Reviewerly/exHarmony huggingface dataset:
Blockchain-based Edge Computing Data Storage Protocol Under Simplified Group Signature

In contrast to the traditional cloud-based IoT structure, which imposes high computation and storage demands on the central cloud server, edge computing can process data at the network edge. In this paper, we present an edge computing data storage protocol employing the blockchain and the group signature, where the blockchain works as the trusted third party, offering a convenient platform for the data storage and protection, and the group signature is utilized for privacy identity information protection. Unlike the common group signature scheme, we propose a notion of \emph{simplified group signature}, which removes the traceable property to improve the computational efficiency. In edge computing, the edge devices and the end devices can easily set up a long-term trust relationship through the cloud services, so the traceability may be of little utility in this scenario. In our protocol, we also design a new data validation scheme that has a proof size of only <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$O(1)$</tex></formula> .
A Lossless Compression Method for Chat Messages Based on Huffman Coding and Dynamic Programming

There is always an increasing demand for data storage and transfer; therefore, data compression will always be a fundamental need. In this article, we propose a lossless data compression method focused on a particular kind of data, namely, chat messages, which are typically non-formal, short-length strings. This method can be considered a hybrid because it combines two different algorithmic approaches: greedy algorithms, specifically Huffman coding, on the one hand and dynamic programming on the other (HCDP = Huffman Coding + Dynamic Programming). The experimental results demonstrated that our method provided lower compression ratios when compared with six reference algorithms, with reductions between 23.7% and 39.7%, whilst the average remained below the average value reported in several related works found in the literature. Such performance carries a sacrifice in speed, however, which does not presume major practical implications in the context of short-length strings.
Research on SVM Fault Diagnosis Method Based on Text Feature Extraction Algorithm

This paper proposed a new fault diagnosis method based on the fault phenomenon description text. This paper used feature extraction algorithm in the field of natural language process to transform unstructured text data into structured data. In this paper, labeled text data is put into a support vector machine to train a prediction model. The model uses fault text description data to diagnose fault.
Parabolic solar water heater performance with optimum tracking system: Baghdad city case study

Abstract The energy extracted from the solar is changing cording to seasons and the way of tracking. In this study, have been five tracking modes are North-South and East-West, which have been rotating over the seasons of the year. Also, the Baghdad city has been chosen to select the optimum tracking mode. A mathematical pattern has been used to solve numerically. From this study, deduced that all the tracking models performance is converging at the noon period of the North-South tracking mode. These will allow the highest solar energy collected is 34.15% from the East-West tracking rotating mode.
Research on 3D Printing Resource cloud Platform Technology based on dynamic Service Composition

Abstract To improve the improve the management and matching of 3D printing resources, a 3Dprinting cloud platform based on dynamic service composition technology is established, and the 3D printing composite service is optimized. First, cloud computing is combined with 3D printing to establish cloud manufacturing mode. According to the characteristics of each stage, the dynamic service composition technology under cloud manufacturing mode is divided into three stages. Then, dynamic service composition technology of 3D printing resource is established. Based on the logical relationship between services, breadth algorithm and depth algorithm are used to find the optimal service composition path in the search graph to meet the different needs of users. The experimental results show that the relationship between user requirements and services can be obtained by calculating the quality of service. In the case, the service quality required by users is less than 20, and service 1 and 3 can meet the needs of users. This exploration provides a reference for the study of 3D printing resources dynamic service composition.
Design and Research of Support System for Multiple Simulation UAVs

In recent years, with the accelerated expansion of applications in the UAV field and the continuous growth of the industry, the increasingly mature UAV market has also put forward new requirements for technological development. "UAV cluster technology" is rising at this moment and is getting more and more attention. The multi-UAV cluster test requires architecture to support it. In order to solve a large number of data stream access, communication and cost issues in the multi-UAV cluster system, it is based on the Internet of Things, wireless ad hoc network, database, etc. A support system for multiple drones. Analysis and demonstration show that, compared with previous related research, this new architecture is systematic and complete, and meets the special application environment of large business data flow and large infrastructure workload for multiple simulation drone cluster tests, and has high reliability and operability.
The media space of the educational sphere as a logistic system: features of management and personality formation

