Reviewerly/exHarmony huggingface dataset:
aqmeological framework for modern higher education as a step towards sustainable development of society

The article considers the issues, connected with the development of acmeological basis of modern higher education, which contributes to the fulfillment of requirements for the provision of sustainable development of society in the context of formation of human ability to learn to live in order to contribute to the own personality prosperity and be able to act, demonstrating independence, judgement independence and personal responsibility. The research results on the use of the acmeological approach in the professional training of applicants for higher education, the organization of acmeological impact and support of future specialists during the professional formation period are presented. Theoretically justified the implementation of the mentioned psychological and pedagogical condition at the professional training system level, sets the necessary strategy in achieving integrative competence by higher education applicants (the organization of educational process depends on this level); at the level of the educational process, sets the action algorithm for achieving the results of mastering theoretical knowledge, practice-oriented and independent activity of a specific topic and discipline as a whole (allows you to use the full potential of the chosen topic to achieve optimum performance in mastering general and special competencies).
The System of Future Teachers’ Professional Development

The article aims to theoretically justify, develop, and experimentally verify the author’s model for implementing the system of future teachers’ professional development as a requirement for professional training of future specialists. The objectives of the article are the following: to identify structural components of future teachers’ professional development; to develop and experimentally verify the effectiveness of the model of future teachers’ professional development; to determine pedagogical conditions for its implementation. Research methods include theoretical analysis and generalization of relevant scientific-methodical literature; pedagogical observations; monitoring of future teachers’ levels of professional development and readiness for it; modeling; pedagogical experiment; methods of mathematical statistics. The value of the obtained results lies in defining and justifying the structure of future teachers’ professional development theoretically, as well as verifying the modeled system of future teachers’ professional development and pedagogical conditions for its implementation experimentally. Besides, one can observe a significant increase in the levels of future teachers’ professional development in the experimental group. Conclusions: The article shows that the system of future teachers’ professional development, which is implemented through the experimental model and whose goals, content, methods, tools, and technologies are aimed at achieving effective results, enhances the level of future teachers’ professional development and their readiness for it.
Increasing momentum-like factors: A method for reducing training errors on multiple GPUs

In distributed training, increasing batch size can improve parallelism, but it can also bring many difficulties to the training process and cause training errors. In this work, we investigate the occurrence of training errors in theory and train ResNet-50 on CIFAR-10 by using Stochastic Gradient Descent (SGD) and Adaptive moment estimation (Adam) while keeping the total batch size in the parameter server constant and lowering the batch size on each Graphics Processing Unit (GPU). A new method that considers momentum to eliminate training errors in distributed training is proposed. We define a Momentum-like Factor (MF) to represent the influence of former gradients on parameter updates in each iteration. Then, we modify the MF values and conduct experiments to explore how different MF values influence the training performance based on SGD, Adam, and Nesterov accelerated gradient. Experimental results reveal that increasing MFs is a reliable method for reducing training errors in distributed training. The analysis of convergent conditions in distributed training with consideration of a large batch size and multiple GPUs is presented in this paper.
Continuous majorization in quantum phase space

We explore the role of majorization theory in quantum phase space. To this purpose, we restrict ourselves to quantum states with positive Wigner functions and show that the continuous version of majorization theory provides an elegant and very natural approach to exploring the information-theoretic properties of Wigner functions in phase space. After identifying all Gaussian pure states as equivalent in the precise sense of continuous majorization, which can be understood in light of Hudson&amp;apos;s theorem, we conjecture a fundamental majorization relation: any positive Wigner function is majorized by the Wigner function of a Gaussian pure state (especially, the bosonic vacuum state or ground state of the harmonic oscillator). As a consequence, any Schur-concave function of the Wigner function is lower bounded by the value it takes for the vacuum state. This implies in turn that the Wigner entropy is lower bounded by its value for the vacuum state, while the converse is notably not true. Our main result is then to prove this fundamental majorization relation for a relevant subset of Wigner-positive quantum states which are mixtures of the three lowest eigenstates of the harmonic oscillator. Beyond that, the conjecture is also supported by numerical evidence. We conclude by discussing some implications of this conjecture in the context of entropic uncertainty relations in phase space.
TEXT ENCRYPTION ALGORITHM FOR SECURE COMMUNICATION

In this paper, a new encryption algorithm is presented, designed for secure text message communication.The proposed cryptographic system is based on pseudorandom generator, constructed with two chaotic maps.For security level determination, extensive cryptographic analysis is performed.The evaluation of the presented cryptographic scheme includes the results of statistical testing, key space analysis, frequency analysis, common correlation analysis, entropy analysis, key sensitivity analysis and speed performance.The results are presented and explained in this paper.
I-Neat: An intelligent framework for adaptive virtual machine consolidation

