Reviewerly/exHarmony huggingface dataset:
Blockchain Based Energy Management System: A Proposed Model

There is tremendous growth in the renewable energy generation due to international awareness and treaty compulsions like Paris Agreement, 2016 etc. for renewable and climate friendly energy generation and distribution. Present power distribution system is centralized in nature and do not have much scope for the distributed energy generation and distribution. Therefore, there is an urgent need to change the present centralized power distribution system with a new decentralized power distribution system integrating both traditional and sustainable energy sources in efficient energy management system. The devolved nature of the smart grids has demanded integration of novel technology like Blockchain to create a secure, immutable and trustless power distribution system for peer-to-peer energy transmission. This paper proposes a model for blockchain based energy management system which integrates all energy sources, identifies the stakeholders as per their roles, generates certificates /token, does energy transactions, and incentivize the use of sustainable or green energy.
Lensless Optical Encryption of Multilevel Digital Data Containers Using Spatially Incoherent Illumination

The necessity of the correction of errors emerging during the optical encryption process led to the extensive use of data containers such as QR codes. However, due to specifics of optical encryption, QR codes are not very well suited for the task, which results in low error correction capabilities in optical experiments mainly due to easily breakable QR code’s service elements and byte data structure. In this paper, we present optical implementation of information optical encryption system utilizing new multilevel customizable digital data containers with high data density. The results of optical experiments demonstrate efficient error correction capabilities of the new data container.
Analysis of Deep Learning Models for Early Action Prediction Using LSTM

Video surveillance is being increasingly adopted for ensuring safety and security both in public and private places. Automated prediction of abnormal events like theft, robbery, murder etc., from continuous observation of surveillance videos is a multidisciplinary study involving computer vision, deep learning and artificial intelligence. Deep learning-based video analysis and categorization is a most researched topic. Many deep learning models based on long short-term memory (LSTM) are proposed for automated prediction of abnormal events. This work does a comparative analysis of six LSTM-based deep learning models for abnormal event prediction from surveillance videos. Deep learning models of ResNet, VGG16, VGG19, 3DCNN, Inception V2 and Inception-ResNet-V2 are combined with LSTM for prediction of abnormal event from past observation of events in the video stream. These six models are run against different benchmarked abnormal event detection datasets and performance is compared in terms of accuracy, loss and execution time. It is observed that Inception-ResNet with LSTM provides training accuracy of 80% and test accuracy of 80% compared to other models.
Research on Vision of Intelligent Car Based on Broad Learning System

The broad learning system (BLS) of intelligent vehicle in different target environments is studied in this article. First, this article provides with the target recognition image data to be trained and detected through the automated guided vehicle (AGV) mobile platform, which can grab the recognition image of different angles and backgrounds. In order to avoid the data generalization phenomenon, the dataset can be expanded by the data normalization and data enhancement. Second, the data are input into the shared convolution layer to extract the feature image and maintain the image. The parameters of image height, width, and channel number are invariable, and the new feature image is obtained by further extraction. Furthermore, the region proposal network (RPN) prefiltering algorithm based on hierarchical clustering is used to filter the objects in the candidate box to determine the region image corresponding to the feature image. Then, the feature images of different sizes input into region of interest (ROI) pooling are used to keep the size of the image in the ROI consistent. Finally, the normalized image is input into the classifier module to obtain the category of the target recognition image to be detected. Through the simulation experiments of different groups, it can be seen that the target recognition system proposed in this design can not only accurately detect the objects but also stably recognize the objects in different environments. The target recognition accuracy for the optimized system is about 95%.
DIGITALIZATION OF EDUCATION FOR THE DIGITAL ECONOMY

