Reviewerly/exHarmony huggingface dataset:
Can Certain Actions in Virtual Space Make a Human Being Impatient?

Summary: In 2005, Research Centre for the Study and Observation of Living Conditions noted that 62% of Internet users are trying to save time in preparing for their purchase. It is now 89% for online buyers [1]. In 2018, a survey carried out for the management of the Lemonde.fr site measured that after five seconds of waiting, out of ten potential readers behind the screen, three abandon [2]. According to the Baromil survey from 2018, because the information is available in real time, we are constantly connected in real time. Google, the most used search engine, does not even take 1 second to display thousands of results for our query, whatever it may be [3]. According to Jean Cottraux, psychiatrist and psychotherapist, we are faced with “a society that pushes buttons. We are constantly waiting for an immediate response” [4]. These few examples and with regard to my own research work on Behavioural Differentiations between the Real and the Virtual, my theories on “Avatarization”, the “Transverse Zone” and the “Virtual Intelligence”, developed in my scientific article and my books, impatience seems to have increased due to the use of the connected tool [5,6]. Behaviours have changed from that. These behaviors can also be reproduced in the real world. What about impatience? How is it characterized in the real and virtual?
A MOVEMENT CONTROL OF A SMALL MOBILE 3-PI ROBOT IN A MAZE USING ARTIFICIAL NEURAL NETWORK

An intention of this article is to present a technical implementation of control of a small mobile 3-Pi robot which moving in a maze along a predefined guide line.To acquire the robotic car position information, the reflectance infra-red sensors serve as input sensors.The control of the direction of the robot's movement is performed by a single-layer neural network including two neurons.The weights (memory) of the neurons are adapting with respect to input sensors signals and the output calculated via Hebbian learning.Based on previous experiments, activation function bipolar sigmoid performs the best for a maze solving problem.
Development and study of a method for cell separation during white blood cell segmentation on images of bone marrow preparations in information and measurement systems for diagnostics of acute leukemia

The issues of using of information and measurement systems based on processing of digital images of microscopic preparations for solving large-scale tasks of automating the diagnosis of acute leukemia are considered. The high density of leukocyte cells in the preparation (hypercellularity) is a feature of microscopic images of bone marrow preparations. It causes the proximity of cells to eachother and their contact with the formation of conglomerates. Measuring of the characteristics of bone marrow cells in such conditions leads to unacceptable errors (more than 50%). The work is devoted to segmentation of contiguous cells in images of bone marrow preparations. A method of cell separation during white blood cell segmentation on images of bone marrow preparations under conditions of hypercellularity of the preparation has been developed. The peculiarity of the proposed method is the use of an approach to segmentation of cell images based on the watershed method with markers. Key stages of the method: the formation of initial markers and builds the lines of watershed, a threshold binarization, shading inside the outline. The parameters of the separation of contiguous cells are determined. The experiment confirmed the effectiveness of the proposed method. The relative segmentation error was 5 %. The use of the proposed method in information and measurement systems of computer microscopy for automated analysis of bone marrow preparations will help to improve the accuracy of diagnosis of acute leukemia.
REGIONAL CONCEPT OF SUPPORT FOR SCHOOLS WITH LOW LEARNING OUTCOMES AND SCHOOLS OPERATING IN UNFAVORABLE SOCIAL CONDITIONS

The article presents the rationale and description of approaches to the development and implementation of the regional concept of support for schools with low learning outcomes (SHNOR) and schools operating in unfavorable social conditions (SHNSU). Here is shown the relevance, the main social and social significance of the support for SHNOR and SHNSU in modern conditions on the basis of a theoretical analysis of the results of international and Russian studies. The factors that determine low educational results and the risk of educational failure in modern schools are fixed. The essence of the concepts «difficult social conditions», «effective school operation», «school with low learning outcomes» and «school operating in unfavorable social conditions» is revealed, clustering of schools is shown. The arguments for the need for a comprehensive solution to the problem of improving the quality of education in modern schools at the level of the region, municipality, secondary schools are described using the experience of the Yaroslavl region. The main provisions of the regional concept of support for SHNOR and SHNSU are presented: goal; tasks; principles; a set of criteria and indicators for assessing the effectiveness of schools; a set of activities aimed at supporting schools; and expected results. The groups of indicators are fixed according to the following criteria: a general integral criterion for the sustainability of students' educational results; school social welfare index; the level of professional competence of teaching staff; additional variable indicators for the SHNOR group and the SHNSU group. A set of measures to support schools has been substantiated, including the organization of activities for: developing regional programs and models for supporting schools; identification of SHNOR and SHNSU; identification of professional needs and deficiencies of teachers; development and implementation of regional and municipal programs to support schools, school programs for the transition to an effective mode of operation; creation of regional and municipal infrastructure for supporting schools and a system of targeted methodological support; the conclusion of partnership agreements; professional development of municipal and school teams, teachers; monitoring the implementation of regional and municipal programs to support schools.
An integrated ambient intelligence system for a Smart Lab environment

