Reviewerly/exHarmony huggingface dataset:
Intelligent Farmhouse Attendance Monitoring System Using Deep Learning

In large farmhouses, attendance is recorded by the supervisor and the record is maintained manually. It results in an increase in time and manpower. It also suffers from human error, and there may be the possibility of missing paper before reaching the owner of the farm for future reference. Deep learning is one of the most popular and emerging technologies in the digital world today. It has become extraordinary in recent years owing to its many applications. In this proposed work, the face recognition technique is used to design a prototype that handles attendance records automatically without any human intervention. To track the workers' attendance in this project, an intelligent attendance tracking system is created by identifying their facial features using efficient algorithms. It also sends the message automatically to the owner of the farmhouse and mails the report to the officers involved in the administration of the farmhouse. In addition to these features, it is also designed with live monitoring facilities. Also the simulation results prove an increase of 1% to 5% in accuracy of identification with different data sets. This low-cost, robust, and portable technology is also very effective and steady, saving the owners' valuable time.
Progressive Temporal Blockchain Framework for Ensuring Secure Management and Access of EHR

This research article proposes a novel framework based on progressive temporal blockchain technology for ensuring secure management and access of personal health records (EHR). With increasing concerns about the privacy and security of sensitive health information, there is a growing need for a robust and tamper-proof data management system that can provide secure access to EHR. The proposed framework integrates advanced encryption mechanisms, identity management protocols, and smart contract technology to ensure that EHR data is stored securely and accessed only by authorized users. The framework also leverages a progressive temporal blockchain architecture that ensures the integrity and immutability of EHR data over time. Through rigorous testing and evaluation, we demonstrate the effectiveness of the proposed framework in ensuring the security, privacy, and accessibility of EHR data. Our results indicate that the framework can provide a scalable and reliable platform for managing EHR, with potential applications in healthcare data management, disease surveillance, outbreak response, and clinical research. The framework also addresses several critical challenges in EHR management, including data privacy, interoperability, and regulatory compliance. Overall, our research contributes to the development of a secure and accessible platform for managing EHR, with significant implications for improving healthcare outcomes and patient satisfaction.
Automated detection of symmetry-protected subspaces in quantum simulations

The analysis of symmetry in quantum systems is of utmost theoretical importance, useful in a variety of applications and experimental settings, and difficult to accomplish in general. Symmetries imply conservation laws, which partition Hilbert space into invariant subspaces of the time-evolution operator, each of which is demarcated according to its conserved quantity. We show that, starting from a chosen basis, any invariant, symmetry-protected subspaces which are diagonal in that basis are discoverable using transitive closure on graphs representing state-to-state transitions under $k$-local unitary operations. Importantly, the discovery of these subspaces relies neither upon the explicit identification of a symmetry operator or its eigenvalues nor upon the construction of matrices of the full Hilbert space dimension. We introduce two classical algorithms, which efficiently compute and elucidate features of these subspaces. The first algorithm explores the entire symmetry-protected subspace of an initial state in time complexity linear to the size of the subspace by closing local basis state-to-basis state transitions. The second algorithm determines, with bounded error, if a given measurement outcome of a dynamically generated state is within the symmetry-protected subspace of the state in which the dynamical system is initialized. We demonstrate the applicability of these algorithms by performing postselection on data generated from emulated noisy quantum simulations of three different dynamical systems: the Heisenberg-XXX model and the ${T}_{6}$ and ${F}_{4}$ quantum cellular automata. Due to their efficient computability and indifference to identifying the underlying symmetry, these algorithms lend themselves to the postselection of quantum computer data, optimized classical simulation of quantum systems, and discovery of previously hidden symmetries in quantum mechanical systems.
An Intelligent Human-Machine Interface Based on Eye Tracking for Written Communication of Patients with Locked-in Syndrome

A deliberate loss of mobility throughout the entire body, with the exception of the muscles that control eye movements, is referred to as "locked-in syndrome" (LIS). Patients are conscious and awake despite this. They can communicate by blinking or moving their eyes, and their mental faculties are frequently unharmed. LIS sufferers are less productive since their everyday routines are substantially affected and they need continual assistance. Currently, many people use assistive technology to make up for their limitations. In order to capture eye movements, gaze courses, a client’s location on the screen at any given moment, and the grouping of their development, eye tracking is now being logically researched in Human PC Interaction. This is because eye tracking technology has advanced from an idea to the physical world. It may be argued that eye development is a crucial constant input technique for human-PC communication, especially for those who are genuinely impaired. The suggested framework aims to provide a straightforward and useful interactive mode using only the client’s eye. The proposed framework’s use stream is designed to flawlessly correspond to human tendencies. A magnifier module is also suggested to enable the precise activity. The Innovation Acknowledgment Model (Cap) measures are used to evaluate the framework’s apparent suitability. The proposed framework’s extreme persuasiveness in terms of usability and interface design is demonstrated.
An Automated Identification of Cervical Cancer disease using Convolutional Neural Network Model

