Reviewerly/exHarmony huggingface dataset:
Approaching Healthy City Ontology: First-Level Classes Definition Using BFO

In recent decades, the concept of the healthy city (HC) has become more and more relevant in many fields, such as city administration and scientific environment, and has become a commonly understood concept in the general public. Due to the breakneck growth of people living in urban contexts, the subsequent necessity to guarantee good urban conditions for all kinds of citizens, and the general deterioration of the hearth environment caused by human activities (concentrated in urban settlements), this issue is increasing in its relevance. In this paper, the authors discuss the concept of the HC from an ontological point of view to organize the highly complex system of elements and the mutual relations that constitute the idea of HC. The main goals of an HC are quite intuitive, but the number of components that define and manage it is vast and related to different disciplines: sustainability, urban management, urban planning, and health and social studies. With the presented research, the authors intend to start an organizational definition of the HC using basic formal ontology (BFO). Considering the definition of HC, the authors focus on the ontology process and the different typologies of ontological structures. Then, the authors describe a first-level scheme of HC ontology and, finally, discuss possible applications of the presented study and next research steps.
History of the Studies in Russian Memoir Literature of the 17th–18th Centuries: The Problem of Classification

The article focuses on the historiography of Russian memoir literature of the 17th– 18th centuries. The authors turned to general studies in humanities (literary, historical, cultural, and philosophical) which discuss theoretical problems of classification and internal systematization of the varieties of memoir literature. The evolution of the terminological apparatus used within memoir studies is shown separately. A special aspect, reflected in the article, concerns attention of the researchers to the manuscript culture and the existence of monuments of memoir literature within this culture. Considering the history of the question chronologically, the authors of the article identify several periods, describe major research schools within the framework of traditional research, and discuss the studies in the field of the “new” methodological paradigm. Addressing the problem of classification in historiography demonstrates the need for further study of key problems in the memoir studies. In conclusion, the authors identify perspective direction towards creating adequate principles of classification, defining genre delimitations and criteria for selecting memoir monuments that exist in the manuscript tradition.
Application of Path Planning and Image Processing for Rescue Robots

The objective of this study was to integrate different position sensors and a camera in a wheeled mobile robot (WMR) for use in a rescue task.A differential global positioning system (DGPS) was used to locate the WMR position and the goal position.A laser sensor was utilized to detect the locations of obstacles and edge values.The edge values were applied to the A * algorithm to search for the shortest path for the WMR.While searching for a target, a webcam was used to capture images of the surroundings.Color segmentation was achieved and the desired color was captured.The captured color target was then used for tracking control of the rescue task.The A * algorithm, together with image processing and laser sensors, was able to obtain the shortest path to the desired color target in an unknown environment.Obstacle avoidance control was based on a fuzzy system.Experiments confirmed that the proposed control scheme worked properly and was suitable for rescue tasks.
QVR: Quranic Verses Recitation Recognition System using PocketSphinx

The recitation of Quran verses according to the actual tajweed is obligatory and it must be accurate and precise in pronunciation. Hence, it should always be reviewed by an expert on the recitation of the Quran. Through the latest technology, this recitation review can be implemented through an application system and it is most appropriate in this current Covid-19 pandemic situation where system application online is deemed to be developed. In this empirical study, a recognition system so-called the Quranic Verse Recitation Recognition (QVR) system using PocketSphinx to convert the Quranic verse from Arabic sound to Roman text and determine the accuracy of reciters, has been developed. The Graphical User Interface (GUI) of the system with a user-friendly environment was designed using Microsoft Visual Basic 6 in an Ubuntu platform. A verse of surah al-Ikhlas has been chosen in this study and the data were collected by recording 855 audios as training data recorded by professional reciters. Another 105 audios were collected as testing data, to test the accuracy of the system. The results indicate that the system obtained a 100% accuracy with a 0.00% of word error rate (WER) for both training and testing data of the said audios via Quran Roman text. The system with automatic speech recognition (ASR) engine system demonstrates that it has been successfully designed and developed, and is significant to be extended further. Added, it will be improved with the addition of other Quran surahs.
INDUSTRIES PIONEERING BLOCKCHAIN TECHNOLOGY FOR ELECTRONIC DATA INTERCHANGE

