Reviewerly/exHarmony huggingface dataset:
Open Science in Seismology: The Role of Citizen Science in the Transition from Seismic Observatory to Science Museum

Catastrophic events, including worldwide pandemics and natural disasters, may lead to ambivalent attitudes toward science among the public. On the one hand, there may be pessimistic feelings toward the limitations of scientific knowledge and technology. On the other hand, there may be optimistic prospects for science-based solutions to the problems caused by these catastrophes. Science communication plays an integral part in shaping societal attitudes toward science. The aim of this research was to build more fruitful relationships between science and society by improving science communication in the field of seismology. Based on the concept of open science, we conducted action research at a seismic observatory as it transitioned from a science facility to a science museum. The museum adopted a citizen-science approach to communicating the science of seismology. In this approach, citizens not only learned about seismology from scientists, but they worked collaboratively with scientists to do science. The results of our research showed that citizen science can play a critical role in making science more accessible and communicating the science of seismology to society.
FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data

Today, recommender systems have played an increasingly important role in shaping our experiences of digital environments and social interactions. However, as recommender systems become ubiquitous in our society, recent years have also witnessed significant fairness concerns for recommender systems. Specifically, studies have shown that recommender systems may inherit or even amplify biases from historical data, and as a result, provide unfair recommendations. To address fairness risks in recommender systems, most of the previous approaches to date are focused on modifying either the existing training data samples or the deployed recommender algorithms, but unfortunately with limited degrees of success. In this paper, we propose a new approach called <u>fair</u> <u>r</u>ecommendation with <u>o</u>ptimized <u>a</u>ntidote <u>d</u>ata (FairRoad), which aims to improve the fairness performances of recommender systems through the construction of a small and carefully crafted antidote dataset. Toward this end, we formulate our antidote data generation task as a mathematical optimization problem, which minimizes the unfairness of the targeted recommender systems while not disrupting the deployed recommendation algorithms. Extensive experiments show that our proposed antidote data generation algorithm significantly improve the fairness of recommender systems with a small amounts of antidote data.
Distinctive mobile learning: where it is different and how it can make a difference

In recent years, the emerging trend of mobile learning (hereinafter “m-learning”) has obtained increasing attention from educators and higher popularity among students worldwide. Partly due to its short history, there seems to be a lack of comprehensive and in-depth understanding on the distinctiveness of m-learning. It is maintained in this paper that m-learning should be described and researched in three intertwined aspects: the mobility of devices, learners and materials. The multi-faceted mobility may help achieve highly portable and genuinely personal learning, which distinguishes m-learning from other forms of technology-enhanced education. This paper also reports an empirical study on m-learning for academic English. With a total of 60 participants (and six in the pilot study) over three progressive phases, the data obtained from mixed methods of automatic logging, questionnaire and interview were used to assess multidimensional learner engagement in the self-directed process of m-learning. The triangulated data can reflect and highlight some known features of m-learning, and they may also imply learners’ expectations towards m-learning. Based on the literature review and data analysis, this paper aims to contribute to the (re)conceptualisation of “m-learning” and also provide some technical and pedagogical implications on future and further development of m-learning.
Efficient Axillary Lymph Node Detection Via Two-stage Spatial-information-fusion-based CNN

Background and objective: Preoperative imaging diagnosis of axillary lymph node (ALN) metastasis is particularly important for breast cancer patients. This paper focuses on developing non-invasive and automatic schemes for accurate localization and classification (metastasis prediction) of ALN via contrast-enhanced computed tomography (CECT) image and deep learning models. Methods: Based on a two-stage strategy, a novel detection neural network is proposed, where the convolutional block attention module is utilized to extract spacial information and the bottleneck feature fusion module is designed for feature fusion in different scales. Results: Owing to the two embedded modules, the proposed convolutional neural network (CNN) model outperforms Faster R-CNN, YOLOv3, and EfficientDet in the sense that the achieved mAP is 0.454, higher than 0.247, 0.335, and 0.329, respectively. In particular, considering the function of classification only, the proposed model reaches the best performance on most indices (accuracy of 0.968, positive predictive value of 0.972, negative predictive value of 0.966, specificity of 0.983), compared with the methods that have been frequently adopted to predict ALN. In addition, the proposed CNN model has the function of locating ALN, which is lacking in existing models. Conclusions: In this paper, a supervised deep learning method is proposed to detect ALN in CECT images. The positive effect of new added modules are verified, and the benefits of spatial information in ALN detection are confirmed. Further, the two subtasks called localization and classification are evaluated separately, where the proposed model achieves the best performance on most indices. The source code mentioned in this article will be released later.
Subset selection strategy-based pancreas segmentation in CT

