Reviewerly/exHarmony huggingface dataset:
Action recognition based on Riemannian manifold distance measurement and adaptive weighted feature fusion

We propose an action recognition method based on Riemannian manifold and adaptive weighted feature fusion. First, we divide the video into several sequence frames, and we propose to use the frame-action relevance to eliminate the slight disturbance of the background, so that the subsequent calculation can focus on the moving region of the target better. On this basis, our proposed framework extracts histogram of oriented gradients (HOG) and histogram of optical flow (HOF) features as original appearance and motion features. Aiming at the problem that the existing action recognition methods do not pay enough attention to the change rate of appearance and motion, an innovative feature names as Riemannian manifold distance feature (RMDF) is proposed, which can represent the topological relationship between features at different times in the same position and capture the change rate of current features. In the feature fusion stage, we use an unsupervised method of solving optimization problems to obtain the weights between different features. The strategy of adaptive weight allocation is adopted to fuse HOG, HOF, and their corresponding RMDFs to obtain the final representation of the video. Finally, it is sent to a back propagation neural network for classification and recognition. Experiments on three benchmark datasets show that our method still has great advantages compared with the most advanced methods.
Fuzzy Control

Control theory deals with the control of dynamical systems in engineered processes and machines. The objective is to develop a model or an algorithm governing the application of system inputs to drive the system to a desired state, while minimizing any delay, overshoot, or steady-state error and ensuring a level of control stability, often with the aim to achieve a degree of optimality. To do this, a controller with the requisite corrective behavior is required. This controller monitors the controlled process variable, and compares it with the reference or set point. Fuzzy Control is introduced as a “model-free control” approach based on expert’s experience and knowledge.
Meta-project as a digital means of individual design in professional education

The relevance of digitalization in education has become widespread and consolidated at the state level. The article deals with the solution of the problem of insufficient formation of competencies among future managers against the background of the fourth industrial revolution and digitalization. Proceeding from this, the problem is seen in the insufficient formation of organizational and managerial competence of future managers in the conditions of digitalization. The purpose of the article is to define digital means of individual design for the formation of organizational and managerial competence. The methodological basis of the study is a competence-based approach, using the principles of a personal-activity approach. The main research methods were: theoretical – analysis, generalization, comparison and systematization; empirical – observation, survey, study of documentation, methods of interpretation and analysis of data. The results of the study consist of the substantiation of the means of individual design – the “Meta-project” of students. The proposed individual project is a means of forming organizational and managerial competence. “Meta-project” is considered as a digital means of professional training and is revealed through the medium of its creation. The environment for creating a “Meta-project” is built around the interaction of traditional and digital education, professional orientation and innovation in management ideas, information environment and technologies. The analysis of psychological and pedagogical literature makes it possible to identify additional educational opportunities by means of a “Meta-project” and dangerous factors of individualization of learning, with ways to overcome them. In conclusion, the article substantiates the design conditions of the “Meta-project”, the stages, design modules and the impact on the development of pedagogical technology and a special course “Platform – a skilled leader”.
Towards Post-Quantum Key-Updatable Public-Key Encryption via Supersingular Isogenies

We present the first post-quantum secure Key-Updatable Public-Key Encryption (UPKE) construction. UPKE has been proposed as a mechanism to improve the forward-secrecy and post-compromise security of secure messaging protocols, but the hardness of all existing constructions rely on discrete logarithm assumptions. We focus our assessment on isogeny-based cryptosystems due to their suitability for performing a potentially unbounded number of update operations, a practical requirement for secure messaging where user conversations can occur over months, if not years. We begin by formalizing two UPKE variants in the literature as Symmetric and Asymmetric UPKE, which differ in how encryption and decryption keys are updated. We argue that Asymmetric UPKE constructions in the literature cannot be straightforwardly instantiated using SIDH nor CSIDH. We then describe a SIDH construction that partially achieves the required security notions for Symmetric UPKE, but due to existing mathematical limitations, cannot provide fine-grained forward secrecy. Finally, we present a CSIDH Symmetric UPKE construction that requires a parameter set in which the class group structure is fully known. We discuss open problems which are applicable to any cryptosystem with similar requirements for continuous operations over the secret domain.
Lang-n-Send: Processes That Send Languages

We present LANG-N-SEND, a π-calculus that is equipped with language definitions.Processes can define languages in operational semantics, and use them to execute programs.Furthermore, processes can send and receive pieces of operational semantics through channels.We present a reduction semantics for LANG-N-SEND, and we offer examples that demonstrate some of the scenarios that LANG-N-SEND captures.
Generative Adversarial Network for Music Generation

