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Malhotra, Kritika, and Amit Prakash Singh. "Implementation of decision tree algorithm on FPGA devices." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (March 1, 2021): 131. http://dx.doi.org/10.11591/ijai.v10.i1.pp131-138.
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<span id="docs-internal-guid-01e673b1-7fff-8dc3-6b99-14ed17cd6b49"><span>Machine learning techniques are rapidly emerging in large number of fields from robotics to computer vision to finance and biology. One important step of machine learning is classification which is the process of finding out to which category a new encountered observation belongs based on predefined categories. There are various existing solutions to classification and one of them is decision tree classification (DTC) which can achieve high accuracy while handling the large datasets. But DTC is computationally intensive algorithm and as the size of the dataset increases its running time also increases which could be from some hours to days even. But thanks to field programmable gate arrays (FPGA) which could be used for large datasets to achieve high performance implementation with low energy consumption. Along with FPGA’s, python is used for accelerating the application development and python is leveraged by using python productivity for zynq (PYNQ), a python development environment for application development. This paper provides the literature review of an implementation of DTC for FPGA devices along with future work that can be done.</span></span>
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Holik, Martin, Tomas Horvath, Vaclav Oujezsky, Petr Munster, Adrian Tomasov, and Sobeslav Valach. "MongoDB Database as Storage for GPON Frames." Sensors 20, no. 21 (October 30, 2020): 6208. http://dx.doi.org/10.3390/s20216208.
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This work is focused on creating an open-source software-based solution for monitoring traffic transmitted through gigabit passive optical network. In this case, the data are captured by the field-programmable gate array (FPGA) card and reassembled using parsing software from a passive optical network built on the International Telecommunication Unit telecommunication section (ITU-T) G.984 gigabit-capable passive optical network GPON recommendation. Then, the captured frames are converted by suitable software into GPON frames, which will be further processed for analysis. Due to the high transfer rate of GPON recommendations, the work describes the issue of writing to the Mongo database system. In order to achieve the best possible results and minimal loss of transmitted frames, a series of tests were performed. The proposed test scenarios are based on different database writing approaches and are implemented in the Python and C# programming languages. Based on our results, it has been shown that the high processing speed is too high for Python processing. Critical operations must be implemented in the C# programming language. Due to rapid application development, Python can only be used for noncritical time-consuming data processing operations.
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Bhat K, Mahesh, and Namita Palecha. "Implementation of a Parallel Fault Simulation System using PODEM in a Hardware Accelerator using Python." Journal of University of Shanghai for Science and Technology 23, no. 06 (June 17, 2021): 530–36. http://dx.doi.org/10.51201/jusst/21/05296.
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VLSI Testing is one of the essential domains in recent times. With the channel length of the transistor decreasing continually, the number of transistors in a chip increases, thus increasing the probability of defects or faults. Automatic Test Pattern Generator is one way to find such input test vectors to the circuit, which will help identify the faults if present. PODEM algorithm is one such algorithm used in this regard. This paper helps in reducing the runtime of this algorithm by the parallelism approach. Different stuck-at faults in the gate level circuit are simulated parallelly.
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Steiger, Damian S., Thomas Häner, and Matthias Troyer. "ProjectQ: an open source software framework for quantum computing." Quantum 2 (January 31, 2018): 49. http://dx.doi.org/10.22331/q-2018-01-31-49.
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We introduce ProjectQ, an open source software effort for quantum computing. The first release features a compiler framework capable of targeting various types of hardware, a high-performance simulator with emulation capabilities, and compiler plug-ins for circuit drawing and resource estimation. We introduce our Python-embedded domain-specific language, present the features, and provide example implementations for quantum algorithms. The framework allows testing of quantum algorithms through simulation and enables running them on actual quantum hardware using a back-end connecting to the IBM Quantum Experience cloud service. Through extension mechanisms, users can provide back-ends to further quantum hardware, and scientists working on quantum compilation can provide plug-ins for additional compilation, optimization, gate synthesis, and layout strategies.
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Sanngoen, Wanayuth, Watcharin Po-Ngaen, Chirot Charitkhuan, and Kitsada Doungjitjaroen. "Development of Parallel Delta Robot System Controller Based on Raspberry Pi and FPGA." Applied Mechanics and Materials 835 (May 2016): 698–704. http://dx.doi.org/10.4028/www.scientific.net/amm.835.698.
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This paper presents a system control design on 2 axes “Delta Industrial Robot”. The control system utilizes both the embedded technology and the “Raspberry Pi" which is a low power consumption PC with open source operating system. The user can communicate with the PC and FPGA (Field Programmable Gate Array) via the system GUI interface. The FPGA acts as dedicated hardware to provide both the efficiency and flexibility for the closed-loop servo control system.To calculate all related kinematics equations for the robot, look up table approach has been implemented to reduce the calculation complexity. Python programs are also implemented to create and search the look up tables. The inverse kinematics is the key to find the position of the robot. A test load of 0.2 kg at the velocity of 2.618 m/s, the S.D. of the end effector’s position of the test result is within 0.06 mm.
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Bocci, Andrea, David Dagenhart, Vincenzo Innocente, Christopher Jones, Matti Kortelainen, Felice Pantaleo, and Marco Rovere. "Bringing heterogeneity to the CMS software framework." EPJ Web of Conferences 245 (2020): 05009. http://dx.doi.org/10.1051/epjconf/202024505009.
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The advent of computing resources with co-processors, for example Graphics Processing Units (GPU) or Field-Programmable Gate Arrays (FPGA), for use cases like the CMS High-Level Trigger (HLT) or data processing at leadership-class supercomputers imposes challenges for the current data processing frameworks. These challenges include developing a model for algorithms to offload their computations on the co-processors as well as keeping the traditional CPU busy doing other work. The CMS data processing framework, CMSSW, implements multithreading using the Intel Threading Building Blocks (TBB) library, that utilizes tasks as concurrent units of work. In this paper we will discuss a generic mechanism to interact effectively with non-CPU resources that has been implemented in CMSSW. In addition, configuring such a heterogeneous system is challenging. In CMSSW an application is configured with a configuration file written in the Python language. The algorithm types are part of the configuration. The challenge therefore is to unify the CPU and co-processor settings while allowing their implementations to be separate. We will explain how we solved these challenges while minimizing the necessary changes to the CMSSW framework. We will also discuss on a concrete example how algorithms would offload work to NVIDIA GPUs using directly the CUDA API.
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Shinde, Piyush N. "Pygame: Develop Games using Python." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 4442–47. http://dx.doi.org/10.22214/ijraset.2021.35735.
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In this paper, we will present the process of creating a simple game(Dracula) made in Python. For the purposes of this work, we created the game Dracula using the Pygame package. The aim of this paper is to show the process of creating a game, i.e the gradual construction of each element of the game. The game was created in several phases, and we will describe and explain each in detail. We will start from the construction of the basic skeleton of the game, that is. a graphical representation of the background image window. We will gradually introduce and add new elements (heroes, enemies, bullets, levels ...) all the way to a fully built and functional game. We will point out the possibilities and limitations that arise when programming the dracula game.
