Reviewerly/exHarmony huggingface dataset:
Lossless Satellite Data Compression for Real-Time Navigation of Autonomous Vehicles

Autonomous vehicles are able to sense their environment and operate without human involvement. In particular, they have to be able to locate themselves. In order to achieve this task, geolocalisation files such as RINEX files are used. Due to the large amount of data, these files are very large and there is a big challenge in reducing their size for real-time navigation. However, this problem has not really been explored for RINEX files. In this paper, we propose an efficient method to losslessly compress RINEX files based on quadratic polynomial interpolation for autonomous vehicles navigation. Indeed, due to the orbits of the satellites around the earth, satellite signals can be modeled by parabolic curves. After interpolation of this model, prediction errors are computed as the differences between each original value of the satellite signal and its associated predicted value. Finally, these prediction errors are compressed using entropy coding. Experimental results show that our proposed method allows us to outperform the compression rate obtained by a previous state-of-the-art approach.
Information and Its Conceptual Perspectives

Information has attributes/positivist concepts that some authors use to better understand it. Others are critical about information subjectivism. This chapter discusses conceptual perspectives of information. The aim is to study the concepts of information from some areas of knowledge of the social sciences looking to propose a concept of information in the field of information science. The methodology of the study is formed by bibliographical research. It is concluded that the concept of information is still in full development.
Cluster Tool Performance Analysis using Graph Database

A cluster tool is the place where wafers are processed. To increase the productivity of wafers, semiconductor manufacturers would like to maximize the utilization of cluster tools. However, a cluster tool may not achieve its optimal utilization rate due to the schedule conflict under different combinations of process recipes. By exploiting the data in the log files of a cluster tool, we may find out the reasons of low utilization rate. In this paper, we propose an approach of cluster tool performance analysis to automatically analyze the root cause of low utilization rate in cluster tools. Experimental results show the effectiveness of the proposed approach.
Knowledge Management Process-Oriented Strategy for Healthcare Organizations

This article explores Knowledge Management (KM) practices among doctors and nurses in Malaysia. A total of 59 interviews were conducted with doctors and nurses from two hospitals. The data analysis employed in vivo coding and process coding techniques. The findings suggest a process-oriented strategy for managing knowledge among doctors and nurses in a clinical work environment. The development of this strategy can help the healthcare workers and management to evaluate and further improve their current KM practices. Additionally, this article adds another KM strategy to the literature that is tailored to supporting healthcare organization. Future studies may want to replicate the proposed strategy in different settings such as other clinical or non-clinical departments within the hospital, other public or teaching hospitals or private hospitals.
Efficient Utilization of IaaS Cloud using Adaptive Evolution Based Technique

In the present era of technology and innovation, computing provides as a service. The service-oriented computing paradigm is a subset of utility and distributed computing paradigm. Hardware specification, platform, and application provide a service to the users in a different time zone. Resource utilization and request completion time is the prime focus of the service providers and consumers. In this work, we proposed a nature-inspired evolution and astrology science-based approach. The Big-Bang Big-Crunch optimization method based task allocation technique focuses on efficient resource utilization of IaaS (Infrastructure as a Service) cloud. We focused on three performance metrics, which may be user-oriented and service provider oriented. Performance metrics measure in terms of average resource utilization cost. Average Finish time, an average start time, and operational cost (customer-oriented) performance metrics are consumer-centric. The simulation performs using five cloud resources, six cloud configurations, and user-defined population size, and number iterations. The selection operator includes a fitness function that depends on estimated resource cost in the duration of execution. BB-BC cost-aware model provides optimal resource utilization, and minimum average finish time, and minimum average start time (ms) of cloudlets. The results exhibit that the proposed astrology, evolution based meta-heuristic approach outperforms the static (first come first serve (FCFS)), dynamic, and meta-heuristic (Genetic cost-aware, Genetic execution time aware and Particle Swarm optimization) approaches.
The Information Explosion

This chapter lays the foundation for the conceptual framework by providing a background of the information explosion. The growth of information is examined from the year 1 AD to 2018 AD. The information explosion produced four major revolutions with intermediate contributions. The four information revolutions were identified by Peter Drucker as the invention of writing, the book, the printing press, and the computer age. This growth is delineated by the years in which information doubled. He compared the advent of the printing press (information revolution three) to the coming of age of the computer and the internet (the fourth of the four information revolutions). Each information revolution generated the need for allied industries. The massive growth of information may be coped with by becoming more effective and efficient learners and teachers.
New approaches in physics teachers ’ professional development courses designed for improvement of the students ’ experimental olympiad tasks solving skills

