Reviewerly/exHarmony huggingface dataset:
Mortality Prediction in ICU Patients Suffering from Stroke

Ischemic stroke is a medical emergency that requires hospitalization and occasionally, specialized care at the Intensive Care Unit. Mortality prediction in the ICUs has been a challenge for intensivists, since prompt identification could impact medical clinical practices and allow efficient allocation of health resources in the ICUs, which are extremely restricted, especially in the era of COVID-19 pandemic. Clinical decision support systems based on machine learning algorithms are taking advantage of the vast amount of information available in the ICUs and are becoming popular in the medical predictive analysis. This study aims to explore the feasibility of interpretable machine learning models to predict mortality in critically-ill patients suffering from stroke. To do so, a vast variety of clinical and laboratory information stored in the electronic health record, are pre-processed to allow taking into account the temporal characteristics of a patient’s stay. An 8-hour sliding observation window was utilized. For the experimental evaluation we used the Medical Information Mart for Intensive Care Database (MIMIC-IV). Results indicate sufficient ability to predict mortality at the end of a given day during the patient’s stay. Moreover, attribute evaluation highlights the important indicators to consider when following up with a patient.
Calibration of the projector in structured light systems using Brox optical flow

In this work, we propose a new calibration technique for locating the projector in the structured light measurement system. Using Brox optical flow, the estimation of three coordinate components of the projector only requires two images captured before and after the motion of the calibration plate. The calibration principle presented on geometry depicts the relation between the position of the projector and that of the camera, to the optical flow caused by the movement of the calibration plate. The accuracy of the proposed method is verified by numerical simulation. The measurement system is calibrated by using the proposed calibration method and by Falcao’s method simultaneously for comparison. After calibration, eight-step phase shift technique of a recognized high precision is performed to reconstruct the 3-D shape. The results show that the height distribution of the specimens is in good agreement with its true values, and the maximum absolute error is less than 0.1 mm.
BloomXNOR-Net

In recent years, the Internet of Things (IoT) has become a dominant data generation framework for establishing a higher level of system intelligence. At the same time, to avail the full advantage of this domain, the adopters of IoT are also keen on applying Machine Learning (ML) algorithms to these datasets to reveal new business insights. However, these datasets contain sensitive information that demands careful processing to prevent privacy breaches. Many existing privacy-preserving ML (PPML) algorithms are unsuitable for these resource-constrained devices. We propose a novel PPML technique that can be executed on IoT devices using the Bloom Filter encoded IoT dataset in XNOR-Net architecture. The preliminary experimental result using the MNIST dataset shows satisfactory performance.
Feature Fusion of LBP, HELBP &amp; RD-LBP for Face Recognition

As literature suggests that single descriptor fails to produce desired results therefore the proposed work launches novel Fused Local Descriptor (FLD), by merging 3 state of art local descriptors. The services of FLD was taken from LBP, HELBP & RD-LBP. As all these 3 descriptors adopts different concepts therefore their integration is done. In LBP, the gray value (neighbors of patch (3 × 3 size)) are involved in comparison to center gray value. In HELBP, the gray value (horizontal neighbors of patch (3 × 5 size)) are involved in comparison to center gray value and in RD-LBP, the different scale pixels are compared. PCA is bring next for reduction and classification is further done by SVMs. FLD attains staggering rates than either of LBP, HELBP & RD-LBP. FLD also proves its significance in front of several other literatures based ones. ORL & GT are used for evaluation in MATLAB R2018a environment.
Chaotic System Based Modified Hill Cipher Algorithm for Image Encryption Using HLS

Lately, information has risen as an imperative asset, and henceforth information safety is of essential concern. A novel and secure image cryptographic system based on the hill cipher algorithm and incorporating a Nose Hoover Chaotic generator has been presented. In the proposed system, the transmission unit mixes an input image with chaotic noise produced by a Chaotic generator and then a bitwise XOR operation is performed to complete the encryption with higher degree of robustness which has been proved by using various measures like entropy and correlation. Moreover, a comparison has been made in the paper. The proposed algorithm includes a key generation system on board which provides an enhanced robustness as well as better computation speed. This makes the algorithm unique and novel since the traditional Hill Cipher has been modified to accommodate pixel values. In addition to the proposed algorithm, we have offered a way to implement the design on a SoC using Xilinx Vivado Design suite.
Performance Estimation of ML Techniques for Pancreatic Tumor Classification in PET/CT Images

