Reviewerly/exHarmony huggingface dataset:
Comparing Explanations between Random Forests and Artificial Neural Networks

The decisions made by machines are increasingly comparable in predictive performance to those made by humans, but these decision making processes are often concealed as black boxes. Additional techniques are required to extract understanding, and one such category are explanation methods. This research compares the explanations of two popular forms of artificial intelligence; neural networks and random forests. Researchers in either field often have divided opinions on transparency, and comparing explanations may discover similar ground truths between models. Similarity can help to encourage trust in predictive accuracy alongside transparent structure and unite the respective research fields. This research explores a variety of simulated and real-world datasets that ensure fair applicability to both learning algorithms. A new heuristic explanation method that extends an existing technique is introduced, and our results show that this is somewhat similar to the other methods examined whilst also offering an alternative perspective towards least-important features.
The Importance of State Practice in the Shaping of International Standards Pertaining to the Clash between Free Speech and the Banning of Negationism: The Contribution of the Greek Legal Order

Considering the tension between freedom of expression and the banning/punishing of historical negationism as a means to fight hate speech, the paper makes three interconnected points. First, it identifies a number of international legal authorities calling on states to enact legislation at the domestic level to prohibit and, in particular, to prosecute denialism. This movement is thinner at the global level, but more tangible within Europe. The authorities at issue acknowledge the importance of FoE, but they offer no clear guidance as to the balance that should be maintained between it and the prohibition/punishment of denialism, or as to the limits of FoE more generally. Moreover, these authorities contain no fully shaped and clear criteria as to how to draw a satisfying distinction between legitimate debate and disagreement, on the one hand, and negationism amounting to hate speech that shall constitute a criminal offence, on the other. Second, the paper argues that establishing such limits and balances involves a value judgment. It is not an objective, “dry” technical legal exercise. In this respect, analysis in the study highlights certain criteria established by the European Court of Human Rights in historical negationism cases arguing that, even though they may be useful, they are not adequate, in the sense that these criteria do not suffice to avoid shifting to a subjective appraisal (by means of stricto sensu proportionality or by declaring certain speech as abusive) based upon the personal values, ideals, preferences and the ideological predispositions of judges. Third, the key argument advanced in this note is that the combination of the previous two points invites us to recognise the weight of state practice in the shaping of international standards in an area (namely the balance between FoE and the prohibition/criminalisation of denialism, particularly as a form of hate speech) that is far less settled than one may think or than what the exigencies of legal certainty, particularly in areas like human rights and especially in criminal law, require. In this respect, the paper outlines the importance of state practice and how, from a technical point of view, such practice may help to shape standards, to give the example of the Greek legal order, briefly discussing relatively recent relevant legislative and judicial practice that allows learning certain lessons regarding, inter alia, the dangers of abusive prosecution of free speech on the basis of defective legislation criminalising denialism.
Improving the Robustness of Hand Gesture Recognition with One-shot-learning Features of Static HOG and SVM by Using Kinect V2 Method

The innovative technologies like dynamic action and hand gesture recognition has improved considerably in the past few years. However, in this paper we are implementing the concept of one short learning with the major features like histogram orientation of gradients and support vector machine for hand gesture recognitions. Here the major utilities of RGB and depth images are pre-recorded by using sensors and 3-D camera of Kinect V2. The combined appearance of the depth images and RGB are very important for motion descriptors. These are used for gesture recognition and temporal segmentation. To measure the differences in Histograms we capture the relationships of cross-bins by using the Quadratic-Chi distance family. Here the proposed methods of HOG descriptors has the ability to remove all unwanted frames from the captured images by using Kinect V2 and it can also trim the video frames by using new algorithms using a combination method in HOG, descriptors together with Support Vector Machines technique to deliver good results in the hand gesture recognition technology.
Towards an Improvement of Complex Answer Retrieval System

Traditional Information Retrieval (IR) systems mainly focus on answering questions about events or objects. However, there are various types of question forms that require IR systems to build complex answers from multiple data sources. Therefore, the idea of building IR systems that can create complex answers automatically, became the aim of TREC CAR 2017-2019. CAR (Complex Answer Retrieval) is one of many tracks, was hosted by TREC (The Text REtrieval Conference) where is a playground for the information retrieval community. In this paper, we built an improved complex answer retrieval system based on the system model of Nogueira et al. [3]. Our method tries to increase the coverage of the retrieval task. Thereby, the performance of our system shows that the MAP, MRR, and NDCG evaluation scores are improved.
Software Agents in Support of Scheduling Group Training