Although convolutional neural network (CNN)-based methods have been widely used in medical image analysis and have achieved great success in many medical segmentation tasks, these methods suffer from various imbalance problems, which reduce the accuracy and validity of segmentation results.We proposed two simple but effective sample balancing methods, positive-negative subset selection (PNSS) and hard-easy subset selection (HESS) for foreground-to-background imbalance and hard-to-easy imbalance problems in medical segmentation tasks. The PNSS method gradually reduces negative-easy slices to enhance the contribution of positive pixels, and the HESS method enhances the iteration of hard slices to assist the model in paying greater attention to the feature extraction of hard samples.The proposed methods greatly improved the segmentation accuracy of the worst case (samples with the worst segmentation results) on the public National Institutes of Health (NIH) clinical center pancreatic segmentation dataset, and the minimum dice similarity coefficient (DSC) was improved by nearly 5%. Furthermore, performance gains were also observed with the proposed methods in liver segmentation (the minimum DSC increased from 75.03% to 84.29%), liver tumor segmentation (the minimum DSC increased from 20.92% to 35.73%), and brain tumor segmentation (the minimum DSC increased from 21.97% to 30.38%) on different neural networks. These results indicate that the proposed methods are effective and robust.Our proposed method can effectively alleviate foreground-to-background imbalance and hard-to-easy imbalance problems, and can improve segmentation accuracy, especially for the worst case, which guarantees the reliability of the proposed methods in clinical applications.
Easy-to-read in Romania: current status and future perspectives in a European context

As the European Union’s Strategy for people with disabilities has been extended to 2030, emphasising the need for these people’s social inclusion, which also comprises accessibility to different information and documents, Romania has to align its legislation and best practices in order to solve its problems concerning this topic. Therefore, this paper focuses on an important aspect of social inclusion, namely Easy-to-Read (E2R) language, which is paramount for the disabled people’s access to information, culture and education. It starts by presenting the concept of Easy-to-Read across Europe, its perception and implementation by various countries, to finally delve into the current situation of Easy-to-Read in Romania. By looking at the analysis and reports on the previous governmental strategies regarding people with disabilities (and especially reading disabilities, for the purpose of this paper), it can be said that Romania still faces challenges in this respect and needs to work on making documents and information accessible by first creating E2R guidelines and then implementing them in printed documents (e.g. adapted textbooks, healthcare leaflets) as well as in the electronic information (e.g. government websites). The paper also traces some future perspectives concerning Easy-to-Read in Romania, namely the Train2Validate “Professional training for Easy-to-Read facilitators and validators” (T2V) project, an Erasmus+ programme, a collaboration between various European countries, that Romania is part of, which goes one step further into helping people with reading disabilities and creating professional roles for them, i.e. facilitators and validators, in order to integrate them on the employment market. Moreover, this research will hopefully raise awareness of the importance of conducting other studies on creating Easy-to-Read guidelines and on simplifying the text according to the different target groups of people with reading disabilities or difficulties and to different text types.
Personalized Medicine for the Critically Ill Patient: A Narrative Review

Personalized Medicine (PM) is rapidly advancing in everyday medical practice. Technological advances allow researchers to reach patients more than ever with their discoveries. The critically ill patient is probably the most complex of all, and personalized medicine must make serious efforts to fulfill the desire to “treat the individual, not the disease”. The complexity of critically ill pathologies arises from the severe state these patients and from the deranged pathways of their diseases. PM constitutes the integration of basic research into clinical practice; however, to make this possible complex and voluminous data require processing through even more complex mathematical models. The result of processing biodata is a digitized individual, from which fragments of information can be extracted for specific purposes. With this review, we aim to describe the current state of PM technologies and methods and explore its application in critically ill patients, as well as some of the challenges associated with PM in intensive care from the perspective of economic, approval, and ethical issues. This review can help in understanding the complexity of, P.M.; the complex processes needed for its application in critically ill patients, the benefits that make the effort of implementation worthwhile, and the current challenges of PM.
Salient edges combined with image structures for image deblurring

