RANCANG BANGUN APLIKASI PERHITUNGAN STATS POKEMON BERBASIS ANDROID

Game development began in the 1950s when computer scientists began making games and simulations that were easy to research or just wanted to play, games that year only used lines and dots, like pong, and from that year until now, game development has been increasingly rapidly from 8-bit games like Super Mario Bros to games that use VR (Virtual Reality) and AR (Augmented Reality). However, there are only two games that have survived from the beginning until now and there are 2 descendants, namely Super Mario and Pokemon, for the purpose of this research, the Researcher will be focusing on the Pokemon franchise. The purpose of this research is to give the players information and the potential of the Pokemon the player use and also decrease the time player spent playing the game. The research method used is using the waterfall model method which includes Requirements, Design, Testing, Implementation, Verification, and Maintenance where the system design uses UML (Unified Modeling Language) diagrams which includes Use Case Diagram, Sequence Diagram, Activity Diagram, and Class Diagram. The results of this research are the Android-based Pokemon Stats Calculator program that has been successfully designed according to the demands and needs of the Players and the Researcher, the Android-based Pokemon Stats Calculator program has been built more efficiently and more effectively and can reduce various errors that occur unlike before using Microsoft Office Excel. Keywords: Android; Pokemon; UML

Prediksi Not Operational Transaction Menggunakan Logistic Regression pada Bank XYZ di Kota Kupang

Bank XYZ is one of the banks in Kupang City, East Nusa Tenggara Province which has several ATM machines and is placed in several merchant locations. The existing ATM machine is one of the goals of customers and non-customers in conducting transactions at the ATM machine. The placement of the ATM machines sometimes makes the machine not used optimally by the customer to transact, causing the disposal of machine resources and a condition called Not Operational Transaction (NOP). With the data consisting of several independent variables with numeric types, it is necessary to know how the classification of the dependent variable is NOP. Machine learning approach with Logistic Regression method is the solution in doing this classification. Some research steps are carried out by collecting data, analyzing using machine learning using python programming and writing reports. The results obtained with this machine learning approach is the resulting prediction value of 0.507 for its classification. This means that in the future XYZ Bank can classify NOP conditions based on the behavior of customers or non-customers in making transactions using Bank XYZ ATM machines.  Bank XYZ merupakan salah satu bank di Kota Kupang, Propinsi Nusa Tenggara Timur yang memiliki beberapa mesin ATM dan ditempatkan dibeberapa lokasi merchant. Mesin ATM yang ada menjadi salah satu tujuan nasabah maupun non-nasabah dalam melakukan transaksi di mesin ATM tersebut. Penempatan mesin-mesin ATM tersebut terkadang membuat mesin  tidak dipakai secara maksimal oleh nasabah untuk bertransaksi sehingga menyebabkan pembuangan sumberdaya mesin dan terjadi kondisi yang disebut Not Operational Transaction (NOP). Dengan adanya data yang terdiri dari beberapa variabel independen dengan tipe numerik maka perlu diketahui bagaimana klasifikasi terhadap variabel dependen yaitu NOP. Pendekatan machine learning dengan metode Logistic Regression menjadi solusi dalam melakukan klasifikasi ini. Beberapa langkah penelitian dilakukan dengan pengumpulan data, analisa menggunakan machine learning menggunakan pemrograman python dan penulisan laporan. Hasil yang diperoleh dengan pendekatan machine learning ini adalah dihasilkannya nilai prediksi sebesar 0,507 untuk klasifikasinya. Hal ini berarti ke depan Bank XYZ dapat melakukan klasifikasi kondisi NOP berdasarkan perilaku nasabah atau non-nasabah dalam bertransaksi menggunakan mesin ATM Bank XYZ. Kata kunci: Machine Learning, Logistic Regression, Python, Klasifikas