The goal of the ambient intelligence system is not only to enhance the way people communicate with the surrounding environment but also to advance safety measures and enrich human lives. In this paper, we introduce an integrated ambient intelligence system (IAmIS) to perceive the presence of people, identify them, determine their locations, and provide suitable interaction with them. The proposed framework can be applied in various application domains such as a smart house, authorisation, surveillance, crime prevention, and many others. The proposed system has five components: body detection and tracking, face recognition, controller, monitor system, and interaction modules. The system deploys RGB cameras and Kinect depth sensors to monitor human activity. The developed system is designed to be fast and reliable for indoor environments. The proposed IAmIS can interact directly with the environment or communicate with humans acting on the environment. Thus, the system behaves as an intelligent agent. The system has been deployed in our research lab and can recognise lab members and guests to the lab as well as track their movements and have interactions with them depending upon their identity and location within the lab.
The Influence of Cyber Informatics as Stable Archetypes on Networking

In this day and age, the quantity of occupants in city and number of vehicles out on the town are extending step by step. With growing in the urban masses and therefore the amount of vehicles, need of controlling paths, thoroughfares and lanes was transformed into a significant issue. The present traffic structure has no complement on live traffic circumstance, which prompts inefficient traffic the administrators frameworks. This endeavor has been realized by using the propelled picture taking care of technique using Mat lab programming and it means to turn away overpowering traffic stop up. Moreover, for executing this endeavor we used the automated Image dealing with procedure. A CCTV is placed in a busy time gridlock way that will catch pictures in a gathering method for the road and the vehicles on where we have to control traffic by using these CCTV pictures are incredible to keep on knowing the traffic thickness. According to the readied data from picture dealing with, the controller sends the solicitation to the traffic LED's to show explicit time on the banner to administer traffic.LED's to demonstrate specific time on the flag to oversee traffic
MATHEMATICAL MODELS OF GEOFILTRRATION AND GEOMIGRATION IN POROUS MEDIA WITH FRACTAL STRUCTURE

In this study 2-D linear models are coming from generalised, Boussinesq eqution describing geofiltration in soils with fractal structures are presented. In this study are presented too mathematical models geomigration of contaminations with groundwater in classical way and in soils with fractal structures
An Efficient Social Spider Optimization for Data Clustering using Data Vector Representation

In this article, we propose a new clustering algorithm namely an efficient social spider optimization for data clustering using data vector representation (ESSODCDI). It uses a data vector representation for each spider so that its memory requirements can be reduced. Unlike other nature-inspired algorithms, it requires lesser memory requirements. We find that its clustering results are by far better than those of other nature-inspired algorithms.
The Establishment of a System of Pre-professional Training “JuniorSkills” by the Forces of Institution of Additional Education

The article is devoted to the analysis of prospects, conditions, opportunities, problems and risks of establishment on the basis of additional education institutions of technical orientation, in particular, the municipal budget institution of additional education for young technicians in Sochi, the system of pre-professional training "JuniorSkills".The paper highlights the relevance, goals and objectives of the project for the preparation of "JuniorSkills" in terms of Sochi city, as well as its conceptual foundations; an analysis of the already done work is made in this direction in the Russian Federation and the possibility of organizing the appropriate infrastructure for Sochi city.
Mobile Health in Uganda: A Case Study of the Medical Concierge Group

