Reviewerly/exHarmony huggingface dataset:
Low-rank adaptive graph embedding for unsupervised feature extraction

Most of manifold learning based feature extraction methods are two-step methods, which first construct a weighted neighborhood graph and then use the pre-constructed graph to perform subspace learning. As a result, these methods fail to use the underlying correlation structure of data to learn an adaptive graph to preciously characterize the similarity relationship between samples. To address this problem, we propose a novel unsupervised feature extraction method called low-rank adaptive graph embedding (LRAGE), which can perform subspace learning and adaptive probabilistic neighborhood graph embedding simultaneously based on reconstruction error minimization. The proposed LRAGE is imposed with low-rank constraint for the sake of exploring the underlying correlation structure of data and learning more informative projection. Moreover, the L2,1-norm penalty is imposed on the regularization to further enhance the robustness of LRAGE. Since the resulting objective function has no closed-form solutions, an iterative optimization algorithm is elaborately designed. The convergence of the proposed algorithm is proved and the corresponding computational complexity analysis is also presented. In addition, we explore the potential properties of the proposed LRAGE by comparing it with several similar models on both synthetic and real-world data sets. Extensive experiments on five well-known face data sets and three non-face data sets demonstrate the superiority of the proposed LRAGE.
The birth of Romanian BERT

Large-scale pretrained language models have become ubiquitous in Natural Language Processing.However, most of these models are available either in high-resource languages, in particular English, or as multilingual models that compromise performance on individual languages for coverage.This paper introduces Romanian BERT, the first purely Romanian transformer-based language model, pretrained on a large text corpus.We discuss corpus composition and cleaning, the model training process, as well as an extensive evaluation of the model on various Romanian datasets.We open source not only the model itself, but also a repository that contains information on how to obtain the corpus, fine-tune and use this model in production (with practical examples), and how to fully replicate the evaluation process.
Rising to the Challenge: Innovative early childhood teachers adapt to the COVID-19 era

Click to increase image sizeClick to decrease image size NotesNotes1 Lynch, R. G., & Vaghul, K. (2015). The benefits and costs of investing in early childhood education: The fiscal, economic, and societal gains of a universal prekindergarten program in the United States, 2016-2050. Washington Center for Equitable Growth.2 Álvarez Marinelli, H., Arias Ortiz, E., Bergamaschi, A., López, Á., Noli, A., Ortiz Guerrero, M., Pérez Alfaro, M., Rieble-Aubourg, S., Rivera, M. C., Scannone, R., Vásquez, M., & Viteri, A. (2020, May 07). La educación en tiempos del coronavirus: Los sistemas educativos de América Latina y el Caribe ante COVID. Banco Interamericano de Desarrollo. http://dx.doi.org/10.18235/00023373 Child Trends Databank. (2019, February 12). Preschool and prekindergarten. https://www.childtrends.org/indicators/preschool-and-prekindergarten; UNICEF. (2020, June). Home environment. https://data.unicef.org/topic/early-childhood-development/home-environment/4 Mercer, R. (2020). How to deal with COVID-19 in early childhood education? [Webinar]. World Organization for Early Childhood Education. https://www.facebook.com/WorldOMEP/videos/209221243811304/?epa=SEARCH_BOX5 Ibid.6 Yonker, L. M., Neilan, A. M., Bartsch, Y., Patel, A. B., Regan, J., Arya, P., Gootkind, E., Park, G., Hardcastle, M., St. John, A., Appleman, L., Chiu, M. L., Fialkowski, A., De la Flor, D., Lima, R., Bordt, E. A., Yockey, L. J., D’Avino, P., Fischinger, S., … Fasano, A. (2020). Pediatric SARS-CoV-2: Clinical presentation, infectivity, and immune responses. The Journal of Pediatrics, Advance online publication. https://doi.org/10.1016/j.jpeds.2020.08.0377 Picchi, A. (2020, August 18). Parents go into debt to pay for kids’ breakfasts and lunch. USA Today. https://www.usatoday.com/story/money/2020/08/18/back-school-2020-parents-go-into-debt-pay-kids-meals/3386643001/
Tensor Low-Rank Reconstruction for Semantic Segmentation

