Reviewerly/exHarmony huggingface dataset:
Practice of Socially Implemented Robotic Education for Life with Robots

Since 1988, the Department of Electronic Control System Engineering at National Institute of Technology, Numazu College has conducted development of Micro Intelligent Robot System ``MIRS'' as a Project-Based Learning (PBL) into the curriculum for the education of system development. We are intended to provide a place of learning ``process of system development'' and ``making education as a creative education'' and ``Proposal and management of competitions with social implementation'' through the development of robot. In this paper, we report on the results of the socially implemented robot education conducted on the theme of Life with Robots. The results of the three years of practice showed that the teams that conducted social implementation experiments had higher technical evaluation scores than those that did not. However, it was also found that even the teams that did not do social implementation experiments got higher evaluation points at the robot presentation. In other words, social implementation is one component of achieving the objectives through robot development education.
Improving K-Means Effectiveness and Efficiency with Initialization Estimates of Cluster Centroids

K-Means is known both for its usefulness in finding clusters of related data as well as its fragility with respect to initialization choices. This paper introduces a 95% more effective and 50% more efficient initialization methods, that could eliminate the need for multiple executions of K-Means to find high quality clustering. To initialize the centroids, it selects a multiple, m, of K real data points, computes (mK)<sup>2</sup> distances and keeps only the K maximum( minimum( distance ) ) points. A consequence of this technique enables O(lnK) binary search to find the optimal K on &#x2019;linearly&#x2019; separable clusters. The effectiveness claim applies both to separable and intertwined clusters although the efficiency is lost on intertwined clusters.
Analysis of visual object tracking algorithms for real-time systems

The article analyzes the latest achievements and decisions in the process of visual support of the target object in the field of computer vision, considers approaches to the choice of algorithm for visual support of objects on video sequences, highlights the main visual features that can be based on tracking object. The criteria that influence the choice of the target object-tracking algorithm in real time are defined. However, for real-time tracking with limited computing resources, the choice of the appropriate algorithm is crucial. The choice of visual tracking algorithm is also influenced by the requirements and limitations for the monitored objects and prior knowledge or assumptions about them. As a result of the analysis, the Staple tracking algorithm was preferred, according to the criterion of speed, which is a crucial indicator in the design and development of software and hardware for automated visual support of the object in real-time video stream for various surveillance and security systems, monitoring traffic, activity recognition and other embedded systems.
Effect of Garbage Collection on Streaming Big Data Workloads

Java applications retrieve memory items as needed. The function of garbage collection (GC) on Java Virtual Machine (JVM) is to automatically determine which memory is no longer in use by the Java system and to deallocate the objects stored in this part of the memory. As memory is restored to JVM automatically, Java developers are not responsible for explicitly deleting unused memory items. We study the performance of various GCs on an Apache Kafka-Storm platform for processing a real-time workload. We demonstrate that the choice of the Garbage collector is sensitive to the configuration of Kafka and the impact of Garbage Collection events on the latency of processing application.
Metric to evaluate virtual courses: case Ecuador

Metric is part of human life and virtual learning environment too. After covid, virtual teaching and learning spaces play an essential role. This paper proposes a metric for evaluate virtual courses. For this purpose, the corresponding formulas are defined, as well as the dimensions and their indicators. The metric is then applied to the case of Ecuador.
An Impact of Digital Technology on Education Industry for Innovative Practices of Teaching and Learning Process: Emerging Needs and Challenges

Digital Technology (DT) is now associated with a slew of issues and solutions that are inextricably tied to the goals of sustained growth for the average economic situation of people in developed countries, particularly India. Globalization is a key enabler, and local uses of technology may help build e-education infrastructure and help young people get higher education and jobs. This includes educational, social, cultural, economic, political, and environmental settings and effects. A new generation of professionals and policymakers is needed to help lead the use of technology to achieve sustainable growth in India's e-education. The advantages of prudent use of increasing information and communication technologies are now widely acknowledged. “The potential benefits penetrated nearly every sector of activity where knowledge and communication are important”. Higher teaching and learning procedures, improved student outcomes, higher student involvement, and seamless communication with teachers and parents are all part of it. There is currently a large gap between what children learn in school and what professionals need in their businesses and communities. In addition to being professional, employers want students who can work in a team cooperatively and analytically solve issues as well as manage a group of individuals. What is the effect of digital technology on education industry for creative business management and sustainability?
Textual Conversational Sentiment Analysis in Deep Learning using capsule network

