Reviewerly/exHarmony huggingface dataset:
Class-Aware Distribution Alignment based Unsupervised Domain Adaptation for Speaker Verification

Existing speaker verification (SV) systems usually suffer from significant performance degradation when applied to a new domain that lies outside the training distribution.Given the unlabeled target-domain dataset, most Unsupervised Domain Adaptation (UDA) methods aim to minimize the distribution divergence between different domains.However, global distribution alignment strategies fail to consider latent speaker label information and can hardly guarantee the feature discriminative capability in target domain.In this paper, we propose a novel UDA approach called WBDA (Within-class and Between-class Distribution Alignment), which aims to transfer the class-aware information (i.e., within-and between-class distributions) learned from the well-labeled source-domain, to the unlabeled target-domain.Motivated by the recent progress of self-supervised contrastive learning, positive and negative pairs are constructed separately for source and target domains, from which the within-and between-class distribution can be estimated.And the SV system can then be learned by jointly optimizing the cross-domain class-aware distribution discrepancy loss and source-domain classification loss in an end-to-end manner.Evaluations on NIST SRE16 and SRE18 achieve a relative performance improvement of about 43.7% and 26.2% over the baseline in terms of Equal Error Rate (EER) separately, significantly outperforming the previous adaption methods based on global distribution alignment.
Earthworm Optimization with Deep Transfer Learning Enabled Aerial Image Classification Model in IoT Enabled UAV Networks

Unmanned aerial vehicles (UAVs) can be placed effectively in offering high-quality services for Internet of Things (IoT) networks. It finds use in several applications such as smart city, smart healthcare, surveillance, environment monitoring, disaster management, etc. Classification of images captured by UAV networks, i.e., aerial image classification is a challenging task and can be solved by the design of artificial intelligence (AI) techniques. Therefore, this article presents an Earthworm Optimization with Deep Transfer Learning Enabled Aerial Image Classification (EWODTL-AIC) model in IoT enabled UAV networks. The major intention of the EWODTL-AIC technique is to effectually categorize different classes of aerial images captured by UAVs. The EWODTL-AIC technique initially employs AlexNet model as feature extractor for producing optimal feature vectors. Followed by, the hyperparameter values of the AlexNet model are decided by the utilization of earthworm optimization (EWO) algorithm. At last, the extreme gradient boosting (XGBoost) model is employed for the classification of aerial images. The experimental validation of the EWODTL-AIC model is performed using benchmark dataset. The extensive comparative analysis reported the better outcomes of the EWODTL-AIC technique over the other existing techniques.
A Practical Interlacing-Based Coverage Path Planning Method for Fixed-Wing UAV Photogrammetry in Convex Polygon Regions

This paper investigates the coverage path planning problem for a fixed-wing UAV in convex polygon regions with several practical task requirements in photogrammetry considered. A typical camera model pointing forward-down for photogrammetric application is developed. In addition, the coordinates of the region vertices are converted from the WGS-84 coordinate system to the local ENU coordinate system for path planning convenience. The relationship between the minimum turning radius and the camera footprint is fully studied and the span coefficient of the fixed-wing UAV is first proposed. A novel flight pattern, named as the interlaced back-and-forth pattern in this paper, is presented accordingly. The proposed algorithm is compared with a traditional back-and-forth pattern in mathematics and several important results are given. Then, a practical low-computation algorithm for waypoints generation is developed. Finally, simulation results validate the effectiveness of the proposed method.
DBRepo: a Semantic Digital Repository for Relational Databases

Data curation is a complex, multi-faceted task. While dedicated data stewards are starting to take care of these activities in close collaboration with researchers for many types of (usually file-based) data in many institutions, this is rarely yet the case for data held in relational databases. Beyond large-scale infrastructures hosting e.g. climate or genome data, researchers usually have to create, build and maintain their database, care about security patches, and feed data into it in order to use it in their research. Data curation, if at all, usually happens after a project is finished, when data may be exported for digital preservation into file repository systems.&#x0D; We present DBRepo, a semantic digital repository for relational databases in a private cloud setting designed to (1) host research data stored in relational databases right from the beginning of a research project, (2) provide separation of concerns, allowing the researchers to focus on the domain aspects of the data and their work while bringing in experts to handle classic data management tasks, (3) improve findability, accessibility and reusability by offering semantic mapping of metadata attributes, and (4) focus on reproducibility in dynamically evolving data by supporting versioning and precise identification/cite-ability for arbitrary subsets of data.
Correction: Enhanced visible light photocatalytic activity of a floating photocatalyst based on B–N-codoped TiO<sub>2</sub> grafted on expanded perlite

