Reviewerly/exHarmony huggingface dataset:
Artificial intelligence and the blood film: Performance of the <scp>MC</scp>‐80 digital morphology analyzer in samples with neoplastic and reactive cell types

Abstract Introduction Implementing artificial intelligence‐based instruments in hematology laboratories requires evidence of efficiency in classifying pathological cells. In two‐Universities, we assessed the performance of the Mindray® MC‐80 for hematology patients with frequent leukemic and dysplastic cells. Methods The Mindray MC‐80® locates and pre‐classifies cells in blood films. In a two‐university study, four films were prepared from 591 samples, two each for the analyser MC‐80 and the microscope reference method, using reagents from two different manufacturers. We used Microsoft Excel® statistics for imprecision and distributional inaccuracy and a matrix table model (H20‐A2 CLSI standard) for sensitivity, specificity and predictive value for atypical cells. Results The results indicate minimal within‐run imprecision (ICSH method) and good intra‐method consistency even on duplicate analysis of 413 samples with a high incidence of hematological abnormalities ( r = 0.942 or more, except for basophils, r = 0.841, and reactive lymphocytes, r = 0.847). Distributional inaccuracy was also very low compared to the microscope reference, with a pass rate higher than 80% for pathological cells (except 75.1% for reactive lymphocytes). The primary causes of discrepancy were bizarre shapes of dysplastic neutrophils and inconsistent nomenclature for lymphoma cells. Sensitivity for critical samples containing cells typically absent in circulating blood (immature or malignant) was 98.8% for immature granulocytes, 83.8% for all types of neoplastic cells, 93.6% for reactive lymphocytes and 97.5% for nucleated red blood cells. The negative predictive values of MC‐80 were 98.8% for immature granulocytes, 88.4% for the different types of neoplastic cells, 97.8% for reactive lymphocytes, and 96.9% for nucleated red blood cells. Conclusion Our study highlights the outstanding diagnostic performance of this artificial intelligence–based blood film analyzer for hematology patients with circulating abnormal cells. We appreciated the morphological harmonization of cells observed on the screen and those seen in the microscope.
re3data – Indexing the Global Research Data Repository Landscape Since 2012

For more than ten years, re3data, a global registry of research data repositories (RDRs), has been helping scientists, funding agencies, libraries, and data centers with finding, identifying, and referencing RDRs. As the world's largest directory of RDRs, re3data currently describes over 3,000 RDRs on the basis of a comprehensive metadata schema. The service allows searching for RDRs of any type and from all disciplines, and users can filter results based on a wide range of characteristics. The re3data RDR descriptions are available as Open Data accessible through an API and are utilized by numerous Open Science services. re3data is engaged in various initiatives and projects concerning data management and is mentioned in the policies of many scientific institutions, funding organizations, and publishers. This article reflects on the ten-year experience of running re3data and discusses ten key issues related to the management of an Open Science service that caters to RDRs worldwide.
An empirical experiment of a usability requirements elicitation method to design GUIs based on interviews

The usability requirements elicitation process is a difficult task that lacks methods to guide and help analysts, who are usually not experts at usability. This paper conducts an experiment with two replications to evaluate a method that elicits usability requirements based on structured interviews named UREM versus an unstructured method. The method consists of guided interviews by the analyst using decision trees. The tree is composed of questions and possible answers. Each question appears when there are different possible design alternatives, and each answer represents one of these alternatives. The tree also recommends the alternative that enhances the usability based on existing usability guidelines. We have conducted an experiment with two replications with 22 and 26 subjects playing two different roles in a within-subjects design. The analysts used a tree to guide the interview and elicit the requirements while the end users had to explain to the analyst the type of system to develop. During the interview, the analyst must design a paper prototype to be validated by the end user. For the analyst, the experiment measures the effectiveness of usability requirements elicitation, the effectiveness of the use of the usability guidelines, the efficiency of the elicitation process, and the satisfaction with the entire elicitation process. For the end user, the experiment measures the satisfaction with the designed prototype at the end of the interview. UREM yielded significantly better results for the effectiveness in the usability requirements elicitation process and for the effectiveness in the use of usability guidelines when compared to unstructured interviews. The use of UREM did not reduce the analysts’ efficiency and both analyst and end user remained the same satisfaction. Eliciting usability requirements is a difficult task if it is done with unstructured interviews and without usability recommendations.
Quality and Readability of Online Information on Cerebral Palsy

