Reviewerly/exHarmony huggingface dataset:
Soft Wrist-Worn Multi-Functional Sensor Array for Real-Time Hand Gesture Recognition

Soft electronics have been widely applied to wearable electronics, hand gesture detection and human robot interaction (HRI). Soft wrist-worn sensor system (SWSS) with electromyography (EMG), strain/pressure sensing abilities has been created for continuous hand gesture recognition, which includes multi-source data sensing, data collection and process, and wireless communication. SWSS is conformal contact with skin surface as the softness of SWSS, and it would improve the wearability with the wrist during long-term hand gesture monitoring. Linear discriminant analysis (LDA) and support vector machines (SVM) algorithms are applied to hand gesture recognition with average accuracy 83.67%, 86.8% for Group #1 (wrist flexion (WF), wrist extension (WE), finger flexion (FF), finger extension (FE), radial deviation (RD), and ulnar deviation (UD)) and 84.71%, 88.53% for Group #2 (sign language 0–9 digits), respectively. It is found that the recognition accuracy has just a little decreased during the long-term hand gesture detection at different sessions (at initial time, one day after, three days after and seven days after). This paper demonstrated the feasibility of gesture recognition via SWSS on wrist, and it could be integrated into wrist-worn electronic system for human robot interaction.
Online Learning as e-Learning: The Quality and Results (Critical Analysis)

This article critically examines the result obtained by researchers at the Higher School of Economics in relation to the results of the experiment to assess the quality of online learning. It is noted that the thesis about the possibility of “complete replacement of traditional forms of education with online education without loss of quality” is distorted in the media, and the conclusions are based on insufficient data. To clarify the conditions of the transition and implementation of online technologies, it is proposed to hold a large-scale experiment with a randomly selection mechanisms for participants of training courses, instructors, disciplines (modules). The article focuses on the fact that while the impact of online education on the psychology and physiology of students has not been sufficiently studied, there is a risk of deteriorating health, as well as a drop in the quality of education. It is proposed to develop temporary standards limiting the maximum time of work online for different categories of students and teachers.The risks of «financial bubbles» in the online education market due to the rush demand after the COVID-19 pandemic for government agencies and private businesses has been identified. The conclusion is made about the priority development of short-term online courses related to the development of rather narrow and applied competencies in demand by the modern information society, about the possible success of online technologies in case of obtaining a “second higher” education and improving qualifications. High-quality online systems in the field of higher education require serious investments in research and development in the field of psychology and pedagogy of education, the use of medical control tools. In the medium term, the benefits of this research, conducted by medical scientists, educators and psychologists, will be used by organizations on the “second wave” of interest to online learning. From an organizational point of view, the correct “rotation” of the contingent of students, when part of the time they study full-time, and part of the time they learn online with computers / tablets and high-quality Internet access, will not lead to a deterioration in the quality of education, but will significantly reduce the spread of the pandemic.
Stablecoins, Central Bank Digital Currencies and US Dollar Hegemony

Abstract Stablecoins are second generation cryptocurrencies, aimed at maintaining their value stable with respect to official currencies. The most famous example is perhaps represented by libra, the cryptocurrency announced by Facebook in 2019 and yet to be issued; the most widespread is tether, with a market capitalization of almost 10 billion dollars and a daily transaction volume of almost 50 billion dollars, which makes it the most used cryptocurrency. The diffusion of stablecoins is hardly surprising. By minimizing volatility – the main flaw of first generation cryptocurrencies, including bitcoin –, stablecoins are expected to play an even more important role on a global scale within a few years. Our contribution deals not with the economic, but specifically with the geopolitical factors that could foster the use of stablecoins for strategic and military purposes. In particular, we focus on how such payment instruments, together with other alternative electronic payment systems, could be used as a means to circumvent economic sanctions and ultimately as a challenge to the hegemony of the US dollar in the international monetary system.
Applications of Machine Learning in Healthcare

Machine learning techniques in healthcare use the increasing amount of health data provided by the Internet of Things to improve patient outcomes. These techniques provide promising applications as well as significant challenges. The three main areas machine learning is applied to include medical imaging, natural language processing of medical documents, and genetic information. Many of these areas focus on diagnosis, detection, and prediction. A large infrastructure of medical devices currently generates data but a supporting infrastructure is oftentimes not in place to effectively utilize such data. The many different forms medical information exist in also creates some challenges in data formatting and can increase noise. We examine a brief history of machine learning, some basic knowledge regarding the techniques, and the current state of this technology in healthcare.
Financial Document Causality Detection Shared Task (FinCausal 2020)

