Reviewerly/exHarmony huggingface dataset:
Tables and Dataframes

The Pandas module for Python [2] came out in 2008, five years before The MathWorks added tables to MATLAB in the R2013b release. Since its release, Pandas has become a core tool for data scientists, financial analysts, statisticians, and machine learning specialists. A comprehensive discussion of Pandas is far beyond the scope of this book; instead, here we show the functional similarities and notational differences between Pandas dataframes and MATLAB tables.
PortNOIE: A Neural Framework for Open Information Extraction for the Portuguese Language

Open Information Extraction (OpenIE) is the task of extracting structured information from text. Recent advances in applying Deep Learning to OpenIE tasks have improved the state of the art for the task, although few works have been produced for languages other than English. In this work, we propose PortNOIE, a neural framework for open information extraction for the Portuguese language. We evaluate our method on a manually annotated corpus of Open IE extractions, obtaining better performance than the current state of the art for OpenIE for Portuguese, both based on rule-based approaches or neural methods.
An Improved SHO Technique for Mathematical and Multidisciplinary Engineering Applications

A newly developed metaheuristic swarm inspired technique named as spotted hyena optimization has got the inspiration from the hunting behavior of hyena for getting the optimal solution for complex and multidisciplinary design problems. However, in order to boost up the local and global search capability of spotted hyena optimization technique during exploration phase a chaotic search method is incorporated which works efficiently and gives positive results. The efficacy of the proposed CSHO is validated on nonlinear and constrained multidisciplinary engineering applications and 23 standard benchmark functions. It is verified and observed that proposed search algorithm, i.e., CSHO, has performed well over other existing algorithms such as spotted hyena optimization, Harris Hawk optimization, grey wolf optimizer, sine cosine algorithm, and other search algorithms.
Generalized Net Model of Telerehabilitation Program for Patients with Socially Significant Diseases

The development of telecommunication technologies and artificial intelligence has led to a significant change in the healthcare trends. A strong example of innovative healthcare provision is the so-called telerehabilitation (TR), which is part of the rapidly evolving field of telemedicine. In the present paper we propose a reduced GN-model representing a real time patient/therapist interaction via video-conferencing platform. The model allows different treatment behavior depending on the disease. The model describes a telerehabilitation protocol and algorithm for choosing the best rehabilitation treatment based on the patient dysfunctions and the possible outcomes.
Introduction to Handling Databases

Data is fundamental to the ways that modern business, communications, science, and even our personal lives are changing. The information we create from our online shopping, social media posts, search engine queries, and location data is collected, managed, and analyzed and can be used for a number of reasons, including to track consumer patterns, to train artificial intelligence algorithms, or even to study the geographic distribution of particular events such as diseases.
Entity Extraction from Portuguese Legal Documents Using Distant Supervision

Most approaches to role-filler entity extraction (REE) rely on large labeled training corpora in which entity mentions are directly annotated in the input document. In this work, we leverage an existing knowledge base (KB) of entities to perform document-level REE from drug seizure petitions. We propose a system that learns to extract entities from petitions to fill 29 roles of a drug seizure event. Although we have access to a KB covering more than 170 thousand entities and six thousand petitions, such that each entity in the KB is linked to a specific petition, the mentions to an entity within a petition’s text are not annotated. The lack of these annotations brings challenges related to mismatches between entity values in the KB and entity mentions in the documents. Additionally, there are entities with same type or same value. Thus, we propose a distant annotation method to overcome these challenges and automatically label petition documents using the available KB. This annotation method includes a parameter that controls the balance between precision and recall. We also propose a strategy to effectively tune this parameter in order to optimize a given metric. We then train a BERT-based sequence labeling model that learns to identify entity mentions and label them. Our system achieves an $$F_1$$ score of 78.59 with precision over 82%. We also report ablation studies regarding the distant annotation method.
Modeling and engineering support of strategic decisions in the security management of financial and economic objects

The article discusses modern approaches to modeling wave processes in the field of ensuring the security of financial and economic objects based on the analysis and use of mathematical methods. The possibilities of the used dynamic models in forecasting the development of processes in the medium and long term, contributing to the implementation of strategic planning, are shown. A method of embedding the developed mathematical models into the security management system of financial and economic objects is proposed. Approaches to the design of engineering support for decisions and optimization of management have developed.
Data Storage Patterns

In Chapter 15 , you learned different patterns for getting data into your destination system. What that chapter did not cover is the question of how to store the data in your destination system.
Optimization and Neural Networks