AbstractAbstractObjective The aim of this study was to investigate the readability and reliability of online content related to cerebral palsy (CP) and to classify sites that contain quality and readable information.Method On April 30, 2022, the term "cerebral palsy" was searched on the Google search engine. The categories, quality, content, readability, Alexa rank values, and backlink numbers of the sites were analyzed. The Journal of American Medical Association score and HONcode seal were used for quality assessment, and the Flesch–Kincaid grade (FKG) and the Simple Measure of Gobbledygook (SMOG) were used for readability assessment.Results Of the 155 sites, 46 had high-quality content. The quality of health portal and scientific publishing sites appeared to be high. The top 10 sites were determined to be of significantly higher quality (p = .017) than the remaining sites. The average readability values of the sites were not at the recommended level.Conclusion Despite the abundance of information available on CP-related websites, most of them were not of high quality. Additionally, many websites did not reach the recommended readability levels. Revision of existing online content related to CP and production of new high-quality content in line with readability recommendations is urgently required.Keywords: Cerebral palsyonline informationqualityreadability Disclosure statementNo potential conflict of interest was reported by the author(s).Ethics statementSince there was no experiment or intervention on any human or animal in the study, no ethics committee application was made.Data availability statementThe data that support the findings of this study are available from the corresponding author [MHT] upon reasonable request.Additional informationFundingThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.Notes on contributorsMustafa Hüseyin TemelMustafa Hüseyin Temel, MD (mhuseyintemel@gmail.com) is at the Clinic of Physical Medicine and Rehabilitation, Üsküdar State Hospital, İstanbul, Turkey.Sevilay BatıbaySevilay Batıbay, MD (sevilaycucen@windowslive.com) is at the Clinic of Rheumatology, Goztepe Prof. Dr. Suleyman Yalçın City Hospital, Istanbul, Turkey.Fatih BağcıerFatih Bağcıer (bagcier_42@gmail.com) is an Associate Professor at the Clinic of Physical Medicine and Rehabilitation, Istanbul Başakşehir Çam and Sakura City Hospital, Istanbul, Turkey.
Augmented reality in the metaverse market: the role of multimodal sensory interaction

Purpose In the growing information systems (IS) literature on metaverse, augmented reality (AR) technology is regarded as a cornerstone of the metaverse which enables interaction services. Interaction has been identified as a core technology characteristic of metaverse shopping environments. Based on previous human–technology interaction research, the authors further explicate interaction to be multimodal sensory. The purpose of this study is thus to better understand the unique nature of interaction in AR technology and highlight the technology's benefits for shopping in metaverse spaces. Design/methodology/approach An experiment has been conducted to empirically examine the authors' research model. The authors use the structural equation modeling (SEM) approach to analyze the collected data. Findings This study conceptualizes image, motion and touchscreen interactions as the three dimensions of multimodal sensory interaction, which can reflect visual-, kinesthetic- and haptic-based sensation stimulation. The authors' findings show that multimodal sensory interaction of AR activates consumers' intention to purchase via a psychological process. To delineate this psychological process, the authors use feelings-as-information theory to posit that experiential factors can influence cognitive factors. More specifically, multimodal sensory interaction is shown to increase multisensory experience and spatial presence, which can effectively reduce product uncertainty and information overload. The two outcomes have been considered to be key issues in online shopping environments. Originality/value This study is one of the first ones that shed light on the multimodal sensory peculiarity of AR interactions in the extant IS literature. The authors further highlight the benefits of AR in addressing major online shopping concerns about product uncertainty and information overload, which are largely overlooked by prior research. This study uses feelings-as-information theory to explain the impacts of AR interactions, which reveal the essential role of the experiential process in sensory-enabling technologies. This study enriches the existing theoretical frameworks that mostly focus on the cognitive process. The authors' findings about AR interactions provide noteworthy guidelines for the design of metaverse environments and extend the authors' understanding of how the metaverse may bring benefits beyond traditional online shopping settings.
Class-Driven Graph Attention Network for Multi-Label Time Series Classification in Mobile Health Digital Twins

