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Android is a popular choice for mobile operating systems. It was essentially designed for mobile touch-screen devices, such tablets and smartphones. Many programming languages are being used to design application for android operating systems. Android programming can be made incisive, cross-platform, and amusing with the help of Kotlin. Since the beginning, Java has been the main programming language for Android development and is still in high stipulation. Programming on a wider scale is made easier for android applications on Scala. It is a general-purpose, practical programming language. For creating rapid apps on any platform, Dart is a client-optimized programming language used for android application development. Here in this paper, we are going to scrutinize these languages based on several factors like implementation and effectiveness, cross-platform compatibility and android runtime. [ABSTRACT FROM AUTHOR]

Traditional methods of watering plants based on timers have been shown to be wasteful when compared to sensor based watering. While sensor based irrigation systems do exist, current implementations are both costly and needlessly complex. Our goal was to design an irrigation sensor system is cheap, easy to implement, and saves more water than traditional watering systems. Our approach was to create a sensor node network that is both reliable and cost effective when installing it. With this approach we were able to create a dependable tree like sensor network that could read moisture data from soil and have it visualized in an Android application. In regards to our Android application, we were able to implement features such as sensor calibration in order to obtain accurate readings. Our tree like network was also able to be consistent in being able to send data packets back and forth with each sensor having a reach of up to 100 meters and being able to have a total of 3125 radio modules in use. In conclusion we were able to create a reliable and cost effective irrigation system that could be used for both farms and home gardens. We were able to meet our initial goals which were to create an user friendly application that could be used to read and visualize data as well creating a sensor network that is low-power and is low cost.  Advisor: Professor Sitao Huang, Department of Electrical Engineering and Computer Science

The increasing demand for Android applications in line with technological evolution and the development of new features often leads to frequent updates and releases of applications. However, in this update cycle, developers sometimes make hasty changes that result in poor design choices, leading to the emergence of bad smell code. The impact of smells involves a decrease in quality, performance, and understanding, and impedes the software maintenance process. Some previously developed smell detection tools rely on JavaParser to parse source code into an Abstract Syntax Tree representation, meaning the extracted information is limited to Java programming language source code. Meanwhile, Google recommends Kotlin as the language for developing Android applications, and over 60% of professional Android developers use Kotlin. This research is using Program Structure Interface representation and applies a software metric-based approach as a method of smell detection and implements it into a Kotlin Android bad smell code detection tool. This approach was chosen because the characteristics of each smell can be represented with software metrics. The tool’s evaluation results on five Kotlin Android projects showed an F-measure of 100% for Brain Class detection, 93.77% for God Class, and 85.71% for Brain Method. Comparing the tool’s detection results with the iPlasma tool on the Quran project showed that the developed tool detected more smell occurrences. Usability testing results indicated that seven participants strongly agreed with the easy-to-understand detection results, easy-to-find presented functionality, clearly presented information, and the tool helped to perform metric calculation and smell detection more quickly and efficiently. The average rating given for each question was between 4.29 – 4.71.

The early lines of code (LOC) estimation in software projects holds significant importance, as it directly influences the prediction of development effort, covering a spectrum of different programming languages, and open-source Kotlin-based applications in particular. The object of the study is the process of early LOC estimation of open-source Kotlin-based apps. The subject of the study is the nonlinear regression models for early LOC estimation of open-source Kotlin-based apps. Objective. The goal of the work is to build the nonlinear regression model with three predictors for early LOC estimation of open-source Kotlin-based apps based on the Box-Cox four-variate normalizing transformation to increase the confidence in early LOC estimation of these apps. Method. For early LOC estimation in open-source Kotlin-based apps, the model, confidence, and prediction intervals of nonlinear regression were constructed using the Box-Cox four-variate normalizing transformation and specialized techniques. These techniques, relying on multiple nonlinear regression analyses incorporating multivariate normalizing transformations, account for the dependencies between variables in non-Gaussian data scenarios. As a result, this method tends to reduce the mean magnitude of relative error (MMRE) and narrow confidence and prediction intervals compared to models utilizing univariate normalizing transformations. Results. An analysis has been carried out to compare the constructed model with nonlinear regression models employing decimal logarithm and Box-Cox univariate transformation. Conclusions. The nonlinear regression model with three predictors for early LOC estimation of open-source Kotlin-based apps is constructed using the Box-Cox four-variate transformation. Compared to the other nonlinear regression models, this model demonstrates a larger multiple coefficient of determination, a smaller value of the MMRE, and narrower confidence and prediction intervals. The prospects for further research may include the application of other data sets to construct the nonlinear regression model for early LOC estimation of open-source Kotlin-based apps for other restrictions on predictors. [ABSTRACT FROM AUTHOR]

