Reviewerly/exHarmony huggingface dataset:
Bioethical Reflection on Access to Education in the Education Sector as an Effect of the COVID-19 Pandemic

The state presents some guidelines to be complied with due to the health emergency that has arisen at the beginning of 2020, decrees that have become mandatory, always thinking of respecting the bioethics of the environment, the proposal is based on complying with the right that the state, establishes standards in the industrial, health, and education sectors, however, these guidelines need resources to be established and guaranteed by the state, especially in the highly vulnerable population, this analysis has focused on the effect of the guidelines regarding access to education, there is talk of high vulnerability to any person who does not have equal access to resources, an order of study has been established that allows recognizing, first of all, the situation by reviewing the scientific publications on the subject of education and the pandemic, effects and actions taken in the pandemic, with which an analysis of this to information directing it to the field of education at all levels presenting some conclusions as a proposal that allows equitable treatment regarding access to the right to education, focusing on opportunities for all.
How much do I Stand Out in Communities Q&amp;A? An Analysis of User Interactions based on Graph Embedding

The interactions in Communities Question Answer (CQA) have high dimensionality, generating dispersed and vast information about the users’ behavior. Understanding this behavior and what characteristics qualify users as the best contributors is still a challenge. In this paper, we rely on Persuasion Theory to identify users who stand out at CQA. We use graph incorporation for reducing data dimensionality to analyze six communities. As a result of the experiments, we found a strong correlation between the intensity of user activity and reputation, but it is not a linear relationship. Also, contrary to the literature, the best contributors are not the top 10-20%, but it varies from community to community. With the results of this work, users can design better strategies for collaboration; headhunters can identify best talents; marketing companies can identify influencers, and developers can adapt their social reputation algorithms.
Agile CMII: A Methodology for Assessing Social Project Impacts Within Agile Contexts

Agile methodologies have been gaining more and more momentum since its inception in the beginning of the year 2000s. Its inherent iterative and user-centered behavior helps teams to dynamically prioritize, plan and execute work packages, which tend to be appropriate for innovative projects where problem-solution duo co-evolve throughout time. Thus, agile adoption, which naturally began within software development teams, has now been widespread throughout other corporate departments and diverse segments. The present work continues an ongoing research over CMII (Conceição Moura Impact Index), which is an analytics model that combines NPS (net promote score), OKRs (objectives and key results) and SROI (social return over investment) methods for measuring project impacts. Specifically, this current research proposes an integration of Scrum (an agile representative) with CMII model. The main objective is to make the model adoptable and practical for agile teams on social projects. Overall, it functions under Scrum’s sprint ceremonies – planning, daily meeting, review and retrospective –, where CMII KPIs and methods will be timely handled or executed, respectively. The main result is a project assessment methodology – comprised of phases, methods and metrics –, making it applicable for professionals when measuring the overall impact of their initiatives. It aims to dynamically informs strategic decision-making and links it to projects’ operational aspects.
THE WORD “SHOOT” ANALYZED BY NATURAL SEMANTIC METALANGUAGE

This study is focusing on the mapping of word shoot meanings. The collected data was qualitatively analyzed, it was starting by reading some sources, finding a word to be analyzed, reading carefully the source text and the target text, taking note, analyzed the text by NSM approach by Wierzbicka (1999). The data in this writing was taken from Colour Magazine Garuda Indonesia and used one of the prevalent comic series of Tintin entitled The Adventures of Tintin: Tintin in America and Tintin the shooting star. The technique of this study were three steps, they are finding the word which want to analyzed, read the text carefully, finding the word with its translation which want to discuss, directly noted the word which was according to the topic. The result of this study showed that the findings of three meanings of one word shoot. Some of the translation could convey the meaning therefore the reader in target language is easily read and understood.
An empirical study of cooperative frequency in distributed cooperative co-evolution

Cooperative co-evolution (CC) is an effective technique for optimizing high dimensional problems. The scalability and efficiency of CC can be further improved by combing with distributed computing, i.e., distributed CC (dCC). How to balance the cooperation and communication among sub-problems is an important issue in dCC. In this paper, we construct series of large-scale benchmark functions, and conduct experimental studies to investigate the effect of different cooperative frequency in dCC. Experiments suggest that frequency of cooperation will not affect the performance of fully separable and partially separable problems unless frequent coordination among sub-populations consumes too many limited computing resources. And overlapping problems need a moderate cooperative frequency to get better performance.
Study on Groundwater Quality Evaluation System Based on Comprehensive Integrated Platform

