Reviewerly/exHarmony huggingface dataset:
Defence performance analysis: Social network analysis

There is little study concerned game structure of women's sevens rugby.Because the game time is only 14 minutes (7 minutes half), the temporal performance would be a decisive factor.It might suggest the characteristics of current world women's sevens rugby and each union.This study clarified the temporal structure of "try scoring rate" (frequency of tries in own possession (secs)), and "try conceding rate" (frequency of tries in oppose possession (secs)).With adding two manipulated factors from "opposition 22m entry try success (attack)" and "own 22m entry try success (defence)", total four factors were investigated for the world ranking clusters.Data were picked up from opened homepage of World Rugby game analysis 2016-2017 women's sevens series.Two-dimensional space showed the relationship of "try scoring rate" and "try conceding rate" (avg.= 79 secs) with mapping the world fifteen union's performances.It suggested world ranking structure.Non-hierarchical cluster analysis was attempted in order to explore the possibility of grouping.Interesting differences were observed among the number of iterations.The 5-iteration process setting showed top group by top 7 unions.The 1000-iteration process setting showed top group by top 3 unions.(explained 96% of point variability).Clustering the world women's sevens rugby was tried from the temporal structure analysis of some attack-defence performances.In non-hierarchical cluster analysis, the number of clusters needs to be determined by the analyst, and there is no established method to calculate the optimal number of clusters automatically.The number of women's sevens games is also limited.Development of the optimal algorithm under this condition would be desired.
New data on titanium mineralization in fenitized precambrian carbonate rocks of Middle Timan bystrinskaya series

Fenitized Precambrian carbonate rocks (Middle Timan) are parent rocks for bauxite — phosphate weathering crusts. Therefore, the mineral composition of fenites, characterized by rich and varied mineralization, is of great interest. Titanium minerals especially attract attention, since their high concentrations are noted in bauxite-phosphate weathering crusts. For example, in the bauxites of the Verkhne-Shchugorsk area, the amount of rutile may reach 6000—7000 g/t. Titanium mineralization is represented by titanite, rutile, anatase, and cassite.
Design and Development of Digital-Analog Seismograph

The analog Seismograph is a device designed and developed to monitor process and record the field seismic data from remote sites. This data contains the timing information which is usually recorded on paper wrapped around drum. Being generating only the hardcopy, the data however cannot be uploaded on seismic software for analysis purpose along with ease of calculations. Not only this, due to limited memory base, frequent changing of drum rolls is required which often leads to delays. These problems can be removed by digitization of the system which with increased efficiency also provides miniaturization of the system. In this work we have used LattePanda a windows 10 development board to run the system. LattePanda has added advantage of raspberry pie and windows 10 IoT core processor. Accelerometer and seismometer is interfaced with LattePanda to measure seismic vibrations. These motions guide the pen movement of the drum which is further incorporated with stepper motors NEMA 14 and NEMA 17 for quick and robust response. Timing and positioning information of the location along with coordinates of the working satellites and present location of the seismometer is decoded using NEO-6m gps module. The graphical visualization of the entire satellites actively passing data to the module is taken through u-blox u-centre gnss evaluation software tool For sending this data on remote servers, Python based TERA TERM software is utilized. With all these parameters now at our disposal, debugging is enhanced and analysis becomes much more easier. Pre-events calculations with digitization not only become feasible but also allow precautionary measures possible.
Efficient Security Base d Video Watermarking Technique u sing Chaotic Encryption with Discrete Cosine Transform

In this paper, a novel chaotic encryption-based blind digital video watermarking technique (DVWT) is presented for grayscale and color images. This method makes use of Discrete Cosine transform (DCT) prior to embed the watermark in the hosting video. The hosting video is split into frames of 8*8 nonoverlapping blocks before employing DCT, and watermark bit is implanted by altering the variations among the nearby DCT coefficients of neighboring blocks. Arnold transform is applied for chaotic encryption to offer extra security to the watermark. A set of three versions of the proposed model has been applied and the results are investigated. The experimental outcome confirms that the DCT-VWT is efficient on different image processing functions such as JPEG compression, sharpening, cropping, and median filtering. The comparative analysis ensures that the presented DCT for digital video watermarking technique (DCT-DVWT) is efficient under subjective as well as objective way.
Stream IO

In this chapter we will explore the Stream I/O model that under pins the way in which data is read from and written to data sources and sinks. One example of a data source or sink is a file but another might be a byte array.
Privacy preservation of clinical dataset using SHA and key base hashing

