Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis for Multi-Attribute Data in OSNs Using Hybrid Approach

Abstract Increasing popularity of social networks like LinkedIn, MySpace and other networks in present days. Communication is also increased in between users present in social networks. Large amount of data being move on social media because of increase data outsourcing. Sentiment analysis is impressive and interest concept for online social networks, while different types of existing methods to find sentiment in online social networks to define communication between different users to categorize patterns with respect to similar attributes to analyze large data. We present and suggest the Hybrid Machine Learning method in this paper.(which is combination of Balanced Window and Classification based on Parts of Speech) to handle outsourced data of social networks from Face Book and other blogging services are trained and then classify the relation based on emotional aspect like positive or negative and other relations in social streams. The performance of our proposed approach is to extensively close to machine learning and identify important relevant features randomly and perform sentiment analysis in different data streams. Our experimental results show exhaustive level of classification results with comparison of existing approaches in real time environment.
Fraud Prevention Within the Brazilian Governmental Public-Key Infrastructure

Digital certificates are massively used in government systems in Brazil. However, in the last few years, cases of fraud were started to be continuously reported. Current Brazil's issuing process is slow, costly and susceptible to failure. Thus, this present work proposes an automatic fraud detection process using machine learning in a hybrid approach. The method aims to categorize the applicant's application at the level of dangerousness, that is, the chances of being a fraud. The proposed method was tested with data from real dossiers, whereas the fraudsters' data were synthetically created. The results were satisfactory, showing that it is possible to reduce the execution time, reduce costs, and increase the process's safety.
Manifold Learning with Intrinsic Distance Estimation Using Kernelized Linear Model for Metric Tensors

We often observe manifolds embedded in a high-dimensional space, which are generated by a latent low-dimensional system via non-linear measurements. If we obtain push-forward metric tensors of the manifold, we can reconstruct a data representation which is intrinsic and isometric with respect to the latent geometry. For that purpose, assuming samples can be drawn from Gaussian distributions in the latent low-dimensional system, we study methods to estimate metric tensors. If we don't have enough amount of samples, the estimation will be failed. One solution to that problem is artificial neural networks, however it can be time-consuming. So, we propose a kernelized linear model which is easy to be implemented and has capability of estimating the metric tensor under the insufficient circumstance. Estimation capability of the proposed method is evaluated in the numerical experiment.
Extreme Learning Machine for Business Sales Forecasts: A Systematic Review

Technology in business is vital, in recent decades technology has optimized the way they are managed making operations faster and more efficient, so we can say that companies need technology to stay in the market. This systematic review aims to determine to what extent an Extreme Learning Machine (ELM) system helps sales forecasts (SF) of companies, based on the scientific literature of the last 17 years. For the methodology, the systematic search for keywords began in the repositories of Google Scholar, Scielo, Redalyc, among others. Documents were collected between 2002 and 2019 and organized according to an eligibility protocol defined by the author. As an inclusion criteria, the sources in which their conclusions contributed to deepening the investigation were taken and those that did not contribute were excluded. Each of the results represented in graphs was discussed. The main limitation was the little information on the subject because it is a new topic. In conclusion, an ELM system makes use of both internal and external data to develop a more precise SF, which can be used not only by the sales and finance area but also to coordinate with the production area a more exact batch to be produced; this has a great impact on the communication and dynamism of companies to reduce costs and increase profits.
University of Excellence: What has Changed for Teachers

This article discovers the evolution of the academic community over the past five decades in the context of theories of the “University of Excellence” (B. Readings), “global academic revolution of the XXI century” (P.J. Altbach) and “transnational model of science” (Y. Gingras). The first chapter of the article provides an analysis of the results of the global academic revolution and its impact on both the institutional academic landscape and the status of the academic profession. The second chapter examines the influence of the practices of “transnational science” on the content and nature of professional activities, lifestyle and values of modern university teachers. In the realities of “fast science”, a scientist is often forced to comply with the principle of “publish or perish”, which can adversely affect his or her creative personality and the ability to conduct long-term and extraordinary research projects. The new business culture, which is transferred from the sphere of corporate business to universities, pays teachers' special attention to the implementation of the result set by the administration, negating any discussion about its content and quality. Based on the analysis of institutional trends and university practices, some conclusions regarding the prospects for higher education are drawn.
MAP segmentation in Bayesian hidden Markov models: a case study

