Reviewerly/exHarmony huggingface dataset:
The Work of The Graduate of Riga Polytechnic Institute Augusts Baumanis (1883–1966) In Engineering Science and Pedagogy

Engineer, entrepreneur and teacher Augusts Baumanis (1883–1966) is one of the graduates of Riga Polytechnic Institute (RPI) who were renown in the 1920s and 1930s. Latvia’s growth without his activities is unthinkable. After graduating from the Department of Chemistry (1913) of RPI, he worked in Russia. In 1919, after returning to Latvia, A. Baumanis became involved in oil production and in export of wooden boards. From 1928 to 1931, he travelled around South Africa with his family and worked in gold mines. After returning to Latvia, he started to work as a teacher. The aim of the article is to reveal the life of engineer A. Baumanis, to introduce readers to the Latvian entrepreneur and to analyse the problems he had to face in business and pedagogy during the interwar period.
Detecting Suspicious Users in Social Networks Using Text Analysis

Twitter is a social networking platform that allows the users to discuss current news, recent trends, facts, and much more. Meanwhile, due to this, popularity also becomes the target for suspicious users to perform spamming activities via their posts or messages. It is true that social media entice as mechanisms to ease the spread of current news, hate, etc. and allow users to discuss their views by posting tweets as their post, but these services create an opportunity for the attacker to do some dishonest activity. Malicious users mostly post tweets having such topics that may attract us to read, based on some news, but in between, the tweet they usually muddled URL that lead users to entirely unrelated websites or suspicious profiles. This paper examines the text for a suspicious user using profile-based extraction of tweet data based on content as well as features-based attributes. This will help in the analysis of text having with some suspicious activity.
Measurement Method/System Development

Achieving quality-assured measurement is particularly challenging when measurements are to be done over a wider scope, in terms of scale (large and small, ranging from cosmological to nanoscales) as well as including more qualitative properties when tackling quality-assured measurement across the social and physical sciences. When embarking on a description of measurement in such challenging circumstances, it will be beneficial to exploit opportunities of observing that the measurement process is a specific example and subset of a production process, where the “product” in this special case is the “measurement result”. In the present chapter, obvious analogies will be drawn between designing production of entities—described in Chap. 1 —and designing measurement systems for experiments. A quality-assurance loop in the special case where the “product” is a measurement. This measurement quality loop will provide the structure for basically the rest of the book. The present chapter—the first in this book to address predominantly measurement rather than product—will cover, so to say, the first quarter of the ‘clock’ of the loop, where the client issue of product demands is interpreted in terms of the corresponding measurement requirements. Modelling of a measurement system will be an important step (Sects. 2.2 and 2.4), as well as advice on designing experiments and measurement methods (Sect. 2.3) and finally validating and verifying them (Sect. 2.5). The chapter concludes with a couple of case studies before the reader is encouraged to continue their chosen example.
Bidirectional Removal of Reverse Gravitational Acceleration Based on Data Segmentation

In this paper, we propose a method called bidirectional removal of the reverse gravitational acceleration algorithm based on data segmentation. The stationary points are used as the endpoint to segment the motion data to avoid inter-segmentation accumulation of error in the angular velocity integration process. Then the bidirectional angle method is presented to remove the reverse gravitational acceleration in each data segment. Our method is able to improve the accuracy of the linear acceleration. The results indicate that our method outperforms the exiting methods.
Medical Image Privacy Using Watermarking Techniques

The amount of digital medical images has increased rapidly in the Internet. The necessity of fast and secure diagnosis is vital in the medical world. Nowadays, the transmission of images is a daily routine and it is necessary to find an efficient way to transmit them over the net. In this project we propose a new technique to cipher an image for safe and denoised transmission. The existing research deals with image cryptography, data hiding and steganography. There are several methods to encrypt binary or grey level images. Watermarking can be an answer to make secure image transmission. For applications dealing with images, the watermarking objective is to embed invisibly message inside the image. To embed the encrypted image in the patient information we have used watermarking technique. In this project, we concentrate to solve the privacy violation problem occurred when images are published on the medical applications without the permission. According to such images are always shared after uploading process. Therefore, the digital image watermarking based on DWT co-efficient. Watermark bits are embedded in uploaded images. Watermarked images are shared in user home page. So images can be difficult to misuse by other persons. In receiver side when the message is arrived then we apply the inverse methods in reverse order to get the original image and patient information and to remove watermark we extract the image before the decryption of message. We have applied and showed the results of our method to medical images.
Innovative psychological and pedagogical technologies in teaching students of economics