Privacy preserving publishing of micro data is one of the most essential concerns in collective data publishing. In this work, we design a problem for data publishing using a new technique called SKHF. Numerous researches have been done in the healthcare domain with privacy preserving, but still nobody had eliminated the data leakage and data loss issues. In the proposed system, a hospital collects various patients' data and publishes the records to an external entity. The proposed system is designed to preserve or maintain the privacy of an individual data in distributed database system by using the slicing algorithm. Basically, it is used for publishing information of specific organisations like healthcare patient information as well as disease information etc. In this paper we studied different techniques for privacy preservation and new techniques for data privacy called secure key base hash function (SKHF) which provides both horizontal and vertical security.
Weight Ward Change Region Plummeting Change for Square based Image Huffman Coding

Image weight is an execution of the data weight which encodes genuine picture with specific bits. The purpose of the image weight is to lessen the redundancy and superfluity of picture data to be capable to record or send data in an incredible structure. Thusly the image weight lessens the period of transmit in the framework and raises the transmission speed. In Lossless technique of picture weight, no data get lost while doing the weight. To handle these sorts of issues various techniques for the image weight are used. By and by inquiries like how to do mage weight and second one is which sorts of development is used, may be rises. In this way normally two sorts' of strategies are explained called as lossless and the lossy picture weight approaches. These techniques are straightforward in their applications and eat up alongside no memory. A count has similarly been familiar and associated with pack pictures and to decompress them back, by using the huffman encoding frameworks.
Extracting image features for classification by two-tier genetic programming

Image classification is a complex but important task especially in the areas of machine vision and image analysis such as remote sensing and face recognition. One of the challenges in image classification is finding an optimal set of features for a particular task because the choice of features has direct impact on the classification performance. However the goodness of a feature is highly problem dependent and often domain knowledge is required. To address these issues we introduce a Genetic Programming (GP) based image classification method, Two-Tier GP, which directly operates on raw pixels rather than features. The first tier in a classifier is for automatically defining features based on raw image input, while the second tier makes decision. Compared to conventional feature based image classification methods, Two-Tier GP achieved better accuracies on a range of different tasks. Furthermore by using the features defined by the first tier of these Two-Tier GP classifiers, conventional classification methods obtained higher accuracies than classifying on manually designed features. Analysis on evolved Two-Tier image classifiers shows that there are genuine features captured in the programs and the mechanism of achieving high accuracy can be revealed. The Two-Tier GP method has clear advantages in image classification, such as high accuracy, good interpretability and the removal of explicit feature extraction process. © 2012 IEEE.
Effects of Knowledge Management and Creation Capability on the Performance of SMEs in Kogi State

This study aimed at determining the effects of knowledge management and creation capability on the performance of SMEs in Kogi State. The research survey design was adopted for this study. For data analysis, descriptive and inferential analytical techniques were used. The analytical techniques employed are survival analysis and multiple regression analysis (stepwise). Findings showed that socialization has a significant effect on the competitive advantage of both Small and Medium Enterprises in Kogi State; externalization has a significant effect on the competitive advantage of Medium Enterprises in Kogi State; the combination of resources can predict the competitive advantage of both Small and Medium Enterprises in Kogi State. Findings further showed that combination, internalization, socialization, and externalization strongly and significantly predict the business growth of SMEs in Kogi State. The study concluded that the ability of SME owners to manage knowledge can translate into a better chance of achieving the enterprise's goals. The study recommended that SME owners should channel their knowledge creation towards socialization and a combination of resources; owners of Medium Enterprises should take advantage of knowledge creation through externalization to facilitate sustainable competitive advantage in Kogi State. Furthermore, SME owners and economic stakeholders should find alternative ways of promoting effective knowledge management and creation in Kogi State.
Evolutionary computation and evolutionary deep learning for image analysis, signal processing and pattern recognition

tutorial Evolutionary computation and evolutionary deep learning for image analysis, signal processing and pattern recognition Authors: Mengjie Zhang Victoria University of Wellington, Wellington, New Zealand Victoria University of Wellington, Wellington, New ZealandView Profile , Stefano Cagnoni University of Parma, Parma, Italy University of Parma, Parma, ItalyView Profile Authors Info & Claims GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2021 Pages 1169–1198https://doi.org/10.1145/3449726.3461412Published:08 July 2021Publication History 0citation84DownloadsMetricsTotal Citations0Total Downloads84Last 12 Months11Last 6 weeks1 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
Natural Language Semantic to Formal Language Semantic

