R Programming and Its Applications in Financial Mathematics

The article explores data mining methods that allow us to get helpful information from the data. The possibility of using these methods in practice in the financial sector was considered. Since financial activity is closely related to our social life, the use of data mining methods plays an essential role in the analysis and forecasting of the financial market in the modern era of big data. However, due to differences in the experience of researchers in different disciplines, it is not easy to use data mining methods when analyzing financial data. Therefore, creating a methodological basis for the practical application of data mining methods in the analysis of financial data is an urgent issue. The purpose of this article is to create a methodological basis for using data mining methods for efficient trading. When processing product data, a priori methods and visualization methods were used, and their implementation in practice was described. As a result, scenarios of computer applications were created as a sample of the practical implementation of the algorithms of these methods. Building a quantitative trading strategy requires first statistical analysis of the information in the market and then testing the quantitative model on the collected data. This study developed a quantitative trading system based on data mining methods. The primary development tool used is the Jupyter web platform, and three cores have been developed: quantitative data selection, strategy testing on data, time series analysis, and visualization. The developed system supports modules for making simple trading decisions.

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.

The emergence of Unified Modeling Language (UML) as a standard for modeling systems has encouraged the use of automated software tools that facilitate the development process from analysis through coding. The UML state machine diagram is used for modeling the dynamic aspects of systems and includes many concepts that are not present in most popular programming languages, like Java or C++. There exists a gap between high level modeling language and a programming language. This paper proposes an approach to narrow the gap between multiple UML models and an implemented system. The narrowing of gap is achieved by generating compact and efficient Java code directly from UML state machine having sequential and concurrent substates. The code generation is achieved by creating a mapping between UML behavior and the Java programming language. Our model can work as a basis for automatic code generation for object-oriented systems.

The object of research in the article are the tools and capabilities of the C# programming language for the implementation of cybersecurity analysis software in local computer networks and computer-integrated systems. The relevance of the study is due to the importance of information security of computer and computer-integrated systems in government, military-industrial complex, private business etc., and due to the importance of training cybersecurity professionals in higher education to consider teaching examples in popular programming languages. The goal of the work is to research the possibilities of the C# programming language for the development of software that analyzes cybersecurity in local computer networks and computer-integrated systems. The tasks to be solved are: to develop software for scanning network device ports in computer networks and computer-integrated systems for information security audit, using tools and libraries of the C# programming language, to research the benefits and possibilities of using this programming language for this task. Research methods: theory of computer networks, object-oriented programming, theory of algorithms and data structures, theory of software testing. Conclusions. In this paper the possibilities and advantages of the C# programming language for developing cybersecurity analysis software for computer and computer-integrated systems were explored. In the course of work software that analyzes information security in local computer networks and computer-integrated systems was developed. This software can be used for educational purposes in learning the C# programming language and cybersecurity of computer systems. The developed software has the potential to be further improved and applied in various fields to test the cybersecurity of local computer networks and computer-integrated technologies.

With the growing usage and migration of interactive applications and systems into today’s environment, the significance of the graphical user interfaces (GUIs) increases as they act as the gates into using systems efficiently. Therefore, extensive efforts are spent to enhance the usability of the GUIs. However, most of the works focus on testing the functional properties of applications’ GUIs rather than nonfunctional features such as performance. For this reason, it is worthwhile to assess the performance of various GUI components at runtime. In this paper, the most popular programming languages used to create GUI based applications namely, Java and C# were compared experimentally to evaluate their performance in terms of the creation and manipulation of GUI components/controls. The experimental results of this study, which is based on 32 testing scenarios, showed that Java outperformed C# in all test scenarios. This might be because of the “HotSpot” performance engine that Java uses. This study is useful for developers to get insights into the performance of different GUI components provided by different programming languages. Also, it helps them to choose the right programming language for their GUI based applications hence enhance the overall applications’ performance and user satisfaction.

In many practical problems, it is important to consider different distributions that could be used to model a data set. In this work, we analyze the generalized trapezoidal (GT) model in financial application. The primary reason for this is that the family of the GT distributions includes models with bounded domain used in risk analysis, and it belongs to the nonparametric class of log-concave densities under determined parametric restrictions. In the literature, one can find several references on the log-concavity and applications with interesting qualitative implications in many areas of economics, actuarial sciences, biology and engineering. Here, we classify the log-concavity of the GT model based on its parameters. We observe that it can be useful in analyzing some data sets, especially a financial market data example is used to illustrate that the GT distribution is better fitting than the symmetric distributions previously considered for this data set. Furthermore, in the particular example, the fitted GT distribution satisfies the log-concavity constraints.

