Reviewerly/exHarmony huggingface dataset:
Improved Privacy of Data Transaction in IoT-Enabled Blockchain Technology Using Privacy-Based Machine Learning Algorithms

Conventional models rely on a trusted third party to check the data integrity of IoT data. These methods may not be applicable for global blockchains, or they may be invalid for transactions. Since most of the transactions in blockchains occur via scalable local ledgers that compromises on peer validation of transaction at local/global level. Hence, it is required to have an interface mechanism that enables the security of data. In this chapter, blockchain is regarded as a potential solution to secure the data via IoT supply chains, and a ML algorithm is utilised data. The ML model processes the data management based on the number of transactions, time of transaction, and transaction confirmation time. The ML algorithm is designed with a consensus protocol, which improves the scalability of data via blockchain networks. It helps in integrating the IoT data transmission while assessing the parameters. The results on the piracy of data transacted via blockchain show that the proposed blockchain-based secured IoT data management in telehealth supply chains has a higher level of security.
Teaching quantum communications through a hands-on laboratory

Abstract Teaching quantum communication is a challenging task when involving different technical and engineering backgrounds. The use of an approach that exploits the knowledge of these profiles, as well as other technological resources available for demonstrations or exercises, enhances this teaching. This paper presents as an example the “Quantum Communications Lab” that took place at the 6th INFIERI Summer School in 2021. In this lab, the access to the Madrid Quantum Communication Infrastructure (MadQCI) was an important resource available.
CONCEPT MAPS AS INCLUSIVE LEARNING TOOLS. THE VISUAL DESIGN PERSPECTIVE

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 615-620ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.0208Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
STRENGTHENING ADULT READER PROFILES: RESULTS OF AN EXPLORATORY RESEARCH

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 5264-5270ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.1361Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
Automatic picking method of microseismic first arrival based on support vector machine based on particle swarm optimization

Automatic and accurate arrival time pickup of microseismic first-arrival waves is an important prerequisite for high precision microseismic source location. Aiming at the low efficiency of the traditional manual pickup method and the low accuracy of the long, short window energy ratio (STA/LTA) method commonly used in automatic pickup for low signal-to-noise ratio signals, an automatic picking method of microseismic first arrival based on support vector machine based on particle swarm optimization is proposed. Firstly, according to the amplitude and energy of microseismic signal and the energy ratio of adjacent time, the signals are marked with different categories. Then the parameters are optimized by particle swarm optimization algorithm to construct the support vector machine model of microseismic first-arrival. Finally, the data is substituted to extract the microseismic first-arrival. The experiment is carried out with the microseismic monitoring data of underground roadway in a gold mine. The experimental results show that, under the condition of low SIGNal-to-noise ratio, the picking accuracy of the proposed method is 96.4%, the average pickup error is 3.9ms, and the picking accuracy and accuracy are better than STA/LTA method.
Blind image super-resolution reconstruction based on dual regression network

Existing deep learning-based Super Resolution (SR) reconstruction algorithms achieve remarkable performance on images with known degradation. Most of the degradation models exists problems in self-adaptations when facing with the deviation of the degradation model of the image of the real scene, and the effect is not good. Therefore, this paper proposes a blind image super-resolution reconstruction algorithm based on dual regression, which aims to solve the problem of poor performance of super-resolution networks in real scenes. Firstly, the closed-loop network is used to constrain the mapping space, and the optimal reconstruction function is found to improve the network reconstruction performance. Secondly, the attention mechanism is adopted into the residual block of feature extraction to expand the receptive field of the feature map, improve the reuse of features, and strengthen the reconstruction of high-frequency information. Finally, the frequency-domain blur kernel map estimates the down sampling kernel and reconstructs the low-resolution image, adaptively extracts the feature expression, enhances the ability to restore texture details, and reconstructs the real-world image better.
Addressing Class Variable Imbalance in Federated Semi-Supervised Learning