This chapter contains a basic introduction to the most important concepts of optimization and explains how they are related to neural networks. This chapter doesn't go into detail, leaving longer discussions to the following chapters. But at the end of this chapter, you should have a basic understanding of the most important concepts and challenges related to neural networks. This chapter covers the problem of learning, constrained and unconstrained optimization problems, what optimization algorithms are, and the gradient descent algorithm and its variations (mini-batch and stochastic gradient descent).
Traffic Matrix Prediction Based on Differential Privacy and LSTM

The traffic matrix (TM) is an important type of information in network management, which is needed in network load balancing and routing configuration. Due to technical and cost reasons, it is difficult to directly measure the matrix, but we can use prediction instead of direct measurement. The Long Short-Term Memory(LSTM) model in deep learning is very suitable for time series forecasting problems. However, due to the characteristics of deep learning, the data samples used to train the network will leave their own traces on the final model, which allows attackers to restore training samples through member inference attacks, causing privacy leakage. This paper focuses on combining the differential privacy mechanism with the LSTM model. In the gradient descent stage of training, a well-controlled noise is added to protect the final model. In the experiments, we verify the feasibility of the method proposed in this paper on the data set Abilene.
FastDCF: A Partial Index Based Distributed and Scalable Near-Miss Code Clone Detection Approach for Very Large Code Repositories

Despite a number of techniques have been proposed over the years to detect clones for improving software maintenance, reusability or security, there is still a lack of language agnostic approaches with code granularity flexibility for near-miss clone detection in big code in scale. However, it is challenging to detect near-miss clones in big code since it requires more computing and memory resources as the scale of the source code increases. In this paper, we present FastDCF, a fast and scalable distributed clone finder, which is partial index based and optimized with multithreading strategy. Furthermore, it overcomes single node CPU and memory resource limitation with MapReduce and HDFS by scalable distributed parallelization, which further improves the efficiency. It cannot only detect Type-1 and Type-2 clones but also can discover the most computationally expensive Type-3 clones for large repositories. Meanwhile, it works for both function and file granularities. And it supports many different programming languages. Experimental results show that FastDCF detects clones in 250 million lines of code within 24 min, which is more efficient compared to existing clone detection techniques, with recall and precision comparable to state-of-the-art approaches. With BigCloneBench, a recent and widely used benchmark, FastDCF achieves both high recall and precision, which is competitive with other existing tools.
Real-Time Path Planning Algorithm Based on ROS Mobile Robot

As a global path planning algorithm, RRT has some advantages in global path planning, but it can’t change the path in random obstacle environment to realize real-time obstacle avoidance. Therefore, two hybrid path planning algorithms, RRT+ improved DWA and RRT+TEB, are proposed to improve the real-time performance of RRT. In addition, an experimental environment was established to verify the two hybrid algorithms and the results were analyzed. Experimental verification and analysis show that the two hybrid algorithms have good real-time performance and can effectively improve the task execution efficiency under unknown environment.
Hydrothermal Mineral Deposits in Orogenic Environments

Hydrothermal deposits: ore minerals precipitated from high-temperature aqueous fluids of different origins. (a) Deposits in orogenic environments Orogenic Au deposits Carlin-type Au deposits Iron oxide–copper–gold (IOCG) deposits (b) Deposits in volcano–sedimentary environments Volcanogenic massive sulfide (VMS) deposits Mississippi Valley–type (MVT) deposits Sedimentary exhalative (SEDEX) deposits Red-bed copper deposits Uranium deposits.
Importing and Creating Data

When you are loading data into RStudio or R, you have options. R reads external objects using connection functions. The connections can be to files, Uniform Resource Locator (URL) addresses, pipes, or socket connections and can be an internal part of a function that reads from the connection, an argument to a function that reads from the connection, or a stand-alone function that opens a connection for reading (and, sometimes, writing). Some functions read textual data, some binary data, and some both kinds of data.
THE IMPORTANCE OF TEACHING THE MODELING DEPARTMENT OF COMPUTER SCIENCE FOR MIDDLE SCHOOL STUDENTS

Today, the rapid development of information and communication technologies leads to radical changes in the field of cognitive activity, in particular in education. An important change is the method of modeling used in the didactic practice of computer science. Modeling is one of the most common methods of studying processes and phenomena, which is widely used in research and teaching. The article analyzes the role of modeling as a universal method of studying complex objects in the teaching of computer science, and shows ways to develop the importance of modeling as a basis for teaching in general software. The role of the model in teaching computer science to high school students focuses on the importance of teaching in the modeling of computer science, and ways to increase students' interest and skills in considering the technology of teaching methods.
Building a Simple GUI

