Reviewerly/exHarmony huggingface dataset:
Evaluation of a Model-Driven Proposal to the Development of Groupware Systems

Building groupware is a complex task. This chapter presents the use of the domain-specific language CSSL v2.0 collaborative software system language. CSSL provides simplicity, expressiveness, and precision to model the main concepts of collaborative systems, especially collaborative processes, protocols, and awareness. Models of collaborative systems are created via visual editors that support the concrete syntax of CSSL. According to the MDD methodology, models are independent of the implementation platform and are formally prepared to be transformed. In this implementation, the target of the transformation is a web application that provides a set of basic functions that developers can refine. Evaluation, validation, and verification of the language is performed, determining that the CSSL tools allow developers to solve central aspects of collaborative systems implementation in a simple and reasonable way. The evaluation determined that the CSSL metamodel has low complexity, with semantics strongly associated with UML and with good configuration possibilities.
SYSTEM-ACTIVITY APPROACH TO FORMATION OF RESEARCH COMPETENCE OF FUTURE TRANSLATORS BY PROJECT-BASED LEARNING METHODS

The article reveals the essence of the system-activity approach to the formation of research competence of future translators by means of project-based learning and identifies its features in the process of their professional training in higher education. The content of the concept of “research competence” is analyzed and it is established that the basis of research competence is the ability to identify the problem in time, formulate a hypothesis, select and analyze the necessary information for research, select the necessary research and data processing methods. The ability to analyse the intermediate results of the study, to discuss the possibility of interpreting the results of the study, to use the results of the study in practice is required. It is noted that the methodological basis for the formation of research competence of future translators by means of project-based learning at higher education institutions is a modern approach to scientific knowledge. This is a system-activity approach. The system-activity approach in the research is considered as a methodological direction of management of socio-pedagogical systems, which involves the presence of interconnected and interdependent activities in the pedagogical process (teaching, education, management, development, learning, etc.). In the course of theoretical analysis, the main ideas of the system-activity approach in the context of formation of research competence of future translators are revealed. Emphasis is placed on the fact that training in the context of the system-activity approach should be organized in such a way as to purposefully form the research competence of future translators. It is established that the use of project-learning technologies allows to bring educational activities as close as possible to the conditions of professional research. The experience of implementation of the systemactivity approach in the formation of research competence of future translators at Alfred Nobel University is revealed. The formation of research competence of future specialists took place in three stages: the formation of skills in working with literature, which is a necessary skill when working on educational or scientific research; implementation of educational and research projects (in the first year – writing essays, in the second and third years – writing term papers and implementation of other projects of educational and research direction); extracurricular work of students (participation of future translators in student conferences, implementation of research projects of various kinds in such disciplines as “Comparative Lexicology of English and Ukrainian”, “Translation Practice”, “Self-Management”, “Linguistics of Second Foreign Languages”).
Design Of A Mobile-Based Geographic Information System Application For The Secretariat Of The Dprd Province Of North Sumatra

The North Sumatra DPRD Secretariat already has an official website containing data and information, namely http://dprd-sumutprov.go.id. However, because this system is based on a website, and the location feature is not available, users will not get information about various DPRDs in North Sumatra along with finding locations and routes to the location accurately. The mobile-based Geographic Information System (GIS) for the North Sumatra DPRD Secretariat is a mobile-based GIS application using Fire Goggle Maps that integrates the Global Positioning System (GPS), either via Android or iOS which is directly connected to Google maps which can be accessed via the internet. The purpose of this system was built for the public to get information about the various locations and routes of DPRD in North Sumatra easily and simply because it can be operated wherever and whenever the user is. Users of this system will be taken to the main page which directs the user to login first so that the user can login directly using a google account or create an account. The purpose of creating a user account is to obtain more accurate mapping information when a user searches for the location of the North Sumatra DPRD, Regency/City DPRD, and City. Applications made can present information based on the selection of each type and provide the closest view of the user's position. The application was made to have an impact on the progress of the North Sumatra Regional House of Representatives Secretariat in technology development.
Accelerated Machine Learning

