Reviewerly/exHarmony huggingface dataset:
CONCEPT OF DEVELOPMENT OF EDUCATIONAL CONDITIONS OF MOSCOW SCHOOLS

The article reveals the content of the concept of development of educational conditions of Moscow schools.This concept is developed on the basis of monitoring of educational conditions of Moscow schools with the use of SACERS scales and the results of expert and methodological support of school teams for the design and development of the educational environment of educational organizations implementing programs of primary and basic General education.The concept involves the development of a monitoring system of the educational conditions of Moscow schools with the use of scales SACERS; design and development of material and technical, information and methodological, personnel and psychological and pedagogical conditions of Moscow schools; formation of a strategy for the development of educational conditions of Moscow schools; formation of management and school teams for the design and development of educational conditions of Moscow schools; providing expert and methodological support of school teams.Based on the concept of the development of educational conditions of Moscow schools should be carried out in the following areas: the organization of the subject-spatial environment of the school; ensuring safety and health; organization of additional education and extracurricular activities, children's leisure in the afternoon; creating conditions for comfortable interaction between the participants of the educational process; organization of the educational process; creating conditions for the professional development of teachers; creating conditions for children with special educational needs.
Experiences from a Data Analysis of Crimes against Humanity

Data analysis is a widely researched field, where innumerable applications allow to discover domain particularities that are specially useful. In this paper, we introduce the data analysis process that we applied to two different systems storing information about statements and testimonies of crimes against Humanity. We describe the activities, design decisions and lessons learned from implementing a specific goal, which involves transforming text data into georeferenced information.
Automatically extracting features for face classification using multi-objective genetic programming

© 2020 Owner/Author. This paper proposes a new multi-objective feature extraction algorithm using genetic programming (GP) for face classification. The new multi-objective GP-based feature extraction algorithm with the idea of non-dominated sorting, which aims to maximise the objective of the classification accuracy and minimise the objective of the number of extracted features. The results show that the proposed algorithm achieves significantly better performance than the baseline methods on two different face classification datasets.
Implementation of multi node Hadoop virtual cluster on open stack cloud environments

Nowadays computing plays a vital role in information technology and all other fields. Yes, the cloud computing is one of the biggest milestone in most leading next generation technology and booming up in IT filed and business sectors. In our day to day life the data is being generated is enormous amount such as tera (TB), peta (PB), zeta (ZB) bytes. Hadoop Map Reduce is the popular distributed computing paradigm to process data intensive jobs in cloud. Completion time goals for deadline of map reduce jobs set by users are becoming crucial in existing cloud based data processing environments like Hadoop. In this paper proposes a real-time implementation of multi node Hadoop virtual cluster on open stack cloud environments and also it processes the huge data sets in parallel different virtual machines (VMs) and it compares average execution time for different node virtual clusters and various size inputs.
Inferences Involving The Mean When σ Is Not Known: One- And Two-Sample Designs

Using numerous examples with real data, this textbook closely integrates the learning of statistics with the learning of R. It is suitable for introductory-level learners, allows for curriculum flexibility, and includes, as an online resource, R-code script files for all examples and figures included in each chapter, for students to learn from and adapt and use in their future data analytic work. Other unique features created specifically for this textbook include an online R tutorial that introduces readers to data frames and other basic elements of the R architecture, and a CRAN library of datasets and functions that is used throughout the book. Essential topics often overlooked in other introductory texts, such as data management, are covered. The textbook includes online solutions to all end-of-chapter exercises and PowerPoint slides for all chapters as additional resources, and is suitable for those who do not have a strong background in mathematics.
Correcting for Model Changes in Statistical Post-Processing – An approach based on Response Theory

The discussion paper presents a method for adapting a post-processing approach after a (small) change in the forecast model, as is often the case in operational weather forecasting.The innovative idea of this paper is to base the approach on response theory.Using a tangential linear model, transient response theory represents a correction method of a post-processing approach as long as the changes in the forecast model are small.Since the consideration of small changes in the prediction model is of great importance, the paper represents a potentially valuable contribution.I particularly congratulate the authors for the courage to base their approach on physical theory, in this C1
ECONOMIC DEVELOPMENT AMONG WOMEN IN IKENNE LOCAL GOVERNMENT AREA, OGUN STATE; AN APPRAISAL OF PROBLEMS AND PROSPECTS