Nowadays, being fit is becoming more and more popular. This includes eating habits – with, for instance, companies offering personalized box-diets – as well as exercising. Obviously, one can go to a fitness club alone, but it is much more fun to go together with friends. Here, it should be obvious that reaching an agreement, between two persons, on the time and location of a fitness club, may be relatively easy. However, it becomes more complex with each additional person that would like to join the group. The aim of this contribution is to show how an agent-based application can be used to negotiate schedule of group training sessions. As a matter of fact, it will be shown that, under a limited number of “good-will assumptions”, the proposed application can fully eliminate human involvement in the scheduling process, as it can find the best suitable place and time for a training session, considering individual preferences.
The task of setting the parameters of metaheuristic optimization algorithms

When solving global optimization problems, many problems arise related to their multi-extremity, nonlinearity, high computation complexity, and other. To solve these problems, a large number of algorithms were created. This article focuses on analyzing the class of metaheuristic algorithms for searching for extrema: testing them on various functions and comparing their behavior at the most optimal parameters. Particle swarm algorithms are considered; bats, swarming bees, swarming fireflies, bacterial. In the framework of this article, a study of various metaheuristic extremum search algorithms, the selection of optimal parameters for each of them using various functions as an example was conducted. Also, these algorithms solved the problem of designing a cylindrical spring with a constant axial load.
The Internet 3.0

Abstract On September 11, 2001, Internet users searched Google for information on the unfathomable events taking place in Manhattan. But the search “New York Twin Towers” came up with no hits. A full month had passed since the words “twin towers” had last been indexed. This meant that at this crucial moment, the term as a search object had not been updated. All the search results Google came up with were frustratingly irrelevant for the acute needs of the users. As an emergency solution, Google created a breaking news page featuring “News and Information on the US Attacks”, which was placed on top of the search list. The page featured links to the websites of news outlets and other news organizations as well as useful links to aid organizations, emergency aid, phone numbers of airlines and hospitals. This episode made Google, during the following year, develop a news filter as part of its search algorithm. This meant that current headlines now came up on top of the search list when a user entered the relevant search words.
Counting the number of the twists of certain polarized abelian varieties

We count the number of isomorphism classes of degree d-twists of some polarized abelian varieties of dimension g over finite fields where either g is an odd prime or else g=2. This can be seen as a higher dimensional analogue of the counting problem for the case of elliptic curves.
Challenges Driven Network for Visual Tracking

Tracking-by-detection is an effective approach for regular objects tracking. However, trackers still drift under challenges like fast motion and occlusion in only several frames. In this paper, we propose the challenges driven network (CDNet) for motion and occlusion handling. First, as for proposal, we provide a motion-aware proposal method utilizing cues of optical flow and previous target states. Second, as for classification, a challenges driven feature learning approach is constructed by combining dual multi-domain learning and category prior learning. The dual multi-domain method improves the network responses to both challenge and common targets, and the steady category prior features are utilized to detect and avoid tracking drift. Experiments on the OTB100 [19] and VOT2015 [10] datasets demonstrate that our CDNet can achieve competitive performance against state-of-art trackers.
Taking into Account Noise and Background Information During Transmitting of Decisions on Signal Detection-Estimation

The article considers problems of detecting and estimating the coordinates of small-sized space objects with onboard passive television systems, observed against a starry background, from a standpoint of the principle of dominant information. The asymmetry of detection threshold that ensures sufficient equality of losses of dominant and noise information, is substantiated. This is achieved by equalizing probability of missing the signal of object and probability of false alarm, multiplied by the number of degrees of freedom of the signal.
Russian Higher Education in XXI Century: Traditions and Trends

The article covers the issue of preservation and rethinking of traditions in present-day Russian higher education. The formulation of paradigm of higher education in Russia at the present stage and the actual trends related to the formation of this paradigm are discussed. The main issues are highlighted, and brief problematic comments are given for each. The absence of the single concept of Russian higher education development is discussed; the values and quality criteria are defined along with traditions (basic milestones) that determined the image of national higher education during Soviet period; the need for adaptation of the teaching community to the modern conditions and requirements is stressed.
Gradually Growing Residual and Self-attention Based Dense Deep Back Projection Network for Large Scale Super-Resolution of Image

