Reviewerly/exHarmony huggingface dataset:
Decentralized Open Authorization Framework: A Novel Approach of Securing Resource Owner’s Credentials

Open Authorization 2.0 is a widely used authorization framework that can be used by any third-party application to access protected resources without sharing actual credentials of resource owner. The proposed framework: Decentralized Open Authorization Framework is an improvement over the conventional Open Authorization 2.0 framework. Unlike Open Authorization 2.0 Framework, the proposed Decentralized Open Authorization Framework doesn&#x2019;t require to fully trust the third-party authorization server and in order to create access tokens, it adopts a decentralized approach for enhancing security and maintaining trust. It may also be configured to remove dependency on third party authorization servers and hence promises better security.
Brain MR Image Lesion Identification Using Threshold-Based Segmentation Techniques

Brain stroke is the major second leading cause of death for the people above the age of 60 and fifth leading cause in people aged 15–59. Automatic ischemic stroke lesion segmentation of Magnetic Resonance Images (MRI) is an important task since manual identification by medical experts is time-consuming. In this work, the Diffusion Weighted (DW) brain MR images of ISLES 2015 challenge dataset and Radiopaedia are pre-processed based on hybrid contrast enhancement and filtering technique. The skull is stripped by thresholding techniques along with mathematical morphology procedure. Later the identification of ischemic stroke lesion is performed by maximum entropy-based and Fuzzy C Means (FCM) thresholding. The segmented results are compared with expert’s ground truth and the performance is analyzed by means of similarity indices. It is observed that FCM performs better in identifying multiple and minute stroke lesion in MR images. There is nearly 98% close match between FCM segmented results and the ground truth scribed by the experts. Thus the proposed automated work flow helps in assisting the clinicians to identify the ischemic stroke lesion in DW modality of brain MR images.
Novel Convex Polyhedron Classifier for Sentiment Analysis

In this paper, we propose a Novel Convex Polyhedron classifier (NCPC) based on the geometric concept convex hull. NCPC is basically a linear piecewise classifier (LPC). It partitions linearly non-separable data into various linearly separable subsets. For each of these subset of data, a linear hyperplane is used to classify them. We evaluate the performance of this classifier by combining it with two feature selection methods (Chi- squared and Anova F-value). Using two datasets, the results indicate that our proposed classifier outperforms other LPC- based classifiers.
Detection of Emotion Intensity Using Face Recognition

The area of face recognition can help to understand the emotional states of user, along with the intensity of those emotions at that particular time, emotion is detected by using feature extraction and process the data to detect the intensity of the emotion.The objective of this paper is to recognaize the intensity of emotions and find out its percentage, for which, a real live video is preperded, Accordingly to the individaul expression in the live video, the emotions are catgorsized in to angry, disgust, scared, happy, sad, surprised, and neutral, emotion intensity is also calculated in real time.
A Secure Network Monitored Balloting System

Voting is used to make cumulative outcomes to express point of views, usually discussions, debates or campaigns. Complications faced when following old systems during balloting are using unfair rules during the process, interference with campaigns, tampering with the election mechanism and data. To address and prevent these drawbacks, an online system is proposed where the voter is first authenticated via voter id details and SMS e-verification. These details are then forwarded to a wireless server through a Domain Name System Security Extensions platform which helps in securing any form of forged DNS data by using public key cryptography. JavaScript, for most client-side computations and .Net are being used to provide faster and accurate results. Each Vote is sent through SHA 256 where it is authenticated. These results are then passed on to a POA consensus block chain algorithm that enables set restrictions on a set of selected known entities to validate. The system even provides randomizing of domains and ports which adds a greater advantage to the proposed model.
Environment Classification for Global Navigation Satellite Systems Using Attention-Based Recurrent Neural Networks

In this paper, an environment classification method for Global Navigation Satellite System (GNSS) is presented. The goal of the study is to characterize the statistical properties of the historical GNSS data in certain typical environments, so that appropriate localization or navigation algorithms can be chosen to achieve better performances once any environments are recognized in real practice. We extract Dilute of Precision (DOP) value, Carrier-to-Noise Ratio (C/N) and Number of Satellite in View from NMEA-0183 data collected in three real typical environments to characterize the environments. Further, an attention-based Recurrent Neural Network (RNN) is constructed; the historical characteristics extracted above are fed into the RNN. Attention values are then calculated using real-time characteristics and the RNN output in each time steps. High dimensional features are then constructed by soft attention and are used as the input of a fully connected network for classification. The performance of proposed method on the classification task of three typical environments has significantly improvement compared to recurrent neural networks without attention mechanism, and achieves an average accuracy of 94% on the testing set.
Modelling global solar radiation to optimise agricultural production