The Natural language has fundamental role in human communication and even in human way of thinking and behavior regarding the world.With the advancement in Natural Language Processing, it is very essential to study it in all the respects especially semantic at different level.The semantic is a basis of both tasks Natural Language Understanding and Natural Language Generation in Natural Language Processing.The ambiguity and vagueness increases the semantic complexity in semantic theory.These two ambiguities and vagueness are socially acceptable while it is highly overlooked academically during the human interactions.These two are consider as distinct and varieties of interpretive uncertainty that has significant meaning in philosophy of language.The Ambiguity deals with uncertainty about levels of representation with different structural characteristics and vagueness with uncertainty about true meanings of particular items.The formal semantics is the study of using the formal logic to analyze the meaning that human beings use in everyday communication.This work presents the review of different semantics and their logics take place in natural language processing with computability of natural language semantics and reflexion on computability of natural language semantic within ambiguous and vagueness situation.It is a computational analysis of currently used logical models and algorithm in natural language semantics with different semantics that will make the foundation for new semantic models and logics.
Efficient Eye Detection and Tracking using Smart Sensors

Smart sensing enables applications with lots of computer-based techniques and algorithms to work in a finer way. We are in era with intelligent machines around us and there sometimes provides a situation of spending of large amount of time due to lack of understanding of those devices. In this paper, we attempt on to find a new interaction method which ease the use of computers or increase interaction efficiency. The most interesting approach that enhances the best way of to find communication between machines and human beings. This enhances the way of communication with the digital devices. Our project enables to have communication using our eye in the desktop environment. This study focus on using eye gazing techniques that allows user to move the mouse cursor using his eye with interpretation of control commands. The desktop environment which has the inbuilt camera enables to achieve the goal in simple and efficient way. The study includes analysis of features using face detection and template matching which have presented promising results.
An Improved Iterative Maximum Correntropy Cubature Kalman Filter

This paper investigates the problem of filtering for nonlinear system under outlier. To improve the reliability and precision of state estimation for nonlinear system with outlier, an improved iterative maximum correntropy cubature Kalman filter is proposed. In this work, cubature Kalman filter is applied to estimate the state of system. The sigma-point-based iterative strategy is designed, which uses the sigma points to iterate instead of the state estimation. Considering the effects of shot noise for nonlinear system, the rule of maximum correntropy is utilized to reduce the effect of shot noise existing in the measurement. Finally, the simulation results proves the validity of the proposed method.
Linear maps

This textbook is a comprehensive introduction to the key disciplines of mathematics - linear algebra, calculus, and geometry - needed in the undergraduate physics curriculum. Its leitmotiv is that success in learning these subjects depends on a good balance between theory and practice. Reflecting this belief, mathematical foundations are explained in pedagogical depth, and computational methods are introduced from a physicist's perspective and in a timely manner. This original approach presents concepts and methods as inseparable entities, facilitating in-depth understanding and making even advanced mathematics tangible. The book guides the reader from high-school level to advanced subjects such as tensor algebra, complex functions, and differential geometry. It contains numerous worked examples, info sections providing context, biographical boxes, several detailed case studies, over 300 problems, and fully worked solutions for all odd-numbered problems. An online solutions manual for all even-numbered problems will be made available to instructors.
Particle Swarm Optimisation for Feature Selection and Weighting in High-Dimensional Clustering

© 2018 IEEE. Clustering, an important unsupervised learning task, is very challenging on high-dimensional data, since the generated clusters can be significantly less meaningful as the number of features increases. Feature selection and/or feature weighting can address this issue by selecting and weighting only informative features. These techniques have been extensively studied in supervised learning, e.g. classification, but they are very difficult to use with clustering due to the lack of effective similarity/distance and validation measures. This paper utilises the powerful global search ability of particle swarm optimisation (PSO) on continuous problems, to propose a PSO based method for simultaneous feature selection and feature weighting for clustering on high-dimensional data, where a new validation measure is also proposed as the fitness function of the PSO method. Experiments on datasets with varying dimensionalities and different number of known clusters show that the proposed method can successfully improve clustering performance of different types of clustering algorithms over using the baseline of the original feature set.
Colour image encryption algorithm based on hyperchaos and DNA sequences

In order to overcome the shortcomings of the single chaotic encryption and the problems of simple structure and low security, the hyperchaotic colour image encryption algorithm based on DNA sequences is proposed. Firstly, the colour images are layered and encoded by DNA, then, the hyper-chaotic sequence is used to scramble the DNA matrix, the hyper-chaos is used to generate natural DNA matrix and DNA addition operations is dynamically selected to perform the DNA rule operations, finally, the cipher-text image is obtained. The performance test of the cipher text image shows that the gray scale distribution of the histogram is relatively uniform; the test values of the related index parameters NPCR and UACI are 99.65% and 33.51%, respectively, which are close to the theoretical value through simulation experiments. The algorithm can effectively improve various anti-attack capabilities of the encryption system and has high security.
Temporal Change Analysis Based Recommender System for Alzheimer Disease Classification

