Reviewerly/exHarmony huggingface dataset:
LSVP

The ubiquity of mobile phones allows video content to be watched on the go. However, users' current on-the-go video learning experience on phones is encumbered by issues of toggling and managing attention between the video and surroundings, as informed by our initial qualitative study. To alleviate this, we explore how combining the emergent smart glasses (Optical Head-Mounted Display or OHMD) platform with a redesigned video presentation style can better distribute users' attention between learning and walking tasks. We evaluated three presentation techniques: highlighting, sequentiality, and data persistence to find that combining sequentiality and data persistence is highly effective, yielding a 56% higher immediate recall score compared to a static video presentation. We also compared the OHMD against smartphones to delineate the advantages of either platform for on-the-go video learning in the context of everyday mobility tasks. We found that OHMDs improved users' 7-day delayed recall scores by 17% while still allowing 5.6% faster walking speed, especially during complex mobility tasks. Based on the findings, we introduce Layered Serial Visual Presentation (LSVP) style, which incorporates sequentiality, strict data persistence, and transparent background, among other properties, for future OHMD-based on-the-go video learning.
Content Selection Network for Document-Grounded Retrieval-Based Chatbots

Grounding human-machine conversation in a document is an effective way to improve the performance of retrieval-based chatbots. However, only a part of the document content may be relevant to help select the appropriate response at a round. It is thus crucial to select the part of document content relevant to the current conversation context. In this paper, we propose a document content selection network (CSN) to perform explicit selection of relevant document contents, and filter out the irrelevant parts. We show in experiments on two public document-grounded conversation datasets that CSN can effectively help select the relevant document contents to the conversation context, and it produces better results than the state-of-the-art approaches. Our code and datasets are available at https://github.com/DaoD/CSN .
Facilitating cognitive processes during EFL smartwatch‐supported learning activities in authentic contexts

Abstract In this study, we designed a game‐based English as a foreign language (EFL) learning activity for junior high school students for the application of newly learned knowledge to the real world. The learning activity was combined with physical exercises such as walking. Smartwatches were used to help students create learning content during the learning activity and monitor their physical activity. We aimed to explore how learning activity supported by smartwatches can facilitate cognitive processes such as Remembering (i.e., remembering new knowledge), Understanding (i.e., understanding new knowledge) and Applying (i.e., applying acquired knowledge in new situations). We also explored student learning experiences during the smartwatch‐supported language learning process. Our results showed that the posttest student's cognitive processes level was significantly higher than that of the pretest. From the interviews, we found that the students’ learning experience can be categorised into EFL learning, physical exercise, smartwatch usage, learning satisfaction and problems. The results revealed that the learning activity supported by smartwatches was useful for language learning and physical exercise, and the students were satisfied with their learning experiences. The students used smartwatch functions such as texting, recording, sharing and monitoring their language practice in the real world. Based on our results, we suggest designing real‐world language learning activities supported by smartwatches as they may facilitate cognitive processes from a basic to a higher level because they enable students to learn new knowledge and apply it in everyday life. Practitioner notes What is already known about this topic? Acquiring new knowledge in class and then applying it to the real world is important. Smartwatches have the potential to facilitate cognitive processes associated with language learning. Exploring learning experiences is important in technology‐assisted language learning. What this paper adds? An understanding of how smartwatch‐supported language learning facilitates cognitive processes. A presentation of student learning experiences in a smartwatch‐supported language learning environment. Our study design and results might serve as a starting point in the research area of smartwatch‐supported learning. Implications for practice or policy The design of learning tasks requires applying newly learned knowledge to the real world and combining it with physical exercise. To make learning experiences more successful, we need to design relevant and meaningful tasks. The use of our proposed eight‐step approach will make learning experiences fruitful.
Naïve Bayes Classifier for Predicting the Novel Coronavirus

These days, the healthcare enterprises procure huge amount of healthcare data that most of the times is not processed to find out the hidden facts and patterns.Data mining along with machine learning performs a prominent role in predicting the diseases.Nowadays, COVID -19 has become a pandemic for the mankind.It is a communicable disease and it takes 12 -24 hours in receiving the reports of diagnose.In various remote and high altitude areas and due to the exponential growth of COVID -19 in various parts of the world, it is not feasible to perform the test on mass population.This research article describes a novel technique to diagnose coronavirus using naïve bayes classifier and we hope that this technique would be useful and fruitful for the humanity and will be a great step to predict the COVID -19.
Random Sequence Generation using Superconducting Qubits

