Reviewerly/exHarmony huggingface dataset:
Introduction

MATLAB’s py module allows it to run Python code which opens a world of new possibilities for MATLAB developers. Among other things it lets MATLAB: run faster with numba-accelerated Python functions; distribute computations to compute clusters with dask; overlay data on maps with Cartopy; interact with Redis, PostgreSQL and MongoDB via pyredis, psycopg2, and pymongo; perform symbolic math operations with SymPy; solve linear programming problems with PuLP; read YAML, ini, and SQLite files; and much more. To take advantage of these capabilities, MATLAB developers need to know Python. _Python for MATLAB Development_ teaches MATLAB users all aspects of Python with a comprehensive collection of MATLAB/Python equivalent expressions. More than twenty complete hybrid MATLAB-Python solutions are presented.
Hydrothermal Mineral Deposits in Volcano–Sedimentary Environments

Hydrothermal deposits: ore minerals precipitated from high-temperature aqueous fluids of different origins. (a) Deposits in orogenic environments Orogenic gold deposits Carlin-type gold deposits Iron oxide–copper–gold (IOCG) deposits. (b) Deposits in volcano–sedimentary environments Volcanogenic massive sulfide (VMS/VHMS) deposits Sedimentary exhalative (SEDEX) deposits Mississippi-Valley Type (MVT) deposits Red-bed copper deposits.
Cartesian Genetic Programming: Some New Detections

In this paper, we propose a variant of the fitness function for Cartesian Genetic Programming, called CGP-nMean. Besides, we also analyze systematically the dependence of this method on its experimental parameters to find the relationship between them and to define what is the best configuration. In order to evaluate the effectiveness of the proposed approach, we run experiments on eighteen symbolic regression problems. The experimental results show that (1) the generalizability of the learned model by CGP-nMean is significantly better than that of standard CGP on the most of tested problems; (2) the performance of CGP is significantly affected by experimental parameters, and controlling these parameters will give us the best configuration of CGP-nMean (CGP-nMean(Best)).
Graphical Analysis of Metamorphic Mineral Assemblages

Building upon the award-winning second edition, this comprehensive textbook provides a fundamental understanding of the formative processes of igneous and metamorphic rocks. Encouraging a deeper comprehension of the subject by explaining the petrologic principles, and assuming knowledge of only introductory college-level courses in physics, chemistry, and calculus, it lucidly outlines mathematical derivations fully and at an elementary level, making this the ideal resource for intermediate and advanced courses in igneous and metamorphic petrology. With over 500 illustrations, many in color, this revised edition contains valuable new material and strengthened pedagogy, including boxed mathematical derivations allowing for a more accessible explanation of concepts, and more qualitative end-of-chapter questions to encourage discussion. With a new introductory chapter outlining the “bigger picture,” this fully updated resource will guide students to an even greater mastery of petrology.
Research on Target Detection Based on Deep Learning

In modern war, the environment is complex and changeable, so how to detect and attack targets automatically and effectively is of great significance. In this paper, through the analysis of the characteristics of targets under complicated environment, six targets, the aeroplanes, bridges, vehicles, ships, submarines and tanks are selected as the objects to be detected, and a large number of corresponding images of them are collected via Internet, and with reference to the dataset format of PASCAL VOC [1], the collected six types of target images are manually annotated to set up the dataset. Then, a corresponding detection model which will be used to detect the six types of targets is built. Base on the dataset built earlier, the detection model is trained and improved by modify its anchors properly.
OAIDS: An Ontology-Based Framework for Building an Intelligent Urban Road Traffic Automatic Incident Detection System

Handling interoperability of data exchange among road traffic sensor devices, connected vehicles, infrastructure components and heterogeneous traffic management center applications has become an important and basic requirement nowadays. To meet this requirement, this paper proposes an ontology based framework to capture the knowledge domain about traffic automatic incident detection system (AIDS) based on Connected Vehicles (CVs) technology. This ontology addresses the semantic data interoperability needed between different heterogeneous entities constituent this AIDS. This contribution aims at modeling and capturing the semantic of the anomaly information used in the incident detection process and describing the AIDS components, their observations, measurements and communications messages features. First, to achieve this goal, NeOn methodology was adopted. Then, we defined the basic concepts and observations of a traffic sensor and CVs that has been extended to define concepts related to the data sensing and gathering layer of this framework based on ontology concepts. In addition, to ensure data interoperability and identify ontology’s restrictions, we used the OWL (Web Ontology Language) language. Furthermore, to build this ontology, we used the OWL under Protégé tool. Finally, OAIDS consisted of 93 concepts and 33 object properties. OntoMetrics was used to confirm the effectiveness of this proposed ontology to carry out the interoperability of CV’s sensor data in the urban road AIDS domain.
Design and Fabrication of Automatic Oxygen Flow Controller for COVID Patient

