Reviewerly/exHarmony huggingface dataset:
Machine-Type Video Communication Using Pretrained Network for Internet of Things

With the increasing demand for internet of things (IoT) applications, machine-type video communications have become an indispensable means of communication. It is changing the way we live and work. In machine-type video communications, the quality and delay of the video transmission should be guaranteed to satisfy the requirements of communication devices at the condition of limited resources. It is necessary to reduce the burden of transmitting video by losing frames at the video sender and then to increase the frame rate of transmitting video at the receiver. In this paper, based on the pretrained network, we proposed a frame rate up-conversion (FRUC) algorithm to guarantee low-latency video transmitting in machine-type video communications. At the IoT node, by periodically discarding the video frames, the video sequences are significantly compressed. At the IoT cloud, a pretrained network is used to extract the feature layers of the transmitted video frames, which is fused into the bidirectional matching to produce the motion vectors (MVs) of the losing frames, and according to the output MVs, the motion-compensated interpolation is implemented to recover the original frame rate of the video sequence. Experimental results show that the proposed FRUC algorithm effectively improve both objective and subjective qualities of the transmitted video sequences.
Quantification of the Spectral Variability of Ore-Bearing Granodiorite under Supervised and Semisupervised Conditions: An Upscaling Approach

Reflectance spectroscopy is a nondestructive, rapid, and easy-to-use technique which can be used to assess the composition of rocks qualitatively or quantitatively. Although it is a powerful tool, it has its limitations especially when it comes to measurements of rocks with a phaneritic texture. The external variability is reflected only in spectroscopy and not in the chemical-mineralogical measurements that are performed on crushed rock in certified laboratories. Hence, the spectral variability of the surface of an uncrushed rock will, in most cases, be higher than the internal chemical-mineralogical variability, which may impair statistical models built on field measurements. For this reason, studying ore-bearing rocks and evaluating their spectral variability in different scales is an important procedure to better understand the factors that may influence the qualitative and quantitative analysis of the rocks. The objectives are to quantify the spectral variability of three types of altered granodiorite using well-established statistical methods with an upscaling approach. With this approach, the samples were measured in the laboratory under supervised ambient conditions and in the field under semisupervised conditions. This study further aims to conclude which statistical method provides the best practical and accurate classification for use in future studies. Our results showed that all statistical methods enable the separation of the rock types, although two types of rocks have exhibited almost identical spectra. Furthermore, the statistical methods that supplied the most significant results for classification purposes were principal component analysis combined with k-nearest neighbor with a classification accuracy for laboratory and field measurements of 68.1% and 100%, respectively.
Exploiting reliable pseudo-labels for unsupervised domain adaptive person re-identification

Person re-identification, getting impressive performance under the single-domain setting, often suffers huge performance drop when deploying to the unseen target domain owing to domain gap. Current research mainly focuses on unsupervised domain adaptation to alleviate the domain gap, and the methods by clustering the target-domain samples have achieved significant results. However, some inaccurate pseudo-labels, i.e., noisy pseudo-labels, may be generated on clustering, which will seriously affect the performance of the model. In order to solve the above problem, we propose a novel unsupervised domain adaptive person re-identification method by exploiting reliable pseudo-labels (RPL) from two aspects, i.e., adaptive dynamic clustering (ADC) and cross-camera similarity evaluation (CCSE). Specifically, firstly, for the methods based on the density-based clustering algorithm, we propose the adaptive dynamic clustering which calculates the clustering radius adaptively and dynamically to obtain more reasonable clustering results in the iterative optimization of the model. Next, for noisy pseudo-labels caused by small inter-class variations under the same camera, we propose the cross-camera similarity evaluation to filter out these noises to further improve the discrimination of the model. Extensive experiments on three publicly available large-scale datasets show that the proposed method can achieve state-of-the-art performance on unsupervised domain adaptation person re-identification.
Reversible Watermarking Using Hyperchaotic System for Secure Image Transmission