The ubiquity of mobile phones offers an opportunity for a paradigm change in health-care delivery, which may offer solutions to some of the challenges faced by the health sector in Uganda. The Medical Concierge Group (TMCG) is a digital health company, headquartered in Uganda, which leverages on mobile phone-based platforms – such as short messaging service (SMS), voice calling – and social media to deliver health services. Just over two-thirds (68%) of users of TMCG’s services are males between 18 and 30 years of age. SMS reminders have improved the honouring of health facility appointments among HIV-positive clients, from 60% to 90%; retention rates at supported health facilities have improved from 45% to 89%. Furthermore, information dissemination has been achieved via mobile SMS, wherein subscribers can access health content on diverse topics – such as HIV/AIDS prevention and family planning – by sending messages to a pre-defined short code to a phone line. Over 900 beneficiaries have accessed health content via SMS subscriptions. Social media platforms, including Facebook and Twitter, are used for health information dissemination and have enabled a wider reach to over 13 million beneficiaries accessing health information on TMCG’s Facebook page alone. Tailoring mobile phone-based health content to meet the target beneficiaries’ needs is critical for TMCG’s impact and uptake. With rising rates of phone ownership and Internet connectivity in Uganda, mobile phones offer an affordable and proven adoptable avenue to overcome the chronic challenges faced by the health sector.
Non-Boolean Quantum Amplitude Amplification and ML applications [Slides]

are amplified at the expense of the basis states with higher values of $$\cos(\varphi)$$. The second algorithm is the quantum mean estimation algorithm, which uses quantum phase estimation to estimate the expectation $$\langle\psi_0|U_\varphi|\psi_0\rangle$$, i.e., the expected value of $$\exp(i\varphi(x))$$ for a random $$x$$ sampled by making a measurement on $$|\psi_0\rangle$$. It is shown that the quantum mean estimation algorithm offers a quadratic speedup over the corresponding classical algorithm. Both algorithms are demonstrated using simulations for a toy example. Potential applications of the algorithms are briefly discussed.
An Algorithm to Find the Directed Minimum Spanning Trees

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Does Refactoring Break Tests and to What Extent?

The International Conference on Software Maintenance and Evolution is the premier international forum for researchers and practitioners from academia, industry, and government to present, discuss, and debate the most recent ideas, experiences, and challenges in software maintenance and evolution.
Research on recognition algorithm of Chinese text image based on Deep Learning

In order to solve the problems of uneven illumination and low character quality in text image recognition, an image enhancement algorithm and a character recognition model based on convolution cyclic neural network are proposed in this paper.Among them, the image enhancement algorithm uses an improved tone mapping function considering local information to increase the visibility of text in dark areas.The method of background estimation and contrast compensation is used to solve the problem of uneven illumination of the image, and the connected domain method is used to locate the text in the image.The convolution and loop depth neural network model is built based on the text region, and the whole string in the image is taken as the recognition target.In this paper, 30 uneven illumination images are collected for experimental verification, and the experimental results show that the text recognition accuracy of the model in this scene is 98.29%.
Advanced Topics in Logic

This chapter presents some advanced topics in logic including fuzzy logic, temporal logic, intuitionistic logic, undefined values, theorem provers and the applications of logic to AI. Fuzzy logic is an extension of classical logic that acts as a mathematical model for vagueness. Temporal logic is concerned with the expression of properties that have time dependencies, and it allows temporal properties about the past, present and future to be expressed. Intuitionism was a controversial theory on the foundations of mathematics based on a rejection of the law of the excluded middle, and an insistence on constructive existence. We discuss three approaches to deal with undefined values, including the logic of partial functions; Dijkstra’s approach with his cand and cor operators and Parnas’s approach which preserves a classical two-valued logic.
Mitotic Cell Classification System Based On Supervised Learning for Histopathological Images of Breast Cancer

Breast cancer is a great threat to the women population throughout the world. Due to the technological advancements in medical science and digital imaging technology, histopathological images are widely utilized for better diagnosis. However, the histopathological images involve complicated structure due to the inconsistent staining, lighting conditions and so on. Considering these challenges, this work presents a mitotic cell classification system based on supervised learning for histopathological images of breast cancer. As the classification solely depends on the effectiveness of nuclei extraction, the proposed approach employs twin stage segmentation for better nuclei extraction. The effectiveness of the proposed mitotic cell classification system is matched with the existing approaches and the proposed approach performs better than the existing works with respect to accuracy, sensitivity, specificity and F-measure rates.
A Novel Interpolation Perspective for Handwritten Digit Recognition using Neural Network