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Glynatsi, Nikoleta, and Vincent Knight. "Game Theory and Python: An educational tutorial to game theory and repeated games using Python." Journal of Open Source Education 4, no. 39 (May 24, 2021): 78. http://dx.doi.org/10.21105/jose.00078.
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Bandura, K., A. N. Bender, J. F. Cliche, T. de Haan, M. A. Dobbs, A. J. Gilbert, S. Griffin, et al. "ICE: A Scalable, Low-Cost FPGA-Based Telescope Signal Processing and Networking System." Journal of Astronomical Instrumentation 05, no. 04 (December 2016): 1641005. http://dx.doi.org/10.1142/s2251171716410051.
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We present an overview of the ‘ICE’ hardware and software framework that implements large arrays of interconnected field-programmable gate array (FPGA)-based data acquisition, signal processing and networking nodes economically. The system was conceived for application to radio, millimeter and sub-millimeter telescope readout systems that have requirements beyond typical off-the-shelf processing systems, such as careful control of interference signals produced by the digital electronics, and clocking of all elements in the system from a single precise observatory-derived oscillator. A new generation of telescopes operating at these frequency bands and designed with a vastly increased emphasis on digital signal processing to support their detector multiplexing technology or high-bandwidth correlators — data rates exceeding a terabyte per second — are becoming common. The ICE system is built around a custom FPGA motherboard that makes use of an Xilinx Kintex-7 FPGA and ARM-based co-processor. The system is specialized for specific applications through software, firmware and custom mezzanine daughter boards that interface to the FPGA through the industry-standard FPGA mezzanine card (FMC) specifications. For high density applications, the motherboards are packaged in 16-slot crates with ICE backplanes that implement a low-cost passive full-mesh network between the motherboards in a crate, allow high bandwidth interconnection between crates and enable data offload to a computer cluster. A Python-based control software library automatically detects and operates the hardware in the array. Examples of specific telescope applications of the ICE framework are presented, namely the frequency-multiplexed bolometer readout systems used for the South Pole Telescope (SPT) and Simons Array and the digitizer, F-engine, and networking engine for the Canadian Hydrogen Intensity Mapping Experiment (CHIME) and Hydrogen Intensity and Real-time Analysis eXperiment (HIRAX) radio interferometers.
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ABBAS, Yasir M. O., and Kenichi Asami. "Design of Software-Defined Radio-Based Adaptable Packet Communication System for Small Satellites." Aerospace 8, no. 6 (June 4, 2021): 159. http://dx.doi.org/10.3390/aerospace8060159.
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Software-defined radio (SDR) devices have made a massive contribution to communication systems by reducing the cost and development time for radio frequency (RF) designs. SDRs opened the gate to programmers and enabled them to increase the capabilities of these easily manipulated systems. The next step is to upgrade the reconfigurability into adaptability, which is the focus of this paper. This research contributes to improving SDR-based systems by designing an adaptable packet communication transmitter and receiver that can utilize the communication window of CubeSats and small satellites. According to the feedback from the receiver, the transmitter modifies the characteristics of the signal. Theoretically, the system can adopt many modes, but for simplicity and to prove the concept, here, the changes are limited to three data rates of the Gaussian minimum shift keying (GMSK) modulation scheme, i.e., 2400 bps GMSK, 4800 bps GMSK and 9600 bps GMSK, which are the most popular in amateur small satellites. The system program was developed using GNU Radio Companion (GRC) software and Python scripts. With the help of GRC software, the design was simulated and its behavior in simulated conditions observed. The transmitter packetizes the data into AX.25 packets and transmits them in patches. Between these patches, it sends signaling packets. The patch size is preselected. Alternatively, the receiver extracts the data and saves it in a dedicated file. It directly replies with a feedback message whenever it gets the signaling packets. Based on the content of the feedback message, the characteristics of the transmitted signal are altered. The packet rate and the actual useful data rate are measured and compared with the selected data rate, and the packet success rate of the system operating at a fixed data rate is also measured while simulating channel noise to achieve the desired Signal-to-Noise Ratio (SNR).
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Heo, Junyoung, and Seukwon Kang. "Simple shooting game engine in Python." International Journal of Computational Vision and Robotics 5, no. 2 (2015): 130. http://dx.doi.org/10.1504/ijcvr.2015.068801.
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Lubis, Fetty Fitriyanti, Yusep Rosmansyah, and Suhono H. Supangkat. "Topic Discovery of Online Course Reviews Using LDA with Leveraging Reviews Helpfulness." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (February 1, 2019): 426. http://dx.doi.org/10.11591/ijece.v9i1.pp426-438.
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Despite the popularity of the Massive Open Online Courses, small-scale research has been done to understand the factors that influence the teaching-learning process through the massive online platform. Using topic modeling approach, our results show terms with prior knowledge to understand e.g.: Chuck as the instructor name. So, we proposed the topic modeling approach on helpful subjective reviews. The results show five influential factors: “learn easy excellent class program”, “python learn class easy lot”, “Program learn easy python time game”, and “learn class python time game”. Also, research results showed that the proposed method improved the perplexity score on the LDA model.
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Gupta, Abhishek. "A Research paper on Typing Speed Tester Game using Python & Tkinter." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 3436–37. http://dx.doi.org/10.22214/ijraset.2021.35767.
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In this Python project , our goal is to build a game in which user can check and even improve typing speed. For a graphical user interface, we will be going to use the Tkinter library in Python which is used for working with graphics. A typing speed test help you to check your typing speed and improve it with regular practice. With the python project, learn to build an application using Tkinter library that can detect typing speed of user with accuracy. The project contains only the user side. It is a simple typing challenge game project. This is like a game, one-by-one random word will pop up on your screen and you have to type as many given words as you can, within 60 seconds. The design of this project is pretty simple so that the user won’t find any difficulties while working on it.
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Naik, Harshad. "Blackjack Game using Object Oriented Programming Methodology and Python." International Journal of Computer Applications 144, no. 7 (June 17, 2016): 7–10. http://dx.doi.org/10.5120/ijca2016910409.
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Parial, Prithwish. "Python the game changer in the field of Machine Learning, Data Science and IoT: A Review." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 1827–37. http://dx.doi.org/10.22214/ijraset.2021.37668.
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Abstract: Python is the finest, easily adoptable object-oriented programming language developed by Guido van Rossum, and first released on February 20, 1991 It is a powerful high-level language in the recent software world. In this paper, our discussion will be an introduction to the various Python tools applicable for Machine learning techniques, Data Science and IoT. Then describe the packages that are in demand of Data science and Machine learning communities, for example- Pandas, SciPy, TensorFlow, Theano, Matplotlib, etc. After that, we will move to show the significance of python for building IoT applications. We will share different codes throughout an example. To assistance, the learning experience, execute the following examples contained in this paper interactively using the Jupiter notebooks. Keywords: Machine learning, Real world programming, Data Science, IOT, Tools, Different packages, Languages- Python.
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Rakshit Tyagi and Prof. Shreya Kapoor. "Comparing 2D game in Lua with game engine of love and in python with pygame." International Journal for Modern Trends in Science and Technology 6, no. 12 (December 18, 2020): 370–72. http://dx.doi.org/10.46501/ijmtst061269.
