Data Block Research Articles

An Adaptive-Iterative Nonlinear Interference Cancellation in Time-Varying Full-Duplex Channels

In this paper, first, the sensitivity of traditional Full-Duplex (FD) communication systems to time variation in the self-interference (SI) channel is demonstrated via performance analysis. It is seen that conventional schemes are not capable of providing efficient operation regarding practical concerns such as spectral efficiency, SI channel aging and learning accuracy, and the contamination of signal-of-interest (SoI). Then, in regard to the aforementioned concerns, a practical FD operation together with a novel iterative SoI-contamination-aware digital SI cancellation (DSIC) algorithm that relieves the sensitivity against time variation considerably is proposed. The proposed scheme, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Turbo</i> DSIC, is practical in terms of training and computational complexity thanks to its two-stage structure such that complex nonlinear parameter learning (NPL) is conducted very rarely in the first stage, and linear processing is performed adaptively at symbol-rate in the second stage, where SoI is iteratively estimated at each data block and eliminated from the received signal to improve SI channel estimation accuracy. Finally, the performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Turbo</i> DSIC, under hardware impairments and time-varying (TV) propagation, is evaluated via both computer simulation environment and actual experimental hardware setup in terms of bit error rate (BER) and achievable information rate (AIR) metrics.

Level-Based Blocking for Sparse Matrices: Sparse Matrix-Power-Vector Multiplication

The multiplication of a sparse matrix with a dense vector (SpMV) is a key component in many numerical schemes and its performance is known to be severely limited by main memory access. Several numerical schemes require the multiplication of a sparse matrix polynomial with a dense vector which is typically implemented as a sequence of SpMVs. This results in low performance and ignores the potential to increase the arithmetic intensity by reusing the matrix data from cache. In this work we use the recursive algebraic coloring engine (RACE) to enable blocking of sparse matrix data across the polynomial computations. In the graph representing the sparse matrix we form levels using a breadth-first search. Locality relations of these levels are then used to improve spatial and temporal locality when accessing the matrix data and to implement an efficient multithreaded parallelization. Our approach is independent of the matrix structure and avoids shortcomings of existing “blocking” strategies in terms of hardware efficiency and parallelization overhead. We quantify the quality of our implementation using performance modelling and demonstrate speedups of up to 3× and 5× compared to an optimal SpMV-based baseline on a single multicore chip of recent Intel and AMD architectures. Various numerical schemes like <inline-formula><tex-math notation="LaTeX">$s$</tex-math></inline-formula> -step Krylov solvers, polynomial preconditioners and power clustering algorithms will benefit from our development.

Digital currency banking using block chain technology

Banks are now the almost sole source of confidence for internet commerce when it comes to processing electronic payments. With a peer-to-peer electronic currency, payments can be conducted online directly between parties without going via a banking organisation. While signatures do contribute in some ways, the main benefits are lost if a trustworthy third party is still required to prevent double spending. So in this project we can implement Bit Coin based banking system can be implemented leveraging the technologies of block chains to create hash functions. Bit coin is a crypto currency, which is not supported by the government or central bank of any nation. It can be traded for goods or services with vendors who the use of bit coins payment. These bit coins are the blocks of secure data. It takes a lot of CPU resources to securely verify each individual transaction as the data is passed from one person to another while also spending money on the transaction. The P2P network monitors and verifies the moving of bit coins between users. Bit coin is more secure than other currencies in terms of cryptographic implementation, and it is difficult to carry out fraudulent transactions. In a Bit coin transaction, the block chain will connect every user on the network, and each time a transaction is entered, the network will broadcast it to all other users after it has been validated. The network will also have a copy of every transaction. The network will group transaction data into blocks and broadcast them throughout the network rather than preserving any transactions in the block chain. Every block in this chain will link to the one before it, which is known as the genesis block. Peer-to-peer networks and a consensus mechanism are used in block chain systems, eliminating the potential of data alteration.

