Reviewerly/exHarmony huggingface dataset:
The ensemble of neural networks to track eye movements

In today’s world, where computers and other electronic devices are in every office and home, cursor control is becoming an increasingly important task. Eye tracking can be used to control a cursor on a computer screen without using a mouse or keyboard. For this, special programs are used that track eye movements and convert them into cursor movements on the screen. This technology is useful for people with limited physical abilities capabilities that cannot use a mouse or keyboard, as well as for those who work with a computer for a long time and want to reduce the load on the muscles of the arms and neck. It allows users to control cursor on the screen, browse various pages and applications, edit documents and perform other tasks.The object of research is the process of eye pupils position recognition on images from a video stream in real-time and development of a system for using the developed algorithms for cursor control. A neural network was developed to determine the position of the eye pupils on images. The system takes video input from a webcam and determines the direction of the gaze of the pupils, allowing for cursor control. This technology is beneficial for individuals with physical disabilities who are unable to use a mouse or keyboard, as well as for those who work with computers for extended periods and wish to reduce strain on their hand and neck muscles. It allows users to control the cursor on the screen, navigate through various pages and applications, edit documents, and perform other tasks.This paper examines the fundamental principles of eye tracking technology for cursor control, its advantages and disadvantages, as well as its practical application in real-world computer control tasks. In this system, the input data will utilize images from the computer’s webcam and interpret eye movements as relative cursor displacement on the screen.The aim of this work is to create a solution that allows controlling the mouse cursor through eye tracking without the use of hands. The object of research is a program for eye tracking using a video stream from a webcam. The subject of research is the methods and algorithms used in eye tracking. The research methods involve machine learning algorithms used for image processing. The practical value lies in the fact that for people with various types of mobility impairments, using traditional mouse and keyboard for computer interaction can be 6 inconvenient or even impossible.
Air Quality Monitoring System Based on Raspberry Pi and Arduino Hardware-Programmable Platform

A system for indoor air quality monitoring based on the Raspberry Pi 4 microcomputer and the Arduino hardware-programming platform has been developed. The system reports on the state of air quality in the room, as well as influencing the state of air quality. The software tools are separated separately for the microcontroller and the microcomputer, respectively. For Raspberry, in addition to the standard development environment for Python, an additional library is used, which allows to conveniently work with infrared signal transmission for control. An information fusion algorithm was used to analyze the measured parameters. A general description of the system’s operation was carried out and the corresponding algorithm was compiled/ a study of the system was carried out.
An Investigation of Acceptance Factors in Adopting Smart ID Card App in Sumenep, Indonesia : Integrating TAM and EUCS

Like most new applications, the implementation of the SIC (Smart ID Card) application as a new staffing application in the Sumenep Regency Government also has several problems. The first problem is that not all civil servants have responded well to the implementation of this application. The second problem is that it is still unknown how the user acceptance of the implementation of the SIC Application follows the actual conditions. The third problem is that the SIC Development Team still does not know what factors need to be the focus of SIC application development in the future. This research has two objectives. The first objective is to know and explain the influence of factors in the integration of the TAM and EUCS models on user acceptance in the implementation of the SIC application. The second objective is to know and explain the recommendations that will be given to the SIC Development Team based on the results of this research. The population of this research is all civil servants in all Regional Apparatus Offices of Sumenep Regency except for sub-district offices, which totaled 2052 people. The research sample amounted to 335 people and was taken using proportionate stratified random sampling. Questionnaires were used as research instruments and distributed by drop-off delivery. This research used a quantitative approach by integrating TAM and EUCS models in a mandatory-use environment. The research was analyzed with two main tests using SmartPLS 3.2.9 and Microsoft Excel 365. The first test was the instrument test, and the second test was the hypothesis test. The results of this research were of the ten hypotheses proposed, there were seven hypotheses accepted, and three hypotheses rejected, and there were 17 recommendations that will be given to the SIC Development Team, with three priority recommendations and 14 usual recommendations.
Implementation of Activation functions using inverse delta coding LUT

Activation functions are essential components of deep neural networks (DNNs). However, the use of exponential functions and dividers for non-linearity significantly increases hardware costs. This article presents a hardware architecture for activation functions that uses inverse delta coding LUT. We implemented an inverse LUT approach by utilizing the fact that the output range is smaller than the input range. Additionally, to lower the bit-depth of LUT components, delta coding was applied, resulting in a 64.45% reduction in the size of the lookup table compared to full size LUT, with no significant decrease in accuracy.
Design of Cattle Feed Measuring System Using Loadcell and MySQL Database