With the increasing use of cloud computing, high energy consumption has become one of the major challenges in cloud data centers. Virtual Machine (VM) consolidation has been proven to be an efficient way to optimize energy consumption in data centers, and many research works have proposed to optimize VM consolidation. However, the performance of different algorithms is related with the characteristics of the workload and system status; some algorithms are suitable for Central Processing Unit (CPU)-intensive workload and some for web application workload. Therefore, an adaptive VM consolidation framework is necessary to fully explore the potential of these algorithms. Neat is an open-source dynamic VM consolidation framework, which is well integrated into OpenStack. However, it cannot conduct dynamic algorithm scheduling, and VM consolidation algorithms in Neat are few and basic, which results in low performance for energy saving and Service-Level Agreement (SLA) avoidance. In this paper, an Intelligent Neat framework (I-Neat) is proposed, which adds an intelligent scheduler using reinforcement learning and a framework manager to improve the usability of the system. The scheduler can select appropriate algorithms for the local manager from an algorithm library with many load detection algorithms. The algorithm library is designed based on a template, and in addition to the algorithms of Neat, I-Neat adds six new algorithms to the algorithm library. Furthermore, the framework manager helps users add self-defined algorithms to I-Neat without modifying the source code. Our experimental results indicate that the intelligent scheduler and these novel algorithms can effectively reduce energy consumption with SLA assurance.
Video Steganography Using Chaos Encryption Algorithm with High Efficiency Video Coding for Data Hiding

Steganography is also known as data hiding, is a method of ensuring the security and confidentiality of digital data by hiding sensitive data in digital media.In video steganography, compression is the most demanding research area in block based video encoders.With the improvement of video coding equipment, the High Efficiency Video Coding (HEVC) delivers better coding efficiency.However, a large proportion of video frames showed system complexity and required a larger Coding Unit (CU) during encoding.Also, as the larger number of video frames were encoded, the time consumption was increased.To overcome such an issue, this research introduced the chaos with enhanced mapping technique to reduce computational complexity and fast encoding.In this research study, the position of each pixel of the secret video frame is calculated by the ELSB technique, where the existing LSB techniques have not considered this secret video frame's position which leads to high video distortion.In proposed chaos encryption with mapping method, the input video frames are encrypted by using logistic and henon mapping that simultaneously maintains quality and efficiency of the model.The HEVC technique compresses the input video frames and the proposed ELSB performs two functions namely replacement of LSB and matching of LSB which effectively embeds the secret video frames into cover video frames.Finally, the secret video frames are retrieved by HEVC technique with chaos decryption which provides security to the model.The results proved that proposed compression technique achieved PSNR of 35.81dB with average execution time of 40 seconds, but the existing H.264 compression technique achieved PSNR of 32.72dB with average execution time of 45 seconds.
Accountable Human Subject Research Data Processing using Lohpi

In human subject research, various data about the studied individuals are collected.Through re-identification and statistical inferences, this data can be exploited for interests other than the ones the subjects initially consented to.Such exploitation must be avoided to maintain trust with the researched population.We argue that keeping data-access policies up-to-date and building accountability on research data processing can reflect subjects' consent and mitigate data misuse.With accountability in mind, we are building Lohpi: a decentralized system for research data sharing with up-to-date access policies.We demonstrate our initial prototype with timely delivery of policy changes along with minimal access control overhead.
Learning Strategies for E-Learning In Primary Schools Through Use of Zoom Meeting Technology in the Covid-19 Pandemic Era

Abstract:During the Covid-19 pandemic, all learning activities were carried out through technology-based learning media, to avoid the spread of the Covid-19 virus. The purpose of this study was to find out how E-learning learning strategies in elementary schools through the use of Zoom Meeting technology in the era of the Covid-19 pandemic. The method in this research is to use the library research model (Library Research). The results showed that the learning media using Zoom Meeting technology as the most effective learning media used during learning in elementary schools, because it provides convenience in face-to-face interaction with teachers and peers.&#x0D; &#x0D; Keywords: Zoom Meeting, Learning Strategies, Learning Media
Goal location prediction based on deep learning using RGB-D camera