The article is focused on the psychological and pedagogical teaching technologies as those having important methodological principles including not only traditional systematic, information and action-related approaches, but also psychological follow-up in teaching and learning. The complex of pedagogical and psychological approaches seems highly innovative since it provides an important synergetic effect for the present-day teaching. The article considers the notion of psychological and pedagogical follow-up, its definition and contents as seemed to be more appropriate and correct in the view of the methodology of teaching, training and learning in higher vocational institutions. The empirical sample research material includes the analysis of activities set up and carried out in the Vladimir branch of Financial University under the Government of the Russian Federation since 2014, and the results of the student survey focused on their satisfaction in psychological assistance. The 2014-2019 experience of the university makes it possible to implement the innovative model of the psychological and pedagogical follow-up for students of economics which centers on the patterns of intellectual and personal identity of future professionals and helps to deal with psychological issues, improve the efficiency of teaching and learning and benefit from the socio-cultural experience in different educational contexts and environments.
EXAM SIMULATION AT A HIGHER EDUCATION ESTABLISHMENT: FACTORS OF POPULARITY

The article considers the problem of spreading “false” interactions, such as exam simulation, in the Russian higher education. The author tackles the problem particularly relevant for a higher education establishment - adequate assessment of students’ educational achievements at an exam. Scientific originality of the study involves identifying objective and subjective factors making the exam simulation practice popular. The proposed hypothesis was empirically confirmed by the results of a sociological survey among teachers and students. The analysis allows concluding that students emphasize subjective factors whereas teachers put a special emphasis on objective factors. The acquired results encourage the discussion of ethical and professional problems of higher education.
AE-CharCNN: Char-Level Convolutional Neural Networks for Aspect-Based Sentiment Analysis

Sentiment Analysis was developed to support individuals in the harsh task of obtaining significant information from large amounts of non-structured opinionated data sources, such as social networks and specialized reviews websites. A yet more challenging task is to point out which part of the target entity is addressed in the opinion. This task is called Aspect-Based Sentiment Analysis. The majority of work focuses on coping with English text in the literature, but other languages lack resources, tools, and techniques. This paper focuses on Aspect-Based Sentiment Analysis for Accommodation Services Reviews written in Brazilian Portuguese. Our proposed approach uses Convolution Neural Networks with inputs in Character-level. Results suggest that our approach outperforms lexicon-based and LSTM-based approaches, displaying state-of-the-art performance for binary Aspect-Based Sentiment Analysis.
Saga: An Open Source Platform for Training Machine Learning Models and Community-driven Sharing of Techniques

With the increasing popularity of machine learning in areas such as multimedia indexing and social media analysis, comes an increasing number of tools for developing and training the machine learning models. These tools assist in the selection and optimization of hyperparameters, but the user becomes locked into the platform's built-in solutions. Therefore, this demo presents an open-source, community-driven platform for sharing machine learning models, training techniques, and datasets. The platform, called Saga, provides a machine learning training pipeline such as those found in machine learning services provided by cloud providers. However, Saga also provides a store where users can upload and share their machine learning training methods. Additionally, the store allows users to rate and comment on other users' uploaded methods, as well as download and run them without any additional setup. In our demo, we will run a scenario where a user wants to train an image classifier using Saga. The demonstration will involve downloading methods from the store and displaying the pipeline provided by the Saga platform.
A New Approach to No-Reference Assessment of Perceived Contrast of Images

This paper addresses the problem of improving the accuracy of quantifying the perceived contrast of images with a large number of objects. In this article, we propose a new approach to quantifying the perceived contrast of a multiobject image. The essence of proposed approach is in measuring the contrast of pairs of objects in a reference image with a specified range of brightness and in the follow-up correction of the measurement results taking into account the ratio of the dynamic ranges of the reference and source images. Based on this approach, proposes a generalized description for the perceived contrast of pair of objects in a multi-object image. Using this description, new definitions for the weighted and relative contrast of a pair of objects in an image are presented.
Layout-Induced Video Representation for Recognizing Agent-in-Place Actions

