Reviewerly/exHarmony huggingface dataset:
Monitoring Of Information Events For The Socio-Cultural Background Analysis During Epidemics

The article is devoted to the study of the issues of forecasting the impact of epidemics and epidemiological informing measures on the psychological state of society by monitoring information events and analyzing the sociocultural background of the population. The aim of the work is to analyze and develop methods of content analysis for a system for monitoring the psychological state of society on information reactions on the Internet, including methods for sentiment analysis of user comments in social networks, using an associative-ontological approach to form a sample of analyzed comments in one thematic area. As a result of the study a monitoring system was developed, built on a microservice architecture and including an information event monitoring server, containing a data collection platform from the Internet, sentiment analysis linguistic services, an ElasticSearch database that stores processed texts in an indexed and searchable form, researcher's workplace with a web interface for setting up tasks and viewing the results of work. The system developed provides additional indicators to refine new models for substantiating epidemiological awareness programs for the population in large metropolitan areas connected by passenger flows of different intensity, as well as the ability to predict the impact of epidemics and epidemiological awareness activities on the psychological state of society by monitoring information events and analyzing the sociocultural background of the population.
Building And Analysing A Skills Graph Using Data From Job Portals

In this paper, the authors consider the process of building and analyzing a skills graph based on vacancies data from the job search portal. The connection between vacancies and skills is identified and formalized as labor market and employers' requirements to employee's qualification in the intellectual educational ecosystem model. The authors describe the collector program which can fetch vacancies data from job search sites with an example of site HeadHunter. Jupyter Notebook interactive notebooks and different Python-based libraries, such as Pandas and Numpy, are used for data processing. As a result, a skills graph is built and stored in the neo4j graph database platform. With the help of the Gephi application, the next properties of this graph are determined: degree distribution, the eigenvector centrality, closeness centrality, betweenness centrality, PageRank coefficient, and modularity. The graph is drawn using the ForceAtlas2 layout algorithm. The modularity coefficient for the vertices is used to find the main clusters of skills. The number of such skills clusters is almost the same as the count of specialties on the example job search portal. It results in the ability to determine specialty skillsets and interspecialty skills using vacancies data from job search sites.
Sign2Speech: A Novel Sign Language to Speech Synthesis Pipeline

The lack of assistive Sign Language technologies for members of the Deaf community has impeded their access to public information, and curtailed their civil rights and social inclusion. In this paper, we introduce a novel proof-of-concept method for end-to-end Sign Language to speech translation without an intermediate text representation.We propose an LSTM-based method to generate speech from hand pose, where the latter can be obtained from applying an off-the-shelf pose predictor to fingerspelling videos. We train our model using a custom dataset of synthetically generated signs annotated with speech labels, and test on a real-world dataset of fingerspelling signs. Our generated output resembles real-world data sufficiently on quantitative measurements. This indicates that our techniques can be used to generate speech from signs, without reliance on text. The use of synthetic datasets further reduces the reliance on real-world, annotated data. However, results can be further improved using hybrid datasets, combining real-world and synthetic data. Our code and datasets are available at https://github.com/DanBigioi/Sign2Speech.
Real-time sign language recognition system

One of the more natural ways of interaction between the machine and human is offered by Human gestures which are useful for sign language recognition (SLR). Sign language is the speaking tongue of a segment of people who are known as Deaf people. they are the most people who benefit from the (SLR) by the Human-Computer Interaction (HCI).Most natural people cannot understand these kinds of languages, so Deaf people fail to communicate with them without this helper which achieves by the (HCI). This study introduced a proposed system that offered SLR in real-time for some gestures from the American sign language (ASL), by using one of the most suitable deep learning-based architectures that were called Convolutional neural networks (CNN) and choosing the Squeezenet module After comparing it with a traditional machine learning system that relied on extracting [HOG] features. Squeezenet presented the best result in accuracy reached to (100%) in the off-time testing and about (97.5%) in the real-time with a competition time of about 3.3sec for capturing the image, predicting it, and converting it to a text and spoken sentence.The system achieved this result without making any preprocessing for the image, which gave it simplicity and low computing time.
Research on image content description in Chinese based on fusion of image global and local features