Rapid development of blockchain technologies promote involvement of methods using categorize research topics, identify the most topical trends, and ascertain the main publishing sources related to electronic data interchange. The study is based on an analysis of scientific publications (2015-2019 years) and investigation on the theory of supply chain and logistic the research is based on theoretical research methods that include a literature review and content analysis. The authors have reviewed 9,780 primary papers published between the years 2015-2019 from major academic databases (EBSCO, Elsevier ScienceDirect, Emerald Insight, Scopus, SSRN; Springer SpringerLink, Web of Science). The results of the research are presented, which at the logistics position describing the usage of blockchain technology for electronic data interchange in multiple industries. In concluding the research, the authors present the research gaps discovered, major trends, most discussed industries and suggestions for future research.
Word Spotting as a Service: An Unsupervised and Segmentation-Free Framework for Handwritten Documents

Word spotting strategies employed in historical handwritten documents face many challenges due to variation in the writing style and intense degradation. In this paper, a new method that permits efficient and effective word spotting in handwritten documents is presented that relies upon document-oriented local features that take into account information around representative keypoints and a matching process that incorporates a spatial context in a local proximity search without using any training data. The method relies on a document-oriented keypoint and feature extraction, along with a fast feature matching method. This enables the corresponding methodological pipeline to be both effectively and efficiently employed in the cloud so that word spotting can be realised as a service in modern mobile devices. The effectiveness and efficiency of the proposed method in terms of its matching accuracy, along with its fast retrieval time, respectively, are shown after a consistent evaluation of several historical handwritten datasets.
Analysis and Research on Human Movement in Sports Scene

As a whole-body sport, skipping rope plays an increasingly important role in daily life. In rope-skipping education, due to the lack of professional teachers, the training efficiency of students is low. The rope-skipping monitoring device is heavy and expensive, and the cost of labor statistics and energy consumption are high. In order to quickly analyze the movement process of students and provide correct guidance, this article implements the movement analysis method of the human body movement process. The problem of limb posture analysis in rope skipping is transformed into a multilabel classification problem, a real-time human motion analysis method based on mobile vision is proposed, and the algorithm model is verified in the rope-skipping scene. The experimental results prove that this paper proposes the improved algorithm, which achieved the expected effect. In the analysis of rope-skipping action, the choice of hyperparameters during the experiment is introduced, and it is verified that the proposed ALSTM-LSTM can solve the problem of multilabel classification in the rope-skipping process. The accuracy rate reaches 95.1%, and it can provide the best in all indicators and good performance. It is of great significance for movement analysis and movement quality evaluation during exercise.
Global Multi-Source Information Fusion Management and Deep Learning Optimization for Tourism

The purpose is to solve the problems of sparse data information, low recommendation precision and recall rate and cold start of the current tourism personalized recommendation system. First, a context based personalized recommendation model (CPRM) is established by using the labeled-LDA (Labeled Latent Dirichlet Allocation) algorithm. The precision and recall of interest point recommendation are improved by mining the context information in unstructured text. Then, the interest point recommendation framework based on convolutional neural network (IPRC) is established. The semantic and emotional information in the comment text is extracted to identify user preferences, and the score of interest points in the target location is predicted combined with the influence factors of geographical location. Finally, real datasets are adopted to evaluate the recommendation precision and recall of the above two models and their performance of solving the cold start problem.
Employee Sentiment Analysis Towards Remote Work during COVID-19 Using Twitter Data

The COVID-19 pandemic has essentially transformed the way of leading a life for millions of people across the world.As offices remained closed for months, employees expressed conflicting sentimental analysis on the workfrom-home culture.People worldwide use social media platforms such as Twitter to talk about their daily lives made a trend in the online platform.This research study aims to gauge the public's sentiment on working from home/ remote locations during the COVID-19 pandemic by tracking their opinions on Twitter.The existing random forest model trained the data faster but failed to predict the results faster.Therefore, an ensemble model is proposed to predict an outcome using a distinct modeling algorithm.An ensemble classifier has been used for enhancing the performances using the base learning classifiers such as Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR) form an Ensemble classifier.The proposed ensemble model aggregates each base model for the prediction and results for the unseen data.These tokens are then passed to the Ensemble classifier that classifies the sentiments and assigns a polarity (positive, negative, neutral) to every tweet.The proposed Ensemble method improve the average prediction performance over any contributing member in the ensemble.The results obtained by the proposed Ensemble model reached accuracy of 97.47 % when compared to the existing models such as Deep LSTM, SVM model that obtained accuracy of 83 %, 84.46 %.
Drone Detection Using Image Processing Based on Deep Learning

The objective of this experimental research is to identify solutions to detect drones using computer vision algorithm. Nowadays danger of drones operating near airports and other important sites is of utmost importance. The proposed techniques resolution pictures with a good rate of detection. The technique is using information concerning movement patterns of drones.&#x0D;
How is FOLIO different from its predecessors?

