Reviewerly/exHarmony huggingface dataset:
Airbag Protection and Alerting System for Elderly People

In the elderly population, falling is an important cause of severe injuries like hip injuries. Injury- or unconsciousness-related immobility implies that the affected are unable to seek assistance. Building fall detection systems is essential since it is usual for senior individuals residing alone to go many hours without being located after a fall, considerably increasing the impact of injuries caused by falls. The primary goal of this paper is to implement an airbag protection and alerting system for elderly people. The system can be installed on a waist belt as an airbag. It is connected wirelessly to the elderly person’s mobile, where an Android mobile application is created to receive alert notifications from the system. The system will detect the fall using a gravity sensor that is connected to an Arduino board. If a fall is detected by the gravity sensor, then the system will activate an air valve and inflate the airbag from the air tank. The system will also send a warning notification to the elderly person’s mobile application via Bluetooth. Then, the elderly person’s GPS location will be determined from their mobile phone and an SMS will be transmitted to a mobile phone belonging to his/her emergency contact of choice. The system is tuned to provide a proper fall detection sensitivity with good accuracy (90%). It has a relatively low cost, very quick response time (only 150 ms delay from the electrical grid), and low power consumption (11.1 Wh).
Solving quantum circuit compilation problem variants through genetic algorithms

Abstract The gate-based model is one of the leading quantum computing paradigms for representing quantum circuits. Within this paradigm, a quantum algorithm is expressed in terms of a set of quantum gates that are executed on the quantum hardware over time, subject to a number of constraints whose satisfaction must be guaranteed before running the circuit, to allow for feasible execution. The need to guarantee the previous feasibility condition gives rise to the Quantum Circuit Compilation Problem (QCCP). The QCCP has been demonstrated to be NP-Complete, and can be considered as a Planning and Scheduling problem. In this paper, we consider quantum compilation instances deriving from the general Quantum Approximation Optimization Algorithm (QAOA), applied to the MaxCut problem, devised to be executed on Noisy Intermediate Scale Quantum (NISQ) hardware architectures. More specifically, in addition to the basic QCCP version, we also tackle other variants of the same problem such as the QCCP-X (QCCP with crosstalk constraints), the QCCP-V (QCCP with variable qubit state initialization), as well as the QCCP-VX that includes both previous variants. All problem variants are solved using genetic algorithms. We perform an experimental study across a conventional set of instances taken from the literature, and show that the proposed genetic algorithm, termed $$GA_{VX}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>G</mml:mi> <mml:msub> <mml:mi>A</mml:mi> <mml:mrow> <mml:mi>VX</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> , outperforms previous approaches in the literature.
Analisis Komunikasi Virtual Dalam Dunia Roleplayer Grup RP Palembang Platform Telegram

With the title "Analysis of Virtual Communication on the Palembang World RP Group Roleplayer Telegram Platform". This study describes how virtual communication in roleplayer games among teenagers, especially teenagers in the city of Palembang. This study aims to find out how the process of roleplayer users form virtual communication and analyze virtual communication in the telegram platform roleplayer world towards members of the RP Palembang group. The theory used in this research is social science theory and new media theory. The research method used in this study is a qualitative research method. The approach used by researchers in this study is through observation obtained by conducting observations and structured interviews to become a reference for researchers to understand in depth about virtual communication in telegram platform roleplayer games. From the research results that have been obtained from the observation and documentation of researchers, the author can explain the process of forming virtual communication between roleplayer players, the types of virtual communication that occur in roleplayer games, supporting factors and inhibiting factors for virtual communication for roleplayer players and the advantages of the telegram platform in forming virtual communication in the roleplayer world of RP Group Palembang members.
Enhanced Machine Learning by a Decorated Quantum Circuit

A novel approach to improve traditional machine learning and enhance the accuracy of predictions is the use of quantum variational circuits. Recent research in this area has concentrated on incorporating the training of quantum variational circuits as a component of the model. We propose a training methodology that segregates the classical and quantum layers during training. This approach is inspired by migration science. Initially, it was suggested that the classical model would feed features into the final density layer, which may possess significant geometric differences. The experiment conducted further bolstered this theory. We verified the hypothesis by conducting four experimental training scenarios on the Cifar dataset and evaluating their accuracy.
Swarm Learning In Autonomous Driving: A Privacy Preserving Approach