Enhanced transformer long short-term memory framework for datastream prediction

In machine learning, datastream prediction is a challenging issue, particularly when dealing with enormous amounts of continuous data. The dynamic nature of data makes it difficult for traditional models to handle and sustain real-time prediction accuracy. This research uses a multi-processor long short-term memory (MPLSTM) architecture to present a unique framework for datastream regression. By employing several central processing units (CPUs) to divide the datastream into multiple parallel chunks, the MPLSTM framework illustrates the intrinsic parallelism of long short-term memory (LSTM) networks. The MPLSTM framework ensures accurate predictions by skillfully learning and adapting to changing data distributions. Extensive experimental assessments on real-world datasets have demonstrated the clear superiority of the MPLSTM architecture over previous methods. This study uses the transformer, the most recent deep learning breakthrough technology, to demonstrate how well it can handle challenging tasks and emphasizes its critical role as a cutting-edge approach to raising the bar for machine learning

funcGNN: A Graph Neural Network Approach to Program Similarity

Program similarity is a fundamental concept, central to the solution of software engineering tasks such as software plagiarism, clone identification, code refactoring and code search. Accurate similarity estimation between programs requires an in-depth understanding of their structure, semantics and flow. A control flow graph (CFG), is a graphical representation of a program which captures its logical control flow and hence its semantics. A common approach is to estimate program similarity by analysing CFGs using graph similarity measures, e.g. graph edit distance (GED). However, graph edit distance is an NP-hard problem and computationally expensive, making the application of graph similarity techniques to complex software programs impractical. This study intends to examine the effectiveness of graph neural networks to estimate program similarity, by analysing the associated control flow graphs. We introduce funcGNN, which is a graph neural network trained on labeled CFG pairs to predict the GED between unseen program pairs by utilizing an effective embedding vector. To our knowledge, this is the first time graph neural networks have been applied on labeled CFGs for estimating the similarity between high-level language programs. Results: We demonstrate the effectiveness of funcGNN to estimate the GED between programs and our experimental analysis demonstrates how it achieves a lower error rate (0.00194), with faster (23 times faster than the quickest traditional GED approximation method) and better scalability compared with the state of the art methods. funcGNN posses the inductive learning ability to infer program structure and generalise to unseen programs. The graph embedding of a program proposed by our methodology could be applied to several related software engineering problems (such as code plagiarism and clone identification) thus opening multiple research directions.Comment: 11 pages, 8 figures, 3 table

Расчет оптических параметров тонких пленок конструкционных материалов теплового неохлаждаемого детектора болометрического типа

The increased interest in utilizing uncooled thermal bolometer-type detectors (microbolometers) within the infrared or terahertz detection field is justified by their operational and technological characteristics, in particular: relatively low manufacturing cost, high detection efficiency, compatibility with silicon CMOS technology, and operation at room temperature. The performance of such detectors depends on optimizing critical parameters, which are dictated by both the geometrical design and the electrical, optical, and thermal properties of the materials used. The determination of optical parameters stands as a decisive factor in the design of microbolometer structures. This article delves into the examination of optical parameters of thin films of structural materials of microbolometer based on thermosensitive vanadium oxide film manufactured at JSC “INTEGRAL”. The investigation showcases the results of determining optical constants (refractive indexes n and absorption coefficients k) of thin films from the transmission curve by applying the reflection-transmission method. Furthermore, a comparison is carried out between the results of computer modeling of the transmission, reflection and absorption spectra – taking into account the obtained values of the coefficients n and k – and the empirical data from the in-situ experiment.Повышенный интерес к применению неохлаждаемых тепловых детекторов болометрического типа (микроболометров) в инфракрасном или терагерцовом поле обнаружения обоснован их эксплуатационными и технологическими характеристиками, в частности: относительно низкой стоимостью изготовления, высокой эффективностью обнаружения, совместимостью с кремниевой КМОП-технологией, работоспособностью при комнатной температуре. Характеристики таких детекторов зависят от оптимизации критических параметров, которые определяются геометрией конструкции, а также электрическими, оптическими и тепловыми свойствами применяемых материалов. Определение оптических параметров является одним из решающих факторов при проектировании приборных структур микроболометров. В статье исследованы оптические параметры тонких пленок конструкционных материалов микроболометра на основе термочувствительной пленки оксида ванадия, изготовленных в ОАО «ИНТЕГРАЛ». Приведены результаты определения посредством применения метода отражения-передачи оптических констант (коэффициентов преломления n и поглощения k) тонких пленок по кривой пропускания. Выполнено сравнение результатов компьютерного моделирования спектров пропускания, отражения и поглощения с учетом полученных значений коэффициентов n и k с данными натурного эксперимента

