Reviewerly/exHarmony huggingface dataset:
Semantic Segmentation With Unsupervised Domain Adaptation Under Varying Weather Conditions for Autonomous Vehicles

Semantic information provides a valuable source for scene understanding around autonomous vehicles in order to plan their actions and make decisions. However, varying weather conditions reduce the accuracy of the semantic segmentation. We propose a method to adapt to varying weather conditions without supervision, namely without labeled data. We update the parameters of a deep neural network (DNN) model that is pre-trained on the known weather condition (source domain) to adapt it to the new weather conditions (target domain) without forgetting the segmentation in the known weather condition. Furthermore, we don't require the labels from the source domain during adaptation training. The parameters of the DNN are optimized to reduce the distance between the distribution of the features from the images of old and new weather conditions. To measure this distance, we propose three alternatives: W-GAN, GAN and maximum-mean discrepancy (MMD). We evaluate our method on various datasets with varying weather conditions. The results show that the accuracy of the semantic segmentation is improved for varying conditions after adaptation with the proposed method.
SAR Image Segmentation Using Region Smoothing and Label Correction

The traditional unsupervised image segmentation methods are widely used in synthetic aperture radar (SAR) image segmentation due to the simple and convenient application process. In order to solve the time-consuming problem of the common methods, an SAR image segmentation method using region smoothing and label correction (RSLC) is proposed. In this algorithm, the image smoothing results are used to approximate the results of the spatial information polynomials of the image. Thus, the segmentation process can be realized quickly and effectively. Firstly, direction templates are used to detect the directions at different coordinates of the image, and smoothing templates are used to smooth the edge regions according to the directions. It achieves the smoothing of the edge regions and the retention of the edge information. Then the homogeneous regions are presented indirectly according to the difference of directions. The homogeneous regions are smoothed by using isotropic operators. Finally, the two regions are fused for K-means clustering. The majority voting algorithm is used to modify the clustering results, and the final segmentation results are obtained. Experimental results on simulated SAR images and real SAR images show that the proposed algorithm outperforms the other five state-of-the-art algorithms in segmentation speed and accuracy.
Improving human–robot interactions in hospitality settings

Purpose Service robotics, a branch of robotics that entails the development of robots able to assist humans in their environment, is of growing interest in the hospitality industry. Designing effective autonomous service robots, however, requires an understanding of Human–Robot Interaction (HRI), a relatively young discipline dedicated to understanding, designing, and evaluating robotic systems for use by or with humans. HRI has not yet received sufficient attention in hospitality robotic design, much like Human–Computer Interaction (HCI) in property management system design in the 1980s. This article proposes a set of introductory HRI guidelines with implementation standards for autonomous hospitality service robots. Design/methodology/approach A set of key user-centered HRI guidelines for hospitality service robots were extracted from 52 research articles. These are organized into service performance categories to provide more context for their application in hospitality settings. Findings Based on an extensive literature review, this article presents some HRI guidelines that may drive higher levels of acceptance of service robots in customer-facing situations. Deriving meaningful HRI guidelines requires an understanding of how customers evaluate service interactions with humans in hospitality settings and to what degree those will differ with service robots. Originality/value Robots are challenging assumptions on how hospitality businesses operate. They are being increasingly deployed by hotels and restaurants to boost productivity and maintain service levels. Effective HRI guidelines incorporate user requirements and expectations in the design specifications. Compilation of such information for designers of hospitality service robots will offer a clearer roadmap for them to follow.
Lost in Anonymization — A Data Anonymization Reference Classification Merging Legal and Technical Considerations

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Thermal maturity and TOC prediction using machine learning techniques: case study from the Cretaceous–Paleocene source rock, Taranaki Basin, New Zealand