Hello again! GUIs are designed to handle specific tasks, such as writing and editing documents or playing videos. Creating any kind of UI can seem like a formidable task with all the different widgets there are to consider. Widgets are the buttons, checkboxes, sliders, and other components that users use to interact with the GUI.
Enhancing the Quality and Security of IoT Software Systems Using Cloud-Based Vulnerability Detector

The increased prevalence of open-source software has become an integral component in many common IoT devices. Open-source software has helped companies and individuals alike implement tools that may not have been within their budget to license or develop. Open-source software allows for code to be audited and viewed by many eyes. In an audit, one can search through code to find non-compliant code, dead code, and vulnerabilities. A cloud-based static analysis tool that has the potential to help the IoT community test and improve their source code concerning source code security is presented. The implementation of known C/C ++ coding patterns susceptible to vulnerabilities within open-source projects is described in this paper as well. The source code of these projects will be transformed into XML and then will be analyzed for these potentially dangerous coding patterns. The coding pattern analysis will search for known unsafe functions, race conditions, buffer overflows, divide by zero errors, implicit typecasting, deallocated memory dereferencing, potential memory corruption, uninitialized variables, and memory leaks. Although many of these dangerous patterns have been made known to the public for years, the results show that the usage of these patterns within C/C++ source code written for IoT devices and applications is still present. The findings also prove the importance and effectiveness of the tool and its checkers within the IoT domain.
Analysis of Ship Resistance Based on Horizontal Placement of Fin Stabilizer Using CFD Software

Fin stabilizer is an anti-rolling system that responds to rolling motion due to external forces and affects the stability and safety of personnel, cargo, and the ship itself. As a result, the addition of a fin stabilizer affects the ship resistance (appendage resistance). This research has been carried out on the type of patrol boat to determine the effect of horizontal fin placement on the ship's resistance. This research focus is based on foil NACA 0010 as fin stabilizer. The study begins by verifying and validating the model before using the fin stabilizer, which aims to check whether the model to be developed has presented the expected conditions, as well as a comparison between the needs of the model that has been tested and the patrol boat design, made using the maxsurf modeler, Rhinoceros and numeca fine marine with a maximum error of 3%. The design results show a maximum error of 2.57%, so the research is continued with the addition of a foil type Naca 0010 with an angle of attack 0°, 5°, 10°, 15° and speed variations from 25 knots to 30 knots. The largest additional resistance addition has been obtained at angle of attack 15 and speed 30 knoots, with the best value at the 24 m fin positin of 18.8% and it is in accordance with the conditions in the fin stabilizer. Seakeeping analysis results using software shipmo best on fin 2 continued to fin 1, fin 3, Fin 4 and bare hull.
In the field: an interview with Harald Wolf

Harald Wolf has recently retired as a Professor at the University of Ulm, Germany, where he investigated navigation, locomotor control and sensorimotor integration in invertebrates. After completing his diploma in biology in 1980 at the Technical University Darmstadt, Germany, he undertook a PhD with Otto von Helversen at Erlangen University, Germany, and postdocs with Keir Pearson at Edmonton University, Canada, and Werner Rathmayer at Constance University, Germany. He then received a Heisenberg Fellowship to complete research at Constance University and the California Institute of Technology, USA, before completing a 2-year lectureship at the University of Zurich, Switzerland.
Intolerance, xenophobia and cross-border supermarket groups’ operations in South Africa

We examine whether there is a relationship between xenophobic attacks and the stock market value of supermarket groups in South Africa. We perform an event study analysis on significant incidents of violent attacks targeted at shops owned by foreigners in South Africa for the period 2006–2017. Using an event study approach with an augmented market model, we uncover evidence that the stock market perceives these events as bad news instead of good news for supermarkets having operations in South Africa and other parts of Africa, as they realise a decline in abnormal returns of about 2.57 percent. We interpret our findings as economic costs of xenophobic attacks emanating from the Johannesburg Stock Exchange. Our analysis attempts to shed light on the peace-building challenges and the potentially grim implications of xenophobia on economic activity in South Africa.
An Analysis of Clustering Techniques

I analyzed different clustering methods of credit card customers data by some machine learning clustering algorithm i approached to classify the high dimensional data by using clustering algorithms in the real world ,It is a type of process where we divide the similar objects into one group The Objective of paper is to identify which method gives a general view of clustering techniques like K-means, algometrive and gaussian distribution ,to analyze Segmentation of customers can be used to define marketing strategies. Clustering comes under unsupervised learning, if we take data set it contains some input patterns, entities etc. The main aim of clustering is to make partition &amp; to make clusters having similarity, so that we can get some useful insights and make further analyses .The process of finding closely connected and fair information from high dimensional datasets is complex and difficult to carry .In this paper we tried to analyze our model through different clustering models like K-means, DBSCAN, Agglomerative Hierarchical and gaussian mixture model) and I explained about all algorithm that I used unique approaches for clustering the high dimensional data.
Parametrized process characterization with reduced resource requirements

