Reviewerly/exHarmony huggingface dataset:
Real-Time Bangla Sign Words Interpretation Uttered at Medical Emergencies

Sign language is a medium of communication using gestures which is developed for people who suffer from auditory or verbal impairment. But sign language is usually not widely familiar to common people. For this reason, a sign language interpreter is essential for translating sign word representation to regular text or voice. Though there are some research works on American Sign Language interpretation, but it is inadequate in number for other languages. This chapter illustrates real time interpretation of medical emergency relevant Bangla sign words using machine learning. Considering medical emergency situations, a real time word level interpreter is designed, and the performance is evaluated. The designed deep neural network uses Mediapipe Holistic network for feature detection from human body in transitional movement and Long short term memory (LSTM) to predict the image sequences of each sign action. The designed system provides 99% model training accuracy and 92.22% test accuracy in real time detection.
Research on the innovation path of university library management and service under the background of big data

Abstract The college library is one of the three pillars of school operation, shouldering the important task of teaching and research services, and is the center of auxiliary teaching, information sharing, academic activities, and cultural inheritance in colleges and universities. The strengths and weaknesses of library construction reflect the level of education and research of the school from one side, and its role in the school construction is self-evident. Therefore, to improve the quality of borrowing by school teachers and students and enhance the utilization rate of library resources. This paper designs a hybrid optimization recommendation algorithm based on the UserCF algorithm and Doc2Vec algorithm in big data to improve the library management and recommendation service. Finally, by summarizing the experimental results, it is concluded that the system running based on this hybrid optimization recommendation algorithm can accurately mine the data of library users’ behavioral preferences, and at the same time can efficiently and precisely recommend the information for readers’ needs. The citation of this optimization algorithm is further demonstrated to achieve the expected results.
Visual Cryptographic Shares Using Color Components

Visual cryptography is an excellent cryptographic method by which the visual information is encrypted and decrypted with or without using of computer systems depending on the human visual system. In visual cryptography techniques, the carrier (with secret information) is divided into multiple shares, in particular any one of which does not reveal any knowledge about the secret information. In this chapter, a newly color component-based visual cryptographic technique, i.e., image share formation for image and video, has been introduced. As video is a sequential amalgamation of image frames, the same has also been implemented for video. In this time, the method supports the {k, n} threshold framework. Out of n transparencies, using k number of shares, the reformation is possible. Using the image, shares data may be embedded to different shares using steganography or watermarking techniques. Furthermore, some suitable comparisons also have been performed to measure the newly developed technique.
Genetic algorithm-based analysis of heat production prediction in electronic devices

Abstract In daily production life, heat generation and dissipation of electronic devices are important issues concerning the safety of the devices. To enhance the prediction and analysis of heat production of electronic devices, this paper analyzes and studies the heat production and dissipation of electronic systems of different electronic devices by collecting their historical operating power, hot spot temperature, ambient temperature, and other data, and iteratively optimizes these data using genetic algorithms to seek the best temperature fitting curve according to the research progress of existing artificial intelligence algorithms. The experimental results show that the population genes are sufficiently optimized as the number of iterations increases. The prediction model established by the genetic algorithm has a global optimization-seeking ability, high prediction accuracy, relatively small absolute and relative errors, and a fast convergence rate. This model has practical feasibility and can play a good role in the operation and maintenance of electronic devices.
Analysis of the innovative development path of university civic education based on the era of big data

Abstract To enrich the content and form of college civic education, this paper constructs a civic education management innovation platform based on big data technology to explore the innovative development path of college civic education. Using the differentiated fusion algorithm, the information processing of college student management data is carried out, and the educational mining information of college students is identified through feature extraction. Bayesian network is introduced in the process of index weight calculation to improve the data mining efficiency to ensure the validity of obtaining the evaluation index of Civic Education. To verify the feasibility of the innovation platform of Civic Education Management based on big data technology, the application effect was tested. The analysis results show that the platform constructed in this paper can make students’ cognitive awareness of course contents reach 0.98, students’ concentration effect reach 0.89, and the overall effect of teaching feedback reach 90% in selecting teaching resources. The average test score of students in the experimental group was 8.5, and the average length range of the independent learning course was 2.78-4.629 hours. Thus, it can be seen that the platform constructed in this paper is conducive to promoting the high-quality development of Civics teaching and enhancing the effectiveness of Civics education in colleges and universities.
Fourier transform-based electronic logic information processing architecture design

