Reviewerly/exHarmony huggingface dataset:
On the performance of particle filters with adaptive number of particles

Abstract We investigate the performance of a class of particle filters (PFs) that can automatically tune their computational complexity by evaluating online certain predictive statistics which are invariant for a broad class of state-space models. To be specific, we propose a family of block-adaptive PFs based on the methodology of Elvira et al. (IEEE Trans Signal Process 65(7):1781–1794, 2017). In this class of algorithms, the number of Monte Carlo samples (known as particles ) is adjusted periodically, and we prove that the theoretical error bounds of the PF actually adapt to the updates in the number of particles. The evaluation of the predictive statistics that lies at the core of the methodology is done by generating fictitious observations , i.e., particles in the observation space. We study, both analytically and numerically, the impact of the number K of these particles on the performance of the algorithm. In particular, we prove that if the predictive statistics with K fictitious observations converged exactly, then the particle approximation of the filtering distribution would match the first K elements in a series of moments of the true filter. This result can be understood as a converse to some convergence theorems for PFs. From this analysis, we deduce an alternative predictive statistic that can be computed (for some models) without sampling any fictitious observations at all. Finally, we conduct an extensive simulation study that illustrates the theoretical results and provides further insights into the complexity, performance and behavior of the new class of algorithms.
A Blockchain-Supported Framework for Charging Management of Electric Vehicles

Profound changes driven by decarbonization, decentralization, and digitalization are disrupting the energy industry, bringing new challenges to its key stakeholders. In the attempt to address the climate change issue, increasing penetration of renewables and mobility electrification augment the complexity of the electric grid, thus calling for new management approaches to govern energy exchanges while ensuring reliable and secure operations. The emerging blockchain technology is regarded as one of the most promising solutions to respond to the matter in a decentralized, efficient, fast, and secure way. In this work, we propose an Ethereum-based charging management framework for electric vehicles (EVs), tightly interlinked with physical and software infrastructure and implemented in a real-world demonstration site. With a specifically designed solidity-based smart contract governing the charging process, the proposed framework enables secure and reliable accounting of energy exchanges in a network of trustless peers, thus facilitating the EVs’ deployment and encouraging the adoption of blockchain technology for everyday tasks such as EV charging through private and semi-private charging infrastructure. The results of a multi-actor implementation case study in Switzerland demonstrate the feasibility of the proposed blockchain framework and highlight its potential to reduce costs in a typical EV charging business model. Moreover, the study shows that the suggested framework can speed up the charging and billing processes for EV users, simplify the access to energy markets for charging station owners, and facilitate the interaction between the two through specifically designed mobile and web applications. The implementation presented in this paper can be used as a guideline for future blockchain applications for EV charging and other smart grid projects.
AVocaDo: Strategy for Adapting Vocabulary to Downstream Domain

During the fine-tuning phase of transfer learning, the pretrained vocabulary remains unchanged, while model parameters are updated.The vocabulary generated based on the pretrained data is suboptimal for downstream data when domain discrepancy exists.We propose to consider the vocabulary as an optimizable parameter, allowing us to update the vocabulary by expanding it with domain-specific vocabulary based on a tokenization statistic.Furthermore, we preserve the embeddings of the added words from overfitting to downstream data by utilizing knowledge learned from a pretrained language model with a regularization term.Our method achieved consistent performance improvements on diverse domains (i.e.,
Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning

Automatic ship detection provides an essential function towards maritime domain awareness for security or economic monitoring purposes. This work presents an approach for training a deep learning ship detector in Sentinel-2 multi-spectral images with few labeled examples. We design a network architecture for detecting ships with a backbone that can be pre-trained separately. By using self supervised learning, an emerging unsupervised training procedure, we learn good features on Sentinel-2 images, without requiring labeling, to initialize our network’s backbone. The full network is then fine-tuned to learn to detect ships in challenging settings. We evaluated this approach versus pre-training on ImageNet and versus a classical image processing pipeline. We examined the impact of variations in the self-supervised learning step and we show that in the few-shot learning setting self-supervised pre-training achieves better results than ImageNet pre-training. When enough training data are available, our self-supervised approach is as good as ImageNet pre-training. We conclude that a better design of the self-supervised task and bigger non-annotated dataset sizes can lead to surpassing ImageNet pre-training performance without any annotation costs.
Efficient Use of GPU Memory for Large-Scale Deep Learning Model Training