The church is a gathering place for Christians to carry out worship. The Sumbawa Redeemer Catholic Church is a church that originates from Sumbawa Besar Regency, West Nusa Tenggara Province. This Catholic Church is the only church in Sumbawa Regency. The purpose of this research is to make the Application of the Catholic Church of the Redeemer of Sumbawa. using the waterfall design method and the ADDIE model. The data collection method was carried out using the Unified Modeling Language (UML) method, use cases and activity diagrams ere used as analysis and design tools. The programming languages used include Kotlin and Java, the software used includes the IDE Android Studio 4.0, MS. This research yielded results, namely a program that can be used to make it easier for people to find information about the Sumbawa Redeemer Catholic Church

With the surge in the number of people using smartphone devices all over the world, the mobile computing industry has been thriving. There are around 3.6 billion smartphone users worldwide as recorded till 2020 and we expect it to be 4.3 billion by 2023. In India itself, we have 696 million smartphone users, and it is expected to be 875 million by 2023. With the increase in the population going online, online shopping has become very convenient. But we still face issues with shopping online at local stores or booking appointments at local hair salons like hunting for a particular item from the store to store only to find it's unavailable or the shop is closed. Through our application "ShopChain", we improve the local store shopping by providing them the opportunity/platform to get their stores online so they can update the customers with live updates about their store. [ABSTRACT FROM AUTHOR]

With the recent rise of the Kotlin programming language as the main contender for Android mobile development, very few courses in Higher Education Institutions exist which incorporate Kotlin as one of the main languages. In addition, various online course platforms which offer learning Kotlin are still very low in number, and the ones that do exist are expensive. In this paper, an e-learning framework for the Kotlin programming language is presented, supporting automatic grading of given assessments. This framework is aimed at students who already have basic knowledge of Java (or similar) programming languages, and want to switch to mobile development. The solution focuses on the development of an interactive course in Kotlin. Furthermore, to compare our solution with commercially available ones, we point out the disadvantages of currently available Kotlin courses, such as the level of previous knowledge needed, or a need for a specific development environment.

Günümüzde nüfus yoğunluğu her geçen gün hızla artmakta ve buna bağlı olarak şehirlerdeki araçların sayısı da artış göstermektedir. Bu nedenle park yeri bulmak sürücüler için oldukça zorlu bir hal almıştır. Sürücülerin park yeri bulmak için trafikte kalması hem trafiği daha da sıkıştırmakta hem de gereksiz yakıt masrafına sebep olmaktadır. Bu çalışmada kullanıcıların kolayca park yeri bulabilecekleri ve boş park yerleri içerisinden seçim yapabilecekleri bir uygulama geliştirilmiştir. Sokakta bulunan kameralardan, güvenlik için yerleştirilmiş kameralardan ya da uydu görüntülerinden bu çalışma için yararlanılabilir. Elde edilen görüntüler işlenerek araçların park edebileceği yerler tespit edilmekte ve kullanıcıya boş olanların bilgisi iletilmektedir. Ardından kullanıcıya boş park yerlerinden kendisine uygun olanı seçme ve o noktaya yol tarifi alma imkânı sağlanmaktadır. Görüntünün işlenmesi ile boş park yerlerinin tespiti için iki yöntemden yararlanılmıştır. Bu yöntemlerden ilki arka plan silme algoritmaları kullanılarak, ikincisi ise Haar cascade sınıflandırıcısı kullanılarak araçların tespit edilmesidir. Bu tespitlerin yapılmasının ardından park yerlerinin dolu veya boş olduğu bilgisi elde edilmektedir. Mobil uygulama, Kotlin yazılım dili ile Android Studio platformu kullanılarak geliştirilmiştir.