RESOLVING SMART HEALTH SECURITY ISSUES USING ONTOLOGIES AND BLOCKCHAIN SERVICES

IoT is one of the most commonly used and emerging technology. It has been used for resolving many issues in daily life. In this paper, we are going to address the security issues in smart health and how we can resolve them by using blockchain technology. Blockchain is evolving rapidly which deals in blocks of data. By using this attribute, we can partially grant access to data to a specific user by identifying their roles in that certain activity. Although there are already many derived solutions by other researchers, we will follow the combination of RFID and EHR techniques of blockchain to propose a better solution to solve this security challenge.

RETRACTED ARTICLE: Blockchain-enabled K-harmonic framework for industrial IoT-based systems

Industrial Internet of Things (IIoT)-based systems have become an important part of industry consortium systems because of their rapid growth and wide-ranging application. Various physical objects that are interconnected in the IIoT network communicate with each other and simplify the process of decision-making by observing and analyzing the surrounding environment. While making such intelligent decisions, devices need to transfer and communicate data with each other. However, as devices involved in IIoT networks grow and the methods of connections diversify, the traditional security frameworks face many shortcomings, including vulnerabilities to attack, lags in data, sharing data, and lack of proper authentication. Blockchain technology has the potential to empower safe data distribution of big data generated by the IIoT. Prevailing data-sharing methods in blockchain only concentrate on the data interchanging among parties, not on the efficiency in sharing, and storing. Hence an element-based K-harmonic means clustering algorithm (CA) is proposed for the effective sharing of data among the entities along with an algorithm named underweight data block (UDB) for overcoming the obstacle of storage space. The performance metrics considered for the evaluation of the proposed framework are the sum of squared error (SSE), time complexity with respect to different m values, and storage complexity with CPU utilization. The results have experimented with MATLAB 2018a simulation environment. The proposed model has better sharing, and storing based on blockchain technology, which is appropriate IIoT.

From #iononriapro to #ioapro: analysing hashtag activism in the Italian restaurant Twittersphere during the 2020 lockdown

PurposeIn this article, the authors analyse the impact of the 2020 lockdown and the subsequent measures to contain the spread of COVID-19 in Italy in the hospitality industry by looking at the social demands brought forward by the restaurant sector.Design/methodology/approachTo analyse social demands, the authors choose Twitter as an observation point using two hashtags as keywords to scratch the data: #iononriapro and #ioapro, which correspond to two different instances conveyed by the same subject: the restaurant sector. The instances linked to the hashtags produced different levels of engagement and penetration within the social structure and digital platform. To analyse the first block of data linked to the first hashtag-flag #iononriapro, the authors used content analysis. To analyse the second and third block of data linked to the hashtag-flag #ioapro, the authors used an automatic procedure, emotional text mining.FindingsThe analysis procedures allow us to reconstruct the positioning of the topics of closures and reopenings due to lockdown in this sector and to identify two explanatory dimensions: structural and affective, which explain the tension that has emerged between the State and the restaurant sector around COVID-related closures.Originality/valueThe study's findings not only contribute to the current understandings of the birth, transformation and penetration of social issues by the restaurant sector over the specific period linked to the COVID-19 pandemic and the measures imposed for its containment but are also valuable to analyse the dynamics through which Twitter hashtags and the social issues they represent find strength or lose interest in the public.

Sparse Multiple Factor Analysis, sparse STATIS, and sparse DiSTATIS with applications to sensory evaluation