To achieve high accuracy when performing deep learning, it is necessary to use a large-scale training model. However, due to the limitations of GPU memory, it is difficult to train large-scale training models within a single GPU. NVIDIA introduced a technology called CUDA Unified Memory with CUDA 6 to overcome the limitations of GPU memory by virtually combining GPU memory and CPU memory. In addition, in CUDA 8, memory advise options are introduced to efficiently utilize CUDA Unified Memory. In this work, we propose a newly optimized scheme based on CUDA Unified Memory to efficiently use GPU memory by applying different memory advise to each data type according to access patterns in deep learning training. We apply CUDA Unified Memory technology to PyTorch to see the performance of large-scale learning models through the expanded GPU memory. We conduct comprehensive experiments on how to efficiently utilize Unified Memory by applying memory advises when performing deep learning. As a result, when the data used for deep learning are divided into three types and a memory advise is applied to the data according to the access pattern, the deep learning execution time is reduced by 9.4% compared to the default Unified Memory.
Constrained Abridged Gaussian Sum Extended Kalman Filter: Constrained Nonlinear Systems with Non-Gaussian Noises and Uncertainties

This work presents a constrained abridged Gaussian sum extended Kalman filter (constrained AGS–EKF) that employs Gaussian mixture models to improve the estimation of extended Kalman filter (EKF) for constrained nonlinear applications involving non-zero mean non-Gaussian process uncertainties and measurement noises. The posterior estimation step in EKF is modified to adopt non-Gaussian measurement noises. An intermediate step is considered to approximate the non-Gaussian prior distribution of the constrained states at each sampling interval. This modified EKF also considers the modified prior estimation step proposed in AGS–EKF (to capture the non-Gaussian process uncertainties). Constrained AGS–EKF performs one (modified) EKF based on the mean value and covariance matrix of the overall Gaussian mixture model, thus avoiding additional computational costs and biased estimations observed in conventional Gaussian sum filters. Computational experiments were performed and showed that the proposed constrained AGS–EKF scheme is computationally efficient and provides appropriate estimates for applications involving active constraints on states, non-Gaussian process uncertainties, and measurement noises.
Key Criteria in the Choice of IoT Platforms in Spanish Companies

The level of implementation of IoT technology in Spanish companies is not low for a novel technology, but there are concerns that hinder its level of implementation from being higher: initial costs, security, and privacy. These concerns could be solved with the choice of the appropriate IoT platform, but it must be taken into account that there are numerous criteria that participate in this decision that influence each other. This could give rise to criteria not being considered at first, even though they should be taken into account in this choice. Existing studies do not consider this. Thus, the objective of this study was to find out what are the key criteria to be taken into account by Spanish companies when choosing an IoT platform. The criteria were located through a literary review, and classified according to their level of importance in this decision through a causal study carried out with the methodology of fuzzy cognitive maps. The results achieved showed that there were key criteria beyond the criteria directly implied by the concerns, such as stability, scalability, flexibility, and interoperability, and these new criteria acquired the same or greater importance.
RESEARCH AND GOOGLE TREND FOR HUMAN NEUROPSYCHIATRIC DISORDERS AND MACHINE LEARNING: A BRIEF REPORT

Objectives:The key characteristics of this study are to highlight the research trend pertaining to the use of machine learning in the diagnosis and management of neuropsychiatric conditions.Methods: The last ten years (2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020) Scopus data related to the use of machine learning techniques in the diagnosis and management of neuropsychiatric disorders in human beings have been collected and examined using VOSviewer.The global internet trend for neuropsychiatric disorders and machine learning techniques during the observation period (1-Jan-2010 to 30-Nov-2020) has been also explored using Google Trend.Results: The mean values of the Google trend for neuropsychiatric disorders and machine learning are 52.09and 40.00 respectively.Moreover, the correlation coefficient for the Google trend of USA, UK and the world found to be significantly (0.98) higher.Likewise, the mean values of web trend for USA, UK, and China are 42.17,38.55, and 30.90.Additionally, the Google trend for the term 'machine learning' in the observation period (1-Jan-2010 to 30-Nov-2020) has been also explored. Conclusion:It is observed that the researchers from the US (32.4%),UK (9.2%) and China (7.4%) are the prime contributors as far as mining and management of the neuropsychiatric disorders using machine learning is concerned.Moreover, the study revealed that neuropsychiatric disorders (seizure, eating, mood, sleep, conduct, and intellectual) need more attention as far as machine learning is concerned.
A cybersickness review: causes, strategies, and classification methods