Autonomous driving technology has made significant progress in recent years due to the development and implementation of cutting-edge models for computer vision and deep learning. These advances have enabled the creation of autonomous vehicles that can navigate roads and make driving decisions without the need for human intervention. However, the use of sensors and cameras in these vehicles has raised concerns about privacy, as they capture a vast amount of data, including location-specific landmarks and personally identifiable information. The identification and obfuscation of such sensitive data during preprocessing can be a costly process. To address these concerns, this paper proposes a Swarm Learning-based training approach for autonomous driving systems. Swarm Learning involves sharing model learnings across nodes rather than raw data, which can help to protect privacy. In addition to addressing privacy concerns, this approach offers performance that is comparable to traditional training methods. It also exhibits improvements over other distributed machine learning techniques such as Federated Learning. Overall, the Swarm Learning approach presents a promising solution for the development of autonomous driving systems that maintain high performance while addressing privacy concerns. By sharing model learnings rather than raw data, Swarm Learning helps to protect sensitive information and reduces the risk of privacy breaches. This approach offers a viable alternative to traditional training methods, enabling the creation of autonomous driving systems that are both effective and respectful of privacy.
Deep Learning Based Sub-Nyquist Modulation Recognition

In this paper, we design a Convolutional Neural Network (CNN) for Sub-Nyquist modulation recognition and compare the performance of Long Short-Term Memory (LSTM) network and Convolutional Long Short-term Deep Neural Network (CLDNN) respectively. Unlike conventional modulation recognition task operates with Nyquist sampled rate, the new network architectures for Sub-Nyquist modulation recognition are specifically designed with a certain number of neurons, layers, and other hyperparameters to effectively extract key features from Sub-Nyquist sampled signals and process larger volumes of data. The simulation results demonstrate that the CNN network has the best recognition accuracy of 98.01% on the GBsense dataset, followed by the CLDNN of 96.81% and LSTM of 87.51% respectively.
FPPFL: FedAVG-based Privacy-Preserving Federated Learning

Edge computing can move storage and computing tasks from cloud data centers to the edge of networks, reducing latency. However, this carries security and privacy risks. Federated learning can protect privacy by transferring training models from edge compute nodes to local devices, which then train models on local datasets. But the parameters transmitted between local devices and edge nodes may contain raw data, which can be stolen. To address this, this paper proposes a FedAVG-based privacy-preserving federated learning (FPPFL) scheme to provide low latency and privacy protection. It optimizes key generation parameters with an improved Paillier homomorphic encryption algorithm, allowing for low-complexity exponential operations in the encryption stage, shortening data transmission time and protecting model parameters, while maintaining accuracy. Experiments demonstrate the improved Paillier algorithm achieves the same security level as benchmark schemes and outperforms them in computational complexity.
Peculiarities of Learning for the Digital Generation Pupils and Students

The article presents general characteristics of nowadays’ pupils and students’ youth as a digital generation. Pedagogical means of leveling the negative features of the digital generation in the educational process of secondary and higher education institutions have been revealed («method of paradoxes», discussions and the search for an alternative point of view, digital detox, etc.). It has been determined that reading is a «universal» means of preventing the youth’s «dumbing down». The need to involve children and young people in reading classical fiction as the most important means of teaching the independent construction of an image system has been proved. The importance of the formation of a reading culture in terms of total manipulation of the consciousness of the average individual with the help of the appropriate mass media has been noted. The technology of presenting educational textual information in the form of so-called creolized text has been characterized. It allows to visualize the educational material in order to bring it as close as possible to the corresponding requirements of «clip» information perception. The technologies and methods of effective teaching of the digital generation in conditions of blended learning has been characterized.
A Breast Cancer Detection Problem using various Machine Learning Techniques in the Context of Health Prediction System

Today, breast cancer is one of the most common diseases that can cause certain complications, sometimes worst-case scenario is death. Thus, there is an urgent need for a diagnosis tool that can help doctors detect the disease at an early stage and recommend the necessary lifestyle changes to stop the progression of the disease; the likelihood of developing cancer at a young age has also been greatly increased by environmental changes in our everyday lives. Machine learning is an urgent need today to enhance human effort and offer higher automation with fewer errors. In this article, a breast cancer detection and prediction system is developed based on machine learning models (SVM, NB, AdaBoost). The achieved accuracies of the developed models are as follows: SVM achieved an overall score of 98.82%, NB achieved an overall score of 97.71%, and finally, AdaBoost achieved an overall score of 97.71%.
Estimating Factors of Agile Software Development Using Fuzzy Logic: A Survey

