Reviewerly/exHarmony huggingface dataset:
Human Factors Affecting the Adoption of Healthcare 4.0

Recent advances in digitalizing healthcare services and related systems show great promise of effective use of technology to enhance the healthcare sector. Healthcare and healthcare-related industries are moving forward with the adoption of various smart services and Healthcare 4.0 systems to reduce costs, improve care, and enhance patient satisfaction. However, the research and development communities are offering a lot of innovative frameworks, applications and techniques to fully transform the healthcare industry into Healthcare 4.0. Adopting such technologies and solutions face various challenges. Some are technical and many of these are solvable, some financial, which can be addressed, somehow; yet, there are other obstacles hindering the efforts, namely, humans! Smart systems in general are invasive and require people to accept how these systems will be involved in their every day life. This leads to another important aspect, trust, as such invasive behavior require humans to trust these systems and those who operate them. They may also replace human interactions, which many see as essential for successful patient/practitioner relationships. Moreover, some view these as replacement for human workers, which may lead to layoffs and financial hardships. In this paper, we investigate the human factor affecting the acceptance and adoption of Healthcare 4.0 and smart healthcare systems among the different stakeholders. We will review current work on the topic and identify the major factors with respect to how they affect stakeholders and whether these are solvable with advances in technology. We see that technology can help solve many of the issues affecting human’s acceptance, yet there are also ones that are not, no matter how advanced the technology can get.
Evaluation of Class Activation Methods for Understanding Image Classification Tasks

Machine Learning systems based on deep neural networks are powerful tools but their wide adoption has shown that both the designers and the users of DNN models must fight the barriers of understanding and controlling what has been learnt from data. To make such systems explainable and interpretable, model-specific post-hoc methods have been developed in the literature. This paper presents a family of such methods, Class Activation Mapping, used to explain the image classification process in Convolutional Neural Networks, and achieves a thorough evaluation of these methods. The analysis is done both a qualitative point of view, through the visualization of the activation heatmap of the image, and from a quantitative point of view, through several metrics that try to objectively quantify the relevant parts of an image which contributed to the classification. Several datasets are used to evaluate the discussed methods and a comparison between the obtained results is presented.
An Electronic Analytical System For Classification of Kidney Abnormalities Aimed At Identifying Abnormalities In Ultrasound Pictures of The Kidney

Kidney disease has become a worldwide public health crisis because of the shocking rise in death and illness it has caused. Due to its low cost and the flexibility to do investigations at the bedside, ultrasound has become the imaging modality of choice for identifying kidney disorders. Radiologists and sonologists are the experts at deciphering ultrasound images for medical purposes. The diagnostics and healthcare of a society can be vastly improved with the introduction of a low-cost imaging modality that eliminates the need for radiation. This problem must be solved in a way that eliminates observer variance. One such option is computer-assisted diagnosis (CAD), which provides more trustworthy and accurate diagnoses that are temporarily immune to observer variances. The renal region has been proposed to be segmented using Distance Regularized Level Set (DRLS), which is driven by a theoretically intensity invariant characteristic. In the past, ultrasound image segmentation has been considered difficult because of the poor contrast of the pictures. There are a number of issues with these photographs, including weak signal and blurry edges. It has been discovered by medical professionals that manual demarcation relies not only on intensity data but also on contextual details. Because of this, properties like local phase and direction, which are theoretically independent of intensity, are often used. The monogenic signal notion is used to retrieve local information in this study. Metrics like the dice similarity coefficient and the mean absolute deviation are used to quantify the potential of the suggested segmentation system. And in the setting of inter-observer and intra-observer variability in organ delineation, PBDRLSE is determined to be effective and promising when compared to the standard and current efforts.
Compression And Decompression Of Files Without Loss Of Quality

This paper proposes the Gzip algorithm for image and document compression and decompression. Gzip is a hybrid algorithm that combines Lz77 and Huffman. In document management and communication systems, picture and document compression and decompression are crucial. Image and document compression technologies are used to lower the amount of data required to represent the file. Image compression has proven to be the most advantageous and practical method in the field of digital image processing. The goal is to reduce the images’ and documents’ redundancy so that data may be stored or sent efficiently. In order to reduce data redundancy and conserve more hardware space and transmission bandwidth, the theory of data compression and decompression is therefore becoming more and more important. Compression is beneficial because it makes use of less expensive resources like hard disc space and transmission bandwidth. When we evaluate the image quality, decompression is beneficial. In the proposed system, there is no reduction in data, but there is a decrease in data size without loss of quality.
A Novel Approach for Nuclei Segmentation Using U-Net

