Reviewerly/exHarmony huggingface dataset:
Adaptive Federated Learning in Presence of Concept Drift

Federated Learning (FL) is a promising research area in the machine learning field. Techniques and solutions belonging to this area operate in distributed scenarios, comprising a server and pervasively distributed clients, aiming at learning a single central model without sending (possibly sensitive) data from the clients to the server. Such an approach allows mitigating the privacy concerns that are nowadays perceived as relevant in distributed machine learning solutions leveraging data belonging to different users or companies. The literature in the field of FL is wide and many state-of-the-art solutions are available. Unfortunately, all these solutions assume (implicitly or explicitly) that the process generating the data is stationary (hence not changing its statistical behavior over time); an assumption that rarely holds in real-world conditions where concept drift occurs due to, e.g., seasonality or periodicity effects, faults in sensors or actuators or changes in the users' behaviour. In this paper, we introduce, for the first time in the literature, a novel FL algorithm called Adaptive-FedAVG, able to operate with nonstationary data generating processes affected by concept drifts. Following a passive approach, Adaptive-FedAVG is able to increase the accuracy in stationary conditions and promptly react to concept drift by adapting the learning rate to increase the plasticity of the learning phase. A wide experimental campaign shows the effectiveness of the proposed Adaptive-FedAVG algorithm by comparing it with a state-of-the-art FL algorithm present in the literature both in stationary and non-stationary conditions.
A Comparatıve Analysis on Three Consensus Algorithms

Blockchain technology has attracted immense attention in recent years from research, business, and governments all over the world. It is regarded as a mechanical advancement that is supposed to disrupt a few application areas that come into contact with all aspects of our lives. Nonetheless, a significant number of these blockchain implementations suffer from genuine flaws in their presentation and security, which must be addressed before any wide-scale acceptance can be achieved. The consensus calculation, which determines the exhibition and security of any blockchain system, is a key element. An algorithm for consensus is a tool that allows clients or machines to coordinate themselves in a decentralized environment. It must ensure that all nodes in the network will agree on a single source of truth even if a few nodes fail. They can usually make improvements, but there is not a complex administration structure in place to reach an agreement among multiple administrators. In a decentralized system, it is an entire other story. Assume we are dealing with a distributed database—how can we decide which records to add? Conquering this test in an atmosphere where nodes do not trust each other was perhaps the most demanding breakthrough in blockchain preparation. In this article, we will investigate how consensus calculations that are fundamental to the working of cryptographic forms of money and distributed ledgers. Therefore, a few current new agreement calculations have been presented to resolve the impediments of different blockchain structures. A methodical investigation of these equations can help us see how and why the way it operates is carried out by a particular blockchain. Consequently, in order to address the impediments of the various blockchain frameworks, some of the existing calculations of the new consensus have also been presented. A methodical investigation of these calculations will help to see how and why a specific blockchain plays out the way it works. In this article, we are talking about three consensus algorithms using an exhaustive empirical classification of properties and looking in depth at the implications of the various issues still prevalent in the agreement calculations.
Path-following by a UGV-UAV Formation Based on Null Space

This paper addresses the control of a formation composed of an unmanned ground vehicle (UGV) and an unmanned aerial vehicle (UAV) using the concepts of null space control. The formation navigates in a coordinated way, with the UAV following the UGV, keeping a relative pose with respect to it, while the UGV follows a specified path. The goal is that the UAV lands on the UGV at any desired instant, a situation quite similar to the return of the UAV to a moving base after delivering a package. Null space control techniques are used in order to get more precise control on the relative pose between the robots, increasing the reliability of the landing operation. Experimental results are presented and discussed, which validate the approach proposed to accomplish the desired task.
Real-time Surveillance Video Salient Object Detection Using Collaborative Cloud-Edge Deep Reinforcement Learning

In recent years, with the advancement of cloud computing technology and the availability of cheaper hardware, surveillance systems have become more and more common. Unfortunately, most existing systems still face many limitations, such as latency and real-time analysis issues, etc. Edge computing effectively expands the boundaries of cloud computing, migrating some computing and analysis tasks to the edge devices for execution. Edge device could perform video analysis, which may be a good solution. In this paper, we adopt the collaborative Cloud-Edge architecture to analyze surveillance video and extract video keyframes for compressing video data at the edge. Then, we provide a residual U-net neural network to perform salient object detection on the extracted keyframes. Finally, we utilize the deep reinforcement learning Asynchronous Advantage Actor-Critic (A3C) algorithm to perform the residual U-net tasks scheduling, adaptive offloading in the cloud or edge, reducing system latency, and improving real-time performance. We verified the system performance using real road surveillance videos and other public datasets. The experiment results are inspiring. It proves that the real-time processing of the surveillance video system based on a collaborative cloud-edge mechanism could obtain the optimal result within the range of tolerable latency.
A survey of medical researchers indicates poor awareness of research data management processes and a role for data librarians