We consider the problem of estimating the maximum posterior probability (MAP) state sequence for a finite state and finite emission alphabet hidden Markov model (HMM) in the Bayesian setup, where both emission and transition matrices have Dirichlet priors. We study a training set consisting of thousands of protein alignment pairs. The training data is used to set the prior hyperparameters for Bayesian MAP segmentation. Since the Viterbi algorithm is not applicable any more, there is no simple procedure to find the MAP path, and several iterative algorithms are considered and compared. The main goal of the paper is to test the Bayesian setup against the frequentist one, where the parameters of HMM are estimated using the training data.
Towards Personalized Recommendations in an IoT-Driven Tour Guide Platform

In this paper we describe a system for personalized recommendations as part of a smart IoT-driven tour guide platform. The system builds user profiles out of historical localization information (movement of users in space within a museum) and indications of their interests, inferred through interaction with digital content, and time spent around exhibits. Users with similar movement style and interests are clustered together in order to provide useful personalized recommendations. The presented system leverages data-streaming technologies for online training and interactive response in coordination with a commercial IoT platform and mobile application. Our evaluation focuses on validating the profile-building and clustering parts of the platform using an experimental testbed that allows for testing under controlled conditions.
Formation of Personal Maturity of Students during the Period of Study at University

Summary: Addressing the problem of the formation of students' personal maturity during the period of study at a university in order to identify dynamic changes in its individual components is relevant for psychology both in theoretical and applied terms. One of the most significant aspects of higher education is the formation of a mature personality, which is capable of making competent, independent, decisions that meet the requirements of subjects of labor relations in professional activity. The process of forming the personal maturity of students, updating their internal resources, ensuring their professional development during the period of study at university will be more successful within the framework of organized support. From this perspective, the article presents an attempt to study the characteristics of the development of personal maturity of students in the process of studying at university, taking into account the issue of the elements of such maturity.. Based on the research and traditions in psychology, which were subjected to theoretical analysis, the main emphasis in our study was placed on those main components of personal maturity that are directly related to such personal constructs as the self-concept, self-awareness, value-semantic sphere. In the context of the studies considered, those that determine its full-fledged functioning were identified as the main components of personal maturity: the value component (self-respect, self-acceptance), the personal component (attitude to one’s “I”, life values and attitudes), achievement motivation (activity) indicators of the ability to psychological closeness (empathy). The analysis of the features of the development of these components of personal maturity became the goal of the experimental work. The data of a formative experiment are presented, as a result of which it is proved that in the course of systemic psychological and pedagogical support of students, it is possible to change the main components of personal maturity.
Analysis of Key Technologies for Artificial Intelligence in Regulation of Power Grid

As science and technology has developed rapidly, artificial intelligence (AI) has become a trend of the development of modern society, and the development of AI has been concerned by many industries. In the power system, regulation of power grid is an important technology which is equivalent to human brain. The combination of regulation of power grid and AI with the use of information technology can improve the accuracy and efficiency of regulation of power grid to promote the development of power industry in China.
Development of Two Axis Solar Tracking System Interface Using Matlab GUI

This study investigates the analysis and modeling of solar tracking system in order to maximize the output power of PV systems by tracking continually the sun position to detect the maximum of solar radiation. The developed man-machine interface using the Matlab guide allows the computation of sun position as well as the PV system position. In addition, the developed interface offers other possibilities like plotting the PV system position and re-initialization of input data in order to compute new PV system position.
Examination of data citation guidelines in style manuals and data repositories

Abstract This study investigated the status of data citation guidelines by analysing 15 style manuals and 100 data repositories in various fields. Analysis results are presented through a comparison with eight items in the Joint Declaration of Data Citation Principles (JDDCP). Only nine style manuals and only 20 repositories contain instructions regarding data citation. The guidelines generally agree on the importance of the author, publication date, title, and publisher, but there is large variation between the different guides. Analysis of the guidelines revealed that they generally reflect the principles of the JDDCP. In particular, the principle of Credit and Attribution is significantly reflected in style manuals and data repositories and supports one of the major motivations for data sharing. The principles of Unique Identification, Access, and Persistence are less well supported in data citation guidelines. However, the principles of Importance, Evidence, Specificity and Verifiability, and Interoperability and Flexibility are rarely supported. The findings of this study reveal that better guidelines are required, and future work could investigate developing and assessing the standard guidelines for data citation in different fields.
Motor Learning based on Presentation of a Tentative Goal