With the recent rise of the Kotlin programming language as the main contender for Android mobile development, very few courses in Higher Education Institutions exist which incorporate Kotlin as one of the main languages. In addition, various online course platforms which offer learning Kotlin are still very low in number, and the ones that do exist are expensive. In this paper, an e-learning framework for the Kotlin programming language is presented, supporting automatic grading of given assessments. This framework is aimed at students who already have basic knowledge of Java (or similar) programming languages, and want to switch to mobile development. The solution focuses on the development of an interactive course in Kotlin. Furthermore, to compare our solution with commercially available ones, we point out the disadvantages of currently available Kotlin courses, such as the level of previous knowledge needed, or a need for a specific development environment. [ABSTRACT FROM AUTHOR]

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Kotlin is a modern JVM language, gaining adoption rapidly and becoming Android official programming language. With its wide usage, the need for code analysis of Kotlin is increasing. Exposing code semantics explicitly with a properly structured format is the first step in code analysis and the construction of such representation is the foundation for downstream tasks. Recently, graph-based approaches became a promising way of encoding source code semantics. However, this work mainly focuses on representation learning with limited interpretability and shallow domain knowledge. The known evolvements of code semantics in new-generation programming languages have been overlooked. How to establish an effective mapping between naturally concise Kotlin source code with graph-based representation needs to be studied by analyzing known language features. Moreover, the feasibility of enhancing the mapping with code semantics automatically learned from the program needs to be explored. In this paper, we first propose a first-sight, rule-based mapping method, using composite representation with AST, CFG, DFG and language features. To examine the possibility of exposing code semantics in the mapped graph, we use Latent Semantic Indexing-based source code summarization to learn more features of each method, and then enrich the attributes of the corresponding node in the graph. We evaluate these mapping strategies with comparative experiments by simulating a code search solution as a downstream task. The experiment result shows that the graph-based method with built-in language features outperforms the text-based way without introducing greater complexity. Comparative experiments also prove that adding code semantics to the graph benefits the capacity of downstream tasks. When exploring the whole mapping process, our study explicitly revealed the practical barriers to extracting and exposing the hidden semantics from Kotlin source code, which may help enlighten source code representations for other modern languages. [ABSTRACT FROM AUTHOR]

El presente trabajo se enfoca en la automatización mediante una aplicación móvil y una página administrativa, de la contratación de servicios profesionales. Esta idea surge de la crisis sanitaria generada por el COVID-19, que afectó principalmente a los aspectos sociales y económicos, lo que provocó un incremento en el indicador de desempleo en la mayoría de los países. Durante esta crisis, empresas que ofrecen sus servicios enfocados en entregas a domicilio, mediante aplicaciones móviles, tuvieron un gran crecimiento debido a su gran demanda por la situación actual; sin embargo, otro tipo de servicios, como los de profesionales informales, se vieron gravemente afectados al no contar con ninguna aplicación que facilite su contratación. Para el desarrollo de este proyecto se utilizó la herramienta de desarrollo Android Studio con el lenguaje de programación Kotlin para la aplicación móvil, y para la página administrativa el Framework Angular, sobre el lenguaje Typescript, ambas aplicaciones se conectan con la plataforma Firebase, que permite la gestión de la información y evita la gestión de un servidor, lo que acelera el desarrollo del lado del cliente.