[This corrects the article DOI: 10.1039/C5RA06056G.].
Social inclusion as a perspective for the validation of the "DigCompEdu Check-In" questionnaire for teaching digital competence

Making use of digital technologies and all the possibilities that benefit education is one of the objectives of the European Framework for the Digital Competence of Educators, as well as their potential for personal development and social inclusion, among other aspects. The aim of this study was to validate the «DigCompEdu Check-In» scale as an instrument for the self-reflection of educators regarding their digital competence from the perspective of social inclusion. This questionnaire provides a more accurate view of the digital competence framework and allows self-evaluating the strengths and weaknesses/needs of educators in digital learning. Exploratory and confirmatory factor analyses were performed, using structural equations. The study sample consisted of 2,262 faculty members from different public universities of Andalusia (Spain). The obtained results show the reliability and validity of the instrument and allow generating accurate scientific knowledge for the improvement of education quality and social inclusion, in both university and non-university institutions.
High-precision camera calibration based on a 1D target

In this paper, the problem of camera 1D calibration is well solved by our proposed high-precision algorithm, which can satisfy actual requirements. We present a viewpoint that the closed-form solution can simply achieve high calibration accuracy in the absence of distortion. So, we abandon the habitual strategy of global nonlinear optimization for all intrinsic and extrinsic parameters. The innovations of the proposed algorithm are three-fold: firstly, cyclic distortion correction method is introduced to ensure that the estimated distortion parameters gradually approach the exact values; secondly, a new criterion for the nonlinear optimization of distortion correction is developed; thirdly, we enhance the anti-noise ability of the closed-form solution by optimally weighting the constraint equations. Extensive experiments prove that the proposed algorithm provides the highest calibration accuracy and robustness, which is comparable to 2D calibration. In addition, our proposed algorithm provides a new approach for desirable distortion correction and an idea for 2D calibration of large field of view.
Automated all in one misspelling detection and correction system for Ethiopian languages

Abstract In this paper, a misspelling detection and correction system was developed for Ethiopian languages (Amharic, Afan Oromo, Tigrinya, Hadiyyisa, Kambatissa, and Awngi). For some of these languages, there have been few works on typo detection and correction systems. However, an effective and all-in-one typo detector and corrector system for Ethiopian languages have yet to be developed. A dictionary-based methodology is used to detect and rectify various forms of misspelling-related issues. The major characteristics of the proposed model can be outlined by presenting suggestions for detected flaws and automatically correcting them utilizing the first suggestion. In addition, the proposed model is evaluated using dictionary-based data sets for all languages. The corpora used were gathered from a variety of sources, including economic, political, social, and related publications, newspapers, and magazines. In this model, the users can perform all spelling-related issues within a single system (all-in-one). That means if the user(s) is (are) working on the Amharic language and then he/she/they can change the language she/he/they prefer(s) without shifting to another graphical user interface (GUI). Here, the users can save time and perform their tasks easily. Similarly, the user(s) can improve their skills in the selected languages accordingly. Finally, precision, recall, and f-measures for each language have been computed following a successful evaluation of the model. The system outperforms an f-measure of 89.57%, 87.57%, 88.31%, 86.83%, 81.83%, and 87.59% for Amharic, Afan Oromo, Tigrinya, Hadiyyisa, Kambatissa, and Awngi languages respectively. Furthermore, recommendations have been provided for future researchers.
NucDETR: End-to-End Transformer for Nucleus Detection in Histopathology Images

Nucleus detection in histopathology images is an instrumental step for the assessment of a tumor. Nonetheless, nucleus detection is a laborious and expensive task if done manually by experienced clinicians, and is also prone to subjectivity and inconsistency. Alternatively, the advancement in computer vision-based analysis enables the automatic detection of cancerous nuclei; however, the task poses several challenges due to the heterogeneity in the morphology and color of the nuclei, their varying chromatin distribution, and their fuzzy boundaries. In this work, we propose the usage of transformer-based detection, and dub it NucDETR, to tackle this problem, given their promising results and simple architecture on several tasks including object detection. We inspire from the recently-proposed Detection Transformer (DETR), and propose the introduction of a necessary data synthesis step; demonstrating its effectiveness and benchmarking the performance of Transformer detectors on histopathology images. Where applicable, we also propose remedies that mitigate some of the issues faced when adopting such Transformer-based detection. The proposed end-to-end architecture avoids much of the post-processing steps demanded by most current detectors, and outperforms the state-of-the-art methods on two popular datasets by 1–9% in the F-score.
Scheme for implementing nonlocal high-fidelity quantum controlled-not gates on quantum-dot-confined electron spins using optical microcavities and photonic hyperentanglement

