Reviewerly/exHarmony huggingface dataset:
Empirical Analysis Of The Effect Of Foreign Direct Investment On Health Status In Sub-Saharan Africa: What Role For Governance?

Purpose of the study: This study aims to empirically examine how governance affects FDI's impact on the health of inhabitants in Sub-Saharan Africa as indicated by the region's infant and child mortality rates. Methodology: The econometric approach is employed in this study. A dynamic panel model is used for a sample of 44 Sub-Saharan African nations encompassing 2002 to 2019 to produce empirical results. The Generalized Method of Moments (GMM) approach is utilized to accomplish the goals per the research and data established. Main Findings: According to the study's results, foreign direct investment (FDI) considerably improves health by lowering baby and child mortality rates. The findings of this study also indicate that better governance, notably the prevention of corruption, has a positive impact on FDI. Research limitation: The lack of data on a long series is the study's primary limitation. Future studies can concentrate on examining the role of governance in the long-term relationship between FDI and health, with a focus on the marginal impact of the two factors' interplay on health. As a result, it would be important to examine how sectoral FDI affects health outcomes in order to identify the primary pathways by which FDI can reliably affect population health. Novelty/Originality of the study: This study is unique from other studies in that it suggests that efforts must be taken to fully capitalize on the benefits of FDI. In order to reach health goals, policymakers must use sound ways to enhance governance indicators in order to attract FDI further. Additionally, the quality of governance is a high key influencer of the characteristics that attract investors.
Formation of Readiness of Future Teachers of Vocational Training to Professional Self-Development by Means of Creative Competition

The relevance of the article is due to the need to train specialists capable of productive work in the conditions of accelerated innovative development, the transition to the use of technologies and equipment of a higher level. This determines the problem of readiness formation for professional self-development, which is especially significant for vocational training teachers who are directly involved in the reproduction of the labor force and are capable of carrying out engineering and technical activities at enterprises and organizations in various sectors of the economy. The authors pointed to the insufficient degree of development of the problem of formation of readiness for professional self-development among future teachers of vocational training, taking into account the multifunctional nature of their work, which is due to the requirements of educational standards. This determined the purpose of the article to identify the essence of professional self-development of a teacher of vocational training and the features of its formation by means of a creative competition. Methods of analysis, synthesis, generalization, systematization, study of pedagogical experience, conversations and observations, as well as leading positions of system and project methodological approaches were used to conduct the research. The author’s definition of professional self-development of the future teacher of vocational training is presented. The potential possibilities of creative competitions in solving the problem of forming the readiness of students of the direction of training “Professional training (by industry)” for professional self-development based on project planning, which underlies the content of creative competitions and corresponds to the nature of the activity of a teacher of vocational training, are indicated. The experience of preparing future teachers of vocational training at the Lugansk State Pedagogical University for participation in creative competitions is characterized. A systematized list of competencies formed by students at different stages of preparation and participation in creative competitions is presented. Ways of organically combining the possibilities of educational and extracurricular processes in the preparation of future teachers of vocational training for participation in creative competitions and the development of the content of such training, which in general will contribute to the formation of students’ readiness for professional self-development, are proposed. Keywords: professional self-development, vocational training teacher, professional and pedagogical activity, readiness for professional self-development of future vocational training teachers, creative competition, project method
On the complexity of quantum link prediction in complex networks

Link prediction methods use patterns in known network data to infer which connections may be missing. Previous work has shown that continuous-time quantum walks can be used to represent path-based link prediction, which we further study here to develop a more optimized quantum algorithm. Using a sampling framework for link prediction, we analyze the query access to the input network required to produce a certain number of prediction samples. Considering both well-known classical path-based algorithms using powers of the adjacency matrix as well as our proposed quantum algorithm for path-based link prediction, we argue that there is a polynomial quantum advantage on the dependence on $N$, the number of nodes in the network. We further argue that the complexity of our algorithm, although sub-linear in $N$, is limited by the complexity of performing a quantum simulation of the network's adjacency matrix, which may prove to be an important problem in the development of quantum algorithms for network science in general.
Comprehensive Analysis of Deep Learning-based Human Activity Recognition approaches based on Accuracy