Abstract Thermal maturity, organic richness and kerogen typing are very important parameters to be evaluated for source rock characterization. Due to the difficulties of high cost geochemical analyses and the unavailability of rock samples, it was necessary to examine and test many different method and techniques to help in the prediction of TOC values as well as other maturity indicators in case of missing or absence of geochemical data. Integrated study of machine learning techniques and well-log data has been applied on Cretaceous–Paleocene formations in the Taranaki Basin, New Zealand. A novel approach of maturity prediction using T max and vitrinite reflectance (VR%) is the first and preliminary objective of this research. Moreover, the organic richness or the total organic carbon (TOC) content has been predicted as well. Geochemical and well-log data collected from the Cretaceous Rakopi and North Cape formations and Paleocene Mangahewa Formation have been processed and prepared to apply the machine learning techniques. Five machine learning techniques, namely Bayesian regularization for feed-forward neural networks (BRNNs), random forest (RF), support vector machine (SVM) for regression, linear regression (LR) and Gaussian process regression (GPR), were employed for prediction of TOC, T max and VR, and their results have been compared. For TOC prediction, the best model achieved the coefficient of determination ( R 2 ) value of 0.964 using RF model. For T max prediction, BRNN with one hidden layer achieved the R 2 value of 0.828. BRNN with two hidden layers produced the best model for VR prediction achieving R 2 = 0.636. A comparison of five ML techniques showed that all of these techniques performed exceedingly well for TOC prediction with a value of R 2 &gt; 0.96. In contrast, BRNN with one hidden layer was the only ML technique able to achieve R 2 &gt; 0.8 for T max and BRNN with two hidden layers was the only ML technique able to achieve R 2 &gt; 0.6 for VR prediction. Therefore, this research provides a strong empirical evidence that ML techniques can capture the nonlinear relationship between the well-log data and TOC as well as the maturity indicators which may not be fully understood by existing linear models.
Attend to Medical Ontologies: Content Selection for Clinical Abstractive Summarization

Sequence-to-sequence (seq2seq) network is a well-established model for text summarization task. It can learn to produce readable content; however, it falls short in effectively identifying key regions of the source. In this paper, we approach the content selection problem for clinical abstractive summarization by augmenting salient ontological terms into the summarizer. Our experiments on two publicly available clinical data sets (107,372 reports of MIMIC-CXR, and 3,366 reports of OpenI) show that our model statistically significantly boosts state-of-the-art results in terms of ROUGE metrics (with improvements: 2.9% RG-1, 2.5% RG-2, 1.9% RG-L), in the healthcare domain where any range of improvement impacts patients’ welfare.
Automated recognition of white blood cells using deep learning

The detection, counting, and precise segmentation of white blood cells in cytological images are vital steps in the effective diagnosis of several cancers. This paper introduces an efficient method for automatic recognition of white blood cells in peripheral blood and bone marrow images based on deep learning to alleviate tedious tasks for hematologists in clinical practice. First, input image pre-processing was proposed before applying a deep neural network model adapted to cells localization and segmentation. Then, model outputs were improved by using combined predictions and corrections. Finally, a new algorithm that uses the cooperation between model results and spatial information was implemented to improve the segmentation quality. To implement our model, python language, Tensorflow, and Keras libraries were used. The calculations were executed using NVIDIA GPU 1080, while the datasets used in our experiments came from patients in the Hemobiology service of Tlemcen Hospital (Algeria). The results were promising and showed the efficiency, power, and speed of the proposed method compared to the state-of-the-art methods. In addition to its accuracy of 95.73%, the proposed approach provided fast predictions (less than 1 s).
Secure Shares Generation via M-Blocks Partitioning for Counting-Based Secret Sharing

Counting-based secret sharing is presented recently as a promising approach serving multi-user authentication applications. The scheme originally generates its shares via simple flipping of one or two 0-bits within the entire secret key at various locations. Reconstructing the secret key combines chosen shares, based on (n,k) threshold, in parallel specific counting to recover back the secret key. This paper proposes modifying the shares generation process, for security enhancement, by dividing the secret key into blocks. Each block involves flipping specific bits simultaneously, generating ambiguous shares improving the access system security. The proposed blocks flipping shares methods is implemented in different 64-bits models via fair testing Java platform. Experimentations showed interesting comparisons results providing remarkable secure contributions. The work can be considered an opening applicability direction for further attractive research in improving the counting-based secret sharing technique.
Embodied Visual Navigation With Automatic Curriculum Learning in Real Environments

We present NavACL, a method of automatic curriculum learning tailored to the navigation task. NavACL is simple to train and efficiently selects relevant tasks using geometric features. In our experiments, deep reinforcement learning agents trained using NavACL significantly outperform state-of-the-art agents trained with uniform sampling -- the current standard. Furthermore, our agents can navigate through unknown cluttered indoor environments to semantically-specified targets using only RGB images. Obstacle-avoiding policies and frozen feature networks support transfer to unseen real-world environments, without any modification or retraining requirements. We evaluate our policies in simulation, and in the real world on a ground robot and a quadrotor drone. Videos of real-world results are available in the supplementary material.
Subradiance with Saturated Atoms: Population Enhancement of the Long-Lived States