A new method for groundwater quality evaluation was proposed to solve the problems of the traditional groundwater quality evaluation, such as difficulty in realizing dynamic, poor visualization effect, and unable to adapt to the spatiotemporal scale change. The new method visualized the groundwater quality monitoring points in the form of nodes by using knowledge graph technology. The groundwater quality evaluation method was componentized by integrating components and Web Service technology. Based on the comprehensive integrated platform, the simulation system of groundwater quality evaluation was built quickly and flexibly. The system realized the dynamic evaluation of groundwater quality under different time scales, different evaluation models and different evaluation factors. Multiple evaluation methods could be integrated, and the analysis results could be comprehensively analyzed and judged through human-computer interaction in a visual environment, so as to provide decision support for managers. The system was applied to the analysis of groundwater quality in Guanzhong area of Shaanxi Province, in order to provide a scientific basis for the utilization and protection of groundwater resources.
Data protection in a European world of ideals: An unforeseeable division of camps

The right to the protection of personal data has taken on a whole new meaning in the technological rebirth of the New Age movement.We are no longer a name and a number; we are an amalgamation of the many digital personalities we both wittingly and unintentionally create online.This ability to "create information about data that were never apparent or intended in the source information" is, nonetheless, yin and yang.What's more, such repurposing undermines the right to informational self-determination, by virtue of which individuals should be able to consent to how and if their personal data should be processed, especially when combined.It remains that the rights to privacy and to the protection of personal data are not absolute.Mass collection and treatment of multiple-source data sets are undeniably indispensable in certain contexts: this is especially true as regards government surveillance in the fight against terrorism and organized crime.The question is, therefore, in what circumstances and to what extent may boundaries be imposed on these state authorities?This article explores the rise in so-called "big data applications," and how the two most instrumental courts in Europe as regards human rights and freedoms are shaping the future of data protection in Europe, albeit their paths are starting to diverge.
Determination of priority locations for the implementation of rice farming insurance: a case study on disaster hazards in Cilacap regency

Abstract In 2018, Cilacap Regency produced a total of 793,265 tons of rice, and was the second largest producer of the commodity in Central Java Province, after Grobogan Regency. However, Cilacap is very prone to flooding, thus is grouped into the high flood hazard category. The aim of this study is to determine the rice fields that need to be included in the Rice Farming Business Insurance program. It was conducted using an overlay and weighting or scoring method. The data were the Flood and Drought Hazard Maps obtained from the National Disaster Management Authority (BNPB), and the Rice Field Map obtained from the Geospatial Information Agency (BIG). The results showed that 17,251 ha of the rice farm land were in the priority I category, while 60,742 and 223 ha(s), were in the Priority II and III categories respectively. Furthermore, the sub-district that was most affected by the flood and drought was Kawunganten. In conclusion, the results can be used in the creation of priority policies for the Rice Farmers Business Insurance program
WAYS TO IMPROVE THE EDUCATIONAL PROCESS OF A UNIVERSITY THROUGH A PROJECT APPROACH

The problems of the quality of higher education are constantly discussed at different levels, but the cardinal changes have not occurred so far. Constantly changing standards dictated by the time and changes in the requirements of the labor market, only aggravate this position. However, the rapid obsolescence of knowledge, the need for timely and effective change in the educational process under the requirements of the market and the employer, the displacement of obsolete professions by new ones, weak basic preparation (in most cases) of school graduates require other approaches to the optimization of the educational process in universities. The article models the educational process of a university in order to identify the factors that determine its effectiveness. Ways to optimize (reengineering) this process are offered, increasing its adaptability to labor market requests and improving the efficiency of the learning process. The educational process is analyzed as a "business process", and substantiates the conclusion that in many respects the educational activity corresponds to the category of "Project". Such interpretation allows you to offer new more flexible tools for quality management of students, which meets the modern qualifications of employers and professional standards.
Enhancing a Personality-Based User Selection Tool to Maximize User eXperience Redesign Effectiveness

PERSEL (PERsonality SELector) is a personality-based selection tool to maximize UX redesign effectiveness by highlighting the best users to involve time by time in the activities due to the target the UX redesign of products aims at. To date, PERSEL knowledge base was populated using a top-down approach; the literature allowed highlighting relationships between personality traits and UX characteristics. Here, the bottom-up approach aims at generating fresh pieces of information to integrate PERSEL knowledge base by looking at experiences in the field. A questionnaire-based survey allows collecting data about the personality of the participants, together with their inclinations towards UX. The statistical analysis of the collected data highlights further relationships among personality traits, UX characteristics and using metrics like novelty, variety and usefulness of the users’ possible suggestions.
Compressive Representations of Weather Scenes for Strategic Air Traffic Flow Management

