Reviewerly/exHarmony huggingface dataset:
Electronic Educational Environment as an Innovative Tool for Growth of Competitiveness and Economic Depolarization of Municipalities (Based on the Materials of the Kabardino-Balkarian Republic)

Innovative development of the economy updates the issues of smoothing social and economic polarization in the development of municipal formations of subsidized regions. One vector of solutions to this problem is the preparation of innovative and active personality through continuing education. In these conditions, a special question is the design and implementation of the electronic educational environment that allows for the preparation of “innovative personality”, which is characterized by a developed motivation for innovative activity, high potential for self-realization, readiness to change existing behavioral stereotypes, tolerance to uncertainty and other qualities. This is what makes it necessary to consider in detail the innovative tools of competitiveness growth and economic depolarization of municipalities. The main purpose of the work is to study the relationship between the impact of the educational environment, the level of human potential and the smoothing of polarization on the example of one of the regions in the North Caucasus Federal District - the Kabardino-Balkarian Republic. This targeted research has led to the formulation and solution of the following tasks: to assess the state of the electronic educational environment of universities in the aspect of continuous education and training of innovative and active personality, to explore the possibility of obtaining IT education, to assess the state of the economy of municipal entities of the Kabardino-Balkarian Republic in the aspect of socio-economic polarization.
An efficient image system-based grey wolf optimiser method for multimedia image security using reduced entropy-based 3D chaotic map

Chaotic maps play an important role in information sharing. In this paper a grey wolf optimiser used with reduced entropy-based 3D chaotic map. The selection and high coefficients are selected based on the reduced entropy value to identify the optimised parameters to get unpredictable random values. Time complexity, autocorrelation of V, H and D elements, histogram of original and cipher images, peak signal to noise ratio and NPCR and UACI values are computed from the cipher image. The empirical results show the proposed method provides good, better imperceptibility and defends various attacks. To prove this accomplishment of the method, several experiments were conducted and compared the results with existing systems.
Mathematical Optimization

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.
Into the Abyss

The first collaborative work composed and performed using a new L2Ork Tweeter platform for socially distant synchronous and collaborative instrument design, improvisation, composition, rehearsal, and performance.
Stacked Autoencoders

The first seven chapters focused on supervised learning algorithms. Supervised learning is a subcategory of ML that uses labeled datasets to train algorithms to classify data and predict outcomes accurately. The remaining chapters focus on unsupervised learning algorithms. Unsupervised learning uses ML algorithms to analyze and cluster unlabeled datasets. Such algorithms discover hidden patterns or data groupings without the need for human intervention.
Introduction

Cryptoassets are an increasingly high profile and at times controversial asset class of financial products aiming to transform how money is transferred, capital is raised, and financial relationships and obligations are memorialized. This book attempts to demystify these new digital instruments through a series of varying and often interdisciplinary investigations, across economics, law, and the social sciences. Contributors include many of the leading experts in the field, with expertise as technologists, academics central bankers, market regulators and attorneys. In tapping this breadth of views and backgrounds, this book seeks to provide cutting-edge analysis and understanding of cryptoassets—and cryptoasset regulatory policy—for generalists and experts alike.
Principles of Feature Learning

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.
The Core ACT-R-Based Model of Retrieval Processes

The core model of sentence processing used in the book is introduced and its empirical coverage relative to the existing reading time data is considered. Here, we also discuss the Approximate Bayesian Computation method for parameter estimation for model evaluation.
Image De-Blurring Using the Least Square Filter Technique in A Neural Network

Image restoration is one of the digital image processing techniques used to repaint or restore image information. One common problem encountered during restoration is image blurring. To solve this problem, a new de-blurring technique was proposed to reduce or remove image blurring. The proposed filter was designed using the least square interpolation calculation controlled by the neural network to select the blurred pixels. The wavelet decomposition technique was used to improve the performance of the proposed filter, which showed good results for fully and partially blurred regions in images.
EventMiner Framework

chapter Share on EventMiner Framework Authors: Laleh Jalali University of California, Irvine (UCI), Hitachi America Ltd. University of California, Irvine (UCI), Hitachi America Ltd.Search about this author , Ramesh Jain University of California, Irvine (UCI) University of California, Irvine (UCI)Search about this author Authors Info & Claims Event Mining for Explanatory ModelingMay 2021 https://doi.org/10.1145/3462257.3462264Published:20 May 2021Publication History 0citation2DownloadsMetricsTotal Citations0Total Downloads2Last 12 Months1Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Basic OWL

Enterprises have made amazing advances by taking advantage of data about their business to provide predictions and understanding of their customers, markets, and products. But as the world of business becomes more interconnected and global, enterprise data is no long a monolith; it is just a part of a vast web of data. Managing data on a world-wide scale is a key capability for any business today.
Robust Discriminative Analysis Framework for Gaze and Headpose Estimation