Dipole-dipole interactions are at the origin of long-lived collective atomic states, often called subradiant, which are explored for their potential use in novel photonic devices or in quantum protocols. Here, we study subradiance beyond linear optics and experimentally demonstrate a two hundred-fold increase in the population of these modes, as the saturation parameter of the driving field is increased. We attribute this enhancement to a mechanism similar to optical pumping through the well-coupled superradiant states. The lifetimes are unaffected by the pump strength, as the system is ultimately driven toward the single-excitation sector.
Self-Perception and Training Perceptions on Teacher Digital Competence (TDC) in Spanish and French University Students

The purpose of this research is, on the one hand, to analyze the self-perception of future teachers of childhood education and primary education, and those studying for a master’s degree in secondary education teacher training on their Teacher Digital Competence (TDC), as well as the potential influence of gender, country and university institution of origin in their representations. On the other hand, it seeks to analyze the perception of future teachers on the TDC of their university trainers (formative perception). In accordance with these aims, a quantitative methodology of a non-experimental nature and of a prospective cross-sectional ex post facto approach has been used. A total of 428 students from two Spanish universities and from a French university agreed to participate in the research. The results report a positive and differential self-perception by gender of the TDC acquired and unfavorable perceptions of the digital competences of their teachers. These results confirm the need to improve the technological-manipulative and didactic training of university teachers, and to adapt the teaching competences to the demands of the Information and Communication Society (ICS) and to the guidelines of the Common Digital Competence Framework.
A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT)

Machine learning techniques are widely used nowadays in the healthcare domain for the diagnosis, prognosis, and treatment of diseases. These techniques have applications in the field of hematopoietic cell transplantation (HCT), which is a potentially curative therapy for hematological malignancies. Herein, a systematic review of the application of machine learning (ML) techniques in the HCT setting was conducted. We examined the type of data streams included, specific ML techniques used, and type of clinical outcomes measured. A systematic review of English articles using PubMed, Scopus, Web of Science, and IEEE Xplore databases was performed. Search terms included "hematopoietic cell transplantation (HCT)," "autologous HCT," "allogeneic HCT," "machine learning," and "artificial intelligence." Only full-text studies reported between January 2015 and July 2020 were included. Data were extracted by two authors using predefined data fields. Following PRISMA guidelines, a total of 242 studies were identified, of which 27 studies met the inclusion criteria. These studies were sub-categorized into three broad topics and the type of ML techniques used included ensemble learning (63%), regression (44%), Bayesian learning (30%), and support vector machine (30%). The majority of studies examined models to predict HCT outcomes (e.g., survival, relapse, graft-versus-host disease). Clinical and genetic data were the most commonly used predictors in the modeling process. Overall, this review provided a systematic review of ML techniques applied in the context of HCT. The evidence is not sufficiently robust to determine the optimal ML technique to use in the HCT setting and/or what minimal data variables are required.
Identifying Unclear Questions in Community Question Answering Websites

Thousands of complex natural language questions are submitted to community question answering websites on a daily basis, rendering them as one of the most important information sources these days. However, oftentimes submitted questions are unclear and cannot be answered without further clarification questions by expert community members. This study is the first to investigate the complex task of classifying a question as clear or unclear, i.e., if it requires further clarification. We construct a novel dataset and propose a classification approach that is based on the notion of similar questions. This approach is compared to state-of-the-art text classification baselines. Our main finding is that the similar questions approach is a viable alternative that can be used as a stepping stone towards the development of supportive user interfaces for question formulation.
Machine learning and serving of discrete field theories

A method for machine learning and serving of discrete field theories in physics is developed. The learning algorithm trains a discrete field theory from a set of observational data on a spacetime lattice, and the serving algorithm uses the learned discrete field theory to predict new observations of the field for new boundary and initial conditions. The approach of learning discrete field theories overcomes the difficulties associated with learning continuous theories by artificial intelligence. The serving algorithm of discrete field theories belongs to the family of structure-preserving geometric algorithms, which have been proven to be superior to the conventional algorithms based on discretization of differential equations. The effectiveness of the method and algorithms developed is demonstrated using the examples of nonlinear oscillations and the Kepler problem. In particular, the learning algorithm learns a discrete field theory from a set of data of planetary orbits similar to what Kepler inherited from Tycho Brahe in 1601, and the serving algorithm correctly predicts other planetary orbits, including parabolic and hyperbolic escaping orbits, of the solar system without learning or knowing Newton's laws of motion and universal gravitation. The proposed algorithms are expected to be applicable when the effects of special relativity and general relativity are important.
Adversarial Robustness on In- and Out-Distribution Improves Explainability