Development of digitization (including education) leads to difficulties in management and regulation of important multidimensional dynamic social economical systems, where problems of efficient management occur requiring for interdisciplinary approach and active application of information technologies and computers. It is required to develop new methods of risk management (in the fields of national security, personal security), since information nonmilitary action as the most important hazard of hybrid wars is precepted as motion along the road of progress, democracy, tolerance. The research objective implies search and implementation of optimum management of stepwise digitization of education by means of monitoring and reconfiguration of digital platforms as elements of global logistic system (with consideration for consciental risks). Targets include analysis of features of dynamics and management of digitization of educational platforms in relation with maturity of social institutions, COVID-19, ecology of information space, degree of informatization of society, role and composition of mental structures, etc.; detection of factors influencing dynamics of persons and social institutions involved in education. The interrelation between digitization of education and development of educational platforms has been described. The conclusions have been made about the advantages and disadvantages of management formed by analogy with management of dynamic processes running on classical logistic systems. Recommendations have been given on how to reduce the dependence of a human on behavioral stereotypes imposed by various algorithmic energy information structures. In the frames of community, this would provide productive existence in unique personal space, efficient communication with surrounding people together with preservation of mentality and self-consciousness, cultural and national identity.
Research on trajectory planning of dual-arm cooperative robot

Abstract To meet the requirements of the market for complex and diverse operation tasks, the reasonable trajectory planning of the manipulator plays an important role in the completion of the task, which affects the motion accuracy and operation efficiency of the manipulator, the trajectory planning of the manipulator needs to meet the smooth change of motion parameters and no sudden change to ensure the smooth operation of the system. In this paper, the method of middle point interpolation is used to calculate and analyze the linear interpolation trajectory planning and arc interpolation trajectory planning of 7-DOF(degrees of freedom) redundant manipulator, and to realize the accurate trajectory planning of the end position of the dual-arm cooperative robot.
Fuzzy Stochastic Timed Petri Nets for Causal Properties Representation

Imagery is frequently used to model, represent and communicate knowledge. In particular, graphs are one of the most powerful tools, being able to represent relations between objects. Causal relations are frequently represented by directed graphs, with nodes denoting causes and links denoting causal influence. A causal graph is a skeletal picture, showing causal associations and impact between entities. Common methods used for graphically representing causal scenarios are neurons, truth tables, causal Bayesian networks, cognitive maps and Petri Nets (PNs). Causality is often defined in terms of precedence (the cause precedes the effect), concurrency (often, an effect is provoked simultaneously by two or more causes), circularity (a cause provokes the effect and the effect reinforces the cause) and imprecision (the presence of the cause favors the effect, but not necessarily causes it). We will show that even though the traditional graphical models are able to represent separately some of the properties aforementioned, they fail trying to illustrate indistinctly all of them. To approach that gap, we will introduce Fuzzy Stochastic Timed PNs as a graphical tool able to represent time, co-occurrence, looping and imprecision in causal flow.
ECDSA-Compatible Privacy Preserving Signature With Designated Verifier

We present the first ECDSA compatible privacy preserving signature scheme, which is based on the work of nominative signature, with a designated verifier. The use case forthis signature scheme is to sign personal information. For example, a user can prove toan insurance company that they are diagnosed with a certain disease and is eligible foran insurance claim. The insurance company will be convinced the doctor's signature is genuine but it cannot leak the information with a convincing proof (i.e. the signature verification proof is not transferable, but leaking information without a proof is still possible). As in our example, the signature generation has to be initiated by the patient, with the help of the doctor, and the signature can only be verified by interacting with the patient directly.
Meteorological Data Driven Prediction of Global Solar Radiation

This paper aims at prediction of global solar radiation (GSR) using artificial neural network (ANN) model. Various meteorological parameters such as relative humidity, average air temperature, wind speed, wind direction, dew point and atmospheric pressure contribute to estimation of global solar radiation. A search has been carried out to find the strongly correlated variables which contribute more to this assessment. On that basis, an ANN model is devised for the prediction of GSR. The results are compared with those obtained using multiple linear regression (MLR) model. The performances of the models are checked using various statistical parameters such as mean absolute deviation (MAD), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE) and correlation coefficient (R). A comparison of the predicted values obtained from the two methods and the actual values obtained from a weather monitoring station shows that the developed ANN model generates values closer to actual. The best MAPE calculated by using ANN model is 2.851%.
A Polystore Based Database Operating System (DBOS)

