Reviewerly/exHarmony huggingface dataset:
Empirical evaluation of Easy Language recommendations: a systematic literature review from journal research in Catalan, English, and Spanish

Abstract Easy Language is a language variety that aims to make information more comprehensible and, ultimately, more accessible. Content in this variety is written and designed following a set of recommendations that have been published in different guidelines. However, it remains uncertain to what extent these recommendations are backed up by empirical research. The aim of this study is to review the existing literature that evaluates current recommendations in Easy Language guidelines, on the basis of the following research questions: (a) is there empirical research that evaluates current international Easy Language recommendations? and, (b) if so, what current international Easy Language recommendations are supported by empirical research and what results were obtained? To this end, we conducted a systematic literature review based on journal articles in three languages: Catalan, English, and Spanish. First, a systematic search was designed and performed in 10 databases of different fields of science. Then, we reviewed every article that resulted from the search and found that 6 publications out of the initial 617 met the inclusion criteria and could be considered relevant for the study. Based on the data extracted from the included publications, and after an overall review of our systematic search results, we safely state that there is indeed empirical research on some current Easy Language recommendations. Nevertheless, empirical research in the field (at least in the publication format and languages considered in our study) is not enough in terms of the number of publications, and the findings obtained are far from generalisable. Our literature review suggests future lines of research, and we hope that it fosters empirical studies in the field that help support the existing findings.
Book Recommendation Using Collaborative Filtering Algorithm

The explosive growth in the amount of available digital information in higher education has created a potential challenge of information overload, which hampers timely access to items of interest. The recommender systems are applied in different domains such as recommendations film, tourist advising, webpages, news, songs, and products. But the recommender systems pay less attention to university library services. The most users of university library are students. These users have a lack of ability to search and select the appropriate materials from the large repository that meet for their needs. A lot of work has been done on recommender system, but there are technical gaps observed in existing works such as the problem of constant item list in using web usage mining, decision tree induction, and association rule mining. Besides, it is observed that there is cold start problem in case-based reasoning approach. Therefore, this research work presents matrix factorization collaborative filtering with some performance enhancement to overcome cold start problem. In addition, it presents a comparative study among memory-based and model-based approaches. In this study, researchers used design science research method. The study dataset, 5189 records and 76,888 ratings, was collected from the University of Gondar student information system and online catalogue system. To develop the proposed model, memory-based and model-based approaches have been tested. In memory-based approach, matrix factorization collaborative filtering with some performance enhancements has been implemented. In model-based approach, K-nearest neighbour (KNN) and singular value decomposition (SVD) algorithms are also assessed experimentally. The SVD model is trained on our dataset optimized with a scored RMSE 0.1623 compared to RMSE 0.1991 before the optimization. The RMSE for a KNN model trained using the same dataset was 1.0535. This indicates that the matrix factorization performs better than KNN models in building collaborative filtering recommenders. The proposed SVD-based model accuracy score is 85%. The accuracy score of KNN model is 53%. So, the comparative study indicates that matrix factorization technique, specifically SVD algorithm, outperforms over neighbourhood-based recommenders. Moreover, using hyperparameter tuning with SVD also has an improvement on model performance compared with the existing SVD algorithm.
Parents’ digital skills and their development in the context of the Corona pandemic

We investigate parents' and guardians' digital skills and the extent of their development in the context of the spread of the Corona epidemic. In addition, we sought to explore the differences in digital skills between parents and their employment status, age, and responsibility in teaching children. We sought to rely on the descriptive-analytical approach and prepared a scale of eight theoretical dimensions with the participation of 250 students' Saudi parents. The application of the study was by online submission form (via Edit Submission). Our findings showed that there was a discrepancy in the performance of the sample, which was very high in the dimensions of operational skills, instrumental skills, and cognitive constructivism skills. There were also differences between the effect of computers on the instrumental skills and cognitive constructivism skills of the parents. Parents' dependence on alternative digital sources in exploring for information, formulating knowledge, manipulating it, and criticizing. The learner can reach the cognitive level in a more flexible manner, which allows him to gain learning objectives. The knowledge navigation can be developed because of different online outdoor exercises and software familiar. This requires self-organization to search for appropriate knowledge to use in the renewal of the cognitive structure.
Co-Design of Augmented Reality Games for Learning with Teachers: A Methodological Approach

