Reviewerly/exHarmony huggingface dataset:
Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm

High dimensionality in data sets is one of the challenges faced in classification, data mining, and sentiment analysis. In the data set, many dimensionalities require effort to simplify. Many of these dimensionalities have a major impact on the complexity and performance of the algorithms used for classification. Various challenges were encountered, including how to determine the optimal combination of pre-processing techniques, how to clean the dataset, and determine the best classification algorithm. This study uses a new approach based on the combination of three powerful techniques which are: tokenizing-lowercasing-stemming (for series of preprocessing), support vector machine (SVM) for supervised classification, and fuzzy matching (FM) for dimensionality reduction. The proposed model was realized using 3 different datasets, namely Amazon product review, movie review, and airline review from Twitter. This study provides better findings than the previous results. Improved performance is generated by SVM combined with FM, resulting in 96% accuracy. So that the SVM-FM combination can be said to be the best combination for sentiment analysis on the given data set.
Machine Learning-Based Hybrid Recommendation (SVOF-KNN) Model For Breast Cancer Coimbra Dataset Diagnosis

An effective way to identify breast cancer is by creating a prediction algorithm using risk factors. Models for ML have been used to improve the effectiveness of early detection. This article analyses a KNN combined with singular value decomposition and Grey wolf optimization(GWO) method to give a detection of breast cancer(BC) at the early phase depending on risk metrics. The SVD technique was utilized to eliminate the reliable feature vectors, the GW optimizer was used to select the feature vectors, and while KNN model was used to diagnose the BC status. The proposed hybrid recommendation model (SVOF-KNN) for BC prediction's main objective is to give an accurate recommendation for BC prognosis through four different steps such as;BCCD dataset collection, data pre-processing, feature selection, and classification/recommendation. It is implemented to classify the consequence of risk metrics connected withregular blood analysis(BA) in the BCCD database. The aspects of the BC dataset are insulin, glucose, HOMA, Leptin, resistin, etc. The error categories such as RMSE and MAE are used to calculate the exception values for each instance of the BC dataset. It hybrid model has recommended the best score instance having the minimumexception rateas the defined features for BC prediction. It improves significance in automatic BC classification with the optimum solution. The hybrid recommendation model (SVOF-KNN) also recommends the accurateclassification method for BC diagnosis. The results of this work shall enhance the QoS in BC care.
Quantum Computers for Weather and Climate Prediction: The Good, the Bad, and the Noisy

Abstract Over the past few years, quantum computers and quantum algorithms have attracted considerable interest and attention from numerous scientific disciplines. In this article, we aim to provide a nontechnical yet informative introduction to key aspects of quantum computing. We discuss whether quantum computers one day might become useful tools for numerical weather and climate prediction. Using a recently developed quantum algorithm for solving nonlinear differential equations, we integrate a simple nonlinear model. In addition to considering the advantages that quantum computers have to offer, we shall also discuss the challenges one faces when trying to use quantum computers for real-world problems involving “big data,” such as weather prediction.
An Attention-PiDi-UNet and Focal Active Contour loss for Biomedical Image Segmentation

Biomedical image analysis has been of paramount importance in modern computer vision recently. While up-to-date procedures have notched up success in medical imaging, there is still necessity to optimise the network and the loss function. Motivated by the PiDiNet architecture and the Active Contour methods, we propose a new Attention-PiDi-UNet architecture and a new Focal Active Contour loss. Our proposed method is evaluated on the three popular datasets: the MRI cardiac ACDC dataset (3-D images), the Skin Lesion ISIC 2018 dataset and the PH2 dataset (both in 2-D images). Several experiments have comfirmed our proposed method effectiveness with outstanding segmentation results.
FAIR and Open Data requires proper incentives and a shift in academic culture

While the benefits of FAIR principles — findable, accessible, interoperable, reusable — and Open Data seem clear to most scientists, significant hurdles need to be overcome to make scientific databases useful and sustainable. The difficulties with incentivizing the community to share data, as encountered by the recently launched Open Membrane Database (OMD), can be used as a starting point to fuel the debate on the power and pitfalls of FAIR and Open Data practices.
Hyperconnected: children and adolescents on social media. The TikTok phenomenon