The period of shock therapy for the education system is over. But the situation requires radical measures at the state level to restore the economy from the impact of the pandemic and the digital transformation of business, and society at a new level, based on an understanding of the reduction of the life cycle of professions and the need to develop digital technologies. The pandemic exposed the problems of the digital transformation of Russian education at all its levels, this is the underdevelopment of the infrastructure of data channels in schools and in many schools and universities, the insufficient readiness of a significant number of pedagogical staff to work remotely, the lack of high-quality and powerful online resources and many others. For universities, the introduction of digital technologies, network interaction with other universities will make it possible to be competitive and provide training for IT personnel for the economy. Today, both the economy and the education system need a large number of employees and teachers with digital skills and the ability to work with advanced technologies. Today, there is a gap between the pace of digital transformation of the education system and the rate of growth of the economy’s need for IT personnel. And the education system is inferior in terms of the speed of digital transformation, which determines the need for high-speed system measures. The government, solving the problem of the existing gap in the education system and the economy, updated the national program «Digital Economy of the Russian Federation» taking into account the challenges of the modern market and the current development of Big Data, Machine Learning and IIoT technologies. In this regard, it is necessary to intensify a wide variety of mechanisms for the participation of all actors, and above all the state, in filling the economy with personnel with the most advanced digital competencies.
The influence of academic integrity on future teachers’ soft skills during the research work

The article considers the problem of students’ research work in compliance with academic integrity as well as its impact on the formation of future teachers’ soft skills. The purpose of the study is to identify and summarize the components of future teachers’ soft skills, acquired in the process of academic writing in compliance with the requirements of academic integrity. These research methods were used: theoretical analysis of scientific and reference sources on the implementation of academic integrity; generalization and systematization of the existing studies related to the acquisition of future specialists’ soft skills; interpretation of one's own views on the acquisition of soft skills by the future teachers during their research work observing academic integrity in the process of the profession-oriented training. The following components of the future teachers’ soft skills facilitating the performance of research work have been proposed: digital skills, critical thinking skills; time management skills; discipline, communication skills. Acquisition of digital skills during the research work requires that students should master the skills of using information and communication technologies which are necessary for the analysis of digital sources, work with a text editor and online communication with a supervisor. Critical thinking skills are important during the research work, i.e. adhering to academic integrity, a student acquires the ability to quote, paraphrase, summarize, specify and compare other authors’ opinions and draw their own conclusions. The components of future teachers’ soft skills include time management skills, which provide planning and organization of work, as well as contribute to timely and effective implementation of all stages of their study. The next component of future teachers’ soft skills is discipline. This is due to the fact that there are compulsory requirements to the research layout, since observance of these requirements is the condition for its acceptance. In addition, the components of soft skills include communication skills, which are acquired by the students during effective communication with their supervisors, agreement with the research base, interpersonal communication in an academic group, presentation of their own research.
Software Defect Prediction: An Ensemble Learning Approach

Abstract Software defect prediction plays an increasingly critical role in emerging software systems. However, existing software defect prediction approaches typically suffer from low accuracy due to the under/over fitting problems. To address this problem, we propose an ensemble learning approach to achieve the accurate defect prediction, where various machine learning algorithms, i.e., artificial neural network, random forest, k-nearest neighbour methods are integrated together. The proposed software defect prediction workflow is introduced. Experiments are conducted to verify the effectiveness of the proposed method. Extensive experiment results verify that our proposed method can improve the defect prediction accuracy when compared with existing methods.
FairTrade: Efficient Atomic Exchange-based Fair Exchange Protocol for Digital Data Trading

Trading of data is increasingly important as data are becoming valuable. This paper studies the fair exchange problem where two untrusting parties need to trade data using digital tokens without anyone being able to cheat the other one. In existing approaches, data are encrypted with some sets of keys, and the keys are traded with digital tokens by leveraging the atomic exchange protocol. There exists a security hole because existing approaches fail to provide a complete and secure design for validating the correctness of the keys. We address this issue by presenting a key verification mechanism that leverages the collision-resistance of hash functions. Additionally, we present a novel atomic exchange protocol based on our key-secret-sharing mechanism, which is more efficient than the existing approach. Our experiments indicate the key verification mechanism incurs negligible overhead and our atomic exchange protocol is more efficient than the state-of-the-art protocol.
ProQuest TDM Studio: A Text and Data Mining Solution

TDM Studio is an integrated platform offered by ProQuest for data and text mining. TDM stands for text and data mining. This cloud-based, all-in-one innovative product is designed to offer researchers a clean interface with rights-cleared content, Jupyter notebook, and data visualization tools. As a result, researchers can now search ProQuest databases, create large datasets, import data to Jupyter notebook for analysis, and download results within a day.
Asynchronous I/O Strategy for Large-Scale Deep Learning Applications