Human Activity Recognition (HAR) is a vital area of Computer Vision.HAR focuses on various activities carried out by humans.Information relative to the human activities is collected by smart sensors and wearable devices.HAR is classified into two categories, e.g.(a) Vision-based, i.e. human activities are captured in form of image and video and (b) Sensor-based, i.e. human activity input can be taken from wearable devices and object tagging techniques.Human activity recognition is an extensive thrust area for Content-based video analysis, Human-machine interaction, animation, healthcare fields.The paper presents a comprehensive analysis of various deep learning-based approaches adopted to implement human activity recognition based on accuracy.It is observed that for the vision-based category the performance of the Depth Camera-based Recurrent Neural Network model is 99.55% accuracy with 12 activities for MSRC-12 datasets and for the sensor-based category, the performance of HAR by Wearable sensors using Deep Neural Network model is 99.93% accuracy with 03 activities for SHO datasets.It is also observed that for Opportunity dataset, InnoHAR: A DNN for complex HAR model gives good performance with 94.6% accuracy along with 18 activities, for PAMAP2 dataset, Multi-input CNN-GRU model gives good performance with 95.27% accuracy along with 12 activities, for WISDM dataset, ConvAE-LSTM model gives good performance with 98.67% accuracy along with 6 activities, and for UCI-HAR dataset, ConvAE-LSTM model gives good performance with 98.14% accuracy along with 6 activities.
Design and Deployment of IoT enabled Blockchain based resilient Supply-chain Management System using Ethereum

With exponential growth of digital consumer purchasing behavior and the increasing implementation of IoT in retail industry, it has become imperative to provide secure, scalable, and manageable data models to the complex interconnected supply chain management systems.Blockchain technology in recent times has huge potential for deployment of secured data framework over a distributed architecture.In this paper, we have proposed a resilient Supply-Chain Management (SCM) system using Ethereum-based blockchain deployment on a distributed computing test-bed.An IoT-based Radio Frequency Identification (RFID) method is used in our use-case prototype for product information tracking at functional nodes (Manufacturers, Distributors, Retailers and Consumers) of SCM implementation.We have implemented Ethereum T M based blockchain using S olidity T M smart contracts with the help of Remix T M Web-based Integrated Development Environment (IDE).A python-based PyAutoGui is used for automation in blockchain deployment at multiple functional nodes distributed across the supply chain.As proof of concept, we have created a test-bed having 28 Oracle virtual machines (VMs) using Linux for successful implementation and validation of our proposed blockchain based SCM system.We have presented comprehensive results showing Ethereum-blockchain containing complete product information, transactions and tracking for secured information access with distributed digital ledger technology on our test-bed implementation.
Handling behavior problems of children with special educational needs based on teacher analysis

Handling of behavior problems in children with special educational needs (CSEN) in the classroom is urgent for the classroom’s conducive atmosphere. Therefore, a review needs to be conducted to determine what steps the teacher may take for coping purposes in handling the behavior problems of CSEN, to determine the predictors for handling the behavior problems of CSEN, to identify the analysis basis for determining teacher predictors, and to figure out the effects of the behavior of CSEN based on the predictors chosen by the teacher. A survey was conducted on 109 teachers of CSEN. This research used a Google Forms questionnaire containing a list of statements to be chosen by teachers as instrument, and analysis was carried out by computing the frequencies at which the teachers chose the statements in percentage and by comparing teachers’ statements on the way they handled behaviors. The results show that the teachers were more inclined toward problem-focused coping (PFC), the predictor chosen was intimacy control, the teacher directed the children to do a task at the time a behaviour problem arose, and in choosing predictors, the teachers would rather calm the children down and give them comfort, making the children calmed.
Enhancement of Blockchain System in Online Transaction by Detecting Attacks using an Intelligent Approach

To maintain the security level in any application, implementing the malicious behavior detection approach is crucial.So, the present research work has intended the central concept of malicious behavior detection in the communication medium.In cloud applications, malicious event detection is a complex task because of the extensive unstructured data.The blockchain-based deep network has been introduced to predict malicious behavior to end these issues.Moreover, the detection-based blockchain model is Recurrent Neural with Serpent Encryption (RNwSE).The unknown or malicious characteristics were detected in the initial phase after the homomorphic serpent encryption model functioned.Moreover, we have implemented the planned work in the python frameworks.The scalability of the developed model has been found in terms of encryption-decryption duration and the exactness score of attack detection.Subsequently, the presented paradigm is compared with recently associated schemes and has earned the most satisfactory outcome as high exactness rate and less processing duration.
The Relationship Between Reading Self-Efficacy and Perceived Use of Reading Strategies Among Saudi EFL Students

