Reviewerly/exHarmony huggingface dataset:
New multiple sclerosis lesion segmentation and detection using pre-activation U-Net

Automated segmentation of new multiple sclerosis (MS) lesions in 3D MRI data is an essential prerequisite for monitoring and quantifying MS progression. Manual delineation of such lesions is time-consuming and expensive, especially because raters need to deal with 3D images and several modalities. In this paper, we propose Pre-U-Net, a 3D encoder-decoder architecture with pre-activation residual blocks, for the segmentation and detection of new MS lesions. Due to the limited training set and the class imbalance problem, we apply intensive data augmentation and use deep supervision to train our models effectively. Following the same U-shaped architecture but different blocks, Pre-U-Net outperforms U-Net and Res-U-Net on the MSSEG-2 dataset, achieving a Dice score of 40.3% on new lesion segmentation and an F1 score of 48.1% on new lesion detection. The codes and trained models are publicly available at https://github.com/pashtari/xunet.
A Survey and Ontology of Blockchain Consensus Algorithms for Resource-Constrained IoT Systems

The basic properties of blockchain, such as decentralization, security, and immutability, show promising potential for IoT applications. The main feature-decentralization of blockchain technology-depends on the consensus. However, consensus algorithms are mostly designed to work in extensive computational and communication environments for network security and immutability, which is not desirable for resource-restricted IoT applications. Many solutions are proposed to address this issue with modified consensus algorithms based on the legacy consensus, such as the PoW, PoS, and BFT, and new non-linear data structures, such as DAG. A systematic classification and analysis of various techniques in the field will be beneficial for both researchers and industrial practitioners. Most existing relevant surveys provide classifications intuitively based on the domain knowledge, which are infeasible to reveal the intrinsic and complicated relationships among the relevant basic concepts and techniques. In this paper, a powerful tool of systematic knowledge classification and explanation is introduced to structure the survey on blockchain consensus algorithms for resource-constrained IoT systems. More specifically, an ontology was developed for a consensus algorithm apropos of IoT adaptability. The developed ontology is subdivided into two parts-CONB and CONIoT-representing the classification of generic consensus algorithms and the ones that are particularly proposed for IoT, respectively. Guided by this ontology, an in depth discussion and analysis are provided on the major consensus algorithms and their IoT compliance based on design and implementation targets. Open research challenges and future research directions are provided.
Defect Detection of Pipeline Inner Surface Based on Coaxial Digital Image Correlation with Hypercentric Lens

A coaxial dual-camera digital image correlation system using a hypercentric lens was proposed to determine the defect position in the inner wall of a pipeline under loads. Compared with the traditional dual-camera system, this system ensures that both cameras can capture a 360-degree panoramic image in the same position. Herein, the imaging principle of the system was introduced in detail. In addition, the effectiveness and accuracy of the proposed method were verified through verification and application experiments.
Simulator-based Explanation and Debugging of Hazard-triggering Events in DNN-based Safety-critical Systems

When Deep Neural Networks (DNNs) are used in safety-critical systems, engineers should determine the safety risks associated with failures (i.e., erroneous outputs) observed during testing. For DNNs processing images, engineers visually inspect all failure-inducing images to determine common characteristics among them. Such characteristics correspond to hazard-triggering events (e.g., low illumination) that are essential inputs for safety analysis. Though informative, such activity is expensive and error prone. To support such safety analysis practices, we propose Simulator-based Explanations for DNN failurEs (SEDE), a technique that generates readable descriptions for commonalities in failure-inducing, real-world images and improves the DNN through effective retraining. SEDE leverages the availability of simulators, which are commonly used for cyber-physical systems. It relies on genetic algorithms to drive simulators toward the generation of images that are similar to failure-inducing, real-world images in the test set; it then employs rule learning algorithms to derive expressions that capture commonalities in terms of simulator parameter values. The derived expressions are then used to generate additional images to retrain and improve the DNN. With DNNs performing in-car sensing tasks, SEDE successfully characterized hazard-triggering events leading to a DNN accuracy drop. Also, SEDE enabled retraining leading to significant improvements in DNN accuracy, up to 18 percentage points.
An overview of structural coverage metrics for testing neural networks

Deep neural network (DNN) models, including those used in safety-critical domains, need to be thoroughly tested to ensure that they can reliably perform well in different scenarios. In this article, we provide an overview of structural coverage metrics for testing DNN models, including neuron coverage, k-multisection neuron coverage, top-k neuron coverage, neuron boundary coverage, strong neuron activation coverage and modified condition/decision coverage. We evaluate the metrics on realistic DNN models used for perception tasks (LeNet-1, LeNet-4, LeNet-5, ResNet20) including networks used in autonomy (TaxiNet). We also provide a tool, DNNCov, which can measure the testing coverage for all these metrics. DNNCov outputs an informative coverage report to enable researchers and practitioners to assess the adequacy of DNN testing, to compare different coverage measures, and to more conveniently inspect the model's internals during testing.
Improving GAN-based Domain Adaptation for Object Detection