Sentiment Analysis technique is frequently used by marketing-related companies, social sites, political parties etc. to view or check the satisfaction level of customers with branding, service or product when feedback is received in huge volume. Social media has become a prime tool in today's time to exchange feelings or opinions between people. Due to this, unstructured data is continuously being generated online in very large volume. The working model of various sentiment analysis techniques has been covered in this work. After surveying various state of art approaches of sentiment analysis using deep learning, a capsule network based novel approach has proposed by this work.
Aesthetic Meanings of Architecture in the System of Architect’s Professional Ethics

The aim of the article is to determine the content of an architect’s professional ethics from the point of view of the aesthetic meanings of architecture. In this regard, an analysis of the relationship of architectural activity with other social spheres is carried out; basing on the conclusions drawn, the specific meanings of architectural activity are noted. It is established that architectural ethics can include the problems of engineering ethics, business ethics, balancing on the border of applied (art ethics) and professional ethics. The connection between ethical and aesthetic in architectural activity is revealed. The scientific originality of the research lies in the analysis of the aesthetic influence on the content of modern professional ethics. As a result, both the moral and aesthetic meanings of the architect’s professional ethics are determined in the context of the cultural and social tasks of architectural practice.
Parole 'difficili' con un sistema di valutazione automatica. Risposte di italofoni e di non italofoni

This paper is concerned with lexical competence in the study of Italian for academic purposes, notably learned words formed with Greek/Latin elements. To improve and check receptive knowledge of such “difficult” vocabulary both by native and by L2 learners, a number of exercises are made available, and can be actually done and evaluated, at https://esploradizionari.i-learn.unito.it/. This is a technology enhanced learning environment integrating Moodle with the Automatic Assessment System Maple®, a software of Canadian origin already successfully used for teaching STEM disciplines. The specific features of the exercises are discussed, along with early results by Italophone and non-Italophone learners.
Identification of Dysmorphic Body Disorders Using the Bayes Theorem Method

Body dysmorphic disorder is a mental disorder that refers to a weak mindset for self-confidence and symptoms of excessive anxiety about one's own physical deficiencies. This disorder often occurs in adolescents to adulthood, sufferers often feel embarrassed and worried about themselves, feel uncomfortable and always keep a distance from their social life. By using an expert system, it will be easier for people with body dysmorphic disorder to diagnose the symptoms they are experiencing. The use of the Bayes theorem method in body dysmorphic confusion can prove the level of confidence contained in a new fact or phenomenon. Using 4 examples of symptoms from 7 symptoms provides significant confidence with the highest level of accuracy reaching 85.26%. Implemented by using an application that helps patients early to find out the disorders suffered so that they can determine the next decision.
Encryption with Covertext and Reordering using Permutated Table and Random Function

Documents for some entities are confidential and important, so security is required. Encryption with Covertext and Reordering (ECR) is a text-based document security model. ECR uses a random key to generate the ciphertext. The ECR's random key was selected using the human-generated method. This research aims to increase the level of document security based on the ECR mechanism. This paper proposes a new method by using a random key in a permutated table. The random key is generated automatically by a function. The entropy is used as a measurement of the security level of the encrypted documents. The experimental shows that the permutated table inside the ECR provides better entropy values. It implies a better security level. The use of permutated tables also makes it easier to use ECR to secure documents.
Contribution Analysis of Scope of SRGAN in the Medical Field

This paper focuses on the concept of generative adversarial networks (GANs) and their scope in the medical field. “Generative adversarial networks” has been one of the most prominent research areas in the domain of machine learning in the past few years. It consists of two neural networks competing against each other. One of them is a generator model which generates fake samples of data and the other is a discriminator model which receives both real data (from the training data) and fake data (from the generator model) and tries to identify them as real or fake. Use of generative adversarial networks has been done here for the purpose of “super-resolution” because GANs work on the concept of generative modeling. Since applying super-resolution to an image means adding more data to the image which was not previously there (2017), it would require generation of data which might not be actually real data but is so close to the real one that one cannot know the difference. Hence, when we apply super-resolution using generative adversarial networks it gives us way better results in comparison to many other approaches such as “SRCNN”.
Peculiarities of Expressing Empathy Among Psychology Students and Law Students