Network communication digital platforms have revolutionized the way we communicate, get information, and entertain ourselves; they have had a major impact on the new generations. The pandemic has increased consumption patterns at all ages and its consequences: a sedentary lifestyle, obesity, sleep, psychological, cognitive, memory/attention, and academic performance disorders, problematic behaviors, and risks of cyberbullying. The activity of the new generations on TikTok -a platform that allows them to create, view, share videos and viral challenges- has affected their behavior. Their posts lead to likes, comments, and followers; this stimulates the dopaminergic reward system, which is the basis of addictive behaviors. Pediatricians should be aware of these behaviors in order to recommend good practices. The objective of this narrative review, based on the bibliography from PubMed Central, SciELO, national and international recommendations, is to provide information and an update to health care providers on TikTok, its characteristics, and the recommendations for safe use.Las plataformas digitales de comunicación en red revolucionaron el modo de comunicarnos, informarnos y entretenernos; impactaron principalmente en las nuevas generaciones. La pandemia incrementó los patrones de consumo en todas las edades y sus consecuencias: sedentarismo; obesidad; alteraciones del sueño, psicológicas, cognitivas, de la memoria/atención, del rendimiento académico; comportamientos problemáticos y riesgos de ciberacoso. La actividad de las nuevas generaciones en TikTok –una plataforma donde pueden crear, visualizar, compartir videos y retos virales– tiene impacto en su comportamiento. Sus publicaciones generan aprobaciones, comentarios y seguidores; esto estimula el circuito dopaminérgico de recompensa, que es la base de los comportamientos adictivos. El pediatra debe conocer estas conductas para recomendar buenas prácticas de uso. Esta revisión narrativa, basada en bibliografía de PubMed Central, SciELO, recomendaciones nacionales e internacionales, tiene como objetivo informar y actualizar a los profesionales sobre la plataforma, sus características y sus recomendaciones de uso seguro.
Design of highly nonlinear confusion component based on entangled points of quantum spin states

Cryptosystems are commonly deployed to secure data transmission over an insecure line of communication. To provide confusion in the data over insecure networks, substitution boxes are the solitary components for delivering a nonlinear mapping between inputs and outputs. A confusion component of a block cipher with high nonlinearity and low differential and linear approximation probabilities is considered secure against cryptanalysis. This study aims to design a highly nonlinear substitution-permutation network using the blotch symmetry of quantum spin states on the Galois field GF (28). To observe the efficiency of the proposed methodology, some common and advanced measures were evaluated for performance, randomness, and cryptanalytics. The outcomes of these analyses validate that the generated nonlinear confusion components are effective for block ciphers and attain better cryptographic strength with a high signal-to-noise ratio in comparison to state-of-the-art techniques.
A Survey on Physical Adversarial Attack in Computer Vision

Abstract In the past decade, deep learning has dramatically changed the traditional hand-craft feature manner with strong feature learning capability, promoting the tremendous improvement of conventional tasks. However, deep neural networks (DNNs) have been demonstrated to be vulnerable to adversarial examples crafted by small noise, which is imperceptible to human observers but can make DNNs misbehave. Existing adversarial attacks can be divided into digital and physical adversarial attacks. The former is designed to pursue strong attack performance in lab environments while hardly remaining effective when applied to the physical world. In contrast, the latter focus on developing physical deployable attacks, which are more robust in complex physical environmental conditions (e.g., brightness, occlusion, etc.). Recently, with the increasing deployment of the DNN-based system in the real world, enhancing the robustness of these systems is an emergency, while exploring physical adversarial attacks exhaustly is the precondition. To this end, this paper reviews the development of physical adversarial attacks against DNN-based computer vision tasks (i.e., image recognition and object detection tasks), which can provide beneficial information for developing stronger physical adversarial attacks. For completeness, we also briefly introduce the works that do not involve physical attacks but are closely related to them. Specifically, we first proposed a taxonomy to summarize the current physical adversarial attacks. Then we briefly discuss the existing physical attacks and focus on the technique for improving the robustness of physical attacks under complex physical environmental conditions. Finally, we discuss the issues of the current physical adversarial attacks to be solved and give promising directions.
Application of improved transformer based on weakly supervised in crowd localization and crowd counting

To the problem of the complex pre-processing and post-processing to obtain head-position existing in the current crowd localization method using pseudo boundary box and pre-designed positioning map, this work proposes an end-to-end crowd localization framework named WSITrans, which reformulates the weakly-supervised crowd localization problem based on Transformer and implements crowd counting. Specifically, we first perform global maximum pooling (GMP) after each stage of pure Transformer, which can extract and retain more detail of heads. In addition, we design a binarization module that binarizes the output features of the decoder and fuses the confidence score to obtain more accurate confidence score. Finally, extensive experiments demonstrate that the proposed method achieves significant improvement on three challenging benchmarks. It is worth mentioning that the WSITrans improves F1-measure by 4.0%.
Vulnerability Assessment with Network-Based Scanner Method for Improving Website Security