Quantum information networks can transmit quantum states and perform quantum operations between different quantum network nodes, which are essential for various applications of quantum information technology in the future. In this paper, a potentially practical scheme for implementing nonlocal quantum controlled-not (CNOT) gate operations on quantum-dot-confined electron spins between two quantum network nodes is presented. The scheme can realize parallel teleportation of two nonlocal quantum CNOT gates simultaneously by employing hyperentangled photon pairs to establish quantum channel, which can effectively improve the channel capacity and operational speed. The core of the scheme are two kinds of photon-spin hybrid quantum CNOT gate working in a failure-heralded and fidelity-robust fashion. With the heralded mechanism, the nonlocal CNOT gates can be implementated with unity fidelities in principle, even if the particularly ideal conditions commonly used in other schemes are not satisfied strictly. Our analysis and calculations indicate that the scheme can be demonstrated efficiently (with efficiency exceeding 99%) with current or near-future technologies. Moreover, the utilized photon-spin hybrid quantum gates can be regarded as universal modules for many other quantum information processing (QIP) tasks. Therefore, the scheme is potential for constructing elementary quantum networks, and realizing nolocal QIP with high channel capacities, high fidelities, and high efficiencies.
End-to-End Optimized Image Compression With Deep Gaussian Process Regression

End-to-end optimization via deep neural networks has facilitated lossy image compression. Existing neural network-based entropy models for end-to-end optimized image compression are limited by parameterized Gaussian distributions with deterministic mean and variance and cannot achieve accurate rate estimation for bottleneck representation with varying statistics. In this paper, we propose a novel entropy model based on deep Gaussian process regression (DGPR) to address this problem. Specifically, the proposed entropy model leverages autoregressive DGPR to flexibly predict the channel-wise posterior distributions of high-dimensional bottleneck representation for entropy coding. Consequently, we develop a well-established bit-rate estimation scheme via posterior inference of DGPR using the learned probabilistic distribution. Furthermore, scalable training is achieved via tensor train decomposition and Monte Carlo sampling to enable tractable variational inference of DGPR. To our best knowledge, this paper is the first attempt to develop the learnable probabilistic model for flexible parameter estimation in entropy modeling. Experimental results show that the proposed model outperforms conventional image compression methods ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., JPEG2000 and BPG) as well as recent end-to-end optimized methods on the Kodak and Tecnick datasets in terms of rate-distortion performance.
Evaluating the integration of eye-tracking and motion capture technologies: Quantifying the accuracy and precision of gaze measures

Integrating mobile eye tracking and optoelectronic motion capture enables point of gaze to be expressed within the laboratory co-ordinate system and presents a method not commonly applied during research examining dynamic behaviors, such as locomotion. This paper examines the quality of gaze data collected through the integration. Based on research suggesting increased viewing distances are associated with reduced data quality; the accuracy and precision of gaze data as participants (N = 11) viewed floor-based targets at distances of 1-6 m was investigated. A mean accuracy of 2.55 ± 1.12° was identified, however, accuracy and precision measures (relative to targets) were significantly (p < .05) reduced at greater viewing distances. We then consider if signal processing techniques may improve accuracy and precision, and overcome issues associated with missing data. A 4th-order Butterworth lowpass filter with cut-off frequencies determined via autocorrelation did not significantly improve data quality, however, interpolation via Quintic spline was sufficient to overcome gaps of up to 0.1 s. We conclude the integration of gaze and motion capture presents a viable methodology in the study of human behavior and presents advantages for data collection, treatment, and analysis. We provide considerations for the collection, analysis, and treatment of gaze data that may help inform future methodological decisions.
HMM-Based Action Recognition System for Elderly Healthcare by Colorizing Depth Map