Agile software development is an iterative process that concentrates on producing a minimal viable product (MVP) quickly and then adjusting and adding features and capabilities in phases based on user feedback and behaviour. It is the most popular software development model nowadays. However, there exists various factors that plays a vital role for software development and are unknown, vague, and imprecision during the software development. Fuzzy logic is widely used for estimation of these factors in agile software development. In this paper, a survey on the use of fuzzy logic in agile software development for estimating various factors is presented. A comparative table of various research articles on different parameters is presented. Various related research questions are framed and finally answered. Overall, it is intended that the study will help advance knowledge creation and information accumulation in the area of agile software development using fuzzy logic by offering readers and researchers a road map to direct future research.
Banks 4.0 in the Context of sustainable development: A literature review and research framework

The digitalization of banking services requires a focus on sustainable development as well as the utilization of new technologies. Understanding and predicting evolving customer needs and expectations is a vital objective for banks. This approach enables achieving and sustaining optimal customer satisfaction, leading to the establishment of customer loyalty It is crucial to examine how Industry 4.0 affects the transformation of the banking industry. It is necessary to prioritize finding a balance between competition, innovation, security, and customer satisfaction to ensure sustainable development. This paper aims to explore the implementation of Industry 4.0 technologies in the banking system and increase awareness among researchers and bank managers about these new technologies and the concept of sustainability. Through an analysis of relevant literature, we hope to provide valuable insights. In this regard, it presents a framework studying the critical components for achieving banking 4.0
Analyzing Optimal Environment for the Text Classification in Deep Learning

Deep learning is an area that emerged from the capabilities of machine learning and artificial intelligence. In the field of deep learning, various parameters are essential to building an efficient model. These parameters include activation function, loss function, number of layers, number of neurons, and many other essentials. Finding an optimal workable environment by setting all these variables is time-consuming and challenging due to time constraints or projects having sharp deadlines. In this paper, the work is done for the exploration of these parameters that make an environment in which deep learning models work. The experimental results will also be discussed to learn about the best environment that makes text classification optimal in deep learning. These results would help the professionals and researchers to extract a ready-to-work environment for the projects associated with the text classification.
A Methodology for Increasing Precision for the Efficient Implementation of Sigmoid Function

In this brief, a high-precision sigmoid function is proposed and compared with the novel approximated method. The novel approximated function equation is modified and respective architecture is proposed. The high-precision sigmoid function is proposed to be used in artificial neural networks as an activation function. Since the activation function is the heart of the neural network, the function is made to give less error when compared with the original sigmoid function. So, increasing precision of sigmoid function will increase the precision of neural networks with high rate.
Load Balancing in Cloud Computing Using Multi-agent-Based Algorithms

In distributed environments, cloud computing is widely used to manage user requests for resources and services. Resource scheduling is used to handle user requests for resources based on priorities within a given time frame. The resource scheduling concept of a virtual machine is used to manage servers. To manage load and perform resource scheduling in a smooth manner in this paper, an attempt has been made to propose a multi-agent-based load-balancing mechanism. In a multi-agent-based load-balancing mechanism, multiple agent nodes at different layers are used to manage load and requests and increase throughput.
Enhancement of Accuracy and Performance of Machine Learning System During Detection of Phishing Emails

Fraudulent attempts to get access to personal data like passwords or bank account details are known as phishing. Phishing scams are among the most common types of online fraud. Online payment, Webmail, banking institutions, file hosting, and cloud storage are just a few of the places where phishing attacks may take place. Among all industries, phishing was most prevalent in Webmail and online payment. Blacklist, heuristics, visual similarity, and machine learning are just a few of the anti-phishing solutions available. Since the blacklist strategy is straightforward to apply and does not detect new phishing attempts, it is the most widely employed. Detecting phishing is easier with the help of ML. It also eliminates one of the main drawbacks of the current method. We conducted a comprehensive review of the literature and developed a novel technique based on feature extraction and a machine learning algorithm for detecting phishing Websites.
Chatbots: Understanding Their World, Classification, and Their Current State