Purpose: This paper takes an incisive examination of economic development with specific focus on some of the problems confronting the involvement of rural women in development activities in Ikenne Local Government.&#x0D; Methodology: The paper addresses this issue through qualitative methodologies by relying heavily on Primary and Secondary Sources of data collection consisting of oral Interviews with selected rural women in the Local Government. Relevant Text books, Journals, and Internet Sources were consulted to corroborate evidences from the findings.&#x0D; Findings: Although women have served as critical agents of rural economic transformation, such roles has been limited by illiteracy, lack of adequate finance, lack of processing and storage facilities, unsteady power supply, poverty. Their impacts have been felt indirectly in subsistent agriculture, and directly at the domestic domain. The role of women in the economic development of Ikenne Local Government Area in Ogun State cannot be overemphasized. This is because, there is basically no aspect of economic development in Ogun State that rural women would not be found. Generally, Ikenne rural women are majorly involved in agricultural activities such as planting, weeding, harvesting, processing and marketing. They also keep some domestic animals and birds. Rural women’s limited access to productive resources, low educational level and illiteracy are contributors to rural women’s poverty. The study discovered that Illiteracy affects extent to which benefits can be accrued from newer, non-traditional methods such as: information and communication technologies.&#x0D; Unique Contribution to Practice and Policy: The research therefore recommended that rural women in Ikenne local government should be exposed to various economic platforms for knowledge mobilization, innovation, and partnerships for sourcing funds to carry out their economic activities. The work concluded that promotion of agricultural development should be through the provision of useful and relevant information to the farming communities by the extension services. There are many constraints making rural women farmers to be lagging behind economically, apart from lack of agricultural information. Further main constraints such as the lack of personal land and credit should be enhanced to suit the necessity of the rural women. Rural women are limited in terms of their potential in contributing to agricultural and socio-economic development.
Extrinsic Factors Influencing Relapse in Alcohol Dependence

Introduction:Relapse is a common problem faced when alcohol dependence is treated.The present study was done to find the influence of extrinsic factors such as stressful life events, peer pressure and perceived social support on relapse in alcohol dependence, which will be useful to develop effective interventions to prevent it.Material and methods: This cross sectional study conducted in tertiary care teaching hospital in Kottayam, included 60 subjects who were grouped into 30 Late Relapsers (who relapsed drinking after 3 months of abstinence) and 30 Early Relapsers-(who relapsed drinking within 3 months of abstinence).Mini-Mental Status Examination, Diagnostic Criteria for Research for ICD-10 (DCR-10) and the Stressful Life Inventory were used for each patient to collect their details. Results:The mean age of onset of alcohol use of Early Relapsers was lower than that of the Late Relapsers.Also the number of stressful life events related to work, marriage, family and finance were more in the Early Relapsers compared to the other and the difference was statistically significant for the above mentioned factors.Conclusions: Early age of onset of alcohol use and occurrence of stressful life events in 6 months preceding the relapse are associated with relapse in alcohol dependence.Community based studies with larger sample size are needed to substantiate the results of this study.
Computation

This chapter introduces the basic building blocks of quantum computing and a variety of specific algorithms. It begins with a brief review of classical computing and discusses how its key elements – bits, gates, circuits – carry over to the quantum realm. It highlights crucial differences to the classical case, such as the impossibility of copying a qubit. The quantum circuit model is shown to be universal, and a peculiar variant of quantum computing, based on measurements only, is illustrated. That a quantum computer can perform some calculations more efficiently than a classical computer, at least in principle, is exemplified with the Deutsch-Jozsa algorithm. Other examples covered in this chapter are the variational quantum eigensolver, which can be applied to the study of molecules and classical optimization problems; quantum simulation; and entanglement-assisted metrology.
Young Scholar Symposium on Transdisciplinary in Education and Environment (YSSTEE)

Young Scholar Symposium on Transdisciplinary in Education and Environment (YSSTEE) is the first conference was held by Universitas Islam Negeri Raden Intan, Lampung, Indonesia. The theme of this conference is the Fourth Industrial Revolution for Smart Society with the Focus and scope of this research including (1) Physics & Physics Education; (2) Mathematics & Mathematics Education; (3) Biology & Biology Education; and (4) Earth & Envirinmental Science.
Online Retrieval and Indexing of Images using Multi Feature Vectors

In technology proliferated era of modern world, health care has witnessed huge developments. The cutting edge technologies have paved way for sophisticated and feature rich image processing in medical field using colour tomography and medical resonance imaging. The images obtained using radiological techniques can be stored in a database and the features and implications can be recorded in the database after the analysis of those images by physicians. These databases can be used in obtaining the meaningful analysis of the images obtained through radiology in rural areas of developing countries like India, where sophisticated medical facilities are a dream for many in developing nations. The dataset of images can be divided into training and testing set. Training set of data is utilized to obtain multi feature vectors based on Caffe. Caffe is used in this training with a focus on image recognition. The image feature is a simple image pattern based on which the description of image can be obtained. The features of an image are transformed to a vector space using computer vision algorithms. Moreover a framework has been evolved in this paper to extract the features from image using image descriptors-white box algorithms and neural nets-black box algorithms. We also present the pros and cons of our novel framework for online retrieval and indexing of images using multi feature vectors.
H.265 Video Streaming in Mobile Cloud Networks