Randomness property is an essential component in fields of cryptography, scientific simulations, secure communications and randomized algorithms. In this study, the reason for pseudo random generators were analyzed and illustrated for which are being slowly replaced by quantum random number generators. Quantum mechanics has emerged as a domain with extreme potential, however, the biggest challenge being faced towards practical utilization is that of scalability. This study proposes a method for Truly Random Bit Sequence (TRBS) generation also determining the most suitable physical implementation of qubits in the process for application in domains of data security, integrity and authentication as a precursor to secure communications. Illustration of the proposed method reaffirms that the probabilistic nature of quantum measurement and inherent randomness being used for the sequence generation lead to a truly random sequence which is also validated through NIST testing suite results.
The Use of Geographic Data Views to Help With Wide-Area Electric Grid Situational Awareness

The paper presents the use of geographic data views (GDVs) to help improve large-scale electric grid situational awareness for power flow and time-domain simulations. GDVs are electric grid display objects whose location is dynamically determined from geographic information embedded in an electric grid model. The paper provides examples using a 2000-bus and an 82,000-bus synthetic electric grid to show how GDVs can be used to help provide wide area understanding of values such as generator outputs, switched shunt values, voltages, and transmission line flows. It also shows the application of force-directed layout of GDVs and GDV summary objects.
A fast underwater calibration method based on vanishing point optimization of two orthogonal parallel lines

This paper proposes a fast underwater calibration method based on vanishing point optimization of two orthogonal parallel lines. Based on the distance optimization, two sets of precise vanishing points corresponding to orthogonal parallel lines are constructed, and then after getting the underwater camera focal length and the equivalent image in air of the underwater image, the camera's external parameters are obtained. This underwater calibration method is easy to operate, which needs only two sets of mutually orthogonal parallel lines from one image, reducing the restriction requirements of underwater camera calibration on scene. In addition, the calibration time is about 2 s. Compared with the classic vanishing point method and the Matlab toolbox, the speed is increased by 231.61% and 387.80%, respectively. The accuracy error of reconstruction model is kept within 2.5%. Results show that the method has enhancement speed while ensuring accuracies, and can be used for underwater robot manipulation.
Object Detection Methods for Improving UAV Autonomy and Remote Sensing Applications

The last decades the Unmanned Aerial Systems (UASs) are being used in a variety of applications, such as civil protection, security, agriculture, armed forces, that need real time object detection of observed information by their sensors. Moreover, the development of fully autonomous UAS is heavily dependent on their capability to detect and track steady or moving objects in a robust, powerful and reliable manner. In this review, we present a comprehensive literature survey and discussion on object detection methodologies for improving UAV autonomy and remote sensing applications. Emphasis is placed on Convolutional Neural Networks (CNN) implementing different object detectors and exploiting cloud processing. Based on these works, we provide a brief discussion and summary of related proposals for UAV-based object detection using different methodologies and approaches, share views for future research directions and draw conclusive remarks.
Multi-View Attention Network for Visual Dialog

Visual dialog is a challenging vision-language task in which a series of questions visually grounded by a given image are answered. To resolve the visual dialog task, a high-level understanding of various multimodal inputs (e.g., question, dialog history, and image) is required. Specifically, it is necessary for an agent to (1) determine the semantic intent of question and (2) align question-relevant textual and visual contents among heterogeneous modality inputs. In this paper, we propose Multi-View Attention Network (MVAN), which leverages multiple views about heterogeneous inputs based on attention mechanisms. MVAN effectively captures the question-relevant information from the dialog history with two complementary modules (i.e., Topic Aggregation and Context Matching), and builds multimodal representations through sequential alignment processes (i.e., Modality Alignment). Experimental results on VisDial v1.0 dataset show the effectiveness of our proposed model, which outperforms previous state-of-the-art methods under both single model and ensemble settings.
Three-dimensional shape measurement based on spatial-temporal binary-coding method

Binary coding methods have been widely used for three-dimensional (3D) shape measurement. However, traditional binary coding methods always require a sequence of binary patterns to encode the fringe order. This paper presents a spatial-temporal binary coding (STBC) method for 3D shape measurement with three binary patterns, including a spatial pattern and two temporal patterns. Specifically, the spatial pattern is used to encode a 4-bit spatial code, and the temporal patterns are used to encode the 2-bit temporal code. By combining the spatial code and the temporal code, 6-bit code words can be obtained that distinguish fringe periods of sinusoidal phase-shifting patterns. A robust dual-region labeling decoding scheme is also developed to extract the 6-bit code word for fringe order determination, and then absolute-phase unwrapping can be carried out. Experiments have been conducted, and their results confirm that the STBC method can reliably reconstruct the 3D shapes of measured objects with only three binary patterns.
The Hierarchical Evolution in Human Vision Modeling