Unlabeled or insufficiently labeled data is a common challenge in autonomous driving applications. Perception tasks like object detection in particular require annotated data, which is expensive to acquire, due to mostly manual labeling processes. One approach to saving label cost is using synthetic data and unsupervised domain adaptation to bridge the visual appearance gap towards the real application data. Knowing the placement of objects in rendered scenes, labels can be extracted automatically. However, lack of realism in rendered scenes leaves a domain gap between real and synthetic data. We transfer image-to-image adversarial domain adaptation methods from semantic segmentation to object detection, leveraging synthetic data for the task of vehicle detection in real images. This improved GAN-based image generation is combined with a standard object detector. This is in contrast to previous works, which also applied domain adaptation methods to the detector. With our adaptations we achieve state of the art results in domain adaption using generative adversarial networks on GTA to Cityscapes synthetic to real unsupervised domain transfer for vehicle detection.
Personalized Federated Learning via Convex Clustering

We propose a parametric family of algorithms for personalized federated learning with locally convex user costs. The proposed framework is based on a generalization of convex clustering in which the differences between different users' models are penalized via a sum-of-norms penalty, weighted by a penalty parameter <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A$</tex> . The proposed approach enables “automatic” model clustering, without prior knowledge of the hidden cluster structure, nor the number of clusters. Analytical bounds on the weight parameter, that lead to simultaneous personalization, generalization and automatic model clustering are provided. The solution to the formulated problem enables personalization, by providing different models across different clusters, and generalization, by providing models different than the per-user models computed in isolation. We then provide an efficient algorithm based on the Parallel Direction Method of Multipliers (PDMM) to solve the proposed formulation in a federated server-users setting. Numerical experiments corroborate our findings. As an interesting byproduct, our results provide several generalizations to convex clustering.
A BIBLIOMETRIC ANALYSIS OF SCIENTIFIC PRODUCTION CONCERNING ONLINE CONSUMER REVIEWS AND THE SALE OF HOME APPLIANCES

Nowadays, online customer evaluations have developed as an essential source of information for both businesses and consumers, replacing and augmenting previous modes of communication. The goal of this work is to perform a bibliometric analysis to investigate the scientific production surrounding online reviews and the sale of home appliances from 2011 to 2022. We used the Scopus database to acquire primary data and the VOSviewer program to analyze the data and perform the bibliometric analysis to achieve the desired objective. The major findings revealed that the year 2021 delivered the greatest amount of scientific contributions and that the United States of America contributed the greatest number of articles overall. Moreover, for the analyzed period the terms with the highest density present in scientific production are: "online reviews", "quality", "applications", "impact", "reviews", "effects", "consumer", "factors", and "purchase intent." At the end of the analysis, we conclude with some relevant future research directions emerging from our bibliometric analysis.
THE EFFECTIVENESS OF VIRTUAL REALITY TECHNOLOGIES TO ENHANCE LEARNING AND TRAINING EXPERIENCE: DURING THE COVID-19 PANDEMIC AND BEYOND

During the global pandemic, we now live and work differently, forcing government decision-makers to find innovative, new ways to learn and train that will still be feasible during the pandemic. As we move slowly away from the Covid-19 shadow, upskilling and reskilling students are crucial for learning and professional development. Consequently, the pandemic has highlighted the necessity for transforming distance learning and opened new opportunities for immersive virtual exchanges. Nowadays, virtual reality (VR) has a significant role in fighting the Covid-19 pandemic. This paper proposes the potential of integrating VR technology in learning and training perspectives as an effective strategy or approach used to complete students experiences. The purpose of this study is to develop and design a virtual environment (VE) that guides students through the educational process at high levels of performance. The focus of this study was to evaluate the effectiveness of a VR simulation for students. In this study, we have designed a VR simulation using Unity 3D software connected with the Hp reverb G2 headset. We collected data by a survey which indicates 50 samples attending a soft skills training course. The results revealed that virtual learning (V-learning) in VR is much more interactive and effective than traditional learning methods like E-learning and classrooms. This study contributes a significant role that the adoption of VR technology can be extremely beneficial to educators in helping students enhance their skills and continue the educational process in universities.
MapReader