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The game flappy bird is used in the process of research .The game is used in both LUA and python programming language and the main aim of the user in game is to move forward by avoiding any contact with tunnel sprite .Also ,Flappy has to stay in air all the time without touching with ground and collects coins in between to increase the score. The game is 2D. [1]Flappy Bird is an arcade-style game in which the player controls the bird Flappy, which moves persistently to the right. The player is tasked with navigating Flappy through pairs of pipes that have equally sized gaps placed at random heights. Flappy automatically descends and only ascends when the player taps control . [2]Each successful pass through a pair of pipes awards the player one point. Colliding with a pipe or the ground ends the gameplay. During the game over screen, the player is awarded a bronze medal if they reached ten or more points, a silver medal from twenty points, a gold medal from thirty points, and a platinum medal from forty points.
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Sideris, Grigorios, and Stelios Xinogalos. "PY-RATE ADVENTURES: A 2D Platform Serious Game for Learning the Basic Concepts of Programming With Python." Simulation & Gaming 50, no. 6 (September 9, 2019): 754–70. http://dx.doi.org/10.1177/1046878119872797.
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Background. Learning programming is a cognitively demanding field of study accompanied with various difficulties. Although there is a high demand in the market for programmers, software analysts and engineers, a high dropout rate is recorded in relevant fields of study. Serious games are a promising means of engaging students in learning programming by giving them more incentives and making the process of learning programming concepts and languages more entertaining. Aim. This article introduces a new serious game called PY-RATE ADVENTURES, which aims to assist young students in their introduction to the basic programming concepts using Python. The game does not have any prerequisites and is suitable for players with no previous knowledge of programming. This article aims to present important information regarding the analysis, design and pilot evaluation of PY-RATE ADVENTURES. Method. The game was evaluated by 31 people that had recently graduated or were students of an Interdepartmental Programme of Postgraduate Studies in Information Systems. The participants voluntarily played the game and answered a questionnaire based on the MEEGA+ model, after their hands on experience with the game. This questionnaire’s purpose was to evaluate PY-RATE ADVENTURES in terms of perceived player experience and short-term learning. Results. The participants positively evaluated the game almost in all the elements of player experience. Furthermore, the majority of the users consider that the game helped them to learn basic programming concepts in Python and stated that they would prefer to learn programming with this game rather than other teaching methods. Conclusion. The positive results of the pilot evaluation give us the motivation to proceed and evaluate the game with students in secondary education, in order to extract stronger and generalisable conclusions regarding the impact of the game as an educational tool for learning programming concepts.
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Koupritzioti, Dimitra, and Stelios Xinogalos. "PyDiophantus maze game: Play it to learn mathematics or implement it to learn game programming in Python." Education and Information Technologies 25, no. 4 (December 23, 2019): 2747–64. http://dx.doi.org/10.1007/s10639-019-10087-1.
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Mukhtar, Harun. "PERANCANGAN DAN PEMBUATAN VISUAL NOVEL SEJARAH KH. AHMAD DAHLAN SEBAGAI MEDIA PEMBELAJARAN BERBASIS ANDROID." Rabit : Jurnal Teknologi dan Sistem Informasi Univrab 3, no. 2 (July 10, 2018): 69–82. http://dx.doi.org/10.36341/rabit.v3i2.444.
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KH. Ahmad Dahlan adalah salah satu tokoh penting dan juga pendiri dari Muhammadyah. Karena beliau adalah tokoh penting dalam Muhammadiyah, maka dari itu akan sangat disayangkan jika mahasiswa itu sendiri tidak mengenal siapa itu Ahmad Dahlan. Maka dari itu dibuat sebuah media yang bisa membantu Mahasiswa untuk memahami bagaimana sosok Ahmad Dahlan yaitu dengan menggunakan media game. Game yang akan digunakan adalah game dengan jenis visual novel. Visual novel terdiri dari cerita, gambar, dan suara yang bisa membantu dalam proses pembelajaran. Game ini akan dibuat menggunakan software Ren’Py untuk membuat visual novel menggunakan bahasa pemrograman Python. Pembuatan game ini menggunakan metode MDLC, yaitu terdiri dari konsep, desain, pengumpulan material, penyusunan dan pembuatan, uji coba, kemudian menyebar luaskan. Setelah semua tahapan selesai, maka didapatlah hasil bahwa game ini ternyata mampu memceritakan, dan memberikan pembelajaran mengenai Ahmad Dahlan.
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Bak, Maciej, and Anna Rozlach. "Moran Pycess: a Python package to simulate Moran processes driven by game theory." Journal of Open Source Software 5, no. 54 (October 26, 2020): 2643. http://dx.doi.org/10.21105/joss.02643.
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Wang, Zhi, Victor Chang, and Gergely Horvath. "Explaining and Predicting Helpfulness and Funniness of Online Reviews on the Steam Platform." Journal of Global Information Management 29, no. 6 (November 2021): 1–23. http://dx.doi.org/10.4018/jgim.20211101.oa16.
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Online review is a crucial display content of many online shopping platforms and an essential source of product information for consumers. Low-quality reviews often cause inconvenience to the platform and review readers. This article aims to help Steam, one of the largest digital distribution platforms, predict the review helpfulness and funniness. Via Python, 480,000 game reviews related data for 20 games were captured for analysis. This article analyzed the impact of three categories of influencing factors on the usefulness and funniness of game reviews, which are characteristics of review, reviewer and game. Additionally, by using the Random Forest-based classifier, the usefulness of reviews could be accurately predicted, while for funniness, Gradient Boosting Decision Tree was the better choice. This article applied research on the usefulness of reviews to game products and proposed research on the funniness of reviews.
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Krishnan, Surenthiran, Pritheega Magalingam, and Roslina Ibrahim. "Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (December 1, 2021): 5467. http://dx.doi.org/10.11591/ijece.v11i6.pp5467-5476.
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<span>This paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reached an accuracy of 98.23% and deep neural network (DNN) has reached 98.5%. The common drawbacks of the existing models are low accuracy due to the complex build-up of the neural network, high number of neurons with redundancy in the neural network model and imbalance datasets of Cleveland. Experiments were conducted with various customized model, where results showed that the proposed model using RNN and multiple GRU with synthetic minority oversampling technique (SMOTe) has reached the best performance level. This is the highest accuracy result for RNN using Cleveland datasets and much promising for making an early heart disease prediction for the patients.</span>
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Donkoh, Elvis Kobina, Rebecca Davis, Emmanuel D. J. Owusu-Ansah, Emmanuel A. Antwi, and Michael Mensah. "Application of Combinatorial Techniques to the Ghanaian Board Game Zaminamina Draft." European Journal of Pure and Applied Mathematics 12, no. 1 (January 31, 2019): 159–75. http://dx.doi.org/10.29020/nybg.ejpam.v12i1.3308.
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Games happen to be a part of our contemporary culture and way of life. Often mathematical models of conflict and cooperation between intelligent rational decision-makers are studied in these games. Example is the African board game ’Zaminamina draft’ which is often guided by combinatorial strategies and techniques for winning. In this paper we deduce an intelligent mathematical technique for playing a winning game. Two different starting strategies were formulated; center starting and edge or vertex starting. The results were distorted into a 3x3 matrix and elementary row operations were performed to establish all possible wins. MatLab was used to distort the matrix to determine the diagonal wins. A program was written using python in artificial intelligence (AI) to help in playing optimally
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Durrant, Jacob D. "BlendMol: advanced macromolecular visualization in Blender." Bioinformatics 35, no. 13 (November 27, 2018): 2323–25. http://dx.doi.org/10.1093/bioinformatics/bty968.