The European Parliament's directive on open data indicates the direction to follow for all public institutions in Europe. The portal Polish Platform of Medical Research (PPM) required more information about researcher attitudes and training requirements for strategic planning.The aim was to assess (1) the status of knowledge about research data management among medical researchers in Poland, and (2) their attitudes towards data sharing. This knowledge may help to inform a training program and adapt PPM to the requirements of researchers.The authors circulated an online survey and received responses from 603 researchers representing medical sciences and related disciplines. The survey was conducted in 2019 at seven Polish medical universities and at the Nofer Institute of Occupational Medicine. Analysis used descriptive statistics.Data sharing was not widespread (55.7% only shared with their research team, 9.8% had shared data on an open access basis). Many cited possible benefits of research data sharing but were concerned about drawbacks (e.g. fraud, plagiarism).Polish medical scientists, like many researchers, are not aware of the processes required for safe data preparation for sharing. Academic libraries should develop roles for data librarians to help train researchers.Fears about the dangers of data sharing need to be overcome before researchers are willing to share their own research data.
Federating Scholarly Infrastructures with GraphQL

A plethora of scholarly knowledge is being published on distributed scholarly infrastructures. Querying a single infrastructure is no longer sufficient for researchers to satisfy information needs. We present a GraphQL-based federated query service for executing distributed queries on numerous, heterogeneous scholarly infrastructures (currently, ORKG, DataCite and GeoNames), thus enabling the integrated retrieval of scholarly content from these infrastructures. Furthermore, we present the methods that enable cross-walks between artefact metadata and artefact content across scholarly infrastructures, specifically DOI-based persistent identification of ORKG artefacts (e.g., ORKG comparisons) and linking ORKG content to third-party semantic resources (e.g., taxonomies, thesauri, ontologies). This type of linking increases interoperability, facilitates the reuse of scholarly knowledge, and enables finding machine actionable scholarly knowledge published by ORKG in global scholarly infrastructures. In summary, we suggest applying the established linked data principles to scholarly knowledge to improve its findability, interoperability, and ultimately reusability, i.e., improve scholarly knowledge FAIR-ness.
Interpretable Deep Learning for Multimodal Super-Resolution of Medical Images

In medical image acquisition, hardware limitations and scanning time constraints result in degraded images. Super-resolution (SR) is a post-processing approach aiming to reconstruct a high-resolution image from its low-resolution counterpart. Recent advances in medical image SR include the application of deep neural networks, which can improve image quality at a low computational cost. When dealing with medical data, accuracy is important for discovery and diagnosis, therefore, interpretable neural network models are of significant interest as they enable a theoretical study and increase trustworthiness needed in clinical practice. While several interpretable deep learning designs have been proposed to treat unimodal images, to the best of our knowledge, there is no multimodal SR approach applied for medical images. In this paper, we present an interpretable neural network model that exploits information from multiple modalities to super-resolve an image of a target modality. Experiments with simulated and real MRI data show the performance of the proposed approach in terms of numerical and visual results.
SSS-AE: Anomaly Detection Using Self-Attention Based Sequence-to-Sequence Auto-Encoder in SMD Assembly Machine Sound

A Surface-Mounted Device (SMD) assembly machine continuously assembles various products in real field. Unwanted situations such as assembly failure and device breakdown can occur at any time during the assembly process and result in costly losses. Anomaly detection techniques using deep learning are effective in detecting such abnormal situations. Two training scenarios, single-product learning and multi-product learning, can be considered for SMD anomaly detection workflows. Since there are not many products in previous studies, single-product learning is sufficient. However, multi-product learning is required when the number of products increases gradually. Successful multi-product learning on various assembly sound data in an industrial environment with limited resources requires efficient and light learning methods. In this paper, we propose robust model and effective data preprocessing method, Self-Attention based Sequence-to-Sequence Auto-Encoder (SSS-AE) and Temporal Adaptive Average Pooling (TAAP). For more accurate evaluation compared with the previous SMD anomaly detection studies, a new large-scale SMD dataset containing observed real abnormal products were collected and evaluated. As a result, we show that SSS-AE and TAAP are powerful and practical approaches for both single-product learning and multi-product learning.
Virtual samples based robust block-diagonal dictionary learning for face recognition

