Estimating Extreme Quantiles of Unknown Distributions using the Peak Over Thresholds Method

Bayesian hierarchical non-stationary hybrid modeling for threshold estimation in peak over threshold approach.

Python is a scripting language that's high- positioned, interpreted, interactive, and object- oriented. Python is intended to be a veritably accessible programming language. It generally uses English terms rather than punctuation, and it has smaller syntactical structures than other languages. Python is a must-have skill for scholars and working professionals who want to become exceptional software masterminds, especially if they work in the web development field. It's a freshman-friendly scripting language. Some of the crucial features of Python programming language are- It supports OOP as well as functional and structured programming methodologies. It can be used as a scripting language or collected into bytecode for large-scale operations. It allows dynamic type verification and provides veritably high-position dynamic data types. It facilitates scrap collection by itself. Numerous different programming languages have been impacted by Python's design and gospel. Some of those languages are Boo, Cobra, CoffeeScript, Go, Swift, Ruby, etc. Some of the advantages of Python programming language are straightforward, free, simple to use, and largely compatible, object- acquainted, has multitudinous libraries, has erected in data structures, has a wide range of uses, boosts productivity and speed, and simple to understand. One of the most extensively used programming languages is Python. It's an open- source language. Python's demand is growing, and its operations are expanding in virtually every assiduity. It's abundant in every way. It has a wide range of capabilities. Python is a popular programming language. It's also developing a strong request in the IT sector. Python is in high demand across the globe. Python helps you negotiate more in lower time. Python has a large community that supports and meets the requirements of inventors. Python is therefore one of the most popular programming languages. It's a veritably reliable and effective programming language. Python programmers are in high demand because Python is being used in a variety of sectors. Python is an extensively used computer language that was created nearly 25 years ago. Python is useful in a variety of fields, including web development, desktop app development, machine literacy, big data, data analysis, and robotics. Clean syntax, extremely clear law, a wide range of uses, packages that help apply features, and a cool community that helps grow this excellent language are just a many of the reasons why people like this language and why it's well suited for different tasks. The Python programming language has a bright future. The advanced technologies like Artificial Intelligence, Machine Learning, Big Data, Cloud Computing, Data Science, etc and world-notorious companies similar as Amazon, Google, Apple, Deloitte, Microsoft, Netflix, and Accenture have the Python programming language as their backbone which states that Python is in demand and AN APPETITE FOR THE SOFTWARE INDUSTRY! A standard and scientific procedure of an Empirical Exploration Methodology (Survey) was conducted to check the statement stated by the author where 900 repliers from colourful corridors of the globe shared their thoughts. From the check, it was concluded that 99.8% of the respondents feel that Python is one of the in- demand programming languages for the digital assiduity in the present time.

BackgroundThe Biological Magnetic Resonance Data Bank (BMRB) is a public repository of Nuclear Magnetic Resonance (NMR) spectroscopic data of biological macromolecules. It is an important resource for many researchers using NMR to study structural, biophysical, and biochemical properties of biological macromolecules. It is primarily maintained and accessed in a flat file ASCII format known as NMR-STAR. While the format is human readable, the size of most BMRB entries makes computer readability and explicit representation a practical requirement for almost any rigorous systematic analysis.ResultsTo aid in the use of this public resource, we have developed a package called nmrstarlib in the popular open-source programming language Python. The nmrstarlib’s implementation is very efficient, both in design and execution. The library has facilities for reading and writing both NMR-STAR version 2.1 and 3.1 formatted files, parsing them into usable Python dictionary- and list-based data structures, making access and manipulation of the experimental data very natural within Python programs (i.e. “saveframe” and “loop” records represented as individual Python dictionary data structures). Another major advantage of this design is that data stored in original NMR-STAR can be easily converted into its equivalent JavaScript Object Notation (JSON) format, a lightweight data interchange format, facilitating data access and manipulation using Python and any other programming language that implements a JSON parser/generator (i.e., all popular programming languages). We have also developed tools to visualize assigned chemical shift values and to convert between NMR-STAR and JSONized NMR-STAR formatted files. Full API Reference Documentation, User Guide and Tutorial with code examples are also available.We have tested this new library on all current BMRB entries: 100% of all entries are parsed without any errors for both NMR-STAR version 2.1 and version 3.1 formatted files. We also compared our software to three currently available Python libraries for parsing NMR-STAR formatted files: PyStarLib, NMRPyStar, and PyNMRSTAR.ConclusionsThe nmrstarlib package is a simple, fast, and efficient library for accessing data from the BMRB. The library provides an intuitive dictionary-based interface with which Python programs can read, edit, and write NMR-STAR formatted files and their equivalent JSONized NMR-STAR files. The nmrstarlib package can be used as a library for accessing and manipulating data stored in NMR-STAR files and as a command-line tool to convert from NMR-STAR file format into its equivalent JSON file format and vice versa, and to visualize chemical shift values. Furthermore, the nmrstarlib implementation provides a guide for effectively JSONizing other older scientific formats, improving the FAIRness of data in these formats.