Generating music artificially using pre-trained Generative Adversarial Networks (GANs) is challenging task as the training involves temporal variations. Unlike training a GAN for synthesizing videos or images, generating music tracks using GAN involves additional inputs. In this paper, we propose a few methods for generating musing using GAN and its variants. The GAN and other variants are trained using a set of midi file accumulated from the piano dataset. The pre-trained model is then used to generate music similar to piano roll. As GAN has both generator and discriminator, the generated music file is similar to the datasets used while training. The accuracy of the network or the quality the music file generated depends both on the generator and on the discriminator. The same pre-trained model is used for several other music tracks like drums, flute, etc., provided the model is trained with appropriate libraries. The outcome of this experiment is evaluated using conventional evaluators and also the esthetics by human observer.
Sentiment Analysis of Customers’ Review in Bangla Using Machine Learning Approaches

Nowadays, the most deliberate subject, with the goal of assisting one in extracting key information from a large dataset is called sentiment analysis. While social platform plays a significant part in worldwide, this transforms the valuable public opinion's medium. It provides an overwhelming amount of textual information. By examining a huge number of documents, sentiment analysis is the best technique to discover the author's thoughts conveyed in evaluations as positive or negative comments. Firstly, collected Bangla's review on KN95, 5 LAYER MASK. So, we prepared a dataset from their statements. We processed our collection of data to eliminate words that are unneeded and phrases taken from the dataset. Afterward, we utilized TF-IDF vectorizer and support vector machine to vectorize the data, Gaussian Naïve Bayes and Multinomial Naïve Bayes classifier for classify the information in visible form. The dataset secondhand in this place experiment was composed of candidly ready for use comments from connected to the internet shops. Using support vector machine treasure, this model achieves 83.04% precision or correctness in contact with the test set and by using Gaussian Naïve Bayes this model brings to a successful conclusion 88.23%, Multinomial Naïve Bayes bring to successful conclusion 86.37%. According to our findings, Gaussian Naive Bayes has higher accuracy, recall, and F1-score than others, whereas multinomial Naive Bayes has higher precision. It also indicates that the Support Vector Machine classifier outperforms the Multinomial Naive Bayes Classifier in terms of F1-score.
Deploying Mission-Critical Spark Applications on Kubernetes

Before diving headfirst into what Kubernetes (K8s) is, and how Apache Spark fits into the distributed K8s ecosystem, it is important to first begin by stating simply that Kubernetes enables Apache Spark applications to run in isolation, pairing elastic scalability with the runtime consistency of containers, collocated in independent micro-environments called pods (which you’ll learn about soon). Ultimately, you can rely on consistent runtime environments, without having to deal with the pain and other hardships of multi-tenancy in a share-everything ecosystem. Rather, imagine each application running in its own isolated world, which at a high-level act similarly to the local environments you’ve run to power Spark applications using docker-compose on the local data platform we’ve been constructing throughout this book.
Blockchain Implementation in IoT Privacy and Cyber Security Feasibility Study and Analysis

The Internet of Things (IoT) has a significant role in next-generation information technology, and its value and importance are broadly recognized. Internet of Things is rising at a huge level; hence there is an extensive scope to explore and improve the data privacy and security in IoT. Evolving research in IoT applications exploits blockchain technology to record transactions, optimize performance, provide additional security, and decentralized platforms. Blockchain has gained a lot of acceptance and started implementing in various arenas such as security of Internet of Things, Banking and Finance, Automobile, Manufacturing, Healthcare, and Supply Chain. We believe that this survey will assist readers in gaining a thorough understanding of blockchain security and privacy features and possible options to implement in various domains which are integrated with IoT.
Application of Intelligent Algorithm Big Data Analysis in Smart Campus Construction

With the introduction of information technology, the construction and effective operation of University intelligent campus is a stage that all universities must go through. Based on digital campus, intelligent campus comprehensively utilizes next-generation information technologies such as Internet of things, cloud computing, big data, social network and artificial intelligence to fully perceive the campus physical environment. It is an advanced form of university information development, the promotion and extension of digital campus, provides extensive IT services for teachers and students, establishes an intelligent application information system for data sharing throughout the University, and carries out campus education and scientific research. Promote the all-round innovative development of management and campus services. High school is different from traditional project construction. It is a complex system engineering integrating the next generation information technology and education management. This is mainly based on the development of Internet communication technology and big data analysis technology based on intelligent algorithms. Its construction pays more attention to the later operation, maintenance and management, which is mainly reflected in safety, applicability and sustainable development innovation. This is an information system project with a long construction cycle. We study the application of big data analysis technology based on Intelligent Algorithm in the construction of intelligent campus.
Statistical analysis of phase values for the determination of step heights in multi-wavelength interferometry

