se ha compilado un conjunto de pruebas experimentales. Los datos experimentales se utilizan para realizar ejercicios de validación de códigos numéricos para simular el comportamiento termohidromecánico y geoquímico acoplado de las bentonitas. La base de datos contiene la información requerida para la simulación de cada prueba experimental que resuelve un problema de valor límite. Los ejercicios de validación cubren una amplia gama de arcillas, incluidas las bentonitas más conocidas (MX-80, FEBEX, GMZ) entre otras. Los resultados recopilados en esta base de datos provienen de pruebas de hinchamiento libre, hinchamiento bajo carga, presión de hinchamiento y compresión. La base de datos se adjunta como material complementario
Perceptions of surveillance: exploring feelings held by Black community leaders in Boston toward camera enforcement of roadway infractions
Roadway camera enforcement programs have been found to effectively reduce vehicle travel speeds, as well as decrease the number and severity of collisions. Despite a wealth of evaluative research confirming this enforcement approach's aptitude at promoting safer roadway behavior, fewer than 50 % of US states currently host camera-based programs. Public opposition is frequently cited as the cause for the slow proliferation of this enforcement strategy. However, with public demand for police reform having an increasing presence on the national political stage, how might feelings toward camera technology currently stand among groups most marginalized by existing enforcement systems, and how might those feelings vary by type of enforcement application? Through a series of focus groups, this work centers Black voices on matters of surveillance and roadway enforcement by discussing sentiment toward camera programs with Black community leaders. This discussion is contextually situated in Boston, Massachusetts, where legislation that would allow for camera enforcement of roadway infractions is actively being deliberated in the State Senate. Findings culminate in a list of right-sizing and procedural recommendations for policy makers hoping to gain support for camera enforcement, improve roadway safety, and advance racial equity in our systems of policing and governance
Corporate Social Responsibility: the institutionalization of ESG
Understanding the impact of Corporate Social Responsibility (CSR) on firm performance as it relates to industries reliant on technological innovation is a complex and perpetually evolving challenge. To thoroughly investigate this topic, this dissertation will adopt an economics-based structure to address three primary hypotheses. This structure allows for each hypothesis to essentially be a standalone empirical paper, unified by an overall analysis of the nature of impact that ESG has on firm performance. The first hypothesis explores the evolution of CSR to the modern quantified iteration of ESG has led to the institutionalization and standardization of the CSR concept. The second hypothesis fills gaps in existing literature testing the relationship between firm performance and ESG by finding that the relationship is significantly positive in long-term, strategic metrics (ROA and ROIC) and that there is no correlation in short-term metrics (ROE and ROS). Finally, the third hypothesis states that if a firm has a long-term strategic ESG plan, as proxied by the publication of CSR reports, then it is more resilience to damage from controversies. This is supported by the finding that pro-ESG firms consistently fared better than their counterparts in both financial and ESG performance, even in the event of a controversy. However, firms with consistent reporting are also held to a higher standard than their nonreporting peers, suggesting a higher risk and higher reward dynamic. These findings support the theory of good management, in that long-term strategic planning is both immediately economically beneficial and serves as a means of risk management and social impact mitigation. Overall, this contributes to the literature by fillings gaps in the nature of impact that ESG has on firm performance, particularly from a management perspective
Identifying Student Profiles Within Online Judge Systems Using Explainable Artificial Intelligence
Online Judge (OJ) systems are typically considered within programming-related courses as they yield fast and objective assessments of the code developed by the students. Such an evaluation generally provides a single decision based on a rubric, most commonly whether the submission successfully accomplished the assignment. Nevertheless, since in an educational context such information may be deemed insufficient, it would be beneficial for both the student and the instructor to receive additional feedback about the overall development of the task. This work aims to tackle this limitation by considering the further exploitation of the information gathered by the OJ and automatically inferring feedback for both the student and the instructor. More precisely, we consider the use of learning-based schemes—particularly, Multi-Instance Learning and classical Machine Learning formulations—to model student behaviour. Besides, Explainable Artificial Intelligence is contemplated to provide human-understandable feedback. The proposal has