Reviewerly/exHarmony huggingface dataset:
Geoscience data publication: Practices and perspectives on enabling the FAIR guiding principles

Abstract Introduced in 2016, the FAIR Guiding Principles endeavour to significantly improve the process of today's data‐driven research. The Principles present a concise set of fundamental concepts that can facilitate the findability, accessibility, interoperability and reuse (FAIR) of digital research objects by both machines and human beings. The emergence of FAIR has initiated a flurry of activity within the broader data publication community, yet the principles are still not fully understood by many community stakeholders. This has led to challenges such as misinterpretation and co‐opted use, along with persistent gaps in current data publication culture, practices and infrastructure that need to be addressed to achieve a FAIR data end‐state. This paper presents an overview of the practices and perspectives related to the FAIR Principles within the Geosciences and offers discussion on the value of the principles in the larger context of what they are trying to achieve. The authors of this article recommend using the principles as a tool to bring awareness to the types of actions that can improve the practice of data publication to meet the needs of all data consumers. FAIR Guiding Principles should be interpreted as an aspirational guide to focus behaviours that lead towards a more FAIR data environment. The intentional discussions and incremental changes that bring us closer to these aspirations provide the best value to our community as we build the capacity that will support and facilitate new discovery of earth systems.
ChainerRL: A Deep Reinforcement Learning Library

In this paper, we introduce ChainerRL, an open-source deep reinforcement learning (DRL) library built using Python and the Chainer deep learning framework. ChainerRL implements a comprehensive set of DRL algorithms and techniques drawn from state-of-the-art research in the field. To foster reproducible research, and for instructional purposes, ChainerRL provides scripts that closely replicate the original papers' experimental settings and reproduce published benchmark results for several algorithms. Lastly, ChainerRL offers a visualization tool that enables the qualitative inspection of trained agents. The ChainerRL source code can be found on GitHub: https://github.com/chainer/chainerrl.
Internet of medical things embedding deep learning with data augmentation for mammogram density classification

Abstract Females are approximately half of the total population worldwide, and most of them are victims of breast cancer (BC). Computer‐aided diagnosis (CAD) frameworks can help radiologists to find breast density (BD), which further helps in BC detection precisely. This research detects BD automatically using mammogram images based on Internet of Medical Things (IoMT) supported devices. Two pretrained deep convolutional neural network models called DenseNet201 and ResNet50 were applied through a transfer learning approach. A total of 322 mammogram images containing 106 fatty, 112 dense, and 104 glandular cases were obtained from the Mammogram Image Analysis Society dataset. The pruning out irrelevant regions and enhancing target regions is performed in preprocessing. The overall classification accuracy of the BD task is performed and accomplished 90.47% through DensNet201 model. Such a framework is beneficial in identifying BD more rapidly to assist radiologists and patients without delay.
Active Learning With Noisy Labelers for Improving Classification Accuracy of Connected Vehicles

Machine learning has emerged as a promising paradigm for enabling connected, automated vehicles to autonomously cruise the streets and react to unexpected situations. Reacting to such situations requires accurate classification for uncommon events, which in turn depends on the selection of large, diverse, and high-quality training data. In fact, the data available at a vehicle (e.g., photos of road signs) may be affected by errors or have different levels of resolution and freshness. To tackle this challenge, we propose an active learning framework that, leveraging the information collected through onboard sensors as well as received from other vehicles, effectively deals with scarce and noisy data. Given the information received from neighboring vehicles, our solution: (i) selects which vehicles can reliably generate high-quality training data, and (ii) obtains a reliable subset of data to add to the training set by trading off between two essential features, i.e., quality and diversity. The results, obtained with different real-world datasets, demonstrate that our framework significantly outperforms state-of-the-art solutions, providing high classification accuracy with a limited bandwidth requirement for the data exchange between vehicles.
FeatureNMS: Non-Maximum Suppression by Learning Feature Embeddings

Most state of the art object detectors output multiple detections per object. The duplicates are removed in a postprocessing step called Non-Maximum Suppression. Classical Non-Maximum Suppression has shortcomings in scenes that contain objects with high overlap: This heuristic assumes that a high overlap between two bounding boxes corresponds to a high probability of one being a duplicate. We propose FeatureNMS to solve this problem. FeatureNMS recognizes duplicates not only based on the intersection over union between the bounding boxes, but also based on the difference of feature vectors. These feature vectors can encode more information like visual appearance. Our approach outperforms classical NMS and derived approaches and achieves state of the art performance.
A Framework for Planning and Execution of Drone Swarm Missions in a Hostile Environment