This work investigates a simple yet effective image deblurring method that combines salient image edges with structures. Typically, the vast majority of edge-based work focuses on advancing the salient edges information for blind image deblurring while ignoring the refined scale of the image structures. With this in mind, we show how to effectively exploit the image structure to blind image deblurring in this paper, i.e., we adopt a mutually guided image filter to guide the restoration of the image structure while salient edges provide edge information for blur kernel estimation. Thus, a more expressive deblurring model that employs L2-regularization for salient edges and image structure, together with the L0-regularization for image gradients is proposed. We find that the quality of the recovered kernel is thereby improved, and the deblurring results are more satisfactory. Experimental results show that our method outperforms state-of-the-art methods in terms of benchmark-based datasets and real scenarios, as well as computational efficiency.
Comparison of breast cancer classification models on Wisconsin dataset

Breast cancer is the leading cause of death for women worldwide. Cancer can be discovered early, lowering the rate of death. Machine learning techniques are a hot field of research, and they have been shown to be helpful in cancer prediction and early detection. The primary purpose of this research is to identify which machine learning algorithms are the most successful in predicting and diagnosing breast cancer, according to five criteria: specificity, sensitivity, precision, accuracy, and F1 score. The project is finished in the Anaconda environment, which uses Python's NumPy and SciPy numerical and scientific libraries as well as matplotlib and Pandas. In this study, the Wisconsin diagnostic breast cancer dataset was used to evaluate eleven machine learning classifiers: decision tree, quadratic discriminant analysis, AdaBoost, Bagging meta estimator, Extra randomized trees, Gaussian process classifier, Ridge, Gaussian nave Bayes, k-Nearest neighbors, multilayer perceptron, and support vector classifier. During performance analysis, extremely randomized trees outperformed all other classifiers with an F1-score of 96.77% after data collection and data analysis.
Monotone Abstractions in Ontology-Based Data Management

In Ontology-Based Data Management (OBDM), an abstraction of a source query q is a query over the ontology capturing the semantics of q in terms of the concepts and the relations available in the ontology. Since a perfect characterization of a source query may not exist, the notions of best sound and complete approximations of an abstraction have been introduced and studied in the typical OBDM context, i.e., in the case where the ontology is expressed in DL-Lite, and source queries are expressed as unions of conjunctive queries (UCQs). Interestingly, if we restrict our attention to abstractions expressed as UCQs, even best approximations of abstractions are not guaranteed to exist. Thus, a natural question to ask is whether such limitations affect even larger classes of queries. In this paper, we answer this fundamental question for an essential class of queries, namely the class of monotone queries. We define a monotone query language based on disjunctive Datalog enriched with an epistemic operator, and show that its expressive power suffices for expressing the best approximations of monotone abstractions of UCQs.
Computational pathology, new horizons and challenges for anatomical pathology

Abstract The emergence of digital pathology environments and the application of computer vision to the analysis of histological sections has given rise to a new area of Anatomical Pathology, termed Computational Pathology. Advances in Computational Pathology may substantially change the routine of Anatomical Pathology laboratories and the work profile of the pathologist.
Instrumentation system for data acquisition and monitoring of hydroponic farming using ESP32 via Google Firebase

This article discusses the design of a hydroponic planting process monitoring system based on the internet of things. This device uses an ESP32 microcontroller board as the main controller. The parameters that were monitored and acquired were the conditions of the hydroponic growing media. Those parameters are; water pH, water temperature, water turbidity level, and ambient air temperature and humidity. The five parameters are measured by analog sensors integrated with the ESP32. These parameters affect the growth process and the quality of crop yields. This article also describes the calibration method for each sensor used for parameter measurement. Then the monitoring of these parameters is carried out by utilizing a real-time database, namely Google Firebase. This platform is very suitable for all IoT-based monitoring and control applications. Measurement result data is uploaded and saved to the real-time database. Then paired by Android-based applications. This application was created to be used by hydroponic farmers who use this device. Thus the results of monitoring can be used to optimize the process of growing hydroponic plants.
Significance of Camera Pixel Error in the Calibration Process of a Robotic Vision System

Although robotic vision systems offer a promising technology solution for rapid and reconfigurable in-process 3D inspection of complex and large parts in contemporary manufacturing, measurement accuracy poses a challenge for its wide deployment. One of the key issues in adopting a robotic vision system is to understand the extent of its measurement errors which are directly correlated with the calibration process. In this paper, a possible source of practical and inherent measurement uncertainties involved in the calibration process of a robotic vision system are discussed. The system considered in this work consists of an image sensor mounted on an industrial robot manipulator with six degrees of freedom. Based on a series of experimental tests and computer simulations, the paper gives a comprehensive performance comparison of different calibration approaches and shows the impact of measurement uncertainties on the calibration process. It has been found from the error sensitivity analysis that minor uncertainties in the calibration process can significantly affect the accuracy of the robotic vision system. Further investigations suggest that inducing errors in image calibration patterns can have an adverse effect on the hand–eye calibration process compared to the angular errors in the robot joints.
Artificial Intelligence Technology in Basketball Training Action Recognition

