Miracle-dz/UniFAR-FTUs huggingface dataset:
Problem Identification on Cardiovascular Disease Prevention using Artificial Intelligence: A Literature Review

Cardiovascular disease (CVD) is a disorder of the blood vessels and heart categorized as a dangerous non-communicable disease worldwide by the World Health Organization (WHO). Many developed and developing countries conduct research to prevent cardiovascular disease, one of the techniques used is to implement artificial intelligence (AI) and machine learning (ML) to detect patterns and provide recommendations to the public. This study applies a systematic literature review to previous studies implementing AI and ML in the prevention of cardiovascular disease. Based on the results of the literature review, five essential things related to cardiovascular disease prevention can be identified, namely (1) High Salt Consumption, (2) High Blood Pressure, (3) Artery Calcium Score, (4) Stress Overload, and (5) Large Clinical Text Data on CVD. The findings obtained in this systematic literature review are that the causes of cardiovascular disease have been identified, and researchers have been able to provide recommendations for prevention. Still, the main problem that occurs is the lifestyle of today's people who are demanded to be fast-paced so that they ignore the guidance of experts and researchers, which is already good. In this SLR, find ways that have the potential to deal with the problems of people's lifestyles today, namely implementing loT technology and chatbots so that people can continue to live fast-paced lifestyles while still paying attention to existing cardiovascular prevention recommendations.
A review of Artificial Intelligence in Foreign Language Learning

The significance of learning a foreign language is commonly accepted as a way to improve our lives. It is, however, a fact that today's young people live and learn in an open, internet-influenced, and diverse cultural environment, potentially leading to reduced language learning engagement, partly because learning a new language in the traditional manner appears to them as outdated. In the last decade, researchers, teachers, and IT experts, have been testing the technology of Artificial Intelligence (AI) in language teaching, in order to increase interest and effectiveness in foreign language learning. This article presents a brief review of the latest attempts of implementation of AI in second language learning. The various aspects of foreign language learning, as influenced by the integration of AI systems and implementations will be presented, highlighting the possibilities of AI in foreign language learning.
Parameterization of Cloud Microphysics Based on Full Integral Moments

AbstractThis paper describes a microphysics parameterization based on integral moments of the full drop size distributions (DSDs) as opposed to a partial moments approach (sometimes referred to as Kessler-type parameterization) based on the moments integrated separately over the cloud and rain drop portion of the drop spectrum. This approach does not assume a prescribed form of a DSD but employs as model variables full moments that have clear physical meaning: drop concentration and surface area, water content, precipitation flux, and radar reflectivity. These variables can be directly measured and assimilated into the model forecast cycle without intermediate retrievals. The approach avoids division of DSDs into cloud and rain drops. This eliminates the problem of defining the threshold between these two categories and subdivision of the physical coagulation process into artificial processes of autoconversion, accretion, and self-collection. The development and testing of the parameterization was made us.
Optimizing Drug Screening with Machine Learning

Drug screening is the process by which potential drugs are identified and optimized before the selection of a candidate drug to progress to clinical trials. To find drug candidates with good pharmacokinetic properties and adequate safety in the human body, pharmaceutical researchers need to comprehensively consider the biological activity of compounds and their influence on the human body. More specifically, only when the compound has good biological activity and ADMET (i.e., absorption, distribution, metabolism, excretion, and toxicity) properties can it qualify as a drug candidate.To improve the efficiency of drug screening, we propose a drug candidate screening approach based on machine learning methods, which not only discovers appropriate compounds but also reveals the potential effects of molecular descriptor (i.e., features) values on the properties of compounds. First, an accurate prediction model is trained based on independent variables (i.e., feature values) and dependent variables (i.e., bioactivity values or ADMET properties). Second, we use a feature interpretation algorithm to pick out features with a significant impact on the dependent variables. Third, we search for the approximate optimal values of these important features and analyze their numerical ranges that are beneficial to obtaining better bioactivity and ADMET properties. Experimental results demonstrate that our scheme is accurate, efficient, and reliable.
Failure Analysis of ABS Tv Bracket Based on Abaqus Fatigue Damage Redevelopment

ABS TV bracket is the main load-bearing part to support TV. The elastic modulus, yield strength, etc. of the ABS TV bracket are crucial and are the key parameters to support TV for a long time. During transportation, the TV support will suffer from alternating fatigue damage under the action of alternating vibration. By combining the polymer fatigue hysteresis theory with the cyclic failure constitutive, the UMAT subroutine was compiled, the material subroutine of the Abaqus finite element software was developed, and the fatigue loss finite element model suitable for ABS was established. Taking the TV bracket as the research object, the failure mechanism and failure process of the TV bracket under vibration fatigue are studied. The research results of this paper provide optimization directions for product design, which help to reduce product development costs and shorten development cycles.
Adversarial Attacks in Some Recognition Models for Vietnamese Speech Classification