In telemedicine applications, the images pertaining to medicines and diseases can undergo tampering and forging, where it increases risk in diagnosis.The reversible watermarking technique is capable to extract the embedded information and retrieve the original images completely from watermarked images.It has some specific features like lower distortion of images and high capacity of payload.To solve such an issue, a reversible watermarking method is proposed to provide security for the medical images using hyperchaotic with Deoxyribonucleic Acid (DNA) sequence.The proposed reversible watermarking method utilized standard images (lena, Barbara, boat and medical images) for encryption and decryption.Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT) are used to compress the cover images and to decompress the stego images.Further, the thresholding with morphological transform is employed for segmentation of Region of Interest (RoI) in secret images.In addition, the encryption and decryption of secret image is done by hyperchaotic system with DNA sequence.Lastly, the embedding and extraction of secret images and cover images from the stego images is performed by reversible watermarking with differential expansion and modulus function.Simulation result shows that the proposed reversible watermarking method achieved Peak Signal to Noise Ratio (PSNR) of 99 dB for secret image construction, which is better compared to other traditional watermarking techniques.
Impact of Organizational Communication Strength on Employee Engagement: The Mediating Role of Perceived Supervisor Support and Moderating Role of Organizational Trust

Companies need employees who are passionate about their job and strive to take organization to height of glory.Owing to high competition in banking sector, it is pertinent to note that banks need to differentiate themselves from competitors. The objective of this study was to examine the relationship of organizational communication strength and employee engagement, with mediating effect of perceived supervisor support and moderating role of organizational trust. Data was collected from a sample 244 employees of banking sector who were through simple random sampling. Cronbach’s alpha was used to test the reliability of the scales used in the study. Correlation and regression analyses were used to analyze the hypothesized relationships. The findings revealed a significant positive relationship between communication strengthand employee’s engagement. Perceived supervisor support mediated the relationship ofcommunication strength and employee’s engagement. Organizational trust moderated the relationship of Perceived supervisor support and employee’s engagement. Organizations should focus on promoting a strong communication system to increase the dedication, vigor and absorption of employees.
COMORBIDADES E ÓBITOS POR COVID-19 NO BRASIL

The new coronavirus outbreak in November 2019 quickly became an international public health emergency. Some studies demonstrate that the poor prognosis of the disease is directly related to the presence of comorbidities, with a risk of death increased by 3.4 times. Thus, the present study aimed to describe the cases of patients with Covid-19 that progressed to death in Brazil, evidencing age, sex, and presence of comorbidities. A retrospective, descriptive study was carried out with secondary data from patients with Covid-19 who progressed to death in Brazil, available on the websites of the health departments of each of the 27 federative units that make up the country. Data were tabulated and frequencies calculated in Microsoft Office Excel® 2007. There were 13,900,091 confirmed cases of Covid-19 until the 59th National Epidemiological Bulletin of 2021, of which 371,678 evolved to death; of them, 61.3% had comorbidities. Among the main comorbidities reported, heart disease (40.1%), diabetes (28.4%), obesity (10.3%), neurological diseases (5.0%), kidney diseases (4.7%) and pneumopathies (4.5%) stand out. In 70% of the cases of patients who died, age equal to or greater than 60 years was observed in males in 55% of the cases. The data obtained corroborate the literature, which indicate a poor prognosis of Covid-19 in patients with one or more comorbidities. Among these, the one that stood out in Brazil during the study period was heart disease.
Using Some Mobile Applications for Teaching Mathematics During COVID-19 Pandemic Through Providing a Suitable Environment

The coronavirus disease 2019 (COVID-19) pandemic and other crises create problems for the normal teaching process in the class. In this case, alternate teaching techniques such as mobile applications are required for continuing the teaching and learning process. This research focuses on teaching mathematics in class 12 by using applications (available on mobiles, laptops, desk tops, iPads etc.), namely Phone, Viber, WhatsApp and Messenger so as to provide an appropriate teaching environment. Class 12 in Gawher Preparatory School (both scientific and literary branches) in Erbil City, Kurdistan Region-Iraq was selected for this study. The sample sizes for both scientific and literary branches during the academic year 2019-2020 were 31 and 53, respectively. Pre and post tests were administrated to check pupils’ achievements. Videos were prepared and uploaded to the teaching groups in Viber and Messenger. Suitable environments for teaching mathematics to class 12 were provided via direct communication, and questions and answers were provided. The results revealed that average scores for pre and post tests for the scientific branch were 20 % and 63.33 %, respectively; while for the literary branch were 17.5 % and 50.83 %, respectively. The mobile applications experimented with have led to the enhancement of the mathematics teaching process outside normal classes and particularly during the COVID-19 lockdown periods and crisis. Also, COVID-19 lockdowns decreased noise pollution, water consumption, production of wastewater, generation of solid waste, and expenditures at schools. Additionally, a number of problems, benefits, and recommendations have been outlined; for instance, providing internet and continuous electricity for schools, supplying mobile phones or laptops for pupils with reasonable prices, and running training courses for teachers and pupils are essential.
Development of Precission Farming Hydropoonic Model Based On Internet of Things Using Arduino