This study aims to explore the influence of decision-making, financial data analysis, and human resource training on company performance, as well as the role of artificial intelligence (AI) moderation in these relationships. Using the path analysis method with AMOS software, data was collected from 100 employees in a Telecommunications company in Indonesia. The results show that decision-making and analysis of financial data have a positive and significant influence on company performance, while human resource training does not show a significant influence. Additionally, AI serves as a significant moderator in the relationship between decision-making and company performance, but not in the relationship between financial data analysis and performance. These findings emphasize the importance of good decision-making and financial data analysis in improving organizational performance, and demonstrate the potential of AI in amplifying the positive impact of decision-making. This study has limitations in sample size and context, which need to be considered for further research.

Java has emerged as a popular programming language and platform for Web applications. JavaBeans defines the software component model in the Java programming language that is used for creating reusable, coarse‐grained components. Beans are used in numerous complementary technologies including Enterprise JavaBeans, Java Abstract Windowing Toolkit, Java Database Connectivity, JavaMail, and Java Management Extensions to create software components. Beans provide a hybrid of object‐oriented and loosely coupled architectural styles, where the interaction between components is in the form of both events and method calls. This chapter focuses on the support for component technology in Java, specifically in the form of JavaBeans and its technology variants that fulfil the needs of Web application development. The chapter also discusses the role of JavaBeans and its technology variants in architecture‐based software development.

Programming language technology has started to achieve one of the dreams of software engineering -- large scale utilization of reusable components. This is due to the standardization of large libraries and frameworks in popular programming languages such as C++, Java, C#, and Python. This standardization and widespread use of libraries will continue to make module specification more and more important. Yet most libraries and frameworks are only specified informally using natural language. This position paper explores research questions related to the specification of such libraries and frameworks.

Memory consistency models, or memory models, allow both programmers and program language implementers to reason about concurrent accesses to one or more memory locations. Memory model specifications balance the often conflicting needs for precise semantics, implementation flexibility, and ease of understanding. Toward that end, popular programming languages like Java, C, and C++ have adopted memory models built on the conceptual foundation of Sequential Consistency for Data-Race-Free programs (SC for DRF). These SC for DRF languages were created with general-purpose homogeneous CPU systems in mind, and all assume a single, global memory address space. Such a uniform address space is usually power and performance prohibitive in heterogeneous Systems on Chips (SoCs), and for that reason most heterogeneous languages have adopted split address spaces and operations with nonglobal visibility. There have recently been two attempts to bridge the disconnect between the CPU-centric assumptions of the SC for DRF framework and the realities of heterogeneous SoC architectures. Hower et al. proposed a class of Heterogeneous-Race-Free (HRF) memory models that provide a foundation for understanding many of the issues in heterogeneous memory models. At the same time, the Khronos Group developed the OpenCL 2.0 memory model that builds on the C++ memory model. The OpenCL 2.0 model includes features not addressed by HRF: primarily support for relaxed atomics and a property referred to as scope inclusion. In this article, we generalize HRF to allow formalization of and reasoning about more complicated models using OpenCL 2.0 as a point of reference. With that generalization, we (1) make the OpenCL 2.0 memory model more accessible by introducing a platform for feature comparisons to other models, (2) consider a number of shortcomings in the current OpenCL 2.0 model, and (3) propose changes that could be adopted by future OpenCL 2.0 revisions or by other, related, models.

Java programming language is one of the most popular programming languages among IT developers. There are common problems in the beginning learning process such as the installation and configuration process of java software like JDK (Java Development Kit). During the online class session, it takes a lot of time and a large internet data packet to solve this problem. This research proposes a website-based system called livE (online - java Exercise). Students can practice coding and also do exams in real-time, simply by using a web browser and the internet. If lecturers want to make corrections, lecturers do not need to download student answer files. They can directly run the program code from student answers in the livE system. This indicates that the system implementation provides process efficiency in the student's job sheet exercises and lecturer's corrections. The result from the test that has been carried out shows that livE is running as expected. Usability testing shows that users are "totally agree" with system quality.