This paper presents a motor learning method based on the presenting of a personalized target motion, which we call a tentative goal. While many prior studies have focused on helping users correct their motor skill motions, most of them present the reference motion to users regardless of whether the motion is attainable or not. This makes it difficult for users to appropriately modify their motion to the reference motion when the difference between their motion and the reference motion is too significant. This study aims to provide a tentative goal that maximizes performance within a certain amount of motion change. To achieve this, predicting the performance of any motion is necessary. However, it is challenging to estimate the performance of a tentative goal by building a general model because of the large variety of human motion. Therefore, we built an individual model that predicts performance from a small training dataset and implemented it using our proposed data augmentation method. Experiments with basketball free-throw data demonstrate the effectiveness of the proposed method.
Morphology on Categorical Distributions

Abstract Mathematical morphology (MM) is an indispensable tool for post-processing. Several extensions of MM to categorical images, such as multi-class segmentations, have been proposed. However, none provide satisfactory definitions for morphology on probabilistic representations of categorical images. The categorical distribution is a natural choice for representing uncertainty about categorical images. Extending MM to categorical distributions is problematic because categories are inherently unordered. Without ranking categories, we cannot use the standard framework based on supremum and infimum. Ranking categories is impractical and problematic. Instead, we consider the probabilistic representation and operations that emphasize a single category. In this work, we review and compare previous approaches. We propose two approaches for morphology on categorical distributions: operating on Dirichlet distributions over the parameters of the distributions and operating directly on the distributions. We propose a “protected” variant of the latter and demonstrate the proposed approaches by fixing misclassifications and modeling annotator bias.
Applications of Feature Engineering Techniques for Text Data

Feature plays a very important role in the analysis and prediction of data as it carries the most valuable information about the data. This data may be in a structured format or in an unstructured format. Feature engineering process is used to extract features from these data. Selection of features is one of the crucial steps in the feature engineering process. This feature selection process can adopt four different approaches. On that basis, it can be classified into four basic categories, namely filter method, wrapper method, embedded method, and hybrid method. This chapter discusses about different techniques coming under these four categories along with the research work on feature selection.
Automatic generation and inferring semantic structure of verbal instructions for a motor task

Expert instructors frequently use verbal instructions in motor learning coaching. The instructors find points to be improved in learners' forms and give advice. However, the verbal expressions strongly depend on instructors' characteristics, and the process of choosing instruction is not systematic in many fields. This study aims to develop an agent that can automatically generate proper verbal instructions from learners' motion data, and to systematize the instructions given by various expressions. We collected shooting motion data of free-throws in basketball from novice players, and verbal instructions to the novices' shooting motions from experienced players. Using multi-label classification, a type of supervised learning, we developed an agent that generates essential instructions to the novices' motions. We also suggested a possibility to extract semantic relationships among instructions with various expressions in the context of motor learning by using network structure inference in graph theory.
Research and Development Housing Rental System with Recommendation System Based on SpringBoot

With the rise of the floating population in the past few years, the housing demand of the society is also increasing. In order to improve the combination of Internet and traditional group properties and explore the feasibility of recommendation system in the housing rental industry, the system adopts spring Boot and other frameworks for research and development. The system realizes the functions of house information editing, house information auditing, house information publishing and so on. In the process of using the system, the system will collect the user’s browsing records, construct user feature vectors, recommend house listings and potential roommates for the tenant through the recommendation algorithm, and improve the rental efficiency.
Workflow Management on BFT Blockchains