AbstractComponent‐based multitable methods, such as multiple factor analysis (MFA), STATIS, and DiSTATIS, are routinely used to analyze multiblock data, which are now common in chemometrics and sensory evaluation studies. These blocks of data form data tables that—for example, in sensory evaluation—describe how different assessors evaluate a set of products either on a set of descriptors or on the similarity between products. To analyze these data, component‐based multitable methods extract orthogonal components explaining most of the variance of the data. However, when the data tables are heterogeneous or have complex structures, a single component space does not represent the data well and can give components that are difficult to interpret. Previous literature solved this interpretation problem by eliminating irrelevant variables—a process called sparsification—while keeping the components orthogonal. Here, we extended such methods to develop sparsification algorithms for three multitable methods, namely, “sparse MFA” (sMFA), “sparse STATIS” (sSTATIS), and “sparse DiSTATIS” (sDiSTATIS). In these sparse methods, we sparsified the data tables to identify the most informative assessors or products. In sMFA, we show how group sparsity can be used to sparsify whole tables (i.e., assessors or products), hereby greatly increasing the interpretability of sMFA's outcome. In sSTATIS and sDiSTATIS, we developed two different sparsification approaches: One approach creates subgroups of products and simplifies the components to facilitate interpretation; whereas the other approach creates subgroups of assessors and alleviates the problem of heterogeneity. We showed with three examples how these sparse methods increase interpretability of the results in sensory evaluation.

ECBC: Efficient Convolution via Blocked Columnizing.

Direct convolution methods are now drawing increasing attention as they eliminate the additional storage demand required by indirect convolution algorithms (i.e., the transformed matrix generated by the im2col convolution algorithm). Nevertheless, the direct methods require special input-output tensor formatting, leading to extra time and memory consumption to get the desired data layout. In this article, we show that indirect convolution, if implemented properly, is able to achieve high computation performance with the help of highly optimized subroutines in matrix multiplication while avoid incurring substantial memory overhead. The proposed algorithm is called efficient convolution via blocked columnizing (ECBC). Inspired by the im2col convolution algorithm and the block algorithm of general matrix-to-matrix multiplication, we propose to conduct the convolution computation blockwisely. As a result, the tensor-to-matrix transformation process (e.g., the im2col operation) can also be done in a blockwise manner so that it only requires a small block of memory as small as the data block. Extensive experiments on various platforms and networks validate the effectiveness of ECBC, as well as the superiority of our proposed method against a set of widely used industrial-level convolution algorithms.

Програма розрахунку коксохімічного обладнання на прикладі сульфатного відділення.

It is stated that Ukraine has recently lost a significant part of its industrial capacities, including in the coke industry. Therefore, after the war, the restoration of industrial potential will become an urgent issue. It is shown that a preliminary calculation of all equipment will become an urgent issue. In this regard, a computer programme for such calculations has been prepared on the basis of the methods developed by SE "UKHIN" and NTU "KhPI". The programme includes a number of typical sections that have the same internal structure. Organizationally, the programme consists of a number of sections, namely: table of contents; general information; initial data; calculations; calculation results; references; reference book; user manual. The programme allows you to perform calculations of both individual technological units and the entire technological chain. The basic principles of this programme are explained using the example of the sulphate department of a coking chemicals recovery shop. The algorithm was developed on the basis of Excel spreadsheets and includes calculations of material and heat flows of the technological process. The calculation allows for the selection of equipment and determination of the main parameters of the equipment. Relevant reference data is provided for selecting and adjusting physical constants and key process parameters. The calculation programme is accompanied by appropriate explanations that facilitate the selection of constants and parameters. This makes it possible to adjust the calculation progress. To prevent erroneous decisions from being made, appropriate blocking of incorrect data has been introduced. Such information will be useful for specialists in the field of coke oven equipment design, as well as for teachers and students of higher education institutions in the relevant field. Keywords: ammonia, coke industry, computer programme, technological units, equipment, ammonium sulphate, saturator, dryer. Corresponding author A.O. Zhuravskiy, e–mail: azhuravskiy@mail.ua