We address scene layout modeling for recognizing agent-in-place actions, which are actions associated with agents who perform them and the places where they occur, in the context of outdoor home surveillance. We introduce a novel representation to model the geometry and topology of scene layouts so that a network can generalize from the layouts observed in the training scenes to unseen scenes in the test set. This Layout-Induced Video Representation (LIVR) abstracts away low-level appearance variance and encodes geometric and topological relationships of places to explicitly model scene layout. LIVR partitions the semantic features of a scene into different places to force the network to learn generic place-based feature descriptions which are independent of specific scene layouts; then, LIVR dynamically aggregates features based on connectivities of places in each specific scene to model its layout. We introduce a new Agent-in-Place Action (APA) dataset to show that our method allows neural network models to generalize significantly better to unseen scenes.
Learning motivation of first-year students of a technical university

The concepts “motive” and “motivation” and their role in the learning process of the technical university are analyzed. The following types of motives were distinguished: internal learning motivation related to learning activities and their content and external learning motivation not related to learning activities and their content but determined by external factors and circumstances. Learning motivation is associated with various changes that occur in students during the learning process. To identify the motivation in firstyear students, they were tested. Driving motives were identified. The results showed that students prefer real learning motives (to use acquired knowledge in life, to continue successful training in subsequent courses, to get good marks, to acquire deep knowledge and intellectual satisfaction). The authors examined motivating methods and techniques. Motivation is a tool used to improve the learning process.
The OntoOO-Method: An Ontology-Driven Conceptual Modeling Approach for Evolving the OO-Method

A conceptual model compiler could be the next step in software engineering terms to achieve conceptual programming. In other words, the model is the code instead of the code is the model. This Ph.D. work faces directly this challenge. To make it feasible, we must answer two main research questions: How to integrate models developed by languages supported by ontologies with different ontological commitments? This will be a question solved by characterizing the ontological framework that determines the basic conceptual building units that ontology-driven conceptual models should include. Finally, what is the best approach for the integration process among those models? This will be a question solved by applying the results of the first research question to the OO-Method approach creating an ontologically well-supported method we call OntoOO-Method. The reported Ph.D. work is an advanced state because the ontology resulting from the first research question is almost complete, and now the author is facing the engineering problem of designing the OntoOO-Method approach.
Watermark Scheme of Database Based on Fuzzy C-Means

In order to protect the copyright of relational database data, a watermark embedding algorithm based on FCM clustering is proposed. When embedding, the embedded watermark data is filtered by the clustering algorithm, and then embedded by the user key watermark. The algorithm can only extract the watermark if it has the key and the secret clustering parameters. The experimental results show that the accuracy of watermark extraction is high, and the algorithm is also excellent in security and concealment. Even in the case of key leakage, the security of the database can be guaranteed.
Activation of the cognitive activity of students of a technical university through the individualization of education

To adapt to work activity, find a creative approach to changes in the job market, it is necessary to have high-quality training of a technical university graduate. This article presents possible ways to solve problems of identifying overt and hidden talents, extraordinary abilities and creative thinking of students. Particular attention is paid to the consideration of the motivating aspects of the cognitive activity ofstudents through the individualization of education in the modern educational system of the higher technical university. The data on the participation of students of the Branch of the Ufa State Petroleum Technological University in the city of Oktyabrsky in the Open International Student Disciplinary Internet Olympiads are given. The issues of enhancing students’ cognitive activity through the identification of individual intellectual abilities of each student are covered. The analysis of the results of Internet Olympiads for the period from 2012 to 2018 in the disciplines of various cycles was carried out, changes in quantitative and qualitative indicators in fifteen disciplines in the profile “Technique and Technology” were revealed, the possibilities of improving the efficiency of preparing students ofhigher educational institutions for scientific activities were shown.
Solving Dynamic Combinatorial Optimization Problems Using a Probabilistic Distribution as Self-adaptive Mechanism in a Genetic Algorithm

In recent years, the interest to solve dynamic combinatorial optimization problems has increased. Metaheuristic algorithms have been used to find good solutions in a reasonably low time, in addition, the use of self-adaptive strategies has increased considerably because they have proved to be a good option to improve performance in these algorithms. In this research, a self-adaptive mechanism is developed to improve the performance of the genetic algorithm for dynamic combinatorial problems, using the strategy of genotype-phenotype mapping and probabilistic distributions. Results demonstrate the capability of the mechanism to help algorithms to adapt in dynamic environments.
Deep Multimodal Network