Calculation of Optical Parameters of Thin Films of Structural Materials of Thermal Uncooled Bolometric Type Detector

Повышенный интерес к применению неохлаждаемых тепловых детекторов болометрического типа (микроболометров) в инфракрасном или терагерцовом поле обнаружения обоснован их эксплуатационными и технологическими характеристиками, в частности: относительно низкой стоимостью изготовления, высокой эффективностью обнаружения, совместимостью с кремниевой КМОП-технологией, работоспособностью при комнатной температуре. Характеристики таких детекторов зависят от оптимизации критических параметров, которые определяются геометрией конструкции, а также электрическими, оптическими и тепловыми свойствами применяемых материалов. Определение оптических параметров является одним из решающих факторов при проектировании приборных структур микроболометров. В статье исследованы оптические параметры тонких пленок конструкционных материалов микроболометра на основе термочувствительной пленки оксида ванадия, изготовленных в ОАО «ИНТЕГРАЛ». Приведены результаты определения посредством применения метода отражения-передачи оптических констант (коэффициентов преломления n и поглощения k) тонких пленок по кривой пропускания. Выполнено сравнение результатов компьютерного моделирования спектров пропускания, отражения и поглощения с учетом полученных значений коэффициентов n и k с данными натурного эксперимента

Силабус навчальної дисципліни «Технології об’єктно-орієнтованого та web-програмування» для здобувачів вищої освіти ступеня «бакалавр», які навчаються за освітньо-професійною програмою «Автоматизація та комп’ютерно-інтегровані технології» спеціальності «Автоматизація та комп’ютерно-інтегровані технології»

У теперішній час найбільш розповсюдженими програмними системами є системі, що розроблені з використанням web-технологій. Це потребує від фахівців з автоматизації та комп’ютерно-інтегрованих технологій знання та вміння використовувати загальні концепції web-програмування та використання сучасних засобів розробки серверної частини web-застосунків. Метою викладання дисципліни є вивчення базових концепцій, механізмів та технік процедурного, об’єктно-орієнтованого, паралельного та web-орієнтованого програмування мовою Python 3, здобуття базових навичок проектування, розробки та тестування програмного забезпечення

Analizador de antenas para las bandas HF y VHF utilizando sistemas embebidos

Nowadays, telecommunications play a fundamental role in daily life, because they are implemented in both the industrial and commercial sector, so it is sought to optimize, improve and measure parameters and physical phenomena that may affect the performance of the components used in a wireless communication system; for this reason it is essential to constantly monitor the antennas used in telecommunications to validate their performance and operability, these activities are costly due to the electronic devices with which they are implemented; this article proposes the development of an embedded system for the analysis of antennas in the high frequency HF and very high frequency VHF bands through a methodology based on four stages: parameter selection, technological integration, application development, and system validation. As a result, a compact system is built that allows obtaining antenna parameters such as impedance, radiation pattern and medium power beam, all with errors lower than 0.1 %, compared to the reference laboratory equipment calibrated and adjusted for the frequency ranges from 10 MHz to 50 MHz, allowing establishing a prototype designed as an alternative for testing and validation in laboratories.En la actualidad las telecomunicaciones juegan un papel fundamental en la vida diaria, debido a que son implementadas tanto en el sector industrial y comercial por lo que se busca optimizar, mejorar y medir parámetros y fenómenos físicos que puedan afectar el rendimiento de los componentes utilizados en un sistema de comunicación inalámbrico; razón por el cual es fundamental realizar el monitoreando de manera constante las antenas utilizadas en telecomunicaciones para validar su funcionamiento y operatividad, estas actividades resultan costosas debido a los dispositivos electrónicos con los que se implementan; este articulo propone el desarrollo de un sistema embebido para el análisis de antenas en las bandas de alta frecuencia HF y muy alta frecuencia VHF mediante una metodología basada en cuatro etapas: selección de parámetros, integración tecnológica, desarrollo del aplicativo, y validación del sistema. Como resultado, se construye un sistema compacto que permite la obtención de parámetros de la antena como la impedancia, patrón de radiación y haz de media potencia, todos con errores inferiores al 0.1 %, en comparación al equipo de laboratorio de referencia calibrado y ajustado para los rangos de frecuencias de 10 MHz a 50 MHz, permitiendo establecer un prototipo diseñado como una alternativa para pruebas y validaciones en laboratorios