Agricultural land in urban areas was needed for community food security. Narrow agricultural land posed obstacles in the implementation of urban agriculture. Hydroponi is a solution for agricultureal development on narrow land. Precesion farming was needed to maintain plant growth. Hydroponic system required regulation of air and water temperature, humidity, water level, pH, water nutrient. This study used SDLC methodology. The result of this research is an automatic hydroponic implementation that can monitor and control an Internet of Things based hydroponic system using Arduino.
Influence of Principals’ Intellectual Stimulation on Students’ Performance at Kenya Certificate of Secondary Education in Public Secondary Schools, Kenya

Transformational leadership is an ideal characteristic which enables the principals to engage with followers and raise consciousness about the significance of specific outcomes and new ways in which those outcomes might be achieved. The purpose of this study was to investigate the influence of principals’ intellectual stimulation on student academic performance in Kenya Certificate Secondary Examination (K.C.S.E) in Makueni County, Kenya. The objective that guided the study was to: assess how the principals’ intellectual stimulation influences students results at Kenya Certificate of Secondary Education. with the hypothesis that there is no relationship between principals’ intellectual stimulation with students’ mean scores at Kenya Certificate of Secondary Education Makueni County, Kenya. The study used transformational leadership theory by Burns (1978) which has four dimensions namely idealized influence, inspirational motivation, intellectual stimulation and individual consideration; all of which are critical in determining academic achievement. The conceptual framework of this study is based on the relationship between the principal transformational with academic performance. The sample comprised of 111 principals, 729 teachers and 12 Ministry of Education officials drawn from 388 secondary schools. Questionnaires and interview guide were used to collect data. Validation of the questionnaires were through test re-test method and use of expert judgment. The coefficient value was 0.85 at alpha = 0.05. Data was analysed using both descriptive Pearson’s correlation coefficient that test showed a strong correlation for Intellectual stimulation and KCSE students’ mean scores. The result indicated a negative and strong correlation between intellection stimulation and students’ KCSE performance (r=-.195, p-value&lt;0.05) respectively. It was concluded that principals’ collective sense of mission and valuing ideas of the followers increased performance. The following recommendations were made: KEMI to carry out transformational leadership seminars for the principals on intellectual practices. The Ministry of Education Policy makers should establish policies on transformational leadership. Teacher trainers especially the universities ought to review curriculum to include analysis of school managers and also have simulated situations.
DESIGN OF INDOOR ROBOT PROTOTYPE GUIDED BY RFID BASED POSITIONING AND NAVIGATION SYSTEM

Abstract. In this study, a robot prototype was designed for indoor spaces guided by an RFID-based positioning and navigation system. First, the work area was prepared from cardboard material and RFID cards were placed at predetermined points in the work area. The unique ID number of each RFID card was defined and the coordinates of their location in the work area are known. The RFID reader in the robot prototype reads from less than 5 cm. With a basic approach, when the robot reads an RFID card that it passes over while in motion, the position of the robot is considered the same as the position of the card it is currently reading. The route is defined for the robot prototype whose location is known before starting the movement. When the robot reads a new RFID card during movement, it must move forward or turn left or right to reach the point where the next RFID card is located according to the route. This decision was predetermined and defined according to its location. Alphabot was used as the prototype. Arduino board and additional auxiliary sensors such as gyro sensor, speed sensors, distance sensors are placed on the prototype. The prototype robot is left at any point in the work area and arrives at a target point determined by the user. The required road route to reach the destination is calculated with the shortest path algorithm depending on the road network on the working area and the route is defined. Thus, it is ensured that the prototype reaches the target without any external intervention by the user other than target determination.
Estimation of infection risk using symptoms of COVID-19: an approach based on fuzzy expert system