Aim of study: To present a complete global radiation model that includes direct, diffuse sky and ground-reflected radiation, and compare the values with those obtained by the pyranometers.Area of study: The data were analyzed at the meteorological station network in Extremadura, Spain, to validate the results calculated by the model.Material and methods: The method uses the maps from meteorological station data are based on a single piece of daily solar radiation data for an area of 8,000 to 9,000 ha, whereas the maps created by the models are obtained using the spatial resolution of the digital elevation model, in this case 25 × 25m.Main results: The analytical model used in the study obtained global radiation values with a difference of 1.44% relative to the values captured by the meteorological stations in Extremadura. Analysis of the data indicates that on days with a specific type of fog or very diffuse cloud, the global radiation captured by sensors is greater than it would be on clear-sky days in the same area. The method was suitable for calculating global solar radiation on any type of terrain with its corresponding diversity of crop types.Research highlights: The research highlights the importance of understanding and modelling solar radiation for efficient use of water resources in agriculture. Adding these global radiation models to a GIS would provide a very valuable tool for developing regions.
A Blockchain-Based Robotic Process Automation Mechanism in Educational Setting

Blockchain technology and robotic process automation are increasingly the focus of attention in research, as these are both used successfully in business, marketing, manufacturing, and finance. However, their application in educational contexts is still emerging. This chapter uses an illustrative example of an online school registration system to explore how a blockchain-based robotic process automation mechanism can resolve the inherent challenges. The proposed system allows parents to register their Grade 1 and Grade 8 children for their school of choice. There is competition for places in certain schools and a history of unfair allocation of educational resources; hence, there is mistrust. To counter this threat, this study proposes a blockchain-based robotic automation process mechanism to fairly and transparently allocate educational resources. It recommends that further design science research be conducted in which the blockchain is supplemented by additional technological processes to enhance data-sharing in educational settings.
SELF-REGULATION OF PERSONALITY AS A PSYCHOLOGICAL AND PEDAGOGICAL PROBLEM

The article reveals the essence of the concept of «self-regulation» and «self-regulation of personality». The current state of the problem self-regulation of personality is considered in the following positions: functions or properties of self-organized systems; complex multilevel systemic education; development of conscious self-regulation in activity and also this concept was studied in the aspect - "personality-activity". The current state of the problem of self-regulation of personality has been considered on the basis of existing scientific approaches such as subject-activity approach through understanding of conscious self-regulation of activity as a system-organized process of internal mental activity of man for initiation, construction, support and management of various types and forms of arbitrary activity that directly implements the achievement of goals that are accepted by man; structural-functional approach through self-regulation of arbitrary activity, which is characterized as the ability of the subject and as a process. Scientific concepts are of considerable interest for our study that have developed within the framework of: systemactivity concept of self-regulation of the state, in which self-regulation is characterized as an activity and as a state; the concept of individual style of self-regulation of arbitrary human activity; concepts that develop a view of selfregulation as a hierarchically organized system, which involves processes of different levels of individuality. A theoretical analysis of the problem of formation of the concept of «self-regulation of personality» in the scientific literature of various fields of knowledge has been done. A retrospective of scientific research on personality selfregulation is revealed: from understanding it as the most important explanatory principle in the system approach based on theories of functional systems as abilities and properties of the individual organism, to studying this category as a process and independent activity in functional state regulation. The basic conceptual positions, directions of researches and formation of the basic scientific concepts are considered. The basic conceptual positions and directions of researches in which the basic concepts from a researched problem are formulated are considered. Mechanisms of personality self-regulation such as self-esteem, level of one's own aspirations, self-control, reflection, self-stimulation are revealed.
MOOCs Recommender based on User Preference and Video Quality