Most image content modelling methods are designed for English description which is different form Chinese in syntax structure. The few existing Chinese image description models do not fully integrate the global features and the local features of an image, limiting the capability of the models to represent the details of the image. In this paper, an encoder-decoder architecture based on the fusion of global and local features is used to describe the Chinese image content. In the encoding stage, the global and local features of the image are extracted by the Convolutional Neural Network (CNN) and the target detection network, and fed to the feature fusion module. In the decoding stage, an image feature attention mechanism is used to calculate the weights of word vectors, and a new gating mechanism is added to the traditional Long Short-Term Memory (LSTM) network to emphasize the fused image features, and the corresponding word vectors. In the description generation stage, the beam search algorithm is used to optimize the word vector generation process. The integration of global and local features of the image is strengthened to allow the model to fully understand the details of the image through the above three stages. The experimental results show that the model improves the quality of Chinese description of image content. Compared with the baseline model, the score of CIDEr evaluation index improves by 20.07%, and other evaluation indices also improve significantly.
ANALÍTICAS DE APRENDIZAJE EN LA EDUCACIÓN SUPERIOR MEDIANTE LA APLICACIÓN DE HERRAMIENTAS COLABORATIVAS

Using collaborative tools under the virtual modality through learning metrics or analytics allows the synchronous interaction of contents of the subject of design and management of learning environments in higher education students. The research was developed with the parameters of the quantitative methodology, of a non-experimental type with a descriptive level of depth such as the application of monitoring tools as an instrument to 28 university students on the use of collaborative tools under the virtual modality. Regarding the evaluation, the project has an innovative character to verify and contrast the aspects that lead the students to know their state of participation and involvement in the contents addressed in each of the 10 class sessions where the monitoring of activities and know through learning routes and analytics the synchronous connection time in the videoconferencing platform and the interaction with collaborative tools, identifying the types of connected devices, in addition to establishing the degree of compliance in the activities through the percentages executed according to the level learning of each participant. It is concluded that having built and designed collaborative learning routes by the teacher, they allowed to monitor the interaction of the students in the activities proposed synchronously based on the Virtual Environment of the Center for Higher Education and the monitoring metric or analytical tools virtual that turned out to be evaluable parameters to make decisions about the circumstances that some students present in their learning.
HUMAN RESOURCE MANAGEMENT PRACTICES AND KNOWLEDGE MANAGEMENT - A STUDY WITH STRUCTURAL EQUATION MODELLING TECHNIQUE

Human resources and knowledge management are considered to be one of the most important factors within organizations that help them to achieve a competitive advantage. However, organizations should take care of the human factor and increase and take advantage of the knowledge within them. The purpose of this research is to investigate the relationship between human resources management practices and knowledge management process. The study is survey research method as a structured questionnaire is used for data collection. The sample for the study is HR managers and IT employees working in the select organizations in the study. A theoretical model was proposed and tested using structural equation modeling (SEM). The results of the SEM analysis indicated that human resources practices (recruitment methods, team work , training and development, performance appraisals, and reward systems) have a significant influence on Knowledge management process. Keywords: Human resource practices, knowledge management process, SEM Modeling, IT
Prevalence of anaemia among college going students in India: a systematic review

Anaemia an important global health problem, which is categorized as one of the 10 most serious health problems by the World Health Organization. More than two billion people worldwide are estimated to have anaemia, with majority coming from the developing countries. Anaemia affects health of an individual varying from poor scholastic performance and cognitive impairment and even causes maternal mortalities. Percentage prevalence of anaemia among adolescent girls in the age group 15-19 years and in the older age group 20-29 years remains almost stagnant at 55.8% and 56.1% respectively. Young age period is formative years in life of an individual and crucial period for undertaking greater responsibilities including decision for study, to earn livelihood and healthy responsible parenthood. Protocol of study selection were prepared before the start of study and studies conducted in various states of India among college students and published between the year January 2000 to December 2021 were included. We searched Pubmed, Research gate, Google scholar for articles and 24 studies were included as per predesigned protocol. Analysis of reported studies shown high prevalence of anaemia among college students and more among female students. Subjects of most of the conducted studies were medical students. More studies should be conducted among Arts and Science college students. It is essential to conduct studies among college students to give not only awareness, but also warn them about health hazards and consequences affecting their day to day life.
Real-Time Query Method Based on Distributed Database Demand Information