The article deals with the problem of the teachers’ post graduate education required due to the need to teach school students to solve experimental Olympiad tasks, and discusses the diffculties that the teachers face with when preparing their students for experimental rounds of the physics Olympiads. The relevance of the problem consists in the following: today teachers face with the lack of information sources on the school experimental physics competitions. The article suggests a possible way to solve the problem - the organization of advanced training courses for physics teachers that include specially developed video materials. The choice of distance learning is grounded. The structural principles and the objectives supposed to be achieved through the developed post graduate courses for teachers are described. The main selection criterion used for choosing the experimental tasks to be included into the courses is clarity. The tasks are rich with bright ideas. In some cases the new training tasks are originally created for certain courses. Each individual lesson is devoted to cover a certain group of techniques and methods of solving tasks. The presented material is structured in a special way that allows the trainee to be able to understand the methodology of solving tasks given as examples. The article describes the required characteristics that the experimental setup should possess, and which steps the experiments include, along with the purpose and methods of image/svg+xml94 measurements. The feedback allows students to contact the teachers in order to be able to keep up with the coverage of every particular topic and regulate this process.
A Parallel Association Rule Mining Algorithm Based on Spark and its application

The research of association rule mining (ARM) is very hot. Along with the increase of data size, single machine ARM algorithms have encountered difficulties in time and memory. Distributed computing could break this bottleneck. In this paper, a parallel association rule mining algorithm is proposed, which is called PFIBM-Growth (parallel frequent item binary matrix growth) algorithm. The algorithm constructs a binary matrix to compress and store data sets, and it uses simple matrix operations to mine frequent itemsets in parallel. In addition, we have applied this algorithm to the failure prognostic of primary air fans. We find that not only the algorithm is not bad, but also the association rules got from it could realize the failure prognostic of primary air fans.
Multimodal Sentiment Analysis Based on Transformer and Low-rank Fusion

Human emotion expressions are usually expressed as multiple modalities of information, such as natural speech, facial expressions and vocal signals. The information of each modality contains the features of human emotion, and the mutual verification between different modal features can greatly improve the accuracy of sentiment analysis. Therefore, it is important for sentiment analysis to fully explore the features of multiple modalities and analyze the correlation between the features of different modalities. A fusion model MulT-LMF based on MulT and LMF is proposed in this paper, and the LMF model is applied to the output part of the MulT model. The MulT model helps to construct the interaction information among the modalities, while the LMF model helps to further extract the correlations among the modal features. The model in this paper is tested on public multimodal sentiment datasets, including CMU-MOSI, CMU-MOSEI and IEMOCAP. Experimental results show that the model achieves better results in most cases.
Feature Extraction

As detailed in Chap. 3 , pre-processing of data improves the quality of data. Now, this chapter proceeds towards the next step of fault diagnosis framework where key characteristics of the data are found. For this purpose, data is analyzed in different domains and thus we obtain a new set of data which we call that good features are obtained. This chapter details how features can be extracted out from the pre-processed data. When the input data to an algorithm is too large to be processed and it is suspected to be redundant (much data but not much information), then the input data will be transformed into a reduced set of features (also named features vector). Transforming the input data into the set of features is called feature extraction. In order to extract useful information from the captured data, it is necessary to represent the data in a suitable form. Generally, for feature extraction purpose, three forms of signal representation as below are utilized.
Resource Provisioning and Scheduling of Big Data Processing Jobs

Cloud Computing has become a buzzword in the IT industry. Cloud Computing which provides inexpensive computing resources on the pay-as-you-go basis is promptly gaining momentum as a substitute for traditional Information Technology (IT) based organizations. Therefore, the increased utilization of Clouds makes an execution of Big Data processing jobs a vital research area. As more and more users have started to store/process their real-time data in Cloud environments, Resource Provisioning and Scheduling of Big Data processing jobs becomes a key element of consideration for efficient execution of Big Data applications. This chapter discusses the fundamental concepts supporting Cloud Computing &amp; Big Data terms and the relationship between them. This chapter will help researchers find the important characteristics of Cloud Resource Management Systems to handle Big Data processing jobs and will also help to select the most suitable technique for processing Big Data jobs in Cloud Computing environment.
Offline Recognition of Malayalam and Kannada Handwritten Documents Using Deep Learning