been evaluated considering a case of study comprising 2,500 submissions from roughly 90 different students from a programming-related course in a Computer Science degree. The results obtained validate the proposal: the model is capable of significantly predicting the user outcome (either passing or failing the assignment) solely based on the behavioural pattern inferred by the submissions provided to the OJ. Moreover, the proposal is able to identify prone-to-fail student groups and profiles as well as other relevant information, which eventually serves as feedback to both the student and the instructor.This work has been partially funded by the “Programa Redes-I3CE de investigacion en docencia universitaria del Instituto de Ciencias de la Educacion (REDES-I3CE-2020-5069)” of the University of Alicante. The third author is supported by grant APOSTD/2020/256 from “Programa I+D+I de la Generalitat Valenciana”
Countermeasures for the majority attack in blockchain distributed systems
La tecnología Blockchain es considerada como uno de los paradigmas informáticos más importantes posterior al Internet; en función a sus características únicas que la hacen ideal para registrar, verificar y administrar información de diferentes transacciones. A pesar de esto, Blockchain se enfrenta a diferentes problemas de seguridad, siendo el ataque del 51% o ataque mayoritario uno de los más importantes. Este consiste en que uno o más mineros tomen el control de al menos el 51% del Hash extraído o del cómputo en una red; de modo que un minero puede manipular y modificar arbitrariamente la información registrada en esta tecnología. Este trabajo se enfocó en diseñar e implementar estrategias de detección y mitigación de ataques mayoritarios (51% de ataque) en un sistema distribuido Blockchain, a partir de la caracterización del comportamiento de los mineros. Para lograr esto, se analizó y evaluó el Hash Rate / Share de los mineros de Bitcoin y Crypto Ethereum, seguido del diseño e implementación de un protocolo de consenso para controlar el poder de cómputo de los mineros. Posteriormente, se realizó la exploración y evaluación de modelos de Machine Learning para detectar software malicioso de tipo Cryptojacking.DoctoradoDoctor en Ingeniería de Sistemas y Computació
Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review
In this paper, a critical bibliometric analysis study is conducted, coupled
with an extensive literature survey on recent developments and associated
applications in machine learning research with a perspective on Africa. The
presented bibliometric analysis study consists of 2761 machine learning-related
documents, of which 98% were articles with at least 482 citations published in
903 journals during the past 30 years. Furthermore, the collated documents were
retrieved from the Science Citation Index EXPANDED, comprising research
publications from 54 African countries between 1993 and 2021. The bibliometric
study shows the visualization of the current landscape and future trends in
machine learning research and its application to facilitate future
collaborative research and knowledge exchange among authors from different
research institutions scattered across the African continent
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Comparison of value perception of children in playing videogames and traditional games: Turkish and British samples
The present study examined the value perceptions of Turkish and British children according to various variables and determined to what extent traditional and videogame genres preferred by children predict their value perceptions, dominant case design, one of the mixed research designs, was used. While the quantitative approach was mainly used in the research, the qualitative approach was used as a supporter. The data collection process of the study lasted for two academic years. In the first year, the study was conducted with 243 primary school students studying in Nottingham (England). In the following year it was conducted with 267 primary school students studying in Ankara (Turkey). A total of 510 primary school students with ages ranging from 9-11 years were recruited for the study. Data were collected using the “Personal Information Form” and “Moral Dilemma Stories Inventory for Children”. Both were developed in English and then adapted into Turkish. It was found that, in both sample, girls’ value perception scores were significantly higher than boys’ scores. Videogames primarily produced for entertainment were the most preferred games by Turkish and British children while educational and serious videogames were the least preferred. Traditional games in sports (soccer, cricket, etc.) were preferred more by British children, while traditional action games (dodgeball, playing tag etc.) were preferred more by Turkish children. It was found that action-adventure and role-playing videogames predicted children’s value perception negatively, and simulation and puzzle videogame genres predicted children’s value perceptions positively. Furthermore, movement-based traditional games (sports, action) predicted children’s value perceptions positively, while traditional competitive (racing) games predicted children’s value perceptions negatively
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