Several automated methods for segmenting micro-scopic images have recently been developed and deployed. Nuclei segmentation is an important yet difficult process for examining histopathology images. This assay allows for the early detection of illnesses such as breast cancer, cardiac issues, and COVID19. Also It provides a thorough understanding of nuclei’s features and functionality. Manual diagnosis is challenging, timeconsuming, and expensive all at the same time. The U-Net deep learning model is the focus of this study since it offers a potential way for efficiently segmenting and detecting cell nuclei. The model was trained using images from the 2018 Data Science Bowl competition. The experimental results show that the proposed techniques perform well, with IoU values of 0.863 and Dice -Coef values of 0.9262.
Evaluation of Machine-Learned Price Prediction Models For Block-Chain Technology

As a new form of technology, blockchain has much to offer. The technology has a wide range of potential uses and advancements, depending on the field of application. However, further study is required to realise the potential of advanced technologies like Blockchain and smart contracts. There is a lot of potential for investigation because the technology is so new. This research looked at the origins of the Blockchain and analysed several different blockchain solutions. Ethereum was discovered to be the most often used platform for constructing distributed apps after being compared to other platforms. Therefore, a distributed smart energy microgrid application will be built on the Ethereum platform. There is a coin, or token, exclusive to each blockchain platform. Bitcoin's relationship to other cryptocurrencies was measured using the Karl Pearson correlation coefficient. The research concludes that ether's price is most sensitive to changes in the price of bitcoin. On a preferred Ethereum network, a distributed application for managing smart microgrids has been built. The smart contract constitutes the heart of this programme. As a result, we also take care of the smart contract's layout, coding, and optimization. In comparison to the original, less GAS is consumed by this smart contract. The entire distributed app has been built and thoroughly tested. Smart Energy Microgrid Distributed Application framework results demonstrate that the application functioned as expected.
The Revelation of Tamil Cryptographic Lexicon –Connecting Tamil Characteristics and Cubic Curve

Data Security is the predominant issue in today’s world as drastic increase in size of data in day today’s life. Even though we have cryptographic algorithms more in hand, the same time how efficient usage of those algorithms are still unknown. Here we propose a novel method to provide high security and easy implementation of existing Galois field cubic curve algorithm using Tamil language. The Tamil language is represented as the product of vowels and consonants in one-toone connection to the Hardy-Ramanujan prime factors. In addition, the mathematical properties of the cubic curves are formed from the tabular representations of Hardy- Ramanujan prime factors and the number digits. It’s experiential shows that, Tamil language based cubic curve encryption provides very stronger security when compared to the modern cubic curve Elliptic curve encryption.
Investigation of Seismic Signal Processing for Detection and Classification of Moving Unarmoured Vehicles

The concept of automatically recognising targets has been actively researched for more than three decades at this point. Despite this, research is still being conducted because the technology has so many potential uses, including monitoring of traffic and perimeters, surveillance, and so on. Research work that focuses on the performance of detection and classification algorithms is in high demand. The current body of work in research investigates potential methods for enhancing the efficiency of detection and classification procedures. The frequency of the seismic signal shifts throughout the course of time, which means that the signal is non-stationary. Using this method, the non-stationary signals are evaluated. The time-frequency representation of these signals does, however, contain the cross-terms in question. In light of this, our working hypothesis for this study is that getting rid of these cross terms will result in an improvement in the performance of the detection algorithm. As a result, an improved method has been suggested, which performs better than STFT and thus verifies the hypothesis that was proposed.
Ontology for Vehicle Function Distribution

Distributing software onto the different controllers of a vehicle has become an increasingly difficult task for architects, as the number of functions to be experienced by the driver or passengers of a car is growing. At the same time, there are research and industrial efforts in breaking the inflexibility of vehicle software architectures after their manufacturing, e.g. by introducing over-the-air updates. However, the nomenclature of distributing software was found to be ambiguous. As a prerequisite for future research, we introduce an ontology that defines a frame of reference for the static as well as the dynamic function distribution and a simplified view on the software and hardware architectures necessary for the definition.
Suspicious Activity Detection Using Deep Learning Approach

Video Surveillance plays a pivotal position in today's global. The technologies have superior an excessive amount of when synthetic intelligence., gadget learning, and deep learning are pitched into the gadget. the usage of the above mixtures, exceptional systems are in a region which helps to differentiate various suspicious behaviors from the live monitoring of photos. Human behavior is the most unpredictable, and it is very difficult to determine whether it is suspicious or normal. In this paper, we have classified human activities into two: Normal and Suspicious. Normal activities include sitting, walking, jogging,hand waving, etc. Suspicious activities include running, boxing, fighting, etc. We achieve this classification by using convolutional neural networks. First, the convolutional neural network is used to extract high- level features from images. The convolutional network classification is taken into account, the final poolinglayer result is extracted and the final prediction is made.
CIFER: Code Integrity and control Flow verification for programs Executed on a RISC-V core