In this work, we present an innovative technique for manually written character recognition that is disconnected, using deep neural networks. Since of the accessibility of enormous knowledge calculation and numerous algorithmic advances that are emerging, it has become easier in this day and age to train deep neural systems. And seeks to classify the numerical digits so that digits can be translated into pixels. Today, the computing force measure required to prepare a neural system has increased owing to the proliferation of GPUs and other cloud-based administrations like Google and Amazon offer tools to prepare a cloud-based neural system. We also developed a system for the recognition of character dependent on manually written image division. This project uses libraries such as NumPy, pandas, sklearn, seaborn to accomplish this either by linear and non-linear algorithm, to know its precision on confusion matrix and accuracy. This idea spins with RBF(radial basis function) which consists of two parameters as C and Gamma and classifying the pixel digits. To train those models, research work includes Convolutional Neural Network (CNN), Dynamic Neural Network(DNN), Recurrent Neural Network(RNN), and TensorFlow algorithms using Keras , which can be accurately used for the classification of the digits.
bitcoind

In this chapter we will learn how to control the Bitcoin Daemon, bitcoind, via Bitcoin’s command line interface, bitcoin-cli. Every function we explored before in the Bitcoin Core Wallet can be replicated in the console as well. This of course makes Bitcoin easy to integrate with other tools and services. We will also learn to use Bitcoin’s API and messaging service.
R Basics

This chapter explains how to download and install R and RStudio. Readers are introduced to the R console, to R Projects, and shown how to execute basic commands.
Glove Sensor with AI system for Deaf and Blind People to Ease of Communication

Deaf and blind people find very difficult to communicate to other people. The latest technological development helps the people having disabilities particularly deaf and blind people to communicate to other people. These people can able to communicate with the help of their sense of touch. So the advance sensors integrating with the recent technology assists the people with disability for communicating with the other people. In this paper Glove based sensors combined with the Artificial Intelligence is demonstrated so that the deaf and blind people can communicate to other people in a big conference or any public places. It is also proved that the device developed is a low cost wearable device which is affordable to such a weaker section of people having disabilities. Raspberry-pi and mobile device are cantered around the sensors to process and communicate. With the help of the device demonstrated in this paper people with disabilities specially deaf and blind can involve freely in day to day regular activities.
Bitcoin

ON 12 JANUARY 2009 a pseudonymous entity signed a transaction that instructed a distributed network to transfer a small amount of digital currency to Hal Finney, one ofthe key figures of the cypherpunk movement. After a few minutes, the transaction was recorded on a distributed public ledger, permanently updating the balance ofbothparties. This transaction— the first Bitcoin transaction—marked the beginning of a new era of decentralized payment systems, ushering in a variety of financial Services that do not depend on any centralized clearinghouse or other financial middleman.
Fast Regions-of-Interest Detection in Whole Slide Histopathology Images

Detecting and localizing pathological region of interest (ROI) over whole slide pathological image (WSI) is a challenging problem. To reduce computational complexity, we introduced a two-stage superpixel-based ROI detection approach. To efficiently construct superpixels with fine details preserved, we utilized a novel superpixel clustering algorithm which cluster blocks of pixel in a hierarchical fashion. The major reduction of complexity is attributed to the combination of boundary update and coarse-to-fine refinement in superpixel clustering. The former maintains the accuracy of segmentation, meanwhile, avoids most of unnecessary revisit to the ‘non-boundary’ pixels. The latter reduces the complexity by faster localizing those boundary blocks. Detector of RoI was trained using handcrafted features extracted from super-pixels of labeled WSIs. Extensive experiments indicates that the introduced superpixel clustering algorithm showed lifted accuracy on lung cancer WSI detection at much less cost, compared to other classic superpixel clustering approaches. Moreover, the clustered superpixels do not only facilitate a fast detection, also deliver a boundary-preserving segmentation of ROI in whole slide images.