Quantum Process Tomography (QPT) is a powerful tool to characterize quantum operations, but it requires considerable resources making it impractical for more than 2-qubit systems. This work proposes an alternative approach that requires significantly fewer resources for unitary process characterization without prior knowledge of the process and provides a built-in method for state preparation and measurement (SPAM) error mitigation. By measuring the quantum process as rotated through the X and Y axes on the Bloch Sphere, we can acquire enough information to reconstruct the quantum process matrix $\chi$ and measure its fidelity. We test the algorithm's performance against standard QPT using simulated and physical experiments on several IBM quantum processors and compare the resulting process matrices. We demonstrate in numerical experiments that the method can improve gate fidelity via a noise reduction in the imaginary part of the process matrix, along with a stark decrease in the number of experiments needed to perform the characterization.
Visualizing Complexity

How can you turn dry statistics into attractive and informative graphs? How can you present complex data sets in an easily understandable way? How can you create narrative diagrams from unstructured data? This handbook of information design answers these questions. Nicole Lachenmeier and Darjan Hil condense their extensive professional experience into an illustrated guide that offers a modular design system comprised of 80 elements. Their systematic design methodology makes it possible for anyone to visualize complex data attractively and using different perspectives. At the intersection of design, journalism, communication and data science, Visualizing Complexity opens up new ways of working with abstract data and invites readers to try their hands at information design New standard work for information design Attractively designed and illustrated manual Innovative presentation solutions for analog and digital media Available in English and German
What Makes a Tutorial Event Effective?

Although tutoring by expert human tutors is usually effective, it is not always. By contrasting cases where tutoring dons and dons not result in learning, we can find out what causes learning during tutoring. Approximately 125 hours of physics tutorial dialog were analyzed to see what features of the dialog were associated with learning. Successful learning appears to require that the student make an error or reach an impasse; too much help can prevent lenenidg. Features of successful tutorial explanations appear to be different for different pieces of knowledge. For instance, some pieces of knowledge are learned only if the tutor emphasizes generalization, whereas other learning requires that the tutor first explain why the student’s error is wrong.
Subgradient methods for non-smooth optimization problems with some relaxation of sharp minimum

In this paper we propose a generalized condition for a sharp minimum, somewhat similar to the inexact oracle proposed recently by Devolder-Glineur-Nesterov. The proposed approach makes it possible to extend the class of applicability of subgradient methods with the Polyak step-size, to the situation of inexact information about the value of the minimum, as well as the unknown Lipschitz constant of the objective function. Moreover, the use of local analogs of the global characteristics of the objective function makes it possible to apply the results of this type to wider classes of problems. We show the possibility of applying the proposed approach to strongly convex non-smooth problems, also, we make an experimental comparison with the known optimal subgradient method for such a class of problems. Moreover, there were obtained some results connected to the applicability of the proposed technique to some types of problems with convexity relaxations: the recently proposed notion of weak $\beta$-quasi-convexity and ordinary quasi-convexity. Also in the paper, we study a generalization of the described technique to the situation with the assumption that the $\delta$-subgradient of the objective function is available instead of the usual subgradient. For one of the considered methods, conditions are found under which, in practice, it is possible to escape the projection of the considered iterative sequence onto the feasible set of the problem.
Necessity of General-Purpose Operating Systems for Wearable Computing Environments

The wearable technology is carefully expected as a next generation business platform which can lead to changes from the smartphone-based hardware and software market. In this paper, we consider whether wearable computing environment with unlimited potential would require an operating system or not by comparing with other computing environment such as a general-purpose computer. In addition, a brief history of wearable technology and the necessity of studying about operating systems for highly merged wearable devices with software systems will be discussed. We also review the features of existing operating systems in different computing environments and discuss the elements that a wearable operating system should have.
Research on nonlinear identification control based on exercise quality analysis and computer numerical simulates with MATLAB

Aiming at the problems of non-linear physical exercise, difficult predictive model identification control and difficult control target, this paper presents an algorithm of non-linear identification control for physical exercise, establishes a control system of non-linear identification control for physical exercise, and simulates it with MATLAB. The simulation results show that the identification ability of physical exercise is strong, the requirements of improving the quality of physical exercise, the control ability and anti-interference ability are increased.