Federated Semi-supervised Learning (FSSL) combines techniques from both fields of federated and semi- supervised learning to improve the accuracy and performance of models in a distributed environment by using a small fraction of labeled data and a large amount of unlabeled data. Without the need tocentralize all data in one place for training, it collect updates of model training after devices train models at local, and thus can protect the privacy of user data. However, during the federal training process, some of the devices fail to collect enough data for local training, while new devices will be included to the group training. This leads to an unbalanced global data distribution and thus af ect the performance of the global model training. Most of the current research is focusing on class imbalance with a fixednumber of classes, while little attention is paid to data imbalance with a variable number of classes. Therefore, in this paper, we propose Federated Semi-supervised Learning for Class Variable Imbalance (FCVI) to solve class variable imbalance. The class-variable learning algorithm is used to mitigate the data imbalance due to changes of the number of classes. Our scheme is proved to be significantly better than baseline methods, while maintaining client privacy.
Experimental demonstration of bandwidth enhancement in photonic time delay reservoir computing

Time delay reservoir computing (TDRC) using semiconductor lasers (SLs) has proven to be a promising photonic analog approach for information processing. One appealing property is that SLs subject to delayed optical feedback and external optical injection, allow tuning the response bandwidth by changing the level of optical injection. Here we use strong optical injection, thereby expanding the SL's modulation response up to tens of GHz. Performing a nonlinear time series prediction task, we demonstrate experimentally that for appropriate operating conditions, our TDRC system can operate with sampling times as small as 11.72 ps, without sacrificing computational performance.
Secure Authentication and Security System for Mobile Devices in Mobile Cloud Computing

The term "mobile cloud computing" refers to the combination of general cloud computing and mobile computing. With mobile devices, we can access resources from the remote cloud data center with the help of mobile electronics and peripherals like mobile smartphones, laptops, gadgets, etc. via cellular technology or wireless communication. Mobile devices have many resource constraints, such as storage capacity, processing speed, and battery life.&nbsp; Because of these factors, mobile devices cannot manipulate on clouds information inside a data center; hence we need to process information or data through external mobile devices. The security of data in cloud computing is often compromised by outside resources. To secure data, researchers have proposed many different methods, including the use of a trusted third party agency. In this thesis article, we will analyze the work of others and propose an approach that allows us to store data securely over mobile devices. We will also review the theory behind how data can be secured on a cloud server by mobile device and offer our own security framework.
Strengthened “Project Duaw” A Policy Guidelines For Pregnant Learners’ Education Based on their Experienced Challenges During Covid-19

This study was conducted to develop policy guidelines for pregnant learners of Upper Mainit National High School during COVID-19. The participants of the study were the identified fifteen (15) pregnant learners of the school year 2021 -2022. Since the study falls under a phenomenological type of qualitative research, the method used to gather data was In-depth Interview (5- WHY Analysis), Interview Guide, and Field Notes/Observation Notes. After that, data analysis and interpretation were done through Thematic Analysis. As to the research framework, the study adopted the action research framework suggested by Kemmis and McTaggart (2005) to present the Innovation, Intervention, and Strategy. The results revealed that the biggest factor leading to the early pregnancy of the participants was the COVID-19 pandemic, which caused school closure. The results also showed that neighbors’ impact and peer pressure/influence were among the major factors that led them to early pregnancy. Furthermore, the major challenge encountered among pregnant learners in terms of the COVID-19 pandemic was the absence of face-to-face classes. In this area, learning performance was a learning comprehension-related challenge.&#x0D;
TEXT SUMMERIZATION USING NLP

Due to major advancements in software and hardware technology, the discipline of data mining has seen tremendous change recently.More forms of data are becoming accessible because to technological advancements, which is particularly advantageous for text data.The internet's and social networks' software and technology platforms have facilitated the quick creation of enormous data repositories.Text data is often managed by a search engine since it lacks structures, whereas structured data is typically kept by a database system.The search engine enables the internet user to find the crucial information from the compiled works with the aid of a keyword query.The process of extracting the most important information from text.
Hindi and Kannada Twitter Sentiment Analysis Using Machine Learning Algorithm

Sentiment analysis is an important task in natural language processing. Twitter sentiment analysis (SA) is the most frequently researched discipline today. Only a few researchers have concentrated on multilingual twitter sentiment analysis, while the majority of scholars have worked on English twitter sentiment analysis. Recently, there seems to be a large increase in the quantity of data sets transmitted via social media in a variety of languages. This work examines the semantic nature of a text in order to classify positive and negative tweets using Hindi and Kannada language models, respectively. Machine learning models such as Naïve Bayes Classifier, KNeighbors’ Classifier, Decision Tree Classifier and Random Forest Classifier are used to tackle the classification task.
A Comparative Study on Deepfake Detection Algorithms

