Analysis of time series – wind speeds and wind directions using neural network models and classification task

В работе приводятся результаты анализа временных рядов – скоростей и направлений ветра в региональном масштабе с использованием моделей нейронных сетей и задачи классификации на основе данных четырех метеорологических станций, расположенных на территории Республики Дагестан. В качестве исходных данных взяты временные ряды за период 2011-2020гг с частотой измерений 8 раз в сутки. Цель работы заключается в изучении закономерностей во временных рядах на основе результатов машинного обучения в задаче классификации. В рамках поставленной цели решаются задачи: спроектировать модели нейронных сетей для классификации метеорологической станции на основе данных скоростей и направлений ветра (вместе и по отдельности); добиться максимально возможной точности предсказания через настройку глобальных параметров; выполнить серию экспериментов по моделированию и оценить результаты. В результате выполнения экспериментов получены зависимости точности классификации от размера блока данных, которые позволяют сделать выводе о минимальном размере блока данных во временном ряде, обеспечивающем точности близкие к максимально возможным. Также установлено и показано, что ошибки классификации модели нейронных сетей явно коррелируют с географическим положением метеорологических станций. По распределению ошибок классификации во временном интервале, установлено, что меньше всего ошибок имеется в весенний период, больше всего – в летний. В целом у расположенных на морском побережье метеорологических станций ошибок классификации больше, что говорит о меньшей уникальности ветрового режима в этих районах. Результаты работы также позволяют сделать общий вывод о том, что нейронные сети могут использоваться не только как инструмент прогноза, распознавания или классификации, но и как инструмент, позволяющий давать аналитическую оценку исходным данным – временным рядам. The paper presents the analytics results of time series – wind speeds and wind directions on a regional scale using neural network models for the classification task based on data from four meteorological stations located on the territory of the Republic of Dagestan. Time series for the period 2011-2020 were taken as the initial data with a frequency of measurements 8 times a day. The purpose of the work is to study patterns in time series based on the results of machine learning in the classification task. Within the framework of this purpose, the following tasks are being solved: to develop neural network models for the classification of a meteorological station based on data of wind speeds and wind directions (together and separately); to achieve the highest possible prediction accuracy by adjusting the global parameters; to run a series of simulation experiments and evaluate the results. As a result of the experiments, the dependences of the classification accuracy on the data block size were obtained, which allow us to conclude about the minimum size of the data block in the time series, which provides the accuracy close to the maximum possible. It was also found and shown that classification errors of the neural network model clearly correlate with the geographical location of meteorological stations. According to the distribution of classification errors in the time interval, it was found that the least number of errors is in the spring period, and most of all – in the summer ones. In general, the meteorological stations located on the sea coast have more classification errors, which indicates a lesser uniqueness of the wind dynamics in these regions. The paper results also allow us to draw a general conclusion that neural networks can be used not only as a forecasting, recognition or classification tool, but also as a tool that allows an analytical assessment of the time series data.

PROPOSAL OF A DATA MODEL FOR MANAGING BLOKCHAIN-BASED TRUST BETWEEN ACTORS IN AN ELECTORAL PROCESS

In this paper we propose a tool (reference data model) for the improvement of trust, based on blokchain technologies, between the different actors of an electoral process. Our contribution focuses in a first step on the implementation of a data model having all these public attributes and thus public classes whose methods are coupled to cryptographic techniques. In a second step, we propose an underlying formalism of this model using a matrix representation of the different actors, the different transactions and the working criteria allowing to validate these transactions and blockchains. This formalism allows to find a transaction performed by one of the actors of the electoral chain and also the data block in which this transaction is validated. Also, an algorithm allowing to reinforce the trust is proposed.

Imagechain—Application of Blockchain Technology for Images

Imagechain is a cryptographic structure that chain digital images with hash links. The most important feature, which differentiates it from blockchain, is that the pictures are not stored inside the blocks. Instead, the block and the image are combined together in the embedding process. Therefore, the imagechain is built from standard graphic files that may be used in the same way as any other image, but additionally, each of them contains a data block that links it to a previous element of the chain. The presented solution does not require any additional files except the images themselves. It supports multiple file formats and embedding methods, which makes it portable and user-friendly. At the same time, the scheme provides a high level of security and resistance to forgery. This is achieved by hashing the whole file with embedded data, so the image cannot be altered or removed from the chain without losing integrity. This article describes the basic concept of an imagechain together with building blocks and applications. The two most important issues are embedding methods and block structure.

Network Pseudohealth Information Recognition Model: An Integrated Architecture of Latent Dirichlet Allocation and Data Block Update

The wanton dissemination of network pseudohealth information has brought great harm to people’s health, life, and property. It is important to detect and identify network pseudohealth information. Based on this, this paper defines the concepts of pseudohealth information, data block, and data block integration, designs an architecture that combines the latent Dirichlet allocation (LDA) algorithm and data block update integration, and proposes the combination algorithm model. In addition, crawler technology is used to crawl the pseudohealth information transmitted on the Sina Weibo platform during the “epidemic situation” from February to March 2020 for the simulation test on the experimental case dataset. The research results show that (1) the LDA model can deeply mine the semantic information of network pseudohealth information, obtain the features of document-topic distribution, and classify and train topic features as input variables; (2) the dataset partitioning method can effectively block data according to the text attributes and class labels of network pseudohealth information and can accurately classify and integrate the block data through the data block reintegration method; and (3) considering that the combination model has certain limitations on the detection of network pseudohealth information, the support vector machine (SVM) model can extract the granularity content of data blocks in pseudohealth information in real time, thus greatly improving the recognition performance of the combination model.

