Miracle-dz/UniFAR-FTUs huggingface dataset:
CENTRAL ASIAN JOURNAL OF LITERATURE, PHILOSOPHY AND CULTURE The Relevance of Games in Teaching a Foreign Language for Students of Non-Linguistic Universities in the Modern Educational System of Universities

: This article discusses topical issues of modern education, which have development prospects and can be widely applied in practice. They are interesting for both experienced teachers and young professionals. The article analyzes the methods of using games and their influence on the modern educational process in non-linguistic universities. We also describe methods for increasing motivation to learn language aspects, the possibility of improving the memorization of complex material, transforming it into a playful and, therefore, easier form.
Low Cost Temperature Controlled Frequency Reconfigurable Antenna with High Selectivity

This paper presents an idea to form a low cost frequency reconfigurable patch antenna based on the temperature change. The use of PET (Polyethylene Terephthalate) material in the substrate gives the flexibility to form a low cost antenna that can work for a range of frequencies. The antenna is flexible and can be used as biological sensors. The use of transparent PET material can have several advantages in terms where transparency is required. The antenna is designed to resonate in K band region. Simple patch antenna is designed with a split ring slot in between to reduce the bandwidth and improve the selectivity, as well as sensitivity of the antenna suitable for controlled reconfigurability in the frequency. At room temperature, antenna resonates at 23.8 GHz, with a gain of 6.16dBi and radiation efficiency of 92.06%. As the temperature increases from room temperature to 100°C, the frequency shifted from 23.8 GHz to 22.66 GHz. Antenna shows a dual band characteristic with equivalent significance of temperature on both the frequency.
Optimization of African LULC Database for Sustainable Development

Land use and land cover information in combination with other thematic datasets related to detailed reference spatial data in localities from an important dataset for different types of analyses in different domains. At the time being, when it comes to the strategy of the SDG, Green Deal, Destination Earth, and construction of Earth’s digital twins, there is no model and database that would effectively gather information about the Earth’s surface in sufficient detail and complex relations. The situation is much worse in Africa compared to Europe since there exist only scattered map layers from public sources across all Africa like Africover and CCI Land Cover 2016. Moreover, the other close option ‘OpenStreetMap’ with a continent-wide coverage collects data on a voluntary basis with minimal attributes. For this reason, it’s prudent to provide validation and harmonisation of this data. Therefore, there was a focus on developing and optimising a new solution based on the Open Land Use (OLU) 2.0 data model. The OLU 2.0 database combined various thematic data with the most detailed reference geometry available. Thematic datasets were focused primarily on the information of land use and land cover and additionally on other themes like soil, topographic characteristics, climatic parameters, data from classification of remote sensing data, vegetation indices of field blocks, etc. and in different time periods. In this way, OLU4Africa 2.0 defined a model which can have large potential in Africa for high-end applications such as food security modelling; environment, biodiversity, and ecosystem protection; planning purposes; forest and water protection. It is worth mentioning that the OLU4Africa 2.0 application has been nominated among WSIS Prizes 2022.
Development of an Integrated Image Acquisition Setup for Assigning Selling Price of Rough and Milled Rice (Oryza Sativa L.) in the Supply Chain

In agro-processing industries, cereal grains acquire a significant place but, many times the consistency in grain quality gets compromised during the supply chain, owing to improper and/or inefficient supervision. In the current work, an integrated computer vision setup, aided with the gravimetric principle, has been developed for assigning the best price for rough and milled rice in the supply chain hierarchy. Three local paddy varieties were classified based on physical and color features extraction, following established image processing techniques. Two unique features i.e., dry mass identifier (DMI) and head rice equivalent (HRE) were introduced for assessing inter-varietal paddy admixture, grain moisture content, and broken fractions at several size segments to achieve consistent pricing. Both features have shown a good correlation with the manual classification which is although accurate but lengthy and tedious. The devised setup can be adopted as an inexpensive, non-invasive, and non-destructive tool in rice trading.
Comparison of Reinforcement Learning Algorithms in Problems of Acquiring Locomotion Skills in 3D Space

In this paper, we compare the effectiveness of modern reinforcement learning methods in solving the problem of acquiring movement skills by an agent in three-dimensional space. Algorithms SAC, PPO, MA-POCA are considered as compared algorithms. The comparison is made in the Unity simulator using the ML-Agents package. The result of the experiments confirms the effectiveness of modern reinforcement learning algorithms in the problem of acquiring the skills of movement in space. At the same time, the Soft Actor-Critic algorithm has the highest sample efficiency, which makes it possible to use it to complete training in real environments with a model previously trained in the simulator.
A Truthful Decision Making for Divorces Using Data Mining Techniques