This study aimed to explore the relationship between reading self-efficacy and perceived use of reading strategies among Saudi EFL senior high school female students. With a correlational research design, the study data were collected by administrating two questionnaires: The Reader Self-Perception Scale (RSPS) questionnaire adapted from Henk &amp; Melnick (1995), and the Survey of Reading Strategies (SORS) adapted from Li &amp; Wang (2010). The two instruments were administrated to 90 Saudi senior high school female students randomly selected from three different public schools in Riyadh, Saudi Arabia. The data were analyzed using Pearson correlation coefficient and regression analysis to determine the relationship between self-efficacy and EFL learners’ perceived use of reading strategies. The study findings revealed how Saudi senior high school students possessed more low and moderate levels than high levels of reading self-efficacy, and that metacognitive processes were the most frequently used strategies among cognitive and compensation strategies. In addition, a positive relationship between students’ reading self-efficacy and perceived use of reading strategies was detected. Pedagogical implications of the study findings and its recommendations were presented and discussed.
THE STUDY OF FINE MOTOR SKILLS DEVELOPMENT IN YOUNGER SCHOOLCHILDREN IN CONDITIONS OF DISTANCE LEARNING

The article studies fine motor skills development in younger schoolchildren during distance learning with the use of digital educational technologies. The purpose of the study is to identify the level of fine motor skills development in younger students in conditions of distance learning. Testing of younger schoolchildren was carried out. The study has revealed the insufficient formation of individual components of fine motor skills in younger schoolchildren during distance learning. The authors have developed some practical solutions of this problem.
Applying BERT for Early-Stage Recognition of Persistence in Chat-Based Social Engineering Attacks

Chat-based social engineering (CSE) attacks are attracting increasing attention in the Small-Medium Enterprise (SME) environment, given the ease and potential impact of such an attack. During a CSE attack, malicious users will repeatedly use linguistic tricks to eventually deceive their victims. Thus, to protect SME users, it would be beneficial to have a cyber-defense mechanism able to detect persistent interlocutors who repeatedly bring up critical topics that could lead to sensitive data exposure. We build a natural language processing model, called CSE-PersistenceBERT, for paraphrase detection to recognize persistency as a social engineering attacker’s behavior during a chat-based dialogue. The CSE-PersistenceBERT model consists of a pre-trained BERT model fine-tuned using our handcrafted CSE-Persistence corpus; a corpus appropriately annotated for the specific downstream task of paraphrase recognition. The model identifies the linguistic relationship between the sentences uttered during the dialogue and exposes the malicious intent of the attacker. The results are satisfactory and prove the efficiency of CSE-PersistenceBERT as a recognition mechanism of a social engineer’s persistent behavior during a CSE attack.
Olympiad in informatics and information technologies for applicants of Yaroslavl State Pedagogical University

The article describes a new type of Olympiad in informatics, its goals and objectives, as well as ways to achieve these goals. Examples of author's tasks are given both to test the knowledge of school informatics and knowledge of information technologies, and to test the logical and algorithmic thinking of the participants in the Olympiad. Special attention in the article is paid to the principles of selecting types of tasks in information technologies for the correspondence and intramural rounds of the Olympiad, the construction of the tasks themselves, and the features of their verification. The authors discuss their experience in conducting such Olympiads and give some recommendations for conducting both correspondence and intramural rounds of the Olympiad for applicants. The material will be useful to the organizers of similar Olympiads, as well as informatics teachers, methodologists, schoolchildren and students.
Multi-Feature Fusion Event Argument Entity Recognition Method for Industrial Robot Fault Diagnosis

The advance of knowledge graphs can bring tangible benefits to the fault detection of industrial robots. However, the construction of the KG for industrial robot fault detection is still in its infancy. In this paper, we propose a top-down approach to constructing a knowledge graph from robot fault logs. We define the event argument classes for fault phenomena and fault cause events as well as their relationship. Then, we develop the event logic ontology model. In order to construct the event logic knowledge extraction dataset, the ontology is used to label the entity and relationship of the fault detection event argument in the corpus. Additionally, due to the small size of the corpus, many professional terms, and sparse entities, a model for recognizing entities for robot fault detection is proposed. The accuracy of the entity boundary determination of the model is improved by combining multiple text features and using the relationship information. Compared with other methods, this method can significantly improve the performance of entity recognition of the dataset.
Geographic Information System for Mapping Agricultural Land in North Samarinda District