Python has emerged as one of the most popular programming languages, extensively utilized in domains such as machine learning, data analysis, and web applications. Python’s dynamic nature and extensive usage make it an attractive candidate for dynamic program analysis. However, unlike for other popular languages, there currently is no comprehensive benchmark suite of executable Python projects, which hinders the development of dynamic analyses. This work addresses this gap by presenting DyPyBench, the first benchmark of Python projects that is large-scale, diverse, ready-to-run (i.e., with fully configured and prepared test suites), and ready- to-analyze (by integrating with the DynaPyt dynamic analysis framework). The benchmark encompasses 50 popular open-source projects from various application domains, with a total of 681k lines of Python code, and 30k test cases. DyPyBench enables various applications in testing and dynamic analysis, of which we explore three in this work: (i) Gathering dynamic call graphs and empirically comparing them to statically computed call graphs, which exposes and quantifies limitations of existing call graph construction techniques for Python. (ii) Using DyPyBench to build a training data set for LExecutor, a neural model that learns to predict values that otherwise would be missing at runtime. (iii) Using dynamically gathered execution traces to mine API usage specifications, which establishes a baseline for future work on specification mining for Python. We envision DyPyBench to provide a basis for other dynamic analyses and for studying the runtime behavior of Python code.

Information technology has experienced significant developments in science and technology, including programming languages. One of the most popular programming languages today is Python. However, many students still have difficulty learning the Python programming language. These difficulties are caused by the lack of learning media that focus on the Python language, module explanations that are difficult to understand, and a lack of understanding of the library in the Python language. This research aims to create a Python programming language learning module focusing on the project-based Learning (PjBL) approach. The methodology used in this research is Research and Development (R&D). The data needed in this study were collected through questionnaires and learning outcomes exams with a subject of 28 students. The data in the effectiveness aspect is using the T-Test Test. This research results in a valid, practical, and effective PjBL-based Python programming language learning module. This module is considered valid by experts in media aspects with a value of 0.85 and material aspects with a value of 0.89. The practicality of this module is proven through positive responses from lecturers of 88.75%, student responses in the limited test of 88.5%, and the usage trial of 86.96%. Meanwhile, the effectiveness of this module can be seen from the classical completion rate of 82% and the gain score of 57%, and the t-test shows that this module significantly increases student learning outcomes in the Python programming language.

Probabilistic planning attempts to incorporate stochastic models directly into the planning process, which is the problem of synthesizing a sequence of actions that achieves some objective for a putative agent. Probabilistic programming has rapidly emerged as a key paradigm to integrate probabilistic concepts with programming languages, which allows one to specify complex probabilistic models using programming primitives like recursion and loops. Probabilistic logic programming aims to further ease the specification of structured probability distributions using first-order logical artefacts. In this article, we briefly discuss the modelling of probabilistic planning through the lens of probabilistic (logic) programming. Although many flavours for such an integration are possible, we focus on two representative examples. The first is an extension to the popular probabilistic logic programming language PROBLOG, which permits the decoration of probabilities on Horn clauses-that is, prolog programs. The second is an extension to the popular agent programming language GOLOG, which permits the logical specification of dynamical systems via actions, effects and observations. The probabilistic extensions thereof emphasize different strengths of probabilistic programming that are particularly useful for non-trivial modelling issues raised in probabilistic planning. Among other things, one can instantiate planning problems with growing and shrinking state spaces, discrete and continuous probability distributions, and non-unique prior distributions in a first-order setting.