Enhancing Cybersecurity Using Blockchain Technology Based on IoT Data Fusion

In this study, blockchain technology is used to strengthen the security of wireless sensing data in the network architecture of the Internet of Things. For many organic farms that contain crops with high unit prices, the environmental parameters of the crops grown on the farm are often regarded as confidential data. First, a wireless sensing network is used to transmit sensing data to the farm’s front-end integrated microcontroller for fusion of the sensing data. After fusion of the sensing data is completed, the processed data is transmitted to the data processing center of the entire farm for encapsulation of the blockchain algorithm. Data are sent to the cloud database for storage after they are packaged by the system. At the same time, the data in the cloud database can be managed and analyzed by remote operators. Data encapsulation based on blockchain technology is used to effectively prevent any data from being stolen or destroyed by hackers. A complete blockchain encapsulation database system is implemented in the experimental stage, and the operator can use this system to encrypt the data that have been fused at the remote end for blockchain technology. In the encryption process of this blockchain, the security of data processing can be specifically enhanced by the system. Finally, each encapsulated block of data is securely stored in a private cloud database. There is a function to check whether the data in the cloud have been tampered with. A very innovative approach to using blockchain technology is proposed in this article to enhance the security of data processing.

CIA: A Collaborative Integrity Auditing Scheme for Cloud Data With Multi-Replica on Multi-Cloud Storage Providers

The emergence of cloud storage has solved many pain points of the traditional storage model. However, the issue of cloud storage data integrity - whether the cloud storage provider has kept the data intact - has raised concerns about cloud storage. Integrity auditing of cloud storage data allows users to know the integrity of the outsourced data without downloading it in its entirety. However it is not enough to know its integrity. Multi-replicas, as a common means of redundancy, improves the reliability of cloud storage data. And storing multi-replicas on multi-cloud storage providers (CSPs) enhances this feature. In a multi-replica multi-CSPs scenario, if the independence of CSPs is given full play and auditing is performed among CSPs, not only the introduction of third party auditor (TPA) can be eliminated, but also the tag generation, which is a huge computational overhead, can be removed. Inspired by this, in this paper we propose a new model for remote data integrity auditing: CIA (Collaborative Integrity Auditing). In addition to the reduction in computational overhead, the proposed scheme provides unprecedented support for free data blocking. The proposed scheme employs only hash functions in the calculation, which has a negligible computational overhead compared to the traditional bilinear pairing-based schemes. Theoretical analysis and experimental results show that the proposed scheme provides high efficiency and flexibility with security assurance, and can be used as a lightweight alternative to traditional remote data integrity auditing schemes in multi-replica multi-CSP scenarios.

METHOD FOR ADAPTING RECURSIVE SYSTEMATIC CONVOLUTIONAL CODES OF TURBO CODES BY BYPASSING THE NODES OF THE SPATIAL GRID

The article is devoted to increasing the efficiency of the functioning of wireless information transmission systems due to the adaptation of the parameters of recursive systematic convolutional codes of turbo codes by bypassing the nodes of the spatial grid and calculating the objective function. The probability of a white error of information decoding is chosen as the objective function. To calculate the chosen probability of a white error of information decoding, it is proposed to use Bowes-Choudhury-Hockingham codes, since for turbo codes there are only approximate asymptotic characteristics that give a large error at small values ​​of the signal-to-noise ratio. Analysis of known works shows that adaptation schemes of these codes are used to improve the reliability of information. At the same time, during adaptation, in the vast majority of works, only one parameter changes - the coding speed, which does not fully increase the effectiveness of corrective coding schemes. That is why it is proposed to use several parameters during adaptation, namely the polynomials of forward and backward connections of recursive systematic convolutional codes and the coding speed of turbo codes. The article consists of an introduction, which highlights the problem, analyzes the latest research and publications on this topic, and formulates the purpose of the article. The results of the research are shown, conclusions and prospects for further research are drawn. The article ends with a list of used sources. As a result of the work of the proposed method, the parameters of the turbo codes, which were found using the method for the channel with additive white Gaussian noise for different sizes of the input data block, are given. We consider the direction of further research to expand the space of the spatial lattice to take into account more parameters of turbo codes during adaptation, while the following can be foreseen: the number of bits in the block, types of interleavers, decoding algorithms, decoding iterations, etc.