This research is motivated by the unavailability of an information system that facilitates data processing of agricultural land for rice commodities at the Agricultural Extension Center of Suluh Manuntung Lempake and the delivery of information that is easily accessible in the form of a web regarding the mapping of agricultural land and land data for rice commodities. North Samarinda sub-district is the center of food crop agriculture and the most cultivated by the community is rice, which has the largest rice harvest area compared to other sub-districts. Ease of access to that information can be presented in the form of a Geographic Information System or GIS (Geographic Information System). GIS is a system designed to capture, store, manipulate, analyze, organize and display all types of geographic data. The results of this study are to build an information system application to facilitate data processing and delivery of information regarding agricultural land mapping as well as farmer data information and farmer group harvest data in North Samarinda District, which is presented in the form of WebGIS mapping of agricultural land for rice commodities, for public knowledge. That the District of North Samarinda has the potential to produce plants that are beneficial to the community.
Retracted: Hand Gesture of Recognition Pattern Analysis by Image Treatment Techniques

[This retracts the article DOI: 10.1155/2022/1905151.].
Transformer-based Natural Language Understanding and Generation

Facilitating the sharing of information between the two complementary tasks of Natural Language Understanding(NLU) and Natural Language Generation(NLG) is crucial to the study of Natural Language Processing(NLP). NLU extracts the core semantics from a given utterance, while NLG, in contrast, aims to construct the corresponding sentence based on the given semantics. However, model training for both research topics relies on manually annotated data, but the complexity of the annotation process involved makes it costly to acquire manually annotated data on a large scale. Also, in the existing research, few scholars have treated NLU and NLG as dual tasks. Indeed, both NLG and NLU can be approached as translation problems: NLU translates natural language into formal representations, while NLG converts formal representations into natural language. In this paper, we propose a Transformer-based Natural Language Understanding and Generation (T-NLU&G) model that jointly model NLU and NLG by introducing a shared latent variable. The model can help us explore the intrinsic connection between the natural language space and the formal representation space, and use this latent variable to facilitate information sharing between the two spaces. Experiment shows that our model achieves performance gains on both the E2E dataset and the Weather dataset, validates the feasibility and effectiveness of performance gains for the respective tasks via the T-NLU&G model, and is competitive with current state-of-the-art methods.
Grammatical and Mechanical Error Analysis as Tool in Writing Improvement Program for Student Journalists

This study aimed to develop a writing improvement program for student journalists in one elementary school in the Philippines. It utilized a case study approach using errors analysis in English written tasks of the target participants. Written tasks were used as main instrument in this study. A total of twelve (12) student journalists who were chosen purposively served as participants. Results show that subject-verb agreement is the most committed error followed by usage of a preposition, tenses, word order, and the last on the ranks involved pronouns. Capitalization ranked first among the three common mechanical errors identified in this study followed by punctuation and spelling Based on these results, a writing improvement program for student journalist was developed. Through this study, root causes of participants’ poor writing were analyzed. While through the study’s output, student-journalists may develop a reasonably good writing skill in English language.
Cybersecurity governance: A scoping review

Purpose: Security measures have become increasingly important due to the expansion of the cyber environments. National and international entities are exposing themselves to cybersecurity risks, and they are growing in number every day.&#x0D; Theoretical Framework: With a comprehensive cybersecurity plan, threats can be eliminated. Implementing this plan is possible by involving all stakeholders in the management processes because the idea of management is insufficient. To ensure cybersecurity, this study highlights the significance of cybersecurity and cybergovernance in the digital world.&#x0D; Design: The study findings and recommendations for cybersecurity governance were reviewed. A scoping review research model was used for this purpose.&#x0D; Findings:A basic and documentary research model related to research philosophy were developed for the application technique. The scope of the research includes publications from Scopus. Studies from the last ten years were downloaded using the selected keywords.&#x0D; Originality: The results show that despite research that has led to local cybersecurity governance solutions in several countries, a comprehensive governance framework has not yet been established. Instead, there is a hidden conflict over control of this region, not its governance.”