Neural networks have led to major improvements in image classification but suffer from being non-robust to adversarial changes, unreliable uncertainty estimates on out-distribution samples and their inscrutable black-box decisions. In this work we propose RATIO, a training procedure for Robustness via Adversarial Training on In- and Out-distribution, which leads to robust models with reliable and robust confidence estimates on the out-distribution. RATIO has similar generative properties to adversarial training so that visual counterfactuals produce class specific features. While adversarial training comes at the price of lower clean accuracy, RATIO achieves state-of-the-art $$l_2$$ -adversarial robustness on CIFAR10 and maintains better clean accuracy.
The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing

Enabling autonomous driving (AD) can be considered one of the biggest challenges in today's technology. AD is a complex task accomplished by several functionalities, with environment perception being one of its core functions. Environment perception is usually performed by combining the semantic information captured by several sensors, i.e., lidar or camera. The semantic information from the respective sensor can be extracted by using convolutional neural networks (CNNs) for dense prediction. In the past, CNNs constantly showed state-of-the-art performance on several vision-related tasks, such as semantic segmentation of traffic scenes using nothing but the red-green-blue (RGB) images provided by a camera. Although CNNs obtain state-of-the-art performance on clean images, almost imperceptible changes to the input, referred to as adversarial perturbations, may lead to fatal deception. The goal of this article is to illuminate the vulnerability aspects of CNNs used for semantic segmentation with respect to adversarial attacks, and share insights into some of the existing known adversarial defense strategies. We aim to clarify the advantages and disadvantages associated with applying CNNs for environment perception in AD to serve as a motivation for future research in this field.
Shallow-circuit variational quantum eigensolver based on symmetry-inspired Hilbert space partitioning for quantum chemical calculations

Development of resource-friendly quantum algorithms remains highly desirable for noisy intermediate-scale quantum computing. Based on the variational quantum eigensolver (VQE) with unitary coupled-cluster Ansatz, we demonstrate that partitioning of the Hilbert space made possible by the point-group symmetry of the molecular systems greatly reduces the number of variational operators by confining the variational search within a subspace. In addition, we found that instead of including all subterms for each excitation operator, a single-term representation suffices to reach required accuracy for various molecules tested, resulting in an additional shortening of the quantum circuit by a factor of 4–8. With these strategies, VQE calculations on a noise-free quantum simulator achieve energies within a few meVs of those obtained with the full unitary coupled-cluster Ansatz with single and double excitations for the H4-square, H4-chain, and H6-hexagon molecules, while the number of cnot gates, a measure of the quantum-circuit depth, is reduced by a factor of as large as 35. Furthermore, we introduced an efficient "score" parameter to rank the excitation operators, so that the operators causing larger energy reduction can be applied first. Using the H4 square and H4 chain as examples, We demonstrated on noisy quantum simulators that the first few variational operators can bring the energy within the chemical accuracy, while additional operators do not improve the energy since the accumulative noise outweighs the gain from the expansion of the variational Ansatz.Received 21 June 2020Accepted 24 November 2020DOI:https://doi.org/10.1103/PhysRevResearch.3.013039Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum algorithmsQuantum gatesQuantum Information
Experimental quantum reading with photon counting

The final goal of quantum hypothesis testing is to achieve quantum advantage over all possible classical strategies. In the protocol of quantum reading this advantage is achieved for information retrieval from an optical memory, whose generic cell stores a bit of information in two possible lossy channels. For this protocol, we show, theoretically and experimentally, that quantum advantage is obtained by practical photon-counting measurements combined with a simple maximum-likelihood decision. In particular, we show that this receiver combined with an entangled two-mode squeezed vacuum source is able to outperform any strategy based on statistical mixtures of coherent states for the same mean number of input photons. Our experimental findings demonstrate that quantum entanglement and simple optics are able to enhance the readout of digital data, paving the way to real applications of quantum reading and with potential applications for any other model that is based on the binary discrimination of bosonic loss.
Towards Explainable Interactive Multi-modal Video Retrieval with Vitrivr

This paper presents the most recent iteration of the vitrivr multimedia retrieval system for its participation in the Video Browser Showdown (VBS) 2021. Building on existing functionality for interactive multi-modal retrieval, we overhaul query formulation and results presentation for queries which specify temporal context, extend our database with index structures for similarity search and present experimental functionality aimed at improving the explainability of results with the objective of better supporting users in the selection of results and the provision of relevance feedback.