Due to the strong capacity of deep learning in handling unstructured data, it has been utilized for the task of single image super-resolution (SISR). These algorithms have shown promising results for small scale super-resolution but are not robust to large scale super-resolution. In addition, these algorithms are computationally complex and require high-end computational devices. Developing large-scale super-resolution framework finds its application in smart-phones as these devices have limited computational power. In this context, we present a novel light-weight architecture-Gradually growing Residual and self-Attention based Dense Deep Back Projection Network (GRAD-DBPN) for large scale image super-resolution (SR). The network is made of cascaded self-Attention based Residual Dense Deep Back Projection Network (ARD-DBPN) blocks to perform super-resolution gradually. Where each block performs 2X super-resolution and fine tuned in an end to end manner. The residual architecture facilitates the faster convergence of network and overcomes the issue of vanishing gradient. Experimental results on different benchmark data-set have been presented to compare the efficacy and effectiveness of the architecture.
Transformer Intelligent Diagnosis Method Based on AFSA-MKELM Algorithm

In the kernel extreme learning machine, a single global kernel function or a local kernel function can not balance the contradiction between fitting and generalization well, so replace the single kernel function with a mixed kernel function to improve the learning and generalization ability of the model. Based on this, a Multi-Kernel Extreme Learning Machine (MKELM) algorithm was proposed. Considering the parameter sensitivity characteristics of the MKELM, the artificial fish swarm algorithm (AFSA) was used to optimize the parameters. In summary, a hybrid algorithm was proposed and applied to the field of transformer fault diagnosis. Compared with the extreme learning machine algorithm of single kernel function and SVM algorithm, the diagnostic accuracy of this algorithm is higher.
Text Sentiment in the Age of Enlightenment

Spectator journals published during the Age of Enlightenment served to enhance morality of readers and focused on a plethora of topics, such as the image of women or men, politics and religion. Although spectator journals have been studied extensively, little is known about the sentiment that they express. In this paper, we analyze text sentiment of spectator journals published in four different languages during a time period of over one hundred years. For that, we conduct (i) a sentiment analysis and (ii) analyze sentiment networks, for which we compute and investigate various network metrics, such as degree distributions and clustering coefficients. Additionally, we study the commonalities and differences between negative and positive words according to the respective metrics. Our results depict a high variability in positive and negative word usage and their linking patterns and extend our knowledge of spectator journals published during the Age of Enlightenment.
Multicriteria Analysis as a Fundamental Tool for Mineral Research. Case Study: Quadrilátero Ferrífero/Brazil

The “Quadrilátero Ferrífero” (Brazil) had a geological influence for the first explorers who entered Brazil, to explore and to search for precious metals. The geological features and their geomorphological consequences led and influenced the beginning of the occupation of the country area in Brazil. Thus, the presence of mineral deposits conditioned the colonization of the region by the Portuguese in the late seventeenth century. Therefore, in this work, the data, treated by Geographic Information System (GIS), were analyzed by multicriteria, in order to obtain the Synthesis Model of the area. Multicriteria Analysis consists of raster-type structure operations in a GIS environment, which produce a new information. It is understood that the Multicriteria Analysis procedure is widely used in the creation of new information. This analysis is done by assigning weights and values, indicated by a specialist in the phenomenon under study, as well as a specialist in the variable in question. The results of this analysis demonstrate the region is suitable for mining and corroborates a better understanding of the territory use for this activity with the synthesis map for a study area based on variables used. For this work, the variables of existing mine pit, lithology and areas with mining requirements, collected directly from the geological survey of Brazil were used as initial data for the study.
Remote Sensing Image Dehazing Algorithm Based on Wavelet Coefficient Weighting

Abstract This paper introduces the principle of the defogging algorithm and the steps of the algorithm implementation. According to the simulated image and result, the data is recorded, and the wavelet coefficient weighting is applied to evaluate the effect of remote sensing image de-clouding. Compared with the homomorphic filtering algorithm and Retinex algorithm, the wavelet coefficient weighting is applied to the remote sensing image. The effect of cloud treatment is very good.
Voting for Superior Services: How to Exploit Cloud Hierarchies