The cost of preventative measures is often lower than that of medical care in most nations. Early diagnosis of disease yields better treatment outcomes than late diagnosis. Unless we have a better idea of how to treat people, whatever help we can provide them would be appreciated. Among these illnesses is cervical cancer, which ranks number four on the list of the most prevalent cancers in women worldwide. Age and the usage of hormonal contraceptives are only two of the numerous variables that raise the risk of cervical cancer. Increased survival and lower mortality rates are the result of cervical cancer screenings that discover the disease at an early stage. The goal of this work is to apply machine learning methods to identify a model that can detect cervical cancer with high specificity and accuracy. Predictions of cervical cancer are made using a CNN model in this study. The Kaggle dataset of risk factors for cervical cancer, including 32 risk factors and 4 goal variables. Lastly, we compared our findings to those of other research and discovered that, based on various assessment metrics, our models performed better than those of the other studies in diagnosing cervical cancer.
Learning to Summarize YouTube Videos with Transformers: A Multi-Task Approach

The wide-range availability of online video content has made people consume more time to keep up with the amount of information available. In this paper, we present a system for summarizing YouTube videos that use NLP and machine learning techniques to retain the important details without any data loss. There are a few techniques for summarization on YouTube, the first approach is YouTube API which has been a popular method used for video summarization. However, it has certain limitations, such as limited accuracy, language barriers, and inability to summarize caption-less videos. To overcome these limitations, a new approach using transformers has been proposed. The proposed approach involves downloading the video' audio, converting it to WAV format, performing speech-to-text conversion using the Hugging Face Automatic Speech Recognition model, and then using transformers and pipeline for summarization. For evaluation we have used Rogue (Recall-oriented understudy for Gisting evaluation) which is a popular metric used for summarization tasks with an f1 score of 0.6. The approach has been tested on multiple videos with different video lengths and has shown good results in terms of accuracy and efficiency. The proposed approach can be used for a wide range of applications, including online education, video marketing, and false thumbnail videos.
Inter-Frame-Relationship Protected Signal: A New Design for Radio Frequency Fingerprint Authentication

Utilizing a multi-frame signal (MFS) rather than a single-frame signal (SFS) for radio frequency fingerprint authentication (RFFA) shows the advantage of higher accuracy. However, previous studies have often overlooked the associated security threats in MFS-based RFFA. In this paper, we focus on the carrier-sense multiple access with collision avoidance channel and identify a potential security threat, in that an attacker may inject a forged frame into valid traffic, making it more likely to be accepted alongside legitimate frames. To counter such a security threat, we propose an innovative design called the inter-frame-relationship protected signal (IfrPS), which enables the receiver to determine whether two consecutively received frames originate from the same transmitter to safeguard the MFS-based RFFA. To demonstrate the applicability of our proposition, we analyze and numerically evaluate two important properties: its impact on message demodulation and the accuracy gain in IfrPS-aided, MFS-based RFFA compared with the SFS-based RFFA. Our results show that the proposed scheme has a minimal impact of only -0.5 dB on message demodulation, while achieving up to 5 dB gain for RFFA accuracy.
NRVC: Neural Representation for Video Compression with Implicit Multiscale Fusion Network

Recently, end-to-end deep models for video compression have made steady advancements. However, this resulted in a lengthy and complex pipeline containing numerous redundant parameters. The video compression approaches based on implicit neural representation (INR) allow videos to be directly represented as a function approximated by a neural network, resulting in a more lightweight model, whereas the singularity of the feature extraction pipeline limits the network's ability to fit the mapping function for video frames. Hence, we propose a neural representation approach for video compression with an implicit multiscale fusion network (NRVC), utilizing normalized residual networks to improve the effectiveness of INR in fitting the target function. We propose the multiscale representations for video compression (MSRVC) network, which effectively extracts features from the input video sequence to enhance the degree of overfitting in the mapping function. Additionally, we propose the feature extraction channel attention (FECA) block to capture interaction information between different feature extraction channels, further improving the effectiveness of feature extraction. The results show that compared to the NeRV method with similar bits per pixel (BPP), NRVC has a 2.16% increase in the decoded peak signal-to-noise ratio (PSNR). Moreover, NRVC outperforms the conventional HEVC in terms of PSNR.
Log-Concave Posterior Densities Arising in Continuous Filtering and a Maximum A Posteriori Algorithm