For a variety of reasons, handwritten text can be digitalized. It is used in a variety of government entities, including banks, post offices, and archaeological departments. Handwriting recognition, on the other hand, is a difficult task as everyone has a different writing style. There are essentially two methods for handwritten recognition: a holistic and an analytic approach. The previous methods of handwriting recognition are time- consuming. However, as deep neural networks have progressed, the approach has become more straightforward than previous methods. Furthermore, the bulk of existing solutions are limited to a single language. To recognise multilanguage handwritten manuscripts offline, this work employs an analytic approach. It describes how to convert Malayalam and Kannada handwritten manuscripts into editable text. Lines are separated from the input document first. After that, word segmentation is performed. Finally, each word is broken down into individual characters. An artificial neural network is utilised for feature extraction and classification. After that, the result is converted to a word document.
A Comparative Analysis of Bitcoin and Ethereum Blockchain

With the development of blockchain, various blockchain-based applications emerged. Of which Bitcoin and Ethereum are the most representative and popular blockchain applications. Therefore, it is important to compare Bitcoin with Ethereum and infer their developing trends. In the paper, we compare Bitcoin and Ethereum from different perspectives, including consensus algorithm, transaction cost, extension, and security. Comparative results demonstrate that Ethereum is better than Bitcoin on extension and transaction mechanism. Also, based on the results, we discuss about the essential development trend about blockchain applications. In the future, blockchain applications will be a platform which can provide more functions than digital currencies.
A Image Enhancement Method for Few-shot Classification

n order to predict the unknown image categories, few-shot image classification has recently become a very hot field. However, many methods need a large number of samples to support in order to achieve enough functions. This makes the whole network de amplification to meet a large number of effective feature extraction, and reduces the efficiency of few-shot classification to a certain extent. To solve these problems, we propose a dilate convolutional network with data enhancement. This network can not only meet the necessary features of image classification without increasing the number of samples, but also has a structure that utilizes a large number of effective features without sacrificing efficiency. The cutout structure can enhance the data by adding a fixed area 0 mask matrix in the process of image input. The structure of FAU uses dilate convolution and uses the characteristics of a sequence to improve the efficiency of the network.
A Bibliometric Analysis and Visualization of Blockchain in Cyberspace Security

In recent years, blockchain and cyberspace security have attracted much attention from researchers and institutions. Blockchain technology has provided new methodologies and innovative solutions for traditional service scenarios of Internet security. In order to intuitively and clearly illustrate the current research status and development trend of blockchain technology in the field of cyberspace security, information visualization analysis tools including BOAP, CiteSpace and VOSviewer were applied. Visualization analyses were conducted on 1021 papers obtained from The Web of Science Core Collection, which ranged from year 2016 to 2021. The knowledge maps were drawn from quantitative analysis, influence analysis, co-citation analysis of literatures, co-occurrence analysis of keywords and so on to intuitively reveal the research situation and development trend of blockchain technology application in the field of network security. The analysis results show that academic research on blockchain technology in the field of network security began in 2016, and showed a trend of rapid development after 2019. Research frontiers mainly focus on Internet of Things, cloud computing, wireless sensor networks, etc.
Representations for evolutionary algorithms

tutorial Representations for evolutionary algorithms Share on Author: Franz Rothlauf Johannes Gutenberg-Universität Mainz, Mainz, Germany Johannes Gutenberg-Universität Mainz, Mainz, GermanyView Profile Authors Info & Claims GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference CompanionJuly 2020 Pages 526–546https://doi.org/10.1145/3377929.3389872Online:08 July 2020Publication History 0citation49DownloadsMetricsTotal Citations0Total Downloads49Last 12 Months10Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Monitoring Method for Relative Position of Insulation Net In Construction of Crossing Power Lines

In order to ensure the safety during construction of crossing power lines, the method for monitoring relative position of insulation net is designed to real-time monitor the distance between the insulation net and above/below adjacent power lines. Firstly, the high-resolution CCD camera is used to real-time collect video images of power lines and insulation net. Then, the target objects (power lines and insulation net) are determined by image preprocessing, Hough transform and Histogram. Finally, the distance between the insulation net and power lines is calculated. The test results show that the proposed method can monitor accurately and reliably the distance between the insulation net and above/below adjacent power lines in a real time, so as to effectively prevent the occurrence of safety accidents.
Some Other Applications of Genetic Algorithms (GAs)