Cloud architecture spreads services throughout several levels from user-close edge to deep cloud, and while this allocation of resources offers versatility and power, it also presents a challenge: where should each aspect of the service be located? Related to this question is, who should decide? A user-centric approach would invite input from the user, and our model allows users to formulate preferences and submit these to the operator through a voting process wherein they express their preferences for the quality of the services they use. The outcome of the vote is a selection of services and related quality levels which receive preferential treatment. This process is distinctive in that it operates with only partial information, which may be as much information that can be reasonably obtained. At the same time, it blends well with information that an operator can collect, statically or in realtime, for the user as well as from content and/or application providers.
A multiuser web-interface for solution of systems of nonlinear equations

In this article, the idea of creating web-applications is considered by the example of developing a multiuser web-interface for the automated search for roots of non-linear equations in the Django package. The automated system is implemented in the Python programming language. It is proposed to create a web application based on a client-server technology, where the client part implements the user interface, forms queries to the server and processes the responses from it. The server part receives the request from the client, performs necessary calculations, then forms a web page and sends it to the client online. The developed application is designed for nonlinear equations systems solution. The system has the ability to save solutions on the server and the PC, and to visualize the step-by-step solution and plotting. The system efficiency was tested on real data.
Neighborhood Encoding Network for Semantic Segmentation

With recent advances of deep neural networks, semantic segmentation algorithms are in rapid development. However, as pixel-level semantic segmentation is often treated as pixel-wise classification task where the neighbor correlation is ignored during inference, the entirety of results is inevitably impaired. In order to increase the correlation ship among the pixels in neural networks, we propose neighborhood encoding network (NENet) to extract the semantics and encode the pixel-level correlation of inputs in a backbone network. In NENet, we use neighborhood prediction module (NPM) to decode the pixel-level correlation and get the result. The NPM can also help the backbone network encode the correlation during training phase. We also design a stage-wise training strategy with NPM for correlation transmission, which eases the training process and increases the performance effectively. The structure of NENet can be expanded to other encoder-decoder network. We evaluate the proposed NENet on CamVid and Cityscpaes datasets, and the NENet achieves impressive results.
Praxi

With today's rapidly-evolving cloud software landscape, users of cloud systems must constantly monitor software running on their containers and virtual machines (VMs) to ensure compliance, security, and efficiency. Traditional solutions to this problem rely on manually-created rules that identify software installations and modifications, but these require expert authors and are often unmaintainable. More recent automated techniques leverage knowledge of packaging practices to aid in discovery without requiring any pre-training, but these practice-based methods cannot provide precise-enough information to perform discovery by themselves. Other approaches use machine learning models to facilitate discovery of software present in a training corpus, but prior approaches have high runtime and storage requirements. This demonstration features Praxi, a new software discovery method that builds upon the strengths of prior approaches by combining the accuracy of learning-based methods with the efficiency of practice-based methods. We demonstrate Praxi's training and detection process in real time while allowing laptop-equipped participants to follow along using a provided remote virtual machine.
Mathematics Teachers Training Problems in the Context of the of New Educational Standards Introduction

The article is devoted to the future mathematics teachers training problems. The problems range of this educational process is represented by the following components: applicants with a low level of mathematical training, insufficient hours for fundamental mathematical disciplines in curricula, reorganization of the educational process with a focus on expanding electronic content. The insufficient number of hours devoted to the study of mathematical disciplines leads to the fact that students do not form subject knowledge. The article outlines ways to eliminate problems in the future mathematics teachers preparation: the propaedeutic courses introduction, the structural and didactic schemes development in the main mathematical sections, the disciplines integration, the system use of practical tasks, the preparing students holistic model for the use of information and communication technologies creation. The results of the training teachers experience in one domestic educational institution of higher education are presented. In accordance with which, such components as sufficient pre-university preparation, integration of disciplines, schematization of mathematical content, creation of conditions for combining the logical and figurative components of the students’ mathematical culture, and informational competence of students become necessary in the structure of building the educational process.
Simulation Study of HIV Temporal Patterns Using Bayesian Methodology

Viral load values and CD4$$^{+}$$T cells count are markersRocha, Diana currently evaluated in the clinical follow-up of HIV/AIDS patients. In this context, it is relevant to developScotto, Manuel methods that provide a more complete temporalPinto, Carla description of these markers, e.g. in betweenTavares, João clinical appointments. To this end, we combineGouveia, Sónia a mathematical model and a Bayesian methodology to estimate trajectories from a set of observed values. Also, we construct a variation band containing the most central trajectories for one patient, by exploring the range of values in the a posteriori distributions. The methods are illustrated with simulated data.