Many scientific applications have started using deep learning methods for their classification or regression problems. However, for data-intensive scientific applications, I/O performance can be the major performance bottleneck. In order to effectively solve important real-world problems using deep learning methods on High-Performance Computing (HPC) systems, it is essential to address the poor I/O performance issue in large-scale neural network training. In this paper, we propose an asynchronous I/O strategy that can be generally applied to deep learning applications. Our I/O strategy employs an I/O -dedicated thread per process, that performs I/O operations independently of the training progress. The I/O thread reads many training samples at once to reduce the total number of I/O operations per epoch. Given the fixed amount of training data, the fewer the I/O operations per epoch, the shorter the overall I/O time. The I/O operations are also overlapped with the computations using the double-buffering method. We evaluate our I/O strategy using two real-world scientific applications, CosmoFlow and Neuron-Inverter. Our experimental results demonstrate that the proposed I/O strategy significantly improves the scaling performance without affecting the regression performance.
Supplementary material to &amp;quot;The role of geochemistry in organic carbon stabilization in tropical rainforest soils&amp;quot;

Supplemented information on valley
Overview of Long-form Document Matching: Survey of Existing Models and Their Challenges

Abstract Long-form document matching is an important direction in the field of natural language processing and can be applied to tasks such as news recommendation and text clustering. However, long-form document matching suffers from noisiness and sparsity of semantic information in long text. Using short-form document matching methods on a long-form matching problem is not satisfactory. Long-form document matching has attracted the attention of researchers, who have proposed many effective methods. Methods for matching long texts can be divided into three categories: traditional bag-of-words-based models, traditional deep learning-based models, and pre-training-based models. This study reviews typical methods of long-form document matching, analyzes their advantages and disadvantages, and discusses possible future developments.
Rebuilding games at runtime

The gaming industry is growing over time, having a considerable amount of fans. Creating a game is a complex process that can take a long time until its release, causing a lot of anxiety for the game fan population. Some of these fans can not wait for the release of a specific game, creating their own versions of games. This process of creating games in an ad-hoc manner from others that already exist is called mod and can be compared to the concept of Software Reuse, where software is created from others. This work aims to increase the systematic of creating mods based on reuse concepts such as Product Line. In this paper, an initial prototype of a game created by applying Product Line concepts to generate new mods in a more systematic way is described.
Technologies for Promoting a Company on the Internet Using Telegram Messenger

The authors focus on the issues of promoting companies using telecommunication technologies to find tools for the effective promotion of the brand, products, and services. Particularly, the authors study the issue of increasing brand awareness using marketing technologies in Telegram messenger. The research methods include collecting, processing, analyzing, and systematizing publicly available information on the discussed issue. The authors analyze the most popular messengers in Russia in 2020 and state that Telegram is the only suitable application for the integrated promotion of a company. The study summarizes the most promising approaches for the company to work with the audience in Telegram messenger. The best combinations of these approaches are offered to achieve maximum effect. The authors consider the technologies of promotion in this messenger, including the creation of a channel (group, bot), the purchase of advertising in other channels and groups, and promotion on external platforms. Moreover, the authors describe the main problems encountered by organizations during the promotion of their brand (products, services) in the Telegram messenger. The recommendations for the solution to these problems are provided. The study shows that the creation of a company’s Telegram channel, group, or chatbot is the most effective and financially optimal algorithm for promoting a company. The authors systematize the independent experience of three years of work with the technologies provided by Telegram messenger and the experience of marketing specialists working in this messenger.
E-MAIL SPAM DETECTION