In recent years, the number of images and videos shared online increased and people have easy ways to access such content. “DeepFake” refers to any multimedia content created using deep learning technology in order to make it appear realistic. The creation of deepfake videos and images using deep learning techniques leads to very realistic “DeepFake” videos and images by changing the digital content of images and videos. Deepfake is widely recognized as one of artificial intelligence’s most dangerous uses. Deepfake makes it possible to place a person in a totally imaginary situation since it is used to imitate an activity that the person did not perform. Deepfakes have been becoming increasingly dangerous to democracy, society’s security and people’s privacy. The distribution of such deepfake content on various platforms urged the international community to revaluate the threat to social security posed by such content. It encouraged the researchers around the world to develop effective deepfake detection methods. In this paper we have discussed such approaches of deepfake detection in videos and images that are available in recent studies and have provided comparative review of research on deepfake detection algorithms. It also compares the different detection techniques and examines their limitations and advantages.
On the use of Blockchain Technology in Supply Chains: A brief review

Recently, The application of Blockchain technology in supply chains has drawn more attention from researchers and increased due to its ability to provide transparency, tracking, tracing, security and enhance confidence and trust among the members participating in the supply chain. In this paper, we provide a brief review which focuses on the use of the Blockchain technology to tackle with three challenges which are facing supply chains, namely traceability, trust and digitization. Besides, the paper provides a discussion about some sectors where the Blockchain technology is adopted at an advanced stage.
Research on Image Information Security Algorithm Based on Wavelet Neural Network

With the continuous emergence of digital images and digital videos, a large number of images containing sensitive information need to be kept secret, and a large number of digital media works need intellectual property protection. Image security and confidentiality technology has become another hot spot in the field of information security. In view of the shortcomings of the statistical processing algorithm for the verification results adopted by various enterprises in the network information security evaluation, through the research on the characteristics of computer security, this paper compares the selection of weights in the commonly used weighted arithmetic average method. Image information encryption originates from the early classical encryption theory. Its purpose is to transform a given image into a disordered image in the spatial or frequency domain according to certain transformation rules, so as to hide the real information of the image information itself. How to protect image information security has become a hot topic in the international research. In order to better adapt to the unique nature of image information and meet the needs of data security and copyright protection, scholars are actively seeking new technologies and methods. This paper studies the basic algorithm of wavelet neural network and applies it to image information security. The main idea of the algorithm is to combine the theory of wavelet transform with the algorithm of neural network, and then get the algorithm of wavelet neural network.
The Network of Power Transmission and Transformation Equipment and Its Application in the Intelligent Power Network

with the rapid development of science and technology, the Internet of things has become an important driving force to promote the development of human society, which is also the main reason for the emergence of the Internet of things of power transmission and transformation equipment. Up to now, the Internet of things of transmission and transformation equipment has become an important part of smart grid, which has a vital impact on the efficient and stable operation of smart grid. In view of this situation, this paper first briefly introduces the Internet of things of power transmission and transformation equipment, and then probes into the application of the Internet of things of transmission and transformation equipment in smart grid, hoping to provide more powerful support and guarantee for the modernization development of our country.
Research on Financial Fraud Identification Based on Machine Learning Algorithm

In recent years, many cases of financial fraud have occurred at home and abroad.The repeated cases of financial fraud not only bring serious property losses to investors, but also seriously affect the credit mechanism of the capital market and slow down the process of healthy development of market economy, so how to efficiently and accurately identify enterprises with financial fraud is a hot topic of academic research.In this paper, a comprehensive identification model is formed by using decision trees in machine learning algorithms as well as artificial neural networks for model construction training.The results show that the accuracy of the two algorithmic models improves after importance selection, with 74.2% and 90%, respectively.It is finally concluded that the importance selection is beneficial to improve the accuracy of the financial fraud identification model, and the artificial neural network can demonstrate better identification results in the algorithm selection.
Performance Benchmarking of Visual Human Tracking Algorithms for UAVs