Virtual reality (VR) and head-­mounted displays are continually gaining popularity in various fields such as education, military, entertainment, and health. Although such technologies provide a high sense of immersion, they can also trigger symptoms of discomfort. This condition is called cybersickness (CS) and is quite popular in recent virtual reality research. In this work we first present a review of the literature on theories of discomfort manifestations usually attributed to virtual reality environments. Following, we reviewed existing strategies aimed at minimizing CS problems and discussed how the CS measurement has been conducted based on subjective, bio­signal (or objective), and users profile data. We also describe and discuss related works that are aiming to mitigate cybersickness problems using deep and symbolic machine learning approaches. Although some works used methods to make deep learning explainable, they are not strongly affirmed by literature. For this reason in this work we argue that symbolic classifiers can be a good way to identify CS causes, once they possibilities human-­readability which is crucial for analyze the machine learning decision paths. In summary, from a total of 157 observed studies, 24 were excluded. Moreover, we believe that this work facilitates researchers to identify the leading causes for most discomfort situations in virtual reality environments, associate the most recommended strategies to minimize such discomfort, and explore different ways to conduct experiments involving machine learning to overcome cybersickness.
Weighting Methods for Rare Event Identification From Imbalanced Datasets

In machine learning, we often face the situation where the event we are interested in has very few data points buried in a massive amount of data. This is typical in network monitoring, where data are streamed from sensing or measuring units continuously but most data are not for events. With imbalanced datasets, the classifiers tend to be biased in favor of the main class. Rare event detection has received much attention in machine learning, and yet it is still a challenging problem. In this paper, we propose a remedy for the standing problem. Weighting and sampling are two fundamental approaches to address the problem. We focus on the weighting method in this paper. We first propose a boosting-style algorithm to compute class weights, which is proved to have excellent theoretical property. Then we propose an adaptive algorithm, which is suitable for real-time applications. The adaptive nature of the two algorithms allows a controlled tradeoff between true positive rate and false positive rate and avoids excessive weight on the rare class, which leads to poor performance on the main class. Experiments on power grid data and some public datasets show that the proposed algorithms outperform the existing weighting and boosting methods, and that their superiority is more noticeable with noisy data.
Adoption of learning technologies in times of pandemic crisis

Journal of Computer Assisted LearningVolume 37, Issue 6 p. 1509-1512 EDITORIAL Adoption of learning technologies in times of pandemic crisis Hendrik Drachsler, Corresponding Author Hendrik Drachsler [email protected] Faculty of Educational Sciences, Open University of the Netherlands, Heerlen, The Netherlands DIPF I Leibniz Institute for Research and Information in Education, Frankfurt am Main, Germany Correspondence Hendrik Drachsler, DIPF - Leibniz Institute for Research and Information in Education, Rostocker Straße 6, D-60323 Frankfurt am Main and Open University of the Netherlands, Faculty of Psychology and Educational Sciences Box 2960, 6401 DL Heerlen,The Netherlands Email: [email protected]Search for more papers by this authorJeroen Jansen, Jeroen Jansen Department of Education, Utrecht University Faculty of Social and Behavioural Sciences, Utrecht, The NetherlandsSearch for more papers by this authorPaul A. Kirschner, Paul A. Kirschner Faculty of Educational Sciences, Open University of the Netherlands, Heerlen, The NetherlandsSearch for more papers by this author Hendrik Drachsler, Corresponding Author Hendrik Drachsler [email protected] Faculty of Educational Sciences, Open University of the Netherlands, Heerlen, The Netherlands DIPF I Leibniz Institute for Research and Information in Education, Frankfurt am Main, Germany Correspondence Hendrik Drachsler, DIPF - Leibniz Institute for Research and Information in Education, Rostocker Straße 6, D-60323 Frankfurt am Main and Open University of the Netherlands, Faculty of Psychology and Educational Sciences Box 2960, 6401 DL Heerlen,The Netherlands Email: [email protected]Search for more papers by this authorJeroen Jansen, Jeroen Jansen Department of Education, Utrecht University Faculty of Social and Behavioural Sciences, Utrecht, The NetherlandsSearch for more papers by this authorPaul A. Kirschner, Paul A. Kirschner Faculty of Educational Sciences, Open University of the Netherlands, Heerlen, The NetherlandsSearch for more papers by this author First published: 15 December 2021 https://doi.org/10.1111/jcal.12626Citations: 5Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume37, Issue6Special Issue: Adoption of learning technologies in times of pandemic crisisDecember 2021Pages 1509-1512 RelatedInformation
Reversal of pixel rotation: A reversible data hiding system towards cybersecurity in encrypted images