Abstract To determine step heights between a few nanometers and several micrometers, we present a statistical evaluation procedure which overcomes the limitations of the unambiguity range of conventional multi-wavelength interferometry. The experimental setup consists of a shear interferometer with two different wavelengths to measure the phase difference between light reflected from an object with regions of two different heights. The statistical averaging over a large area of both regions of the object for both wavelengths generates a pair of phase difference values. A diagram of the space of phase values converts the pair of phase difference values into a step height. This statistical method enables the determination of step heights greater than the synthetic wavelength with an accuracy of a few nanometers even with small tilts of the object.
Enhancing Data Sets From Rudna Deep Copper Mine, SW Poland: Implications on Detailed Structural Resolution and Short-Term Hazard Assessment

&amp;lt;p&amp;gt;Applying the software BackTrackBB (Poiata et al., 2016) for automated detection and location of seismic events to data sets from Rudna Deep Copper Mine, SW Poland, lead to an enhancement of existing routine catalogs by about a factor of 10.000 in number of events. Following our hypothesis that all types of seismic events contribute to seismic hazard in a mine, we included all events from major mine collapses (M&amp;gt;3), recorded blasting works and detonations, to machinery noise. These enhanced data sets enabled a detailed spatio-temporal distribution of seismicity in the mine and a short-term hazard assessment on a daily basis.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this study, we focus on the data from two days with major mine collapses: the 2016-11-29 Mw=3.4, and the 2018-09-15 Mw=3.7 events. The spatio-temporal distribution of seismicity of both days deciphered detailed horizontal and vertical structures and revealed the increase of seismic activity after the daily blasting work. The daily histograms exhibit similar patterns, suggesting the dominant influence of explosions on the overall seismicity in the mine. Using the enhanced data sets for short-term hazard assessment, we observed gaps in the activity rates before the main shocks. They were followed by sudden increase of seismicity, a simultaneous drop in seismic b-value, and an increase in exceedance probability for the assumed largest magnitude events. This demonstrates the usefulness of enhanced data sets from surface networks for revealing precursory phenomena before destructive mine collapses and suggests a testing strategy for early warning procedures.&amp;lt;/p&amp;gt;
Scalability Analysis of Proof-of-Work Based Blockchain

Recent advancements in technologies and today’s digital era demands a high level of security and privacy in all aspects. The concept of Blockchain has emerged as a promising technology. As any node in a peer-to-peer network does not fully trust the other nodes, the technology uses cryptographic hash functions (essentially the message digest) as digital signatures. The chapter focuses on discussing Blockchain basics, its working, types of Blockchain, its architecture along with various Blockchain applications. Also, we have provided a detailed discussion and comparison between various existing Blockchain consensus mechanisms viz. Proof-of-stake, Proof-of-Work, Proof-of-Activity, etc. The block size of proof-of-work based Blockchain, such as Bitcoin, is generally limited to 1 MB. As a result, when the number of transactions grow, the systems tend to become slower, expensive, and inefficient. Hence, it is necessary to perform scalability analysis on a network and incorporate necessary scalability solutions. The chapter discusses various system parameters involved with Blockchain scalability issues in detail. Further, we will present our series of experiments that have been performed for scalability analysis of Proof-of-work based Blockchain using a simulation tool. It provides details of the process along with the analysis results to examine the impact of varying parameters such as block-size, number of nodes, and transaction fee.
Prototype Decision Support System To Detect Disaster Prone Areas With Saw Method (Tanggamus District Case Study)

Most of Tanggamus regency is a disaster-prone area, such as floods, landslides, earthquakes, and so on. To determine the area that is really potentially catastrophic is something complicated and the determination process there are many errors, because the determination process is based on subjectivity. In this case it is most likely that the area that is really potential for disaster does not enter into the territory prioritized by the government to be given socialization of insights about disasters or reduce the risk of disasters. This paper discusses the Simple Additive Weighting (SAW) method that can be used in determining disaster-prone areas in Tanggamus Regency. The area to be designated as a disaster-prone area has criteria that have been set. Criteria needed include: Flood disaster data, landslide disaster data, earthquake disaster data, tsunami disaster data, and fire disaster data. The result of this system is a list of disaster-prone areas that comply with the criteria specified as areas of special attention from local governments.
Action Node Graph: Graph Design for Mobile Robot Route Planning in Cities