The last decade witnessed the tremendous development of Artificial Intelligence (AI). More and more real-life problems are involved with AI. Nonetheless, Machine Learning/Deep Learning models depend strongly on the data we feed in training/validation time. Training with noisy data was proved to cause harmful problems. This paper addresses adversarial attacks against recognition models for Vietnamese speech classification. The attacks are processes of creating audio samples that carry the original content; however, they distract the models with noises, which are hard to detect. Notably, we make attacks for different models that can be applied widely in practice to Vietnamese. This research is an essential first step in building measures to prevent such attacks in the future.
Waveform Analysis of DFT-spread OFDM with Phase Rotation and π /2-4PAM for Efficient Wireless Transmission

DFT-s-OFDM is derived from the precoding by discrete Fourier transform (DFT) before the inverse DFT modulation for orthogonal frequency-division multiplexing (OFDM) and adopted for the uplink (UL) in the 4G and 5G due to the practical peak power characteristics. On the other hand, demands to further reduce the peak power of UL OFDM signals with small computational complexity get stronger to sufficiently support the internet of things and device-to-device communications by low-cost devices. For this purpose, phase rotation schemes are promising and partially adopted in the DFT-s-OFDM systems. However, it has not been discussed well why the phase rotation enhances the PAPR reduction in the DFT-s-OFDM. This study analyzes the mechanism of the phase rotation and proposes $\pi / 2 \mathbf{-shifted }$ 4-level pulse amplitude modulation $(\pi / 2-4 \mathbf{P A} \mathbf{M})$ for the DFT-s-OFDM. Numerical experiments verify that the proposed $\pi/2-4\mathbf{PAM}$ provides equivalent PAPR performance with almost half computational complexity although the bit error rate is degraded compared to the conventional $\pi/4-\mathbf{OPSK}$.
Consumer Document Analytical Accelerator Hardware

Document scanning devices are used for visual character recognition, followed by text analytics in the software. Often such character extraction is insecure, and any third party can manipulate the information. On the other hand, near-edge processing devices are restrained by limited resources and connectivity issues. The primary factors that lead to exploring independent hardware devices with natural language processing (NLP) capabilities are latency during cloud processing and computing costs. This paper introduces a hardware accelerator for information retrieval using memristive TF-IDF implementation. In this system, each sentence is represented using a memristive crossbar layer, with each column containing a single word. The number of matching scores for the TF and IDF values was implemented using operational amplifier-based comparator accumulator circuits. The circuit is designed with a 180nm CMOS process, Knowm Multi-Stable Switch memristor model, and WOx device parameters. We compared its performance with that of a standard benchmark dataset. Variability and device-to-device related issues were also taken into consideration in the analysis. This paper concludes with implementing TF-IDF score calculation for applications such as information retrieval and text summarization.
Indonesian Traditional Food Image Recognition using Convolutional Neural Network

In the era of globalization, the popularity of traditional food has been replaced by imported food. In Indonesia, traditional food is starting to lose its meaning and popularity among various generations, including the millennial generation. There are many kinds of traditional food in Indonesia, but some people may not acknowledge the name of traditional food. In this study, an automatic recognized system was developed to classify the traditional food in Indonesia. A Convolutional Neural Network is used to provide a reliable classification of recognizing ten types of Indonesian traditional food: Asinan (Pickled Vegetable), Gado-gado (Mixed vegetables with peanut sauce), Gudeg, Kerak Telor (Egg crust), Kolak (compote), Papeda (sago porridge), Rawon (Indonesian beef soup), Sate Bandeng (Milkfish satay), Sop Buntut (Oxtail soup) and Soto (clear soup). The data set used is 20 images for each food, in total 200 images and divided into three parts training, testing, & validation as much as 70% (140 images), 15% (30 images), 15% (30 images) respectively. By using the EfficientNet architecture, the proposed system can reach an accuracy of 90%. And the implementation of this research is applied to mobile devices using TensorFlow Lite.
Phonatory Analysis on Parkinson's Disease Patients Attending Singing and Discussion Therapy (Parkinsonics) using Signal Processing Techniques

Persons with Parkinson's Disease (PD) frequently have speech and voice disorders. Regular speech therapy with a speech-language pathologist is essential to mitigate progressive symptom deterioration. Speech-related therapies, such choral singing groups are alternative approaches designed to be more naturalistic and enhance participant enjoyment. It is important to measure and quantify the effects of these therapies on the vocal features of PD patients to determine efficacy. We performed a prospective crossover study of 25 PD patients attending discussion or choral-singing groups for 12 weeks each (Parkinsonics NCT02753621). Every six weeks, each participant produced several recordings of the sustained vowels /a:/ and /e:/ at ‘normal’ and ‘maximum’ loudness. The goal was to identify if there are signal-processing-based features that can help track changes in the voice of PD patients over time. Voice features were extracted from these recordings using the Automatic Voice Condition Analysis (AVCA) library and were compared using non-parametric statistical tests. Results suggest that neither therapy caused any significant improvements in the analyzed phonatory aspects of the patients' voices. Future work should require use of connected speech to analyze articulation and comparison with a control group of participants with PD not attending any therapy to evaluate if therapy can mitigate the progressive effects of PD on the voice of patients.
A Scene Change and Noise Aware Rate Control Method for VVenC, An Open VVC Encoder Implementation