Terse representation of high-dimensional weather scene data is explored, in support of strategic air traffic flow management objectives. Specifically, we consider whether aviation-relevant weather scenes are compressible, in the sense that each scene admits a possibly-different sparse representation in a basis of interest. Here, compression of weather scenes extracted from METAR data – including temperature, flight categories, and visibility profiles for the contiguous United States – is examined, for the graph-spectral basis. The scenes are found to be compressible, with 75-95% of the scene content captured using 0.5-4% of the basis vectors. Further, the dominant basis vectors for each scene are seen to identify time-varying spatial characteristics of the weather, and reconstruction from the compressed representation is demonstrated. Finally, potential uses of the compressive representations in strategic TFM design are briefly scoped.
The Bionic Plaintiff and the Cyborg Defendant: Liability in the Age of Brain-to- Computer Interface

Human-enhancing devices via machine interface are rapidly approaching mass marketability. These devices include, for example, exoskeletons that allow functionality for those neurologically impaired are powered hydraulically, mechanically, or electrically. Newer devices, recently approved by the FDA, power such devices via brain waves transmuted into electrical signals. This Brain to Computer Interface (BCI) technology has been utilized in advanced designs, such as controlling a stylus or robotic arms, and more mundane contraptions, such as wheelchairs, via brain waves signaling intention. All are governed under Class II FDA designation for devices posing low and moderate risks. Of concern are studies that have recorded the existence of a readiness potential that precedes brainwaves involved in conscious movement, recordable shortly before intent to move -- or even awareness of such intent -- is acknowledged by the user. This raises the question regarding whether BCI technology can mobilize devices based on unconscious or subconscious thoughts – creating the possibility of “unintended” harm, calling into question the legal definition of “intent” needed to prove assault and battery. The BCI devices also render it nearly impossible to divine relative contribution of fault in the event of an accident: was it due to the intent (conscious or not) of the user- or product malfunction, the subject, perhaps, of a product liability suit against the manufacturer? It appears the technology is poised to throw the tort system into disarray.Here I postulate that FDA Class III regulation is warranted for BCI devices. This would assure greater oversight and protection – not just for the user- but for bystanders and the public at large for devices allowing remote movements engineered by pure thought. I further suggest that enhanced testing is warranted – and that failure to pursue such testing might render the manufacturer liable in tort breaching pre-emption bars, thereby furnishing double protection: deterrence via lawsuit, plus FDA oversight. This double protection, I suggest, is warranted in such potentially dangerous situations. Finally, I highlight the difficulties in assessing fault and recklessness in law when actions are committed without full awareness.
Better, Faster Small Hazard Detection: Instance-Aware Techniques, Metrics and Benchmarking

Vision-based detection of hazards in the path of ego-vehicle is a challenging task because of the variability in the type of hazards. In this paper, we present a detailed review of vision-based hazard detection methods followed by a set of new architectures and methods include semantic segmentation, instance segmentation, object detection, monocular vision with depth fusion based methods and ensembles. Additionally, we propose a set of new (and some old) benchmarking metrics that accurately capture the effectiveness of hazard detection algorithms, in terms of both algorithmic accuracy and deployability in vehicles. Detailed performance evaluations show that the proposed methods using Mask-RCNN, ensembles and monocular-stereo fusion surpass current state-of-the-art techniques in terms of accuracy and computational speed. Additionally, our fusion based object detection architectures provide a good tradeoff between accuracy (e.g. Average Precision) and computation requirements, with operating speeds that are 15 times faster than existing techniques.
Applying NuPOV to support students’ three-dimensional visualization skills

In chemistry education, the visualization of three-dimensional elements has always played an important role. However, it is challenging to teach such concepts because most of them are being done on two-dimensional media. With the recent prevalent rise of virtual reality and augmented reality (VR/AR) technologies in higher education, there have been many attempts at using these technologies to present the concept to students. However, those attempts have their limitations, and our AR project, “Nucleophile’s Point of View” (NuPOV), aims to address those limitations. By using NuPOV, users could not only view chemistry concepts from the AR interface, but they can also interact with them. These methods enable users to have an individualized and self-directed experience, which enable them to have a better understanding of the concepts. Our study has shown that such an approach proved to be relatively well received by students.
Traveling Salesman Problem via Swarm Intelligence

Traveling Salesman Problem (TSP) is one of the most classic combinatorial optimization problems. It can be widely applied in many real-world applications. In this paper, we propose an efficient method via swarm intelligence to handle the traveling salesman problem, which may not be suitable for the standard particle swarm optimization due to its domain’s discrete nature. Compared to the classic Ant Colony Optimization method, the SIB method performs well in terms of efficiency and accuracy in the TSP problem. For TSP with cities size between 15 to 25, SIB has a significantly lower average executing time to obtain an adequate solution with close distance.
Benchmarking gradient-free optimizers for 3D performance capture in the nevergrad platform