Biodiversity data exchange depends on established standards, such as Darwin Core, Audiovisual Core, Taxon Concept Schema, etc. Standards provide terms with defined semantic meaning and structures for data exchange. Standards simplify interchange of biodiversity data among government agencies, researchers, and engineers. A notable challenge during such data transfers is the complexity involved in processing data for various uses. Most data exchanges are performed via Comma Separated Value (CSV), Extensible Markup Language (XML), or JavaScript Object Notation (JSON), and need to be parsed and imported into a database before being queryable. For example, to explore the data in a Darwin Core Archive zipped file, it is necessary to decompress it, parse XML-based metadata about the content of the file, parse and read Ecological Metadata Language (EML) to extract provenance metadata, and correctly open the text-delimited files that use a wide variety of character encodings, delimiters, enclosures, and escape characters. All of it requires non-trivial data management and programming skills from users. We propose a paradigm shift toward using queryable SQLite-based files for more straightforward data interchange. This approach would reduce friction in data processing by directly using Structured Query Language (SQL). SQLite is an open source, high performance, lightweight database already installed on most computers (SQLite 2024). It integrates a database into a single file, facilitating straightforward data exchange and compression. Its robust engine is able to manage terabytes of data. The SQLite developers are committed to maintaining backward compatibility of both binary and SQL text file versions until 2050 (SQLite 2024). Connectivity to SQLite databases is supported by all popular programming languages. Furthermore, the United States Library of Congress endorses SQLite alongside XML and JSON for data archives, attesting to its long-term reliability (United States Library of Congress 2024). We at the Species File Group are experimenting with using SQLite to create a universal data converter, in which an SQLite database serves as an intermediate data storage format. This provides several useful advantages: 1. Universal data converter By learning a standard SQL schema for biodiversity data, users are able to efficiently write importers and exporters to/from the intermediate schema for a variety of other biodiversity data-exchange formats. Any programming language can be used to develop the conversion scripts. Import and export are completely decoupled from each other, allowing mixing and matching of converters in a flexible way. For example, we aim to use SQLite as an intermediary archive to convert data between Darwin Core Archives, Catalogue of Life Data Package (ColDP), Global Names Verifier, and TaxonWorks (Fig. 1). 2. Semantic versioning of the archive Like any other data format, the intermediate data exchange SQLite schema will evolve over time. Using Git tags and branches, it is straightforward to follow a semantic versioning paradigm (Semantic Versioning 2024). For example, in "v1.2.5" the numbers respectively indicate major, minor, and patch versions. Major versions include substantial changes that might not be backwards compatible, while minor versions are backwards compatible and patch versions indicate tiny bug fixes. 3. Data querying With SQLite-based archives being relational databases, they can immediately be queried without any additional parsing and importing. This allows the archives to be explored with ease by anyone familiar with SQL. The Carpentries also provide online learning resources and courses for researchers to learn SQLite (Martinez and Poisot 2017). 4. Biodiversity application development As SQLite-based archives are relational databases, and the intermediate data exchange schema is standardized and stable, a database file can be used directly by a variety of applications. For example, it would be possible to develop a web application frontend that utilizes the SQLite archive as a backend. It could be possible to develop an ecosystem of biodiversity informatics applications based on SQLite archives. We believe that adopting SQLite for biodiversity data exchange can reduce operational friction, enhance data accessibility, and promise a streamlined and universally compatible data management framework.

The purpose of this research is to evaluate tax compliance based on the behavior of taxpayers' financial and non-financial data. This research is expected to be useful as material for evaluating the level of tax compliance of taxpayers. This study uses a case study method (single case) and uses a mixed method. Descriptive analysis is used to analyze data from the research object in the form of a single case unit analysis. The results of this study indicate the perceived behavior control of taxpayers that shows low tax compliance behavior. The combination of analysis of financial and non-financial data from taxpayers produces a useful evaluation in determining the level of tax compliance. Based on this, the low achievement of tax revenue realization and high tax audit performance is directly influenced by the low tax compliance behavior of taxpayers.
 Keywords: Tax Compliance; Behavior; Financial Data; Non-Financial Data.

The object-oriented theory has been widely used in software design and development and Java programming language is a popular object-oriented programming language for computer science related majors in higher education. With the huge demand for intelligent manufacturing employees in the work market, more and more universities have listed Java programming language in their curriculum for non-CS undergraduates. However, learning object-oriented programming languages, such as Java, is tough even for CS students. This paper presents how to improve non-CS undergraduate students’ learning outcomes of java language by adopting industry standard professional integrated development tool Eclipse and interactive visual educational tool BlueJ. Our Java programming language course is an alternative 8-week course including 16 h lecture and 16 h lab. Forty-seven junior students from mechanical engineering major were divided into two groups, 23 in the control group (CG) and 24 in the experimental group (EG). Students in both groups used Eclipse to write and run programs during the course while BlueJ was adopted to demonstrate the object-oriented concepts during the fifth and sixth weeks only for students in the EG. A pre-test, which was taken by students from both groups, shows no statistical differences in programming knowledge between the two groups. Results from the post-test at the end of the sixth week show students in EG achieved better performance than students in CG. Results of the survey for the EG reveal that students thought BlueJ was helpful for mastering the concepts of object-oriented programming.KeywordsJava programmingLearning outputHigher educationInteractiveVisualBlueJEclipse

In this paper we measure the speed of several popular and recent programming languages performing the most usual operators in the canonical evolutionary algorithm, mutation and crossover, as well as an usual fitness function, OneMax. These three operations are representative of the kind of the ones performed in binary chromosomes. Our main objectives are, first, to create programs in programming languages that use the fastest available implementation. Second, to find out the differences in speeds for the different languages. Third, to find out whether the usual assumptions about the speed of languages really holds. And, finally, to find if the assumed order of speed in languages used in evolutionary algorithms holds true for all kinds of operations. In order to do that, we use available implementations or perform our own, concluding that the evolutionary algorithm scenario is more complex than usually assumed and finding out some surprising winners and losers among the languages tested.