Contemporary motion picture content, consisting of scenes with different amounts of visual complexity or camera noise, represents demanding input for video encoders operating in rate control (RC) modes. This paper presents improvements to the 2-pass RC method integrated into VVenC, an open VVC encoder implementation, outlined in previous publications. We specifically introduce three extensions to our RC solution: first, frame type adaptation operating near scene cuts, along with an associated simple detector; second, rate stabilization means to allow for more reliable lookahead based 2-pass RC operation in on-the-fly encoding applications; and third, a low-complexity approach for estimating the instantaneous intensity of camera noise or film grain to avoid large variations in bit consumption when encoding individual frames in the final RC pass. Experimental evaluation confirms that these extensions significantly improve both the objective (BD rate) and subjective (visual) RC performance of VVenC especially on challenging video content.
Application and Challenges of an IoT Bird Repeller System As a result of Bird Behavior

Northern Thailand has a high market price and high demand for strawberries, which has a critical role in the local economy. Chiang Rai is the northern Thai province with the most strawberry farms and the potential for high profits for farmers. While gravitating to the harvest, pests and animals will ingest and demolish the strawberries. The majority of strawberry farms must repel an abundance of birds that try to eat strawberries, causing damage and stopping them from harvesting the required quantity of products. This work proposes to use the bird repeller system designed to address the problem of birds entering the farm and eating the produce by using an IoT system. The proposed system will monitor and repel birds throughout the day without the need for humans or farmers to repel birds inside the farm area. This work was tested with two parts of the system in an agricultural area where birds are always attacking. The first part tested the performance of the PIR motion sensor detection. The second part tested the performance of our repellent system that used sound and lights to repel birds. The results of our system had an accuracy of 91.93%.
Patch Antenna Design using Artificial Intelligence Methods for 4G/5G Applications

The advent of fifth generation wireless communications (5G), highlights the need for new priorities in research. Especially, the growing mobile networks require abilities for mas-sive connectivity, advanced security, increased network capacity and extremely low-latency. Antennas play a crucial part of this ecosystem, thus, their design is a challenging task. In this work, a Boundary - Seeking Generative Adversarial Network is trained to serve as a surrogate model for the design of a patch antenna with random slots and two operating frequencies at 2.6 GHz and 3.5 GHz, making it suitable for 4G/5G applications. The outcome of the trained model is used as an initial population for further op-timization using the biogeography-based optimization algorithm. The final design achieves $S_{11}=-37$ dB and $S_{11}=-33$ dB at 2.6 GHz and 3.5 GHz respectively.
Knowledge Extraction and Discrimination Based Calibration on Medical Imaging Classification

The calibration of modern deep learning methods is often neglected when they are applied to medical diagnosis. Meanwhile, the effectiveness of traditional calibration methods heavily relies on the size of validation set, which is not suitable for scenarios with limited medical images. To this end, we propose a knowledge extraction and discrimination model (KED), in which the discrimination network is set to generate fine-grained pseudo-labels based on the information of the training process; the classification network is trained with both binary classification and fine-grained pseudo-labels. The calibration of the model is achieved during the training process by explicit modeling. Experiments are conducted on breast MRI images based on BI-RADS assessment categories, and the results show that our model achieves the best overall calibration level and the classification accuracy is also improved compared with other calibration methods.
A Novel Single Phase Grid Connected Transformer-Less Solar Micro-Inverter Topology With Power Decoupling Capability

A novel transformer-less micro-inverter topology suitable for interfacing a 35 V, 220 W solar PV module to a single phase 220–230 V ac grid is proposed in this paper. It employs only six switches, out of which two switches operate at high frequency, three at line frequency and one switch at high frequency during the negative half cycle of the grid voltage. The micro-inverter is operated in discontinuous conduction mode of operation for all possible operating conditions to achieve high gain as well as negligible turn on losses for the high frequency switches. In this topology the negative terminal of the PV module is shorted with the grid neutral which makes the magnitude of the leakage current flow to be zero. The power decoupling between the dc and ac sides is inherent in this topology, and it is achieved without incorporating any active power decoupling circuit. Thin film capacitors are utilized to improve the reliability of the micro-inverter. The detailed analyses of the proposed micro-inverter are carried out. The effectiveness of the proposed scheme is verified by performing detailed simulation studies. A 220 W laboratory prototype of the proposed micro-inverter is fabricated, and detailed experimental studies are carried out to confirm the viability of the proposed scheme.
Teaching Mechatronic Systems based on a Physical Model of an Elevator