In this ongoing pandemic situation, an acute shortage of oxygen due to number of patients required the pure oxygen supply becoming high, showed us the level of importance of this gas. People getting affected by COVID-19 are suffering from low saturation level which needs to be increased by the supply of pure oxygen. The oxygen is used to bring the saturation level to 94%. Any drop below 84% lead to serious respiratory failures. We propose a system where the oxygen is supplied to the patient as per the requirement preventing the wastage of oxygen. This is done by constantly monitoring the oxygen level of the patient and releasing the exact amount of oxygen needed by the patient, using a microcontroller device, to control the flow rate of oxygen. This helps automatically to control the oxygen flow and also alert the doctor if the patient goes into a critical condition.
Basic Cryptography: Hash Function

The algorithms in this chapter are very different from those in the previous chapter (symmetric key encryption). A Hash Function is a one-way process (there is no way to recover the original information from a message digest). There is no key (a given plaintext will always produce exactly the same message digest). With symmetric key cryptography, the output is roughly the same size as the input. With a hash function, the output is always a fixed length (based on the algorithm used, e.g. 256 bits for SHA2-256). Hash functions are used in very different ways than encryption algorithms.
Enhanced Encodings for White-Box Designs

Designing a robust white-box implementation against state-of-the-art algebraic and differential computational analysis attacks is a challenging problem. The study of white-box security was revamped by recent advances involving grey box attacks. Since then, many authors have struggled to protect implementations against such new attacks. New designs as well as new security notions appeared, and white-box research in general seems to have greatly benefited from such advances. The current research aims at finding the best encodings and masking schemes to resist tracing attacks. In this perspective we suggest a new encoding scheme that can be applied to white-box designs. By using a modified version of the Benaloh cryptosystem, our design introduces semi-homomorphic properties to the encoding. To the best of our knowledge, this is the first time such properties are applied to an encoding design. This allows reducing the memory requirements and providing a better resistance against tracing attacks. Our encoding is versatile and can be adapted to different ciphers, and in most cases it provides performance improvements with respect to the state-of-the-art.
Latent Multi-view Subspace Clustering Based on Schatten-P Norm

In this paper, we aim at the research of rank minimization to find more accurate low-dimensional representations for multi-view subspace learning. The Schatten-p norm is utilized as the rank relaxation function for subspace learning to enhance its ability to recover the low rank matrices, and a multi-view subspace clustering algorithm via maximizing the original feature information is proposed under the assumption that each view is derived from a latent representation. With the Schatten-p norm, the proposed algorithm can improve the quality and robustness of the latent representations. The effectiveness of our method is validated through experiments on several benchmark datasets.
Hyper-Parameter Tuning

Hyper-parameters can be loosely defined as parameters that do not change during training. For example, the number of layers in an FFNN, the number of neurons in each layer, activation functions, learning rate, and so on. This chapter deals with how to tune hyper-parameters in the most efficient way.
Soccer Robots Modeling Project Based on RoboCupJunior: Simulation Environment for Physical Robot Improvement

This article aims to present the contributions of the soccer robot project based on RoboCupJunior Soccer Open. The project proposes the 3D modeling of the robot in order to create a virtual version before the physical robot. In the virtual robot, it is implemented the algorithm, are validated the game strategies, and tested the design functionality in the robot structure through the 3D simulation environment. This virtual environment was developed within the parameters of the competition to provide greater consistency and integrity during the simulations, which allows time optimization, project improvement, problem correction and solution implementation before robot construction. The potential of the simulation environment is visible, which can encourage a 3D Simulation modality of Soccer at RoboCupJunior.
Classification of the quality attributes of the food delivery platform from the seller’s perspective based on the Kano model