The digital world has seen a significant increase in security threats in recent years, with hacker attacks on websites being a major concern in cybersecurity. One platform that is particularly vulnerable is WordPress, which is widely used and therefore a popular target for hackers. About 95.62% hacked website in 2021 is WordPress based site. Therefore, to improve website security we conducted a vulnerability assessment on a WordPress based website, in order to identify vulnerabilities that may be exploited by hackers. To do the vulnerability assessment, we used the network-based scanner based to detect vulnerabilities on the WordPress website. Our results showed that the website had several vulnerabilities that needed to be addressed and fixed immediately. The conclusion of our research highlights the importance of conducting regular vulnerability assessments on WordPress-based websites to reduce the risk of vulnerabilities being exploited. By taking proactive measures to identify and fix vulnerabilities, website owners can better protect their sites from potential hacker attacks. It is crucial for website owners to be aware of the risks posed by security threats in the digital world and to take steps to mitigate these risks to protect their businesses and their customers.
Privacy in electronic health records: a systematic mapping study

Abstract Main Electronic health record (EHR) applications are digital versions of paper-based patient health information. Traditionally, medical records are made on paper. However, nowadays, advances in information and communication technology have made it possible to change medical records from paper to EHR. Therefore, preserving user data privacy is extremely important in healthcare environments. The main challenges are providing ways to make EHR systems increasingly capable of ensuring data privacy and at the same time not compromising the performance and interoperability of these systems. Subject and methods This systematic mapping study intends to investigate the current research on security and privacy requirements in EHR systems and identify potential research gaps in the literature. The main challenges are providing ways to make EHR systems increasingly capable of ensuring data privacy, and at the same time, not compromising the performance and interoperability of these systems. Our research was carried out in the Scopus database, the largest database of abstracts and citations in the literature with peer review. Results We have collected 848 articles related to the area. After disambiguation and filtering, we selected 30 articles for analysis. The result of such an analysis provides a comprehensive view of current research. Conclusions We can highlight some relevant research possibilities. First, we noticed a growing interest in privacy in EHR research in the last 6 years. Second, blockchain has been used in many EHR systems as a solution to achieve data privacy. However, it is a challenge to maintain traceability by recording metadata that can be mapped to private data of the users applying a particular mapping function that can be hosted outside the blockchain. Finally, the lack of a systematic approach between EHR solutions and existing laws or policies leads to better strategies for developing a certification process for EHR systems.
The Remittance, Foreign Direct Investment, Export, and Economic Growth in Bangladesh: A Time Series Analysis

This research empirically examined the effect of internal and external factors on the economic growth of Bangladesh using the annual data from 1976 to 2019.ARDL bounds tests were used to detect cointegration and understand the dynamic effect.This study mainly identified the positive and negative casualties because remittance and export have a significant positive role, and FDI has an adverse role in the economic growth process.Furthermore, this study recommends that the government take appropriate steps to increase export, the inflow of remittance and FDI to achieve longrun economic growth, considering the impact of COVID-19 and present Russia-Ukraine war.
Dilemmas Between Freedom of Speech and Hate Speech: Russophobia on Facebook and Instagram in the Spanish Media

On March 11, 2022, Russia opened a criminal case against Meta, the parent firm that owns Facebook, Instagram, and WhatsApp. It alleged that Facebook had modified its community standards, broadening its concept of freedom of speech to allow alleged hate speech against Russian citizens, amid the conflict in Ukraine. Reuters (2022, para. 1) refers to a “temporary change in the company’s hate speech policy,” according to confidential Facebook documents. The Office of the High Commissioner for Human Rights called the change “worrying” (“Rusia y Ucrania,” 2022, para. 11). In this context, this article addresses two objectives: (a) to explore and comment on the state of the art on freedom of expression in social networks and its deontological limitations to prevent hatred against nationalities (EU legislation, scientific research, Twitter, and Instagram deontological limitations); and (b) to study the emergence of possible cases of Russophobia, in a mediatized form, through the news of Spanish media and the comments they generated on their Facebook and Instagram sites. A triangular methodology is used: analytical and longitudinal commentary on EU definitions and standards on hate speech; quantitative analysis of news items in Spanish media on Russophobia, on Facebook and Instagram, published between January 1, 2022, and October 20, 2022; and mixed analysis of the engagement of these news items, thanks to the Fanpage Karma tool. The media coverage of Russophobia is scarce, with an average of one news item per media and, exceptionally, with two news items in very few cases. It is also striking that in such a long period, only six hashtags are used.
The Application of Morphogo in the Detection of Megakaryocytes from Bone Marrow Digital Images with Convolutional Neural Networks