Nonlinear filtering is fundamental to many engineering problems, as it involves inferring the state of a system given complicated dynamics and noisy observations. Some approaches to nonlinear filtering use the analysis of the underlying PDE or stochastic PDE governing the evolution of the posterior probability distribution over time, one approach, in particular, being the Yau–Yau method. In this paper, we give a maximum a posteriori (MAP) framework for the Yau–Yau method. Furthermore, we propose convex filtering, intermediate between linear and nonlinear filtering, which gives criteria under which the posterior preserves log-concavity. The key tool from the PDE is a result from Korevaar, giving criteria under which a quasilinear parabolic PDE preserves convexity. A bridge between the MAP estimator and the structure of the posterior is explained. Finally, we propose a novel numerical method based on iteration and apply this method to a tracking problem.
Optimized Adsorption of Pb(II) ION from Aqueous Solution Using Sharp Sand: Ann and RSM Modelling

Aim: This study was aimed at using sharp sand as an adsorbent to optimize the removal of Pb ionsfrom simulated water and water obtained from Enyigba River.&#x0D; Study Design: Adsorption experiment was designed using Response Surface Methodology (RSM). Artificial Neural Network (ANN) was also used to predict % Pb(II)removal.&#x0D; Place and Duration of Study: Department Of Pure and Industrial Chemistry, Nnamdi Azikiwe University, Awka, June 2012 to June 2023.&#x0D; Methodology: Sharp sand was modified with salicylic acid, noted as SSM and then characterized. Experiments were carried out by varying four adsorption factors: concentration, time, pH and temperature. Adsorption experiments were also done for equilibrium isotherm, kinetics and thermodynamic studies.&#x0D; Results: The modified sharp sand showed a high percentage of silicon. RSM and ANN predicted the sorption of Pb(II) well with R2 values &gt; 0.9. The optimal values for concentration, time, pH and temperature are 0.0125mg/l, 100sec, 2, 20oC respectively. Langmuir isotherm gave the better fit and adsorption was endothermic with positive ∆H. Adsorption followed second-order kinetics. FTIR analysis showed the presence of functional groups that were responsible for adsorption. Enyigba River water is slightly polluted with lead ions with concentrations higher than WHO standard.&#x0D; Conclusion: The adsorbent was able to remove over 70% of the metal ions from both the simulated and Enyigba River water.
Augmented reality and education in electronics: Relationship between usability, academic performance, and emotions experienced in learning

Abstract Students often find difficult to understand the concepts and working details of electricity because its mechanisms of operation are invisible. The visualization of electricity through an augmented reality (AR) app could assist students in understanding these concepts more intuitively and in improving their academic achievement. Due to the lack of studies on AR apps for electricity education, this study aimed to investigate the effects of an interactive AR app designed for teaching electrical circuits on students. The study investigates its impact on students' academic performance, explores its influence on their emotions, and examines the relationship between the perceived usability of the app and the student's learning outcomes and emotional experiences. The study was conducted in an electromagnetism laboratory with the participation of 28 engineering students. The findings revealed that the students who used the augmented reality application presented a better academic performance than those who participated in the traditional laboratory. Except for the students in the experimental group feeling less shame, there were no discernible variations between the students' feelings in the two groups. Anxiety increases in both groups. The AR application proved to have usability rated as good, but it was not evident that it correlated with academic performance, or the emotions students experienced. Only one relationship was determined between the perceived consistency of the system and hopelessness.
Investigations on the Swiftness of a Quantum and a Classical Processor

The cutting-edge technologies cloud computing and IoT are taking an upper hand in every domain. A huge and wide variety of data is being handled and processed by clouds. The cloud federation technique further adds up to this. In the coming years, quantum computers will replace the conventional computers. Pulling out particular data from the gigantic data set processed by clouds in a conventional computer would take a considerable amount of time. In the chapter, Grover's algorithm, a search algorithm, is implemented on traditional computers on IBM quantum simulator and also on QUIRK quantum simulator. Three qubit data is considered in the proposed scheme. The objective of this chapter is to compare the execution time taken to run the Grover's algorithm on IBM and Quirk quantum simulators and on classical computers. The work carried out proves that quantum computer execution speed is high compared to the classical counterpart. This could be effectively used in the future in searching for specific data from a mammoth data set using quantum simulators.
Biometric Authentication Model Based on Transformation of Face Image Into a PIN Number Usable During the Covid-19 Pandemic