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Abstract Summary Programs such as VMD and PyMOL are excellent tools for analyzing macromolecular structures, but they do not implement many of the advanced rendering techniques common in the film and video-game industries. In contrast, the open-source program Blender is a general-purpose tool for industry-standard rendering/visualization, but its user interface is poorly suited for rigorous scientific analysis. We present BlendMol, a Blender plugin that imports VMD or PyMOL scenes into Blender. BlendMol-generated images are well suited for use in manuscripts, outreach programs, websites and classes. Availability and implementation BlendMol is available free of charge from http://durrantlab.com/blendmol/. It is written in Python. Supplementary information Supplementary data are available at Bioinformatics online.
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Mishra, Akshansh, and Tarushi Pathak. "Estimation of Grain Size Distribution of Friction Stir Welded Joint by using Machine Learning Approach." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 10, no. 1 (December 10, 2020): 99–110. http://dx.doi.org/10.14201/adcaij202110199110.
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Machine learning has widely spread in the areas of pattern recognition, prediction or forecasting, cognitive game theory and in bioinformatics. In recent days, machine learning is being introduced into manufacturing and material industries for the development of new materials and simulating the manufacturing of the required products. In the recent paper, machine learning algorithm is developed by using Python programming for the determination of grain size distribution in the microstructure of stir zone seam of Friction Stir Welded magnesium AZ31B alloy plate The grain size parameters such as an equivalent diameter, perimeter, area, orientation etc. were determined. The results showed that the developed algorithm is able to determine various grain size parameters accurately.
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Lee, Dae-Hyun. "An Alternative Approach for Implementing Interactive Media Contents - a Case Study of Teaching Computer Game Programming using Python." CONTENTS PLUS 13, no. 1 (February 27, 2015): 145–56. http://dx.doi.org/10.14728/kcp.2015.13.01.145.
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Wang, Lei, and Chang Liu. "Evolutionary Game Analysis on Government Supervision and Dairy Enterprise in the Process of Product Recall in China." International Journal of Information Systems in the Service Sector 12, no. 1 (January 2020): 44–66. http://dx.doi.org/10.4018/ijisss.2020010104.
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On the basis of stating recall and regulation mode, this paper analyzes long-term evolutionary trend between dairy enterprise and government supervision on bounded rationality with evolutionary game. The authors use Python matplotlib to simulate research results. Studies show that it is helpful to build a standard recall system of defect and dairy products. This system should reduce the costs of government supervision. In addition, in case of mandatory recall, it should strengthen punishment intensity of the government supervision branch on dairy enterprise, increase more losing costs of dairy enterprise, and decrease external environment benefits of dairy enterprise. In case of voluntary recall, the system should encourage various strategies and subsidy of the government supervision branch on dairy enterprise and amplify social influence of dairy enterprise. Especially, the paper puts forward detailed strategies for dairy enterprise.
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Steinmaurer, Alexander, Johanna Pirker, and Christian Gütl. "sCool – Game-Based Learning in Computer Science Class: A Case Study in Secondary Education." International Journal of Engineering Pedagogy (iJEP) 9, no. 2 (April 15, 2019): 35. http://dx.doi.org/10.3991/ijep.v9i2.9942.
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In this paper we present a case study of the mobile learning game sCool [1]. Based on previous work presented Steinmaurer et. al. (2018) [2] we expand-ed our study by a second experiment and additional aspects. sCool is a mul-ti-platform game that should encourage children learning computational thinking and coding in Python. The learning content is highly adaptable; therefore, educators can create own courses, individual for the needs of the students. These courses involve a concept-learning and a practical mode. First, the students learn a certain concept and in a second step, they have to apply it in a practical task. For this purpose, we created a course to teach some basic programming concepts. Two student groups of different school types participated in class as a formal learning activity. In this paper we pre-sent the results of the evaluation of sCool in coding classes. Therefore, we focus on the performance, game engagement, emotions and the girls’ percep-tion. Within this study we found out, that the students are interested in learn-ing to code but do have problems to transfer the learned content to similar fields. We also found out, that there are slightly differences in the perfor-mance of the different types of students in terms of gender and school type.
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Miler, Ryszard K., Andrzej Kuriata, Anna Brzozowska, Akram Akoel, and Antonina Kalinichenko. "The Algorithm of a Game-Based System in the Relation between an Operator and a Technical Object in Management of E-Commerce Logistics Processes with the Use of Machine Learning." Sensors 21, no. 15 (August 3, 2021): 5244. http://dx.doi.org/10.3390/s21155244.
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Machine learning (ML) is applied in various logistic processes utilizing innovative techniques (e.g., the use of drones for automated delivery in e-commerce). Early challenges showed the insufficient drones’ steering capacity and cognitive gap related to the lack of theoretical foundation for controlling algorithms. The aim of this paper is to present a game-based algorithm of controlling behaviours in the relation between an operator (OP) and a technical object (TO), based on the assumption that the game is logistics-oriented and the algorithm is to support ML applied in e-commerce optimization management. Algebraic methods, including matrices, Lagrange functions, systems of differential equations, and set-theoretic notation, have been used as the main tools. The outcome is a model of a game-based optimization process in a two-element logistics system and an algorithm applied to find optimal steering strategies. The algorithm has been initially verified with the use of simulation based on a Bayesian network (BN) and a structured set of possible strategies (OP/TO) calculated with the use of QGeNie Modeller, finally prepared for Python. It has been proved the algorithm at this stage has no deadlocks and unforeseen loops and is ready to be challenged with the original big set of learning data from a drone-operating company (as the next stage of the planned research).
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Guo, Hangxin, Zheng Liu, Bin Hu, Huidan Lin, Lihua Shi, and Cuicui Meng. "Pricing and Coordination Mechanism of Supply Chain considering Product Recycling under Asymmetric Power." Mathematical Problems in Engineering 2021 (March 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/5579655.
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The concept of “green supply chain” puts forward new requirements for the recycling management of waste products. The waste electronic products are taken as the research object, and a two-stage closed-loop supply chain composed of a manufacturer, a retailer, and consumers is considered in this paper. By constructing a Stackelberg game model, the pricing strategy and profit distribution of the supply chain under different decision modes considering asymmetric powers are studied. Finally, Python is used for simulation. The results show that the gross profit of supply chain under decentralized decision is always lower than that under centralized decision-making, and asymmetric power has an impact on the pricing decision-making of forward supply chain and reverse supply chain; compared with transfer cost, wholesale price has a greater impact on coordination effect.
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Yegoshyna, G. A., S. M. Voronoy, and A. A. Ovdieichuk. "LEARNING SYSTEM DESIGN FOR GAME APPLICATIONS." Proceedings of the O.S. Popov ОNAT 1, no. 2 (December 31, 2020): 82–91. http://dx.doi.org/10.33243/2518-7139-2020-1-2-82-91.
