Reviewerly/exHarmony huggingface dataset:
A Fine Grainedresearch Over Human Action Recognition

Human Action Recognition from videos has been an active research is in the computer vision due to its significant applicability in various real-time applications like video retrieval, human-robot interactions, and visual surveillance, etc. Though there are so many surveys over Human action Recognition, they are limited to various constraints like only focusing on the methods in few orientations only. Unlike the earlier ones, this paper provides a detailed survey according to the basic working methodology of Human action recognition system. Initially, a detailed illustration is given about various standard benchmark datasets. Further, following the methodology, the survey is accomplished in two phases, i.e., the survey over feature extraction approaches and the survey over action classification approaches. Further, a fine-grained survey is also accomplished under every phase based on the individual strategies
Search for a measurement standard in the modifications of the first generation AHP for the method of effective projects selection and other fields of science

It is author’s another article in the series of publications to develop the previously formulated core and two fundamental provisions of the method of effective projects selection through different solutions of modification of the method of analysis of hierarchies (AHP) for financial, mathematical and other fields in science. The author paid special attention to improving the accuracy of measurements of matrix, normalized and vector estimates for the development of universal properties of AHP due to the following solutions with different qualities of scientific novelty: introduction of new formulas for calculating matrix estimates with detailed instructions for their application; offers nine different variants of AHP combinations, each including four classifiers (AHPMS-M1.N, AHPMS(AM) - M1.N, FAHPMS-M1.N and AHPDD-M1.N) on the basis of integer and fractional T. Saati 9-point scale with eight measurement intervals. This article presents a volumetric experimental data, which proved the scientific validity of these and previously disclosed solutions having scientific novelty in the direction of improving the accuracy of measurements in the AHP using different modifications of the first generation. The results of the experiment really allowed us to find and prove the validity of applying the measurement standard within science in the form of the 9 proposed combinations. The distinctive features of the reference combination are as follows: fractional scale [0; …;8]+1 in 9 main measurement intervals; when evaluating two equal objects (Ai(j)= Aj(i)), their matrix estimates are equal to units (0+1=1); these new solutions. Thus, the experimentally obtained and confirmed measurement standard from the first generation of AHP modifications is recommended to be used not only in the selection of effective projects, but also in other fields of science, taking into account its universal properties.
Visibility detection of single image in foggy days based on Fourier transform and convolutional neural network

Visibility is a very important meteorological observation factor, the reduction of which will cause inconvenience to traffic, and even cause traffic accidents. It is of great significance to estimate and monitor the visibility. In this paper, a foggy image visibility detection algorithm based on Fourier transform and convolution neural network was proposed. Firstly, Fourier transform was used to obtain the image characteristics in the frequency domain, which can reflect the general law of the image changing with visibility. Secondly, convolutional neural network was established to predict the image visibility. The experimental results show that the network convergence speed can be improved by processing the samples with Fourier transform. The detection accuracy can reach about 74.59%.
VALUE RELATIONS OF PARTICIPANTS - CONDITION OF EFFECTIVE PEDAGOGICAL PARTNERSHIP

Accessibility and openness of information and reference bases with spreading of the distal format of training is determined by the main advantage during both stages of the empirical research. The level of satisfaction is higher than in directions: Information formatting, simplicity of algorithmic submission and clear content of tasks with artistic, productive activity.The article makes it clear that parity through equal accessibility contributes to the harmonization of educational goals and objectives for the formation of valuable attitudes of the participants of the educational process is achieved in a constructive dialog. Mobility of educational interaction in the system "Teacher – pupils, parents of the child". The study confirmed that parity presupposes the use of the information offered and available in the network through educational cooperation. It is important to establish a strong consistent connection for detailed discussion of hot topics, avoiding the diversity of the proposed primary sources, personalization of educational experience gained during the distance execution of specific educational tasks due to individual possibilities (knowledge of language, ability to work with different content, etc.). Parity provides stable open access to the necessary educational information, cognitive activity, which is clearly observed during use of distance learning tools. This reveals the possibility of reflexia as an element of the logical structure of parity of educational interaction. Careful attitude to the position, opinions of each participant of the educational process, equality in the right to express a personal vision of one or another element of the educational and methodical complex will help to form valuable attitudes.
Application of Waterfall model in development of family planning participants information system