Accelerated machine learning is an exciting new paradigm in the field of AI which aims to improve the efficiency of training machine learning models. This is achieved by providing users with the ability to execute end-to-end data science pipelines on GPU’s or large-scale CPU based clusters. Although this is a widespread practice for DL applications, historically the training of traditional machine learning models such as SVM’s and RF’s have been restricted to CPU compute. Architectures based on single CPU’s which are found in all types of computer hardware (Intel/AMD) have started to stall in terms of their performance due to restrictions on the manufacturing process and heat dissipation issues. The performance of CPUs has seen limited improvement in recent years which is in stark contrast to the early decades which saw CPU performance double every year (Moores Law) [1]. To combat some of these limitations’ CPU vendors have switched to the development and manufacture of multi-core CPUs that enable multithreading tasks. These processors facilitate rapid performance and enable giga floating-point operations per second (GFLOPS). Traditionally applications were designed to run sequentially and therefore dramatically slowing down the execution of the running process.
Research on Higher Education Collaborative Management Platform Based on Relationship Graph

According to the current problems of higher education management informatization, this paper puts forward a development scheme of collaborative platform on education management.The main technology includes three parts.First, integrate the distributed database and use two-tier linked list to realize dynamic data access.Second, the relation graph is used to display the data of each student, so as to realize the visual sharing of data.Third, realize the collaborative information security mechanism from three aspects to ensure the legal sharing of data.Finally, the platform development is completed with Java.It can help to improve the effectiveness of educating students.
Un-Supervised Learning

Following on from Chap. 3 where we discussed supervised learning, we will now begin to explore an alternative approach called unsupervised learning. The supervised learning aspect of the previous approach is reliant on labelled data to train a model. In supervised learning, the algorithms are designed to look for patterns within a dataset with no predetermined labels. There are two primary types of unsupervised learning which includes cluster analysis and principal components. Additionally, in this chapter, we will provide an overview of association rule mining which allows us to understand the relationships between clusters.
Task Roadmaps: Speeding up Task Replanning

Modern industrial robots are increasingly deployed in dynamic environments, where unpredictable events are expected to impact the robot's operation. Under these conditions, runtime task replanning is required to avoid failures and unnecessary stops, while keeping up productivity. Task replanning is a long-sighted complement to path replanning, which is mostly concerned with avoiding unexpected obstacles that can lead to potentially unsafe situations. This paper focuses on task replanning as a way to dynamically adjust the robot behaviour to the continuously evolving environment in which it is deployed. Analogously to probabilistic roadmaps used in path planning, we propose the concept of Task roadmaps as a method to replan tasks by leveraging an offline generated search space. A graph-based model of the robot application is converted to a task scheduling problem to be solved by a proposed Branch and Bound (B&B) approach and two benchmark approaches: Mixed Integer Linear Programming (MILP) and Planning Domain Definition Language (PDDL). The B&B approach is proposed to compute the task roadmap, which is then reused to replan for unforeseeable events. The optimality and efficiency of this replanning approach are demonstrated in a simulation-based experiment with a mobile manipulator in a kitting application. In this study, the proposed B&B Task Roadmap replanning approach is significantly faster than a MILP solver and a PDDL based planner.
Design and Implementation of Obstetric Central Monitoring System Based on Medical Image Segmentation Algorithm

At present, the incidence of emergencies in obstetric care environment is gradually increasing, and different obstetric wards often have a variety of situations. Therefore, it can provide great help in clinical medicine to give early warning and plan coping plans according to different situations. This paper studied an obstetrics central surveillance system based on a medical image segmentation algorithm. Images obtained by central obstetrics monitoring are segmented, magnified in detail, and image features are extracted, collated, and trained. The normal distribution rule is used to classify the features, which are included in the feature library of the obstetric central monitoring system. In the gray space of the medical image, the statistical distribution of gray features of the medical image is described by the mixture model of Rayleigh distribution and Gaussian distribution. In the gray space of the medical image, Taylor series expansion is used to describe the linear geometric structure of medicine. The eigenvalues of Hessian matrix are introduced to obtain high-order multiscale features of medicine. The multiscale feature energy function is introduced into Markov random energy objective function to realize medical image segmentation. Compared with other segmentation algorithms, the accuracy and sensitivity of the proposed algorithm are 87.98% and 86.58%, respectively, which can clearly segment small medical features.
“The Rodney Dangerfield of Stylistic Devices”: End-to-End Detection and Extraction of Vossian Antonomasia Using Neural Networks