Abstract Augmented Reality Game-Based Learning (ARGBL) is becoming increasingly relevant in Technology-Enhanced Learning. Games with AR characteristics, or even AR applications structured with rules and game elements, are proving to be effective and successful learning experiences. There is a need to include teachers in the design process. In this paper, two case studies are shown in order to validate a methodological approach for the co-design of ARGBL, in which 6 teachers participated. This is a co-design method that proposes a thorough, iterative process guided by design principles and mediated by dialogue among the stakeholders. Here, the process of co-design with teachers is analyzed and assessed using mixed-methods observations on the use of the produced ARGBL games with students on naturalistic environments. The validation process links the usefulness of the ensuing products with the use of the method and shows the benefits of using co-design methods to create ARGBL experiences.
MAE-BG: dual-stream boundary optimization for remote sensing image semantic segmentation

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<scp><i>DeepFixCX</i></scp>: Explainable privacy‐preserving image compression for medical image analysis

Abstract Explanations of a model's biases or predictions are essential to medical image analysis. Yet, explainable machine learning approaches for medical image analysis are challenged by needs to preserve privacy of patient data, and by current trends in deep learning to use unsustainably large models and large datasets. We propose DeepFixCX for explainable and privacy‐preserving medical image compression that is nimble and performant. We contribute a review of the field and a conceptual framework for simultaneous privacy and explainability via tools of compression. DeepFixCX compresses images without learning by removing or obscuring spatial and edge information. DeepFixCX is ante‐hoc explainable and gives privatized post hoc explanations of spatial and edge bias without accessing the original image. DeepFixCX privatizes images to prevent image reconstruction and mitigate patient re‐identification. DeepFixCX is nimble. Compression can occur on a laptop CPU or GPU to compress and privatize 1700 images per second of size 320 × 320. DeepFixCX enables use of low memory MLP classifiers for vision data; permitting small performance loss gives end‐to‐end MLP performance over 70× faster and batch size over 100× larger. DeepFixCX consistently improves predictive classification performance of a Deep Neural Network (DNN) by 0.02 AUC ROC on Glaucoma and Cervix Type detection datasets, and can improve multi‐label chest x‐ray classification performance in seven of 10 tested settings. In all three datasets, compression to less than 5% of original number of pixels gives matching or improved performance. Our main novelty is to define an explainability versus privacy problem and address it with lossy compression. This article is categorized under: Fundamental Concepts of Data and Knowledge &gt; Explainable AI Commercial, Legal, and Ethical Issues &gt; Security and Privacy Fundamental Concepts of Data and Knowledge &gt; Big Data Mining
An Assessment of Cryptomixing Services in Online Illicit Markets

The internet has become a popular marketplace for the sale of illicit products, including stolen personal information, drugs, and firearms. Many of these products are acquired using cryptocurrencies, which are generally defined as forms of digital currency that is traceable through blockchain ledger technology. These currencies are thought to be more secure than other forms of digital payment, though law enforcement and financial service providers have found ways to investigate account holders and their transactions. Consequently, several service providers have begun to offer cryptomixing services, which effectively launders payments to circumvent detection and investigation tools. Few have explored the practices of cryptomixing services, or the ways in which they are marketed on the Open and Dark Web. This inductive qualitative analysis will examine a sample of 18 cryptomixing services advertised on both the Open and Dark Web to better understand cryptomixing and its role in facilitating illicit transactions across the internet.
Robust transfer learning of high-dimensional generalized linear model

This paper studies transfer learning of a high-dimensional generalized linear model with the target model as well as source data from different but possibly related models. Both known and unknown transferable domain settings are considered. On the one hand, an improved two-step transfer learning algorithm is proposed and the optimal rate of convergence for estimation is proved when the set of transferable domain is known. On the other hand, when the set of transferable domain is unknown, we propose a data-driven procedure for transfer learning, called Stepwise Selection algorithm, and investigate its finite-sample performance through simulations studies. Experimental results on six datasets demonstrate that the proposed method can perform better.
Hourly and sub-hourly ahead global horizontal solar irradiation forecasting via a novel deep learning approach: A case study

