Reviewerly/exHarmony huggingface dataset:
A Rapid Computing Technology on Profound Computing Era with Quantum Computing

The area of quantum computing bring the strong bridge of new computing era. For the automotive sector, quantum computing will have real-world ramifications, benefits, and concerns. It merely gives a cursory overview of current efforts to create quantum computers, a lively field that is still so new that even specialists cannot anticipate which approaches will be most effective. They define quantum computation topics precisely and underline important distinctions. There are many ways to explore the scientific laws in general and physics laws in particular. Knowledge, like physical laws of nature, can be conveyed in a diversification of ways. These ideas form the foundation for quantum algorithms, which use quantum computers to tackle complicated problems. This article explains some of the fundamental quantum physics concepts that underpin quantum computing. As they endeavor to outfit significantly a greater amount of the quantum world's latent capacity, there might be very nearly second quantum unrest. Many industries, including healthcare, energy, banking, security, and entertainment, could be affected by quantum computing and quantum communication. According to recent studies, the quantum sector will be worth billions of dollars by 2030.
Camera Metrics

This chapter elaborates observation equations for cameras. The pinhole camera model is discussed with relation to a reprojection error. Intrinsic calibration of perspective camera is elaborated. Calculating pinhole camera external orientation using Plücker lines is given. The colinearity and coplanarity observation equations for metric and equirectangular cameras are given. All of these observation equations can be used for Bundle Adjustment.
Enhancing Fireworks Algorithm in Local Adaptation and Global Collaboration for Solving ICSI 2022 Benchmark Problems

Fireworks algorithm is a swarm intelligence optimization algorithm with superior performance, which can be used to solve various practical optimization problems. To enhance the performance of fireworks algorithm, we introduce a powerful local search mechanism and add multiple cooperative strategies. These strategies improve the local exploitation capability and global exploration capability of fireworks algorithm. The experimental results on the ICSI’2022 test set demonstrate that the performance of the algorithm is satisfactory.
Leukaemia Classification Using Machine Learning and Genomics

The field of genomics is vast and innovation is happening at a rapid pace today. With the availability of lots of medical data and extensive research, the tools at our disposal are sharper than ever. One such tool that has quite a lot of untapped potential is Machine Learning. Machine Learning is the field of computer science that gives computers the ability to understand data and make decisions based on that understanding, in quite a similar way as we humans do. Machine learning has proven to be the next big thing in almost all industries today including medicine. The use of Machine Learning in the field of genomics however, is yet to reach its true momentum. With the help of machine learning, patterns in genetic data can be found that were unknown to us earlier and these patterns can be very useful in making conclusions about diseases and disorders that are inherently genetic in nature. In this work, we have classified the patients based on the two cancer classes, namely acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL).
Framework for Estimating Software Cost Using Improved Machine Learning Approach

A software cost estimation is one of the integral parts of project management in every software development organization, which deals with accounting for all the measurable effort required to develop software. This topic in software engineering has been consistently being investigated for the last decade with the intermittent publication of research papers. After reviewing existing approaches, it is found that still, the problem is an open end. Therefore, this paper introduces a machine learning-based approach where a project manager computes the software cost based on the standard input. In contrast, the project manager has estimated cost is further fed to neural network processors subjected to multiple learning algorithms to perform accurate software cost prediction considering all the practical project management scenario. The comparative study outcome shows extensively better accuracy only in three stages of evaluation in the presence of multiple learning approaches.
Lexical Tone is Different and Special: Evidence from a Speeded Repeated Production Task

A distinctive feature of tonal languages such as Mandarin Chinese is that the same consonant-vowel (CV) sequence is a different word depending on the tone it is spoken with. However, there is currently no strong evidence on how tone is represented in speech planning (i.e., attached to the vowel or CV as a whole, or represented independently), as well as whether tone is involved in early or late encoding in phonological encoding. We explored these questions with a word sequence repetition task and measured how quickly speakers produced sequences of CV-tone syllables. Across two experiments, we obtained unexpected results showing that the number of unique CV or tone units in the sequence did not predict speech rate, nor did their repetition pattern. Instead, speech rate was robustly faster when each CV needed to be produced with only one tone (i.e., about equal speech rate for ba2 di1 da1 bi2 and ba1 ba1 ba1 ba1), compared to when a particular CV needed to be produced with more than one tone (i.e., slower speech rate for ba1 ba2 ba1 ba2). We suggest that Mandarin speakers represent CVs as syllable “chunks,” integrating each with a structural frame involving tone, so that producing the same chunk with more than one tone in a sequence is difficult. Our results support models of phonological encoding that select syllables as proximate units, represent tone separately but not independently from segments, and include a late integration stage of syllable and tone.
Gragnostics: Evaluating Fast, Interpretable Structural Graph Features for Classification and Visual Analytics