We present the FinCausal 2020 Shared Task on Causality Detection in Financial Documents and the associated FinCausal dataset, and discuss the participating systems and results. Two sub-tasks are proposed: a binary classification task (Task 1) and a relation extraction task (Task 2). A total of 16 teams submitted runs across the two Tasks and 13 of them contributed with a system description paper. This workshop is associated to the Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (FNP-FNS 2020), held at The 28th International Conference on Computational Linguistics (COLING'2020), Barcelona, Spain on September 12, 2020.
Qutrit-based semi-quantum key distribution protocol

This article provides the unconditional security of a semi quantum key distribution (SQKD) protocol based on 3-dimensional quantum states. By deriving a lower bound for the key rate, in the asymptotic scenario, as a function of the quantum channel's noise, we find that this protocol has improved secret key rate with much more tolerance for noise compared to the previous 2-dimensional SQKD protocol. Our results highlight that, similar to the fully quantum key distribution protocol, increasing the dimension of the system can increase the noise tolerance in the semi-quantum key distribution, as well.
Upping uptake of COVID contact tracing apps

To contain the COVID-19 pandemic, many countries around the world rushed to develop digital contact tracing apps. However, the low rates of app installation have undermined the efficacy of such tools. A study by Munzert et al. shines light on potential barriers to adoption, as well as levers that could be used to increase uptake.
Use of physics to improve solar forecast: Physics-informed persistence models for simultaneously forecasting GHI, DNI, and DHI

Observation-based statistical models have been widely used in forecasting solar energy; however, existing models often lack a clear relation to physics and are limited largely to global horizontal irradiance (GHI) forecasts over relatively short time horizons (<1 h). Incorporating physics into observation-based models, increasing forecast time horizons and developing a model system for forecasting not only GHI but also direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) remain challenging, especially under cloudy conditions because of complex cloud-radiation interactions. This work attempts to address these challenges by developing a hierarchy of four new physics-informed persistence models that can be used to simultaneously forecast GHI, DNI and DHI. The decade-long measurements (1998 to 2014) at the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM)’s Southern Great Plains (SGP) Central Facility site are used to evaluate the performance of the new models. The results show that the new physics-informed forecast models generally outperform the simple and smart persistence models, and improve the forecast accuracy at lead times from 1.25 h up to 6 h. Further analysis reveals that the forecast error is highly related to the error and temporal variability of the assumed cloud predictor. The best model for forecasting different radiative components can be explained by the relationship between solar irradiances and cloud properties.
Formal verification of higher-order probabilistic programs: reasoning about approximation, convergence, Bayesian inference, and optimization

Probabilistic programming provides a convenient lingua franca for writing succinct and rigorous descriptions of probabilistic models and inference tasks. Several probabilistic programming languages, including Anglican, Church or Hakaru, derive their expressiveness from a powerful combination of continuous distributions, conditioning, and higher-order functions. Although very important for practical applications, these features raise fundamental challenges for program semantics and verification. Several recent works offer promising answers to these challenges, but their primary focus is on foundational semantics issues. In this paper, we take a step further by developing a suite of logics, collectively named PPV for proving properties of programs written in an expressive probabilistic higher-order language with continuous sampling operations and primitives for conditioning distributions. Our logics mimic the comfortable reasoning style of informal proofs using carefully selected axiomatizations of key results from probability theory. The versatility of our logics is illustrated through the formal verification of several intricate examples from statistics, probabilistic inference, and machine learning. We further show expressiveness by giving sound embeddings of existing logics. In particular, we do this in a parametric way by showing how the semantics idea of (unary and relational) ⊤⊤-lifting can be internalized in our logics. The soundness of PPV follows by interpreting programs and assertions in quasi-Borel spaces (QBS), a recently proposed variant of Borel spaces with a good structure for interpreting higher order probabilistic programs.
The Identity Fragmentation Bias

Consumers interact with firms across multiple devices, browsers, and machines; these interactions are often recorded with different identifiers for the same consumer. The failure to correctly match different identities leads to a fragmented view of exposures and behaviors. This paper studies the identity fragmentation bias, referring to the estimation bias resulted from using fragmented data. Using a formal framework, we decompose the contributing factors of the estimation bias caused by data fragmentation and discuss the direction of bias. Contrary to conventional wisdom, this bias cannot be signed or bounded under standard assumptions. Instead, upward biases and sign reversals can occur even in experimental settings. We then compare several corrective measures, and discuss their respective advantages and caveats.
A Review of Speaker Diarization: Recent Advances with Deep Learning