Threshold-Based Post-Quantum Secure Verifiable Multi-Secret Sharing for Distributed Storage Blockchain

Blockchain systems store transaction data in the form of a distributed ledger where each node stores a copy of all data, which gives rise to storage issues. It is well-known that the tremendous storage and distribution of the block data are common problems in blockchain systems. In the literature, some types of secret sharing schemes are employed to overcome these problems. The secret sharing method is one of the most significant cryptographic protocols used to ensure the privacy of the data. The main purpose of this paper is to improve the recent distributed storage blockchain systems by proposing an alternative secret sharing method. We first propose a secure threshold verifiable multi-secret sharing scheme that has the verification and private communication steps based on post-quantum lattice-based hard problems. We then apply the proposed threshold scheme to the distributed storage blockchain (DSB) system to share transaction data at each block. In the proposed DSB system, we encrypt the data block with the AES-256 encryption algorithm before distributing it among nodes at each block, and both its secret key and the hash value of the block are privately shared among nodes simultaneously by the proposed scheme. Thereafter, in the DSB system, the encrypted data block is encoded by the Reed–Solomon code, and it is shared among nodes. We finally analyze the storage and recovery communication costs and the robustness of the proposed DSB system. We observe that our approach improves effectively the recovery communication cost and makes it more robust compared to the previous DSB systems. It also improves extremely the storage cost of the traditional blockchain systems. Furthermore, the proposed scheme brings to the DSB system the desirable properties such as verification process and secret communication without private channels in addition to the known properties of the schemes used in the previous DSB systems. As a result of the flexibility on the threshold parameter of the scheme, a diverse range of qualified subsets of nodes in the DSB system can privately recover the secret values.

HadoopSec 2.0: Prescriptive analytics-based multi-model sensitivity-aware constraints centric block placement strategy for Hadoop

Like many open-source technologies such as UNIX or TCP/IP, Hadoop was not created with Security in mind. Hadoop however evolved from the other tools over time and got widely adopted across large enterprises. Some of Hadoop’s architectural features present Hadoop its unique security issues. Given this security vulnerability and potential invasion of confidentiality due to malicious attackers or internal customers, organizations face challenges in implementing a strong security framework for Hadoop. Furthermore, given the method in which data is placed in Hadoop Cluster adds to the only growing list of these potential security vulnerabilities. Data privacy is compromised when these critical and data-sensitive blocks are accessed either by unauthorized users or for that matter even misuse by authorized users. In this paper, we intend to address the strategy of data block placement across the allotted DataNodes. Prescriptive analytics algorithms are used to determine the Sensitivity Index of the Data and thereby decide on data placement allocation to provide impenetrable access to an unauthorized user. This data block placement strategy aims to adaptively distribute the data across the cluster using innovative ML techniques to make the data infrastructure extra secured.

Research and Application of High-Speed and Adjustable Synchronous Data Transfer System Based on USB 3.0 Peripheral Controller

Since bulk transfer bandwidth of the host is unstable, the universal serial bus (USB) 3.0 hyperspectral data transfer system can only achieve a data transfer rate of about 30 MBps which is less than one-fifteenth of USB 3.0 theoretical transfer rate of 5 Gbps. For aerial hyperspectral imager, data transfer system is required to meet different frame rates of detector for different speed-to-height ratios. In this paper, we propose a high-speed and adjustable synchronous transfer system. The USB 3.0 peripheral controller uses synchronous first in first out (FIFO) and automatic direct memory access (DMA) to achieve the highest data transfer bandwidth. The USB acquisition software collects a data block in every fixed time interval. The size in bytes of every data block must be an integer multiple of the maximum data packet payload size, which is a necessary condition for using automatic DMA and bulk transfers. The data transfer rate of the system could be adjusted by directly changing the data block size and acquisition time interval. The experimental results show that the synchronous transfer mechanism could facilitate the 100-MBps error-free and high data transfer bandwidth application on a hyperspectral data processing system.

Perfectly Secure Shannon Cipher Construction Based on the Matrix Power Function

A Shannon cipher can be used as a building block for the block cipher construction if it is considered as one data block cipher. It has been proved that a Shannon cipher based on a matrix power function (MPF) is perfectly secure. This property was obtained by the special selection of algebraic structures to define the MPF. In an earlier paper we demonstrated, that certain MPF can be treated as a conjectured one-way function. This property is important since finding the inverse of a one-way function is related to an N P -complete problem. The obtained results of perfect security on a theoretical level coincide with the N P -completeness notion due to the well known Yao theorem. The proposed cipher does not need multiple rounds for the encryption of one data block and hence can be effectively parallelized since operations with matrices allow this effective parallelization.