The primary research purpose lies in studying the intelligent detection of movements in basketball training through artificial intelligence (AI) technology. Primarily, the theory of somatosensory gesture recognition is analyzed, which lays a theoretical foundation for research. Then, the collected signal is denoised and normalized to ensure that the obtained signal data will not be distorted. Finally, the four algorithms, decision tree (DT), naive Bayes (NB), support vector machine (SVM), and artificial neural network (ANN), are used to detect the data of athletes' different limb movements and recall. The accuracy of the data is compared and analyzed. Experiments show that the back propagation (BP) ANN algorithm has the best action recognition effect among the four algorithms. In basketball training athletes' upper limb movement detection, the average accuracy rate is close to 93.3%, and the average recall is also immediate to 93.3%. In basketball training athletes' lower limb movement detection, the average accuracy rate is close to 99.4%, and the average recall is immediate to 99.4%. In the detection of movements of upper and lower limbs: the recognition method can efficiently recognize the basketball actions of catching, passing, dribbling, and shooting, the recognition rate is over 95%, and the average accuracy of the four training actions of catching, passing, dribbling, and shooting is close to 98.95%. The intelligent basketball training system studied will help basketball coaches grasp the skilled movements of athletes better to make more efficient training programs and help athletes improve their skill level.
Improving biomedical named entity recognition by dynamic caching inter-sentence information

Abstract Motivation Biomedical Named Entity Recognition (BioNER) aims to identify biomedical domain-specific entities (e.g. gene, chemical and disease) from unstructured texts. Despite deep learning-based methods for BioNER achieving satisfactory results, there is still much room for improvement. Firstly, most existing methods use independent sentences as training units and ignore inter-sentence context, which usually leads to the labeling inconsistency problem. Secondly, previous document-level BioNER works have approved that the inter-sentence information is essential, but what information should be regarded as context remains ambiguous. Moreover, there are still few pre-training-based BioNER models that have introduced inter-sentence information. Hence, we propose a cache-based inter-sentence model called BioNER-Cache to alleviate the aforementioned problems. Results We propose a simple but effective dynamic caching module to capture inter-sentence information for BioNER. Specifically, the cache stores recent hidden representations constrained by predefined caching rules. And the model uses a query-and-read mechanism to retrieve similar historical records from the cache as the local context. Then, an attention-based gated network is adopted to generate context-related features with BioBERT. To dynamically update the cache, we design a scoring function and implement a multi-task approach to jointly train our model. We build a comprehensive benchmark on four biomedical datasets to evaluate the model performance fairly. Finally, extensive experiments clearly validate the superiority of our proposed BioNER-Cache compared with various state-of-the-art intra-sentence and inter-sentence baselines. Availabilityand implementation Code will be available at https://github.com/zgzjdx/BioNER-Cache. Supplementary information Supplementary data are available at Bioinformatics online.
Digital Competences of Students

AbstractStudents are often assumed to be “digital natives”, i.e., to be competent in the use of digital technologies. However, observations in the teaching context show that students do not (or cannot) necessarily transfer skills acquired in their leisure time to the study context. To provide concepts for developing appropriate teaching/learning quality and for the efficient use of corresponding technologies, a database is required to document students’ digital competences. We therefore refer to the European Reference Framework DigComp2.1 as a conceptual basis as well as selected results from surveys of several large universities in Germany. These conceptualise a new self-report questionnaire to assess digital competences. In this chapter, we first address the question how precisely digital competences can be assessed? Second, we stress the significance of digital competences in the first year of higher education under pandemic conditions. While it has been proven that self-efficacy is a predictor of study success, satisfaction and dropout intentions, this chapter attempts to examine the extent to which digital competences mediate this relationship when students experience their first year in higher education only in a virtual environment. For this purpose, we conducted a longitudinal study spanning the first year in higher education. Ultimately, a recording of digital competences serves as the basis for quality-enhancing concepts for higher education teaching to coordinate the sensible use of digital teaching/learning technologies with existing competences or to promote the acquisition of missing competences.