In this paper, a Long Short-Term Memory Neural Networks (LSTM) Deep Learning Approach is proposed to forecast Global horizontal solar Irradiation (GHI) in Erfoud Moroccan city. The main objective is to predict simultaneously hourly and sub-hourly GHI for time horizons from h + 1 to h + 6, and from 10 min + 10 to 10 min + 60, based only on endogenous historical data. For this purpose, two scenarios have been proposed, an annual performances scenario to consider all meteorological conditions of the year and seasonal performances scenario to distinguish the meteorological conditions characterizing each season of the year. In addition, LSTM performances are compared to those of the Multi-Layers Perceptron Artificial Neural Networks (MLP-ANN) and Random Forest (RF) ensemble method. The annual performances results show that LSTM models are the most efficient and predict precisely hourly and sub-hourly GHI for all time horizons against ANN and RF models with nRMSE varies from 11.63% for h + 1 to 17.35% for h + 6, and from 11.28% for 10 min + 10 to 14.88% for 10 min + 60. As well, the improvement in terms of nRMSE due to LSTM utilization compared to ANN and RF is more important with the forecast horizon increase. This study also reveals that sub-hourly GHI prediction performances are more accurate than for those of hourly GHI prediction, especially for a long time horizon (from 10 min + 40 to 10 min + 60). Moreover, the seasonal study shows that the global horizontal irradiation is more difficult to predict during autumn than other seasons due to the high meteorological variability observed in this period. However, the LSTM deep learning approach is the most robust even in the case of the high variability of GHI. While the ANN and RF are the most sensitive ones.
LHP: Logical hypergraph link prediction

Logical knowledge mining has become increasingly important as it is the foundation of precise reasoning, such as for clinical diagnosis or chemical reaction discovery. Knowledge hypergraphs provide a natural format for expressing inherently high-order relationships beyond pairwise associations. Although logical knowledge (the conjunct operator in first-order logic) is apparently a higher-order relation that can be represented by a hypergraph, methods of representing logical knowledge in this form and completing knowledge by hyperlink prediction have not yet been explored. In this study, logical knowledge is represented by directed hyperedges and effectively quantified by geometric operations in a neural network. The proposed logical hyperlink predictor (LHP) leverages logical knowledge features including permutation invariant and information aggregation of the logical 'conjunct' operation. LHP captures logical representation as a whole unit containing the different relationship types embedded within separate hyperedges, making it the novel method to integrate logical knowledge contained within hyperedges. We conduct extensive experiments on the iAF1260b, iJO1366, USPTO, and our Chinese Medical High-order Relational (CMHR) dataset. LHP achieved best performance with linear complexity compared to the state-of-the-art hyperlink prediction methods on a mean area under the receiver operating characteristic curve (AUC), Macro_F1 and accuracy. It was particularly effective on the CMHR demonstrating the importance of logical knowledge representation in the medical field. LHP also achieved a mean AUC of 0.924 in performing hyperedge relationship classification on the CMHR.
HFedDI: A novel privacy preserving horizontal federated learning based scheme for IoT device identification

As the number of IoT devices that are getting connected to the Internet is increasing, there is a need to automatically detect the IoT devices connected to the network for efficient network resource management and planning, identification of security attacks and anomalies in the network. Centralized machine learning techniques for device identification lead to the use of excessive communication bandwidth and can at times lead to privacy issues as data has to be transported to the central location. In this paper, we propose HFedDI- a novel horizontal learning based federated learning scheme for IoT device identification. The proposed federated learning technique is scalable as an edge device that does device identification can identify the devices which were never connected to it previously due to the achieved generalization accuracy as a result of model updates from other edge devices. Our work has indicated significant performance improvement in device identification across IID scenarios, and non-IID (specifically tested for data and label distribution skew) scenarios when tested on three publically available datasets. The proposed device identification technique is privacy preserving and is promising as the existing work in literature which utilized federated learning for other applications has indicated very poor results under non-IID label skew scenarios.
Investigation of semi- and self-supervised learning methods in the histopathological domain

Training models with semi- or self-supervised learning methods is one way to reduce annotation effort since they rely on unlabeled or sparsely labeled datasets. Such approaches are particularly promising for domains with a time-consuming annotation process requiring specialized expertise and where high-quality labeled machine learning datasets are scarce, like in computational pathology. Even though some of these methods have been used in the histopathological domain, there is, so far, no comprehensive study comparing different approaches. Therefore, this work compares feature extractors models trained with state-of-the-art semi- or self-supervised learning methods PAWS, SimCLR, and SimSiam within a unified framework. We show that such models, across different architectures and network configurations, have a positive performance impact on histopathological classification tasks, even in low data regimes. Moreover, our observations suggest that features learned from a particular dataset, i.e., tissue type, are only in-domain transferable to a certain extent. Finally, we share our experience using each method in computational pathology and provide recommendations for its use.
Image deblurring to improve the grain monitoring in a rice combine harvester