Divorce rates are increasing around the world. During the procedure of divorce, the couple is under a lot of emotional and mental strain. Moreover, divorce also has social and economic ramifications. Considering these points, it is important to examine and analyze the divorce phenomenon. To reduce and prevent the occurrence of future divorce cases, it is very crucial to know what are the factors which lead a couple to divorce. In our proposed work, we have primarily used four classification algorithms namely Naive Bayes, Random Forest, KNN, and GLM then further applied the data mining technique of MCA on KNN and GLM to improve the performance of divorce prediction. The models were evaluated based on Leave-One-Out-Cross-Validation (LOOCV). We have been able to achieve the least test error of 0.58% with GLM + MCA and KNN + MCA.
A Review on Swarm intelligence & Evolutionary Algorithms based Approaches for Diabetic Retinopathy Detection

Diabetic retinopathy has overtaken cataracts as the primary cause of new blindness globally. Diabetics are more likely to develop cataracts, visual loss, glaucoma, excessive intraocular pressure, and, most importantly, diabetic retinopathy (DR). If blood vessels in the retina are compromised, vision loss is irreversible. The patient may not exhibit any symptoms early on, and by the time they do, the damage has already been done. Early diabetes treatment helps to retain vision and permits a patient to see. Diabetic retinopathy is a worldwide health problem. To address the medical community’s requests for early identification of diabetes and other illnesses, several professionals have advocated a computer assisted diagnosis technique. In this work, image processing techniques and image classifiers that sort images based on the status of the disease will be used to describe automated ways to look at retinal images for important signs of diabetic retinopathy. There are compelling motivations to create retinopathy risk reduction models and strategies that can be used widely. The difficulty of acquiring accurate diabetic retinopathy at a reasonable cost needs a major investment in creating and testing computer-assisted diagnosis (CAD). This study looks at the different stages, traits, and types of models that may be used to reduce the risk of diabetic retinopathy and detect it early using Evolutionary computing and Swarm optimization.
The Performance Analysis of the FSO System Utilization in the Long Reach PON Network using the HPON Network Configurator

Free Space Optics (FSO) systems present a new possibility for supplementing optical access infrastructures to support various applications of service providers. In the near future, we can expect an expansion of next-generation passive optical networks utilizing similar or soft revised topologies as common optical access networks where an extended long reach scenario can be also expected. Therefore, an integration of FSO systems into the Long Reach Passive Optical Network (LR PON) is probably realizable. In this contribution, a presentation of the simulation program for Hybrid Passive Optical Network (HPON) configuration together with requirements for the LR PON network is introduced. Next, possible configurations of the FSO access point are presented. Subsequently, the FSO system utilization for various specific network parameters is analyzed. Finally, recommendations for possible FSO system integration into LR PON networks based on simulation results are presented.
Energy-Efficient Ultra-Dense Network with Deep Reinforcement Learning

With the explosive growth in mobile data traffic, ultra-dense network (UDN) where a large number of small cells are densely deployed on top of macro cells has received a great deal of attention in recent years. While UDN offers a number of benefits, an upsurge of energy consumption in UDN due to the intensive deployment of small cells has now become a major bottleneck in achieving the primary goals viz., 100-fold increase in the throughput in 5G+ and 6G. In recent years, an approach to reduce the energy consumption of base stations (BSs) by selectively turning off the lightly-loaded BSs, referred to as the sleep mode technique, has been suggested. However, determining an appropriate active/sleep modes of BSs is a difficult task due to the huge computational overhead and inefficiency caused by the frequent BS mode conversion. An aim of this paper is to propose a deep reinforcement learning (DRL)-based approach to achieve a reduction of energy consumption in UDN. Key ingredient of the proposed scheme is to use decision selection network to reduce the size of action space. Numerical results show that the proposed scheme can significantly reduce the energy consumption of UDN while ensuring the rate requirement of network.
Multi Tile Synchronization and Calibration of Xilinx RF SoC ZCU216 for Digital Beamforming