Context information plays an indispensable role in the success of semantic segmentation. Recently, non-local self-attention based methods are proved to be effective for context information collection. Since the desired context consists of spatial-wise and channel-wise attentions, 3D representation is an appropriate formulation. However, these non-local methods describe 3D context information based on a 2D similarity matrix, where space compression may lead to channel-wise attention missing. An alternative is to model the contextual information directly without compression. However, this effort confronts a fundamental difficulty, namely the high-rank property of context information. In this paper, we propose a new approach to model the 3D context representations, which not only avoids the space compression but also tackles the high-rank difficulty. Here, inspired by tensor canonical-polyadic decomposition theory (i.e, a high-rank tensor can be expressed as a combination of rank-1 tensors.), we design a low-rank-to-high-rank context reconstruction framework (i.e, RecoNet). Specifically, we first introduce the tensor generation module (TGM), which generates a number of rank-1 tensors to capture fragments of context feature. Then we use these rank-1 tensors to recover the high-rank context features through our proposed tensor reconstruction module (TRM). Extensive experiments show that our method achieves state-of-the-art on various public datasets. Additionally, our proposed method has more than 100 times less computational cost compared with conventional non-local-based methods.
Introduction to Artificial Intelligence and Machine Learning for Pathology

Recent developments in machine learning have stimulated intense interest in software that may augment or replace human experts. Machine learning may impact pathology practice by offering new capabilities in analysis, interpretation, and outcomes prediction using images and other data. The principles of operation and management of machine learning systems are unfamiliar to pathologists, who anticipate a need for additional education to be effective as expert users and managers of the new tools.To provide a background on machine learning for practicing pathologists, including an overview of algorithms, model development, and performance evaluation; to examine the current status of machine learning in pathology and consider possible roles and requirements for pathologists in local deployment and management of machine learning systems; and to highlight existing challenges and gaps in deployment methodology and regulation.Sources include the biomedical and engineering literature, white papers from professional organizations, government reports, electronic resources, and authors' experience in machine learning. References were chosen when possible for accessibility to practicing pathologists without specialized training in mathematics, statistics, or software development.Machine learning offers an array of techniques that in recent published results show substantial promise. Data suggest that human experts working with machine learning tools outperform humans or machines separately, but the optimal form for this combination in pathology has not been established. Significant questions related to the generalizability of machine learning systems, local site verification, and performance monitoring remain to be resolved before a consensus on best practices and a regulatory environment can be established.
AIM 2020 Challenge on Rendering Realistic Bokeh

This paper reviews the second AIM realistic bokeh effect rendering challenge and provides the description of the proposed solutions and results. The participating teams were solving a real-world bokeh simulation problem, where the goal was to learn a realistic shallow focus technique using a large-scale EBB! bokeh dataset consisting of 5K shallow/wide depth-of-field image pairs captured using the Canon 7D DSLR camera. The participants had to render bokeh effect based on only one single frame without any additional data from other cameras or sensors. The target metric used in this challenge combined the runtime and the perceptual quality of the solutions measured in the user study. To ensure the efficiency of the submitted models, we measured their runtime on standard desktop CPUs as well as were running the models on smartphone GPUs. The proposed solutions significantly improved the baseline results, defining the state-of-the-art for practical bokeh effect rendering problem.
BLOCKCHAIN OPPORTUNITIES IN HEALTHCARE

Blockchain technology is finding its way into every industry and healthcare is no exception. By decentralising patient health history, tracking pharmaceuticals and improving payment options, blockchain holds the promise of remodeling the collaborative exchange of vital research and useful healthcare data. At the same time, there are still significant obstacles to the broader use of blockchain. Among other things, its novel and distinct characteristics, particularly its consensus-driven decision making and decentralised authority, are somewhat at odds with the clearly delineated framework of rights and obligations. Therefore, for any blockchain project today, it is crucial to understand the application of existing laws in relation to blockchain, especially in the context of data protection, allocation of liability and determination of governing law. This chapter describes key characteristics of blockchain technology, analyses possible use cases in healthcare and delves into legal issues and questions that arise in relation to these blockchain applications.
Resource-Aware Distributed Differential Evolution for Training Expensive Neural-Network-Based Controller in Power Electronic Circuit

The neural-network (NN)-based control method is a new emerging promising technique for controller design in a power electronic circuit (PEC). However, the optimization of NN-based controllers (NNCs) has significant challenges in two aspects. The first challenge is that the search space of the NNC optimization problem is such complex that the global optimization ability of the existing algorithms still needs to be improved. The second challenge is that the training process of the NNC parameters is very computationally expensive and requires a long execution time. Thus, in this article, we develop a powerful evolutionary computation-based algorithm to find a high-quality solution and reduce computational time. First, the differential evolution (DE) algorithm is adopted because it is a powerful global optimizer in solving a complex optimization problem. This can help to overcome the premature convergence in local optima to train the NNC parameters well. Second, to reduce the computational time, the DE is extended to distribute DE (DDE) by dispatching all the individuals to different distributed computing resources for parallel computing. Moreover, a resource-aware strategy (RAS) is designed to further efficiently utilize the resources by adaptively dispatching individuals to resources according to the real-time performance of the resources, which can simultaneously concern the computing ability and load state of each resource. Experimental results show that, compared with some other typical evolutionary algorithms, the proposed algorithm can get significantly better solutions within a shorter computational time.
INSTANCE – the Italian seismic dataset for machine learning