Estimating peanut and soybean photosynthetic traits using leaf spectral reflectance and advance regression models

One proposed key strategy for increasing potential crop stability and yield centers on exploitation of genotypic variability in photosynthetic capacity through precise high-throughput phenotyping techniques. Photosynthetic parameters, such as the maximum rate of Rubisco catalyzed carboxylation (V-c,V-max) and maximum electron transport rate supporting RuBP regeneration (J(max)), have been identified as key targets for improvement. The primary techniques for measuring these physiological parameters are very time-consuming. However, these parameters could be estimated using rapid and non-destructive leaf spectroscopy techniques. This study compared four different advanced regression models (PLS, BR, ARDR, and LASSO) to estimate V-c,V-max and J(max) based on leaf reflectance spectra measured with an ASD FieldSpec4. Two leguminous species were tested under different controlled environmental conditions: (1) peanut under different water regimes at normal atmospheric conditions and (2) soybean under high [CO2] and high night temperature. Model sensitivities were assessed for each crop and treatment separately and in combination to identify strengths and weaknesses of each modeling approach. Regardless of regression model, robust predictions were achieved for V-c,V-max (R-2 = 0.70) and J(max) (R-2 = 0.50). Field spectroscopy shows promising results for estimating spatial and temporal variations in photosynthetic capacity based on leaf and canopy spectral propertiesThe authors would like to thank the technical help during the experiment of Mr. Robert Icenogle, Barry Dorman (USDA-ARS), Seth Johnston, and Mary Durstock (Crop Physiology Laboratory, Auburn University). The authors also would like to thank to Dr. Jose A. Jimenez Berni for statistical support to analyze the data. This research was supported by the Action CA17134 SENSECO (Optical Synergies for Spatiotemporal Sensing of Scalable Ecophysiological Traits) funded by COST (European Cooperation in Science and Technology, www.cost.eu).This research was also supported by Auburn University and Alabama Agricultural Experimental Station Seed Grant

Estimating peanut and soybean photosynthetic traits using leaf spectral reflectance and advance regression models

One proposed key strategy for increasing potential crop stability and yield centers on exploitation of genotypic variability in photosynthetic capacity through precise high-throughput phenotyping techniques. Photosynthetic parameters, such as the maximum rate of Rubisco catalyzed carboxylation (Vc,max) and maximum electron transport rate supporting RuBP regeneration (Jmax), have been identified as key targets for improvement. The primary techniques for measuring these physiological parameters are very time-consuming. However, these parameters could be estimated using rapid and non-destructive leaf spectroscopy techniques. This study compared four different advanced regression models (PLS, BR, ARDR, and LASSO) to estimate Vc,max and Jmax based on leaf reflectance spectra measured with an ASD FieldSpec4. Two leguminous species were tested under different controlled environmental conditions: (1) peanut under different water regimes at normal atmospheric conditions and (2) soybean under high [CO2] and high night temperature. Model sensitivities were assessed for each crop and treatment separately and in combination to identify strengths and weaknesses of each modeling approach. Regardless of regression model, robust predictions were achieved for Vc,max (R2 = 0.70) and Jmax (R2 = 0.50). Field spectroscopy shows promising results for estimating spatial and temporal variations in photosynthetic capacity based on leaf and canopy spectral properties