Reviewerly/exHarmony huggingface dataset:
A physical downscaling algorithm for the generation of high-resolution spatiotemporal solar irradiance data

The National Solar Radiation Database (NSRDB) provides more than 20 years of solar resource information covering North and South America at a 30-min, 4-km by 4-km resolution. However, high-resolution solar resource data is generally required for solar generation models such as the System Advisor Model (SAM), power system models such as the Integrated Grid Modeling System (IGMS), and renewable integration studies. The models and tools require data at a temporal resolution of minutes and spatial resolution of 1–2 km. This study developed a physics-based algorithm to downscale the NSRDB data to higher spatiotemporal resolution for use in various downstream models. The model uses the NSRDB radiative transfer models and is sufficiently generic to downscale solar resource data from the NSRDB without site-specific tuning. The downscaled solar resource data shows comparable accuracy to the native NSRDB when validated against high-temporal-resolution ground measurements. The downscaled solar resource data is also used to generate power production profiles that are compared to high-temporal-resolution photovoltaic plant power generation profiles with good qualitative agreement. Finally, an initial high-resolution publicly accessible data product developed using this method is presented for Puerto Rico and the U.S. Virgin Islands.
ADS-B anomaly data detection model based on VAE-SVDD

As a key technology of the new generation air traffic surveillance system, ADS-B (Automatic Dependent Surveillance-Broadcast) is vulnerable to cyber security challenges because it lacks data integrity and authentication mechanism. For detecting ADS-B data attacks accurately, an anomaly detection model is proposed which fully considers temporal correlations and distribution characteristics of ADS-B data. First, VAE (Variational AutoEncoder) is used to reconstruct ADS-B data so that the reconstructed values can be obtained. Then, for the sake of solving the adaptive problem of anomaly detection threshold, the difference values between the reconstructed values and the actual values are put into SVDD (Support Vector Data Description) for training, and a hypersphere classifier that can detect ADS-B anomaly data is obtained. In addition, in order to prevent overfitting and underfitting, appropriate reconstructed values are selected which can reduce FPR (False Positive Rate) and FNR (False Negative Rate) of anomaly detection. Experiments show that the VAE-SVDD model can detect ADS-B anomaly data which is generated by attacks such as random position deviation and constant position deviation. Moreover, compared with other machine learning methods, this model is not only more adaptable, but also has a lower FPR and FNR.
Regulatory Responses to ‘Fake News’ and Freedom of Expression: Normative and Empirical Evaluation

Abstract National authorities have responded with different regulatory solutions in attempts to minimise the adverse impact of fake news and associated information disorder. This article reviews three different regulatory approaches that have emerged in recent years—information correction, content removal or blocking, and criminal sanctions—and critically evaluates their normative compliance with the applicable rules of international human rights law and their likely effectiveness based on an evidence-based psychological analysis. It identifies, albeit counter intuitively, criminal sanction as an effective regulatory response that can be justified when it is carefully tailored in a way that addresses legitimate interests to be protected.
Explaining Black-Box Models for Biomedical Text Classification

In this paper, we propose a novel method named Biomedical Confident Itemsets Explanation (BioCIE), aiming at post-hoc explanation of black-box machine learning models for biomedical text classification. Using sources of domain knowledge and a confident itemset mining method, BioCIE discretizes the decision space of a black-box into smaller subspaces and extracts semantic relationships between the input text and class labels in different subspaces. Confident itemsets discover how biomedical concepts are related to class labels in the black-box's decision space. BioCIE uses the itemsets to approximate the black-box's behavior for individual predictions. Optimizing fidelity, interpretability, and coverage measures, BioCIE produces class-wise explanations that represent decision boundaries of the black-box. Results of evaluations on various biomedical text classification tasks and black-box models demonstrated that BioCIE can outperform perturbation-based and decision set methods in terms of producing concise, accurate, and interpretable explanations. BioCIE improved the fidelity of instance-wise and class-wise explanations by 11.6% and 7.5%, respectively. It also improved the interpretability of explanations by 8%. BioCIE can be effectively used to explain how a black-box biomedical text classification model semantically relates input texts to class labels. The source code and supplementary material are available at https://github.com/mmoradi-iut/BioCIE.
WHAT IS YOUR FAVORITE MOVIE?: A STRATEGY OF ENGLISH EDUCATION STUDENTS TO IMPROVE ENGLISH VOCABULARY