With the evolution of robotic systems, unmanned aerial vehicles (UAV) have become a target of interest for domains such as computer vision (CV) and artificial intelligence (AI), contributing to a variety of applications for surveillance, transportation and many more. A very hot topic that is the playground of the proposed benchmark is visual human tracking in images acquired by a camera mounted on a UAV. This target application troubles CV and deep learning (DL) research community in recent years and it has created serious demands for visual tracking algorithms. Some of the most important demands are high performance under hard visual tracking conditions and deployment in edge devices with limited computation resources. These two challenges are the main motivation of the presented paper, where 37 tracking algorithms have been benchmarked in visual object tracking (VOT) images. For each tracking algorithm two metric categories, relative to detection performance and hardware resources consumption, have been considered. The objective of the proposed paper is to highlight the most lightweight and high performance tracking algorithms for usage in UAV based applications.
Per una rassegna dei tratti (morfo)sintattici dell’italiano scientifico

This contribution describes the most salient (morpho)syntactic features of scientific Italian, focusing on the characteristics found at phrase and clause levels. The specificities identified, which become evident through a comparison with the common use of the Italian language, are valid for the most part regardless of the scientific discipline considered and can be explained based on general pragmatic and textual characteristics such as objectivity and algidity, abstraction and specialization, economy and clarity of exposition.
Data from Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers

&lt;div&gt;Abstract&lt;p&gt;Whole-slide histology images contain information that is valuable for clinical and basic science investigations of cancer but extracting quantitative measurements from these images is challenging for researchers who are not image analysis specialists. In this article, we describe HistomicsML2, a software tool for learn-by-example training of machine learning classifiers for histologic patterns in whole-slide images. This tool improves training efficiency and classifier performance by guiding users to the most informative training examples for labeling and can be used to develop classifiers for prospective application or as a rapid annotation tool that is adaptable to different cancer types. HistomicsML2 runs as a containerized server application that provides web-based user interfaces for classifier training, validation, exporting inference results, and collaborative review, and that can be deployed on GPU servers or cloud platforms. We demonstrate the utility of this tool by using it to classify tumor-infiltrating lymphocytes in breast carcinoma and cutaneous melanoma.&lt;/p&gt;Significance:&lt;p&gt;An interactive machine learning tool for analyzing digital pathology images enables cancer researchers to apply this tool to measure histologic patterns for clinical and basic science studies.&lt;/p&gt;&lt;/div&gt;
Water Animosity Detection and Tainting Emulsion Remover for Lakes

Unwanted and toxic chemicals in water are increasing rapidly which not only makes the aquatic life suffer but also the Animalia on the plains. The froth formed due to the toxic disposals on those lakes is harmful as well as inflammable which is prone to result in wildfires. The paper proposes a robot structure that would sail on the water surface collecting solid waste. A part of the robot structure would reside inside the water body which will treat water chemically. An autonomous system that can traverse over the water body and cleans the water body both physically and chemically.
Supplementary Data from Raman Spectroscopy Reveals That Biochemical Composition of Breast Microcalcifications Correlates with Histopathologic Features

&lt;p&gt;The supplementary data file contains detailed patient's characteristics including the number of MC detected for each sample, statistical significance calculations, classification and validation results, wide angle X-ray scattering (WAXS) diffractograms, WAXS and small angle X-ray scattering (SAXS) images, mean Raman spectra of different diagnostic categories, box-plot reporting phosphate peak position shift among diagnostic categories, loading of the first principal components emerging from PCA and correlation between Raman variables and a) age, b) BIRADS classification and c) MC size.&lt;/p&gt;
Implicit Cross-Lingual Word Embedding Alignment for Reference-Free Machine Translation Evaluation

As we know, cross-lingual word embedding alignment is critically important for referencefree machine translation evaluation, where source texts are directly compared with system translations.In this paper, it is revealed that multilingual knowledge distillation for sentence embedding alignment could achieve cross-lingual word embedding alignment implicitly.A simplified analysis is given to explain the implicit alignment reason.And according to the analysis, it could be deduced that using the last layer embeddings of the distilled student model will have the best alignment effect, which is also validated by the experimental results on the WMT19 datasets.Furthermore, with the assistant of a target-side language model, BERTScore and Word Mover's Distance using the cross-lingual word embeddings get very competitive results (4 best average scores on 3 types of language directions and ranking first among more than half of all 18 language pairs for the system-level evaluations) in the WMT19's reference-free machine translation evaluation tasks when the current state-of-the-art (SOTA) metrics are chosen for comparison. INDEX TERMS Cross-lingual word embedding,