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The presented paper investigates the problem of designing a learning system for agents in intelligent game applications based on Unity Game Engine and reinforcement machine learning algorithms. Modern trends in the game applications development are characterized by the active using of the concept of an intelligent agent as a behavior model of an active element in various situations with applying various strategies for interactions with other active elements and the environment. In recent years, there have been a significant number of advances in this area, such as DeepMind and the Deep Q learning architecture, the winning of the Go Game Champion with AlphaGo, OpenAI and PPO. Unity developers have implemented a support for machine learning and, in particular, for deep reinforcement learning in order to create a deep reinforcement learning the SDK (Software Development Kit) for game and simulation developers. With Unity and ML-Agents toolkits we can create physically, visually, and cognitively rich environments, including ones for evaluating new algorithms and strategies. However, learning system design for agents in Unity ML-Agents is possible only by using the Python API. The possibility of a learning system design for agents in the Flappy Bird game application based on the Unity Game Engine with using its own environment is discussed in this paper. Separately, the paper highlights typical features of the Flappy Bird gaming application environment. The environment can be implemented as a fully observable environment or a partially observable environment. The fully observable environment is suggested to be used due to all environment states in this case are seen in the playfield. Thus, the problem of strategy formation is considered as a Markov decision-making process and the agent directly observes the current state of the environment. Temporal Difference Learning is used as a learning method; it involves the assessment of a reward at each stage. Two separate environments, deterministic and stochastic, have been implemented, that allows to conduct further research and evaluation of strategy formation algorithms.
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Bandyopadhyay, Anjan, Vikash Kumar Singh, Sajal Mukhopadhyay, Ujjwal Rai, Fatos Xhafa, and Paul Krause. "Matching IoT Devices to the Fog Service Providers: A Mechanism Design Perspective." Sensors 20, no. 23 (November 26, 2020): 6761. http://dx.doi.org/10.3390/s20236761.
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In the Internet of Things (IoT) + Fog + Cloud architecture, with the unprecedented growth of IoT devices, one of the challenging issues that needs to be tackled is to allocate Fog service providers (FSPs) to IoT devices, especially in a game-theoretic environment. Here, the issue of allocation of FSPs to the IoT devices is sifted with game-theoretic idea so that utility maximizing agents may be benign. In this scenario, we have multiple IoT devices and multiple FSPs, and the IoT devices give preference ordering over the subset of FSPs. Given such a scenario, the goal is to allocate at most one FSP to each of the IoT devices. We propose mechanisms based on the theory of mechanism design without money to allocate FSPs to the IoT devices. The proposed mechanisms have been designed in a flexible manner to address the long and short duration access of the FSPs to the IoT devices. For analytical results, we have proved the economic robustness, and probabilistic analyses have been carried out for allocation of IoT devices to the FSPs. In simulation, mechanism efficiency is laid out under different scenarios with an implementation in Python.
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Su, Yi, Wen Guo, and Zaoli Yang. "Reverse Knowledge Transfer in Cross-Border Mergers and Acquisitions in the Chinese High-Tech Industry under Government Intervention." Complexity 2021 (January 4, 2021): 1–18. http://dx.doi.org/10.1155/2021/8881989.
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The high-tech industry is the main force promoting the development of China’s national economy. As its industrial economic strength grows, China’s high-tech industry is increasingly using cross-border mergers and acquisitions (CBM&A) as an important way to “go out.” To explore the rules governing the process and operation mechanism of reverse knowledge transfer (RKT) through the CBM&A of China’s high-tech industry under government intervention, a tripartite evolutionary game model of the government, the parent company, and the subsidiary as the main subjects is constructed in this paper. The strategies adopted by the three subjects in the RKT game process are analysed, and the factors influencing RKT through CBM&A under government intervention are simulated and analysed using Python 3.7 software. The results show that, under government intervention, the parent company and subsidiary have different degrees of influence on each other. Subsidiaries are highly sensitive to the compensation rate of RKT. Positive intervention by the government tends to foster stable cooperation between the parent company and the subsidiary. However, over time, the government gradually relaxes its intervention in the RKT and innovation of multinational companies.
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Song, Yuhang, Andrzej Wojcicki, Thomas Lukasiewicz, Jianyi Wang, Abi Aryan, Zhenghua Xu, Mai Xu, Zihan Ding, and Lianlong Wu. "Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7253–60. http://dx.doi.org/10.1609/aaai.v34i05.6216.
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Learning agents that are not only capable of taking tests, but also innovating is becoming a hot topic in AI. One of the most promising paths towards this vision is multi-agent learning, where agents act as the environment for each other, and improving each agent means proposing new problems for others. However, existing evaluation platforms are either not compatible with multi-agent settings, or limited to a specific game. That is, there is not yet a general evaluation platform for research on multi-agent intelligence. To this end, we introduce Arena, a general evaluation platform for multi-agent intelligence with 35 games of diverse logics and representations. Furthermore, multi-agent intelligence is still at the stage where many problems remain unexplored. Therefore, we provide a building toolkit for researchers to easily invent and build novel multi-agent problems from the provided game set based on a GUI-configurable social tree and five basic multi-agent reward schemes. Finally, we provide Python implementations of five state-of-the-art deep multi-agent reinforcement learning baselines. Along with the baseline implementations, we release a set of 100 best agents/teams that we can train with different training schemes for each game, as the base for evaluating agents with population performance. As such, the research community can perform comparisons under a stable and uniform standard. All the implementations and accompanied tutorials have been open-sourced for the community at https://sites.google.com/view/arena-unity/.
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Donnelly, Jim. "Giving Away the Game – Scattershot Notes on Social Class and Other Afflictions." Journal of Working-Class Studies 6, no. 1 (June 29, 2021): 89–98. http://dx.doi.org/10.13001/jwcs.v6i1.6443.
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One of my earliest jobs was driving for an unregulated car service in New York. In the days before Uber they were called ‘gypsy cabs.’ One night I found myself on the business end of a revolver. Telling the tale to my dispatcher next day, he was staggeringly nonplussed. ‘Ya gotta put up with a lot,’ he said, ‘when you’re tryin’ t’ get ahead.’ ‘Yeah,’ I replied, disgusted, ‘even gettin’ your head blown off.’ Some time later, another driver, an African-American in a similar scenario, didn’t make it, emphasizing how much higher the stakes for a person of color. These are the real wages of work, I thought, and the rules of the game.
My dispatcher’s nonchalance bespoke how invested in the game he was; in a set of beliefs, assumptions, and animating myths that keep the wheel of fortune going. Like the Monty Python skit about the collapsing tower, if too few invest in those myths, the entire edifice crumbles.
The following is a personal essay that attempts to navigate the game’s parameters - social class, aspiration, and its attendant neurosis - and the myths that animate such notions as ‘getting ahead,’ ‘climbing the ladder,’ and the ‘American Dream,’ my country’s main (ideological) export. The approach is less theory-driven than empirical, phenomenological. Hence the numbered sections, a style popularized by Wittgenstein, Herbert Read and others. Here it doesn’t represent chronology so much as the elusive, episodic nature of the beast.