Vocabulary is one of the most important language components in learning the English language. A vocabulary is several English words that are taught to students in the teaching and learning process which goes hand in hand with all four English skills, especially listening. By increasing vocabulary, students’ ability for better understanding in listening, speaking, reading, and writing will likely increase. The more they listen, the more words they will add to their mastering vocabulary. Moreover, many kinds of media may be used to exercise students’ ability nowadays, which include music, video, movies, etc. English movies can provide a great chance for students to learn English vocabulary by listening. This could happen because in watching movies students often hear new words that they may never hear before. The instruments used were questionnaires and interviews. The result showed that by watching English Movie they could add some vocabularies to their memory through listening and watching the scene in the English Movie.
Humanoid Loco-Manipulation Planning Based on Graph Search and Reachability Maps

come from teaching and research institutions in France or abroad, or from public or private research centers.L'archive ouverte pluridisciplinaire
Imaginary-time propagation on a quantum chip

Evolution in imaginary time is a prominent technique for finding the ground state of quantum many-body systems, and the heart of a number of numerical methods that have been used with great success in quantum chemistry, condensed matter, and nuclear physics. We propose an algorithm to implement imaginary time propagation on a quantum computer. Our algorithm is devised in the context of an efficient encoding into an optimized gate, drawing on the underlying characteristics of the quantum device of a unitary operation in an extended Hilbert space. However, we prove that for simple problems it can also be successfully applied to standard digital quantum machines. This work paves the way for porting quantum many-body methods based on imaginary-time propagation to near-term quantum devices, enabling the future quantum simulation of the ground states of a broad class of microscopic systems.
Subspace Clustering via Structured Sparse Relation Representation

Due to the corruptions or noises that existed in real-world data sets, the affinity graphs constructed by the classical spectral clustering-based subspace clustering algorithms may not be able to reveal the intrinsic subspace structures of data sets faithfully. In this article, we reconsidered the data reconstruction problem in spectral clustering-based algorithms and proposed the idea of “relation reconstruction.” We pointed out that a data sample could be represented by the neighborhood relation computed between its neighbors and itself. The neighborhood relation could indicate the true membership of its corresponding original data sample to the subspaces of a data set. We also claimed that a data sample’s neighborhood relation could be reconstructed by the neighborhood relations of other data samples; then, we suggested a much different way to define affinity graphs consequently. Based on these propositions, a sparse relation representation (SRR) method was proposed for solving subspace clustering problems. Moreover, by introducing the local structure information of original data sets into SRR, an extension of SRR, namely structured sparse relation representation (SSRR) was presented. We gave an optimization algorithm for solving SRR and SSRR problems and analyzed its computation burden and convergence. Finally, plentiful experiments conducted on different types of databases showed the superiorities of SRR and SSRR.
Lanczos recursion on a quantum computer for the Green's function and ground state

A state-preserving quantum counting algorithm is used to obtain coefficients of a Lanczos recursion from a single ground-state wave function on the quantum computer. This is used to compute the continued fraction representation of an interacting Green's function for use in condensed matter, particle physics, and other areas. The wave function does not need to be reprepared at each iteration. The quantum algorithm represents an exponential reduction in memory over known classical methods. An extension of the method to determining the ground state is also discussed.
Real-time and accurate object detection in compressed video by long short-term feature aggregation

Video object detection is a fundamental problem in computer vision and has a wide spectrum of applications. Based on deep networks, video object detection is actively studied for pushing the limits of detection speed and accuracy. To reduce the computation cost, we sparsely sample key frames in video and treat the rest frames are non-key frames; a large and deep network is used to extract features for key frames and a tiny network is used for non-key frames. To enhance the features of non-key frames, we propose a novel short-term feature aggregation method to propagate the rich information in key frame features to non-key frame features in a fast way. The fast feature aggregation is enabled by the freely available motion cues in compressed videos. Further, key frame features are also aggregated based on optical flow. The propagated deep features are then integrated with the directly extracted features for object detection. The feature extraction and feature integration parameters are optimized in an end-to-end manner. The proposed video object detection network is evaluated on the large-scale ImageNet VID benchmark and achieves 77.2% mAP, which is on-par with the state-of-the-art accuracy, at the speed of 30 FPS using a Titan X GPU. The source codes are available at https://github.com/hustvl/LSFA.
A low‐light image enhancement method based on bright channel prior and maximum colour channel

Low-light image enhancement algorithms have been introduced to improve the visual quality of low-light images that may degrade the performance of many computer vision and multimedia systems designed for high-quality images. However, the existing bright channel prior and maximum colour channel enhancement algorithms introduce halo artifacts and colour distortions while enhancing the images. To overcome these limitations, in this paper, an effective fusion-based low-light image enhancement algorithm is proposed. In the proposed algorithm, the illumination of the low-light image is estimated from both the bright and maximum colour channels to overcome the halo artifacts and colour distortion problems. Further, an effective refinement method is utilized to improve the sharpness of the initial enhanced image representing the scene reflectance. Experiment results show that the proposed algorithm outperforms the state-of-the-art algorithms qualitatively and quantitatively. Moreover, the proposed algorithm reduces the halo artifacts and colour distortion and enhances the details while preserving the naturalness.
‘Desire to learn, learn to shine’: Idolizing motivation in enhancing speaking skill among L2 learners