Head movements, combined with gaze, play a fundamental role in predicting a person’s action and intention. In non-constrained head movement settings, the process is complex, and performance can degrade significantly in the presence of variation in head-pose, gaze position, occlusion and ambient illumination. In this thesis, a framework is therefore proposed to fuse and combine head-pose and gaze information to obtain more robust and accurate gaze estimation. Specific contributions include: the development of a newly developed graph-based model for pupil localization and accurate estimation of the pupil center; the proposal of a novel iris region descriptor feature using quadtree decomposition, that works together with pupil localization for gaze estimation; the proposal of kernel-based extensions and enhancements to a fusion mechanism known as Discriminative Multiple Canonical Correlation Analysis (DMCCA) for fusing features (proposed and traditional) together, to generate a refined, high quality feature set for classification; and the newly developed methodology of head-pose features based on quadtree decompositions and geometrical moments, to better integrate roll, yaw, pitch and jawline into the overall estimation framework. The experimental results of the proposed framework demonstrate robustness against variations in illumination, occlusion, head-pose and is calibration free. The proposed framework was validated on several datasets and scored: 4.5° using MPII, 4.4° using Cave, 4.8° using EYEDIAP, 5.0° using ACS, 4.1° using OSLO and 4.5° using UULM datasets respectively.
Computer Programs

The semantic formalisms presented in Chap. 7 define the meaning of programming languages and thus implicitly also of computer programs written in these languages. However, they are too low-level to be of practical use when our actual goal is to verify the correctness of a program with respect to its specification. In this chapter, we are going to discuss various (closely related) calculi that allow us to reason about programs on this high level. These calculi can be subsumed under the term axiomatic semantics, because they are based on axioms and inference rules that describe how we can derive expected program properties. The soundness of these calculi can be shown from the underlying denotational semantics of the programming language.
Ensuring competitiveness of professional education in the condition of the digital educational environment

The problem of lack of competitiveness in state-of-the-art education is getting a substantial problem for Russian education development. The phenomenon of competitiveness, applied to the system of education, provides an overview of different rating scales, allowing us to evaluate different aspects of competitiveness and quality of Russian education. According to the set of criteria, Russia demonstrates consistent indicators in many ways: results of education, internal unity, effectiveness of resource utilization. In that time, the most problematic results are related to indicators of economic effectiveness of education, development of scientific- educational sets and collaborations, practical effect of education. In these conditions, the digital transformation is based on one of the most important systemic solutions to all the problems, which means that it is especially important in connection with pandemics. The main result of the articles is the annotations of the six major projects of modernization and the increase of competitiveness of the Russian system of vocational education in the context of digital transformations of educational means.
Vulnerability Tracking in Cloud using Encryption

Secure hunt over encoded remote information was pivotal in distributed computing to guarantee information protection and ease of use. To square unapproved information use, fine-grained get to control is vital in multi-client framework. Nonetheless, approved client may purposefully uncover the mystery key for monetary advantage. Along these lines, finding and revoking the pernicious client who manhandles mystery key should be illuminated quickly. In this paper, we propose an escrow free detectable property based numerous catchphrases subset look framework with adulterated off shoring unscrambling. The key escrow free instrument could adequately obstruct the Key Generation Center from unscrupulous seeking and decoding all scrambled records of clients. Additionally, the unscrambling procedure just requires ultra-lightweight calculation, which is a fundamental component for vitality constrained gadgets. What's more, efficient client denial is empowered after the malignant client is made sense of. Moreover, the proposed framework can bolster adaptable number of properties as opposed to polynomial limited. Adaptable numerous catchphrase subset look structure or organization is acknowledged and the difference in the question watchwords request does not have any effect to the query output.
DuPont adds degasification to water treatment solutions

Ligasep membrane degassing modules are now part of DuPont Water Solutions’ portfolio of water purification and separation technologies, through an exclusive global partnership with Sun Chemical and its parent company the DIC Corporation.
Multi-valued Verification of Strategic Ability

Some multi-agent scenarios call for the possibility of evaluating specifications in a richer domain of truth values. Examples include runtime monitoring of a temporal property over a growing prefix of an infinite path, inconsistency analysis in distributed databases, and verification methods that use incomplete anytime algorithms, such as bounded model checking. In this paper, we present multi-valued alternating-time temporal logic (mv-ATL*→), an expressive logic to specify strategic abilities in multi-agent systems. It is well known that, for branching-time logics, a general method for model-independent translation from multi-valued to two-valued model checking exists. We show that the method cannot be directly extended to mv-ATL*→. We also propose two ways of overcoming the problem. Firstly, we identify constraints on formulas for which the model-independent translation can be suitably adapted. Secondly, we present a model-dependent reduction that can be applied to all formulas of mv-ATL*→. We show that, in all cases, the complexity of verification increases only linearly when new truth values are added to the evaluation domain. We also consider several examples that show possible applications of mv-ATL*→ and motivate its use for model checking multi-agent systems.
Convolutional Long Short-Term Memory Hybrid Networks for Skeletal Based Human Action Recognition