Digital Twins for Mobile Networks (DTMN) can enhance mobile health (mHealth) by increasing diagnostic and monitoring capabilities. Classifying multi-label time series mHealth data in DTMN is challenging due to complex class relevance and feature extraction difficulties. This paper proposes a Class-Driven Graph Attention network learning framework (C-DGAM) for Multi-label classification of mHealth data in DTMN. C-DGAM captures the complex class relationships by constructing a unique class relevance graph for each time series. It uses a temporal context attention module to generates class representation vectors by fusing multi-dimensional features of time and class. Then, it dynamically models different relevance among the class representation vectors through a dynamic graph attention module which improves the performance of multi-label time series classification while maintaining a smaller parameter size and lower computational complexity. The mean Average Precision achieved by C-DGAM on two different multi-label time series datasets are 0.955 and 0.776, respectively, with corresponding F1 scores of 0.867 and 0.80. It demonstrates leading performance compared to existing state-of-art works. It provides more accurate and generalized algorithmic support for DMTN systems.
Sentiment analysis of imbalanced datasets using BERT and ensemble stacking for deep learning

The Internet is a crucial way to share information in both personal and professional areas. Sentiment analysis attracts great interest in marketing, research, and business today. The instability faced by imbalanced datasets on sentiment analysis is examined in this research. Balancing the datasets using techniques based on under-sampling and over-sampling is examined to achieve more efficient classification results as the effects of using BERT as word embedding and ensemble learning methods for classification. The effects of the resampling training set algorithms on different deep learning classifiers were investigated using BERT as a word embedding model and Cohen's kappa, accuracy, ROC-AUC curve, and MCC as evaluation metrics with k-fold validation on three sentiment analysis datasets containing English, Arabic, and Moroccan Arabic Dialect texts. Also, we did those performance metrics for all models when scaling the dataset for training and testing, and we calculated the memory and the execution time for each model. Finally, we analyzed the National Office of Railways of Morocco (ONCF) customers' Facebook comments in Modern Standard Arabic (MSA) and MD to determine customer satisfaction as positive, negative, and neutral comments.
Enhanced Harris hawks optimization-based fuzzy k-nearest neighbor algorithm for diagnosis of Alzheimer's disease

In order to stop deterioration and give patients with Alzheimer's disease (AD) early therapy, it is crucial to correctly diagnose AD and its early stage, mild cognitive impairment (MCI). A framework for diagnosing AD is presented in this paper, which includes magnetic resonance imaging (MRI) image preprocessing, feature extraction, and the Fuzzy k-nearest neighbor algorithm (FKNN) model. In particular, the framework's novelty lies in the use of an improved Harris Hawks Optimization (HHO) algorithm named SSFSHHO, which integrates the Sobol sequence and Stochastic Fractal Search (SFS) mechanisms for optimizing the parameters of FKNN. The HHO method improves the quality of the initial population overall by incorporating the Sobol sequence, and the SFS mechanism increases the algorithm's capacity to get out of the local optimum solution. Comparisons with other classical meta-heuristic algorithms, state-of-the-art HHO variants in low and high dimensions, and enhanced meta-heuristic algorithms on 30 typical IEEE CEC2014 benchmark test problems show that the overall performance of SSFSHHO is significantly better than other comparative algorithms. Moreover, the created framework based on the SSFSHHO-FKNN model is employed to classify AD and MCI using MRI scans from the ADNI dataset, achieving high classification performance for 6 representative cases. Experimental findings indicate that the proposed algorithm performs better than a number of high-performance optimization algorithms and classical machine learning algorithms, thus offering a promising approach for AD classification. Additionally, the proposed strategy can successfully identify relevant features and enhance classification performance for AD diagnosis.
Topic research in fuzzy domain: Based on LDA topic modelling

Over the past few decades, the field of fuzzy research has experienced rapid growth. However, there remains a dearth of a comprehensive overview and summary of fuzzy research that provides a macro understanding of the field, particularly from the lens of topic analysis. To fill this void and capture the knowledge structure and trends in fuzzy research, this paper employs Latent Dirichlet Allocation (LDA) topic models to extract ten potential crucial scientific topics from a dataset comprising 33,957 articles published in renowned journals within the fuzzy domain. The identified topics are thoroughly discussed, and their evolution over time, as well as their distribution characteristics and dynamic trends at both journal and country/region levels, are analyzed. This research contributes to a better understanding of the distribution and trends of fuzzy research topics, providing valuable insights into the future of the field. Furthermore, the findings guide identifying appropriate journals, adjusting journal strategies, and fostering international collaborations in fuzzy research.
Linked Data Interfaces: A Survey