The paper is devoted to the analysis of the problem of determining the source code author , which is of interest to researchers in the field of information security, computer forensics, assessment of the quality of the educational process, protection of intellectual property.
 The paper presents a detailed analysis of modern solutions to the problem. The authors suggest two new identification techniques based on machine learning algorithms: support vector machine, fast correlation filter and informative features; the technique based on hybrid convolutional recurrent neural network.
 The experimental database includes samples of source codes written in Java, C ++, Python, PHP, JavaScript, C, C # and Ruby. The data was obtained using a web service for hosting IT-projects – Github. The total number of source codes exceeds 150 thousand samples. The average length of each of them is 850 characters. The case size is 542 authors.
 The experiments were conducted with source codes written in the most popular programming languages. Accuracy of the developed techniques for different numbers of authors was assessed using 10-fold cross-validation. An additional series of experiments was conducted with the number of authors from 2 to 50 for the most popular Java programming language. The graphs of the relationship between identification accuracy and case size are plotted. The analysis of result showed that the method based on hybrid neural network gives 97% accuracy, and it’s at the present time the best-known result. The technique based on the support vector machine made it possible to achieve 96% accuracy. The difference between the results of the hybrid neural network and the support vector machine was approximately 5%.

Programming error messages have proven to be notoriously problematic for novices who are learning to program. Although recent efforts have focused on improving message wording, these have been criticized for attempting to improve usability without first understanding and addressing readability. To date, there has been no research dedicated to the readability of programming error messages and how this could be assessed. In this paper we examine human-based assessments of programming error message readability and make two important contributions. First, we conduct an experiment using the top twenty most-frequent error messages in three popular programming languages (Python, Java, and C), revealing that human notions of readability are highly subjective and dependent on both programming experience and language familiarity. Both novices and experts agreed more about which messages are more readable, but disagreed more about which messages are not readable. Second, we leverage the data from this experiment to uncover several key factors that seem to affect message readability: message length, message tone, and use of jargon. We discuss how these factors can help guide future efforts to design a readability metric for programming error messages.

Python programming language is a popular programming language in the field of data analysis. In this research, researchers will analyze data on the cause of lung cancer, because lung cancer has the highest death case among other cancer types. The purpose of the research is to explore how dataset processing related to lung cancer-causing factors using the Python programming language and the datasets used are derived from Kaggle. The research method used is quantitative with a population of 462,000 people and a sample of 999 people. The analysis technique used is an explorative data analysis technique. From the results of the research, the highest cause of lung cancer was found in people aged 32-42 years, level 7 genetic factor, 7th level in dust allergies, level 7 obesity and male sex has higher risk than women.

In triplex pumps, due to the higher speeds of the pistons, it is required to use back-up pumps, which are used as centrifugal pumps. The flow part of the pump consists of three main elements – the inlet, the impeller and the outlet. Through the supply, the liquid is fed into the impeller from the supply pipeline. The purpose of the impeller is to transfer fluid energy from the engine. The impeller of a centrifugal pump consists of a drive and a driven disc, between which there are blades bent, as a rule, in the direction opposite to the direction of rotation of the wheel. The impeller is mounted on the shaft with the drive disc. The liquid moves through the wheel from its central part to the periphery. Upon discharge, the liquid is diverted from the impeller to the pressure nozzle or, in multistage pumps, to the next wheel. Centrifugal back-up pumps, creating excessive pressure in the pipeline supplying the clay solution to the drilling pump, brings the filling of the pumping chambers with liquid to 100% and improves the hydraulic action of the drilling pump by achieving the closest possible correspondence between the movement of liquids, valves and pistons. Therefore, special attention should be paid to improving their design and using advanced computer calculation methods. The axes of the blade can be constructed using analytical calculation, using the Python programming language. Python is one of the most popular general purpose programming languages. It is among the fastest available programming languages in the world and is used by software engineers, mathematicians, data analysts, scientists, network engineers, students and accountants. An algorithm and a program in Python were compiled and the result was obtained using the source data. The practical significance of the study lies in the fact that the results of the work performed on the topic "Numerical calculations of the blade axis of a centrifugal pump of a pumping and circulation complex of a drilling rig" can be used as a base for theoretical research, analytical and design activities of authors studying this area. The second aspect of the practical significance of this study is the possibility of using the results of the study within the framework of the topic "Numerical calculations of the blade axis of a centrifugal pump of a pumping and circulation complex of a drilling rig" in order to develop the mechanism of functioning of the object under study.