The article discusses some aspects of teaching mechatronic systems based on a physical model of an elevator. After introducing the major building blocks of a typical mechatronic system, state-of-the-art control approaches are discussed in the paper. They include the software-centric jump table, the hardware-oriented finite state machines, and the use of the modern meta-operating system for robots ROS. The described physical laboratory mock-up and the suggested control methods provide a solid platform for teaching mechatronics in a real or remote environment without loss of realism.
E-Warung Systems Development

The development of information technology is currently growing rapidly and has been implemented in many sectors including trade. Warung is one of the economic drivers of an area that sells basic needs, daily needs. The wa are relatively small and are located in residential areas, with extended working hours seven days a week and very limited availability of raw materials. This study aims to develop a warung information system that is integrated to all stakeholders. The method used to develop ewarung systems is the Prototype method, where this method describes customer needs in more detail. prototype is an unfinished program that provides an interface with a software flow simulation so that it looks like ready-made software. The prototype has three stages, namely Listen to Customer collects user needs, then Build/revice mock-ups, at this stage design and prototype systems are carried out, lastly Customer Test Drives/Mock-ups, Prototypes of the system are tested by users who will use the system until found specifications that match the wishes of the customer or user. In addition, to get maximum results, this study conducted observations used to find out information about business processes, culture and daily warung activities in the field. Interviews were conducted to obtain variations of authentic research results with the process of verifying and validating the data obtained and the last one being to do documentation, which is a record of every event that has occurred or can be said to be an event that has passed. The results of this study were in the form of an e-warung prototype design by obtaining results using alpha testing to respondents, namely running 15 processes and the results obtained were that the entire process was running accordingly. The next test, namely beta testing, was carried out on 5 respondents by asking 6 questions on the questionnaire that had been distributed, the results were that respondents who answered “Yes” were 83.3% and respondents who answered “No” were 16.6%. So it can be concluded that the prototype ewarung system is feasible to use and the user is quite satisfied with the prototype designed, and it is hoped that it can help the shop integrate to all stakeholders so that the shop develops and competes in the future.
Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Classification

Study on the identification and classification of fish is challenging and valuable because of its role in advancing the marine and agricultural fields. This research has benefits interms of monitoring fish populations and ecosystems in a particular area. Furthermore, this research helps monitor fish that are considered threatened or endangered so that it makes iteasier to map prohibited areas for fishing. This research aims to know performance of MobileNetV2 and VGG16 with parameter tuning process by identifying the value of batch size, epoch, learning rate, and optimizer for fish image dataset. The proposed research phase consists of five main stages, including experimental setup, dataset construction, dataset preprocessing, dataset training and modelling and evaluation. As the result, VGG16 obtained the highest accuracy value. For VGG16 without fine-tuning, the testing accuracy is 98.07%. For VGG16 with fine-tuning, the testing accuracy is 96.56%.
Convolutional Neural Network and Dropout Technique for Recognition of Thai Food Image

One of the main causes of diabetes, heart disease, and obesity in today's world is consumption behavior. To help people prevent such diseases, the consumption advice is required for behavior changes. A comparative study was proposed in this paper to find out a method for recognition of Thai food image using Convolutional Neural Network (CNN) and dropout technique to recognize the type of Thai food. CNN is used for distinguishing the complexity among Thai food images, while dropout technique is used for noise reduction. Sample used in the experiment were 16,170 images for recognizing 52 food types. The proposed method is compared with CNN without dropout, and Nu-In Net 1.1. The comparison results showed that CNN with dropout performed the best with 96.67% accuracy.
Toward a Predictive Smart Parking System in IoT-enabled Cities

One of the main traffic problems that need to be taken care of is traffic congestion, which causes many harmful consequences such as air pollution and waste of fuel. The ineffectiveness of parking vehicles is the main reason for traffic congestion due to the shortage of parking spaces and the lack of guidance information leading to spending considerable time searching for parking spaces, which causes traffic delays. In this paper, we proposed a smart parking system that can predict parking availability based on long short-term memory (LSTM) network. The system then notifies the drivers about forecast information that help drivers save time in choosing parking lots. Subsequently, we deploy a license plate recognition (LPR) mechanism on the Jetson nano developer kit that automatically recognizes the vehicle's plate at the parking lot entrance. Experimental results show that LSTM can outperform the popular time series forecasting mechanisms (AutoTS, Darts) on the Birmingham parking lot dataset, and our LPR mechanism performance can reach 47fps in license detection on the Jetson nano developer kit.