Pancreatic cancer is one of the deadliest diseases and detecting the cancer at an early stage is the difficult task and the reason that ‘cancer’ symptoms happen only at a problematical period. The reliable screening tool to spot high-risk patients is lacking. Machine Learning (ML) algorithms were utilized in Positron Emission Tomography (PET)/Computed Tomography (CT) pancreatic images. Combination of PET/CT is a current mechanism, which combines both the functional information of ‘PET’ with the anatomic features of ‘CT’. Incorporated ‘PET/CT’ devices show ‘PET’ with contrast medium enhanced ‘CT’ images of the complete body in single condition. Classification techniques have a significant function in ‘Computer Aided Diagnostic (CAD)’ systems in identifying the malicious (cancerous) area from any kind of medical images. Supervised ML techniques, for example K-Nearest Neighbor (KNN), Decision Tree (DT) and Support Vector Machine (SVM) have been accepted as strong ideal in classifying features for medical images. Classification using SVM achieved 80.7% accuracy; KNN achieved 90.8% accuracy and decision tree achieved 89.9% accuracy in detecting pancreatic cancer. From the above deliberations, this paper attempts to evaluate the latest ‘ML’ techniques for ‘Pancreatic Cancer’ classification using second order statistical features from ‘PET/CT’ images. The results indicate that KNN can be achieved the maximum accuracy than SVM and DT algorithms.
Improved Control Design for Autonomous Vehicles

In this paper, the autonomous vehicle presented as a discrete-time Takagi-Sugeno fuzzy (T-S) model. We used the discrete-time T-S model since it is ready for the implementation unlike the continuous T-S fuzzy model. The main goal is to keep the autonomous vehicle in the centreline of the lane regardless the external disturbances. These disturbances are the wind force and the unknown curvature; they are applied to test if the autonomous vehicle moves from the centreline. To ensure that the autonomous vehicle remain on the centreline we propose two discrete-time fuzzy lateral controllers called also steering controllers.
Ultra-Short Pulse Non-Classical Light Emitters Utilizing Multiple Wide Quantum Wells

We report a novel ultra-short pulse emitter utilizing transient charge carrier behavior and cooperative emission in multiple wide-quantum-well heterostructures. Its optical cavity reassembles a tandem-cavity laser diode with electroabsorber section surrounded by two gain sections. With selection of the electrical pump pulse and the absorber voltage, due to quantum-confined Stark effect in wide quantum well, it is possible to spatially pull the electrons and holes apart from each other. When the pump bias is removed, an ultra-short pulse due to cooperative recombination of electrons and holes emerges. We demonstrate that such cooperative superradiance produces non-classical high-order correlations in the emitted light while Wigner function of the state reveals quantum oscillations.
Motion recording via the Fourier-phase camera

An original motion recording framework is proposed, which records dynamic scene through the Fourier-phase and a sharp image. Pixel-wise motion-offsets are inferred through the trained Phase-net, and guide the video reconstruction from a static image.
Data Driven GNSS Based Interference Signal Detection Technology

Most of the traditional GNSS interference detection algorithms have disadvantages such as low detection accuracy and complicated algorithms when detecting interference signals. In this regard, a GNSS interference signal detection algorithm based on convolutional neural network is proposed in this paper. Firstly, various types of interference signals are simulated and generated by MATLAB software. The simulated signal RF output is performed by controlling the vector signal generator (VSG) and data acquisition is performed in the microwave laboratory. Subsequently, the received baseband signal data is simulated over the channel, which is used to construct training data sets for various JNR scenarios. Finally, the neural network designed in this paper is trained using the sample data, and the conventional interference detection algorithm is compared with the proposed neural network algorithm in this paper on the same sample data set. The experimental results confirm that the proposed neural network algorithm has better detection performance compared with the existing traditional detection algorithms.
Deep Stacked Random Forest

Current deep forest are mostly built upon multi-grained cascade forest, i.e. a novel decision-tree ensemble, with a cascade structure that enables representation learning by forest. In this paper, we propose the Deep Stacked Random Forest(DSRF) based on Deep Forest. We have made the following three enhancements: (1) The enhancement of data learning strengthens the cascade part of the deep forest model. DSRF applies a Random Feature Extraction (RFE) method to obtain more diverse feature subsets from the original data, which benefits feature learning, thus laying a solid foundation for the performance improvement of the model. (2) Designing the Mean Stacked Forest(MSF), which adopts mean output, reduces the impact of part of the classifiers reaching local optima on the accuracy of the algorithm, reduces the convergence speed of the representation learning model. To a certain extent, MSF reduces memory consumption. (3) Adding the null space matrix on training data promotes the model’s ability to learn mapping rules from the input data, thereby improving the generalization and fault tolerance of the model. Experiment results show that DSRF achieves advanced performance on four low-dimensional datasets and two high-dimensional datasets. Especially, the model achieves state-of-the-art performance in terms of convergence speed and learning efficiency on low-dimensional datasets.
POST-COVID TOURISMS: VIRTUAL TOURISMS AS THE NEW TRAVEL TRENDS