This article presents a framework for planning a drone swarm mission in a hostile environment. Elements of the planning framework are discussed in detail, including methods of planning routes for drone swarms using mixed integer linear programming (MILP) and methods of detecting potentially dangerous objects using EO/IR camera images and synthetic aperture radar (SAR). Methods of detecting objects in the field are used in the mission planning process to re-plan the swarm’s flight paths. The route planning model is discussed using the example of drone formations managed by one UAV that communicates through another UAV with the ground control station (GCS). This article presents practical examples of using algorithms for detecting dangerous objects for re-planning of swarm routes. A novelty in the work is the development of these algorithms in such a way that they can be implemented on mobile computers used by UAVs and integrated with MILP tasks. The methods of detection and classification of objects in real time by UAVs equipped with SAR and EO/IR are presented. Different sensors require different methods to detect objects. In the case of infrared or optoelectronic sensors, a convolutional neural network is used. For SAR images, a rule-based system is applied. The experimental results confirm that the stream of images can be analyzed in real-time.
Social Values of Students in Conditions of Digitalization of Education and COVID-19

Introduction. During the COVID-19 pandemic variety of digital tools and technologies were demonstrated owing to the global digitalization of education and the total transition to online learning. The purpose of present research is to determine factors of digitalization of education and their effect on transformation of social values among high school students in conditions of the COVID-19. The results of research on social values of students were considered using comparative analysis. It was revealed that the paradigm of values among young people changed from an individual-personal one at the beginning of the XXI century to the social oriented paradigm – tolerance, openness, public recognition through social networks, self-development and contribution to the community. Materials and Methods. The analysis of pedagogical experience in the field of digitalization of education allowed to formulate the main factors of global digitalization of education and to highlight positive and negative aspects of online learning. According to M. Rokich’s method, an empirical study of instrumental values transformations was carried out among 137 students of Ural Federal University in the conditions of full-time online learning. A questionnaire method was implemented to determine studentsʼ attitude to online learning during COVID-19 pandemic and possibility for further development of this form of learning. Results. It was revealed that only 13.7% of respondents did not notice any decrease in the quality of learning and did not experience discomfort in obtaining knowledge in online form. According to the studentsʼ opinion the values that are subjected to transformation are the following: efficiency, self-control, responsibility, honesty. It can be concluded that the era of network personality and new scientific knowledge in pedagogy has begun. Online learning is a fait accompli which requires scientific substantiation of a new didactics and social values system development among students in a digital education environment. Discussion and Conclusion. Achieved results contribute to ecological system of new digital services that change life and educational activities of a human, as well as transform his personality and values system. Thus, for the purpose of health and emotional intelligence preservation in students the continuation of interdisciplinary study of various aspects of education digitalization is required.
Environment Inference for Invariant Learning

Learning models that gracefully handle distribution shifts is central to research on domain generalization, robust optimization, and fairness. A promising formulation is domain-invariant learning, which identifies the key issue of learning which features are domain-specific versus domain-invariant. An important assumption in this area is that the training examples are partitioned into domains or environments. Our focus is on the more common setting where such partitions are not provided. We propose EIIL, a general framework for domain-invariant learning that incorporates Environment Inference to directly infer partitions that are maximally informative for downstream Invariant Learning. We show that EIIL outperforms invariant learning methods on the CMNIST benchmark without using environment labels, and significantly outperforms ERM on worst-group performance in the Waterbirds and CivilComments datasets. Finally, we establish connections between EIIL and algorithmic fairness, which enables EIIL to improve accuracy and calibration in a fair prediction problem.
Novel Ensemble Algorithm for Multiple Activity Recognition in Elderly People Exploiting Ubiquitous Sensing Devices

Ambient assisted living is good way to look after ageing population that enables us to detect human’s activities of daily living (ADLs) and postures, as number of older adults are increasing at rapid pace. Posture detection is used to provide the assessment for monitoring the activity of elderly people. Most of the existing approaches exploit dedicated sensing devices as cameras, thermal sensors, accelerometer, gyroscope, magnetometer and so on. Traditional methods such as recording data using these sensors, training and testing machine learning classifiers to identify various human postures. This paper exploits data recorded using ubiquitous devices such as smart phones we use on daily basis and classify different human activities such as standing, sitting, laying, walking, walking downstairs and walking upstairs. Moreover, we have used machine learning and deep learning classifiers including random forest, KNN, logistic regression, multilayer perceptron, decision tree, QDA and SVM, convolutional neural network and long short-term memory as ground truth and proposed a novel ensemble classification algorithm to classify each human activity. The proposed algorithm demonstrate classification accuracy of 98% that outperforms other algorithms.
Ensemble Based Filter Feature Selection with Harmonize Particle Swarm Optimization and Support Vector Machine for Optimal Cancer Classification