Current operating systems are complex systems that were designed before today’s computing environments. This makes it difficult for them to meet the scalability, heterogeneity, availability, and security challenges in current cloud and parallel computing environments. To address these problems, we propose a radically new OS design based on data-centric architecture: all operating system state should be represented uniformly as database tables, and operations on this state should be made via queries from otherwise stateless tasks. This design makes it easy to scale and evolve the OS without whole-system refactoring, inspect and debug system state, upgrade components without downtime, manage decisions using machine learning, and implement sophisticated security features. We discuss how a database OS (DBOS) can improve the programmability and performance of many of today’s most important applications and propose a plan for the development of a DBOS proof of concept.
An Unsupervised Machine Learning Technique for Recommendation Systems

Due to the advancement of the Internet, various kinds of data are easily found online which helps users to find useful information which are of their interest. However, the exponential growth of data has caused it to be complex and huge, so it has become difficult to filter valuable information from it. Recommendation systems can help to overcome this issue and give recommendations to the users which matches the area of their interest. Most of the systems rely on a rating prediction algorithm where the items are taken as recommended for a user if the user's predicted rating is high on those items. This research aims to increase the accuracy and reduce the processing time for recommendation using the prediction algorithm based on the unsupervised machine learning method. The proposed solution consists of Autoencoder to enhance the accuracy of prediction and reduce the processing time. Partially observed interaction matrix is used as input for the neural network model which outputs a complete rating matrix. The proposed solution achieved an improvement by 1.83%, 0.85% and 3.72% in cold start case for MSE, RMSE and MAE evaluation metrics respectively. The proposed solution performs better and will be used in cold start cases for datasets where timestamp value (user creation time) is used.
Target Localization in Multistatic Systems Without the Bistatic Range Measurement Noise Power

This letter investigates the problem of determining the static target in multistatic systems using bistatic range (BR) measurements, which can be obtained by some local sensor signal processing methods. The localization problem is first formulated as a semidefinite programming problem by using the semidefinite relaxation technique. Then, an initial position estimate is given by augmenting the unknown parameter space, which is required for solving the SDP problem. Specifically, this initialization is also a closed-form solution. Compared with existing algorithms, the proposed estimator has the advantage of not relying on the prior knowledge of BR measurement noise power. Simulation results validate the performance of the proposed estimator.
PCA effect on the 3D face recognition system speed

Abstract In this paper, a system of three-dimensional (3D) face recognition is not done through 3D face reconstruction method but directly uses the data retrieved from Kinect Xbox camera system. From a previous study, there exists a possibility to increase the speed and accuracy of the system. In order to accelerate the recognition speed, a single step in the said study is eliminated, which is the reconstruction of 3D face data. The algorithms used in this research are Backpropagation Neural Network and PCA. Testing is done in two ways. The first test uses a combination of Backpropagation and PCA, while the second test uses Backpropagation only. PCA has the function of simplifying the amount of data, thereby reducing the amount of computing that can increase speed system. The results show that the system that uses a combination of Backpropagation and PCA achieved an increase in speed up to 34.2 times, but a reduced accuracy of 8.5%.
Sentiment Analysis Tool for Spanish Tweets in the Ecuadorian Context

The huge amount of textual information that exists on social networks added by users through comments, has aroused a great interest in companies and research groups, which seek to use this information to identify trends and acceptance levels of brands, products, and services. A technique to know the level of acceptance or rejection of a particular topic, in an automated way, is the Sentiment Analysis. Some informatics tools incorporate this technique, however, there are few contributions to texts in Spanish. It is because of the difficulty of identifying different contexts, dialects, complex grammatical structures, and semantic language variances in each region. This article presents a web tool for the analysis of sentiments in texts written in Spanish that include Ecuadorian dialect or idioms. The tool was developed in R-Shiny with an approach lexicon-based. The tool allows the customization of the lexicons based on context and facilitates the automatic download of tweets according to search criteria such as the place, dates, and topic. To evaluate the effectiveness of the application, their result was compared with two commercial tools (Azure Text Analytics and IBM Watson NLU) and a manual score carried out by a group of people. The tests include the analysis of three corpora created from tweets. The results show the effectiveness of the tool to identify the sentiment polarity, especially in texts that include dialects, colloquial words, and negative expressions.
Feature-Weighted Sampling for Proper Evaluation of Classification Models