The purpose of the family planning program is to improve the welfare of mothers and children, which is the basis for the realization of a prosperous society by controlling birth control as well as controlling population growth. The Department of Population Control and Family Planning in Labuhanbatu Regency has the task of carrying out family planning programs in the Labuhanbatu Regency area whose implementation is assisted by Family Planning Extension Officers in 9 existing sub-districts. District officers record and report family planning data in each sub-district every month. Recording and reporting of family planning data is still done manually. This manual system resulted in the process of submitting reports being delayed due to distance problems between sub-districts and districts. The delay in submitting this report has an impact on the performance of the institution itself. This study aims to overcome these problems with the solution of making a web-based family planning participant data collection information system. The system development method used is the Waterfall model. From the results of the implementation and testing of the system, it is found that the Codeigniter framework can be implemented in a family planning participant data collection information system quickly and easily. Tests using the Blackbox testing method also show valid results. From the results of this study, it can be concluded that to apply the waterfall method in designing and developing a KB participant data information system in Labuhanbatu Regency, it must go through five stages, namely, analyzing system requirements, designing systems using UML, implementing systems with a codeigniter framework, conducting trials with blackbox testing methods, and perform system maintenance. With this system, it is hoped that it can become a recommendation and reference for the Labuhanbatu Regency Population Control and Family Planning Service to implement it so that the problem of delays in reporting family planning data can be handled properly.
Prevent Road Accident using Machine to Machine (M2M) Learning

The number of road casualties is steadily rising, while the age of driverless vehicles on the road is rapidly coming. Machine-to-machine (M2M) communication and the use of Big Data created by M2M communication have enormous promise for improving road safety. A training dataset-less Deep Learning strategy that uses only a safety model and optimizes it sequentially through M2M learning over time can prevent a lack of suitable Knowledge Base while also improving the capacity to handle unpredictable scenarios. The article outlines an M2M learning model based on in-vehicle sensors that can be used to reduce traffic accidents.
SCMT: Self-Correction Mean Teacher for Semi-supervised Object Detection

Semi-Supervised Object Detection (SSOD) aims to improve performance by leveraging a large amount of unlabeled data. Existing works usually adopt the teacher-student framework to enforce student to learn consistent predictions over the pseudo-labels generated by teacher. However, the performance of the student model is limited since the noise inherently exists in pseudo-labels. In this paper, we investigate the causes and effects of noisy pseudo-labels and propose a simple yet effective approach denoted as Self-Correction Mean Teacher(SCMT) to reduce the adverse effects. Specifically, we propose to dynamically re-weight the unsupervised loss of each student's proposal with additional supervision information from the teacher model, and assign smaller loss weights to possible noisy proposals. Extensive experiments on MS-COCO benchmark have shown the superiority of our proposed SCMT, which can significantly improve the supervised baseline by more than 11% mAP under all 1%, 5% and 10% COCO-standard settings, and surpasses state-of-the-art methods by about 1.5% mAP. Even under the challenging COCO-additional setting, SCMT still improves the supervised baseline by 4.9% mAP, and significantly outperforms previous methods by 1.2% mAP, achieving a new state-of-the-art performance.
A deep learning-based method for ultra-short-term PV power prediction

Abstract With the rapid development of photovoltaic industry, the application accuracy of photovoltaic power prediction technology in the field of power system control, dispatching and operation is more precise. Due to the volatility and randomness of photovoltaic power in the ultra short term, high-precision prediction has more theoretical and practical significance. This paper presents an ultra short-term photovoltaic power prediction model based on dual attention mechanism and GRU, which realizes the high-precision prediction of photovoltaic power in ultra short term. Firstly, attention mechanism is introduced to realize the dual extraction of temporal and spatial features; Then, the power is predicted by combining the extracted features with the characteristics of GRU long-term memory ability and fast calculation. The time series attention mechanism is introduced to independently extract the information of historical key moments to improve the stability of long-time series prediction effect; The feature attention mechanism effectively calculates the correlation of each meteorological feature quantity, and alters the feature weight. Through the comparison experiment with the baseline model, it is verified that the proposed model has higher prediction accuracy, better generalization ability and robustness.
Others’ Books, Catalogues of our own: non-roman scripts in SBN