Explosive increase of dataset features may intensify the complexity of medical data analysis in deciding necessary treatment for the patient. In most cases, the accuracy of diagnosis system is vitally impacted by the data dimensionality and classifier parameters. Since these two processes are dependent, conducting them independently could deteriorate the accuracy performance. Filter algorithm is used to eliminate irrelevant features based on ranking. However, independent filter still incapable to consider features dependency and resulting in imbalance selection of significant features which consequently degrade the classification performance. In order to mitigate this problem, ensemble of multi filters algorithm such as Information Gain (IG), Gain Ratio (GR), Chi-squared (CS) and Relief-F (RF) are utilized as it can considers the intercorrelation between features. The proper kernel parameters settings may also influence the classification performance. Hence, a harmonize classification technique using Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is employed to optimize the searching of optimal significant features and kernel parameters synchronously without degrading the accuracy. Therefore, an ensemble filter feature selection with harmonize classification of PSO and SVM (Ensemble-PSO-SVM) are proposed in this research. The effectiveness of the proposed method is examined on standard Breast Cancer and Lymphography datasets. Experimental results showed that the proposed method successfully signify the classifier accuracy performance with optimal significant features compared to other existing methods such as PSO-SVM and classical SVM. Hence, the proposed method can be used as an alternative method for determining the optimal solution in handling high dimensional data.
Decoupled Feature-Temporal CNN: Explaining Deep Learning-Based Machine Health Monitoring

Machine learning, especially deep learning, has been extensively applied and studied in the area of machine health monitoring. For machine health monitoring systems (MHMS), major efforts have been put into designing and deploying more and more complex machine learning models. Those black-box models are nontransparent toward their working mechanism. However, this research trend brings huge potential risks in real life. Since machine health monitoring itself belongs to high stake decision applications, the outputs of the autonomous monitoring systems should be trustworthy and reliable, which refers to obtain explainability. Then, it comes to the following key question: why the deployed MHMS predicts what they predict. In this article, we shed some light on this meaningful research direction: explainable MHMSs (EMHMS). In EMHMS, the machine doctor could act like a real doctor who can not only make a diagnosis but also describe the patient's symptoms. First, we propose a specific convolutional neural network (CNN) structure, named DecouplEd Feature-Temporal CNN (DEFT-CNN), to balance precision-explainability tradeoff. Specifically, feature information and temporal information have been encoded in different stages of our model. The spatial attention module is added to boost the performance of the model. Then, to explain the decision of the model, we adopt gradient-based methods to generate features and temporal saliency maps highlighting which kinds of features and time steps are keys for the model's predictions. Finally, we conduct the experimental studies on two real datasets to verify the effectiveness of our proposed framework.
A Survey on Socially Assistive Robotics: Clinicians’ and Patients’ Perception of a Social Robot within Gait Rehabilitation Therapies

A growing interest in Socially Assistive Robotics in Physical Rehabilitation is currently observed; some of the benefits highlight the capability of a social robot to support and assist rehabilitation procedures. This paper presents a perception study that aimed to evaluate clinicians’ and patients’ perception of a social robot that will be integrated as part of Lokomat therapy. A total of 88 participants were surveyed, employing an online questionnaire based on the Unified Theory of Acceptance and Use of Technology (UTAUT). The participants belong to two health care institutions located in different countries (Colombia and Spain). The results showed an overall positive perception of the social robot (&gt;60% of participants have a positive acceptance). Furthermore, a difference depending on the nature of the user (clinician vs. patient) was found.
Tailoring Embedding Function to Heterogeneous Few-Shot Tasks by Global and Local Feature Adaptors

Few-Shot Learning (FSL) is essential for visual recognition. Many methods tackle this challenging problem via learning an embedding function from seen classes and transfer it to unseen classes with a few labeled instances. Researchers recently found it beneficial to incorporate task-specific feature adaptation into FSL models, which produces the most representative features for each task. However, these methods ignore the diversity of classes and apply a global transformation to the task. In this paper, we propose Global and Local Feature Adaptor (GLoFA), a unifying framework that tailors the instance representation to specific tasks by global and local feature adaptors. We claim that class-specific local transformation helps to improve the representation ability of feature adaptor. Global masks tend to capture sketchy patterns, while local masks focus on detailed characteristics. A strategy to measure the relationship between instances adaptively based on the characteristics of both tasks and classes endow GLoFA with the ability to handle mix-grained tasks. GLoFA outperforms other methods on a heterogeneous task distribution and achieves competitive results on benchmark datasets.
High-entropy chaos generation using semiconductor lasers subject to intensity-modulated optical injection for certified physical random number generation