The digitization trend is developing throughout the crisis caused by the COVID-19 pandemic. The volume of digital payments is increasing. The most common way of checking the authentication in electronic payment systems is the PIN number that users type in
Touching the future of training: investigating tangible interaction in virtual reality

Virtual reality offers exciting new opportunities for training. This inspires more and more training fields to move from the real world to virtual reality, but some modalities are lost in this transition. In the real world, participants can physically interact with the training material; virtual reality offers several interaction possibilities, but do these affect the training’s success, and if yes, how? To find out how interaction methods influence the learning outcome, we evaluate the following four methods based on ordnance disposal training for civilians: 1) Real-World , 2) Controller-VR , 3) Free-Hand-VR , and 4) Tangible-VR in a between-subjects experiment ( n = 100). We show that the Free-Hand-VR method lacks haptic realism and has the worst training outcome. Training with haptic feedback, e.g ., Controller-VR , Tangible-VR , and Real-World , lead to a better overall learning effect and matches the participant’s self-assessment. Overall, the results indicate that free-hand interaction is improved by the extension of a tracked tangible object, but the controller-based interaction is most suitable for VR training.
Study on Copyright Infringements of We Media Based on Media Integration Blockchain

With the maturity and improvement of blockchain theory and technology, many media have created platforms based on blockchains such as Civil, Decentralized News Network, Steemit and so on. All these successful cases showed us the potential in protecting media industry especially in digital copyright area. In 2021, the Xinhua News Agency’s search engine ChinaSo created Media Integration Blockchain to protect copyright of news media. This paper introduces the design of the Media Integration Blockchain and try to give some advice to upgrade it to increase the range of it. The paper focuses on not only news media but also on We Media. The purpose of this study is to avoid the copyright infringements and contribute to the protection of digital copyright based on the application of blockchains in China’s digital copyright protection.
Face Mask and Social Distancing Detection in Real Time

With the recent outbreak and rapid transmission of the COVID-19 pandemic, the need for the people to follow social distancing and wear masks in public is only increasing. So, the main objective of this chapter is to build a machine learning model based on TensorFlow object detection API and YOLO Objection Detection that will determine a green and red rectangle around the face if the person detected in the camera wears or does not wear a mask, along with an email alert being sent to the authority in charge informing about a person's violation of face mask policy and will return a green or red bounding box accordingly if social distancing is maintained between two people and at the same time alert others by a beep alarm. The accuracy of the model is nearly 97% so it can be used by governments to alert people if the situation turns serious.
Modeling of Russian–Ukrainian war based on fuzzy cognitive map with genetic tuning

The Russian–Ukrainian conflict is considered as a dynamic system, whose variables are factors affecting the losses of the Russian army and the threat of the use of nuclear weapons. A fuzzy cognitive map (FCM) is used for modeling, that is, a directed graph whose vertices are model variables, and the weights of arcs are the degrees of positive and negative influences of variables on each other. The following factors influencing the losses of the Russian army and the threat of a nuclear strike were selected: resistance of the Ukrainian army, support of Ukraine with weapons, economic sanctions against Russia, opposition to the Russian government and its self-preservation instinct. The degrees of the influence of factors on each other and on the possibility of using nuclear weapons are evaluated by experts using fuzzy terms, which correspond to numeric values. To adjust the FCM, a genetic algorithm is used to select the degrees of influence of factors that minimize the discrepancy between the simulation results and expert estimations. The obtained FCM is used for scenario modeling of the conflict according to the “what if” scheme and ranking of factors according to their degree of influence on the level of nuclear threat.
Emotions of students from online and offline texts

Students generally face tremendous pressure – from their competing peers, expectant guardians, and the society as a whole. These tend to make them vulnerable, and may lead to development of mental quirks that are very hard to remove with time. This chapter reports a venture to detect negative streaks existing within the human mind – especially, amongst the student community. Some machine-learning tools are utilized to study their sentiments from their day-to-day communications and practices. The end-product is a minuscule model to detect participants who are experiencing alarmingly stressed conditions. The system reports encouraging results within the limited periphery in which it has been tested so far. The main objective is to open up the vista of research on the virgin ground of detecting emotion from word usage and writing hand.