Computational models of primate vision took a significant advance with David Marr's tripartite separation of the vision enterprise into the problem formulation, algorithm, and neural implementation; however, many subsequent parallel developments in robotics and modeling greatly refined the algorithm descriptions into very distinct levels that complement each other. This review traces the time course of these developments and shows how the current perspective evolved to have its alternative internal hierarchical organization.
Bibliometric analysis: Augmented reality-based physics laboratory with VOSviewer software

Abstract Augmented reality (AR) is a breakthrough in future technology trends that combine information and virtual objects that draw the user’s imagination into a real scene. The implementation of AR into the world of education will greatly affect the transformation of education, especially in the physics education laboratory. The purpose of this research was to analyze the application of the AR in the physics laboratory by providing bibliometric analysis so that the opportunities for further research can be presented. The data had been collected from the Scopus database. There were 80 articles found using the keywords “Augmented Reality and Physics”, 21 articles were found using the keywords “Augmented Reality and Physics Education” and 10 articles were found using the keywords “Augmented Reality and Physics Laboratory”. Then all data was classified according to the type of co-occurrence analysis based on the keywords of the author, co-authorship for unit countries, publication density, and year of publication using VOSviewer software. Based on the analysis, research on the augmented reality-based physics laboratory on the Higher-Order Thinking Skill (HOTS) aspect has never been studied. Previous research discussed physics laboratory experiments based on AR technology using Smart glasses to foster the concept of heat conduction and the effects of AR technology on students’ cognitive in the physics laboratory. Thus, HOTS on AR-based physics laboratories can be opportunities for future research.
Hyperplane Neural Codes and the Polar Complex

Hyperplane codes are a class of convex codes that arise as the output of a one layer feed-forward neural network. Here we establish several natural properties of stable hyperplane codes in terms of the polar complex of the code, a simplicial complex associated to any combinatorial code. We prove that the polar complex of a stable hyperplane code is shellable and show that most currently known properties of hyperplane codes follow from the shellability of the appropriate polar complex.
The program LOS for interactive seismic and infrasonic data processing

For many years, the Kola Division of the Geophysical Survey of the Russian Academy of Sciences carries out work on testing and implementation of modern techniques and algorithms for seismic and infrasonic data processing and event location. The KoD staff has developed several original algorithms that appeared to be useful for seismic and infrasonic event location and discrimination. In 2020, the LOS program was created. The most efficient tools for data processing and analysis, event location algorithms have been united in the program. The program also contains a modern mapping system and database. The following tools have been implemented: bandpass and adaptive filtration, polarization analysis and backazimuth computation for 3C stations, computation of backazimuths, and apparent velocities for seismic and infrasonic arrays (beamforming). To analyze records of infrasonic arrays the program has a cross-correlation tool, which enables to observe changes of signal’s backazimuths and apparent velocities in time. For seismic event location, the program uses two basic algorithms: minimization of origin time estimation residual and grid search based on generalized beamforming approach. These algorithms can be used in different combinations depending on the location scenario selected by a user. In addition, a new location algorithm based on a presentation of the seismic medium in a form of random graph where vertices correspond to points in the medium and edges are wave paths between the points, has been implemented. It can be useful for locating events in a substantially heterogeneous media, possibly with voids and cavities, as well as for taking into account the relief. This algorithm can be used, in particular, when locating events in mines using local mine seismic networks. The LOS program has been put into the practice of the Kola Division.
A Hierarchical Energy-Efficient Service Selection Approach With QoS Constraints for Internet of Things

With the emergence of Service-oriented Architecture (SoA) technologies, service-defined Internet of Things (IoT) applications have been widely developed. However, IoT system is resource-constrained. Devices in IoT usually have limited storage, computation and especially limited energy. Unfortunately, traditional service selection technologies ignore the energy efficiency of services. Although a few studies address energy consumption in service selection or composition, they fail to pay more attention to the dynamic characteristic of IoT, which is not suitable for resource-constrained IoT. In order to resolve this issue, we propose a hierarchical energy efficient service selection approach for resource-constrained IoT. In our approach, a Quality of Service (QoS) guaranteed global constraints decomposition is performed to acquire a group of local QoS constraints. Then, based on the selected local QoS constraints, an energy efficient service selection scheme can be carried out independently according to the dynamic network state. Experimental evaluations demonstrate the capability of our proposed approach.
Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification

Employing clustering strategy to assign unlabeled target images with pseudo labels has become a trend for person re-identification (re-ID) algorithms in domain adaptation. A potential limitation of these clustering-based methods is that they always tend to introduce noisy labels, which will undoubtedly hamper the performance of our re-ID system. To handle this limitation, an intuitive solution is to utilize collaborative training to purify the pseudo label quality. However, there exists a challenge that the complementarity of two networks, which inevitably share a high similarity, becomes weakened gradually as training process goes on; worse still, these approaches typically ignore to consider the self-discrepancy of intra-class relations. To address this issue, in this paper, we propose a multiple co-teaching framework for domain adaptive person re-ID, opening up a promising direction about self-discrepancy problem under unsupervised condition. On top of that, a mean-teaching mechanism is leveraged to enlarge the difference and discover more complementary features in target domain. Comprehensive experiments conducted on several large-scale datasets show that our method achieves competitive performance compared with the state-of-the-arts.
Estimating surface solar irradiance from geostationary Himawari-8 over Australia: A physics-based method with calibration

Surface Solar Irradiance (SSI) is required for solar energy planning and adoption, and is a fundamental parameter in modelling weather, climate, ecosystem and agricultural activities. Herein a time series based radiative transfer model was developed to simultaneously retrieve properties of clouds, aerosols and surface albedo, which were in turn used to calculate the components of SSI: i.e., global, direct and diffuse irradiance. The calculated results were calibrated across the Australian continent against in-situ measurements to account for minor factors (e.g., water vapor) not considered by the physics-based method. Detailed validation of the SSI components was performed against three years of in-situ measurements at 11 sites across Australia, at a range of time scales (i.e., instantaneous, hourly, daily and monthly) and under both all-sky and cloudy-sky conditions. The main advantage of the present method is the reliable separation of the direct and diffuse components with consistently low biases (~4 W/m2) at all four time scales, while still maintaining relatively low RMSE (root-mean-square-error) and MAE (mean-absolute-error). Once calibration has been performed the model does not require any ancillary data when implemented operationally: that is the model only requires geostationary satellite data. This model may be implemented across the globe using widely available next generation geostationary satellite data with a handful of ground-data for calibration.
Automatic seismic phase picking based on unsupervised machine-learning classification and content information analysis

Accurate identification and picking of P- and S-wave arrivals is important in earthquake and exploration seismology. Often, existing algorithms are lacking in automation, multiphase classification and picking, as well as performance accuracy. We have developed a new fully automated four-step workflow for efficient classification and picking of P- and S-wave arrival times on microseismic data sets. First, time intervals with possible arrivals on waveform recordings are identified using the fuzzy c-means clustering algorithm. Second, these intervals are classified as corresponding to P-, S-, or unidentified waves using the polarization attributes of the waveforms contained within. Third, the P-, S-, and unidentified-waves arrival times are picked using the Akaike information criterion picker on the corresponding intervals. Fourth, unidentified waves are classified as P or S based on the arrivals moveouts. The application of the workflow on synthetic and real microseismic data sets indicates that it yields accurate arrival picks for high and low signal-to-noise ratio waveforms.
Multiple-shot and unambiguous discrimination of von Neumann measurements

We present an in-depth study of the problem of multiple-shot discrimination of von Neumann measurements in finite-dimensional Hilbert spaces. Specifically, we consider two scenarios: minimum error and unambiguous discrimination. In the case of minimum error discrimination, we focus on discrimination of measurements with the assistance of entanglement. We provide an alternative proof of the fact that all pairs of distinct von Neumann measurements can be distinguished perfectly (i.e. with the unit success probability) using only a finite number of queries. Moreover, we analytically find the minimal number of queries needed for perfect discrimination. We also show that in this scenario querying the measurements <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">in parallel</mml:mtext></mml:mrow></mml:math> gives the optimal strategy, and hence any possible adaptive methods do not offer any advantage over the parallel scheme. In the unambiguous discrimination scenario, we give the general expressions for the optimal discrimination probabilities with and without the assistance of entanglement. Finally, we show that typical pairs of Haar-random von Neumann measurements can be perfectly distinguished with only two queries.