Most of the research on the quality of the existing food delivery platform was conducted on consumers. However, from the perspective of platform operators, restaurant sellers that enter the platform and provide restaurant services are also major members of the platform, and research is needed accordingly, but it is insufficient. Therefore, this study conducted a study on the quality attributes of sellers using the food delivery platform based on the Kano model, a quality planning theory. In addition, the difference in the Kano model according to the seller's usage behavior was analyzed and statistical significance was tested, and the customer satisfaction coefficient and potential customer satisfaction improvement index (PCSI) were derived to supplement the Kano model. Sellers using the food delivery platform were classified as a unified quality factor that causes satisfaction when satisfied and dissatisfaction when not satisfied with all the quality attributes of the platform. In the case of the Kano model by seller's usage behavior, there was no difference, but as a result of the chi-square test, statistical significance was confirmed for a specific item. As a result of deriving the customer satisfaction coefficient, the top items of the satisfaction coefficient were “advertising effect on the platform,” “number of platform users,” and “support of Coupon/Event costs,” and “quickness of customer center response” as the top items. The Potential Customer Satisfaction Improvement Index (PCSI) was found to have the top items such as 'Instant Action Function for Negative Reviews', 'Fee Price Appropriateness', and 'Rationality of Advertising Costs'. This study is expected to provide strategic implications for platform quality strategies for sellers from the standpoint of operators operating the food delivery platform by analyzing the quality attributes perceived by sellers using the food delivery platform based on the Kano model.
An Automatic Design Method of Similarity Fusion Neural Network Based on SG-CIM Model

The State Grid Enterprise Public Data Model (SG-CIM 4.0) is a semantically unified data model that can be provided for smart grid business applications. The model is based on a standard table to find similar entities in the physical model for consistency checking. The standard table entities and physical model entities contain both continuous attributes and discrete attributes. How to accurately calculate the similarity of these different attributes and fuse them into a unique similarity, which can be used to efficiently and accurately mine the entity pairs with the highest similarity, are problems to be solved. In order to solve the above problems and make this similarity calculation and fusion method scalable, this paper calculates the syntactic similarity of continuous attributes, semantic similarity, and discrete attribute similarity to the content of different attributes in the entity and introduces a NAS (Neural Architecture Search) based on these similarities Similarity Fusion Neural Network automatically designed a method to achieve the fusion of similarity, which will be called SFNAS (Similarity Fusion NAS). The neural network fusion similarity calculated using SFNAS is better than the traditional linear weighted average similarity in terms of entity pair matching hit rate. This paper can provide useful references for subsequent research on SG-CIM models.
Customer Satisfaction in Software Development Projects

In this study, research is being conducted about the Customer's Satisfaction (CS) along with factors that have an impact on it and on the Software Project Management. The research method of this study is the Systematic Literature Review (SLR) and the main conclusion derived from this study is that the customer's active involvement through agile methods in all phases of the IT project development, leads to a higher level of satisfaction.
The education language and its influence on the pupils incompetence in text formation (The third class of the basic stage) as an example

The pupils incompetence in text writing lesson has lots of causes, text formation lesson is not like the dictation lesson in which the sentences to be written without paying attention to the meaning and status of the words and good marks will be obtained in. A proper writing is the result of thinking and benefiting from imagination and the mental activities in this lesson, in addition to writing the words correctly, the pupils should have a good command of syntax and have the ability of describing the visible matters and their inner thoughts. To strengthen their competence; the pupils should be given exercises in their schools. In this research, we have attempted to showcase the reasons behind the pupils incompetence; we should answer some questions and for this purpose we have taken the opinion of the pupils, teachers and the parents of the pupils. The research has been conducted through a questionnaire that has been distributed over them and the collected data and answers have been analyzed later. The results show that the incompetence problem among the pupils of the third stage of the basic study in text formation lesson can be summarized in four reason: 1- The word and sentence status problem. 2- The practical problem. 3- The problem related to the teaching method. 4- The problem related to directing the pupils and choosing the topic.
Forecasting Cryptocurrencies’ Price with the Financial Stress Index: A Graph Neural Network Prediction Strategy

This paper proposes a graph neural network strategy (GNN), in which the long short-term memory (LSTM) and graph convolution network (GCN) are applied to capture both temporal and spatial features to forecast the price of Bitcoin, Litecoin, Ethereum, and Dash Coin with the 'stable-coin' Tether (USDT) and financial stress index (FSI). The main results show that the GNN strategy has better performance than univariate LSTM and multivariate LSTM in all of the seven steps forward forecasting. A sensitivity check shows that USDT and FSI/sub-FSI are important factors in the construction of the graphs and they verify the validity of the results.
Clustering Educational Items from Response Data Using Penalized Pearson Coefficient and Deep Autoencoders

Educational data mining techniques are very useful to analyze learner performance in purpose to optimize the approach of item-to-skill mapping. Therefore computing a degree of similarity between items using different measures based on the performance of the learner toward items, enhance the clustering of different items into knowledge components. This paper proposes a computational framework to group the elements of the corresponding knowledge component. The first phase of the framework represents a variation of Pearson coefficient to measure item similarity by applying a penalty score that is calculated from the number of hints taken by the learner during solving two items. The second phase applies a dimensionality reduction using deep auto encoders to improve the clustering accuracy. The experimental results show that clustering based on the penalized Pearson coefficient and the deep dimensionality reduction (PPC+DDR) outperforms basic clustering based on different similarity methods, with approximately +0.2 in Mean silhouette coefficient.
Convolutional Neural Networks