Subject of Research. The paper considers a method for solution of the nondeterministic polynomial hard problem (NP-hard problem) of a minimal Boolean formula synthesis from a given truth table. The solution of this problem is proposed based on its reduction to the Boolean satisfiability problem (SAT). The issues of efficient and convenient software implementation are discussed for reducing nondeterministic polynomial hard problems to the satisfiability problem. Method. For a minimal Boolean formula synthesis, the constraint programming approach was used: a SAT-formula was created for a given truth table, satisfiable if and only if, there exists a Boolean formula of a given size that satisfies the given truth table. The developed method accent is the application of incremental satisfiability problem solvers. Main Results. A method is proposed for synthesis of a Boolean formula, minimal with respect to the number of operators and terminals, for a given truth table. The method is based on reducing to satisfiability problem and provides the usage of incremental satisfiability problem solvers. The kotlin-satlib framework is developed with the possibility to use the Kotlin and Java languages effectively and conveniently for the software implementation of reducing various nondeterministic polynomial-time hard problems to satisfiability problem. Native interaction with satisfiability problem solvers by Java Native Interface (JNI) technology is used. The proposed method for the minimal Boolean formula synthesis is implemented in the Kotlin programming language using the developed kotlin-satlib framework. Practical Relevance. An experimental study on the example of minimal Boolean formula synthesis has shown that the usage of incremental satisfiability problem solvers for nondeterministic polynomial hard problems is reasonable, since it reduces the total solving time in comparison with the non-incremental approach application.

The article presents a comparison of Kotlin coroutines with analogous solutions in Java and Scala in parallel programming using chosen metric and non-metric criteria. For that purpose, a multi-module project with corresponding implementations of selected algorithms in all of the three languages was created and then analyzed. The studies were preceded by a description of the created project.

This paper discusses the issue of comparing Java and Kotlin technologies based on the web application framework. The criteria taken into account for testing purposes are: execution time, memory usage, CPU load, database response in set time. A series of tests and their in-depth comparative analysis are carried out. For this case, tests and code analysis were carried out to draw comparative conclusions. The performance in terms of web frameworks, database response speed and tests implementation in different languages - in all these Kotlin proved to be less efficient. There is no significant difference between CPU load between individual easurements, the difference does not exceed 2%. Implementation in the Kotlin language has never achieved the best result in any group of measurements.

The problem of the lack of instruments for identifying the characteristics of low-quality code in Android projects that are written in the Kotlin language is determined. A review of modern approaches for identifying antipatterns in program code is accomplished. The analysis of the methods used to find problems with code in Android projects is performed. DECOR and Paprika approaches are considered. Conclusions are drawn about the importance of finding design flaws in program code for the mobile software development and its further support. An antipatterns identification approach for Kotlin language program code in Android projects is proposed. An algorithm for identifying low-quality Kotlin code is presented. The technology for detecting poor quality code characteristics consists of four stages: collecting metrics about an analyzed software system, building a quality model, converting a quality model into a graph representation, and identifying predefined antipatterns. The collection of metrics, including the search for both Androidspecific and object-oriented metrics of Chidamber and Kamerer, is proposed to be implemented through parsing source code and converting it into an abstract syntax tree using the KASTree library. The implementation of KASTree library usage is offered through the Adapter design pattern. The construction of a quality model is implemented using the Paprika tool, supplemented by a number of introduced metrics. Conversion of quality model exactly into graph representation is used to identify antipatterns in order to ensure the speed and quality of complex queries execution for identifying antipatterns. Antipatterns identification using database queries is based on various template rules, including the Catolino rules. Different features of applying the Cypher query language to a graph database are used to represent the rules in form of queries. Results of the work can be used in development of software for poor quality code identification in mobile applications written in Kotlin language, as well as in studies of mobile development antipatterns for this language.

This paper aims to create infrastructure-free networking capabilities for people in need such as when inadequate infrastructure including mobile cell towers and power supplies are damaged or when all communication means are restricted. In general, this means finding a way to communicate without mobile cell towers, repeaters, or Wi-Fi hotspots on mobile devices. This paper focuses on introducing an ad-hoc network based on Bluetooth where users can communicate without barriers. The proposed network implementation uses Google’s API Nearby Connection which is one of Google Play Services that can provide high bandwidth, low latency, encrypted data transfers between nearby devices in a fully-offline P2P mode. Kotlin language was used to program an application called BlueEmergency installed on Android devices to broadcast data as bytes payloads in a form of text messages from one node to all nodes in proximity using Bluetooth only. The test and verification of the system are performed showed that successful communication is achieved with the latest versions of both the Android operating system and Bluetooth. Finally, the designed network can be extended to cover large geographical areas by multi-hopping the messages between devices to make the system more efficient to use in the time of need.