The catalogue of the Italian National Library System (SBN) is a roman-script-only database. Local libraries, on the other hand, are facing requests from an increasing number of users from other countries, and are filling the gaps of the national system by developing isolated, non-collaborative ways to manage resources in non-roman scripts. Though a couple of Institutions have recently, but independently each other, developed projects to catalogue resources in native scripts, it is still impossible to predict when this feature will be finally implemented. In the next future, Italian cataloguers will therefore still rely on romanization only. After having briefly reviewed the romanization methods recommended by the Italian cataloging rules (REICAT), the article suggests to expand its repertoire by establishing a method for identifying the most suitable romanization standards.
Development of a portable electromyograph for recording bioelectric potentials appearing in muscles

The paper discusses the diagnostic method of electromyography, which allows assessing the state of functioning of the nerve that innervates the damaged muscle. The method is based on registration and analysis of bioelectrical potentials of muscles and peripheral nerves. Purpose of the study: develop an algorithm of operation and a structural diagram of an electromyography device with the possibility of converting the recording results to a PC, display and smartphone via USB and bluetooth for their analysis. Materials and methods. A method is proposed for measuring muscle function, which involves observing the movement of muscles from the surface of the skin over the muscle. The muscle sensor gives an output signal depending on its activity. Using a surface electrode, you can track the overall movement of the muscles. Using the program for interfacing the Myoware EMG muscle sensor with the Arduino, we obtain an analog signal. The sensor values can be observed on the serial plotter. The next task was to read the sensor data or analog voltage on the LCD1602 display. We have connected the I2C pins (SDA and SCL) of the LCD1602 to A4 and A5 of the Arduino. LCD1602 module displays 2 lines of 16 characters. After downloading the special code LCD1602 via I2C, the display began to display the value of the EMG analog signal, as well as the voltage. The Arduino can communicate with other devices via Bluetooth using the HC-05 (zs-040) module. This module allows the Arduino to connect and communicate with other devices such as a smartphone, computer or other. Results. Based on the results of the study, an idea was obtained of the bioelectric potential that occurs in the muscle. The applied method of fixation of bioelectrical signals in muscles is of practical importance for clinical neurology; its implementation will facilitate and increase the diagnostic accuracy of electrophysiological studies. Conclusion. In this study, a structural diagram of the device and the solution of a technical problem based on technical requirements have been developed, and an algorithm for the device has been developed. 4,963 rubles were spent on the implementation of practical study taking into account the costs of basic and auxiliary materials, as well as other direct costs.
COVAC: A Blockchain-based COVID Testing and Vaccination Tracking System

Blockchain is an emerging technology based on a distributed digital ledger system. Decentralized trust is one of the key factors behind the blockchain-based system. The transparency of such a system is better than a conventional centralized ledger system. By using a blockchain-based transaction system, any business organization can harness key benefits like data integrity, confidentiality, and anonymity without involving any third party in control of the transactions. Since the blockchain is used in numerous applications, the horizon is expanding at an unprecedented pace. It was found that tracking COVID vaccination in a transparent and accountable way is an emerging need, especially after the pandemic outbreak around the world. The blockchain platform is a good match for such applications. In this study, a blockchain-based COVID-19 testing and vaccination tracking system, called COVAC, has been designed to manage the COVID testing and vaccination process for local organizations. The “Prototype Software Development" approach was used to determine the system requirements according to the practical knowledge obtained through the vaccine monitoring and screening tests process and then communicated with local healthcare facilities to determine whether these requirements were satisfied. The blockchain-based implementation ensured the system transparency, integrity, and security of data on COVID-19 testing and vaccination
Practical application-specific advantage through hybrid quantum computing

Quantum computing promises to tackle technological and industrial problems insurmountable for classical computers. However, today's quantum computers still have limited demonstrable functionality, and it is expected that scaling up to millions of qubits is required for them to live up to this touted promise. The feasible route in achieving practical quantum advantage goals is to implement a hybrid operational mode that realizes the cohesion of quantum and classical computers. Here we present a hybrid quantum cloud based on a memory-centric and heterogeneous multiprocessing architecture, integrated into a high-performance computing data center grade environment. We demonstrate that utilizing the quantum cloud, our hybrid quantum algorithms including Quantum Encoding (QuEnc), Hybrid Quantum Neural Networks and Tensor Networks enable advantages in optimization, machine learning, and simulation fields. We show the advantage of hybrid algorithms compared to standard classical algorithms in both the computational speed and quality of the solution. The achieved advance in hybrid quantum hardware and software makes quantum computing useful in practice today.
From Psychology Management Innovation and Education Reform in the Digital Age: Role of Disruptive Technologies