Beamforming is one of the powerful techniques used in modern wireless technology for transmitting and receiving signals efficiently. Transmitting signals from the antenna array with a particular phase and amplitude is crucial to forming the beam in the desired direction. However, transmitting chains contains different nonlinear components and different PLLs, which produce random phase shifts to the transmitting signals. Therefore, synchronization and calibration are needed between different transmitting paths to transmit signals with the desired phase shift. In this work, the multi-tile synchronization and calibration have been done for the latest Xilinx RF SoC consisting of 16 transmitters and 16 receiver chains. An LTE signal of 20 MHz bandwidth is used for testing. The NMSE of around −28 dBm between the different transmitting path signals has been achieved after synchronization and calibration. As a proof of concept, the digital beamforming is implemented to validate the presented synchronization and calibration of all transmitters.
Research and Implementation of an Embedded Image Classification Method Based on ZYNQ

FPGA was a programmable chip with powerful high parallel computing capabilities. Through the FPAG&ARM collaborative processing of neural networks, it can improve computing efficiency and reduce energy consumption. Aiming at a large number of matrix operation problems involved in the AlexNet network, this paper uses the OPENBLAS acceleration algorithm to optimize the matrix operation, and proposes a research and implementation of an embedded image classification method based on ZYNQ. This paper uses FPGA to realize the real-time image acquisition, takes the acquired image as the input of the convolutional neural network model, uses the AlexNet network on the ARM side to realize the classification of the image, and finally deploys the neural network model to the ZYNQ-7000 platform. The experimental results show that in the case of ensuring that the accuracy is not reduced during the image classification process, compared with using the AlexNet network to achieve image classification in 23.4s, using the OPENBLAS acceleration algorithm to accelerate the matrix calculation in the AlexNet network, the image classification consumes time is about 4.5s, and the classification performance has been improved by nearly 5.2 time.
Predicting the Trajectories of the Development of Situations from a Given Phase Based on the Logical Conclusion of the Consequences

A method is proposed for predicting the trajectories of situations from a given phase based on the logical conclusion of the consequences. The method makes it possible to solve problems arising when using other forecasting methods related to the complexity of interpretability and justification of the results obtained. The formal description of situations is carried out using first-order predicate calculus formulas, which differ in relative simplicity of interpretation. Forecasting is based on deductive logical deduction of consequences from a knowledge base containing rules for the development of the situation and facts characterizing a given phase of the situation. The method of inference of final consequences with the formation of a description of the logical inference scheme is used. The output scheme represents a long-term forecast, which in general contains several branches that determine different trajectories of the situation. The logical inference scheme clearly represents a long-term forecast and reflects the rationale for the results obtained. It is assumed that the situation develops in discrete time, the report of which begins simultaneously with the adoption of a long-term forecast. As the situation develops, new facts are received that correspond to the events taking place. It is proposed to use a series of short-term forecasts. The short-term fore-cast is made for the next moment in time in accordance with the long-term fore-cast and new facts and determines the events (facts) that are expected at the next moment of the situation development. At the same time, it is taken into account that the incoming facts may not confirm some predicted statements and lead to the exclusion of certain branches of logical inference, thus reducing the number of trajectories of the situation. An example of predicting the trajectories of the development of situations from a given phase based on the logical conclusion of the consequences is given.
Research on Iron Ore Price Prediction Based on AdaBoost-SVR

This study aims to use the support vector regression (SVR) theory, according to the nonlinear characteristics of iron ore price series fluctuation, based on the 5000 daily transaction data of iron ore in Dalian Commodity Exchange as the research object, the Adaboost -SVR iron ore price prediction model optimized by the novel BAT algorithm (NBA) is established. The model takes the maximum, minimum, closing price and trading volume of the daily transaction data as input parameters and the closing price of the next trading day as output parameters. The prediction results of the research model are compared and analyzed. The results show that the prediction value of the research model is closer to the real value, and the mean relative error (MRE) and root mean square error (RMSE) of the research model are 0.006 and 20.19, respectively, which are better than the prediction results of the traditional support vector regression model. The research model provides technical support and decision-making basis for the market monitoring and early warning of iron ore, and has advantages in accuracy compared with traditional forecasting methods.
Ensemble Learning for Coronary Heart Disease Prediction

Disease diagnosis is an important and tedious job in medicine. Nowadays, cardiovascular diseases are the major cause of death in the world, especially in the low income countries. The variation in blood pressure, blood sugar and heart rate leads to heart disease that in turn leads to narrowing of blood vessels. Therefore, diagnosing of heart disease has foreseen massive amount of attention globally. There is a huge quantity of data in healthcare sector; therefore the need is to convert this raw data into useful information. Although medical practitioners have been using the conventional clinical methods such as blood tests and electrocardiography for heart disease prediction, however computerized diagnosis systems are also in use. Some currently existing studies have applied various machine learning techniques for heart disease prediction purpose. But the performance achieved by most of the basic machine learning algorithms is not up to the mark. There are different types of heart diseases and one of them is the coronary heart disease or CHD. In this paper various ensemble machine learning classifiers have been used for coronary heart disease prediction. Ensemble classifiers combine varying set of individual models to improve the reliability and stability of individual algorithms. Ensemble classifiers combine the output from multiple classifiers to get the aggregated output with extended prediction accuracy. Ensemble classifiers that have been used in this study include adaboost, majority voting, weighted average, bagging and gradient boosting. According to the results the classifier that came up with the highest prediction accuracy was bagging ensemble classifier predicting with an accuracy of 85% on the given dataset.
The effect of pulse amplitude on the linearity of the weight update in the HfO2-based memristor