Speaker diarization is a task to label audio or video recordings with classes that correspond to speaker identity, or in short, a task to identify who spoke when. In the early years, speaker diarization algorithms were developed for speech recognition on multispeaker audio recordings to enable speaker adaptive processing. These algorithms also gained their own value as a standalone application over time to provide speaker-specific metainformation for downstream tasks such as audio retrieval. More recently, with the emergence of deep learning technology, which has driven revolutionary changes in research and practices across speech application domains, rapid advancements have been made for speaker diarization. In this paper, we review not only the historical development of speaker diarization technology but also the recent advancements in neural speaker diarization approaches. Furthermore, we discuss how speaker diarization systems have been integrated with speech recognition applications and how the recent surge of deep learning is leading the way of jointly modeling these two components to be complementary to each other. By considering such exciting technical trends, we believe that this paper is a valuable contribution to the community to provide a survey work by consolidating the recent developments with neural methods and thus facilitating further progress toward a more efficient speaker diarization.
Robust Minimum Error Entropy Based Cubature Information Filter With Non-Gaussian Measurement Noise

In this letter, a robust minimum error entropy based cubature information filter is proposed for state estimation in non-Gaussian measurement noise. A new combined optimization cost is defined based on the error entropy. Through cubature transform, a statistical linearization regression model is constructed, and a new information filter is then developed by minimizing the error entropy based cost. The fixed-point iteration approach is used to compute the state estimate. Further, the convergence of the proposed information filter is analyzed, and the convergence conditions are derived. Simulations are performed to demonstrate the effectiveness of the proposed algorithm. It is shown that the estimation performance of the proposed filter is more robust than that of traditional methods against the complicated non-Gaussian noises, such as outliers and noises from multimodal distributions.
The euBusinessGraph ontology: A lightweight ontology for harmonizing basic company information

Company data, ranging from basic company information such as company name(s) and incorporation date to complex balance sheets and personal data about directors and shareholders, are the foundation that many data value chains depend upon in various sectors (e.g., business information, marketing and sales, etc.). Company data becomes a valuable asset when data is collected and integrated from a variety of sources, both authoritative (e.g., national business registers) and non-authoritative (e.g., company websites). Company data integration is however a difficult task primarily due to the heterogeneity and complexity of company data, and the lack of generally agreed upon semantic descriptions of the concepts in this domain. In this article, we introduce the euBusinessGraph ontology as a lightweight mechanism for harmonising company data for the purpose of aggregating, linking, provisioning and analysing basic company data. The article provides an overview of the related work, ontology scope, ontology development process, explanations of core concepts and relationships, and the implementation of the ontology. Furthermore, we present scenarios where the ontology was used, among others, for publishing company data (business knowledge graph) and for comparing data from various company data providers. The euBusinessGraph ontology serves as an asset not only for enabling various tasks related to company data but also on which various extensions can be built upon.
Logo Detection Using Deep Learning with Pretrained CNN Models

Logo detection in images and videos is considered a key task for various applications, such as vehicle logo detection for traffic-monitoring systems, copyright infringement detection, and contextual content placement. The main contribution of this work is the application of emerging deep learning techniques to perform brand and logo recognition tasks through the use of multiple modern convolutional neural network models. In this work, pre-trained object detection models are utilized in order to enhance the performance of logo detection tasks when only a portion of labeled training images taken in truthful context is obtainable, evading wide manual classification costs. Superior logo detection results were obtained. In this study, the FlickrLogos-32 dataset was used, which is a common public dataset for logo detection and brand recognition from real-world product images. For model evaluation, the efficiency of creating the model and of its accuracy was considered.
Running an XR lab in the context of COVID-19 pandemic: Lessons learned from a Norwegian university

Universities and companies were not prepared to the changes introduced to limit the spread of COVID-19 in Norway. Universities had to switch to online teaching overnight. There is still uncertainty how measures to control the pandemic will keep affecting universities in the short and middle term. Such measures have consequences on how to carry out research that usually relies on students, researcher and volunteers using the equipment and applications. Our group carries out research on virtual/augmented/extended reality (VR/AR/XR) for immersive training and learning. This research often involves user studies. We had established procedures on how to use the equipment, carry out demonstrations and teaching for students, teachers and visitors, develop projects as part of bachelor and master projects and test new applications with volunteers. The measures taken by authorities to control the spread of the pandemic made it difficult or unfeasible to carry out some of those activities. In this paper we describe how our group and XR lab reacted after universities were closed to students' presence in campus in March 2020. We present our actions to keep research ongoing, our evaluation of some of those actions and discuss how we had to change the way we operate our XR lab in order to continue teaching and research in the near future, under the assumption that restrictions due to the pandemic can be re-implemented at short notice. We propose procedures to run an XR lab in a manner that inspires visitors to feel safe and confident of using the equipment. Our contribution is the proposal of procedures to run an educational XR lab safely and contribute towards the conversation about how to carry out research involving users in XR under pandemic restrictions.
Convolutional neural network with median layers for denoising salt-and-pepper contaminations