FOLIO is a future-oriented library service platform. Enlightened by the results of the 2021 International Survey of Library Automation, the author shared thoughts on how FOLIO meets librarians’ expectations, why it’s a good time to get involved in the FOLIO project and what challenges FOLIO is facing at the current development stage.
L2 Motivation

Globalization has led to the spread of English, which has become the world’s international language. As a result of its importance in social, economic, and political spheres, many countries have introduced English language learning in their school curricula. However, despite its importance many learners still seem to struggle with English second language (L2) motivation, which is an important element of L2 learning success. The aim of this study was to analyse the relationship between several phases of learners’ motivation, including learners’ attributions of past English L2 learning success, current motivational characteristics, and motivation to learn English in the future. Using the L2 Motivational Self System (L2MSS) as a framework, it was found that past learning experiences did not have a significant effect on current learner motivational dispositions. However, elements of the L2MSS had an effect on learners’ intentions to exert effort into future English language learning. In other words, Croatian university students’ L2 motivation was based on their visions for the future rather than on past learning experiences. Moreover, it appears that the ideal L2 self plays a much stronger role in learners’ motivation compared to the ought-to L2 self, and this motivation is tied to utilitarian motives for learning English. In addition, the results showed that other motivational components, such as interest, as well as L2 anxiety, are important elements of learners’ motivation, suggesting cognitive as well as affective aspects in this motivational profile. The complexity of motivation as shown in this study suggests the need for further investigations in various contexts.
System for Fusion of Face and Speech Modalities Using DTCWT+QFT and MFCC+RASTA Techniques

Objectives:The main objective is to propose a multimodal biometric system by forming a fusion of Face and Speech modalities using DTCWT+QFT techniques for face and MFCC+RASTA Techniques for Speech recognitions.The experimental results are compared with existing works and analysed the performance with counterparts.Methods: The proposed model, make use of DTCWT and QFT techniques to extract the features of face images and perform fusion of both.The MFCC and RASTA techniques are implemented to extract features of speech data and then fusion is applied.Various databases discussed and utilized for both face and speech recognition system proposed.Findings: The results of experimentation are compared with existing systems and analysis proved than the proposed system is placed in better position.The fusion of DTCWT and QFT techniques for face recognition system is implemented and the results using performance parameters such as False Acceptation Ratio (FAR), False Rejection Ratio (FRR), Total Success Rate (TSR), Partial Error Rate (PER), Equal Error Rate (EER) are tabulated for six different types of face data sets.The average performance of the results is compared with four existing fusion techniques and showed that the proposed system performs better.The fusion of MFCC and RASTA techniques for speech recognition system is implemented and the performance is measured by calculating accuracy, precision, recall and F1-score.These results are compared with five different schemes and proved that proposed system of fusion of face and speech traits works better for human recognitions.Novelty: Fusion of two algorithms for face recognition is implemented and the results analysed.Then the fusion of two algorithms for speech recognition is implemented and the results are analysed.The novel approach is presented to combine both face and speech recognition system in to single system to improve the security using multimodal biometrics.
Examining the Content Validity of Android-Based Augmented Reality Media for Chemical Bonding using Rasch Model

21st-century learning presents chemistry teachers with new challenges in teaching abstract chemistry concepts with various technologies that continue to develop. One of the media that can visualize abstract chemical concepts is Augmented Reality (AR). AR media developed in chemistry learning needs to be valid so that it can be used properly. Therefore, this study aims to examine the validity of the Android-based AR media developed. Four education practitioners carried out the validation as experts, and 13 students participated as respondents. The instrument used is a questionnaire with a Likert scale. The data obtained were analyzed quantitatively with the Rasch model using the facet software. The results of the analysis show that the Exact Agreements are 41.10%, and the Expected Agreements are 42.20%. The most challenging aspect to achieve is media design, while profit is the most easily agreed-upon aspect. Based on this assessment, in the development of augmented reality media, it is necessary to pay attention to display aspects and 3D objects suitable for viewing a material so that it is easier for users to understand the material.
Mechatronic Anti-Collision System for Electric Wheelchairs Based on 2D LiDAR Laser Scan