Abstract. The Italian earthquake waveform data are collected here in a dataset suited for machine learning analysis (ML) applications. The dataset consists of nearly 1.2 million three-component (3C) waveform traces from about 50 000 earthquakes and more than 130 000 noise 3C waveform traces, for a total of about 43 000 h of data and an average of 21 3C traces provided per event. The earthquake list is based on the Italian Seismic Bulletin (http://terremoti.ingv.it/bsi, last access: 15 February 2020​​​​​​​) of the Istituto Nazionale di Geofisica e Vulcanologia between January 2005 and January 2020, and it includes events in the magnitude range between 0.0 and 6.5. The waveform data have been recorded primarily by the Italian National Seismic Network (network code IV) and include both weak- (HH, EH channels) and strong-motion (HN channels) recordings. All the waveform traces have a length of 120 s, are sampled at 100 Hz, and are provided both in counts and ground motion physical units after deconvolution of the instrument transfer functions. The waveform dataset is accompanied by metadata consisting of more than 100 parameters providing comprehensive information on the earthquake source, the recording stations, the trace features, and other derived quantities. This rich set of metadata allows the users to target the data selection for their own purposes. Much of these metadata can be used as labels in ML analysis or for other studies. The dataset, assembled in HDF5 format, is available at http://doi.org/10.13127/instance (Michelini et al., 2021).
Learning to Pre-train Graph Neural Networks

Graph neural networks (GNNs) have become the defacto standard for representation learning on graphs, which derive effective node representations by recursively aggregating information from graph neighborhoods. While GNNs can be trained from scratch, pre-training GNNs to learn transferable knowledge for downstream tasks has recently been demonstrated to improve the state of the art. However, conventional GNN pre-training methods follow a two-step paradigm: 1) pre-training on abundant unlabeled data and 2) fine-tuning on downstream labeled data, between which there exists a significant gap due to the divergence of optimization objectives in the two steps. In this paper, we conduct an analysis to show the divergence between pre-training and fine-tuning, and to alleviate such divergence, we propose L2P-GNN, a self-supervised pre-training strategy for GNNs. The key insight is that L2P-GNN attempts to learn how to fine-tune during the pre-training process in the form of transferable prior knowledge. To encode both local and global information into the prior, L2P-GNN is further designed with a dual adaptation mechanism at both node and graph levels. Finally, we conduct a systematic empirical study on the pre-training of various GNN models, using both a public collection of protein graphs and a new compilation of bibliographic graphs for pre-training. Experimental results show that L2P-GNN is capable of learning effective and transferable prior knowledge that yields powerful representations for downstream tasks. (Code and datasets are available at https://github.com/rootlu/L2P-GNN.)
XLM-E: Cross-lingual Language Model Pre-training via ELECTRA

In this paper, we introduce ELECTRA-style tasks (Clark et al., 2020b) to cross-lingual language model pre-training.Specifically, we present two pre-training tasks, namely multilingual replaced token detection, and translation replaced token detection.Besides, we pretrain the model, named as XLM-E, on both multilingual and parallel corpora.Our model outperforms the baseline models on various cross-lingual understanding tasks with much less computation cost.Moreover, analysis shows that XLM-E tends to obtain better cross-lingual transferability.
Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center

Broad adoption of digital pathology (DP) is still lacking, and examples for DP connecting diagnostic, research, and educational use cases are missing. We blueprint a holistic DP solution at a large academic medical center ubiquitously integrated into clinical workflows; researchapplications including molecular, genetic, and tissue databases; and educational processes.We built a vendor-agnostic, integrated viewer for reviewing, annotating, sharing, and quality assurance of digital slides in a clinical or research context. It is the first homegrown viewer cleared by New York State provisional approval in 2020 for primary diagnosis and remote sign-out during the COVID-19 (coronavirus disease 2019) pandemic. We further introduce an interconnected Honest Broker for BioInformatics Technology (HoBBIT) to systematically compile and share large-scale DP research datasets including anonymized images, redacted pathology reports, and clinical data of patients with consent.The solution has been operationally used over 3 years by 926 pathologists and researchers evaluating 288 903 digital slides. A total of 51% of these were reviewed within 1 month after scanning. Seamless integration of the viewer into 4 hospital systems clearly increases the adoption of DP. HoBBIT directly impacts the translation of knowledge in pathology into effective new health measures, including artificial intelligence-driven detection models for prostate cancer, basal cell carcinoma, and breast cancer metastases, developed and validated on thousands of cases.We highlight major challenges and lessons learned when going digital to provide orientation for other pathologists. Building interconnected solutions will not only increase adoption of DP, but also facilitate next-generation computational pathology at scale for enhanced cancer research.
On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification

This article aims to investigate how circuit-based hybrid quantum convolutional neural networks (QCNNs) can be successfully employed as image classifiers in the context of remote sensing.The hybrid QCNNs enrich the classical architecture of convolutional neural networks by introducing a quantum layer within a standard neural network.The novel QCNN proposed in this work is applied to the land-use and land-cover classification, chosen as an Earth observation (EO) use case, and tested on the EuroSAT dataset used as the reference benchmark.The results of the multiclass classification prove the effectiveness of the presented approach by demonstrating that the QCNN performances are higher than the classical counterparts.Moreover, investigation of various quantum circuits shows that the ones exploiting quantum entanglement achieve the best classification scores.This study underlines the potentialities of applying quantum computing to an EO case study and provides the theoretical and experimental background for future investigations.
The Ninth Visual Object Tracking VOT2021 Challenge Results

The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative. Results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years. The VOT2021 challenge was composed of four sub-challenges focusing on different tracking domains: (i) VOT-ST2021 challenge focused on short-term tracking in RGB, (ii) VOT-RT2021 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2021 focused on long-term tracking, namely coping with target disappearance and reappearance and (iv) VOT-RGBD2021 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2021 dataset was refreshed, while VOT-RGBD2021 introduces a training dataset and sequestered dataset for winner identification. The source code for most of the trackers, the datasets, the evaluation kit and the results along with the source code for most trackers are publicly available at the challenge website <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Blockchain Technology Enhances Sustainable Higher Education

This research investigates blockchain technology, focusing on the influence of motivation on collaborative work, which positively influences learning performance in Higher Education Institutions (HEI). In addition, blockchain technology is correlated with decentralisation, security and integrity, and anonymity and encryption. It can also be perceived as a consensus mechanism, rewarding students, professors, and universities as a smart contract. Therefore, this technology has been used to improve higher education. It also allows less informed people to interact with better-informed peers and mentors. Finally, this study aims to enhance the current state of blockchain applications comprehension. The methodology used for this research includes document analysis, literature review, content analysis (blockchain platforms), the case study method, and the survey method. In statistical considerations, aiming to evaluate indicators, this research presents the Composite Reliability Analysis, Cronbach Alpha Coefficients, and the Bootstrapping method (Variance Inflation Factor). All these analyses aimed to present a designed research model. This exploratory research gathered data from 150 students at 3 universities in Serbia, Romania, and Portugal. As demonstrated, using student motivation has a significant and positive impact on the quality of student collaborative work. Student collaborative work also correlates with students’ higher level of engagement in the educational process, and the more engaged students are, the better their learning outcomes will be. As a result, in higher education, student involvement boosted learning outcomes. Researchers found that motivation, teamwork, and student involvement were important factors in improving student learning outcomes, as were blockchain-based tools. The results from the quantitative analysis indicate that Collaborative work, Motivation, Engagement, MOOCs, AR, VR, Gamification, and Online class were associated with learning performance.
Federated against the cold: A trust-based federated learning approach to counter the cold start problem in recommendation systems

• Federated learning-based recommendation system for cold-start items. • Trust establishment for recommenders that considers resource utilization and credibility. • Recommender selection strategy based on Double Deep Q Learning . • Simulations on the MovieLens 1M dataset suggest better accuracy compared to two benchmark approaches. Recommendation systems are often challenged by the existence of cold-start items for which no previous rating is available. The standard content-based or collaborative-filtering recommendation approaches may address this problem by asking users to share their data with a central (cloud-based) server, which uses machine learning to predict appropriate ratings on such items. But users may be reluctant to have their (confidential) data shared. Federated learning has been lately capitalized on to address the privacy concerns by enabling an on-device distributed training of a single machine learning model. In this work, we propose a federated learning-based approach to address the item cold-start problem in recommendation systems. The originality of our solution compared to existing federated learning-based solutions comes from (1) applying federated learning specifically to the cold-start problem; (2) proposing a trust mechanism to derive trust scores for the potential recommenders, followed by a double deep Q learning scheduling approach that relies on the trust and energy levels of the recommenders to select the best candidates. Simulations on the MovieLens 1M and Epinions datasets suggest that our solution improves the accuracy of recommending cold-start items and reduces the RMSE, MAE and running time compared to five benchmark approaches.