MOOCs (Massive Open Online Courses) are a new revolution in the field of e-learning. MOOCs are capable of providing several thousands of learners with access to courses over the internet. MOOCs are produced in many different video production styles and these styles play an important role in helping the consumer stay engaged and interested in the courses. MOOCs provide a large number of courses in different domains to a wide range of learners. It has become difficult and a time-consuming task for a user to find the most suitable courses that suit a learner's personal preferences. This paper describes how to recommend a course based on the preferred video style of the learner and the basic learning style of the learner which determines the learner's preferences on other materials in a course. In the course recommendation process, this paper also describes how to classify the course in order to recommend the most appropriate massive open online courses for users according to their most preferred video production style.
Quality Characteristics of CRM Systems

Customer Relationship Management systems (CRMS) support companies in managing their relationship with their customers. They are sophisticated systems composed of various functionalities. The software quality models defined by ISO/IEC 25010 international standard are generic and provide software quality characteristics that are intended to be pertinent to all software types. Nevertheless, the same international standard indicates the need to individualize software quality models to specific software product types. This paper proposes a set of quality characteristics and sub characteristics specific to CRMS based on the quality models of ISO/IEC 25010. To achieve this goal, after a systematic mapping review and the identification of several qualities related to CRMS, a survey was conducted to confirm its pertinence with professionals and users of CRMS.
The experience of distance learning in the bologna process countries during the COVID-19 pandemic

In this article, the authors analyze various aspects of the distance learning discourse of the countries participating in the Bologna Process from the point of view of the impact of the pandemic on the development of higher education. The author pays special attention to the practical aspects of the activities of Russian universities during the pandemic, their interaction with each other and the problems of cooperation in the context of distance learning. Naturally, the stress caused by the epidemic and the transition to distance learning, especially for senior teachers, could not but affect the quality of training, but competently and professionally selected materials provided students with an educational result, and teachers with positive feedback and opportunities for self - development. The aim of the article is to demonstrate the experience of distance learning in the context of the pandemic and its impact on the higher education system. The author used the following methods: comparative research; methods of collecting empirical information; conceptual and terminological analysis; a method for solving problems. The results of the study can certainly be considered as revealing the positive dynamics of the development of the Bologna process in the higher education system, especially in the context of the pandemic.
A Real and Accurate Fake Product Detection System and Generate Original Reviews Using Data Mining Mechanism

Most of the people requires genuine information about the online product. Before spending their economy on particular product can analyze the various reviews in the website. In this scenario, they did not identify whether the product may be fake or genuine. In general, some reports in the websites are good, company technical people itself add these for making the product famous. These people belong to media and social organization teams, they give reviews with a good rating by their own firm. Online purchasers did not identify the fake product because of this falsification in the reviews of the website. In this research,the SVM classification mechanism has been used for detect the fake reviews by using IP address. This implementation helpful for users find out the correct review of online product. In this accuracy is improved by 98.79%, F1-Score increases by 10%.
Public Sentiment Big Data Query Processing and Optimization with Unified Storage of Source and Meta Data

Abstract Public sentiment big data has the characteristics of mass, multi-source, heterogeneity and multi-mode. At present, it mostly adopts separate storage strategies and has low storage and query efficiency. To this end, a distributed storage model is proposed in which structured and unstructured data correspond and are stored uniformly. By establishing a unified storage framework for source metadata, the original source data and characteristic metadata are stored uniformly according to different storage methods. Based on the above storage architecture, a hierarchical index structure is proposed to improve the efficiency of big data query. The unified storage model of source metadata is compared with the common storage methods, and is in the leading position in terms of Block number and query processing efficiency.
A message-passing multi-task architecture for the implicit event and polarity detection

Implicit sentiment analysis is a challenging task because the sentiment of a text is expressed in a connotative manner. To tackle this problem, we propose to use textual events as a knowledge source to enrich network representations. To consider task interactions, we present a novel lightweight joint learning paradigm that can pass task-related messages between tasks during training iterations. This is distinct from previous methods that involve multi-task learning by simple parameter sharing. Besides, a human-annotated corpus with implicit sentiment labels and event labels is scarce, which hinders practical applications of deep neural models. Therefore, we further investigate a back-translation approach to expand training instances. Experiment results on a public benchmark demonstrate the effectiveness of both the proposed multi-task architecture and data augmentation strategy.
Two-hybrid sine cosine algorithm based on Invasive weed optimization algorithm and Bat algorithm