This paper proposes a graph design for the action planning of autonomous mobile robots in cities. When moving in city environments, not only movements but also various other actions such as opening a door or crossing the street need to be considered. Autonomous robots are required to plan their routes while balancing the cost of both movements and required actions. To enable this, we developed the "action node graph," a graphical representation of a robot&#x2019;s mobility environment. Autonomous robots can use this action node graph to obtain the optimal route to the destination and determine the required actions according to their specifications. An action node graph can be easily constructed from geospatial information and is automatically converted into a behavior tree to be used for the autonomous navigation of a variety of mobile robots. We used a wheeled robot to demonstrate autonomous navigation around crossings and buildings.
An Intelligent System for Diagnosis and Prediction of Breast Cancer Malignant Features using Machine Learning Algorithms

Artificial intelligence is the most emerging technology in computer science of which Machine Learning (ML) is one of the branches that facilitates systems to learn from data and recognize patterns that provide decisions with less human intervention. Various ML techniques are being devised to cure breast cancer (BC), which is the second most common cancer in women. These techniques are able to classify BC as malignant or benign by predicting breast cancer at early stages, which can lower the mortality rate to a certain extent. This chapter presents how to categorize BC as malignant or benign, early diagnosis using various machine learning techniques, and curing BC as well as the best technology in terms of accuracy rate. In this chapter, we conducted tests on machine learning algorithms, namely KNN, RF, NB, and SVM. The results show that RF 98.11 and SVM 92.6 are best with less error rates.
Classification of healthy and affected lungs by pneumonia disease from x-ray images of lungs and gene sequencing using inception model

This project is entitled to predict the lung disease using chest x-rays by deep learning technique. Lung disease is a term that refers to improper functioning of lungs. There are many diseases which occur due to the abnormal functioning of lungs. It includes tuberculosis, pneumonia, lung cancer, asthma. The infection can be bacterial, viral or fungal. It causes inflammation of trachea and respiratory failure. If found earlier it can be cured else it can even lead to death. This project classifies the normal and abnormal x-ray with a percentage of accuracy so that we can give the treatment to the patient accordingly by seeing the x-ray. The algorithms used are Convolutional Neural Network (CNN) Inception Neural Network (INN) and Tensor Flow which is Google's open-source algorithm. The project is helpful for finding lung disease using chest x-ray.
Towards a Historical Ontology for Arabic Language: Investigation and Future Directions

Despite its diversity, Arabic does not yet have a historical dictionary which helps to monitor its semantic development throughout history and to understand its knowledge and scientific heritage properly. There are only some incomplete attempts with various semantic problems. At first, an Arabic word can carry different types of meaning depending on the specific context. The relationship between Arabic words should be analysed and modeled in order to have a comprehensive dictionary. However, all dictionary versions that have been constructed till now are either unstructured or semi-structured. Also, they neither define nor present the relationship between different Arabic words. Hence, the use of a standardized and well-structured format is necessary to implement the Arabic historical dictionary. In this respect, it is worth noting that Semantic Web Technologies (SWT) including ontologies are a promising solution for building a unified standard model for the Arabic language. This paper examines the possibility of representing the Arabic historical dictionary using SWT in order to capture the evolution of Arabic words and their diverse meaning over centuries.
Black Widow Spider Algorithm Based on Differential Evolution and Random Disturbance

Aiming at the problems of unbalanced exploitation and exploration and falling into local optimization in black widow algorithm. In this paper, differential evolution strategy is introduced to avoid unnecessary exploration. At the same time, the random disturbance factor is used to improve the local exploitation performance of the black widow algorithm, and the memory function is added to the individual to improve the population's reproductive strategy, it further reduces the possibility of falling into local and ensures the balance between exploitation and exploration. By testing benchmark optimization functions and engineering problems, it shows the advantages of the improved algorithm, and has better progress in global search ability and convergence speed.
Tree-based machine learning models for photovoltaic output power forecasting that consider photovoltaic panel soiling

This article presents direct regression models for forecasting photovoltaic (PV) system output power based on machine learning (ML) methods, in which the Cleanness Index (CI) was introduced as an indicator of the PV panel soiling level. Three different ML methods based on decision trees were used: random forests (RF), extra trees (ET) and gradient boosting (GB). The research results showed that the introduction of the CI has significant potential to improve the forecasting of tree-based ML models. The mean average error of forecasts of the best model, which contained CI, and the model created by excluding CI from the set of input variables were 0.22% and 1.24%, respectively, as related to the nominal power of the PV panel. For the observed models, it was shown that the CI is the second-most important input variable and more significant than the PV panel temperature.