In the navigation system, the desired destination position plays an essential role since the path planning algorithms takes a current location and goal location as inputs as well as the map of the surrounding environment. The generated path from path planning algorithm is used to guide a user to his final destination. This paper presents a proposed algorithm based on RGB-D camera to predict the goal coordinates in 2D occupancy grid map for visually impaired people navigation system. In recent years, deep learning methods have been used in many object detection tasks. So, the object detection method based on convolution neural network method is adopted in the proposed algorithm. The measuring distance between the current position of a sensor and the detected object depends on the depth data that is acquired from RGB-D camera. Both of the object detected coordinates and depth data has been integrated to get an accurate goal location in a 2D map. This proposed algorithm has been tested on various real-time scenarios. The experiments results indicate to the effectiveness of the proposed algorithm.
Sketched Stochastic Dictionary Learning for large‐scale data and application to high‐throughput mass spectrometry

Abstract Factorization of large data corpora has emerged as an essential technique to extract dictionaries (sets of patterns that are meaningful for sparse encoding). Following this line, we present a novel algorithm based on compressive learning theory. In this framework, the (arbitrarily large) dataset of interest is replaced by a fixed‐size sketch resulting from a random sampling of the data distribution characteristic function. We apply our algorithm to the extraction of chromatographic elution profiles in mass spectrometry data, where it demonstrates its efficiency and interest compared to other related algorithms.
Mirroring-Based Data Hiding in Audio

Information technology has multiplied for some decades.It has affected the way how the public interacts with their environment.Specifically, this technology helps us to work more efficiently than before.Nevertheless, some problems arise due to the exploitation of its weaknesses, like capturing confidential data.Some schemes have been introduced, such as embedding the secret in a multimedia file.However, the quality of the file after the embedding process may be different from its original form significantly.Moreover, there is a limitation on the secret size that can be hidden.In this research, we propose to improve the existing method by taking audio as the carrier.Specifically, we explore the mirroring technique after the audio samples are interpolated.It is to reflect the samples to the specified mirroring point, purposing to minimize the difference between before and after the payload is embedded.The more similar the stego to the cover file, the higher the quality; besides, the embedding space has been enlarged.The experimental result shows that this proposed method can increase the quality of the stego file by about 20 dB for various payload sizes.
Research on the Integration of 5G+AI Technology to Empower Digital Education Under the Background of Industrial Internet

With the continuous maturity and gradual application of 5G technology, this technology will break the traditional traffic service model and shift to a full factor ecological chain model. The integration of 5G technology and industrial industries will greatly improve industrial production efficiency and accelerate the digital transformation of industrial upgrading. When 5G + AI technology is accelerated into the education and learning process, what effect will this have on the educational scene? Undoubtedly, this is a new subject worthy of attention and research. 5G + AI technology will reconstruct the future education information ecosystem, bringing subversive technological changes and educational application scenarios. The integration of 5G + AI and education and teaching practice will innovatively bring many important scenarios, such as technology integration, teacher training, student learning, digital campus management, teaching context, curriculum development, environmental design, teaching evaluation, special education, etc.
A Neural Network based Integer Frequency Offset Estimation and PSS Detection in 5G NR Systems

With an increase in defined carrier frequency for the 5G new radio system, the need for synchronization also increases.The transceiver loses synchronization due to the occurrence of timing and carrier frequency offsets.Carrier frequency offsets often occur due to mismatch between transmitter and receiver oscillator frequency as well as the occurrence of doppler shifts due to transmitter/receiver movements.When frequency offsets exceed subcarrier spacing, integer frequency offset occurs that results in performance loss due to subcarrier indices shifts.Conventional approach i.e. maximum likelihood and sequential method is already employed to estimate integer frequency offset and to detect sector id.In this paper, the deep learning-based method is demonstrated to estimate the integer frequency offset and sector id detection.The neural network containing multiple convolution layers with activation layers is used to find the optimum received signal.Then, by calculating the number of cyclic shifts in the optimum received signal, the integer frequency offset is estimated.Using the corrected optimum received signal, the primary synchronization signal is also detected that gives sector id.This proposed estimator is tested for different profiles of tapped delay line models with different desired delay spread and compared with conventional methods i.e. maximum likelihood estimation method and sequential estimation method.Simulation results show that the proposed Neural Network based estimator outperforms in all delay profiles.
Cloud Computing Security Using Cryptographic Algorithms

Cloud computing is a new and fast emergent technology in field of computation and data storage as a service at very eye-catching facilities. It provides fast and well organized on demand services for storage, network, software, and hardware through the Internet. Applications that run in the cloud can balance various elements comprising load balancing, bandwidth, security and size of data. Major problems to cloud adoption are data privacy and security, because the data owner and the service provider are not within the similar trusted domain. Safety issues are increasingly significant in lowest layer Infrastructure as a Service (IaaS) to higher Platform as a Service (PaaS). In this paper we present range of dissimilar techniques or security algorithms exploiting to uphold the secrecy and security of the cloud with cryptographic algorithms.
Research on Management Model Based on Deep Learning