Abstract This research paper takes two hybrids. First hybrid is a sine cosine algorithm with evolution algorithm invasive weed optimization (IWO) resulting that improved algorithm has highly properties that we so called SCA-IWO. Second hybrid is a sine cosine algorithm (SCA) with Bat algorithm optimization (BA). Which is considered one of the intelligent swarm algorithms related to intelligent arithmetic branches and have got on hybrid algorithm, so called SCA-BA. In these two improved algorithms that we have gained which have high properties that they can overcome over the problems that confront the SCA, IWO and BA, each separately. In addition to that, it can pass the falling in local solutions and slow converge, so they gained the global value in vast majority of numerical calculation. SCA-IWO and SCA-BA haven been applied on set of function by specialists in testing optimization that has high measurement with certain iterations. Numerical results proved the efficiencies of hybrids of SCA-IWO and SCA-BA with excellent results compared with original SCA, as well as getting a global and minimum value in many used functions.
A Context Based Approach for Recommending Move Class Refactoring

Misplacement of classes makes poor design quality of an application in terms of software modularization. It causes inefficient software design and increases maintenance cost, time and effort. So, placement of classes is one of the most important design activities to optimize modularization in any object oriented application. Move class, a refactoring technique assists to achieve this design aspect by placing a target class in a more appropriate package (or folder). Although it is an important design aspect, there exist few works regarding this refactoring technique. However, existing works have not considered contextual information of an application, though context of classes in a package provides significant information to group related classes. Therefore, this paper proposes an idea of recommending move class refactoring for a target class based on the contextual similarity between the class's current package and the other packages of the application. An Information Retrieval (IR) technique, cosine similarity is used to measure context based similarity which provides a new dimension in the refactoring research field.
An Experimental Design Approach to Analyse the Performance of Island-Based Parallel Artificial Bee Colony Algorithm

The Artificial Bee Colony (ABC) is a novel nature-inspired metaheuristic optimization algorithm that mimics the behavior of honey bees searching for food sources. The main drawback of ABC, similar to the most of metaheuristics, is the premature convergence (i.e., the earlier stuck into local optima). Recently, the structured population approach, in which the individuals are distributed into multiple sub-populations (called islands), has been widely exploited to maintain the required diversity during the search process and thus reducing the prematurity problem. In this paper, the island model, which is a common structured population approach, is incorporated with the ABC to introduce a parallel variant called (iABC). Besides, an experimental design approach is proposed to analyze the sensitivity of iABC to the parameters of the island model as well as the main specific parameters. The linear regression model and the Analysis of variance (ANOVA) are utilized to estimate the effect of parameters and identify the importance of them. Two well-known benchmark functions are used for evaluation purposes. Experimental results revealed that most parameters and their low-order interactions have a significant influence on the performance of the iABC. Furthermore, the proposed iABC proved its superiority compared to other state-of-the-art algorithms.
Control-based event-driven bandwidth allocation scheme for video-surveillance systems

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Study on the Digitalization Method of Intelligent Emergency Plan of Power System

This paper puts forward a formalized method of emergency plan based on ontology, sums up the main concepts such as system, event, rule, measure, constraint and resource, and analyzes the logical relationship among concepts. A digital intelligent emergency plan storage scheme based on relational database model is proposed. In this paper, full-text search, data search and knowledge search are comprehensively used to adapt to the information needs and characteristics of different users' query plans. Finally, an example of emergency plan made by a power supply company is given to illustrate the effectiveness of the method.
Advance Techniques in Medical Imaging under Big Data Analysis: Covid-19 Images

Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest.Accurate segmentation is required for volume determination, 3D rendering, radiation therapy, and surgery planning.In medical images, segmentation has traditionally been done by human experts.Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered.Therefore, automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use.Many methods have been proposed to detect and segment 2D shapes, most of which involve template matching.Advanced segmentation techniques called Snakes or active contours have been used, considering deformable models or templates.The main purpose of this work is to apply segmentation techniques for the definition of 3D organs (anatomical structures) when big data information has been stored and must be organized by the doctors for medical diagnosis.The processes would be implemented in the CT images from patients with COVID-19.