Abstract To better develop the electronic information industry and make the electronic logic information processing architecture more widely used in production life. This paper uses the Fourier transform model based on the discrete Fourier transform and fast Fourier transform data simulation, for the electronic logic information architecture of single-signal and dual-signal signal spectrum analysis for simulation analysis. The electronic logic signal, whether it contains whole harmonics, non-harmonics, or attenuated DC components, can be accurately decomposed even when the system frequency deviates from 50 Hz, and the error value is guaranteed to be around 0.0016%. It is also pointed out that the presence of the attenuated DC component does not affect the results. The simulation results show that the design of electronic logic information processing architecture based on Fourier transform is better than other models under the sample data conditions. And the Fourier transform model can effectively guarantee the probability strength of logic information transfer and enhance the reliable line of results, which provides a research direction for the design of electronic logic information processing architecture.
IoT Based Coal Mine Safety and health Monitoring System

There are many sectors that depend on coal, and it is often referred to as the "Black Diamond" by many. In order to meet India's ever-increasing electricity needs, coal mining has become a national priority However, this work is hazardous and could be fatal for those who perform it. The environment poses several challenges to coal miners, including high temperatures that cause excessive sweating and can have negative health effects as well as the emission of hazardous gases, to name a few. These elements make the workplace a dangerous place for employees to be, endangering their very lives. In this research, a data transmission platform called Think speak IoT is used as a medium to pass the data. The technology is used to monitor the different parameters of human health parameter are heart rate, sweat rate, and spo2 level. These sensors are all deemed one unit and are installed in coal mines. All sensor values are constantly transmitted to the think speak IoT platform for analysis. The developed system is primarily used to rescue employees' health conditions and to guarantee workers' safety. So that appropriate actions can be done whenever a potentially dangerous situation appears.
Message from the HCW 2023 Technical Program Committee Co-Chairs

Welcome to the 32 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> HCW at IPDPS. Heterogeneity can now be found in computing systems ranging from embedded systems to multi-clouds. How to best handle this growing Heterogeneity of modern and emerging parallel and distributed computing systems represents both challenges and exciting opportunities for advancing scientific discovery as well as impactful innovations.
Developing Website-Based Information System Applications to Map PT. XYZ’s Properties Using Next.JS Framework with Haversine Method

The presence of technology in society fosters rapid innovation in the industrial sector. Information Systems in the property industry is necessary for the development of marketing and sales. The customer's attitude towards the Website Application is determined by the perceived benefits, trust and customer satisfaction with the Website which helps users to obtain information about the current property of the XYZ company and trust PT. XYZ to make predictions using location calculations with the Haversine Calculation method. Through this prediction, it is hoped that we discover the University’s closest location to the PT. XYZ property. Application specially designed to make it easier for PT. XYZ customers in server-side rendering is very useful when loading single page applications because it results in a better user experience with Next Js. This application utilizes the Geographic Information System feature supported by Google Maps equipped with a geolocation feature that can pinpoint a location based on latitude and longitude, as well as using the Haversine Formula technique in a web application which is useful for users.&#x0D; &#x0D;
Performance Evaluation of Lightweight ASCON-HASH Algorithm for IoT Devices

Lightweight cryptographic hashing algorithms are designed to be efficient and fast, making them suitable for use in resource-constrained milieus such as Internet of Things (IoT) devices, other embedded systems. These algorithms are optimized for limited computational power, memory, and bandwidth, while still providing a high level of security. Resources constrained devices typically have limited computational power, memory, and bandwidth, making it challenging to use traditional, computationally intensive cryptographic algorithms. Lightweight hashing algorithms are designed to address this challenge by providing a high level of security while being optimized for the limited resources of these devices. To verify the authenticity of messages and prevent tampering or replay attacks. Digital signatures: To provide non-repudiation and ensure that messages have not been altered in transit. Data integrity: To detect accidental or intentional modification of data. Key derivation: To generate cryptographic keys from a shared secret or password. This paper is focused on ASCON-Hash LWC, which his based-on sponge structure. Detailed study on hash function of ASCON is done and the algorithm is implemented on raspberry Pi to analyze the actual result. After performing experiments on the ASCON-Hash, it has been observed that there is possibility to optimize the algorithm by optimizing the permutation layer of hash function.
Hyperparameter Study: An Analysis of Hyperparameters and Their Search Methodology