Mobile video applications have started to dominate the global mobile data traffic in recent years, and both opportunities and challenges have arisen when the emerging mobile cloud paradigm is introduced to support the resource-demanding video processing and networking services. This chapter offers in-depth discussions for content- and context-aware, adaptive, robust, secure, and real-time video applications in mobile cloud networks. The chapter describes and analyses the essential building blocks including the state-of-the-art technologies and standards on video encoding, adaptive streaming, mobile cloud computing, and resource management, and the associated security issues. The focus is context-aware adaptive streaming based on the latest video coding standard H.265 in the context of Internet-centric mobile cloud networking. Built upon selected building blocks underpinned by promising approaches and emerging standards, an integrated architecture is proposed towards achieving next-generation smart video streaming for mobile cloud users, with future research directions in this field identified.
Detection of Human Faces in Video Sequences using NGLBP Feature

Machine analysis of face detection is an interesting topic for study in Human-Computer Interaction. The existing studies show that discovering the position and scale of the face region is difficult due to significant illumination variation, noise and appearance variation in unconstrained scenarios. This paper suggests a method to detect the location of face area using recently developed YouTube Video face database. In this work, each frame is formulated by normalization technique and separated into overlapping blocks. The Gabor filter is tuned to extract the Gabor features from the individual blocks. The averaged Gabor features are manipulated and local binary pattern histogram features are extracted. The extracted patterns are passed to the classifier with training images for face region identification. Our experimental results on YouTube video face database exhibits promising results and demonstrate a significant performance improvement when compared to the existing techniques. Furthermore, our proposed work is uncaring to head poses and sturdy to variations in illumination, appearance and noisy images
The Core Python Language II

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Learning a causal structure: a Bayesian Random Graph approach

A Random Graph is a random object which take its values in the space of graphs. We take advantage of the expressibility of graphs in order to model the uncertainty about the existence of causal relationships within a given set of variables. We adopt a Bayesian point of view in order to capture a causal structure via interaction and learning with a causal environment. We test our method over two different scenarios, and the experiments mainly confirm that our technique can learn a causal structure. Furthermore, the experiments and results presented for the first test scenario demonstrate the usefulness of our method to learn a causal structure as well as the optimal action. On the other hand the second experiment, shows that our proposal manages to learn the underlying causal structure of several tasks with different sizes and different causal structures.
Efficient Multi-Keyword Search Through Ciphertext Data in the Cloud

Cloud computing has become essential for storing sensitive data sets that are centralized in the cloud. The need for privacy and protection of files and documents has increased day by day. Data users typically dump the most powerful information in cloud storage to prevent third parties from accessing data in cloud storage. In legacy systems, end users safely retrieved encrypted information using keyword search. However, in existing systems, we recommend only individual keywords and Boolean keywords, which is not yet sufficient to ensure efficient data usage for a vast number of data users and the number of documents in the cloud repository. This work aims to develop a systematic approach to searching multi keywords in the cloud with ciphertext data. The cloud server carries out risk-free investigations with no clear information about keywords and trap doors. The client or user uses multiple keywords to perform data retrieval. When the client enters a question for many words, the server breaks the question into one word and retrieves the word from the index. In this task, the cipher text policy attribute-based encryption (CPABE) algorithm is used to perform encryption of files and documents. Experimental results show 95% accuracy with a data set size of 1000, for both single and multiple keyword searches. Because previous research were limited to single searches, this new work performs multiple keyword searches with unique security aspects to create a multi-keyword search system rather than the cryptographic data in the cloud.
Health Information Literacy: The Saving Grace During Traumatic Times

t When it comes to engaging with health information in their daily lives, people face different challenges. In the context of COVID-19, the aim of this study is to determine whether health information literacy can assist people in making informed health-related decisions. An empirical study was conducted to investigate such an effect. Building on a dataset composed of 155 respondents, the research model was examined through structural equation modelling. The results showed that health information literacy – as an individual ability – not only influences health decision making but also has a direct impact on the awareness of the challenges imposed by the current pandemic situation. In addition, the results show that too much information leads to information fatigue, and consequently negatively impacts decision making. The findings of this paper show that the concept of health information literacy should be understood and developed separately from the health literacy concept. Theoretical contributions and practical implications are discussed.
Review of Lamarque and Julia, Solid Earth Discussions, 2019