In relation to cattle farming, many researchers conduct research to find solutions to problems that arise, especially with cows in the barn. Early research on the importance of clean and strong cowsheds was conducted in the early 20th century. In increasing the productivity of cattle development, another important thing is to pay attention to the food intake or feed of cows. Delays or waiting times in feeding cows can affect their growth. The information obtained indicates that each cow is expected to increase its weight by 1.5 kg. To achieve this goal, each cow is given 15 kg of feed per day, which is divided into three feedings. In each feeding, each cow will be given a ration of 5 kg, but from the observations obtained, it appears that the feed given is not really measured because no weighing is done before it is given to the cow and only uses estimates. With the creation of an automatic cow feed measurement system using an internet of things-based load cell, it can make it easier for cattle farmers to feed cows in a scheduled, measured, and monitored manner. The results of load cell calibration with standard scales give an error value of 0.5%, so the load cell scales are feasible to use as an automatic feed weight measuring tool.
A Study of Skin Visualization with Smartphones Using Deep Learning from Medical Cameras

A method is proposed to realise skin visualisation in the beauty industry using a smartphone-based consumer electronics. The smartphone is used to capture a macro image of a part of the face, while a medical camera is utilized to capture color and ultraviolet images of the same facial area. The smartphone and medical camera images are manually aligned with precision, and these images are then employed for deep learning. By utilizing a well-trained neural network, it becomes feasible to generate a UV image from the color image obtained from the smartphone. Additionally, it is also possible to convert a smartphone color image into an image that closely resembles the color image captured by a medical camera. By focusing on a specific facial region rather than the entire face, alignment becomes achievable, enabling the generation of various types of images using smartphones.
Traffic Sign Recognition Using Local Vision Transformer

Recognition of traffic signs is a crucial aspect of self-driving cars and driver assistance systems, and machine vision tasks such as traffic sign recognition have gained significant attention. CNNs have been frequently used in machine vision, but introducing vision transformers has provided an alternative approach to global feature learning. This paper proposes a new novel model that blends the advantages of both convolutional and transformer-based networks for traffic sign recognition. The proposed model includes convolutional blocks for capturing local correlations and transformer-based blocks for learning global dependencies. Additionally, a locality module is incorporated to enhance local perception. The performance of the suggested model is evaluated on the Persian Traffic Sign Dataset and German Traffic Sign Recognition Benchmark and compared with SOTA convolutional and transformer-based models. The experimental evaluations demonstrate that the hybrid network with the locality module outperforms pure transformer-based models and some of the best convolutional networks in accuracy. Specifically, our proposed final model reached 99.66% accuracy in the German traffic sign recognition benchmark and 99.8% in the Persian traffic sign dataset, higher than the best convolutional models. Moreover, it outperforms existing CNNs and ViTs while maintaining fast inference speed. Consequently, the proposed model proves to be significantly faster and more suitable for real-world applications.
Survey For Evaluating the Improvement of User's Mandarin Proficiency as a Second Language by Using Duolingo's Mobile App

The Chinese language, also commonly known as Mandarin is the second most widely spoken language with 1.10 billion speakers, trailing only behind English. In addition, Mandarin is recognized as one of the most difficult languages in the world. Nowadays, the use of language-learning applications has become prevalent, with Duolingo standing out as one of the most popular choices. This study aims to assess the effectiveness of the Duolingo application in teaching Mandarin to second language users (L2). This research will encompass evaluations of the app's user interface (UI), user experience (UX), user development, and overall improvement. The primary objective of this study is to ascertain the suitability of Duolingo as a platform for learning Mandarin and how the application’s interface impacts the learning experience of its users. The results indicate that the survey participants demonstrated significant improvement in their Mandarin skills and the UI/UX of the application received a positive rating thus contributing to effective learning. In conclusion, Duolingo is well-suited for L2 beginners aiming to grasp the basics of a new language, and consistency in learning is crucial for achieving positive advancements.
Communications disruptor in project-oriented organisations

The article is devoted to the role of communication disruptors in project-oriented organisations. The key components of the communication environment, channels, system, and cognitive aspects are analysed. Domains of communication are defined. The term disruptor is given, such as the directions of its influence. Disruptors can impact organisations, leading to misunderstandings and conflicts. Solutions are presented, which include identifying the disruptor.
A Flexible and Efficient Hardware Accelerator Architecture Design for Multiple CNN