Vossian Antonomasia (VA) is a well-known stylistic device based on attributing a certain property to a person by relating them to another person who is famous for this property. Although the morphological and semantic characteristics of this phenomenon have long been the subject of linguistic research, little is known about its distribution. In this paper, we describe end-to-end approaches for detecting and extracting VA expressions from large news corpora in order to study VA more broadly. We present two types of approaches: binary sentence classifiers that detect whether or not a sentence contains a VA expression, and sequence tagging of all parts of a VA on the word level, enabling their extraction. All models are based on neural networks and outperform previous approaches, best results are obtained with a fine-tuned BERT model. Furthermore, we study the impact of training data size and class imbalance by adding negative (and possibly noisy) instances to the training data. We also evaluate the models' performance on out-of-corpus and real-world data and analyze the ability of the sequence tagging model to generalize in terms of new entity types and syntactic patterns.
An Interpretable Word Sense Classifier for Human Explainable Chatbot

Explainable Artificial Intelligence (AI) based chatbot is one of the most ambitious and unsolved sectors of conversational AI. Recently, there has been a boom in the neural network architecture such as BERT and GPTs that understand the sense of such words/phrases in the sentence. However, such models fail to explain the logical reasoning behind the language understanding thereby making the base of chatbot unreliable. In this paper, we design and extend the previous TM based Word Sense Disambiguation task on complete 20 words as well as design a fully explainable word sense classifier using the Tsetlin Machine (TM) that supports the chatbot to understand the concept of the word/phrases. Our experiments show that the proposed model performs on par with the state-of-the-art accuracy on the publicly available CoarseWSD-balanced dataset. In addition, we explore in-depth how each interpretable clause of TM carries context information that can be easily explained by a human for designing a trustful chatbot.
Human Activity Recognition Systems Based on Audio-Video Data Using Machine Learning and Deep Learning

Human Activity Recognition (HAR) has attracted great attention from the researchers in pervasive computing for smart healthcare. Patients with cardiac disease, obesity, diabetes have to perform some routine physical exercises as a treatment of their disease. Some of the mental disorder patients have to monitor their daily physical activities to prevent undesirable conditions. Therefore, identification and monitoring of physical human activities are helpful to provide the health assessment to the health person regarding patients behaviour. So, it is necessary to make HAR system available to the end users for the safety measures of their health. However, it is important to design the HAR system with proper experimental data set. Various research communities evolved and came up with various datasets for HAR. Audio-Video data are very much useful to create an appropriate HAR system along with smartphone based sensory data. Different types of data have their own advantages and disadvantages. This chapter discusses the implications of various types of data in HAR. Moreover, the primary objective is to enhance the performance of the identification of the human physical activities to make the HAR system reliable. So far, various “Machine Learning” (ML) and “Deep Learning” (DL) approaches are applied to enhance the recognition accuracy of various human physical activities. Analysis of the performance of various HAR systems on audio-video and sensory data using various ML and DL algorithms is discussed. Moreover, detailed case study on the application HAR is also discussed in this chapter.
Introduction to Deep Learning

Like Machine Learning, Deep Learning (DL) is a subset of AI which uses biologically inspired networks to solve a given problem. Neural networks which are a key component in most DL architectures are modelled on the human brain which allows computers to learn in a similar manner. Some of the common problems currently solved using DL includes pattern recognition, time-series predictions, signal processing, anomaly detection, control and automation and computer vision [1]. Like with traditional ML as discussed in previous chapters you will find that most of the common tools and techniques are also applicable in the DL domain. For example, you still need to undertake elements of the EDA process, partition your data into train, validation and test sets and evaluate your model using some of the metrics previously discussed (In specific cases of DL we will introduce additional metrics where appropriate). However, many of the similarities end here as the fundamental architecture of DL models differ from their traditional ML counterparts.
Application of DBOA in Structural Engineering Design Problems

In order to validate any algorithm, its efficiency is required to be verified. Therefore, in this present chapter the performance of the DBOA is tested and verified on 13 unconstrained benchmark problems and 8 structural engineering design optimization problems (SEDOP). The proposal is further validated on five Himmelblau’s nonlinear constrained optimization problems (HNCOP). Comparative experimental and non-parametric statistical result analysis with DE variants, BOA and some other state-of-art algorithms stated in the literature confirms the improved or at-par presentation of DBOA. Comparative experimental results and non-parametric statistical result analysis with DE variants, BOA and some other state-of-art algorithms stated in the literature endorses the better or at-par performance of DBOA.
Human Activity Recognition Systems Based on Sensor Data Using Machine Learning