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Garrison, Stephanie, and Claire Wallace. "Media Tourism and Its Role in Sustaining Scotland’s Tourism Industry." Sustainability 13, no. 11 (June 2, 2021): 6305. http://dx.doi.org/10.3390/su13116305.
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Popular media, including films, television, comics, videogames, and books, are an increasingly important aspect of contemporary tourism. This is especially the case in Scotland, where popular culture led to the development of Scotland’s tourism industry. In this article, we will describe the phenomenon of media-related tourism in Scotland with respect to three selected case studies within Scotland: First, Glenfinnan Viaduct, made famous by the Harry Potter film series; Second, Doune Castle, used as a set for Monty Python, Game of Thrones and more recently, Outlander; Third, Abbotsford, home of Sir Walter Scott, a classical novelist now celebrating his 250th Birthday Anniversary. In examining these case studies, the article will consider how sustainable media tourism is. This approached is from the lens of media tourism and its impact on rural communities, concerns over local infrastructure, wider understandings of media tourism as a growing sub-sector, and the sustainability of the wider Scottish tourism industry in relation to the coronavirus pandemic.
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Ulrich, Thomas A., Ronald L. Boring, Timothy McJunkin, and Aunshul Rege. "A visualization approach to performing task analysis of time series event log data from a microworld simulation." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 1867–71. http://dx.doi.org/10.1177/1071181319631534.
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This paper presents a novel visualization technique to perform a task analysis using only information from data logs collected from users interacting with the system. Traditional task analysis includes data acquired from system specifications, observations of the user interacting with the system, and interviews with the user. There are however situations, in which this information is not available such as forensic investigations. In situations in which these other data sources are available, the visualization approach provides an additional tool the analysist can use to further their understanding of the system and begin qualitatively and quantitatively describing a user’s behavior. As a demonstration of this technique, data logs from a microworld simulator, the Grid Game, were analyzed using a custom designed python based graphing tool. The Grid Game is a gamified and simplified simulation of an electric microgrid. Teams of players compete by maintaining and optimizing their grid configuration in the presence of cyber-attacks. Several different visualization graphing techniques were examined to identify their suitability for representing the grid event data in a format capable to support an analyst performing a task analysis. The most suitable graphing technique for this particular use case was a discrete event graphing technique including a common time dimension to provide performance based context to the graphical depiction of logged simulation events.
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Yun, Heuijee, and Daejin Park. "Virtualization of Self-Driving Algorithms by Interoperating Embedded Controllers on a Game Engine for a Digital Twining Autonomous Vehicle." Electronics 10, no. 17 (August 30, 2021): 2102. http://dx.doi.org/10.3390/electronics10172102.
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Computer simulation based on digital twin is an essential process when designing self-driving cars. However, designing a simulation program that is exactly equivalent to real phenomena can be arduous and cost-ineffective because too many things must be implemented. In this paper, we propose the method using the online game GTA5 (Grand Theft Auto5), as a groundwork for autonomous vehicle simulation. As GTA5 has a variety of well-implemented objects, people, and roads, it can be considered a suitable tool for simulation. By using OpenCV (Open source computer vision) to capture the GTA5 game screen and analyzing images with YOLO (You Only Look Once) and TensorFlow based on Python, we can build a quite accurate object recognition system. This can lead to writing of algorithms for object avoidance and lane recognition. Once these algorithms have been completed, vehicles in GTA5 can be controlled through codes composed of the basic functions of autonomous driving, such as collision avoidance and lane-departure prevention. In addition, the algorithm tested with GTA5 has been implemented with a programmable RC car (Radio control car), DonkeyCar, to increase reliability. By testing those algorithms, we can ensure that the algorithms can be conducted in real time and they cost low power and low memory size. Therefore, we have found a way to approach digital twin technology one step more easily.
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Kroustalli, Chrysoula, and Stelios Xinogalos. "Studying the effects of teaching programming to lower secondary school students with a serious game: a case study with Python and CodeCombat." Education and Information Technologies 26, no. 5 (May 31, 2021): 6069–95. http://dx.doi.org/10.1007/s10639-021-10596-y.
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Waithe, Dominic, Mathias P. Clausen, Erdinc Sezgin, and Christian Eggeling. "FoCuS-point: software for STED fluorescence correlation and time-gated single photon counting." Bioinformatics 32, no. 6 (November 20, 2015): 958–60. http://dx.doi.org/10.1093/bioinformatics/btv687.
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Abstract Motivation: Fluorescence Correlation Spectroscopy (FCS) is a popular tool for measuring molecular mobility and how mobility relates to molecular interaction dynamics and bioactivity in living cells. The FCS technique has been significantly advanced by its combination with super-resolution STED microscopy (STED-FCS). Specifically, the use of gated detection has shown great potential for enhancing STED-FCS, but has also created a demand for software which is efficient and also implements the latest algorithms. Prior to this study, no open software has been available which would allow practical time-gating and correlation of point data derived from STED-FCS experiments. Results: The product of this study is a piece of stand-alone software called FoCuS-point. FoCuS-point utilizes advanced time-correlated single-photon counting (TCSPC) correlation algorithms along with time-gated filtering and innovative data visualization. The software has been designed to be highly user-friendly and is tailored to handle batches of data with tools designed to process files in bulk. FoCuS-point also includes advanced fitting algorithms which allow the parameters of the correlation curves and thus the kinetics of diffusion to be established quickly and efficiently. Availability and implementation: FoCuS-point is written in python and is available through the github repository: https://github.com/dwaithe/FCS_point_correlator. Furthermore, compiled versions of the code are available as executables which can be run directly in Linux, Windows and Mac OSX operating systems. Contact: dominic.waithe@imm.ox.ac.uk
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MF Amin, R., R. FA Yasmin, A. N. Azlina, AB Zanariah, and Y. Faridah. "Intelligent home automated system." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 2 (August 1, 2019): 733. http://dx.doi.org/10.11591/ijeecs.v15.i2.pp733-742.
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<p>Home automation system is one of the intelligent systems meant to create new opportunities for industry and business, as well as new experiences for users and consumers. This project is about designing, developing and testing a web-based intelligent home automation system called i-Home. The main users will be residents in any residential area i.e. gated community, terrace housing area, condominium and others. Its function is to ease residents in controlling the lights, fans, air conditioning, the house temperature and monitoring their CCTVs through online. For the hardware component Raspberry Pi was used due to its low cost, credit card-sized single-board computer that includes a processor, GPU, RAM and MicroSD as the server. Raspberry Pi primarily uses Linux-kernel-based operating system. It also requires PHP, JavaScript, Python and MySQL. The prototype of the system was built and performed most of the functions of home automation system. Usability and functional testing were carried out and both results showed that all functions worked perfectly.</p>
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Gu, Bonwoo, and Yunsick Sung. "Enhanced Reinforcement Learning Method Combining One-Hot Encoding-Based Vectors for CNN-Based Alternative High-Level Decisions." Applied Sciences 11, no. 3 (February 1, 2021): 1291. http://dx.doi.org/10.3390/app11031291.