This paper presents a novel design for a neural network accelerator that supports multiple networks and incorporates various convolution operations, including Dilated convolution and Transposed convolution. To address the SRAM data read conflicts caused by the systolic execution of convolution operations, a Forwarding mechanism is introduced to mitigate these conflicts, and its effectiveness is demonstrated through the implementation of the Enet and Unet network. This work is of significant importance in improving the computational capabilities and expanding the application range of neural network accelerators.
An Adaptive Budget and Deadline-aware Algorithm for Scheduling Workflows Ensemble in IaaS Clouds

Nowadays, cloud computing with pay-per-use infrastructures provides an ideal environment for processing large-scale scientific workflows. Workflows that are interrelated and have specific tasks in scientific applications are referred to as workflows ensemble. Mapping scientific workflow tasks based on their priorities onto computing resources while adhering to deadline and budget constraints is one of the most challenging problems in cloud computing. In this paper, we present an intelligent and adaptive algorithm for scheduling workflow ensemble with optimized resource provision under given deadline and budget constraints. The proposed method makes decisions based on workflow priorities and attempts to execute the many high-priority workflows as possible. To observe budget, deadline, and resource utilization as Quality of Service (QoS) parameters, we introduce three approaches: FastestScheduling, SchedulingWithMinCost, and GapRate analysis. By using different strategies for the main problem, an optimal scheduling map is created. To evaluate the proposed algorithm, we conduct simulations with a set of scientific workflows ensemble and present the related results. The experimental outcomes demonstrate that resource utilization is increased by using gap analysis in the public cloud while executing the best possible number of workflows with high priority under deadline and budget constraints, in comparison to the state-of-the-art approach.
Sentiment Analysis With Lipschitz Recurrent Neural Networks

At present, sentiment analysis is used in almost every sector. In medical chatbots and other healthcare domains, government agencies and other essential industries use sentiment analysis for public safety, improving the service quality of products, and other necessary purposes. A large amount of data is required to analyze and briefly overview any product, movie, or comment. This is a challenging task to determine the sentiment from plain text. However, researchers use Natural Language Processing (NLP) to do this task accurately. Our model is based on the Lipschitz Recurrent Neural Networks (LRNN), which analyzed the IMBD dataset. The ultimate goal was to reduce the vanishing gradient problem and find the best accuracy number and a good number of evaluation metrics. The LRNN is based on hidden-to-hidden matrices, which update the hidden state, and this hidden state is used to predict the sentiment with reasonable accuracy. Finally, we got 86.75 % accuracy.
Jakarta Flood Map: Map Visualization for Flood Area in Jakarta Using Spatio-Temporal Analysis

Floods are a frequent occurrence in Indonesia, particularly in DKI Jakarta. Flood disasters cause significant losses to human health, the economy, and environmental damage. This research aims to assist government data collection efforts and raise public awareness through Map Visualization of flood-prone areas in Jakarta using ArcGIS. The data processing begins with data collection and data cleaning methods applied to a dataset encompassing flood quantities from 2021 to 2022. The processed dataset is then used for modeling in ArcGIS to generate map visualizations and analyze them with the spatiotemporal analysis method. The research results are expected to effectively inform the government and community to collaborate in future flood prevention initiatives in Jakarta.
Farsi Text in Scene: A new dataset

Due to the recent advancements in computer vision and scene understanding, detection & recognition of text within scene images have attracted significant interest from both academia and industry. To develop a reliable and effective text recognition system, having access to a suitable dataset is essential. While many datasets containing English text in scene images are available, the lack of a comparable dataset for Farsi is evident. To lay the groundwork for the advancement of Farsi text detection and recognition systems in the future, a new dataset of Farsi text in scene images is introduced. Images are taken by a smartphone mainly from shop panels and advertising banners in the street. Printed texts on the images are in different fonts, sizes, and colors on diverse and complex backgrounds which give this dataset variety needed for building a robust text detection and recognition system. In total, 1182 images are collected and annotated. To validate our dataset, we trained a deep object detection model and achieved an mAP50 of 46.4% on the test set.
Crop &amp; Match: RoI Cropping and Feature Matching for Segmentation of Small Objects

In semantic segmentation, predicting RoI (Region of Interest) classes or objects within small regions faces challenges. To address this issue, we propose Crop&Match, a method to combine RoI cropping and feature matching, to enhance model performance of small RoI classes. The cropping ensures the network to focus on small RoI regions by cropping image regions to contain RoI. And feature matching aligns input images with their RoI cropped versions at a representation level, improving predictive consistency across different perspectives on the same image, thereby boosting RoI class recognition.
Real-time Translation of American Sign Language into Text Using Machine Learning