Researchers approach of the concept of empathy the consideration of this concept from the point of view of the cognitive component, behavioral, activity, and affective. The ability to empathize is quite an important addition to the professional activities of many specialists. The authors conducted a detailed theoretical analysis of modern approaches to the description of empathy and identified three main approaches to its understanding. In this study, empathy is understood as the ability to consciously understand the emotional state and inner world of another person, during which the subject of empathy has a need to help the object of empathy, the desire to overcome his negative state. Using the test method “Emotional Response Scale”, developed by A. Mehrabyan and adapted by N. Epstein, the level of empathy expression in students was determined. Based on the criteria of mathematical statistics, the Friedman chi-square and the Mann–Whitney U-test, we determined the leading content of empathy and the leading way of expressing empathy in groups of psychology and law students. This article examines the relationship between the chosen profession, personal characteristics and the content and method, as well as the subject of the expression of empathy. Different levels of expression of empathy towards different population groups were revealed. The results of the study can be used in the practice of improving the education system by relying on individual psychological characteristics, the development of which will contribute to increasing the empathic potential of the individual.
On the Cost of ASIC Hardware Crackers

Setting security goals requires understanding the current possibilities and limitations in terms of attacks can be considered practical and what cannot. To do so, this chapter discusses the hardware implementation of a group of cryptanalytic attacks against SKE primitives. As a case study, we consider attacks against once of the recently broken primitives, SHA-1. It is an attempt at answering three important research questions
Attribute-induced Attractiveness Regression of Facial Images with Multi-task Convolution Neural Network

Facial attractiveness prediction is a significant problem that involves many research areas. Recently, deep-learning-based methods have made significant progress for this task, while many challenges remain tackled. Firstly, it is hard to guarantee the objectivity of manual evaluation facial attractiveness, which results in labeling bias of training data. Secondly, the training data with attractiveness score required for a robust predictor is in severe shortage. For the former problem, we adopt the ELO rating algorithm prevalently used in sports to dynamically reorder the ranking of training images according to the comparisons between images. We propose a method based on attribute-induced regression with a multi-task Convolution Neural Network to overcome training data shortage for the last challenge. The method employs facial attributes of facial parts such as eyes, nose, mouth to restrain and reduce the facial attractiveness prediction problem&#x2019;s solution space. We collected, labeled, and ranked an Asian female face dataset with attributes for facial attractiveness analysis. Comprehensive experiments conducted on the dataset demonstrate the effectiveness of our method.
Blockchain Based Secure Cloud Storage System for Range Activity Data

To understand the actual behavior of any ammunition in different situations, dynamic firing needs to be carried out with various instruments for capturing data. On premise storage and processing of that high volume and high veracity data are not only compute intensive and sub optimized but also vulnerable during any disaster. Cloud based storage systems [1] are widely popular for off premise data storage and processing. At the same time data confidentiality, integrity and availability are also prime concerns for range activity data, which can be addressed by using blockchain technology. Though blockchain technology has been used in many areas like healthcare, business and finance, there has not been significant work on cloud storage security based on blockchain technology. In this paper various cloud storage challenges are discussed with their blockchain based solutions. Finally a working model is discussed with all the required features for storing high volume and sensitive range activity data.
Image Super-Resolution Using Quality Aware Generative Adversarial Networks

It has been demonstrated that GAN-based algorithms can generate realistic images in single image super-resolution. However, these methods usually generate undesired artifacts in the accompanied images. We proposes a new GAN-based super resolution method to further improve the performance of super-resolved results. In this fashion, we introduce dense compression unit as our basic unit. Then, we use an additional noise into the generator to enhance the quality of generator network. To enhance the supervision of texture recovery, we use a novel quality aware function that is inspired by the SSIM index as excellent regularizer for GAN objective functions. Finally, we demonstrate our method in extensive experiments that the generated images has more realistic textures and it has a great potential in remote sensing tiny-object detection.
Considering Context in Procedures of Personal Data Discovery