An increase in life expectancy is leading to an aging society, where gradual degradation in physical/cognitive health could result in fall injuries, cardiovascular, neurological/psychological problems, or a sedentary lifestyle. Sensor-based human activity recognition (HAR) is found to be a potential assistive technology in this context to improve the quality of life of an aging society. This chapter presents the state of the art and future trends of Internet of Things (IoT)-enabled sensor data-based Human Activity Recognition (HAR) systems, where machine learning (ML)/deep learning (DL) is used to classify human actions in the context of healthcare applications. In particular, the impact of IoT on the healthcare industry is so immense that a dedicated specialization, termed as Internet of Healthcare Things (IoHT), has emerged. Smart healthcare systems are equipped with different categories of sensors (wearable, wireless, and ambient). The huge amount of sensor data acquired by IoHT sensors allows for performing accurate HAR. The HAR involves detection/classification problems, e.g. fall detection and seizer detection, where ML is used extensively, at its heart. A lot of ML/DL techniques are being used in HAR including support vector machine (SVM), artificial neural network (ANN), recurrent neural network (RNN), convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTM), etc. However, there are a few special aspects of HAR sensor data that make it difficult to handle. Involved research is still being pursued across the globe to solve these problems.
VGL Rating: A Novel Benchmarking Suite for Modern Supercomputing Architectures

This paper presents a novel project aimed to rank modern supercomputing architectures. The proposed rating is based on an architecture-independent Vector Graph Library (VGL) framework. The initial integration with VGL greatly simplifies the process of ranking new supercomputing architectures due to the fact that VGL provides a convenient API for developing graph algorithms on a large variety of supercomputing architectures. Unlike existing projects (such as Graph500), the proposed rating is based on a larger number of graph algorithms and input graphs with fundamentally different characteristics, which makes it significantly more representative when certain architectures have to be compared for a specific real-world problem. Moreover, the proposed flexible software architecture of our rating allows one to easily supplement the rating with new graph algorithms and input data, if necessary.
Hybrid Learning of Future Bachelor’s-Translators in Higher Education Institutions

Based on the modern approaches the article has identified the features of hybrid learning and has substantiated the benefits of its usage during force majeure in the country. The terms «blended learning» and «hybrid learning» are considered synonymous and are understood as learning when the online and life formats alternate each other with the same intensity. The main characteristics of hybrid training of future bachelors-translators have been highlighted: the simultaneous existence of old and new technologies; the focus on the modern educational environment; the replacement of the obsolete part of the traditional educational process with innovative methods and technologies; the efficiency of material development increase. The stages of hybrid education of future bachelors-translators have been described and the procedure of evaluating the effectiveness, advantages and disadvantages of hybrid learning has been described on the example of the selective educational component «Fundamentals of Literary Translation». The results of the questionnaire on the satisfaction of the use of hybrid learning in the process of training students in the system of higher education have been analyzed. It has been established that hybrid learning that includes various components combined harmoniously and forms the holistic methodological system of education with the use of innovative technologies, is optimal in the current challenges facing our state.
Constructing an Expert System for Solving Astrophysical Problems Based on the Ontological Approach

The current state of the methods for solving computational problems of mathematical physics and supercomputer systems poses a complicated task for the researcher associated with the choice of numerical methods and a multicore computer architecture for efficiently solving the problem in a reasonable time with the required accuracy. We are developing an intelligent support system for solving mathematical physics problems on supercomputers. The system includes a knowledge base and an expert system based on the ontological representation of numerical methods, computing architectures, and inference rules that connect them. This paper discusses in detail the issues related to the formation of inference rules for solving astrophysical problems. The formalization of these rules is described, and their application for constructing a solution scheme of the problem according to the user’s specification is shown. An example of solving the problem of modeling the spiral instability evolution in a protostellar disk based on the proposed approach is given.
Visual Rewards From Observation for Sequential Tasks: Autonomous Pile Loading

One of the key challenges in implementing reinforcement learning methods for real-world robotic applications is the design of a suitable reward function. In field robotics, the absence of abundant datasets, limited training time, and high variation of environmental conditions complicate the task further. In this paper, we review reward learning techniques together with visual representations commonly used in current state-of-the-art works in robotics. We investigate a practical approach proposed in prior work to associate the reward with the stage of the progress in task completion based on visual observation. This approach was demonstrated in controlled laboratory conditions. We study its potential for a real-scale field application, autonomous pile loading, tested outdoors in three seasons: summer, autumn, and winter. In our framework, the cumulative reward combines the predictions about the process stage and the task completion (terminal stage). We use supervised classification methods to train prediction models and investigate the most common state-of-the-art visual representations. We use task-specific contrastive features for terminal stage prediction.