This paper involves the development of an application that translates American Sign Language (ASL) gestures into English text and speech in real-time. It utilizes a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model for video recognition, achieving a training accuracy of 94.87 % and a testing accuracy of 80.1 %. The CNN-LSTM model also demonstrates a precision of 89.0%, a recall of 84.0%, and an F1-score of 84.0%. For ASL image recognition, an Extreme Gradient Boosting (XGBoost) model is employed, achieving a training ac-curacy of 99.8% and a testing accuracy of 93.64%. The XGBoost model exhibits a precision of 93.99 %, a recall of 93.64 %, and an F1-score of 93.64 %. By incorporating these models into the application, users can interact with a web cam to capture ASL sign gestures and receive instant text and speech output. This innovative solution aims to break down communication barriers and foster effective communication between individuals who are deaf or use ASL and those who do not. Overall, the project demonstrates promising results in terms of accuracy, precision, recall, and F1-score, contributing to improved inclusivity and equal opportunities for the deaf community.
Techno-economic Feasibility Analysis of a Floating Solar Photovoltaic System in San Roque Dam, Philippines

The Philippines is heavily reliant on fossil fuels for electricity generation. Solar Photovoltaic systems present a sustainable alternative, but deploying such a system requires large land areas and high installation costs. A Floating Solar Photovoltaic (FSPV) system is an emerging alternative that can be implemented in water bodies. A techno-economic feasibility assessment of an FSPV system in 2% of the San Roque Dam's total coverage area was conducted using the System Advisory Model (SAM). The results showed that the proposed system can generate an installed capacity of 21.42MW and an annual AC energy output of approximately 32.68 GWh. The LCOE of floating photovoltaics is found to be generally lower than the market price, indicating the project's viability. The FSPV system also demonstrated its ability to generate favorable returns on investment throughout its operational lifetime, with an IRR of 13.67% and a positive NPV.
Multi-Level Speech Encryption Using Chaotic Maps

With the rapid improvement of digital communications and multimedia applications, protecting digital data, such as images, speech, and videos, has become a crucial challenge. Chaotic encryption is an important, essential, and effective encryption method for securing speech signals as the most natural communication mode. Encrypted signals using chaotic systems are noisy signals that increase the level of confusion during signal transmission over a channel. In this study, in addition to reviewing recent works on speech encryption, a novel approach for chaos-based speech encryption based on signal multi-level partitioning is proposed. Signal multi-level partitioning allows for the use of multiple mappings, which increases the number of initial parameter values. Consequently, we will have a larger key space, which will lead to increased security against various attacks. The speech signal encrypted using the approach proposed in this study is noisier than the results of previous research studies, and because of the significantly larger key space (more than three times that of previous studies), it provides higher security against attacks. It is worth mentioning that, despite the higher efficiency of the proposed encryption algorithm, its computational complexity does not affect the speed of the algorithm, and speech signal reconstruction during decryption is performed with high efficiency.
Sentiment Analysis to manage Health Crisis in Smart Cities

Sentiment analysis is a branch of natural language processing that analyzes textual data and provides insights into users’ opinions. In the context of smart cities, sentiment analysis seeks to seamlessly integrate technological innovations into people’s daily lives, fostering sustainability and enhancing overall quality of life while monitoring citizen satisfaction levels. This approach allows for more precise analysis and facilitates the implementation of strategies geared towards improving services and the general quality of life. The aim of this paper is to propose an approach for managing healthcare crises in smart cities. The proposal relies on a hybrid approach that combines LSTM for tweet classification with EHO for optimizing classification parameters. Experimental results have demonstrated the effectiveness of this proposed technique.
High-speed eye tracking based on a synchronized image acquisition mechanism by dual-ring infrared lighting source

It is a challenge for conventional monocular-camera single-light source eye tracking methods to achieve high-speed eye tracking. Human gaze motion is a high-speed and miniature eye movement. Eye tracking requires a high-speed sampling frequency. In this work, an eye tracking method was proposed to overcomes the above limitation. The dual-ring infrared lighting source was designed to achieve bright and dark pupils in high-speed. The eye tracking method used a dual-ring infrared lighting source and synchronized triggers for the even and odd camera frames to capture bright and dark pupils. A pupillary corneal reflex was calculated by the center coordinates of the Purkinje spot and the pupil. A map function was established to map the relationship between pupillary corneal reflex and gaze spots. The gaze coordinate was calculated based on the mapping function. The detection time of each frame was less than five milliseconds, which achieved the purpose of high-speed eye tracking of the human gaze.