Online advertisement is an important source of revenue for internet companies, so increasing click-through rates (CTR) on ads is crucial. the traditional CTR prediction model only extracts the classification features and numerical features of the advertisement, but ignores the title, description and other text features of the advertisement. However, we know that such information is very important for an advertisement. Therefore, we propose a deep multimodal network (DMN) to solve this problem. on the basis of the traditional deep model, DMN add the text features of cyclic neural network learning, so as to improve the performance of the model. we also did a lot of visual data analysis on the dataset, Finally, we conducted a comparative experiment on the real-world dataset.
COMPARISON OF OPTIMIZATION ALGORITHMS OF CONNECTIONIST TEMPORAL CLASSIFIER FOR SPEECH RECOGNITION SYSTEM

This paper evaluates and compares the performances of three well-known optimization algorithms (Adagrad, Adam, Momentum) for faster training the neural network of CTC algorithm for speech recognition. For CTC algorithms recurrent neural network has been used, specifically Long-Short-Term memory. LSTM is effective and often used model. Data has been downloaded from VCTK corpus of Edinburgh University. The results of optimization algorithms have been evaluated by the Label error rate and CTC loss.
EU Data Protection Law: An Ally for Scientific Reproducibility?

This keynote will introduce some of the key concepts of European data protection law, and clarify how and why this is not equivalent with privacy law. Next, I will explain why and how EU data protection law could enhance the methodological integrity of machine learning applications, also in the domain of multimedia.
SAR Image Segmentation based on Multifractal Features

Synthetic Aperture Radar (SAR) imaging is based on airborne or satellite active microwave sensors that can capture the earth surface by emitting a signal and receiving the backscattered signal that forms the resulting image. Since microwave radiation is not interfered by sunlight and can pass through clouds, SAR imagery can be generated oblivious to weather and daylight conditions. However, the active nature of the imaging process determines that SAR images are contaminated by an inherent speckle noise that may degrade significantly the quality and usefulness of the images, and specific noise-removal processes may also filter out relevant textural information. In this article, we propose a texture-based method that can be applied for region segmentation in SAR imagery. The method is based on local analysis of the multifractal spectrum and a clustering procedure. The outcomes obtained both with synthetic and real SAR images show better region segmentation results than with state-of-the-art proposals.
Approximation of quantum assemblages

In this paper, we derive a set of projectors on a large Hilbert space which can universally work for approximating quantum assemblages with binary inputs and outputs. The dimension of the Hilbert space depends on the accuracy of the approximation.
Review of Digital Data Protection Using the Traditional Methods, Steganography and Cryptography

The following paper reviews software protection methods by using the methods of steganography, cryptography to safeguard the running applications on pc as well as mobile applications. The software applications these days gets most of the work done for professional use as well as other purposes. The major issues faced by the software developer or publisher are of software piracy. All the major software these days are circulated and pirated through the internet. Thus using steganography and other techniques the goal is to solve the problem of software piracy. The solutions for protecting software from getting pirated are stated in this paper.
A Method to Easily Configure the Monitoring Platform

Due to the advantages we discussed in Section 4.2, we based our monitoring platform on E-GSM.
Arabic speech recognition using phase parameter

Abstract In this paper, the main idea is to investigate the ability of the information presented in the speech signal phase in contributing in the speech recognition system. The database that we used is Arabic words (20 words) pronounced by 10 speakers in different times. The system adopts single frequency signals strategy by using filter bank in feature extraction stage. Gaussian model is used as a classifier. The results shows that will be absolutely error to ignore the phase parameter, especially in some system like MFCC and PLP models, which used only spectrum information. No comparisons are made; the paper is just examining the phase parameter under different circumstance, which include different Filterbank channels and different database styles (clean and noisy utterance).
Atom-atom entanglement in a not-resonant two-photon Tavis-Cummings model

The entanglement between two two-level atoms (qubits) interacting not-resonantly with a one mode of thermal field in a lossless cavity via effective degenerate two-photon transitions is investigated. Based on the exact solution for the time-dependent density matrix of the system under consideration, negativity is calculated as a measure of the entanglement of atoms. The influence of a detuning on the dynamics of entanglement of atoms for separable and entangled initial atomic states and thermal cavity state is investigated.