This paper proposes a real-time query method for distributed database requirement information. First, we propose to extract the database information according to the submitted query keyword. Then, we establish a real-time model of distributed database demand information query and a real-time information classification model, using a distributed multivariate hash algorithm model and a query of real-time information. When there is a query keyword, the proposed method directly reads the data above the hash ring based on the keyword to improve query efficiency. Extensive experimental results indicate that our proposed method reduces the number of queries, query pressure, and processing time of information queries in the distributed database. Moreover, the distributed database’s real-time processing rate and amount have increased.
Object Localization Based on a Fast Recurrent Regional Based Network (OL-FR-RBN)

Image recognition includes the categories of object localization, object identification, and image categorization. Despite the fact that these terms are sometimes used interchangeably, object detection and object localization are not the same. Similar to this, categorizing photographs and pinpointing their locations are two separate processes that shouldn't be mixed up. It's important to evaluate how well these models work with data points that haven't been seen before while creating machine learning models for use in the real world. To assess how well different models perform when applied to actual data points, we use an assessment metric. The most popular way for gauging the success of an image localization operation is IoU. "Intersection over Union" is referred to as IoU. In this work, object localization was carried out utilizing a Fast Recurrent Reginal Based Network (OL-FR-RPN). The accuracy and recall of each of the best-matching bounding boxes for the recognised objects in the image are used to measure how good an proposedOL-FR-RPN object recognition model is
Grenade Detection in Complex Environment Based on YOLOv5

The artificial blasting method of dealing with dud bombs has high risks and low efficiency. Based on the target detection technology, the realization of autonomous detonation by an unmanned platform can effectively reduce the risk of human operation and improve the efficiency of detonation. Aiming at the problem of grenade recognition in complex environments, this paper studies the target detection algorithm, analyzes the structure and principle of the algorithm, and finally verifies the feasibility of the algorithm on the self-made grenade data set. The results show that the weight of the YOLOv5 model is small, the inference speed is fast, and the model detection accuracy rate reaches 99.5%, which can effectively meet the needs of practical applications.
UUV Fast Route Planning Algorithm Based on Improved Visual Graph Method

Route planning technology is one of the key technologies of underwater unmanned vehicle (UUV), and it is an important means to ensure UUV safe navigation. UUV operation sea area usually has the characteristics of large range and sparse distribution of environmental constraints. Combined with this characteristic, how to give consideration to the timeliness and optimality of route planning scheme is an important problem to be solved urgently. Aiming at the effectiveness of route planning, the number of vertices to be considered is further reduced by using the geometric characteristics of graph theory, and the traditional visual graph method is improved. Besides, the dynamic guidance strategy is used to give the visual graph method the ability to deal with multiple ordered areas that must be reached, which improves the adaptability of the visual graph method under the coexistence of multiple types of constraint areas. The simulation results show that the improved visual graph method has strong adaptability to the fast route planning problem in multi constraint and large-scale environment.
Research on Knowledge Extraction of Maritime Operational Decision-Making Sentences

This paper details the challenges and explores the technical novelty associated with extracting knowledge of maritime operational decision-making sentences. Due to the specificity of the operational decision-making text, many specialized terms will be included in the operational decision-making sentences. For further extraction, it is necessary to first extract the specialized terms in the operational planning sentences to provide support for subsequent synonym identification. In order to identify the terms with the same meaning in the operational decision document when building the operational decision domain knowledge base, avoid the entity redundancy of the domain knowledge base, and at the same time, when extracting the operational decision sentence relationship, it is possible to identify different description methods of the same entity, it is necessary to perform synonym identification on the extracted operational decision terms.
Semantic Web Services Architecture Design and Service Discovery Research

Registry Center Structure and Peer to Peer Structure are two leading Web Services Architectures. But neither of them meets the requirements to construct large-scale and complex systems for modern enterprises, nor can they eliminate fuzziness and ambiguity of Web Services description due to the lack of necessary semantic information. In order to solve these problems, this paper proposes a semantic Web Services Architecture based on structured Hierarchical Overlay Network, describes the service discovery process of the architecture, studies Web Services functional matching algorithm based on OWL-S description. Finally, it takes online shopping as a case to verify the feasibility of the method.
AN IMPROVED ARTIFICIAL ATOM ALGORITHM WITH THE OPERATOR OF SHUFFLED FROG LEAPING ALGORITHM