We present MapReader, a free, open-source software library written in Python for analyzing large map collections. MapReader allows users with little computer vision expertise to i) retrieve maps via web-servers; ii) preprocess and divide them into patches; iii) annotate patches; iv) train, fine-tune, and evaluate deep neural network models; and v) create structured data about map content. We demonstrate how MapReader enables historians to interpret a collection of ≈16K nineteenth-century maps of Britain (≈30.5M patches), foregrounding the challenge of translating visual markers into machine-readable data. We present a case study focusing on rail and buildings. We also show how the outputs from the MapReader pipeline can be linked to other, external datasets. We release ≈62K manually annotated patches used here for training and evaluating the models.
Blockchain in the Energy Sector—Systematic Review

The article provides an overview of academic contributions to blockchain technology over the past three years. A large number of practical implementations are proving the versatility of blockchain across industries. Some of these areas are easy to deduce, but for some, the benefits of using blockchain technology may not be obvious. Real applications of blockchain can be found in sectors such as cyber security and the financial sector, but also in various categories of the public sector, healthcare, and industry. This paper focuses on the use of blockchain technology in the energy industry. The paper aims to present the current trends of blockchain in the energy sector and provide a summary of blockchain technology discussed in academia. The research questions are formulated to correspond to the basic goals of the energy sector today. The core of the paper forms a systematic review based on the PRISMA guidelines. The output of this systematic review brings an up-to-day insight into the issue and introduces potential areas for further research.
Diverse cloud and aerosol impacts on solar photovoltaic potential in southern China and northern India

Abstract Cloud and aerosol are two important modulators that influence the solar radiation reaching the earth’s surface. It is intriguing to find diverse impacts of clouds and aerosols over Southern China (SC) and Northern India (NI) which result in remarkable differences in the plane-of-array irradiance (POAI) that signifies the maximum available solar photovoltaic potential by combining the latest satellite retrieval results and modeling tools. By separating the impacts of cloud and aerosol on the POAI, it is found that clouds are responsible for the most reduction of POAI in the SC, while aerosols and clouds are equally important for the NI region. The frequent occurrences of low and middle level clouds with high optical depth in the SC, as compared with the much lower occurrences of all levels of clouds with lower optical depth in the NI, is regarded as the major reason for the differences in the POAI. The differences in the main compositions of aerosols in the SC (sulfate) and the NI (dust) could be essential to answer the question of why higher aerosol optical depth in the SC whereas leads to weaker reduction in the POAI than that in the NI. The mitigation measures targeting on the controls of different types of aerosols should be considered for different regions.
Comparative Study on Arabic Text Classification: Challenges and Opportunities

There have been great improvements in web technology over the past years which heavily loaded the Internet with various digital contents of different fields. This made finding certain text classification algorithms that fit a specific language or a set of languages a difficult task for researchers. Text Classification or categorization is the practice of allocating a given text document to one or more predefined labels or categories, it aims to obtain valuable information from unstructured text documents. This paper presents a comparative study based on a list of chosen published papers that focus on improving Arabic text classifications, to highlight the given models and the used classifiers besides discussing the faced challenges in these types of researches, then this paper proposes the expected research opportunities in the field of text classification research. Based on the reviewed researches, SVM and Naive Bayes were the most widely used classifiers for Arabic text classification, while more effort is needed to develop and to implement flexible Arabic text classification methods and classifiers.
Fine-grained classification of drug trafficking based on Instagram hashtags

Social networks have become important platforms for the marketing and sale of illicit drugs. Hashtags make it easier for users to engage in drug trafficking, further increasing the risk of drug abuse. However, there are significant challenges in the detection and management of drug trafficking activities. In addition, the rapid legalization of some drugs has required a fine-grained classification of drugs to distinguish them from those that are illegal. Motivated by these observations, in this paper, our aim is to develop a methodology using the latest advances in AI technology to classify hashtags from posts advertising illicit drugs for sale on social networks. We present a semi-supervised deep learning approach to classify hashtags from posts advertising illicit drugs. An elegant combination of Bidirectional Encoder Representations from Transformers (BERT) with Graph Convolutional Network (GCN) allows us to analyze the characteristics (e.g., shipping region and platform self-regulation) of illegal drug trafficking. Our BERT+GCN model achieved the best performance with more than 75% accuracy compared to the other three baseline models. Then, fine-grained hashtags identified are applied to explore the characteristics of drug trafficking. Finally, we report our results for further exploration of shipping regions and self-regulation of drug trafficking on the platform in two analysis scenarios. Our developed approach has shown its effectiveness in detecting hashtags for different types of drugs from illegal drug sellers. Based on hashtag classification, we also provide two case studies that indicate that (1) there are differences in self-regulation for different types of drugs on social media, (2) there are regional differences in the demand for different types of drugs. • We provide a systematic study of the fine-grained classification of drug-related hashtags in posts or comments on Instagram. • We formulate hashtag classification as a semi-supervised deep learning problem, and we propose a method that combines BERT and GCN to infer the labels for the unlabeled hashtags. • We demonstrate the effectiveness of the proposed method in hashtag classification using real-world datasets that we collected from Instagram. • Based on the classification of hashtags, we also provide two case studies that indicate (1) that there are differences in self-regulation for different types of drugs on social media, (2) that there are regional differences in demand for different types of drugs.
Digital microscopy and artificial intelligence could profoundly contribute to malaria diagnosis in elimination settings