Due to privacy and security concerns, the researches of reversible data hiding in encrypted images (RDHEI) have become increasingly important. Conventional schemes vacate the spare room after image encryption (VRAE) suffer from the low embedding rate, high error rate of data extraction, and imperfect image recovery. To address these issues, we propose a separable reversible data hiding scheme for encrypted images that utilizes a novel pixel rotation technique to embed data into fully encrypted images. The block complexities of four decrypted rotation states are considered when recovering image. To realize perfect image recovery, we further devise a lossless version (LPR-RDHEI). Experimental results demonstrate that the proposed PR-RDHEI scheme achieves an embedding rate of 0.4994 bpp on average and ensures lossless data extraction. Meanwhile, the proposed LPR-RDHEI scheme still has a 0.4494 bpp embedding rate on average. The embedding rates of our two schemes are significantly improved compared with state-of-the-arts.
Solutions to Big Data Privacy and Security Challenges Associated With COVID-19 Surveillance Systems

The growing dependency on digital technologies is becoming a way of life, and at the same time, the collection of data using them for surveillance operations has raised concerns. Notably, some countries use digital surveillance technologies for tracking and monitoring individuals and populations to prevent the transmission of the new coronavirus. The technology has the capacity to contribute towards tackling the pandemic effectively, but the success also comes at the expense of privacy rights. The crucial point to make is regardless of who uses and which mechanism, in one way another will infringe personal privacy. Therefore, when considering the use of technologies to combat the pandemic, the focus should also be on the impact of facial recognition cameras, police surveillance drones, and other digital surveillance devices on the privacy rights of those under surveillance. The GDPR was established to ensure that information could be shared without causing any infringement on personal data and businesses; therefore, in generating Big Data, it is important to ensure that the information is securely collected, processed, transmitted, stored, and accessed in accordance with established rules. This paper focuses on Big Data challenges associated with surveillance methods used within the COVID-19 parameters. The aim of this research is to propose practical solutions to Big Data challenges associated with COVID-19 pandemic surveillance approaches. To that end, the researcher will identify the surveillance measures being used by countries in different regions, the sensitivity of generated data, and the issues associated with the collection of large volumes of data and finally propose feasible solutions to protect the privacy rights of the people, during the post-COVID-19 era.
Bluetooth-Low-Energy-Based Fall Detection and Warning System for Elderly People in Nursing Homes

Due to the ever growing population of elderly people, there is a dramatic increase in fall accidents. Currently, multiple ideas exist to prevent the elderly from falling, by means of technology or individualised fall prevention training programs. Most of them are costly, difficult to implement or less used by the elderly, and they do not deliver the required results. Furthermore, the increasingly older population will also impact the workload of the medical and nursing personnel. Therefore, we propose a novel fall detection and warning system for nursing homes, relying on Bluetooth Low Energy wireless communication. This paper describes the hardware design of a fall-acceleration sensing wearable for the elderly. Moreover, the paper also focuses on a novel algorithm for real-time filtering of the measurement data as well as on a strategy to confirm the detected fall events, based on changes in the person’s orientation. In addition, we compare the performance of the algorithm to a machine learning procedure using a convolutional neural network. Finally, the proposed filtering technique is validated via measurements and simulation. The results show that the proposed algorithm as well as the convolutional neural network both results in an excellent accuracy when validating on a common database.
Yes, I Do: Marrying Blockchain Applications with GDPR

Due to blockchains' intrinsic transparency and immutability, blockchain-based applications are challenged by privacy regulations, such as the EU General Data Protection Regulation.Hence, scaling blockchain use cases to production often fails to owe to a lack of compliance with legal constraints.As current research mainly focuses on specific use cases, we aim to offer comprehensive guidance regarding the development of blockchain solutions that comply with privacy regulations.Following the action design research method, we contribute a generic framework and design principles to the research domain.In this context, we also emphasize the need for distinguishing between applications based on blockchains' data integrity and computational integrity guarantees.
A Novel Distributed Gravitational Search Algorithm With Multi-Layered Information Interaction

Population structures play a crucial role in individuals' evolution.Gravitational search algorithm (GSA) inspired by physical laws is a population-based algorithm.Its population structure is able to influence the individuals' search behavior.In this paper, we propose a distributed GSA with multi-layered information interaction, termed as MGSA, to offer a good balance between exploitation and exploration.A historical information layer and an elite top layer are designed to improve individuals' interaction.A distributed structure maintains the population diversity.Experimental results on CEC2017 benchmark functions and a real-world static economic dispatch problem confirm the effectiveness of MGSA in comparison with several state-of-the-art algorithms.In addition, landscape search trajectory and population diversity analyses verify the excellent convergence of MGSA.