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Gomoku is a two-player board game that originated in ancient China. There are various cases of developing Gomoku using artificial intelligence, such as a genetic algorithm and a tree search algorithm. Alpha-Gomoku, Gomoku AI built with Alpha-Go’s algorithm, defines all possible situations in the Gomoku board using Monte-Carlo tree search (MCTS), and minimizes the probability of learning other correct answers in the duplicated Gomoku board situation. However, in the tree search algorithm, the accuracy drops, because the classification criteria are manually set. In this paper, we propose an improved reinforcement learning-based high-level decision approach using convolutional neural networks (CNN). The proposed algorithm expresses each state as One-Hot Encoding based vectors and determines the state of the Gomoku board by combining the similar state of One-Hot Encoding based vectors. Thus, in a case where a stone that is determined by CNN has already been placed or cannot be placed, we suggest a method for selecting an alternative. We verify the proposed method of Gomoku AI in GuPyEngine, a Python-based 3D simulation platform.
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Ivan, Jeremias, Rizky Nurdiansyah, and Arli Aditya Parikesit. "Mathematical Problem Solving: One Way to Prevent Dementia." Frontiers in Health Informatics 8, no. 1 (May 13, 2019): 10. http://dx.doi.org/10.30699/fhi.v8i1.179.
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Introduction:Dementia is a common medical condition of older people which is marked by the decline of multiple cognitive abilities, such as memory and communication. Currently, there is no effective treatment for curing dementia, making prevention the most priority to this disease. Previous studies showed that cognitive ability training, such as mathematical problem solving, has a potential to slow down cognitive decline. The aim of this project is to create a simple yet interactive mathematical quiz as a way to train one’ cognitive ability and reduce the risk of getting dementia.Material and Methods: The quiz was created by using tkinter module and its built-in functions in Python programming language.Results: The result showed that the quiz was simple but involved an active role of the user to input the answer. It also did not have certain time limit, preventing the user to feel rushed or burdened in doing the quiz. In addition, three different types of difficulty were provided to give a challenging atmosphere to the game. Conclusion:As a conclusion, this quiz provides a simple way for people to regularly train their cognitive skill, so the risk of getting dementia, especially in elderly stage, can be reduced.
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Su, Yumin, Jianfeng Lin, Dagang Zhao, Chunyu Guo, Chao Wang, and Hang Guo. "Real-Time Prediction of Large-Scale Ship Model Vertical Acceleration Based on Recurrent Neural Network." Journal of Marine Science and Engineering 8, no. 10 (October 4, 2020): 777. http://dx.doi.org/10.3390/jmse8100777.
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In marine environments, ships are bound to be disturbed by several external factors, which can cause stochastic fluctuations and strong nonlinearity in the ship motion. Predicting ship motion is pivotal to ensuring ship safety and providing early warning of risks. This report proposes a real-time ship vertical acceleration prediction algorithm based on the long short-term memory (LSTM) and gated recurrent units (GRU) models of a recurrent neural network. The vertical acceleration time history data at the bow, middle, and stern of a large-scale ship model were obtained by performing a self-propulsion test at sea, and the original data were pre-processed by resampling and normalisation via Python. The prediction results revealed that the proposed algorithm could accurately predict the acceleration time history data of the large-scale ship model, and the root mean square error between the predicted and real values was no greater than 0.1. The optimised multivariate time series prediction program could reduce the calculation time by approximately 55% compared to that of a univariate time series prediction program, and the run time of the GRU model was better than that of the LSTM model.
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Guntuboina, Chakradhar, Aditya Porwal, Preet Jain, and Hansa Shingrakhia. "Video Summarization for Multiple Sports Using Deep Learning." ELCVIA Electronic Letters on Computer Vision and Image Analysis 20, no. 1 (May 27, 2021): 99–116. http://dx.doi.org/10.5565/rev/elcvia.1286.
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This paper proposes a computationally inexpensive method for automatic key-event extraction and subsequent summarization of sports videos using scoreboard detection. A database consisting of 1300 images was used to train a supervised-learning based object detection algorithm, YOLO (You Only Look Once). Then, for each frame of the video, once the scoreboard was detected using YOLO, the scoreboard was cropped out of the image. After this, image processing techniques were applied on the cropped scoreboard to reduce noise and false positives. Finally, the processed image was passed through an OCR (Optical Character Recognizer) to get the score. A rule-based algorithm was run on the output of the OCR to generate the timestamps of key-events based on the game. The proposed method is best suited for people who want to analyse the games and want precise timestamps of the occurrence of important events. The performance of the proposed design was tested on videos of Bundesliga, English Premier League, ICC WC 2019, IPL 2019, and Pro Kabaddi League. An average F1 Score of 0.979 was achieved during the simulations. The algorithm is trained on five different classes of three separate games (Soccer, Cricket, Kabaddi). The design is implemented using python 3.7.
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Halim, Zulkifli, Shuhaida Mohamed Shuhidan, and Zuraidah Mohd Sanusi. "Corporation financial distress prediction with deep learning: analysis of public listed companies in Malaysia." Business Process Management Journal 27, no. 4 (February 19, 2021): 1163–78. http://dx.doi.org/10.1108/bpmj-06-2020-0273.
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PurposeIn the previous study of financial distress prediction, deep learning techniques performed better than traditional techniques over time-series data. This study investigates the performance of deep learning models: recurrent neural network, long short-term memory and gated recurrent unit for the financial distress prediction among the Malaysian public listed corporation over the time-series data. This study also compares the performance of logistic regression, support vector machine, neural network, decision tree and the deep learning models on single-year data.Design/methodology/approachThe data used are the financial data of public listed companies that been classified as PN17 status (distress) and non-PN17 (not distress) in Malaysia. This study was conducted using machine learning library of Python programming language.FindingsThe findings indicate that all deep learning models used for this study achieved 90% accuracy and above with long short-term memory (LSTM) and gated recurrent unit (GRU) getting 93% accuracy. In addition, deep learning models consistently have good performance compared to the other models over single-year data. The results show LSTM and GRU getting 90% and recurrent neural network (RNN) 88% accuracy. The results also show that LSTM and GRU get better precision and recall compared to RNN. The findings of this study show that the deep learning approach will lead to better performance in financial distress prediction studies. To be added, time-series data should be highlighted in any financial distress prediction studies since it has a big impact on credit risk assessment.Research limitations/implicationsThe first limitation of this study is the hyperparameter tuning only applied for deep learning models. Secondly, the time-series data are only used for deep learning models since the other models optimally fit on single-year data.Practical implicationsThis study proposes recommendations that deep learning is a new approach that will lead to better performance in financial distress prediction studies. Besides that, time-series data should be highlighted in any financial distress prediction studies since the data have a big impact on the assessment of credit risk.Originality/valueTo the best of authors' knowledge, this article is the first study that uses the gated recurrent unit in financial distress prediction studies based on time-series data for Malaysian public listed companies. The findings of this study can help financial institutions/investors to find a better and accurate approach for credit risk assessment.
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Zheng, Junjun, Mingyuan Xu, Ming Cai, Zhichao Wang, and Mingmiao Yang. "Modeling Group Behavior to Study Innovation Diffusion Based on Cognition and Network: An Analysis for Garbage Classification System in Shanghai, China." International Journal of Environmental Research and Public Health 16, no. 18 (September 11, 2019): 3349. http://dx.doi.org/10.3390/ijerph16183349.