Addressing the problems facing the elderly, whether living independently or in managed care facilities, is considered one of the most important applications for action recognition research. However, existing systems are not ready for automation, or for effective use in continuous operation. Therefore, we have developed theoretical and practical foundations for a new real-time action recognition system. This system is based on Hidden Markov Model (HMM) along with colorizing depth maps. The use of depth cameras provides privacy protection. Colorizing depth images in the hue color space enables compressing and visualizing depth data, and detecting persons. The specific detector used for person detection is You Look Only Once (YOLOv5). Appearance and motion features are extracted from depth map sequences and are represented with a Histogram of Oriented Gradients (HOG). These HOG feature vectors are transformed as the observation sequences and then fed into the HMM. Finally, the Viterbi Algorithm is applied to recognize the sequential actions. This system has been tested on real-world data featuring three participants in a care center. We tried out three combinations of HMM with classification algorithms and found that a fusion with Support Vector Machine (SVM) had the best average results, achieving an accuracy rate (84.04%).
Evaluating the Use of Uncertainty Visualisations for Imputations of Data Missing At Random in Scatterplots

Most real-world datasets contain missing values yet most exploratory data analysis (EDA) systems only support visualising data points with complete cases. This omission may potentially lead the user to biased analyses and insights. Imputation techniques can help estimate the value of a missing data point, but introduces additional uncertainty. In this work, we investigate the effects of visualising imputed values in charts using different ways of representing data imputations and imputation uncertainty— <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">no imputation</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mean</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">95% confidence intervals</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">probability density plots</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">gradient intervals</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hypothetical outcome plots</i> . We focus on scatterplots, which is a commonly used chart type, and conduct a crowdsourced study with 202 participants. We measure users' bias and precision in performing two tasks—estimating average and detecting trend—and their self-reported confidence in performing these tasks. Our results suggest that, when estimating averages, uncertainty representations may reduce bias but at the cost of decreasing precision. When estimating trend, only hypothetical outcome plots may lead to a small probability of reducing bias while increasing precision. Participants in every uncertainty representation were less certain about their response when compared to the baseline. The findings point towards potential trade-offs in using uncertainty encodings for datasets with a large number of missing values. This paper and the associated analysis materials are available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://osf.io/q4y5r/</uri>
Experimental Preparation and Manipulation of Squeezed Cat States via an All‐Optical In‐Line Squeezer

The squeezed cat state, an essential quantum resource, can be used for quantum error correction and slowing decoherence of the optical cat state. However, preparing a squeezed cat state with high generation rate, and effectively manipulating it, remain challenging. In this work, a high-performance all-optical in-line squeezer is developed to prepare a squeezed cat state and manipulate the phase of the quadrature squeezing. This scheme has the advantages that the phase of the quadrature squeezing of the squeezed cat state can be manipulated by changing the working condition of the squeezer, and that a higher generation rate can be achieved via the deterministic squeezing operation of the in-line squeezer. The generation rate of squeezed cat states reaches 2 kHz, the same as that of the initial cat state. The all-optical in-line squeezer proposed here removes the requirements of electro-optic and opto-electric conversions necessary for an off-line squeezer, thus enabling high-bandwidth squeezing operations on non-Gaussian states. These results provide an efficient method to prepare and manipulate optical squeezed cat states, which makes a step closer to their applications in all-optical quantum information processing.
Guidelines for Artifacts to Support Industry-Relevant Research on Self-Adaptation

Artifacts support evaluating new research results and help comparing them with the state of the art in a field of interest. Over the past years, several artifacts have been introduced to support research in the field of self-adaptive systems. While these artifacts have shown their value, it is not clear to what extent these artifacts support research on problems in self-adaptation that are relevant to industry. This paper provides a set of guidelines for artifacts that aim at supporting industry-relevant research on selfadaptation. The guidelines that are grounded on data obtained from a survey with practitioners were derived during working sessions at the 17th International Symposium on Software Engineering for Adaptive and Self-Managing Systems. Artifact providers can use the guidelines for aligning future artifacts with industry needs; they can also be used to evaluate the industrial relevance of existing artifacts. We also propose an artifact template.
Acoustic-to-articulatory Speech Inversion with Multi-task Learning

Multi-task learning (MTL) frameworks have proven to be effective in diverse speech related tasks like automatic speech recognition (ASR) and speech emotion recognition. This paper proposes a MTL framework to perform acoustic-to-articulatory speech inversion by simultaneously learning an acoustic to phoneme mapping as a shared task. We use the Haskins Production Rate Comparison (HPRC) database which has both the electromagnetic articulography (EMA) data and the corresponding phonetic transcriptions. Performance of the system was measured by computing the correlation between estimated and actual tract variables (TVs) from the acoustic to articulatory speech inversion task. The proposed MTL based Bidirectional Gated Recurrent Neural Network (RNN) model learns to map the input acoustic features to nine TVs while outperforming the baseline model trained to perform only acoustic to articulatory inversion.