The development of recommender systems gathered momentum due to its relevance and application in providing a personalized recommendation on a product or a service for customer relations management. It has proliferated into medicine and its allied domains for the recommendations on disease prediction/detection, medicine, treatment, and other medical services. This chapter describes a new composite and comprehensive recommender system named Temporal Change Analysis based Recommender System for Alzheimer Disease Classification (TCA-RS-AD) using a deep learning model. Its performance is evaluated on the dataset with T1-weighted MRI clinical temporal data of OASIS and the results were recorded in terms of Precision, Recall, F1-Score and Accuracy, Hamming Loss, Cohens Kappa Coefficient, and Matthews Correlation Coefficient. The improved accuracy of this recommendation model endorses its suitability for its application in the classification of AD.
An Efficient VLSI Design of AES Cryptography in Memory Implementation

This paper depicts a novel sub bytes strategy for executing the executing the advanced encryption standard (AES) algorithm that offers a considerably enhanced cryptographic strength. Our strategy depends on composite field math randomization, which involves a low cost of execution while not adjusting the algorithm, does not decrease the recurrence of the work and maintains an ideal similarity to the distributed standard. In this document, we suggest a fast and knowledgeable execution of AES in memory (AIM) to scramble the whole part of the memory only when needed. We use NVM’s intrinsic logic working ability to implement the AES algorithm instead of adding extra processing parts to the cost-sensitive memory. The proposed design is implemented using Modelsim 6.4 C and Xilinx tool Verilog HDL and simulated. The proposed framework actualized in FPGA Vertex or Spartan-3.The proposed AES system has been made into an IP and effectively connected in encryption application.
A Consideration on Cooperative Control between Driver and Self-driving Car Aided by Extended Laplace Transform Type Machine Learning

While proceeding with the examination of Level 3, we came to think that the concept of cooperative control is effective not only for Level 3 but also for all levels. In Level 3, when the system cannot stand, the driver receives Request to Intervene(RtI) from the system. This will be only possible if the system itself understands why it requests RtI to the driver and the system can the driver what to do. To achieve this situation, It is important for the machine learning to be short calculation time and to get smarter with training. Extended Laplace transform type machine learning HNN(Holographic neural network) is the one that meats these requirements.
Interactive comment on “The Mineral Aerosol Profiling from Infrared Radiances (MAPIR) algorithm: version 4.1 description and validation” by Sieglinde Callewaert et al.

The authors present a useful multi-sensor study, a fairly comprehensive validation of the updated version 4.1 IASI MAPIR dust retrieval algorithm with respect to data from numerous AERONET sites, CALIOP spaceborne lidar data, ground-based lidar data, and lidar data from the CATS instrument onboard the International Space Station.Updates to the previous version of the algorithm include a change of the radiative transfer model used (RTTOV), a change to logarithmic aerosol concentrations, and the use of the Levenberg-Marquardt modification of the Gauss-Newton iteration scheme.The updates seem to enhance the performance of the MAPIR AOD retrieval algorithm with respect to AERONET measurements, reducing the previous bias of +0.28 to -0.04 in the new version.Validation with various lidar instruments indicates that the MAPIR
A Cloud-based Storage and Retrieval Solution for RDF Data

In recent years, RDF (Resource Description Framework) has been widely recognized as a standard data storage format.There is a real issue that how to store and retrieve RDF data efficiently, because it is the foundation of all semantic-based application development.In this paper, we propose a novel storage and retrieval solution based on cloud and R*-tree.Different to existing approaches, we use cloud-based storage approach to compress RDF data, meanwhile R*-tree is adapted to retrieve compressed data.The advantage is that it not only reduces the local storage pressure but also improves the retrieval performance compare to existing approaches.Because we separate the storage and retrieval and adapt dictionary-based compression approaches, in terms of security and flexibility, our solution is better than existing approaches.
"Syllabic Units Automatically Segmented Data for Continuous Speech Recognition"

We present novel approach for constant speech processing in which the detection and recognition tasks are separated A syllable is utilized as a measure both to detection and localization. A minimal phase’s group delay characteristic approach and an utterance isolated style are used to segment the speech signal at the boundaries of syllabic units. For two Indigenous languages, an HMM recognizing system has been created. Viterbi algorithm-based methods are suggested to solve recognition problems caused by shifts in segment borders and syllabic unit merging.