Deep neural networks have significantly improved people’s daily lives since they were created. Deep learning provides more rational direction than humans can in almost every aspect of daily life. Though it has been likened to alchemy, developing, and training neural networks continues to be a challenging and unpredictable process. Automated hyperparameter tuning has gained popularity in both academic and commercial circles to minimize consumer technical barriers. The most important areas of hyperparameter tuning are reviewed in this work. The primary neural network model hyperparameters are introduced in the initial section, along with their significance and methods for defining the value range. The discussion continues with the most popular optimization approaches and their efficiency. Finally, the second-order optimization and its usefulness over first-order optimization are discussed in this study.
Product Review and Recommendation

Websites that compare product reviews have emerged as an essential informational resource for customers trying to make informed judgments about their purchases. Through these platforms, customers may browse user reviews and ratings, search and compare products, and base their decisions on the expertise and experiences of other shoppers. Due to the significance of user-generated material in the purchasing process, there is an increasing need for sophisticated and user-friendly product review comparison websites. With technologies like artificial intelligence, social integration, and better verification procedures, a proposed system aims to improve the user experience in order to meet this demand. As a result, users will experience a platform that is more personalized, entertaining, and outfitted with cutting-edge technologies to deliver reliable information to customers. Product review comparison websites have the ability to play an even more significant part in the decision-making process by staying on the cutting edge of technical developments and consistently enhancing the user experience. This study presents an overview of websites that compare product reviews, the suggested system, and their influence on the choice to buy.
Medical Image Processing and Machine Learning: A Study

Machine Learning Field is a rapidly growing field in the research sector since 2 decades and a growing field in the medical imaging processing sector. So, to diagnose the diseases at the early stage machine learning provides various techniques and algorithms such as supervised learning technique, unsupervised learning technique, Reinforcement learning technique, Active learning, Semi-supervised learning, Evolutionary learning and lastly deep learning. It enables machine to automatically learn data from the past experience and predict things without any need of being explicit programmed. First we need to provide input of the past data then train the data using some sort of machine learning algorithm and then build logical models and finally providing output. The main aim of these survey paper is to give brief detail regarding how machine learning can be useful in the medical sector.
Indian Sign Language Recognition: A Comparative Study

The principal form of communication for the deaf community in India is Indian Sign Language (ISL). Growing interest has been seen recently in developing systems that can automatically recognize ISL signs, which could greatly improve community interaction among hearing as well as deaf people. This paper provides a comparative analysis of various cutting-edge ISL recognition techniques in this research. They evaluate and compare the performance of these techniques on a publicly available dataset of ISL signs, utilizing several criteria, including F1-score, recall, accuracy, and precision. Our findings demonstrate that classic machine learning (ML) techniques like Support Vector Machines (SVMs) and Random Forests (RF) are outperformed by deep learning-based approaches like Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks. Furthermore, we also compare the performance of these techniques on diverse subsets of the dataset, based on the complexity and variability of the signs. Our study provides useful insights into the strengths and weaknesses of various ISL recognition techniques and can serve as a valuable resource for researchers and practitioners working in this field.
MONETARY POLICY, INFORMAL SECTOR, AND ECONOMIC GROWTH IN NIGERIA

This article examines the influence of monetary policy instruments and the size of Nigeria's informal sector on economic development from 1991 to 2020.From the ADF unit root test result, the OLS-based Auto Regressive distributed Lag is the most appropriate technique for investigating the long-and shortrun relationships among variables and the Granger Causality test to examine the causal relationship between the informal economy and monetary policies imposed by the CBN.The estimated model was evaluated using diagnostic tests (serial correlation test, normality test, and stability test).The empirical results showed that the interest rate had a negative (-0.60) and significant impact on economic growth in Nigeria in the short run.In the long run, money supply had a negative relationship with GDP with a coefficient of (-10.26).In the long run, the private sector credit and the size of the informal sector had significant positive impacts on economic growth in Nigeria.This study emphasizes the need to include informal sectors in model estimations of economies, especially in countries that engage in extensive informal activities.Using the dynamic general equilibrium model proves to be a better fit as it captures shocks and disturbances in the economy.
Research on the Precise Employment and Education Training Mode of Engineering Colleges under the Internet Big Data