In this study, the focus was on the development of management models and future prediction for the cost and risk by using an improved deep learning (DL) algorithm. Management model can be defined as the management activities that are interlinked and organized inside organization of institutions. Different opportunities and different organizations are offered by different management models. Proper management models lead to strategies and decisions help to success organization. Deep neural network (DNN) is proposed to make good prediction for organization for increasing the cost and reduce risk in companies and institutions. The error of prediction is updated according to variable hidden layers and nodes within iteration. Improved DNN is used and modify weights that have an effect on the features extracted in advance to increase the accuracy and precisions are used. The proposed method is based on dynamic hidden layers with backpropagation and feedforward. Absolute mean relative error (AMRE) and variance (R2) are used for evaluation in term of accuracy. The training system is used with three available datasets from big company, health issue, and industry. Gained result proves the worth of the proposed system and is suitable for predicting complex data and reducing the risk as possible.
Reverse logistics of Indian E-Commerce Industry- Consumer Perspective and Ways to Improve

The modern supply chain has become complex and multi-locational, and efficient movement of material contributing to business success. The industry that deals with the movement of goods from one point to another is known as the logistics industry. Reverse logistic is a process of moving materials back from their consumer destination to their point of manufacturer or the distribution center in order to recapture their value for redistribution, or to dispose them properly. The movement of goods backwards in the supply chain is as important as the movement of goods forward in the supply chain. With the introduction of ecommerce and increase in the footprint of digitalization, the expectations of the consumers and the industries have reached an all-time high. We have used customer survey to know their perspective and then we have analyzed and visualized the data using Tableau. This research paper will focus on the Indian ecommerce industry and how end-consumers think differently than the industry, and how the gap in the quality of service is too wide between tier-1 cities and tier-2 or tier- 3 cities. We have used our data to analyze and find how can big data and blockchain help these industries in finding modern day solutions.
Automatic post-selection by ancillae thermalization

Tasks such as classification of data and determining the ground state of a Hamiltonian cannot be carried out through purely unitary quantum evolution. Instead, the inherent nonunitarity of the measurement process must be harnessed. Post-selection and its extensions provide a way to do this. However, they make inefficient use of time resources---a typical computation might require $O({2}^{m})$ measurements over $m$ qubits to reach a desired accuracy and cannot be done intermittently on current (superconducting-based) NISQ devices. We propose a method inspired by thermalization that harnesses insensitivity to the details of the bath. We find a greater robustness to gate noise by coupling to this bath, with a similar cost in time and more qubits compared to alternate methods for inducing nonlinearity such as fixed-point quantum search for oblivious amplitude amplification. Post-selection on $m$ ancillae qubits is replaced with tracing out $O[log\ensuremath{\epsilon}/log(1\ensuremath{-}p)]$ (where $p$ is the probability of a successful measurement) to attain the same accuracy as the post-selection circuit. We demonstrate this scheme on the quantum perceptron, quantum gearbox, and phase estimation algorithm. This method is particularly advantageous on current quantum computers involving superconducting circuits.
A Fact-Finding Procedure Integrating Machine Learning and AHP Technique to Predict Delayed Diagnosis of Bladder Patients with Hematuria

Bladder cancer, the ninth most common cancer worldwide, requires fast diagnosis and treatment to prevent disease progression and improve patient survival. However, patients with bladder cancer often experience considerable delays in diagnosis. One reason for such delays is that hematuria, a major symptom of bladder cancer, has a high probability of also being a warning sign for urinary tract diseases. Another reason is that the sensitivity of the body parts affected by bladder cancer deters patients from undergoing cystoscopy and influences patients’ “physician shopping” behavior. In this study, the analytic hierarchy process was used to determine critical variables influencing delayed diagnosis; moreover, the variables were used to construct models for predicting delayed diagnosis in patients with hematuria by using several machine learning techniques. Furthermore, the critical variables associated with delayed diagnosis of bladder cancer in patients with hematuria were evaluated using GainRatio technology. The study sample was selected from a population-based database. The model evaluation results indicated that the prediction model established using decision tree algorithms outperformed the other models. The critical risk factors for delayed diagnosis of bladder cancer were as follows: (1) cystoscopy performed 6 months after hematuria diagnosis and (2) physician shopping.