E-mail is the most important form of communication. Used for a wide range of people including individuals and organizations. But these people using this e-mail they find it difficult to use because of spam mail. These spam emails are also called unsolicited bulk mail or junk mail. Spam emails are available randomly sent messages to people by anonymous users. Sites are trying to steal yours personal, electronic and financial information. An increase in spam emails leads to crime of theft of sensitive information, reduced productivity. Spam detection is dirty. The line between spam and non-spam messages is blurred, and the condition changes over time. From various attempts to automate spam detection, machine learning has so far proven to be the most effective and popular method of email providers. While we still see spam emails, a quick look at the trash folder will show how many spam is removed from our inbox daily due to machine learning algorithms. It is estimated that 40% of emails are spam mail. These spam wastes time, storage the space and width of the communication band. There are a few ways to receive spam emails but spam senders make it difficult for you to send users from a random sender address or by adding special characters at the beginning or end of the email. There are several machine learning methods for filtering spam emails including Naïve Bayes classifier, Vector support equipment, Neural Networks, Close Neighbour, Rough Sets and Random Forests. In this project we use the Naïve Bayes classifier to identify spam mail. The vast majority of people depend on what is available email or messages sent by a stranger. Possibly anyone can leave an email or message provide gold the opportunity for spam senders to write a spam message about us different interests. Spam fills in the inbox with a number of funny things mails. Slow down our internet speed. Theft useful information such as our details on our contact list. Identifying these people who post spam and spam content can be a a hot topic for research and strenuous activities. Email Spam is functionality of mass mailings. From the cost of Spam is heavily censored by the recipient, it is a successful post proper advertising. Spam email is a form of commercial advertising economically viable because email can be costly effective sender method. With this proposed model some message may be declared spam or not use Bayes' theorem and Naive Bayes’ Classifier and IP addresses of sender is usually found.
A scalable ideal progressive visual cryptography scheme

Visual cryptography is a kind of image-based secret sharing scheme to share secret image based on the human visual system (HVS). For instance, a ()-visual cryptography scheme (VCS) encrypts a secret image into shares and distributes them to different participants, the secret image can be recovered by at least shares. However, most of the existing VCSs are designed for binary images with limited flexibility and scalability. In this paper, a scalable ideal progressive visual cryptography scheme (PVCS) is proposed by scalable basis matrices and random Gaussian noise, which can process non-binarized images directly without pixel expansion. Our proposed basis matrix expansion method greatly improves the flexibility of the scheme and thus the efficiency is improved. In addition, since the visual effect of the VCS is standardized by the HVS and lacks an accurate quantitative index, we measure our scheme by using the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM), we also propose a method to derive the noise volume according to SSIM. Furthermore, sufficient experiments show that the proposed scheme is visually efficient and superior.
Towards a Formal Approach for Data Minimization in Programs (Short Paper)

As more and more processes are digitized, the protection of personal data becomes increasingly important for individuals, agencies, companies, and society in general. One principle of data protection is data minimization, which limits the processing and storage of personal data to the minimum necessary for the defined purpose. To adhere to this principle, an analysis of what data are needed by a piece of software is required. In this paper, we present an idea for a program analysis which connects data minimization with secure information flow to assess which personal data are required by a program: A program is decomposed into two programs. The first projects the original input, keeping only the minimal amount of required data. The second computes the original output from the projected input. Thus, we achieve a program variant which is compliant with data minimization. We define the approach, show how it can be used for different scenarios, and give examples for how to compute such a decomposition.
Use of WhatsApp in Synchronous and Asynchronous Education in Students of the IV Cycle of Peruvian basic Education

The study proposed to delimit the use of WhatsApp and its relationship with synchronous and asynchronous education in students of the IV cycle of basic education. For this purpose, the correlational descriptive study method was used and 74 students were considered a sample. The result revealed the existence of a positive relationship expressed in Spearman's Rho = 0.315, p = .000; where it was found that the use of WhatsApp is an interactive technological resource that supports students in synchronous and asynchronous education. That is, WhatsApp is a didactic technological resource that supports synchronous and asynchronous learning, allowing students to build meaningful knowledge in a dynamic, attractive, interactive and autonomous way.
A Novel Image Encryption Algorithm Based on Circular Shift with Linear Canonical Transform

This paper proposes a new image encryption based on circular shift on linear canonical transform, i.e., gyrator transform. We review various optical image encryption techniques proposed and inspired by the double random phase encoding (DRPE) system in the literature review. Also, we discussed optically inspired encryption techniques Fourier transform, fractional Fourier transform (FrFT), quadratic phase system (QPS), gyrator transform (GT), Fresnel transform (FrT), possible attacks and challenges in literature review and then explained various image scrambling techniques Jigsaw transform (JT) and Arnold transform (ART). Image encryption transforms are numerically implemented and compared with different DRPE schemes. Applied circular shift with Linear canonical transform (LCT) based transforms Fresnel transform, Gyrator transform, and compare the results of MSE, PSNR, and Histogram. The challenges faced in this area and the objectives of research have been discussed. This new circular shift approach provides adequate means security with acceptable complexity.