Колекції котових (Felidae) у природничих музеях України та їх значення у поширенні природничих знань

The study is devoted to the analysis of collections of the felid family, which are housed in natural history museums of Ukraine, two academic and eight university. The basics of the modern taxonomy of felids are considered with the vernacular names of tribes, genera and their type species, which is important for the unification of exhibition and catalogue information. For each of the considered museums, four blocks of data are presented: a general summary for the collection, presentation of particularly valuable or unique specimens, and description of the exhibition and scientific collections. The most complete felid collections (in terms of the number of specimens) are housed in the Museum of Nature of Kharkiv University (109 specimens), in the National Museum of Natural History (152 specimens), and in the Zoological Museum of Odesa University (66 specimens); in other collections the amount of material is much smaller (7–36 specimens). The total volume of collections of this family reaches 460 specimens of 21 species of 12 genera (out of 14 living). The examples of materials important for the dissemination of knowledge about this group are given, from folklore to important and interesting scientific facts for the general public. For all species, there is an organised catalogue, classified by tribe, genus, and species (all alphabetically), in which key information about the available specimens is given for each museum. Such information for each species is arranged according to the scheme ‘museum, location, number of samples, collection numbers and types of specimens, date, and collector or preparator.’ The present study is the first such generalisation of zoological collections of Ukraine, the experience of which can be extended to other groups of animals. The development of this study showed a high interest of colleagues in such generalisations, which clearly activate the attention of museum workers, researchers of variability and diversity, potential museum visitors and society to the topic of the selected focus groups and activation of exhibiting and research work in relation to those groups.

Multithreaded Convolution Implementation Based on Block Methods

A multithreaded convolution implementation based on block algorithms is considered. Convolution is essentially the basis of many methods that solve the problem of determining the degree of similarity or independence of two processes, in other words, when it is necessary to determine the degree of correlation. The algorithm itself is executed with a significant delay, because for its execution it is necessary to accumulate the entire signal and then process it. The analysis showed that one of the possible ways to reduce time costs is a multithreaded implementation of convolution based on block algorithms. The article shows the main features of the convolution implementation by the overlap method with addition and the overlap method with addition, as well as numerical examples. The results obtained show that the application of these methods without the use of a window function leads to significant distortions in the signal spectrum. Based on the results of the analysis, a universal scheme for performing convolution based on multithreaded processing of an input data block is proposed. This allows to achieve a good compromise between computational complexity, system architecture, and time costs.

Prediction of Future NBA Games’ Point Difference: A Statistical Modeling Approach

Nowadays, the popularity of the NBA along with the penetration of gambling ideas is rising all around the world. More and more fans prefer to bet on sports gambling. People want to predict the final score difference for each individual game. In addition, there are many either superficial or underlying factors that will affect the game’s result. The central claim of this article is to establish a reliable model that could predict each game’s result by extracting and analyzing previous games’ outcomes. The major analysis is done by modeling and exploratory data analysis, and is composed of several graphical methods. For the modeling part, ideally the model could predict the games’ score differences by analyzing previous dozens of games’ data sets. The database consists of four seasons 2013-2017 on Goldsheet website to explore more analysis. Then step by step checking and demonstrating to prove the feasibility and precision of the model. The model will collect and analyze the data information, like teams, rebounds, assists, turnovers, three points, free throws, blocks, and injury data. Considering the transfer between teams and other changes, the model will predict the future game result in each season independently by using each team’s previous game result in that season. This article indicates the idea of linear regression model to find the best fit by comparing the correlation strength of each variable, which include the home-field advantage, teams’ technical statistics, and injury data, to the result of a game. The model final outcomes implies that the game’s result has the most correlation with the home-field advantage and player injury; however, team’s basic technical statistics, included the rebounds, blocks, turnovers, free throws, three points, and assists, have low correlation coefficient with the result to demonstrate that they are not significant to a game’s result.