To date, logic-based technologies are either built on top or as extensions of the Prolog language, mostly working as monolithic solutions tailored upon specific inference procedures, unification mechanisms, or knowledge representation techniques. Instead, to maximise their impact, logic-based technologies should support and enable the general-purpose exploitation of all the manifold contributions from logic programming. Accordingly, we present 2P-Kt, a reboot of the tuProlog project offering a general, extensible, and interoperable ecosystem for logic programming and symbolic AI.

Application for university users for easy navigation inside the building during events.

Assessment of replicate quality is an important process for any shotgun proteomics experiment. One fundamental question in proteomics data analysis is whether any specific replicates in a set of analyses are biasing the downstream comparative quantitation. In this paper, we present an experimental method to address such a concern. PeptideMind uses a series of clustering Machine Learning algorithms to assess outliers when comparing proteomics data from two states with six replicates each. The program is a JVM native application written in the Kotlin language with Python sub-process calls to scikit-learn. By permuting the six data replicates provided into four hundred triplet non redundant pairwise comparisons, PeptideMind determines if any one replicate is biasing the downstream quantitation of the states. In addition, PeptideMind generates useful visual representations of the spread of the significance measures, allowing researchers a rapid, effective way to monitor the quality of those identified proteins found to be differentially expressed between sample states.

While many cluster and grid computing frameworks are available, the task of building secure distributed systems or implementing distributed algorithms continue to be a challenging task due to the inherent distributed nature of such systems with multiple failure modes and security issues. In this paper, we present the design and development of remote method delegation (RMD), which is a secure lightweight grid computing platform with load balancing and code migration. RMD is focused on improving the usability issues that plague related industry solutions. The platform is implemented on the JVM (Java Virtual Machine) and supports the Java and Kotlin programming languages, however, the platform should theoretically work with other JVM languages. RMD was designed to simplify the implementation of distributed algorithms by providing a Kotlin DSL (domain specific language) that allows the programmer to define jobs within dedicated code blocks. Users from around the world can donate their own computing resources by hosting their own job server. RMD is secured by placing all untrusted code within a sandbox environment that prevents potentially malicious actions from taking place. To demonstrate the feasibility of the proposed model, a proof of concept implementation has been constructed with real examples demonstrating the usefulness of the proposed solution. [ABSTRACT FROM AUTHOR]

This article presents the results of a literature study related to the construction and operation of Java Virtual Machine, as well as performance tests of selected languages using the aforementioned runtime environment on the example of Java, Scala and Kotlin. Performance testing was carried out using two applications built using the Apache Maven archetype with the built-in Java Microbenchmark Harness library.

This publication presents the results of a comparative analysis of Java and Kotlin programming languages used to create mobile applications for system Android. The analysis covers performance aspects such as CPU load, RAM load, as well as the compilation and execution times. Aspects such as code structure, availability of libraries, supported databases, popularity and community support were taken under consolidation.

Context: During the last years, developers of mobile applications have the possibility to use new paradigms and tools for developing mobile applications. For instance, since 2017, Android developers have the official support to write Android applications using Kotlin language. Kotlin is programming language fully interoperable with Java that combines object-oriented and functional features. Objective: The goal of this paper is twofold. First, it aims to study the degree of adoption of Kotlin language on the development of open-source Android applications and to measure the amount of Kotlin code inside those applications. Secondly, it aims to measure the quality of Android applications that are written using Kotlin and to compare it with the quality of Android applications written using Java. Method: We first defined a method to detect Kotlin applications from a dataset of open-source Android applications. Then, we analyzed those applications to detect instances of code smells and computed an estimation of the quality of the applications. Finally, we studied how the introduction of Kotlin code impacts on the quality of an Android application. Results: Our experiment found that 11.26% of applications from a dataset with 2,167 open-source applications have been written (partially or fully) using Kotlin language. We found that the introduction of Kotlin code increases the quality, in terms of the presence of 10 different code smells studied, 4 object-oriented and 6 Android, of the majority of the Android applications initially written in Java. [ABSTRACT FROM AUTHOR]