An extraordinary growth is being witnessed for mobile devices and their ever-growing computational powers. Also, the chip demand is very high. This demand is not only coming from the software industry, but almost all the other sectors also contribute to this enormous growth. With the recent innovations in chip manufacturing, complemented with very high demands, compute power costs are getting reduced at a significant rate. The computation power of chips fuels the artificial intelligence (AI) technologies to harness them. AI is a very computation resource demanding technology, and, in many cases, it requires specialized computational resources. Due to deep mobile penetration in the society, AI technologies such as Data Science and NLP are taking great pace. On the other hand, chatbots are gaining momentum beyond customer care and typical use cases. This paper analyzes the history, growth, and new trends of chatbots. Especially the impact of AI on bot technologies and the road ahead. This paper will take a look at the chatbot journey. It also examines where chatbots are making an impact on human lives.
Distributed storage optimization using multi-agent systems in Hadoop

Understanding data and extracting information from it are the main objectives of data science, especially when it comes to big data. To achieve these goals, it is necessary to collect and process massive data sets, arriving at the system in different formats at great velocity. The Big Data era has brought us new challenges in data storage and management, and existing state-ofthe-art data storage and processing tools are poised to meet the challenges while posing challenges to the next generation of data. Big Data storage optimization is essential for improving the overall efficiency of Big Data systems by maximizing the use of storage resources. It also reduces the energy consumption of Big Data systems, resulting in financial savings, environmental protection, and improved system performance. Hadoop provides a solution for storing and analysing large quantities of data. However, Hadoop can encounter storage management problems due to its distributed nature and the management of large volumes of data. In order to meet future challenges, the system needs to intelligently manage its storage system. The use of a multi-agent system presents a promising approach for efficiently managing hot and cold data in HDFS. These systems offer a flexible, distributed solution for solving complex problems. This work proposes an approach based on a multi-agent system capable of gathering information on data access activity in the HDFS cluster. Using this information, it classifies data according to its temperature (hot or cold) and makes decisions about data replication based on its classification. In addition, it compresses unused data to manage resources efficiently and reduce storage space usage.
A Hybrid Multi-Objective Optimization Algorithm for Scheduling Heterogeneous Workloads in Cloud

Cloud computing provides a platform for hosting and providing services to users in an on-demand manner over the Internet. Numerous challenges are faced due to the dynamic nature of the cloud environment. One such key challenge involves the scheduling of tasks submitted by various users at the same time on virtual machines (VMs). Task scheduling involves the optimized resource allocation to various tasks while considering several performance metrics such as the turnaround time and the throughput. To improve these performance measures, a variety of single-criteria and multi-criteria decision-making (MCDM) methods have been suggested. In this study, we have proposed a hybrid algorithm BWM_ELECTRE (Best Worst Method EliminationEt Choice Translating REeality). The performance of this algorithm has been analyzed and evaluated in terms of the turnaround time and the throughput performance measures. The efficiency of BWM_ELECTRE has been compared with the best-known MCDM method BWM_TOPSIS (Best Worst Method Technique for Order Preference by Similarity to Ideal Solution). A dataset of 2400 cloudlets has been considered to compare the performance of these algorithms. Experiments have been carried out in an open-source simulation tool CloudSim 3.0.1. The results validate that BWM_ELECTRE outperforms the BWM_TOPSIS algorithm by 1.27%, and 2.08% in terms of the average turnaround time and the throughput, respectively.
A Systematic Review on Blockchain-Based e-Healthcare for Collaborative Environment

The utilization of recent data management support can be utilized to continuously carry out a variety of analytical activities, speeding up interactions between parties. Conventional healthcare service systems can help build a secure network. The existing EHR software’s security and privacy concerns restrict how much information about specific patients can be gathered from the multiple health providers’ records. Conventional e-healthcare solution technologies are built on centralized access regulation and are not adequately suitable for cooperative groups. The necessity of a decentralized strategy in the e-healthcare system stems from this centralized approach’s difficulties. In this work, an integrated blockchain technology conceptual model of an e-hospital emergency management platform that correlates the Internet of things (IoT) is proposed. In Sect. 3, a critical examination of 50+ papers on blockchain-based health systems that have been published during 2015 and 2020 is presented. Thirty-six of which were articles, seven are all from conferences, four were from different symposiums, and three were from seminars, while one was a section from a book. Current e-healthcare system is trust less in collaborative environment. Three key questions will be covered after literature survey. Firstly, what are challenges that occur with traditional e-healthcare system in collaborative environment? Secondly, need of decentralized access control for digital e-healthcare system. Third, see challenges with established methods in the e-healthcare system and whether a blockchain-based model can be a fine replacement.