The paper reviews what the Julia programming language is and its staying power relative to other popular programming languages. The following focuses on two popular programming languages (C programming and Python programming) to obtain a deeper understanding of what Julia derives from them, how Julia deviates from them, and what Julia leverages to be a more reasonable choice for performance.

Lab sessions in engineering education are designed to reinforce theoretical concepts. However, usually there are not enough time to reinforce all of them. Remote and virtual lab give students more time for reinforce those concepts. In particular, with remote labs, this can be done interacting with real lab equipment and specific configurations. This work proposes a flexible configuration for Remote Lab Sessions, based on some of 2019 most popular programming languages (Python and JavaScript). This configuration needed minimal network privileges, it is easily to scale and reconfigure. Its structure is based on a unique Reception-Server (which hosts User database, and Time Shift Manager, it is accessible from The Internet, and connect Users with Instruments-Servers) and some Instrument-Servers (which manage hardware connection and host experiences). Users always connect to Reception-Server, and book a shift for an experience. During the time range associate to that shift, User is internally forwarded to Instrument-Server associated with selected experience, so User is still connected to Reception-Serer. In this way, Reception-Server acts as a firewall, protecting Instrument-Servers, which never are open to The Internet. A triple evaluation system is implemented, User session logging and auto-evaluation (objectives accomplished) a knowledge test and an interaction survey. An experience is implemented, controlling a DC source using Standard Commands for Programmable Instruments.

Undergraduate research experiences are a promising way to broaden participation in computer architecture research and have been shown to improve student learning, engagement, and retention. These outcomes can be more profound and lasting if students experience research early. However, there are many barriers to early research in computer architecture some of which include the gap between pedagogy and research, the lower emphasis on hardware design compared to software in first year courses, and the lack of online resources. We propose lowering these barriers through a methodical approach by involving undergraduates in early research and by creating freely available and innovative educational tools for designing hardware.We present the experience of a team of undergraduate students with research over one academic year using a Python hardware description language, PyRTL. PyRTL was developed to enable early entry into digital design. Its overarching goals are simplicity, usability, clarity, and extensibility, a stark contrast to traditional languages like Verilog and VHDL that have a steep learning curve. Instead of introducing traditional languages early in the undergraduate curriculum, PyRTL takes the opposite approach, which is to build on what students already know well: a popular programming language (Python), software design patterns, and software engineering principles. The students conducted their research in the context of the Early Research Scholars Program (ERSP), a program designed to expand access to research among women and underrepresented minority students in their second year through a well designed support structure.

Engineering involves many areas of Science, and implies the explanation of many different theoretical concepts. Lab sessions are designed to reinforce those concepts, but generally they are not enough to cover all of them. Remote and virtual labs help this proposal. With them, students can put in practice and reinforce academic concepts at home. Specifically, Remote labs grant them access to lab equipment and configurations, impossible in other situations that request lecturer. In this work, a flexible configuration is presented, based on 2019 most popular programming languages (Python (for the back-end) and JavaScript (for the front-end)), for Remote Lab Sessions, with a minimum network privileges, scalable and reconfigurable. It is based on a Reception-Server, which hosts the User-database and the Shift Management, for use of Instrument-Servers, and controls the User data flow. These Instrument-Servers, host the instruments interfaces, the experience, and manage the connection with the instruments and different hardware involved. Users connect to Reception-Server, which uses a public IP address, and forward the Users to Instrument-Servers, on schedule reserved. This avoids the need for Instrument-Servers external access for, and protects the network, acting as firewall (only communications among Reception-Server and Instrument-Servers are allowed). The experiences are evaluated through a three-fold system, logging the User session, with a survey of interaction experience, and with a knowledge questionnaire related to the experience. An example of use is provided, in which the user experiences control of a DC power supply, throw GPIB using standard commands for programmable instruments (SCPI).

Quite a few educational programming languages have been developed to make programming easier to learn. These languages have seen very little adoption in academia. This raises questions about their suitability for their intended purpose as introductory programming languages. A lot of factors are taken into consideration in choosing an introductory programming language. This work presents the results of a survey of computer science degree programs across the United States. The survey shows not only which introductory programming languages are popular but the reasons behind their popularity. It shows the features that are considered important for an introductory programming language and how well the popular introductory languages support those features. This work shows that some popular educational programming languages do not support some of those features that are considered important.