It is an open question to learn an over-complete dictionary from a limited number of face samples, and the inherent attributes of the samples are underutilized. Besides, the recognition performance may be adversely affected by the noise (and outliers), and the strict binary label based linear classifier is not appropriate for face recognition. To solve above problems, we propose a virtual samples based robust block-diagonal dictionary learning for face recognition. In the proposed model, the original samples and virtual samples are combined to solve the small sample size problem, and both the structure constraint and the low rank constraint are exploited to preserve the intrinsic attributes of the samples. In addition, the fidelity term can effectively reduce negative effects of noise (and outliers), and the ε-dragging is utilized to promote the performance of the linear classifier. Finally, extensive experiments are conducted in comparison with many state-of-the-art methods on benchmark face datasets, and experimental results demonstrate the efficacy of the proposed method.
Burning Sage: Reversing the Curse of Dimensionality in the Visualization of High-Dimensional Data

In high-dimensional data analysis, the curse of dimensionality reasons that points tend to be far away from the center of the distribution and on the edge of high-dimensional space. Contrary to this, is that projected data tends to clump at the center. This gives a sense that any structure near the center of the projection is obscured, whether this is true or not. A geometric transformation to reverse the curse, is defined in this article, which uses radial transformations on the projected data. It is integrated seamlessly into the grand tour algorithm, and we have called it a burning sage tour, to indicate that it reverses the curse. The work is implemented into the tourr package in R. Several case studies are included that show how the sage visualizations enhance exploratory clustering and classification problems. Supplementary files for this article are available online.
Generation of Background Model Image Using Foreground Model

Proper consideration of the temporal domain and the spatial domain is essential to perform robust foreground object detection in visual surveillance. However, there are difficulties in considering long-term temporal information with CNN-based methods. To solve this limitation, classical algorithms and some deep learning-based algorithms have used a background model image. However, acquiring a sophisticated background model image is also one of the complex problems. Most of the algorithms take a lot of time to initialize the background model image and generate many errors in the presence of a static foreground. This paper proposes an algorithm for generating a background model image using a deep-learning-based segmenter to solve this problem. The proposed method shows a 66.25% lower mean square error (MSE) than the background subtraction (BGS) algorithm and 79.25% lower than the latest deep learning algorithm in the SBI dataset. In addition, in the deep learning-based segmenter that uses a background image as input, replacing the background image of BGS algorithm with the background image of the proposed method shows a 38.63% reduction in the false detection rate (PWC).
Generating Explainable Rule Sets from Tree-Ensemble Learning Methods by Answer Set Programming

We propose a method for generating explainable rule sets from tree-ensemble learners using Answer Set Programming (ASP).To this end, we adopt a decompositional approach where the split structures of the base decision trees are exploited in the construction of rules, which in turn are assessed using pattern mining methods encoded in ASP to extract interesting rules.We show how user-defined constraints and preferences can be represented declaratively in ASP to allow for transparent and flexible rule set generation, and how rules can be used as explanations to help the user better understand the models.Experimental evaluation with real-world datasets and popular tree-ensemble algorithms demonstrates that our approach is applicable to a wide range of classification tasks.
Research on the Integration and Development of Culture and Tourism Based on Wireless Communication and Virtual Reality Technology

The interconnection of all things and industrial integration is the current trend of the times. Among them, the interconnection of all things is the demand of informationization of the times, and the industrial integration is the demand of industrial development. The interconnection of all things is realized based on wireless communication technology. It is necessary to combine the development of the tourist area and the surrounding culture. The relationship between tourist attractions and culture needs to be fully and effectively developed. In order to fully explore the advantages of the cooperation between the two, it is necessary to combine modern technology to package the tour process of each characteristic culture of the scenic spot. Virtual reality is a modern technology that can combine culture and tourism. Wireless communication and VR technology are applied to the development of integration of culture and tourism. The process of tourism will promote profound changes in the tourism model. Under the demand for informatization in various industries, the tourism industry is gradually developing in this direction. The integration of culture and tourism will also be driven by informatization and technology. This paper analyzes the current situation of culture and tourism, summarizes the problems existing in the current process of integration of culture and tourism, and finally puts forward targeted solutions. Mainly in the process of the integration and development of tourism and cultural industries, the traditional culture and scenic spots are the basic factors, combined with wireless communication and virtual reality technology, to develop a tourism industry with technological characteristics of the new era.
App-Based Saccade Latency and Directional Error Determination Across the Adult Age Spectrum