This study investigates high-entropy chaos generation using a semiconductor laser subject to intensity-modulated optical injection for certified physical random number generation. Chaos with a continuous spectral profile that is not only widely distributed but also broadly flattened over a bandwidth of 33 GHz is generated. The former suggests that the chaos can be sampled at a high rate while keeping sufficient un-correlation between data samples, and the latter indicates that the chaos possesses high entropy, both of which enhance the generation rate of physical random numbers with guaranteed unpredictability. A minimum entropy value of 2.19 bits/sample is obtained without any post-processing and by excluding the contribution from measurement noise, suggesting that, to the least extent, the chaotic source can be used as a 2-bit physical random number generator at a rate of 160 Gbits/s.
Blockchain: an inter-organisational innovation likely to transform supply chain

In current times, supply chains are gaining momentum because of customers’ many requirements, such as more flexibility, velocity, information, and traceability of products. Several factors can explain this phenomenon: the acceleration of technological innovations, which allow for better performance of systems and processes; globalisation of trade, fostering faster access to products for customers; and environmental responsibility, which drives customers to inquire about the sustainability of their purchases. In this scenario, the disruptive technology of ‘blockchain’ could be a solution to meeting these challenges. This system is characterised by high safety and traceability, and is becoming revolutionary in supply chains, which could have a substantial impact on companies. This exploratory study aims to investigate the current existing research on blockchain applied to supply chains and the main drivers and obstacles that companies face in implementing this technology. Interviews were conducted with ten companies to understand the motivations and risks of embracing this technology. The results show traceability and communication to be the main incentives for its deployment, while interoperability represents its main obstacle.
Efficient Discrete Feature Encoding for Variational Quantum Classifier

Recent days have witnessed significant interests in applying quantum-enhanced techniques for solving a variety of machine learning tasks. Variational methods that use quantum resources of imperfect quantum devices with the help of classical computing techniques are popular for supervised learning. Variational quantum classification (VQC) is one of such methods with possible quantum advantage in using quantum-enhanced features that are hard to compute by classical methods. Its performance depends on the mapping of classical features into a quantum-enhanced feature space. Although there have been many quantum-mapping functions proposed so far, there is little discussion on efficient mapping of discrete features, such as age group, zip code, and others, which are often significant for classifying datasets of interest. We first introduce the use of quantum random-access coding (QRAC) to map such discrete features efficiently into limited number of qubits for VQC. In numerical simulations, we present a range of encoding strategies and demonstrate their limitations and capabilities. We experimentally show that QRAC can help speeding up the training of VQC by reducing its parameters via saving on the number of qubits for the mapping. We confirm the effectiveness of the QRAC in VQC by experimenting on classification of real-world datasets with both simulators and real quantum devices.
Time-aware Path Reasoning on Knowledge Graph for Recommendation

Reasoning on knowledge graph (KG) has been studied for explainable recommendation due to its ability of providing explicit explanations. However, current KG-based explainable recommendation methods unfortunately ignore the temporal information (such as purchase time, recommend time, etc.), which may result in unsuitable explanations. In this work, we propose a novel Time-aware Path reasoning for Recommendation (TPRec for short) method, which leverages the potential of temporal information to offer better recommendation with plausible explanations. First, we present an efficient time-aware interaction relation extraction component to construct collaborative knowledge graph with time-aware interactions (TCKG for short), and then we introduce a novel time-aware path reasoning method for recommendation. We conduct extensive experiments on three real-world datasets. The results demonstrate that the proposed TPRec could successfully employ TCKG to achieve substantial gains and improve the quality of explainable recommendation.
ImageNet-21K Pretraining for the Masses

ImageNet-1K serves as the primary dataset for pretraining deep learning models for computer vision tasks. ImageNet-21K dataset, which is bigger and more diverse, is used less frequently for pretraining, mainly due to its complexity, low accessibility, and underestimation of its added value. This paper aims to close this gap, and make high-quality efficient pretraining on ImageNet-21K available for everyone. Via a dedicated preprocessing stage, utilization of WordNet hierarchical structure, and a novel training scheme called semantic softmax, we show that various models significantly benefit from ImageNet-21K pretraining on numerous datasets and tasks, including small mobile-oriented models. We also show that we outperform previous ImageNet-21K pretraining schemes for prominent new models like ViT and Mixer. Our proposed pretraining pipeline is efficient, accessible, and leads to SoTA reproducible results, from a publicly available dataset. The training code and pretrained models are available at: this https URL