The linearity of the memristor conductivity (or weight) variation affects the training accuracy of analog neuromorphic systems. Usually, the weight update linearity can be achieved by changing the waveform, but this requires the inclusion of additional peripherals in the analog circuit. The paper considers the influence of the signal pulse amplitude on the long-term potentiation of a HfO2-based memristor. It is observed that the pulse amplitude decrease leads to the higher device's linearity. The linearity optimization procedure is shown. Additionally, we consider the effects of unusual conductance changing during consecutive cycles of long-term potentiation and long-term depression with a constant voltage amplitude. We demonstrate the possibility of a linear weight update of the device once identical pulses are applied due to unusual behavior.
An Unsupervised Based Enhanced Anomaly Detection Model Using Features Importance

Anomaly Detection, one of the important use cases of cybersecurity, allows us to know the out of distribution behavior of processes in the network which can help us to flag any kind of attack or issue on the network. Several works have been proposed in the literature that aim to detect anomalies based on supervised algorithms. However, they fail to detect zero-day attacks. Keeping this limitation in mind, in this work, we have proposed an anomaly detection model based upon features' importance. A newly introduced dataset called BETH is used for our anomaly detection research and we outperform the baseline scores using the existing Principal Component Analysis, Histogram and Clustering based approaches which performed really well with 0.96, 0.95 and 0.94 ROC-AUC scores respectively on the test set by selecting the features extracted through different approaches such as difference in metric and assignment of weights.
DDCC-based MISO type Voltage-mode First-order Universal Filter

This paper introduces a new voltage-mode first-order Universal filter (FOUF) using a single differential difference current conveyor (DDCC) along with single resistor and single capacitor. The proposed FOUF is a multiple-input single-output (MISO) type and offers low pass, high pass and all pass responses. The achieved pole frequency is 15.9 MHz. It is simulated using OrCAD Pspice with 180nm CMOS process parameters.
A New Benchmark Dataset for Egyptian License Plate Detection and Recognition

Automatic license plate detection and recognition have become an integral part of the new ecosystems to ensure safety and traffic management. It is also challenging because it depends on several factors, including the country’s design, colors, language, and texture. One of the main issues that face the deep learning models is the lack of resources and datasets, especially in the Arabic language. In this work, we propose a large-scale dataset, namely Egyptian Automatic License Plate Recognition (EALPR), to address this issue. The images are collected from social-media websites using scraping tools. After cleaning and sanity checks, the YOLO detector is used for annotation. The total number of vehicles is 2,450, including different locations, times (night and day), and backgrounds to guarantee the balance of the dataset. We also provide the detailed steps of the dataset collection and annotation. Finally, we discuss the comparison of the state-of-the-art open-source datasets. The dataset and models will be publicly available on https://github.com/ahmedramadan96/EALPR
Overview of Closed-Loop Control Systems and Artificial Intelligence Utilization in Greenhouse Farming

The increased demand for agricultural land and the more frequent occurrence of extreme weather conditions lead to increased greenhouses usage in agriculture. Greenhouses offer an efficient alternative to traditional agriculture as all environmental parameters are controlled, leading to a higher yield per land use. In older greenhouses, the different environmental factors are set manually, resulting in unfavorable climate conditions due to over- and undershoots of the factors. Optimizing environmental conditions, closed-loop control systems (CLCS) are used to reduce the susceptibility to over- and undershoots as well as to disturbance variables. The greenhouse actuators respond to various input values bringing changes to the environmental parameters. For controlling the actuators, artificial intelligence (AI) offers the potential to control the environmental parameters more accurately than a simple CLCS. With the help of predicting the influence of actuators regarding the greenhouse climate, AI-based climate systems might outperform CLCS systems and human experts. This paper provides an overview of the different architectures in which AI is used for controlling complex systems and discusses its potential for greenhouses. The results will show that AI offers the possibility for yield increase and resources saving.