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In real life, garbage has caused great pollution to the environment. A garbage classification system is an effective way to manage this issue, and is an innovation in Shanghai, China. Innovation diffusion is the topic of this paper. This study uses a mathematical statistics method to formulate individual bounded rationality, and uses the specific graph structure of a scale-free network to characterize group structure. Then, a model of group behavior is constructed and the simulation experiment is run on the Python platform. The results show that: (1) In the case of general cognitive ability and high value innovation, most individuals in the group will accept the innovation in the process of innovation dissemination in a garbage classification system after several rounds of the game; (2) it is more helpful to improve the cognitive ability of individuals and the true value of innovation for the diffusion of innovation; and (3) the larger a group, the greater the scope of innovation diffusion and the more time is needed. It is helpful to expand the scope and reduce the time of innovation diffusion by increasing connections among individuals. The innovation of this study is the characterization of individual bounded rationality, which has a certain theoretical value. Meanwhile, the research results of this paper have important practical significance for the promotion of garbage classification, which can be used to popularize the concept of garbage classification.
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Lakhno, Valerii, Berik Akhmetov, Borys Husiev, Andrii Blozva, Dmytro Kasatkin, and Tetiana Osypova. "PROCEDURES FOR INVESTMENT IN CYBER SECURITY, TAKING INTO ACCOUNT MULTIFACTORITY AND FUZZY STATEMENT." Cybersecurity: Education, Science, Technique 3, no. 11 (2021): 6–15. http://dx.doi.org/10.28925/2663-4023.2021.11.615.
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It is shown that the application of multi-step quality games theory allows financing of various information technologies considering various factors. In particular, there are lots of approaches to building effective information security systems in the enterprise. Using such model will make it possible to develop, based on game models, decision support systems (DSS), for example, software products (PP). Which, in turn, will allow making rational decisions on investing in the development of such technologies. This circumstance makes it necessary and relevant to develop new models and software products that can implement decision support procedures in the process of finding rational investment strategies, including in information security field of enterprises, and obtaining forecast assessment for feasibility of a specific strategy. The model proposed by us is based on analysis of financing process by investors in information technology for protecting information tasks for the case of their multi-factoring in fuzzy setting. The investment process management model is proposed, using the example of investing in the information security of informatization objects taking into account multi-factoring and in fuzzy setting for DSS computational core. The difference between the model and previously developed ones is that it considers the investment process as complex structure, for which it is not enough to model it as a single-factor category. Computational experiments were performed for the developed model. The simulation results are visualized in the Python programming language, which allows you to optimize the procedures for investment process managing.
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Дорофеева, Ю. А., and П. Д. Ермолаев. "REVIEW, Classification AND VISUALISATION of opinion dynamics IN SOCIAL COMMUNITIES." Южно-Сибирский научный вестник, no. 4(38) (August 31, 2021): 56–61. http://dx.doi.org/10.25699/sssb.2021.38.4.009.
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В теории игр существует множество различных динамик мнений для социальных сообществ. Процессы обмена мнениями, формирование и изменение как коллективного, так и индивидуального мнений напрямую зависят от вида социального сообщества, условий взаимодействия всех участников, возможности достижения консенсуса, вида взаимосвязи между каждым из членов коллектива и других факторов. Каждая из закономерностей имеет определенные характеристики, некоторые из которых являются обобщающими. Важной задачей является численное моделирование с визуализацией процесса изменения мнений. Это позволит анализировать взаимодействие участников более эффективно. Также графическое представление позволяет рассматривать формирование новых мнений и сходимость более детально, оценивать поведение каждого участника и попытку «уклониться» от консенсуса. Другой не менее важной задачей является классификация социальных динамик по следующим признакам: сходимость мнений (достижение консенсуса); наличие порога доверия (эта характеристика позволит разбивать все сообщество на подгруппы согласно уровню доверия участников друг к другу); виды взаимодействия между участниками (парное и групповое). Оба этих аспекта позволяют систематизировать наиболее «популярные» закономерности, а также наглядно и обстоятельно рассмотреть процесс изменения мнений участников и сходимость их мнений. В работе рассматриваются различные динамики мнений внутри социальных сообществ. Выбор социальных динамик, представленных в данном исследовании, обусловлен двумя критериями. Первым является цитируемость научных работ с их описанием в поисковых системах и научных базах. Вторым - техническая возможность визуализации на языке программирования Python. There is a bunch of different opinion dynamics for social communities in game theory. The processes of opinions exchange, the formation and change of both collective and individual opinions directly depend on the social community type, the participants interaction conditions, the consensus possibility, the relationship type between each of the team members and other factors. Every regularity has certain characteristics, some of them are generalizing. An important task is numerical modeling with visualization of the changing opinions process. This will allow to analyze the interaction of participants more effectively. Also, the graphical presentation allows us to consider the formation of new opinions and convergence in more detail, to evaluate the behavior of each participant and an attempt to “avoid” the consensus. Another important task is the classification of social dynamics according to the following criteria: convergence of opinions (the achievability of consensus); the trust threshold presence (this characteristic will allow the entire community to be divided into subgroups according to the trust level of the participants to each other); types of interaction between participants (paired, group). Both of these aspects make it possible to systematize the most "popular" patterns, as well as to visually and thoroughly examine the changing participants’ opinions process and the convergence of their opinions. There are different opinion dynamics within social communities examined in this paper. Dynamics presented in this study were selected due to two criteria. The first one is the citation of scientific works with their description in search engines and scientific databases. The second criterion is the technical possibility of visualization in the Python programming language.
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Apaydin, Halit, Hajar Feizi, Mohammad Taghi Sattari, Muslume Sevba Colak, Shahaboddin Shamshirband, and Kwok-Wing Chau. "Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting." Water 12, no. 5 (May 24, 2020): 1500. http://dx.doi.org/10.3390/w12051500.
Full textAbstract:
Due to the stochastic nature and complexity of flow, as well as the existence of hydrological uncertainties, predicting streamflow in dam reservoirs, especially in semi-arid and arid areas, is essential for the optimal and timely use of surface water resources. In this research, daily streamflow to the Ermenek hydroelectric dam reservoir located in Turkey is simulated using deep recurrent neural network (RNN) architectures, including bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), long short-term memory (LSTM), and simple recurrent neural networks (simple RNN). For this purpose, daily observational flow data are used during the period 2012–2018, and all models are coded in Python software programming language. Only delays of streamflow time series are used as the input of models. Then, based on the correlation coefficient (CC), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency coefficient (NS), results of deep-learning architectures are compared with one another and with an artificial neural network (ANN) with two hidden layers. Results indicate that the accuracy of deep-learning RNN methods are better and more accurate than ANN. Among methods used in deep learning, the LSTM method has the best accuracy, namely, the simulated streamflow to the dam reservoir with 90% accuracy in the training stage and 87% accuracy in the testing stage. However, the accuracies of ANN in training and testing stages are 86% and 85%, respectively. Considering that the Ermenek Dam is used for hydroelectric purposes and energy production, modeling inflow in the most realistic way may lead to an increase in energy production and income by optimizing water management. Hence, multi-percentage improvements can be extremely useful. According to results, deep-learning methods of RNNs can be used for estimating streamflow to the Ermenek Dam reservoir due to their accuracy.