In machine learning applications, classification schemes have been widely used for prediction tasks. Typically, to develop a prediction model, the given dataset is divided into training and test sets; the training set is used to build the model and the test set is used to evaluate the model. Furthermore, random sampling is traditionally used to divide datasets. The problem, however, is that the performance of the model is evaluated differently depending on how we divide the training and test sets. Therefore, in this study, we proposed an improved sampling method for the accurate evaluation of a classification model. We first generated numerous candidate cases of train/test sets using the R-value-based sampling method. We evaluated the similarity of distributions of the candidate cases with the whole dataset, and the case with the smallest distribution–difference was selected as the final train/test set. Histograms and feature importance were used to evaluate the similarity of distributions. The proposed method produces more proper training and test sets than previous sampling methods, including random and non-random sampling.
Elementary school in the conditions of digital transformation of the education system

Informational support of education is an area of pedagogical science that has been actively developing in Russia over the past few decades. Today, it has become the basis for the digital transformation of education, whose essence is transforming the methods of professional teaching by integrating digital technologies and moving to a data-based decision-making model. Among the participants of educational relations, there are diametrically opposite views on the possibility and feasibility of digitalization of education, especially at its initial stage. The purpose of this research is to present an authorial standpoint on the problem of digitalization of education with regard to both elementary general education in general, and digitalization of the Mathematics and Computer Science subject area, in particular, at the level of primary general education. The research methods include content analysis of scientific articles and methodological publications, the regulatory framework of school education, as well as generalization and systematization of international research in the field of the digital transformation of education. Authors considered external aspects of the digital transformation of primary education, determined by the general requirements for the information educational environment; highlighted activity areas related to the formation of students’ basic digital skills in the course of mastering all subject areas without exception; as well as revealed the problems of internal digitalization of the Mathematics and Computer Science subject area at the level of primary general education. The article substantiates the expediency of strengthening the logical and algorithmic components of the initial course of mathematics, the content line responsible for working with data, as well as several other issues related to theoretical computer science, and outlines prospects of the digital transformation of training of future primary school teachers to work in a modern digital educational environment.
Subquadratic-Time Algorithms for Normal Bases

For any finite Galois field extension K/F, with Galois group G = Gal (K/F), there exists an element $$\alpha \in $$ K whose orbit $$G\cdot\alpha$$ forms an F-basis of K. Such an $$\alpha$$ is called a normal element, and $$G\cdot\alpha$$ is a normal basis. We introduce a probabilistic algorithm for testing whether a given $$\alpha \in$$ K is normal, when G is either a finite abelian or a metacyclic group. The algorithm is based on the fact that deciding whether $$\alpha$$ is normal can be reduced to deciding whether $$\sum_{g \in G} g(\alpha)g \in$$ K[G] is invertible; it requires a slightly subquadratic number of operations. Once we know that $$\alpha$$ is normal, we show how to perform conversions between the power basis of K/F and the normal basis with the same asymptotic cost.
Performance Analysis of Feature sets in Speaker Diarization techniques

Abstract Speech is the most important communication among humans. Processing of speech signal has many strategies including speech coding, speaker recognition, speaker verification, etc. Speaker diarization is the pre-processing stage for many applications of speaker recognition systems. Speaker Diarization is the mission of determining “who Spoke when” for any audio recording that carries an unknown quantity of records and an unknown variety of audio systems. Speaker diarization has come to be achief era for many tasks like navigation, retrieval, or higher-level interference on audio data. It mainly performs three operations feature extraction, voice activity detection, and classification. In this paper, we’ve reviewed the few speaker diarization Techniques. The trendy speaker diarization structures finished nice outcomes. In this paper, few speaker diarization device performances are evaluated for Diarization mistakes, Tracking time, and False alarm.
A fast adaptive convergence control of underwater vehicle based on ELM

In view of the underwater vehicle system, this paper presents a new fast nonlinear sliding mode algorithm based on adaptive ELM to approximate the indeterminate items in the system as a whole. Moreover, a new nonlinear slip mode super-surface is used to enable the system to reach the equilibrium point faster than the linear slip and terminal slip mode from any state on the slip face. By using Lyapunov stability theory, the existence of bounded disturbance and bounded neural network approximation error conditions, the control system of underwater vehicle tracking error bounded stable are proved. And relevant simulation verification is carried out.