We aid in neurocognitive monitoring outside the hospital environment by enabling app-based measurements of visual reaction time (saccade latency) and directional error rate in a cohort of subjects spanning the adult age spectrum.We developed an iOS app to record subjects with the frontal camera during pro- and anti-saccade tasks. We further developed automated algorithms for measuring saccade latency and directional error rate that take into account the possibility that it might not always be possible to determine the eye movement from app-based recordings.To measure saccade latency on a tablet, we ensured that the absolute timing error between on-screen task presentation and the camera recording is within 5 ms. We collected over 235,000 eye movements in 80 subjects ranging in age from 20 to 92 years, with 96% of recorded eye movements either declared good or directional errors. Our error detection code achieved a sensitivity of 0.97 and a specificity of 0.97. Confirming prior reports, we observed a positive correlation between saccade latency and age while the relationship between directional error rate and age was not significant. Finally, we observed significant intra- and inter-subject variations in saccade latency and directional error rate distributions, which highlights the importance of individualized tracking of these visual digital biomarkers.Our system and algorithms allow ubiquitous tracking of saccade latency and directional error rate, which opens up the possibility of quantifying patient state on a finer timescale in a broader population than previously possible.
TaxoRef: Embeddings Evaluation for AI-driven Taxonomy Refinement

Taxonomies provide a structured representation of semantic relations between lexical terms. In the case of standard official taxonomies, the refinement task consists of maintaining them updated over time, while preserving their original structure. To date, most of the approaches for automated taxonomy refinement rely on word vector models. However, none of them considers to what extent those models encode the taxonomic similarity between words. Motivated by this, we propose and implement TaxoRef, a methodology that (i) synthesises the semantic similarity between taxonomic elements through a new metric, namely HSS, (ii) evaluates to what extent the embeddings generated from a text corpus preserve those similarity relations and (iii) uses the best embedding resulted from this evaluation to perform taxonomy refinement. TaxoRef is a part of the research activity of a 4-year EU project that collects and classifies millions of Online Job Ads for the 27+1 EU countries. It has been tested over 2M ICT job ads classified over ESCO, the European standard occupation and skill taxonomy.
A Global Universality of Two-Layer Neural Networks with ReLU Activations

In the present study, we investigate a universality of neural networks, which concerns a density of the set of two-layer neural networks in function spaces. There are many works that handle the convergence over compact sets. In the present paper, we consider a global convergence by introducing a norm suitably, so that our results will be uniform over any compact set.
Learning Behavior-Aware Cognitive Diagnosis for Online Education Systems

Cognitive diagnosis, which aims to diagnose students' knowledge proficiency, is crucial for numerous online education applications, such as personalized exercise recommendation. Existing methods in this area mainly exploit students' exercising records, which ignores students' full learning process in online education systems. Besides, the latent relation of exercises with course structure and texts is still underexplored. In this paper, a learning behavior-aware cognitive diagnosis (LCD) framework is proposed for students' cognitive modeling with both learning behavior records and exercising records. The concept of LCD was first introduced to characterize students' knowledge proficiency more completely. Second, a course graph was designed to explore rich information existed in course texts and structures. Third, an interaction function was put forward to explore complex relationships between students, exercises and videos. Extensive experiments on a real-world dataset prove that LCD predicts student performance more effectively, the output of LCD is also interpretable.
COVID-19 Economic Vulnerability and Resilience Indexes: Global Evidence

The study complements the extant literature by constructing Covid-19 economic vulnerability and resilience indexes using a global sample of 150 countries which are categorized into four principal regions, namely: Africa, Asia-Pacific and the Middle East, America and Europe.Seven variables are used for the vulnerability index and nine for the resilience index.Both regions and sampled countries are classified in terms of the two proposed and computed indexes.The classification of countries is also provided in terms of four scenarios pertaining to vulnerability and resilience characteristics, notably: low vulnerability-low resilience, high vulnerability-low resilience, high vulnerability-high resilience and low vulnerability-high resilience to respectively illustrate, sensitive, severe, asymptomatic and best cases.The findings are relevant to policy makers especially as it pertains to decision making in resources allocation in the fight against the global pandemic.