Blockchains have been proposed as infrastructure technology for a wide variety of applications. They provide an immutable record of transactions, making them useful when business actors do not trust each other, and their distributed nature makes them suitable for inter-organizational applications. However, widely-used proof-of-work based blockchains are computationally inefficient and do not provide final consensus, although they scale well to large networks. In contrast, blockchains built around Byzantine Fault Tolerance (BFT) consensus algorithms are more efficient and provide immediate and final consensus, but do not scale well to large networks. We argue that this makes them well-suited for workflow management applications, which typically include no more than a few dozen participants. This paper is motivated by a use case in the resource extraction industry. We develop an architecture for a BFT blockchain based workflow management system (WfMS) and present a prototype implementation. We discuss its advantages and limitations with respect to proof-of-work based systems and provide an outlook to future research.
Improving the Quality of Scientific Articles Machine Translation While Writing Original Text

Currently, the publication of scientific articles in peer-reviewed journals in most cases assumes translation into English. In this context, it is possible to reduce translation costs by using machine translation systems with post-editing. Modern machine translation systems have come a long way from bilingual dictionaries to multi-lingual localization platforms and continue to develop rapidly. By the use of neural networks deep learning, translation quality has almost reached the level of manual translation. However, machine translation has disadvantages and limitations, understanding of which allows using such systems to reduce time and costs for scientific texts translation. The paper formulates and systematizes main features of machine translation application, as well as gives recommendations for preparing a source text for translation, which are relevant not only when using machine translation systems, but also for human translation, especially for texts of specific subjects. By the example of a scientific and technical text and performing an experiment it is demonstrated how the quality of machine translation changes when described recommendations are applied. BLUE score metric is used to evaluate translation quality.
The Research of Aerospace Software Product Line Engineering

The “mass, low cost, fast response” development mode proposed by commercial aerospace has become the trend of future aerospace missions. However, the current aerospace software development is twisting with severe issues such as quality, progress, and efficiency, etc., and software product line (SPL) technology turns out to be a good resolution to these issues. Software product line method is one software reuse technology which is oriented to a particular field, massive and in large particle. The method has attracted much attention in the software reuse field in recent years. This paper firstly presents detail discussion on extensive aspect of software product line, such as the concept, theory and methodology, etc. Then after further analysis of the characteristics of software in the aerospace field, this paper points out the application feasibility of SPL and proposes a method of software development and management based on SPL suitable for aerospace field.
Distributed Anonymity Based on Blockchain in Vehicular Ad Hoc Network by Block Size Calibrating

The network connectivity problem is one of the critical challenges of an anonymous server implementation in the VANET. The objective and main contribution of this paper are to assure the anonymity in VANET environments. In the proposed blockchain method, before packaging transactions into blocks, anonymity risk reduced through techniques such as k-anonymity, graph processing, dummy node, and silence period. This paper addresses the challenges of anonymous servers, such as update challenges and single point of failure, by exploiting append-only, distributed, and anonymity features. Although mounting the blockchain process with asymmetric cryptography solves the connectivity challenge, start-up delay and network overhead are severe. The significant feature of the proposed method solves this delay challenge by aggregating many transactions into a block and fixing constraint range of multicasting blocks. Also, aggregating transactions of various end-users into a block preserves the path anonymity. The asymmetric cryptography with ring public and private keys protects the identity anonymity as well as unlinkability. The robust anonymity mechanism existence and the traceability of all transactions constitute the main advantages of the proposed method. The simulation is running by the python to evaluate blockchain performance in VANET with connectivity failure and rapidly changing topology. The results indicate the stabilization of the proposed method in the VANET environment.
Spatially Constrained Likeliness-based Fuzzy Entropy Clustering Algorithm and its Application to Noisy 3D Brain MR Image Segmentation

This paper proposes a spatially constrained likeliness-based fuzzy entropy clustering algorithm for noisy 3D brain MR image segmentation. It introduces a likeliness measure with respect to a voxel under consideration by using intensity distribution surrounding its local neighborhood. We use this measure as an additional membership function and named as fuzzy likeliness measures. We integrate these two fuzzy membership functions into a objective function by means of a regularizing parameter. Further, we introduce a fuzzy entropy using the fuzzifier weighted fuzzy likeliness measures to define the information uncertainty associated with a voxel in order to finding its cluster. By integrating weighted fuzzy membership function and fuzzy likeliness measure we generate the final membership function. The experiments on noisy 3D brain MR image volumes that include simulated and clinical data suggest that the proposed algorithm is superior while comparing with several state-of-the-art algorithms in terms of Dice coefficient, partition coefficient and partition entropy.