In this paper we document our work that extends the nevergrad platform by adding a real-world benchmark at the intersection of computer vision and graphics: 3D Performance Capture. Detailed documentation can be found at vcl3d.github.io/nevergrad/
Contextual-Aware Fire Product Interaction Design

This study demonstrates the practical significance of context awareness theory in the interaction design of fire products. The environmental, user and task contextual data were collected by real-time monitoring and subjected to a dimensionless and comprehensive analysis. The context awareness model transformed the deep demands of users to statistically analyzable data in a simulated environment, and a product-function design list was established by combining main component analysis with the design criteria of fire products. By optimising the development of the software and hardware sides of fire products, this study guides the best function and interactive experience between humans and fire products.
Exer-NN: CNN-Based Human Exercise Pose Classification

To enjoy the glow of good health, you must exercise [Gene Tunney], because it helps us to feel happier, increase energy levels, reduce chronic disease and helps us to keep our brain and body refresh. Today's computer vision technology is supported by deep algorithms which use a special type (CNN) of neural networks to sense objects. In this work, we propose a novel system to classify different types of exercise pose detection automatic self-ruling decision making and predictive models using convolutional neural networks (CNN). In earlier, some research has been conducted to pose detection in image classification problems. For strong architecture, we retrained the final layer of the CNN architecture, VGG16, MobileNet, Inception V3 for classification approach. Predicting among five different classes. We will create a new model "Exer-NN" to successfully classify human exercise pose. We proposed an average accuracy is 88% approximately that can be used for different purposes like tool kit assistance, helping management system automatically.
I See You: An Anomaly Detection Framework for System Alerts

One of the main challenges in anomaly detection is the broad way in which they are defined. While this problem has been studied over the past ninety years, previous anomaly detection studies focus on a technique to solve a broad range of use-cases. Our motivation to revisit this problem is the rise of system alerting within production queuing systems. We consider the problem of anomaly detection as a probabilistic one. Using an enterprise dataset, we address the question as to whether existing anomaly detection techniques can detect subtle changes in alert inter-arrival times, and if not can a robust technique be used to detect queue anomalies. Our results show that two existing techniques perform poorly (F1 = 0.38), and our two parameter ranking algorithm and probabilistic detection method identifies anomalies with a higher degree of precision.
Setup of fuzzy hybrid particle swarms

This paper presents a framework for systematically investigating and designing fuzzy rulesets for Adaptive Fuzzy Particle Swarm Optimization (AFPSO) algorithms. Training is achieved through Gaussian Process (GP) supported by Gradient Boosted Regression Trees (GBRT). Meta-objective was defined by ranks on various benchmark functions. Validation benchmarks were also performed on GECCO '20 bound-constrained optimization competition. The resulting variants, particularly those controlling hybridization with Quantum Particle Swarm Optimization (QPSO) surpassed classical Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE) on the training functions. Some level of generalization was also observed on most of the validation set.
Skin Detection Using YCbCr Colour Space for UAV-Based Disaster Management

Crushing impact of inappropriate disaster management and expanded death rate during a disaster compelled to search for powerful disaster management. Rapid technological advancement and research on unmanned aerial vehicle (UAV) urged them to use in disaster management. UAV captures the disaster site images and upon further analysis by image processing techniques helps in human detection through skin detection technique. The study involves skin detection using YCbCr colour space. Experiment test was performed on both outdoor and indoor, and the results largely depend on light conditions and various environmental factors. The UAV was hovered above 15 m from the ground to capture the outdoor samples This technique helps to get better output such that humans could be detected based on their skin and quick recovery can be made reducing the mortality and risk of rescue operators.
Efficient Keyword Spotting System Using Deformable Convolutional Network

Keyword spotting (or wake-word detection) establishes a major component of human–machine interactions. Maximizing the detection accuracy at a low false alarm rate, while minimizing the footprint size and computation are the goals of keyword spotting systems. To better satisfy these requirements, we propose an end-to-end neural architecture with deformable convolution combined with the attention mechanism. The deformable convolution layer drives the model to focus more on the human speech region, while the attention mechanism further focuses on the most important part of the speech segment for keyword spotting. Our experimental results on real-world dataset “Hey Snips” show that our system significantly outperforms recent approaches in terms of quality of detection and size and complexity. With only 78 K parameters, the model achieves a false rejection rate (FRR) of 0.005% on clean samples, and 0.531% in noisy conditions, at 0.5 false alarm (FA) per hour.