This paper aims to analyze the effect of motivation in the development of English speaking skills on second language acquisition. There are so many excellent teaching methods that vary in effectiveness. Motivation is the driving force for learning another language, and integration of that language into the person’s identity. Motivation is the practical reason for learning a second language. Data collection was done from two Indian universities named: Patna University and Patliputra University, from each university 50 students were randomly selected. Speaking English is one of the best needs of individual in both the students’ academic and a professional field. The second language students should be encouraged not only in the classroom but also outside to speak in English. In achieving this goal, motivation can be used as magical catalyst in learning L2. With this viewpoint, this research focuses on 3 main motivational factors to analyze the role of motivation in developing speaking skills: identifying the motivation function in promoting speaking skills; researches on motivational factors for English as second language; suggesting impact and strategies in stimulating learners in developing speaking skills. close-ended questionnaires using relevant types of questions are chosen to conduct data collection. For the research, descriptive statistical analysis was performed using SPSS software. The mean value was used to represent the analysis results. Within this paper, the researcher identified several factors that affect students’ motivation to learn second language. Motivation is surely, of great importance in this phenomenon, and is often critical in its development.&#x0D; &#x0D; Keywords: language skills, L2 learner, motivation, second language; speaking skill;
Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning

It is necessary to monitor, acquire, preprocess, and classify microseismic data to understand active faults or other causes of earthquakes, thereby facilitating the preparation of early-warning earthquake systems. Accordingly, this study proposes the application of machine learning for signal–noise classification of microseismic data from Pohang, South Korea. For the first time, unique microseismic data were obtained from the monitoring system of the borehole station PHBS8 located in Yongcheon-ri, Pohang region, while hydraulic stimulation was being conducted. The collected data were properly preprocessed and utilized as training and test data for supervised and unsupervised learning methods: random forest, convolutional neural network, and K-medoids clustering with fast Fourier transform. The supervised learning methods showed 100% and 97.4% of accuracy for the training and test data, respectively. The unsupervised method showed 97.0% accuracy. Consequently, the results from machine learning validated that automation based on the proposed supervised and unsupervised learning applications can classify the acquired microseismic data in real time.
Future Primary School Teachers’ Digital Competence in Teaching Science through the Use of Social Media

The digital revolution has transformed the ways of doing, acting, and training in the university field. For this reason, as an objective of this work, social media was used to generate and share knowledge related to sustainability, classification of living beings, and the functioning of the human body. The information was collected through questionnaires, that were completed before and after the intervention program, as well as the work carried out by the students. The participants were 131 students enrolled on the Primary Education degree course of the University of Murcia. The students were well aware of social media, although initially not their educational use. This perception changed when having to make concrete proposals with Instagram and Pinterest, since students valued the wide potential of the use of these networks in teaching. As a result of this intervention, 266 images were generated on Pinterest related to the classification of living beings and 67 about the devices and systems of the human body. Additionally, in terms of the activity on Instagram, 213 publications were created, reaching 1454 followers and 7885 likes. In addition, the participants recognized social media as a source of resources for education, and therefore teachers must explore all the possibilities they offer.
IoT Data Qualification for a Logistic Chain Traceability Smart Contract

In the logistic chain domain, the traceability of shipments in their entire delivery process from the shipper to the consignee involves many stakeholders. From the traceability data, contractual decisions may be taken such as incident detection, validation of the delivery or billing. The stakeholders require transparency in the whole process. The combination of the Internet of Things (IoT) and the blockchain paradigms helps in the development of automated and trusted systems. In this context, ensuring the quality of the IoT data is an absolute requirement for the adoption of those technologies. In this article, we propose an approach to assess the data quality (DQ) of IoT data sources using a logistic traceability smart contract developed on top of a blockchain. We select the quality dimensions relevant to our context, namely accuracy, completeness, consistency and currentness, with a proposition of their corresponding measurement methods. We also propose a data quality model specific to the logistic chain domain and a distributed traceability architecture. The evaluation of the proposal shows the capacity of the proposed method to assess the IoT data quality and ensure the user agreement on the data qualification rules. The proposed solution opens new opportunities in the development of automated logistic traceability systems.
Ferromagnetically Shifting the Power of Pausing