This conceptual paper discussed about the COVID-19 pandemic caused many industries to face their downfall, and one of the industries is the tourism industry. As a corollary, a new type of tourism, virtual tourism, is required to promote and restore the tourist sector to its former glory while not dismissing the need for social distancing. Through virtual tourism, people no longer need to travel to a particular tourist destination to experience tourism, but they can now stay at their home and escape to another destination virtually. Aside from giving tourists another level of tourism pleasure, virtual tourism also contributes a lot to the industries such as heritage preservation, tourism marketing, tourism planning and as an opportunity for disabled person to experience tourism. Even though virtual reality technology does not provide the users with enough authentic experience due to the inability to touch the object in the virtual environment, but it can be considered as a good start to advertise virtual tourism as an alternative to conventional tourism especially during the pandemic era where every movement is limited.
Lightweight and Secure Authentication Protocol for Wearable Device in Smart Healthcare

The information sent by wearable devices usually includes sensitive data. There are great risks that can occur to the data sent due to smart devices that collect it through wearable devices, so it is very necessary to authenticate all wearable devices and protect the data sent through them to the smart device and then to the server. That is why in this paper we proposed a lightweight protocol for authentication between wearable devices and the smart device used by the patient, and this paper also explains how the proposed protocol can prevent potential attacks on data. And also compare it with some of the proposed protocols in terms of communication cost and computation cost.
Email Spam Detection using Naïve Bayes Algorithm

Abstract: Email Spam has become a vital issue currently, with high-speed growth of internet users. Some people are using them for illegal conducts, phishing and fraud. Sending malicious link through spam emails which can harm our system and may also they will seek into our system. The need of email spam detection is to prevent spam messages from lagging into user’s inbox so it’ll improve user experience. This project will identify those spam emails by using machine learning approach. Machine learning is one amongst the applications of Artificial Intelligence that allow systems to read and improve from experience without being specific programmed. This paper will discuss the machine learning algorithm which is Naïve Bayes. It is a probabilistic classifier, which means it predicts on the idea of the probability of an object and it is selected for the email spam detection having best precision and accuracy.
Physically Accurate Learning-based Performance Prediction of Hardware-accelerated ML Algorithms

Parameterizable ML accelerators are the product of recent breakthroughs in machine learning (ML). To fully enable the design space exploration, we propose a physical-design-driven, learning-based prediction framework for hardware-accelerated deep neural network (DNN) and non-DNN ML algorithms. It employs a unified methodology, coupling backend power, performance and area (PPA) analysis with frontend performance simulation, thus achieving realistic estimation of both backend PPA and system metrics (runtime and energy). Experimental studies show that the approach provides excellent predictions for both ASIC (in a 12nm commercial process) and FPGA implementations on the VTA and VeriGOOD-ML platforms.
Tests of QED at Low Energy

Written for a two-semester Master's or graduate course, this comprehensive treatise intertwines theory and experiment in an original approach that covers all aspects of modern particle physics. The author uses rigorous step-by-step derivations and provides more than 100 end-of-chapter problems for additional practice to ensure that students will not only understand the material but also be able to apply their knowledge. Featuring up-to-date experimental material, including the discovery of the Higgs boson at CERN and of neutrino oscillations, this monumental volume also serves as a one-stop reference for particle physics researchers of all levels and specialties. Richly illustrated with more than 450 figures, the text guides students through all the intricacies of quantum mechanics and quantum field theory in an intuitive manner that few books achieve.
Parity Violation in Weak Interactions

Written for a two-semester Master's or graduate course, this comprehensive treatise intertwines theory and experiment in an original approach that covers all aspects of modern particle physics. The author uses rigorous step-by-step derivations and provides more than 100 end-of-chapter problems for additional practice to ensure that students will not only understand the material but also be able to apply their knowledge. Featuring up-to-date experimental material, including the discovery of the Higgs boson at CERN and of neutrino oscillations, this monumental volume also serves as a one-stop reference for particle physics researchers of all levels and specialties. Richly illustrated with more than 450 figures, the text guides students through all the intricacies of quantum mechanics and quantum field theory in an intuitive manner that few books achieve.
Digital Transformation in Educational Institutions: Scrutinizing the Debate and Hhighlighting Success Factors