As we had already seen that genetic algorithms (GAs) are smart in their working. Here, the authors explore the rich working of genetic algorithms (GAs) in various diversified fields. Until now, they had discussed the historical nature of genetic algorithms (GAs). They have also discussed the programming code to run simple genetic algorithms (SGA). Lastly, they are going to take an overview of the application of genetic algorithms (GAs) in various fields (i.e., from business to non-business). Already, they have discussed the robust working of genetic algorithms (GAs) in various adverse conditions. Here, they discuss the application of genetic algorithms (GAs) in various other diversified fields.
Estimation of the synchronization error for spaced-apart ground points of satellite communication

A significant number of patents and publications in the open press show the intensity of the ongoing research to create new methods of synchronization and specialized ground-based equipment for frequency-time synchronization. The article considers the implementation of an autonomous synchronization system between ground-based satellite communication points, which is used in conditions of unfavorable reception or absence of signals from global navigation satellite systems. The paper presents studies on the assessment of the error of time synchronization of geographically separated ground points of satellite communications. The basics of constructing the receiving equipment of a radio engineering system are considered and the mutual correlation of the reference signal of the receiving equipment and the received input synchronization signal is calculated. The results of field tests of mutual synchronization equipment are presented, where the potential synchronization accuracy of equipment at ground objects with previously unknown coordinates was determined, while the synchronization equipment provided measurement of the signal propagation time from one ground object to another with the formation of universal time scales on points. The method of mutual synchronization of linked points is carried out by the duplex method at a frequency of 1065 MHz (request) and 625 MHz (response) by phase-code-manipulated signals with a base equal to 1023 and a duration of 0,1 ms.
Boosting

In this chapter we continue the use of fitting ensembles. We focus on boosting with an emphasis on classifiers. The unifying idea is that the statistical learning procedure makes many passes through the data and constructs fitted values for each. However, with each pass, observations that were fit more poorly on the last pass are given more weight. In that way, the algorithm works more diligently to fit the hard-to-fit observations. In the end, each set of fitted values is combined in an averaging process that serves as a regularizer. Boosting can be a very effective statistical learning procedure.
Modeling the Ranking of Evaluation Criteria for Cloud Services

The cloud market has become increasingly dense as vendors of all size compete for customers who have gravitated to technology as a way to run their operations. Today traditional players, as well as new vendors, are showing greater agility in the cloud business of India. At present, there are limited industry standards for the cloud-related businesses to assess their services. End-user government departments are puzzled as to which CSP is best suited for their requirements. An evaluation of cloud service providers should not only be driven by cost, but importance should also be given towards service provider ability and commitment to deliver the services. The absence of a common framework to assess the evaluation criteria of CSPs, combined with the fact that no two CSPs are the same, complicates the matter of CSP selection. This article proposes a framework for ranking of evaluation criteria for CSPs. The framework leverages the capability analysis of CSPs, measurable metrics and ranks the evaluation criteria for assessing the best CSP suitable for end-user government organizations.
Cybercrime

Abstract This chapter explores the legal framework regarding cybercrime, with a focus on Europe. In the case of cybercrime, competent authorities face a moving target, as technological developments, both on the side of perpetrators and on the side of policing and forensics, often outwit prevalent and tested strategies against traditional crime. This chapter first raises the question of what makes cybercrime ‘cyber’, and then introduces the international and supranational legal frameworks that are meant to cope with cybercrime, with a focus on the Cybercrime Convention. Finally, the chapter offers a reflection on the image of the weighing scale where it comes to balancing safety and security against rights and freedoms.
Towards a New Combination and Communication Approach of Software Components

The interest behind the use of component based software engineering is to divide an information system in subsystems with less complexity, the reduction of time, faster development, and enhancing the productivity. The software systems are developed by assembling components which are software units that offer a set of services exposed as interfaces. The indispensable role of component-based development is the component model that defines how components can be built and how they can be assembled. A component is intended to provide specific services as the management of the combination and the communication between the system units. The Manager Component is an important and complementary paradigm for the development of software systems. The functionalities that it encapsulates must be related and consistent. The model presented in this paper proposes a component called Manager Software component based on the viewpoint (vision of each system user and actor) by the assembly of Base Component and system components.