The objective is to develop a time series image representation of the skeletal action data and use it for recognition through a convolutional long short-term deep learning framework. Consequently, Kinect captured human skeletal data is transformed into a Joint Change Distance Image (JCDI) descriptor which maps the time changes in the joints. Subsequently, JCDIs are decoded spatially well with a Convolutional (CNN). Temporal decomposition is executed on long short term memory (LSTM) with data changes along x , y and z position vectors of the skeleton. We propose a combination of CNN and LSTM which maps the spatio temporal information to generate a generalized time series features for recognition. Finally, scores are fused from spatially vibrant CNNs and temporally sound LSTMs for action recognition. Publicly available action datasets such as NTU RGBD, MSR Action, UTKinect and G3D were used as test inputs for experimentation. The results showed a better performance due to spatio temporal modeling at both the representation and the recognition stages when compared to other state-of-the-arts.
Achieving Authentication of Remotely Stored Data

Data storage over cloud is a demanding service but it is vulnerable for various attacks. It is required to provide strong security to this data. An authenticity of the data on remote storage is necessary but data owners have to trust on cloud storage for their outsourced data. There is a need of some mechanism to check correctness of the data without compromising its confidentiality. We have studied homomorphic algorithms so that can be applied on the cipher text. Aim of the framework designed here is to enable any authentic user or auditor to verify hash value generated by owner with the hash value of encrypted data which is stored at remote storage for assuring authentication of the data. This can be done with strong mathematical cryptographic algorithms those can be used in the public environment and can provide better performance complexity.
Phishing Website Detection using Supervised Deep Learning

The website phishing is the tremendously growing problem over the internet which will lead to the loss of personal information. This process will run like, when ever user clicks a website link it will lead them to the web page that is created by the phisher to deceive the user. After this phishing has been started in order to stop it many techniques came into existence to detect the phished web site and help the user from being deceived by the attacker. Even though many techniques have adapted to stop the attackers, it is difficult because as many phished web pages are generated by the attackers within few hours. Most of the techniques to detect these phishing websites are not able to decide the fake website with legitimate one because the accuracy of getting results are much less. There are many supervised machine learning techniques which are supervised, where a primary set of data is given to the algorithm and depending on that set the algorithm will be trained and it will predict the results for the same. One of the most important techniques that is deep Learning classifiers is applied with significant features to detect phishing websites. By using this algorithm we can classify the phishing websites from genuine websites by using effective features. In this algorithmic approach to detect genuine websites a feature set is used so by analyzing these features using deep neural networks we can detect a website is phished or not. We can also increase the accuracy of our algorithm by adding certain more features and increasing the hidden layers in neural networks.
QUALIDADE DE VIDA E DIABETES MELLITUS TIPO 2 NA ATENÇÃO PRIMÁRIA: UMA REVISÃO NARRATIVA

Type II Diabetes Mellitus (DM2) is a metabolic disorder characterized by high blood glucose levels, caused by impaired β cell secretory function and insulin resistance.Objective: To review what has been produced in the scientific literature on the quality of life of patients with DM2 followed up by primary care.Methodology: Literature review of the narrative type as a whole, 42 articles were found in the databases: PubMed, LILACS, SciELO, MEDLINE, of which three were selected after the inclusion and exclusion criteria.Results: Refer to different approaches in relation to quality of life and DM2 in primary care.Conclusion: The multidisciplinary team is very important for treatment, therefore, it is suggested that studies show what are the factors that influence the quality of life of patients with DM2, in order to improve well-being and treatment.
Blockchain technology solutions in healthcare: a systematic review

The healthcare systems are making changes by incorporating technological innovations to serve patients and secure medical data. Blockchain is an emerging field that has become one of the most examined techniques for managing and securing data transfer over trusted peer-to-peer systems. This study focuses on the existing literature to identify the challenges and solutions in healthcare systems. Several research articles were thoroughly examined and analysed to conclude how blockchain technology can overcome security challenges in the healthcare sector. This paper was completed using a systematic literature review to find and aggregate all relevant studies to answer specific research questions. The anticipated contribution of this paper is to provide an overview for healthcare organisations and practitioners to understand the significance of blockchain technology solutions in healthcare. In addition, to motivate and provide direction for advancing knowledge to better secure, distribute, and manage medical data.
Innovation in Pedagogy and Technology Symposium, 2019: Selected Conference Proceedings

Opening Remarks • Mary Niemiec, Associate Vice President for Digital Education, Director of University of Nebraska Online 6
AGILE APPLICATION IN CONSTRUCTION INDUSTRY

The construction industry is dynamic in nature because it involves a wide number of stakeholders, such as consumers, manufacturers, consultants, regulators and others. Construction projects suffer from many challenges and complex performance issues, such as low coordination, job delays, changes in goals. Study on the contrast of conventional and modern methodologies for project management in construction projects. Introduction of Agile project management approach as modern project management methodology and its framework effects on construction project performance. With the use of Agile software development tools comparison of individuals experts and team solutions will be analyzed and evaluate optimum solutions and their comparison with traditional project management tools. In traditional project management, validation of the results has been done through survey of the experienced experts. However, as agile software development tools Jira and Trello software would be used for managing project. Analysis of the research will compare traditional and agile project management tools and suggest the project management team an optimum solution of any problem occur at construction project.
Linear Unsupervised Learning

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.