We study the interplay between quantum annealing parameters in embedded problems, providing both deeper insights into the physics of these devices and pragmatic recommendations to improve performance on optimization problems. We choose as our test case the class of bounded-degree minimum-spanning-tree problems. Through runs on a D-Wave quantum annealer, we demonstrate that pausing in a specific time window in the anneal provides improvement in the probability of success and in the time to solution for these problems. The time window is consistent across problem instances and its location is within the region suggested by prior theory and seen in previous results on native problems. An approach to enable gauge transformations for problems with the qubit coupling strength $J$ in an asymmetric range is presented and shown to significantly improve performance. We also confirm that the optimal pause location exhibits a shift with the magnitude of the ferromagnetic coupling, $|{J}_{F}|$, between physical qubits representing the same logical one. We extend the theoretical picture for pausing and thermalization in quantum annealing to the embedded case. This picture, along with perturbation-theory analysis and exact numerical results on small problems, confirms that the effective pause region moves earlier in the anneal as $|{J}_{F}|$ increases. It also suggests why pausing, while still providing significant benefit, has a less pronounced effect on embedded problems.
Bright Single-Photon Emitters with a CdSe Quantum Dot and Multimode Tapered Nanoantenna for the Visible Spectral Range

We report on single photon emitters for the green-yellow spectral range, which comprise a CdSe/ZnSe quantum dot placed inside a semiconductor tapered nanocolumn acting as a multimode nanoantenna. Despite the presence of many optical modes inside, such a nanoantenna is able to collect the quantum dot radiation and ensure its effective output. We demonstrate periodic arrays of such emitters, which are fabricated by focused ion beam etching from a II-VI/III-V heterostructure grown using molecular beam epitaxy. With non-resonant optical pumping, the average count rate of emitted single photons exceeds 5 MHz with the second-order correlation function g(2)(0) = 0.25 at 220 K. Such single photon emitters are promising for secure free space optical communication lines.
Moving Human Target Detection and Tracking in Video Frames

The conventional method for moving human target detection and tracking has come across a major setback due to various hindering factors such as environmental lighting conditions, temperature, etc.Similarly, it has been noticed that the manual selection of moving human targets in a video sequence does not provide convincing results either.In this paper, a new method for moving human target detection and tracking is proposed.It involves two stages.The first stage consists in the detection of moving human targets and the second one in target tracking based on the Continuously Adaptive Mean-Shift (CAMShift) algorithm.In the first stage, in order to select the moving target, the background subtraction method and frame subtraction method are combined.The Region Of Interest (ROI), which is usually the moving target is identified.In the second stage, target tracking is performed by choosing a centroid pixel point over the ROI, which is then used by the CAMShift algorithm.The proposed method has shown outperforming results for various performance parameters such as precision, accuracy, recall, and the F1-score under three different lighting conditions.The results obtained also show a reduction in time complexity in comparison with the state-of-the-art algorithms.
Bloody Mahjong playing strategy based on the integration of deep learning and XGBoost

Bloody Mahjong is a kind of mahjong. It is very popular in China in recent years. It not only has the characteristics of mahjong's conventional state space, huge hidden information, complicated rules, and large randomness of hand cards but also has special rules such as Change three, Hu must lack at least one suit, and Continue playing after Hu. These rules increase the difficulty of research. These special rules are used as the input of the deep learning DenseNet model. DenseNet is used to extract the Mahjong situation features. The learned features are used as the input of the classification algorithm XGBoost, and then the XGBoost algorithm is used to derive the card strategy. Experiments show that the fusion model of deep learning and XGBoost proposed in this paper has higher accuracy than the single model using only one of them in the case of high-dimensional sparse features. In the case of fewer training rounds, accuracy of the model can still reach 83%. In the games against real people, it plays like human.
High Precision Optical Tracking System Based on near Infrared Trinocular Stereo Vision

A high precision optical tracking system (OTS) based on near infrared (NIR) trinocular stereo vision (TSV) is presented in this paper. Compared with the traditional OTS on the basis of binocular stereo vision (BSV), hardware and software are improved. In the hardware aspect, a NIR TSV platform is built, and a new active tool is designed. Imaging markers of the tool are uniform and complete with large measurement angle (&gt;60°). In the software aspect, the deployment of extra camera brings high computational complexity. To reduce the computational burden, a fast nearest neighbor feature point extraction algorithm (FNNF) is proposed. The proposed method increases the speed of feature points extraction by hundreds of times over the traditional pixel-by-pixel searching method. The modified NIR multi-camera calibration method and 3D reconstruction algorithm further improve the tracking accuracy. Experimental results show that the calibration accuracy of the NIR camera can reach 0.02%, positioning accuracy of markers can reach 0.0240 mm, and dynamic tracking accuracy can reach 0.0938 mm. OTS can be adopted in high-precision dynamic tracking.