Fault Injection Attacks (FIA) are powerful threats that can modify the intended behavior of a program running on a processor. Control Flow Integrity (CFI) is used to check at runtime that a program’s execution path follows its corresponding Control Flow Graph (CFG) and is not altered by these attacks. Recent works have stated that developers do not sufficiently consider hardware specifications while designing software countermeasures. Moreover, most hardware and codesign CFI solutions do not cover the integrity of the processor microarchitecture. This paper presents CIFER, a Code Integrity and control Flow verification system for programs Executed on a RISC-V core. It ensures instruction execution in the core while checking the microarchitectural control signals. This is known as a Control Flow and Execution Integrity (CFEI) approach. Our solution is built upon the RISC-V Trace Encoder (TE) which provides information about the execution path of the user’s program. CIFER proposes an evolution of the TE standard and an analysis of the targeted core’s architecture to monitor the pipeline control signals. The average hardware area overheads of our solution range from 35.2% to 55%. Compared to existing CFI and CFEI countermeasures, CIFER presents no performance costs. It does not modify the RISC-V Instruction Set Architecture (ISA), the compilation process nor the user code.
Automating Hadoop Cluster On Aws Cloud Using Terraform

Nowadays, companies need a lot of storage to store their data. Massive amounts of data can be stored with Hadoop. Studies show that by 2023, 90% of all Fortune 500 companies will have adopted Hadoop. However, it may take hours or even days to set up a Hadoop Cluster for storing data. When businesses manage their time well, they can consistently deliver their products and services on time. To make smart and calculated decisions, they need this data. Business forecasting is a practice that has been around for a long time, but in the past, it was often done with limited data. However, in today’s data-driven world, businesses must utilize data to make informed decisions and stay ahead of their competitors. By analyzing large amounts of data, businesses can make more accurate predictions and decisions about consumer preferences, market trends, and potential fraud activities. The insights gained from data analysis can benefit professionals across all industries, allowing them to make better decisions and improve their business outcomes.The main objective is to automate the formation of a Hadoop Cluster on AWS Cloud with the help of Terraform. The Hadoop Cluster will have the following nodes: Master Node, Slave Nodes, Client Nodes
Data Encryption of Blood-chain data in Blockchain Network

Blockchain technology is more advantageous in supply chain management since there is a constant need to record the transaction history of each node/party in the blockchain network. One such example is storage of blood donation and transfusion data on blockchain. As multiple parties such as blood donor, blood bank and hospital are involved, it is necessary to keep the part of their transactional data (such as donor’s personal data, patient’s personal data) as confidential. Hence in this paper, with the intension to provide more data confidentiality and security, the popular data encryption algorithm ‘Advanced Encryption Standard (AES)’, a symmetric key crypto-system is employed on part of blood-chain data before sending transactions to the ordering service and adding blocks to the ledger. Therefore, only a person who has access to the appropriate key that was used to create the cipher text can decrypt the encrypted data. In addition, the performance of the transaction using different parameters such as data encryption/decryption speed, storage is evaluated using Hyperledger Caliper Tool.
Improving the Estimation of Object mass from images

Image2mass and Pix2Vox++ are works that focus on the estimation of mass and the shape of objects in images, respectively. Having the aid of the Pix2Vox++ in mind, the proposed work aims to further explore the image2mass’s architecture by modifying its mass estimation pipeline. This pipeline contains a Geometry Module which estimates a thickness mask and 14 geometric features and uses them to predict the density and volume of objects, which result in the mass value when multiplied.Firstly, in this work we present a method to turn the output of the Pix2Vox++ into a thickness mask and 14 features, analogous to image2mass. Secondly, it is fine-tuned the Pix2Vox++ architecture with different configurations, using a dataset called GraspNet. The three best Pix2Vox++ models are then chosen to substitute, or aid, the Geometry Module, and the image2mass is fine-tuned using the same dataset. Additionally, texture randomization is executed to evaluate the generalization of results given the variability of the input data.The results demonstrate that the Pix2Vox++ help the image2mass to achieve a better performance. The fine-tuning configurations of both models, however, should be further explored.
A Gradient Descent Multi-Algorithm Grid Search Optimization of Deep Learning for Sensor Fusion