One of the most important sources of grain losses happens during harvesting. A machine vision system is capable to send online information of the grain condition to the adjustment units in order to reduce the grain losses. However, the continuous vibration of combine during harvesting prevents to acquire desirable images for grain monitoring. To solve this issue, several image deblurring algorithms were investigated and the best one was introduced. The x-y-z accelerometer sensors were installed on the combine tank cap and then the acceleration data was extracted. These data were then used for fabrication of a small-scale combine tank simulator for preparation of the image dataset. Five algorithms were used for deblurring, including Fast Image Deconvolution Hyper-Laplacian Priors, Wiener, Lucy-Richardson, Maximum likelihood deconvolution, and regularized iterative image restoration algorithm. Also, three filters of Motion, Average, and Disk were used. Blind/Reference-less Image Spatial QUality Evaluator (BRISQUE) and Natural Image Quality Evaluator (NIQE) were used as the image quality indices. The results showed that Lucy-Richardson had the best performance in image deblurring (NIQE=6.0539 and BRISQUE=43.0489). Maximum Likelihood showed similar performance (NIQE=6.1974 and BRISQUE=43.4288). Analyzing the execution time of the algorithms showed that Lucy-Richardson was the fastest one (0.8538 s) and therefore, this is preferred to Maximum Likelihood in online applications. The results of the applied filters based on the subjective criterion showed that motion filter has produced sharper images while, disk filter performs better in de-noising. Comparing two objective indices showed that both indices performed well in evaluating the quality of deblurred images. However, the results of original images (blurred images) revealed that NIQE showed more sensitivity to image contrast and noise, compared to BRISQUE.
TCNN: A Transformer Convolutional Neural Network for artifact classification in whole slide images

The production of pathological slides is a complex task requiring several physical and chemical procedures that are often done manually. Occasionally, such procedures may end up in patterns (called artifacts) appearing in the images that were not present in the biological tissue but are produced during the slide processing. This paper presents a new method for the automatic detection of such artifacts in pathological images. This detection is seen as a binary classification task that is solved by means of a new Transformer Convolutional Neural Network (TCNN). The proposed method can assist laboratory technicians in avoiding the time-consuming manual labeling of images, preventing the risk of sending artifact patches to pathologists and physicians. The artifact patches include misleading data and should not be considered. In addition, artifact patches jeopardize the accuracy of Computer-Aided Diagnosis (CAD) systems. The proposed method works on the Hue-Saturation-Value colormap layer, which is related to color representation, to improve model robustness against external lighting changes. Spatial Transformer layers are used to make the model spatially invariant on input data, and a set of convolutional layers of EfficientNet Convolutional Neural Network improve pattern recognition. Images used in this research are more than 13,000 and represent 22 types of tissue affected by 7 diverse types of artifacts. Through performance measurement, we evaluate the model using several metrics: Probability Calibration graph, Mean Prediction Probability, Brier Loss, ROC-AUC curve, Confusion Matrix, Accuracy, Sensitivity, Specificity, F1-Score, and Confidence Interval.
DissecTLS: A Scalable Active Scanner for TLS Server Configurations, Capabilities, and TLS Fingerprinting

Abstract Collecting metadata from Transport Layer Security (TLS) servers on a large scale allows to draw conclusions about their capabilities and configuration. This provides not only insights into the Internet but it enables use cases like detecting malicious Command and Control (C &amp;C) servers. However, active scanners can only observe and interpret the behavior of TLS servers, the underlying configuration and implementation causing the behavior remains hidden. Existing approaches struggle between resource intensive scans that can reconstruct this data and light-weight fingerprinting approaches that aim to differentiate servers without making any assumptions about their inner working. With this work we propose DissecTLS, an active TLS scanner that is both light-weight enough to be used for Internet measurements and able to reconstruct the configuration and capabilities of the TLS stack. This was achieved by modeling the parameters of the TLS stack and derive an active scan that dynamically creates scanning probes based on the model and the previous responses from the server. We provide a comparison of five active TLS scanning and fingerprinting approaches in a local testbed and on toplist targets. We conducted a measurement study over nine weeks to fingerprint C &amp;C servers and analyzed popular and deprecated TLS parameter usage. Similar to related work, the fingerprinting achieved a maximum precision of 99 % for a conservative detection threshold of 100 %; and at the same time, we improved the recall by a factor of 2.8.