Graph comparison is important for many common analysis tasks, such as machine learning classifiers and visual analytic tools. Researchers have developed many graph comparison methods, such as graph-level statistics that are often not scalable, or machine learning methods that are often uninterpretable. To address the need for fast, interpretable graph comparison methods, this work proposes gragnostics, a set of fast, interpretable structural graph features. Gragnostics is comprised of 10 structural graph features that can each be computed in linear time. An evaluation shows that these features can discriminate graph topologies better than DDQC, an alternative set of features based on degree distribution. Example usage scenarios of Chiron, a visual analytic tool designed using gragnostics, show that gragnostics can be effective in a rank-by-feature framework. This book chapter also presents several new analyses: A deeper analysis showing relationships between features, and showing how individual features separate some graph classes; a new comparison of gragnostics and DDQC to graph kernels, showing that gragnostics has substantially faster runtime than graph kernels and better accuracy than DDQC and graph kernels; and additional depth and new figures illustrating gragnostics in the Chiron visual analytics usage scenarios.
Study of Markov Chains for the Identification of the Hate Contents in Hinglish

Sreelakshmi, K. Premjith, B. Gopalakrishnan, E. A. Soman, K. P.In the past years, the activity on social media platforms is rising. Social media platforms ease the communication between users; nevertheless, this has led to hate speech proliferation. Hate speech detection has become a hot research topic, this is not only reflected by the hiked media coverage, but also by the political attention, it is receiving. Automatic hate speech detection is quite demanding owing to the non-standard variations in spelling and grammar. The task becomes strenuous for countries with a multilingual and bilingual population due to the CodeMix nature of the hate content. In this paper, we study the third-order Markov chain model for hate speech detection from CodeMix Hindi-English text. The main motive to use the Markov chain model is that it is language-independent, less time-consuming, and does not require any large domain-specific pretrained models. The promising results indicate that the setup put forward can be used for hate speech detection from any language.
Fractures Classification and Detection Using DIP and ML

In modern years, the identification and categorization of fractured bone has been an extensively debated field and various scholars have anticipated various models to solve this setback. A good collective methodology to sort all types of fractures that occur in body hasn’t been outlined yet. The article targets to investigate and assess the kinds of fractures that occur in humans through DL and DIP. This paper uses a different approach to recognize bone fractures. For easy diagnosis of types, we are using only X-ray, technology available in hospitals and classifying them through machine learning.
Robustness Optimization Based on Genetic Evolution

We discuss the self-organizing robustness optimization methods improving reliability of IoT topology in Chap. 3 . In this chapter, we further improve the efficiency of optimization methods to solve multi-parameter and nonlinear problems. Researchers always pursue an optimal solution with less overhead and pay more attention to Genetic algorithm (GA). We analyze the topology structure characteristics from genetic evolution perspective, and propose an optimization method based on multi-population co-evolution to address the premature convergence problem in GA. Furthermore, in order to solve the problem that computational overhead is larger with the population size increasing, we present an adaptive optimization algorithm based on self-competition, which ensures the evolution direction and convergence speed of the population. This chapter rigorously demonstrates the correctness and effectiveness of the methods from a large number of experiment simulations.
Statistics of RDF Store for Querying Knowledge Graphs

Many RDF stores treat graphs as simple sets of vertices and edges without a conceptual schema. The statistics in the schema-less RDF stores are based on the cardinality of the keys representing the constants in triple patterns. In this paper, we explore the effects of storing knowledge graphs in an RDF store on the structure of the space of queries and, consequently, on the definition of the framework for the computation of the statistics. We propose a formal framework for an RDF store with a complete conceptual schema. The poset of schema triples defines the structure of the types of triple patterns and, therefore, the structure of the query space. The set of schema triples, together with the ontology of classes and predicates, form the conceptual schema of a knowledge graph, referred to as a schema graph. We present an algorithm for computing the statistics of a schema graph that consists of the schema triples from the stored schema graph and the schema triples that are more general/specific than the stored schema triples up to a user-defined level.
Research on the Application of Ant Colony Clustering in Commodity Classification