In the era of big data, linked data interfaces play a critical role in enabling access to and management of large-scale, heterogeneous datasets. This survey investigates forty-seven interfaces developed by the semantic web community in the context of the Web of Linked Data, displaying information about general topics and digital library contents. The interfaces are classified based on their interaction paradigm, the type of information they display, and the complexity reduction strategies they employ. The main purpose to be addressed is the possibility of categorizing a great number of available tools so that comparison among them becomes feasible and valuable. The analysis reveals that most interfaces use a hybrid interaction paradigm combining browsing, searching, and displaying information in lists or tables. Complexity reduction strategies, such as faceted search and summary visualization, are also identified. Emerging trends in linked data interface focus on user-centric design and advancements in semantic annotation methods, leveraging machine learning techniques for data enrichment and retrieval. Additionally, an interactive platform is provided to explore and compare data on the analyzed tools. Overall, there is no one-size-fits-all solution for developing linked data interfaces and tailoring the interaction paradigm and complexity reduction strategies to specific user needs is essential.
A solar radiation intelligent forecasting framework based on feature selection and multivariable fuzzy time series

Accurate solar radiation forecasting can effectively improve solar energy utilization efficiency and decrease the operational cost of solar photovoltaic power plants. However, some common forecasting methods have certain limitations, such as neglecting data fuzziness, meteorological factors, feature selection, and seasonal adjustment. Therefore, a solar radiation intelligent forecasting framework based on feature selection and multivariable fuzzy time series is proposed. Specifically, a combined fuzzy strategy is used to fuzzy the data, and an improved multi-objective optimization algorithm is proposed to search for the optimal parameters. A seasonal multivariable fuzzy time series is proposed to achieve multivariable inputs and seasonal adjustments. The experimental analysis, statistical test, and robustness analysis all verify the superiority of the proposed forecasting framework compared with competitive models. The MAPE values of the proposed forecasting framework for two regions are about 6% and 4%, respectively, which outperform some common basic models such as BPNN(about 11% and 9%), ELM(about 12% and 10%), LSTM(about 9% and 7%). The comparison analysis further indicates that the vital parts of the forecasting framework containing feature selection, seasonal adjustment, multi-objective optimization, and multivariable inputs can all have a positive influence on forecasting performance.
Building a Sustainable Social Feedback Loop: A Machine Intelligence Approach for Twitter Opinion Mining

This paper presents a sustainable machine intelligence approach for Twitter opinion mining, focusing on building a socially responsible feedback loop. We propose a methodology that combines advanced machine learning algorithms with eco-conscious practices to extract sentiment-related insights from Twitter data while minimizing environmental impact. The preprocessing steps involve removing special characters, tokenization, stop word removal, handling user handles and URLs, and lemmatization or stemming. Sentiment classification is performed using the Extra Tree Classifier, an ensemble learning algorithm that incorporates random feature selection and bagging techniques. Experimental results demonstrate the effectiveness of our approach in accurately classifying tweets into positive, negative, and neutral sentiment categories. The visualizations of class distribution, number of tokens per tweet, and word clouds provide further insights into the sentiment landscape on Twitter. Our research contributes to the development of sustainable and inclusive approaches for Twitter opinion mining, ensuring minimal environmental impact while capturing valuable sentimental information.
Adversarial camouflage for node injection attack on graphs

Node injection attacks on Graph Neural Networks (GNNs) have received increasing attention recently, due to their ability to degrade GNN performance with high attack success rates. However, our study indicates that these attacks often fail in practical scenarios, since defense/detection methods can easily identify and remove the injected nodes. To address this, we devote to camouflage node injection attack, making injected nodes appear normal and imperceptible to defense/detection methods. Unfortunately, the non-Euclidean structure of graph data and the lack of intuitive prior present great challenges to the formalization, implementation, and evaluation of camouflage. In this paper, we first propose and define camouflage as distribution similarity between ego networks of injected nodes and normal nodes. Then for implementation, we propose an adversarial CAmouflage framework for Node injection Attack, namely CANA, to improve attack performance under defense/detection methods in practical scenarios. A novel camouflage metric is further designed under the guide of distribution similarity. Extensive experiments demonstrate that CANA can significantly improve the attack performance under defense/detection methods with higher camouflage or imperceptibility. This work urges us to raise awareness of the security vulnerabilities of GNNs in practical applications.