Sensor fusion approaches combine data from a suite of sensors into an integrated solution that represents the target environment more accurately than that produced by an individual sensor. Deep learning (DL) based approaches can address challenges with sensor fusion more accurately than classical approaches. However, the accuracy of the selected approach can change when sensors are modified, upgraded or swapped out within the system of sensors. Historically, this can require an expensive manual refactor of the sensor fusion solution.This paper develops 12 DL-based sensor fusion approaches and proposes a systematic and iterative methodology for selecting an optimal DL approach and hyperparameter settings simultaneously. The Gradient Descent Multi-Algorithm Grid Search (GD-MAGS) methodology is an iterative grid search technique enhanced by gradient descent predictions and expanded to exchange performance measure information across concurrently running DL-based approaches. Additionally, at each iteration, the worst two performing DL approaches are pruned to reduce the resource usage as computational expense increases from hyperparameter tuning. We evaluate this methodology using an open source, time-series aircraft data set trained on the aircraft’s altitude using multi-modal sensors that measure variables such as velocities, accelerations, pressures, temperatures, and aircraft orientation and position. We demonstrate the selection of an optimal DL model and an increase of 88% in model accuracy compared to the other 11 DL approaches analyzed. Verification of the model selected shows that it outperforms pruned models on data from other aircraft with the same system of sensors.
Ensemble Learning-based Smartbed System for Enhanced Patient Care

A growing number of feature learning methods, particularly those based on deep learning, have been investigated to derive useful feature representations from large quantities of data. However, applying each model in real time for various research requirements can be challenging. With the common use of smartphones equipped with sensors, ensemble learning has become an area of interest among researchers. By obtaining knowledge of a patient’s mobility, a wide range of services can be provided. Therefore, in this research work, the authors endeavor to detect a patient’s state using sensors attached to the patient’s smartbed. The authors specifically create an ensemble network for greater precision and improved accuracy. This paper is based on using ensemble learning techniques to determine a patient’s state of mobility, and data are gathered from integrated devices in the smartbed. In this study, the authors use ensemble learning to distinguish between various forms of transit, including sleeping, standing, sitting, walking, and emergency states. The authors propose an ensemble network model based on deep learning to enhance the performance and resolve issues that may arise in a singular network. The characteristics generated by the neural networks are merged and relearned in this model. The data used in the trials are taken from the sensors attached to the patient and their smartbed.
Augmented Reality For Education Based On Markerless Dynamic Rendering

Augmented Reality (AR) technology has the potential to revolutionize education by providing a new way for students to visualize and interact with complex concepts. In this project, a system is proposed to develop an AR smartphone application that allows students to visualize objects and scenarios that the teacher is teaching in real-time. The application will employ the smartphone’s camera and sensors to materialize a user-friendly and easy-to-use dynamic AR experience, with the teacher allowing the students to simply access their smartphone to project 3D models of objects or scenarios onto a flat surface. Students will be able to view these models from any angle and interact with them in a variety of ways, such as by rotating them or zooming in on specific details. In addition to enhancing student’s understanding of the material being taught, the AR application will also provide an engaging and immersive learning experience. The distinguishing factor is the storage of the 3D assets on the cloud that will equip the educator with the option of pre-planning and customizing their entire lesson as well as storing any number of models. This can help to increase student engagement and motivation, leading to better retention of the material being taught. Overall, the proposed AR smartphone application has the potential to significantly improve the way students learn and understand complex concepts, making education more effective and enjoyable for all.
Improving the Diagnostic of Contrast Enhanced Ultrasound Imaging using Optical Flow for Focal Liver Lesion Detection

Our proposal aims an automatic method used for obtaining the ultrasound image of a region of interest based on the optical flow computation. Combined with a kernel correlation filter tracking algorithm and a Xception deep convolutional neural network architecture, our solution provides state-of-the-art results (over 90% accuracy) in the automatic diagnosis of liver lesion using contrast enhanced ultrasound.
Dual Channel EM/Power Attack Using Mutual Information and its Real-time Implementation

Cryptosystem implementations often leak information about a secret key due to correlation with side channels such as power, timing, EM, etc. Based on this principle, statistical and machine-learning-based side-channel attacks have been investigated, most often using a single channel or modality such as power; however, EM is growing in popularity. Since power and EM channels can leak distinct information, the combination of EM and power channels could increase side-channel attack efficiency. In this paper, we combine EM and power channels in a linear fashion by using mutual information to determine the optimal coefficients for each feature. Mutual information is also systematically applied for lightweight dimensionality reduction. Further, the proposed methodology is implemented onto a platform to simultaneously measure power and EM traces and process them in real time to extract AES subkeys. With the proposed dual channel approach, the success rate increases by at least 30% compared to single power/EM channels in the offline mode and over 50% in the real-time mode.