The manuscript under review presents a detailed accounting of lithospheric anisotropy through the use of Ps receiver function analysis and data collected at 75 seismic stations within the Borborema Province of NE Brazil.The importance of their analysis rests in the fact that they can provide firm constraints on anisotropic boundary depth, in contrast to shear wave splitting which is a path integrated measurement.Their results show a clear correlation between tectonic deformation and orientation of seismic anisotropy.Within the continent, they find that the orientation of anisotropy is coincident with the orientation of large-scale shear zones thought to be associated with Printer-friendly version Discussion paper were more clearly written as they appear washed out and are difficult to read.
Optimising QoS with load balancing in cloud computing applying dual fuzzy technique

Cloud computing has become a necessity when the internet usage has increased drastically. This research paper objective is to optimise quality of service in cloud computing using dual fuzzy technique. With the competition to provide the best quality service at cloud data centre, we are analysing the parameters of average response time, average completion time, average CPU utilisation and job success. Cloud-sim simulator along with the mathematical model is used to provide reliable and valid result. To achieve the best result, the load in data centre needs to be efficiently distributed, so that it is managed to process maximum service requests with the best service response time and very few failures. In this paper, we applied dual fuzzy technique for the load balancing in the cloud data centre and the findings were extensive and support the proposed technique. With this technique, cloud computing service provider can provide better quality service.
PENGEMBANGAN SISTEM INFORMASI AKADEMIK USER FRIENDLY UNTUK SMA MUHAMMADIYAH KOTA TEGAL (SIATA)

EnglishAn Academic Information System of Senior High School (SMA) is called SIATA. The system is designed and made to make easy on data processing of a school. SIATA is made only for offline web or local host. The method on designing SIATA uses waterfall model, because it is flexible model. SIATA needs an alpha and beta trial testing to find the weakness and quality of the system before it is implemented as the running system. The result of the design is an academic information system, which is easy used by the user. The operating system is not difficult, so it is can be user friendly system. Indonesian Sistem Informasi Akademik SMA Muhata atau yang disebut SIATA, merupakan suatu sistem yang dirancang dan dibangun untuk mempermudah sekolah dalam proses pengolahan data akademik. Saat ini SIATA dibangun sebatas web offline atau localhost. Metode yang digunakan dalam perancangan SIATA ini menggunakan model waterfall karena sifatnya yang fleksibel. Sebelum SIATA diimplementasikan menjadi sistem yang berjalan akan dilakukan pengujian alpha dan pengujian beta guna menemukan kelemahan sistem dan menentukan kualitas sistem. Hasil dari perancangan ini adalah sebuah sistem informasi akademik yang mudah digunakan oleh user dan pengoperasiannya tidak rumit sehingga menjadi sistem yang user friendly.
Message from the General Chair

On behalf of the organizing committee and IEEE Computer Society’s Technical Committee (TC) on Security & Privacy, it is my honor and privilege to welcome you to the 42nd Symposium on Security & Privacy (S&P). This year marks the second time we hold the IEEE Symposium on Security and Privacy virtually, and we hope to maintain and even improve the online experience for all attendees. In addition to the Symposium, we are also holding the Security and Privacy Workshops (SPW) on Thursday, May 27. I am very grateful to Gabriela Ciocarlie, the 2020 General Chair, who managed the first transition of the event to an online experience (in record time) last year and thus created a solid foundation for the event this year. She was always ready to hear my concerns and provide advice.
An Attribute-Oriented Task Scheduling Strategy for Improvement of Quality of Service in Cloud Computing

This thesis describes an innovative task scheduling and resource allocation strategy by using thresholds with attributes and amount (TAA) in order to improve the quality of service of cloud computing. In the strategy, attribute-oriented thresholds are set to decide on the acceptance of cloudlets (tasks), and the provisioning of accepted cloudlets on suitable resources represented by virtual machines (VMs,). Experiments are performed in a simulation environment created by Cloudsim that is modified for the experiments. Experimental results indicate that TAA can significantly improve attribute matching between cloudlets and VMs, with average execution time reduced by 30 to 50% compared to a typical non-filtering policy. Moreover, the tradeoff between acceptance rate and task delay, as well as between prioritized and non-prioritized cloudlets, may be adjusted as desired. The filtering type and range and the positioning of thresholds may also be adjusted so as to adapt to the dynamically changing cloud environment.