As a swarm intelligence algorithm, ant colony algorithm is simple to implement, and its application range and fields are very wide, it can also effectively solve complex problems, especially unsupervised clustering problems and combinatorial optimization problems. At the same time, ant colony algorithm has the characteristics of self-organization, scalability, robustness and anti-noise data. Since the ant colony algorithm is a global search, it can be applied to the e-commerce system to deal with the problems of cold start and data scarcity, the ant colony clustering model is closer to the actual clustering problem. In this paper, ant colony algorithm is used to cluster users according to user behavior characteristics to achieve the purpose of product recommendation. By constructing the pheromone matrix and the optimal solution set of the ant colony, the iterative results of the algorithm are stored. On this basis, iterate continuously, mark the behavior data of different users, and update the optimal solution to find the category of each user. Secondly, through different parameter settings, the experimental results are analyzed. If the clustering algorithm can be effectively applied in the e-commerce system, the system can improve the accuracy and efficiency of the recommendation results when making personalized recommendations for users.
FACTORS OF «ENGRAFTMENT» AND INCREASE OF AGILE METHODOLOGY EFFECTS

The purpose of the study is to analyze the transformation of Agile. The article discusses the experience of implementing the provisions of the Agile manifesto and its impact on increasing the performance indicators of project teams. Detailed analysis of the Agile methodology as a management tool was carried out. Research methods are based on the use of a system-activity approach methodology, statistical analysis, specified by a set of general scientific methods: analysis, synthesis, comparison, grouping. Based on a comparison of Agile application practices in Russia and abroad, as well as among certain industries, factors contributing to the effectiveness of the methodology have been investigated.
Dynamic Interaction between Saving, Investment and Economic Growth in Ethiopia

The purpose of this research is to inves-tigate the dynamic interaction between savings, investment and economic growth in a case of Ethiopia by using quarterly time series data of National Bank of Ethiopia (NBE) from the periods 1992/93 to 2019/20. Consequently, the Vector Error Correction Model (VECM) results showed that there was a positive long run relationship between savings, investment and economic growth in Ethiopia. Granger causality from domestic invest-ment and real GDP to domestic savings and domestic investment granger causes economic growth. The response of do-mestic investment to a positive shock of real GDP is positive. Investment and money supply reacts positively to shocks of domestic saving. Whereas, the ex-change rate responds adversely to the shock of investment. In the short run the reaction of saving to shocks of real GDP and investment was positive and negative respectively. Whereas, shocks of saving generate a negative effect in the short run and insignificant effect on economic growth in the long run. Real GDP re-sponds negatively to shocks of interest rate and exchange rate. Besides, the var-iance decomposition results show that the variation of economic growth is largely explained by shocks to itself and in-vestment. The variation in saving ema-nates from the money supply, domestic savings, real GDP and investment. In-vestment deviation is also emanating from Real GDP and saving. Thus, the govern-ment and stakeholders are supposed to practice macroeconomic policies that will promote economic growth and gross domestic investment and thus saving will increase.
The visual aspect of slides

This chapter answers the following questions. How can I get an overall view of my presentation? How can I limit the amount the number of words in a slide? How big should slide titles be? Are Design Ideas useful? How can I repeat an element from one slide in the next slide? What kind of slides are overused and thus have little effect? Should I use fun images? What if there are restrictions on the number of slides that can be used?
Random Variables and Their Distributions

Random variables can be continuous or discrete, and they are described by a distribution function that measures the probability of occurrence of any value of the variable. The purpose of performing experiments and collecting data is to gain information on random variables of interest. Useful measures of the distribution of random variables are the mean, variance, and other higher-order moments. Measurements of the variable can be used to estimate these moments by calculating quantities such as the sample mean and the sample variance.
Judgment of Computer Software Infringement and Possible Civil Liability

In the information age, there are many kinds of computer software. Nowadays, the number of software is extremely large, and there are many kinds of software. Some of the software is favored by people, and has become a common tool in people’s life and work, which makes the developers or operators of such software get a lot of benefits. However, this phenomenon may involve civil liability, so we must combat this phenomenon, but it needs to make clear the judgment conditions of tort and the civil liability that may be undertaken. In this regard, this paper will carry out relevant research, discusses the two forms of computer software infringement, and analyzes the judgment conditions of the two forms, and finally puts forward the possible civil liability.
Application of Deep Learning in Mental Disorder: Challenges and Opportunities