In German education, digitalization is evolving, and the COVID-19 pandemic has accelerated this process due to closed schools and universities. While these external conditions were almost the same for all institutions, they have dealt with remote teaching in different ways and developed various new learning concepts. We conducted 27 interviews with headmasters, e-learning managers, and teachers from Germany to gain insights into varying attitudes towards learning management systems, as well as the motivations and concerns about future teaching with digital support. Using an inductive method, we identify different obstacles, including lack of digital competencies, necessity of technical equipment, concerns about replacing traditional lessons, but also enthusiasm for new opportunities. We argue that a meaningful use of learning management systems and learning analytics depends on an open and impartial debate on data protection and equal opportunities. Overall, this paper contributes to current research with proposition development about privacy rights and inequalities.
UGV Digital Twin for garbage detection through AI

The efforts to maintain low level of pollution in the environment need to meet sustainable and innovative solutions in order to be effective in the modern and complex world. Indeed, currently we are facing critical issues in terms of sustainability and new technologies are crucial to identify new solutions: recycling the abandoned wastes is a critical issue that needs to be considered and implemented beside their collection and processing. Due to these reasons, the purpose of this research is to develop an intelligent cleaning complex system based on UGV (Unmanned Ground Vehicle), which focus is to enhance the ability of strategic decisions making and achieve satisfactory trash detection in the wild for deployment. The system stands on AI (Artificial Intelligence) based on deep neural networks, which behaviour is simulated in a 3D Unity synthetic environment, through the usage of a digital twin, that simulates the desired solution of detecting and collecting garbage. The developed virtual environment chosen by the authors is a contaminated beach, in which an armed and autonomous robot manages to collect and recycle the wastes; the scenario is flexible to changes and further ones can be developed for training and testing of the intelligent system.
Open Science for Better FAIRness: A biodiversity virtual research environment point of view

"FAIR (Findable, Accessible, Interoperable, Reusable) principles" (Wilkinson et al. 2016) and "open science" are two complementary movements in biodiversity science. Although we need to transition to making scientific data and associated material more FAIR, this does not necessarily imply open data or open source algorithms. Here, based on the experience of the French Biodiversity Data Hub ("Pôle national de données de Biodiversité" - PNDB), which is an e-infrastructure for and by researchers, we want to showcase how focusing on openness can be a strategy to efficiently reach greater FAIRness. Following DataOne guidance, we can build a complete data/metadata ecosystem allowing us to structure heterogeneous environmental information systems. Using the Galaxy analysis platform and its related initiatives (Galaxy training network, European Erasmus+ Gallantries project, bioconda, bioContainer), we can thus illustrate how we can create transparent, peer-reviewed and accessible tools and workflows and collaborative training material. The Galaxy platform also facilitates use of high performance computing infrastructure through notably the European Open Science Cloud marketplace. Finally, through our experiences contributing to open source projects like EML (Ecological Metadata Language (Michener et al. 1997)) Assembly Line, EDI (Environmental Data Initiative, or PAMPA (Indicators of Marine Protected Areas performance for managing coastal ecosystems, resources and their uses), a French platform to help protected areas managers to standardize and analyse their data, we also show how building open source "doors" through the R Shiny programming language to these environments can be beneficial for all.
Replication: Carrying out an Empirical Study

Having considered repetition in the previous chapter, we now look at replication in this chapter. Beginning with a discussion of replication, we move on to attempt to replicate some features of Leech’s study of modal verb decline, building on similar work by Paul Baker and focusing on the results produced in support of claims of modal decline from the early twenty-first century. Our study undertakes a form of replication rare in corpus linguistics, but common in the sciences – a study based on new datasets using the same sampling frame as the original dataset. Our replications then force us through a process of hypothesis reformulation, corroboration, falsification and, at times, rejection in line with the scientific approach to corpus data outlined in Chapters 1 to 5 of the book.
The effects of introducing language learning software during the COVID-19 pandemic

The research aimed to investigate the impact of introducing language learning software to university students in the learning environment influenced by COVID-19. It describes the results of an experience with adding daily activities in Elevate, a free language learning application marketed as brain training software for the undergraduate English curriculum. The experiment took place at RUDN University (Moscow, Russia) in 2021 during the COVID-19 pandemic. Participants were engaged in synchronous distance and face-to-face learning, depending on their location and the current epidemiological situation. Some students alternated between online and offline environments. Research methods included a theoretical analysis of current trends in language training and gamification. Statistical data were collected and processed using the university’s digital ecosystem. The research included quantitative data from the experiment and surveys, which consisted of multiple-choice, single-choice, and open-ended questions. The research sample included students from eight countries with the common european framework of reference for languages (CEFR) levels of English from B1 to C1. The research results show the effects of language learning software on students’ language proficiency (P). From the results of this research, it can be concluded that schools and universities can use multi-level feedback from participants to maximize the effectiveness of language training and diversify the range of activities included in the curriculum. The results also provide the theoretical basis for e-learning software development and represent a new contribution to the literature.