In the main body of colleges and universities, the effect of college teaching psychology management is an important standard to test the quality of college teaching psychology management and its effects on the development of college teaching psychology management. However, the psychology management system used by traditional colleges and universities is challenging to meet the needs of the innovation of the new talent training model of higher vocational education. The construction of the new micro-level teaching organization inevitably requires the psychology management practice, structure, process, and technology at the medium and macro-levels to adapt to it. However, there are many mismatches in Higher Vocational Colleges' internal and external administrative systems in China, such as low administrative efficiency, generalization of administrative power, low degree of specialization in administrative psychology management, and unreasonable administrative and organizational structure. This paper innovates the psychology management of colleges and universities reforms in combination with the characteristics of the digital age. Based on the collaborative innovation theory of colleges and universities, this paper puts forward the collaborative psychology management innovation network model of colleges and universities. The model combines the psychology management theory with the practical research of collaborative innovation in colleges and universities, expands the application and research field of innovation network theory, constructs the evaluation index system of students' satisfaction in teaching psychology management in colleges and universities, and discusses it through reading and consulting materials and communication with teachers and students. A questionnaire of college teaching psychology management based on student satisfaction is formed. The experiment shows that teaching satisfaction is improved by nearly 31% compared with before innovation and reform. It can initially get the effect of innovation and reform, meet students' needs, and promote the improvement of teaching psychology management quality.
Photometric Visual Gyroscope for Full-View Spherical Camera

This article presents a visual gyroscope based on the spherical representation of dual-fisheye cameras. By using the fully available view of the scene thanks to a dual-fisheye camera and projecting the photometric information on a sphere, a highly precise orientation estimation along with a great convergence domain can be achieved. This method is validated with the help of images taken from the PanoraMIS dataset to evaluate quantitatively the performances.
Virtual special issue on advances in digital security: Biometrics and forensics

This work evaluates the use of synthetic data to train deep 6DoF pose estimation models that use a monocular RGB camera as input. We have compared different training strategies combining real and synthetic data (with domain randomization) to investigate how to better handle real-world challenges. We show that it is possible to obtain accurate models using less real data and suggest how to utilize this strategy. In this work, we have captured and made available two datasets: one real and one synthetic, totaling over 110,000 annotated frames. These datasets are organized according to the different cameras used and the challenges present in the sequences, all featuring textureless 3D printed objects. We also show that synthetic data can help models generalize, handling challenges such as fast motion, occlusion, illumination changes, color variation, scale changes, and unexpected geometry. Finally, we evaluated 70 different models to understand how a model trained for one camera sensor performs when used with a different sensor. To this end, we also suggest how to handle this challenge better by using synthetic simulations to supplement training.
Developing plastic recycling classifier by deep learning and directed acyclic graph residual network

Recycling is one of the most important approaches to safeguard the environment since it aims to reduce waste in landfills while conserving natural resources. Using deep Learning networks, this group of wastes may be automatically classified on the belts of a waste sorting plant. However, a basic set of connected layers may not be adequate to give satisfactory accuracy for such multi output classifier tasks. To optimize the gradient flow and enable deeper training for network design with multi label classifier, this study suggests a residual-based deep learning convolutional neural network. For network training, ten classes have been explored. The Directed Acyclic Graph (DAG) is a structure with hidden layers that have inputs, outputs, and other layers. The DAG network's residual-based architecture features shortcut connections that bypass some levels of the network, allowing gradients of network parameters to travel freely among the network output layers for deeper training. The methodology includes: 1) preparing the data and creating an augmented image data store; 2) defining the main serially-connected branches of the network architecture; 3) defining the residual interconnections that bypass the main branch layers; 4) defining layers, and finally; 5) creating a residual-based deeper layer graph. The concept is to split down the multiclass classification problem into minor binary states, where every classifier performs as an expert by concentrating on discriminating between only two labels, improving total accuracy. The results achieve (2.861 %) training error and (9.76 %) a validation error. The training results of this classifier are evaluated by finding the training error, validation error, and showing the confusion matrix of validation data