In this chapter, application of deep learning techniques in mental disorder is proposed to enable medical practitioners to detect the disease accurately and help them in efficient decision making subsequently. Mental illness is a type of health condition that changes a person’s mind, emotions or behaviour (or all three), which witnessed its impact on an individual’s physical health. The idea behind using deep learning lies in its potential in classifying medical data, detecting objects, recognizing speech, translating languages and knowledge discovery processes. An extensive review is conducted based on state-of-the-art machine learning approaches considering the trends, gap and challenges in mental health disorder scenarios and then proposed several deep learning methods including convolution neural network (CNN) and autoencoder; for building an effective and efficient model to address the potential pitfalls. Experimental studies are conducted using publicly available mental disorder dataset with various performance metrics such as accuracy, complexity and error rate. The obtained results and discussions will pave the way for the researchers and practitioners to understand the suitability of deep learning applications to address the mental health issues in terms of developing plans for effective mental health treatment, identifying behavioural biomarkers and predicting crises with utmost caution to name a few.
Using Earth Systems Paleoclimate Predictions to De-Risk a Frontier Margin for Source Rocks

Summary Earth Systems paleoclimate predictions can be used to address hydrocarbon exploration questions and reduce risk. We have developed a first-pass screening workflow that integrates data, geological principles and ‘ground-truthed’ paleoclimate models that allow us to predict the likely distribution and driving mechanism for source rocks in deep geological time. We demonstrate how this workflow can be applied to the East African margin. Despite numerous gas discoveries, the East African margin remains a frontier region for oil exploration as there have been very few substantial oil discoveries. Our geodynamic reconstructions show that the southern half of this margin formed the northern margin of the paleo-Southern (Austral) Ocean. This ocean would have been highly restricted, stratified, and anoxic in the Early Cretaceous — conditions strongly favoring the preservation of organic matter. Our climate predictions indicate that organic matter productivity in this ocean was caused by seasonal upwelling supplemented by localised inputs of terrestrial organic nutrients from rivers. We predict the most likely locations for Early Cretaceous, oil-prone, oil-mature source rocks on the East African margin together with a proxy for thickness: these results correspond to recent exploration successes in South Africa.
Global Horizontal Solar Irradiance Forecasting Based on Data-Driven and Feature Selection Techniques

With the rapidly expanding infrastructure of the solar energy system, the need for an accurate solar prediction has become an essential part of the renewable energy sector. Over the past decade, various machine learning (ML) algorithms have been used for this purpose. Although the prediction of solar irradiance forecasting has been discussed in a large number of studies, the use of meta-heuristic optimization techniques has not been explored to select features for the forecasting model. This study comprises two meta-heuristic optimization techniques such as simulated annealing (SA) and ant colony optimization (ACO) for feature selection. The results show that feature selection based on meta-heuristics gave better results than models without feature selection. Amongst the two optimization methods, ACO outperformed SA with some exceptions. For SA, the declining order of performance observed is extreme gradient boosting (XGBoost), random forest (RF), multilayer perceptron (MLP) , decision tree (DT) and support vector regression (SVR), while for ACO the declining order observed is XGBoost followed by MLP, RF, DT and SVR. This manuscript indicates the potential capability of meta-heuristic techniques for accurate prediction of global horizontal irradiance (GHI) given a wide array of feature variables.
The Comprehensive Art of Atmospheric Turbulence Mitigation Methodologies for Visible and Infrared Sequences

Heat scintillation also known as atmospheric turbulence distorts the image. This distortion is due to the propagation of light that passed through the volatile surroundings. The distortion created by the atmospheric turbulence is proportional to the distance between camera and object. This paper gives an overview of state-of-the-art techniques, working principles, and challenges in the field of the atmospheric turbulence. The techniques include first register then average and subtract (FRTAAS), independent component analysis (ICA), Lucas–Kanade, and control grid interpolation (CGI). These techniques use image registration for mitigation atmospheric turbulence. Different standard datasets mostly used in this field are represented in the paper. Finally, all state-of-the-art algorithms are evaluated based on standard evaluation performance parameter.