The mode of employment education in colleges and universities has always been the focus of employment work in colleges and universities in China, but there are some problems in the construction of employment education mode in colleges and universities at present.Taking engineering colleges as an example, this paper explores the mode of employment education in colleges and universities under the big data of the Internet, analyzes the factors affecting its development, and puts forward solutions to the existing problems.By constructing the dual-subject education mode of "major+employment", students' training is combined with career planning.Guided by industry demand and centered on the improvement of students' ability, a set of dual-subject education system of "major+employment" and dual-subject education mode of "career planning+enterprise demand" are constructed, which combines students' personal development with career planning to achieve the purpose of improving students' employability and enhancing their professional competitiveness.
Sequential Model-based Optimization Approach Deep Learning Model for Classification of Multi-class Traffic Sign Images

Autonomous vehicles are currently gaining popularity in the future mobility ecosystem.The development of autonomous driving systems is still challenging in the research area of image processing and signal processing.Extensive research work was conducted on various traffic sign datasets.It achieved respectable results, but a robust network structure is still needed to develop to improve the traffic sign recognition (TS R) system.In this research work, there is an alternative approach to designing deep learning models, which are implemented in TS R systems.The proposed deep learning model was also tested with different datasets to obtain the generalized model.The proposed model was based on a convolutional neural network (CNN), and Bayesian Optimization optimizes the model's hyperparameters to find the best hyperparameters grid.After that, the optimized CNN model was used to classify the traffic sign images from three different datasets, including the German traffic sign recognition benchmark (GTS RB), the Belgium traffic sign classification (BTSC) dataset, and the Chinese traffic sign database, achieving the average accuracy scores of 99.57%, 99.15%, and 99.35%, respectively.
DeepCyberDetect: Hybrid AI for Counterfeit Currency Detection with GAN-CNN-RNN using African Buffalo Optimization

Modern technology has made a big contribution to the distribution of counterfeit money and the valuation of it.This paper recommends a deep learning-based methodology for currency recognition in order to extract attributes and identify money values; machine learning's binary classification task of fake currency detection.One can train a model that can distinguish between real and fake banknotes if one has enough information about actual and fake notes.The vast majority of older systems relied on hardware and techniques for image processing.Using such strategies renders identifying fake currency more challenging and inefficient.The proposed system has suggested deploying a deep convolution neural network to figure out fake currency in order to solve the aforementioned issue.By analyzing the images of the currency, our technique finds counterfeit notes.The transfer-learned convolutional neural network is trained using data sets that represent 2000 different currency notes in order to learn the unique characteristics map of the currencies.After becoming familiar with the feature map, the network is capable of real-time phoney cash detection.It is surprising how well deep learning models perform in photo classification tasks.The Deep CNN model that has been created in the proposed approach helps in the detection of the fake note without really manually extracting the properties of photographs.The model trains from the data set produced during training, letting us to identify fake currency.In multiple instances, techniques for deep learning have been shown to be more effective.Thus, deep learning is used to boost currency recognition accuracy.Among the techniques used are the African Buffalo Optimization Approach (ABO), recurrent neural networks (RNN), convolutional neural networks, generative adversarial networks (GAN) for identifying bogus notes, and classical neural networks.
Exploring traditional and advanced cloud computing architecture

This paper explores the fundamental elements of cloud computing architecture, discussing the integration of cloud computing with Big Data, the use of homomorphic encryption, and remote sensing data in cloud architecture. It also examines advanced cloud architectures, such as Hypervisor Clustering architecture, Load Balanced Virtual Server Instances architecture, Zero Downtime architecture, and Resource Reservation architecture, emphasizing their role in enhancing availability and scalability. The paper concludes by showcasing the significance of cloud computing architecture in addressing modern computational and data management challenges.