The evaluation of megakaryocytes is an important part of the work up on bone marrow smear examination. It has significance in the differential diagnosis, therapeutic efficacy assessment, and predication of prognosis of many hematologic diseases. The process of manual identification of megakaryocytes are tedious and lack of reproducibility; therefore, a reliable method of automated megakaryocytic identification is urgently needed. Three hundred and thirty-three bone marrow aspirate smears were digitized by Morphogo system. Pathologists annotated megakaryocytes on the digital images of marrow smears are applied to construct a large dataset for testing the system's predictive performance. Subsequently, we obtained megakaryocyte count and classification for each sample by different methods (system-automated analysis, system-assisted analysis, and microscopic examination) to study the correlation between different counting and classification methods. Morphogo system localized cells likely to be megakaryocytes on digital smears, which were later annotated by pathologists and the system, respectively. The system showed outstanding performance in identifying megakaryocytes in bone marrow smears with high sensitivity (96.57%) and specificity (89.71%). The overall correlation between the different methods was confirmed the high consistency (r ≥ 0.7218, R2 ≥ 0.5211) with microscopic examination in classifying megakaryocytes. Morphogo system was proved as a reliable screen tool for analyzing megakaryocytes. The application of Morphogo system shows promises to advance the automation and standardization of bone marrow smear examination.
Corporate digital responsibility: bibliometric landscape – chronological literature review

Over the last decade, we have witnessed how new technologies, such as AI in the form of automation or machine learning, have proliferated in business processes. Although digitalisation has led to a significant increase in efficiency, it raises certain concerns related to privacy, data protection and other human rights, which might be at stake when huge amounts of data are being collected and processed or when AI is used for decision making. Digitalisation, apart from increasing efficiency, has a strong potential to contribute to sustainable development if responsibility and trust are guaranteed. Therefore, companies should critically reflect upon different ethical criteria to avoid compromising democratic rights and values when engaging in digitalisation. In our study, we wanted to draw attention to and increase awareness of an evolving area of corporate digital responsibility. In addition to the bibliometric analysis of the CDR literature, a summary of the definitions is provided.
Social Metaverse: Challenges and Solutions

Social metaverse is a shared digital space combining a series of interconnected virtual worlds for users to play, shop, work, and socialize. In parallel with the advances of artificial intelligence (AI) and growing awareness of data privacy concerns, federated learning (FL) is promoted as a paradigm shift towards privacy-preserving AI-empowered social metaverse. However, challenges including privacy-utility tradeoff, learning reliability, and AI model thefts hinder the deployment of FL in real metaverse applications. In this article, we exploit the pervasive social ties among users/avatars to advance a social-aware hierarchical FL framework, i.e., SocialFL for a better privacy-utility tradeoff in the social metaverse. Then, an aggregator-free robust FL mechanism based on blockchain is devised with a new block structure and an improved consensus protocol featured with on/off-chain collaboration. Furthermore, based on digital watermarks, an automatic federated AI (FedAI) model ownership provenance mechanism is designed to prevent AI model thefts and collusive avatars in social metaverse. Experimental findings validate the feasibility and effectiveness of proposed framework. Finally, we envision promising future research directions in this emerging area.
Modelling the adoption of Internet of things (IoT) for sustainable construction in a developing economy

Purpose The need to improve sustainable construction with the appropriate technology adoption in the fourth and fifth industrial revolutions cannot be overemphasized. This study modelled the adoption of Internet of things (IoT) technologies in the construction industry to improve sustainable construction. Consequently, the study would assist construction stakeholders and policymakers in achieving sustainable construction through concerted efforts toward improving IoT adoption. Design/methodology/approach The study obtained the IoT technologies and the challenges of IoT adoption in the construction industry through a literature review. Subsequently, the awareness (knowledge of the existence and uses) of IoT technologies, the significance of the challenges and the level of adoption of the IoT technologies were tested on construction stakeholders in Kigali with a structured questionnaire. Consequently, a structural equation model of IoT adoption was developed. Findings The structural equation model indicates the relationship between the awareness, challenges and adoption of IoT technologies. It equally describes the effect sizes of the awareness and challenges of IoT on the adoption of IoT technologies. Research limitations/implications This study is beneficial to construction stakeholders as it portrays the exclusive impacts of the awareness and the challenges of IoT technologies on the adoption of IoT. The study also benefits the government and policymakers as it provides a basis for intensifying efforts to create awareness and mitigate the challenges of IoT adoption for sustainable construction. The study also provides a basis for future studies on IoT adoption in the construction industry. Originality/value There are several studies on the adoption of IoT. There is however scarcity of studies on how the awareness and the challenges of IoT technologies impact the adoption of IoT in the construction industry. Thus, this study bridges the gap by modelling the adoption of IoT technologies for improved sustainable construction.