A convolutional neural network (CNN) is a feedforward neural network with layers for specialized functions for applying filter to the input image by sliding a filter across small sections of the image to produce an activation map. Recall that regular feedforward networks are made up of individual computation units or neurons, and the training process requires backpropagation to learn their weights and biases. Each computation unit combines the input with weights using a dot product and passes it to a nonlinear function that produces an output. This output is often used as input to the neurons in the next layer till we reach the output layer. CNNs are developed and centered on finding features within images with the use of a mathematical operation called convolution in at least one of their layers.
A New Approach for Unified Characters Cluster Segmentation of Ancient Handwritten Modi Documents

In the recognition of the historical handwritten Modi documents, character segmentation is a significant task. The complexity in Modi character segmentation is increased because of non-uniformed, unconstrained, stylish and cursive writing nature of the Modi text. Here, a modified and robust character segmentation approach is presented for the unified clusters of archaic handwritten Modi script documents. Three different strategies are presented here to tackle the different challenges in segmentation of these clusters. The selection strategy is based on the degree of connected component overlap ratio. Highly overlapping characters are segmented by employing the local zoning-based background pixel intensity method. Foreground pixel intensity and vertical projection profile are analysed to segment the partial overlapping/touching characters and entirely touching characters respectively. Experiment was conducted using the handwritten unified Modi characters clusters. It has been proved that with the proper selection strategy, the proposed unified characters cluster segmentation system achieves 86.33% segmentation accuracy with reduced bad segmentation rate.
Asynchronous Multi-sensor Data Fusion with Updating Bias Correction

Multi-sensor data fusion is capable of providing more reliable and accurate estimations of targets than that from a single sensor. However, the performance of multi-sensor fusion is greatly affected by updating intervals, especially in asynchronous scenarios. A method correcting updating bias for asynchronous multi-sensor data fusion is proposed. Linear resampling for updating interval is applied in time domain for each individual surveillance source, then the state estimation of a target from a single source is given by Kalman filtering. The fused state estimation of each target then can be obtained under the condition of the optimized minimum of the system error covariance. Simulation results show that the proposed method is feasibility and effectiveness for asynchronous multi-sensor data fusion problem.
Descriptive Functions and Manipulating Objects

For arrays, matrices, vectors, lists, and expressions, in command line R, there are a number of functions that describe various attributes of an object. In RStudio, many attributes, such as the number of columns in a matrix or the length of a list, are given to the right of the object name under the Environment tab in the upper right window. Also, there are functions that manipulate objects to create new objects.
Propositional Calculus

Propositional formulas are elements of the free algebra generated by the set of propositional variables V and constants $$\top $$ , $$\bot $$ in the Boolean-type $$\langle \vee , \wedge , \lnot \rangle $$ .
On Filtration in a Rectangular Interchange with a particularly Unpermatable Vertical wall in the Evaporation

We consider a plane steady-state filtration in a rectangular bridge with a partially impermeable vertical wall in the presence of evaporation from a free surface of groundwater. To study the effect of evaporation, a mixed multiparametric boundary-value problem of the theory of analytic functions is formulated and using the method of P. Y. Polubarinova-Kochina. Based on the proposed model, an algorithm is developed to calculate the dependence of efficiency and productivity of hydrodynamic analysis.
The Rearrangement Algorithm

In this lecture, we discuss certain optimization problems related to copula theory. A numerical solution to these problems is presented, namely, the so-called rearrangement algorithm.
A Term Selection Method Combined with Sentiment Score for Twitter Sentiment Analysis

research-article Share on A Term Selection Method Combined with Sentiment Score for Twitter Sentiment Analysis Authors: Longyang Zhao Guilin University of Electronic Technology, China Guilin University of Electronic Technology, ChinaView Profile , HangTao Wang Guilin University of Electronic Technology, China Guilin University of Electronic Technology, ChinaView Profile , Kaili Zheng FlashEx Corporatio, China FlashEx Corporatio, ChinaView Profile , Ruimin Meng Guilin University of Electronic Technology, China Guilin University of Electronic Technology, ChinaView Profile Authors Info & Claims ICCBD 2021: 2021 4th International Conference on Computing and Big DataNovember 2021 Pages 27–32https://doi.org/10.1145/3507524.3507529Online:08 March 2022Publication History 0citation20DownloadsMetricsTotal Citations0Total Downloads20Last 12 Months20Last 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access