Computing Offloading and Resource Optimization of Adaptive Data Block Size in Edge Environments

Computing Offloading and Resource Optimization of Adaptive Data Block Size in Edge Environments

A Review on Machine Learning &amp; It’s Algorithms

Machine learning is important because it gives us accurate predictions based on data. It can teach computers to perform complex tasks without any human intervention. Machine learning can analyze complex blocks of data. Machine learning enables entrepreneurs and businesses to quickly recognize potential business opportunities and risks. Businesses that rely solely on large amounts of data are using machine learning as the best way to analyze data and build models. Machine learning is not only considered as the backbone of artificial intelligence, but machine learning also plays a significant role in the development and advancement of artificial intelligence. Using algorithms to solve classification problems with different sets of parameters yields dramatically different classification accuracies. The machine learning challenge of finding the most appropriate parameter values for algorithms that best solve technical problems related to performance metrics. In this paper, the author discussed various types of machine learning such as supervised, unsupervised and reinforcement machine learning. The main emphasis is on supervised machine learning such as classification and regression using various machine learning algorithms such as Decision Tree, Naïve Bayes, K-Nearest Neighbor, Random Forest and SVM Classifier. The author explains all classification-based algorithms well with examples and diagrams. The authors also mention applications or domain areas where these classification algorithms can be used.

Blockchain Technology for Electronic Health Records.

Compared with traditional paper-based medical records, electronic health records (EHRs) are widely used because of their efficiency, security, and reducing data redundancy. However, EHRs still manifest poor interoperability and privacy issues are unresolved. As a distributed ledger protocol composed of encrypted blocks of data organized in chains, blockchain represents a potential tool to solve the shortcomings of EHRs in terms of interoperability and privacy. In this paper, we define EHRs and blockchain technology and introduce several classic schemes based on blockchain technology to strengthen EHR interoperability and privacy protection. We then review ongoing challenges in the areas of data management efficiency, fairness of access, and trust in the systems. In this commentary, we suggest ongoing research needs for health informatics, data sciences, and ethics to establish EHRs based on blockchain technology. Blockchain-based EHR schemes must address the potential inequality of healthcare resources, the huge carbon footprint of computational needs, and potential distrust of health providers and patients that may ensue with wider use of blockchain technology.

Research on Enterprise Financial Information Management System Based on Large Data Technology and Block Chain Fusion

In recent years, the rapid development of big data and cloud computing has had a profound impact on the integration of accounting and management accounting. In the Internet era, enterprises' financial information is more transparent and open. For modern enterprises' needs of financial management system informationization, the enterprise financial management information system based on big data scenarios is designed and implemented. The system is designed based on the philosophy of software engineering, and the functions to be implemented are selected rationally by investigating the requirements of the financial management work system of the enterprise. The system is implemented based on B/S architecture, and the design of database follows the relevant specifications in software engineering. The system can enable fine management of enterprise financial data, reduce enterprise operation cost, and thus improve the comprehensive efficiency of enterprise business.

Distributed random vector functional link network with subspace-based local connections

In order to solve the problem of poor generalization ability and high computational complexity of random vector functional link (RVFL) network when dealing with large-scale data classification, we design and implement a distributed RVFL network with subspace-based local connections in Spark framework (DRVFL-SLC). Firstly, in order to take advantage of the partition parallelism of resilient distributed dataset (RDD), the large-scale dataset stored in the Hadoop distributed file system HDFS is randomly divided into random sample partition (RSP) data blocks and each RSP data block corresponds to a partition of the RDD, where the RSP data block is a subset of data that maintains probability distribution consistency with the big data at a given significance level. After that, the mapPartitions transformation is invoked on the RDD containing multiple partitions in a distributed environment and this operation trains the corresponding optimal RVFL-SLC efficiently in parallel. Then, the collect execution operator is used to efficiently fuse the optimal RVFL-SLC corresponding to each partition of the RDD to obtain DRVFL-SLC for realizing the classification of big data. Finally, the feasibility and effectiveness of DRVFL-SLC are verified based on several large-scale data set with at least million records on a Spark cluster deployed with 6 computing nodes. The results show that DRVFL-SLC has a good speedup ratio, scalability and scale growth, and can achieve better generalization performance than RVFL-SLC trained on a single machine with full data.