According to the published reports and studies, the symptoms of the disease caused by the COVID-19 virus have not yet been fully determined. It is a major stress on clinicians to make a correct and consistent decision about whether to apply the test or not, as many factors with extreme uncertainty need to be evaluated at once. In this study, it is aimed to provide assistance to the clinicians by processing the data using fuzzy logic based decision support system at the time of the decision-making process. In the designed fuzzy logic based decision support system, a fuzzy rule-base was created with linguistic information by interpreting the symptoms that are naturally uncertain by experts. With the help of the obtained fuzzy rule base, the input data of symptoms will be processed and the risk of a person being infected will be obtained as an output. As the results of the estimation module constructed with the existing parameters are examined, it is observed to be compatible with the data published before. In this context, a data set with 50 different patients were designed randomly to evaluate the system. For the analysis of the nonlinear mapping obtained with the Mamdani type fuzzy inference system, random test data is used and infection risk at rates varying between 12.5-83% was determined. The fuzzy logic based decision support system for COVID-19 can be accepted as applicable, flexible, and trustworthy for clinicians. It can be said that this system is not only suitable for COVID-19 but also applicable for future epidemics.
The Missions of the Regional Pedagogical University: Post-Non-Classical Understanding

Pedagogical universities are in the area of great attention from the society and the state because of their increasing role in forming the human capital of the territories. The purpose of this article is to explain the necessity and possibility of creating a mission and strategy for the development of pedagogical universities in (macro) regions on the basis of a post-non-classical understanding of pedagogical education. Methodology and methods: to construct the mission of the pedagogical university, claiming to play an active role in the ecosystem of education in the region, the post-non-classical methodology was used in understanding pedagogical education as an open, system-synergetic approach (ecosystem as its kind), as well as the typical of the post-non-classical methodology, methods of reconstructing the experience of designing an open pedagogical education in a heterogeneous (academic and teaching) professional community. Research results: three missions of the regional pedagogical university are characterized: education, research and impact on society, from the standpoint of the ecosystem approach and the concept of an entrepreneurial university. A pedagogical university acts as a key element of the ecosystem if it demonstrates an entrepreneurial type of behavior, overcomes resource constraints due to the dominance of the third mission over education and research, subordinating them to the task of creating a cohort of agents of change – vanguard groups of future a working teachers in the territory. Conclusion: the missions of the pedagogical university should be subordinated to the influence on the entire pedagogical corps of the region by increasing the density of connections in educational and professional communities.
User-Centric Adaptive Password Policies to Combat Password Fatigue

Today, online users will have an average of 25 password-protected accounts online, yet use, on average, 6.5 passwords. The excessive cognitive burden of remembering large amounts of passwords causes Password Fatigue. Therefore users tend to reuse passwords or recycle password patterns whenever prompted to change their passwords regularly. Researchers have created Adaptive Password Policies to prevent users from creating new passwords similar to previously created ones. However, this approach creates user frustration as it neglects users’ cognitive burden. This paper proposes a novel User-Centric Adaptive Password Policy (UCAPP) Framework for password creation and management that assigns users system-generated passwords based on a cognitive-behavioural agent-based model. The framework comprises a Password Policy Assignment Test (PassPAST), a Cognitive Burden Scale (CBS), a User Profiling Algorithm, and a Password Generator (PassGEN). The framework creates tailor-made password policies that maintain password memorability for users of different cognitive thresholds without sacrificing password strength and entropy. The framework successfully created 30-40% stronger passwords for Critical users and random (non-mnemonic) passwords for Typical users based on each individual’s cognitive password thresholds in a preliminary test.
A Proposal of Parameter Extension for Multilanguage in 5G CBS

This paper is the result of studying the parameters to be expanded to support multilanguage in 5G CBS (Cell Broadcasting Service) service and studying the multilanguage support method. We examine protocols and interfaces from the system that issues CBS message orders to transmission networks and terminals, and also examine IE (Information Element) and language coding methods related to multilanguage services. In addition, the 30 seconds rule and segmentation issue are investigated, which are obstacles to CBS multilanguage service. And an operating policy for multilanguage service is proposed in conclusion.
NeuralDoc-Automating Code Translation Using Machine Learning

Source code documentation is the process of writing concise, natural language descriptions of how the source code behaves during run time. In this work, we propose a novel approach called NeuralDoc, for automating source code documentation using machine learning techniques. We model automatic code documentation as a language translation task, where the source code serves as the input sequence, which is translated by the machine learning model to natural language sentences depicting the functionality of the program. The machine learning model that we use is the Transformer, which leverages the self-attention and multi-headed attention features to effectively capture long-range dependencies and has been shown to perform well on a range of natural language processing tasks. We integrate the copy attention mechanism and incorporate the use of BERT, which is a pre-training technique into the basic Transformer architecture to create a novel approach for automating code documentation. We build an intuitive interface for users to interact with our models and deploy our system as a web application. We carry out experiments on two datasets consisting of Java and Python source programs and their documentation, to demonstrate the effectiveness of our proposed method.