Electric wheelchairs make it easier for disabled and elderly people to live, move, interact, and participate in society. Moving a wheelchair in open spaces is relatively easy, but in closed and small spaces, maneuvering is difficult. Solutions to such problems for people with disabilities are applicable to a relatively small group of recipients and are mostly custom-made solutions, whose considerable cost is a significant barrier to accessibility. New technologies can provide an opportunity to improve the quality of life of people with disabilities in this aspect. Using selected elements of complex automation and control systems, cost-effective solutions can be created that facilitate the functioning of people with disabilities. This paper presents an analysis of hazards and problems when maneuvering a wheelchair in narrow passageways, as well as the authors’ solution to this problem, and the concept and assumptions of a mechatronic anti-collision system based on 2D LiDAR laser scanners. This solution is composed of a proprietary 2D rotating scanner mechanism that ensures the acquisition of 3D images of the environment around the wheelchair. Preliminary tests of this solution yielded promising results. Further research will include miniaturization of the device.
Assessing the Role of Social Media in the Fight Against Sexual Assault

We examine the challenges faced by women for effective communication on social media. The majority of the data used in this study was primary, and it was gathered in the field using a questionnaire. The questionnaire utilized in this study was a written list of questions that had been carefully designed and selected to collect data. The questions were closed-ended and focused on women's issues and their use of social media. Cronbach alpha was used to test the dataset's validity, consistency, and reliability based on the obtained information. Cronbach's Alpha for all constructs or items was more significant than the minimum acceptable reliability coefficient of 0.5 to 0.9, indicating strong internal consistency. Furthermore, Cronbach's Alpha for all constructs was more than the minimal permissible reliability coefficient, indicating that they were valid and trustworthy and hence suitable for use. We notice that most respondents in this study reported that they had experienced social media harassment. Our findings show, in particular, that the majority of respondents in this study believe that women are usually overlooked when it comes to matters requiring social media opinions. We opined that information, education, and communication are empowering tools from the preceding. It is the challenge of women in leadership to dismantle the obstacles in the women's use of information media. No form of women empowerment will succeed until these challenges are met.
Studying the Evolution of Neural Activation Patterns During Training of Feed-Forward ReLU Networks

The ability of deep neural networks to form powerful emergent representations of complex statistical patterns in data is as remarkable as imperfectly understood. For deep ReLU networks, these are encoded in the mixed discrete-continuous structure of linear weight matrices and non-linear binary activations. Our article develops a new technique for instrumenting such networks to efficiently record activation statistics, such as information content (entropy) and similarity of patterns, in real-world training runs. We then study the evolution of activation patterns during training for networks of different architecture using different training and initialization strategies. As a result, we see characteristic- and general-related as well as architecture-related behavioral patterns: in particular, most architectures form bottom-up structure, with the exception of highly tuned state-of-the-art architectures and methods (PyramidNet and FixUp), where layers appear to converge more simultaneously. We also observe intermediate dips in entropy in conventional CNNs that are not visible in residual networks. A reference implementation is provided under a free license.
Efficient Task Scheduling and Fair Load Distribution Among Federated Clouds

The federated cloud is the future generation of cloud computing, allowing sharing of computing and storage resources, and servicing of user tasks among cloud providers through a centralized control mechanism. However, a great challenge lies in the efficient management of such federated clouds and fair distribution of the load among heterogeneous cloud providers. In our proposed approach, called QPFS_MASG, at the federated cloud level, the incoming tasks queue are partitioned in order to achieve a fair distribution of the load among all cloud providers of the federated cloud. Then, at the cloud level, task scheduling using the Modified Activity Selection by Greedy (MASG) technique assigns the tasks to different virtual machines (VMs), considering the task deadline as the key factor in achieving good quality of service (QoS). The proposed approach takes care of servicing tasks within their deadline, reducing service level agreement (SLA) violations, improving the response time of user tasks as well as achieving fair distribution of the load among all participating cloud providers. The QPFS_MASG was implemented using CloudSim and the evaluation result revealed a guaranteed degree of fairness in service distribution among the cloud providers with reduced response time and SLA violations compared to existing approaches. Also, the evaluation results showed that the proposed approach serviced the user tasks with minimum number of VMs.