We propose a deep fully convolutional neural network with a new type of layer, named median layer, to restore images contaminated by salt-and-pepper (s&p) noise. A median layer simply performs median filtering on all feature channels. By adding this kind of layer into some widely used fully convolutional deep neural networks, we develop an end-to-end network that removes extremely high-level s&p noise without performing any non-trivial preprocessing tasks. Experiments show that inserting median layers into a simple fully-convolutional network with the L2 loss significantly boosts signal-to-noise ratio. Quantitative comparisons testify that our network outperforms the state-of-the-art methods with a limited amount of training data.
Path Planning for Manipulation Using Experience-Driven Random Trees

Robotic systems may frequently come across similar manipulation planning problems that result in similar motion plans. Instead of planning each problem from scratch, it is preferable to leverage previously computed motion plans, i.e., experiences, to ease the planning. Different approaches have been proposed to exploit prior information on novel task instances. These methods, however, rely on a vast repertoire of experiences and fail when none relates closely to the current problem. Thus, an open challenge is the ability to generalise prior experiences to task instances that do not necessarily resemble the prior. This work tackles the above challenge with the proposition that experiences are “decomposable” and “malleable,” i.e., parts of an experience are suitable to relevantly explore the connectivity of the robot-task space even in non-experienced regions. Two new planners result from this insight: experience-driven random trees (ERT) and its bi-directional version ERTConnect. These planners adopt a tree sampling-based strategy that incrementally extracts and modulates parts of a single path experience to compose a valid motion plan. We demonstrate our method on task instances that significantly differ from the prior experiences, and compare with related state-of-the-art experience-based planners. While their repairing strategies fail to generalise priors of tens of experiences, our planner, with a single experience, significantly outperforms them in both success rate and planning time. Our planners are implemented and freely available in the Open Motion Planning Library.
A New Real-Time Detection and Tracking Method in Videos for Small Target Traffic Signs

It is a challenging task for self-driving vehicles in Real-World traffic scenarios to find a trade-off between the real-time performance and the high accuracy of the detection, recognition, and tracking in videos. This issue is addressed in this paper with an improved YOLOv3 (You Only Look Once) and a multi-object tracking algorithm (Deep-Sort). First, data augmentation is employed for small sample traffic signs to address the problem of an extremely unbalanced distribution of different samples in the dataset. Second, a new architecture of YOLOv3 is proposed to make it more suitable for detecting small targets. The detailed method is (1) removing the output feature map corresponding to the 32-times subsampling of the input image in the original YOLOv3 structure to reduce its computational costs and improve its real-time performances; (2) adding an output feature map of 4-times subsampling to improve its detection capability for the small traffic signs; (3) Deep-Sort is integrated into the detection method to improve the precision and robustness of multi-object detection, and the tracking ability in videos. Finally, our method demonstrated better detection capabilities, with respect to state-of-the-art approaches, which precision, recall and mAP is 91%, 90%, and 84.76% respectively.
Benefits of AWS in Modern Cloud

This article gives an overview of the benefits of AWS in the modern cloud. Cloud computing is performing well in today’s World and boosting the ability to use the internet more than ever. Cloud computing gradually developed a method to use the benefits of it in most of the organizations. It is very demanding in all businesses tasked with improving the quality of service reducing costs as the organization pays for the service only what they consume based on the incoming and outgoing traffic.
Polymer Physics by Quantum Computing

Sampling equilibrium ensembles of dense polymer mixtures is a paradigmatically hard problem in computational physics, even in lattice-based models. Here, we develop a formalism based on interacting binary tensors that allows for tackling this problem using quantum annealing machines. Our approach is general in that properties such as self-avoidance, branching, and looping can all be specified in terms of quadratic interactions of the tensors. Microstates realizations of different lattice polymer ensembles are then seamlessly generated by solving suitable discrete energy-minimization problems. This approach enables us to capitalize on the strengths of quantum annealing machines, as we demonstrate by sampling polymer mixtures from low to high densities, using the D-Wave quantum computer. Our systematic approach offers a promising avenue to harness the rapid development of quantum computers for sampling discrete models of filamentous soft-matter systems.