Artificial Atom Algorithm is an optimization technique that developed inspired by nature. This algorithm used for both continues problems and discrete problems in previous studies. In this study, an arrangement that would increase the success of this algorithm was envisaged. For this purpose, the ionic bond function of Artificial Atom Algorithm has been improved benefiting an algorithmic step of Shuffled Frog Leaping Algorithm. As a result of the updates, the search space was narrowed for the ionic bond operator. Thus, the state of getting away from the solution in each iteration was prevented. The success of Improved Artificial Atom Algorithm was tested with benchmark functions. Experimental results for the proposed method were interpreted comparatively.
Exploration of Image Inpainting approaches and challenges: A Survey

The process of restoring missing areas of an image is referred to as "image inpainting."It is a significant challenge in the field of computer vision and a crucial feature that is utilized in a wide variety of image and graphics programs.Although image inpainting, also known as the art of repairing old and worn images, has been around for a couple of years, it has recently gained even more popularity due to recent developments in image processing techniques.Image inpainting can be thought of as the art of restoring old and worn images.Automatic image inpainting has become an important and challenging area of research in image processing due to the advancement of tools for image processing and the flexibility of digital image editing.Automatic image inpainting has found important applications in computer vision and is also becoming an important image processing application.Due to its high significance and effectiveness in a variety of image processing applications such as, for example, object removal, image restoration, manipulation, re-targeting, compositing, and image-based rendering, researchers have studied the image inpainting problem intensively over several decades.The process of eliminating or filling in a missing area in an image is referred to as "image inpainting," It is defined as in-depth knowledge of the image details in terms of its structure and texture.It is considered to be one of the most challenging subjects in image processing.This article presents a survey of most image inpainting techniques and summarizes them, along with comparisons that include the benefits and drawbacks of each method.These comparisons and summaries can assist researchers in evaluating their own proposed techniques against existing ones.
Teaching and learning through platforms during the pandemic: An alternative resource to teaching at the university [Enseñanza y aprendizaje por medio de plataformas durante la pandemia: Un recurso alternativo de impartir clases en la universidad]

Technological innovation in the use of platforms during the pandemic is vital in learning. Therefore, teaching and learning through platforms during the pandemic was investigated. The objective was to know the efficiency of teaching through the use of platforms in the learning of university students. The research is based on the descriptive method with a cross-sectional approach, for which it was questioned what platforms do they use for teaching and learning? Do they understand the topics covered during the classes? And what proposals do you propose to improve teaching through the use of platforms? Once the data for each month was obtained, they were processed using basic statistics. The results determined that in the use of platforms used for teaching, Google Meet stood out with 90%, they understood the teachings through the use of platforms with 90% in June and constant communication as a proposal to improve teaching with 55% in May and June. It is concluded that there was efficiency in the teachings through the use of platforms such as Google Meet and constant communication improves the learning of the university student.
Optimization of Support Vector Machines by Meta-Heuristic Approach for Differential Diagnosis and Classification of Oral Diseases

&lt;p&gt;The dataset has been done at Gilan University of Medical Sciences. It included 210 clinical images of oral mucosa, 154 images of which were related to the three studied lesions: 50 cases of erosive lichen planus and ulcerative, 53 cases of leukoplakia, 51 cases of oral SCC, and 56 images were related to healthy mucosa. The dataset contained 30 features.&lt;/p&gt;
Bibliometric Study and Visualisation of Research Trends in Hybrid Blockchain Technology

This paper is a summary of literature from the Scopus database on hybrid blockchain technology, since 2016. The bibliometric patterns of research trends were visualized using a VOS viewer and the scope for future research was discussed. The most active authors, countries, and organizations were identified. The most active author was Byun C and the most active country was China. The most active research organization was Beijing University of Posts and Telecommunications, China. Future research can be on implementation challenges of hybrid blockchain technology and finding newer horizons for application. This paper highlights the domination of China, Chinese authors, and Chinese research organizations on research related to hybrid blockchain technology.