In a connected and not fully regularized world, the protection of personal data is one of the obligations of government authorities towards their citizens. The GDPR (General Data Protection Regulation) is the European directive instituted to oblige organizations and in particular Internet commercial companies to respect the data concerning their customers and to forge a bond of trust with them. The anonymization of data is one of the methods of complying with GDPR. Protecting sensitive business data, and in particular customer personal data, starts with knowing what sensitive data is and where it is located. Data discovery inspects metadata and real data in different databases to discover personal data and provides comprehensive results listing sensitive fields storing that data. The specification of personal data is a very time-consuming phase spent by business experts in the analysis, especially in the case of databases with large amount of data stored in thousands of fields in the tables. There are data discovery tools that use established procedures to determine personal and sensitive data in applications of organizations and companies. However, these tools cannot always extract the exhaustive list of personal data or incorrectly specify certain data as personal. The error deviation in automatic detection is due to the context in which the data is used. For instance, a field typed as first name will not be considered as storing personal data if all the records in the database for this field always return the values “Zoe” and “Megane” which are certainly the first names of people but in the application context, the values correspond to names given to categories of car models. The procedure for identifying personal data must consider the practices of the business analyst who is familiar with the data included in these applications. These practices correspond to user actions performed in the different contexts of use of data to confirm whether the data is personal. This paper presents how to contextualize procedures used in personal data discovery to improve the anonymization process adopted by a company to protect its corporate applications. The anonymization is reinforced by different contextualization of connected data groups through user practices added to well-established procedures. We use the Contextual Graphs Formalism to improve a procedure of sensitive personal data discovery used in the anonymization process.
Application of the CORAL GMDH Algorithm for Modeling and Forecasting the National Income of Ukraine

CORAL GMDH is a method of the inductive approach. In this article it is used for modeling and forecasting socio-economic processes. Here the CORAL GMDH algorithm is applied to solve three problems: recovery of missing data, modeling of macroeconomic indicators, and forecasting the gross national income (GNI). Also, the CORAL GMDH algorithm is used to build models in the problem of modeling the dependence of GNI on socio-demographic indicators and develop recommendations on how the state can influence the level of human capital development in the country by influencing certain socio-demographic indices. The results of modeling for Ukraine, Belarus, and Poland are compared.
Emoji-Based Reactions to the Said Construction in Spanish and English

The said construction (SC), a relatively common but understudied standard English construction, is usually characterized by the use of said in place of a determiner, followed by a noun (N2), typically given (in some sense) and licensed by an antecedent noun (N1). (e.g. “I made coffee… said coffee was bitter.”) I present the results of a social media based experiment, focusing on the way participants react to SC-containing sentences using the “reactions” buttons provided on many social media sites. Participants were presented with stimuli that looked like a Facebook profile; each item in the experiment had a “profile photo”, a sentence that contained either a standard determiner like the or that, or SC, as well as an emoji-based reactions schema with the reaction options provided on Facebook. Participants were told to read each sentence and react to it using the provided emoji buttons. Results showed that SC-containing sentences were more likely to receive the “
Trajectory Planning of Manipulator Based on DQN Algorithm Guided by MPC Sampling

In response to the problems of slow convergence and high randomness of the original DQN (Deep Q-Learning Network) algorithm in manipulator trajectory planning, an MPC (Model Predictive Control) algorithm advantage is fused with deep reinforcement learning, and a DQN algorithm of MPC-guided sampling is proposed. Firstly, the method reduces the number of failures during training by providing constraint control of the manipulator based on a dynamic model. Secondly, the MPC algorithm is run in different initial states. The trajectories are sampled and stored after iterative optimization by a linear Gaussian controller. These samples with high success rate improve the training speed of the neural network. Finally, a virtual simulation environment for the manipulator is built on the CoppeliaSim platform to validate the algorithm. The results show that the improved DQN algorithm improves the learning efficiency by nearly 1.5 times, whose effect is significantly better than that of the original DQN algorithm.
Teaching Scheme Design of UAV Control Experiment Based on the Combination of Virtuality and Reality

Based on the background of the field of unmanned system and agricultural unmanned aerial vehicle (UAV), which integrates mechanical engineering, control, computer and other disciplines, and considering the importance of Python programming combined with engineering application for the undergraduates’ engineering training, the College of Engineering of China Agricultural University has opened an elective experimental course “UAV Control experiment” for the undergraduates majoring in mechanical-electronic engineering. This course guides students through their interests and focuses on cultivating students’ practicality and innovation. The content of the course combines the virtual with the real, which is designed from two aspects of UAV virtual simulation control flight and UAV flight control by programming. The experimental content is in a gradual way and the difficulty is moderate. The grading scheme for this course is reasonable, which focuses on the students’ participation during the experiments. The grading is based on three aspects: performance in the experiment, experimental demonstration results, and the course paper submitted after the course. In the end, this course realized the guidance to the undergraduates in technological innovation, the inspiration to the postgraduates in research direction, and the impact on admissions promotion for the university.