OPINION article Front. Artif. Intell., 17 November 2022Sec. Medicine and Public Health https://doi.org/10.3389/frai.2022.510483
Machine-learning prediction for hospital length of stay using a French medico-administrative database

Prolonged Hospital Length of Stay (PLOS) is an indicator of deteriorated efficiency in Quality of Care. One goal of public health management is to reduce PLOS by identifying its most relevant predictors. The objective of this study is to explore Machine Learning (ML) models that best predict PLOS.Our dataset was collected from the French Medico-Administrative database (PMSI) as a retrospective cohort study of all discharges in the year 2015 from a large university hospital in France (APHM). The study outcomes were LOS transformed into a binary variable (long vs. short LOS) according to the 90th percentile (14 days). Logistic regression (LR), classification and regression trees (CART), random forest (RF), gradient boosting (GB) and neural networks (NN) were applied to the collected data. The predictive performance of the models was evaluated using the area under the ROC curve (AUC).Our analysis included 73,182 hospitalizations, of which 7,341 (10.0%) led to PLOS. The GB classifier was the most performant model with the highest AUC (0.810), superior to all the other models (all p-values <0.0001). The performance of the RF, GB and NN models (AUC ranged from 0.808 to 0.810) was superior to that of the LR model (AUC = 0.795); all p-values <0.0001. In contrast, LR was superior to CART (AUC = 0.786), p < 0.0001. The variable most predictive of the PLOS was the destination of the patient after hospitalization to other institutions. The typical clinical profile of these patients (17.5% of the sample) was the elderly patient, admitted in emergency, for a trauma, a neurological or a cardiovascular pathology, more often institutionalized, with more comorbidities notably mental health problems, dementia and hemiplegia.The integration of ML, particularly the GB algorithm, may be useful for health-care professionals and bed managers to better identify patients at risk of PLOS. These findings underscore the need to strengthen hospitals through targeted allocation to meet the needs of an aging population.
BChainGuard: A New Framework for Cyberthreats Detection in Blockchain Using Machine Learning

Recently, blockchain technology has appeared as a powerful decentralized tool for data integrity protection. The use of smart contracts in blockchain helped to provide a secure environment for developing peer-to-peer applications. Blockchain has been used by the research community as a tool for protection against attacks. The blockchain itself can be the objective of many cyberthreats. In the literature, there are few research works aimed to protect the blockchain against cyberthreats adopting, in most cases, statistical schemes based on smart contracts and causing deployment and runtime overheads. Although, the power of machine learning tools there is insufficient use of these techniques to protect blockchain against attacks. For that reason, we aim, in this paper, to propose a new framework called BChainGuard for cyberthreat detection in blockchain. Our framework’s main goal is to distinguish between normal and abnormal behavior of the traffic linked to the blockchain network. In BChainGuard, the execution of the classification technique will be local. Next, we embed only the decision function as a smart contract. The experimental result shows encouraging results with an accuracy of detection of around 95% using SVM and 98.02% using MLP with a low runtime and overhead in terms of consumed gas.
SDG-Meter: A Deep Learning Based Tool for Automatic Text Classification of the Sustainable Development Goals

The 17 Sustainable Development Goals (SDGs) are a “shared blueprint for peace and prosperity for people and the planet, now and into the future”. Since 2015, they help pointing out pathways to solve interlinked challenges being faced globally. The monitoring of SDGs is essential to assess progress and obstacles to realise such shared goals. Streams of SDG-related documents produced by governments, academia, private and public entities are assessed by United Nations teams to measure such progress according to each SDG, requiring labelling to proceed to more in-depth analyses. Such laborious task is usually done by the experts, and rely on personal knowledge of the links between the documents contents and the SDGs. While UNEP has experts in many fields, links to the SDGs that are outside their expertise may be overlooked. In this context, we propose to solve this problem with a multi-label classification of texts using Bidirectional Encoder Representations from Transformers (BERT). Based on this method, we designed the SDG-Meter, an online tool able to indicate to the user in a fully automatic way the SDGs linked to their input text but also to quantify the degree of membership of these SDGs.