Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative analysis of QGIS plugins for Web Maps creation

QGIS is a free and open-source software that allows viewing, editing, and analyzing georeferenced data. It is a Geographic Information System (GIS) software composed by tools that allow to manipulate geographic information and consequently to create maps which help to get a better understanding and organization of geospatial data. Unfortunately, maps created directly in the GIS desktop software are not automatically transferred to a website. This research aimed to compare publishing capabilities in different QGIS plugins to create Web Maps. This study analyzes four QGIS plugins (QGIS2Web, QGIS Cloud, GIS Cloud Publisher and Mappia Publisher), performing a comparison between them, considering their advantages and disadvantages, the free and subscription plans, the tools offered by each plugin and other generic aspects. The four plugins were tested in a specific case study to automatically obtain different Web Maps. This study could help users to choose the most adequate tools to publish Web Maps under QGIS software.
Neural Text Denormalization for Speech Transcripts

This paper presents a simple sequence-to-sequence approach to restore standard orthography in raw, normalized speech transcripts, including insertion of punctuation marks, prediction of capitalization, restoration of numeric forms, formatting of dates and times, and other, fully data-driven adjustments. We further describe our method to generate synthetic parallel training data, and explore suitable performance metrics, which we align with human judgment through subjective MOS-like evaluations. Our models for English, Russian, and German have a word error rate of 6.36%, 4.88%, and 5.23%, respectively. We focus on simplicity and reproducibility, make our framework available under a BSD license, and share our base models for English and Russian.
Preservation and Restoration of Roadside Tree Alleys in Line with Sustainable Development Principles—Mission (Im)possible?

The main challenge in the efforts to protect roadside tree alleys is to achieve a balance between the local development and the conservation of biodiversity. The problem of aging and disappearing roadside tree alleys was analyzed based on a critical review of the literature, various types of reports related to this topic and the provisions of international and Polish acts of law on biodiversity conservation and landscape protection. The following research hypotheses were formulated: 1. The preservation and restoration of roadside tree alleys promotes biodiversity conservation and sustainable development, and it constitutes an important social and scientific problem, and 2. detailed guidelines are needed to formulate effective programs for the protection of roadside tree alleys. The results of the study were used to formulate the universal guidelines for designing programs for the protection of roadside tree alleys. The potential barriers and obstacles that can occur in each stage of the proposed algorithm were identified and discussed on the example of Poland. In the summary, the answer to the question used as the title of the article should be affirmative. However, many actions must be taken. Most of them are included in the proposed guidelines and some of them are given as recommendations.
QISTA-Net-Audio: Audio Super-Resolution via Non-Convex ℓ_q-Norm Minimization

Audio super-resolution (ASR) aims to reconstruct the high-resolution signal from its corresponding low-resolution one, which is hard while the correlation between them is low. In this paper, we propose a learning model, QISTA-Net-Audio, to solve ASR in a paradigm of linear inverse problem. QISTA-Net-Audio is composed of two components. First, an audio waveform can be presented as a complex-valued spectrum, which is composed of a real and an imaginary part, in the frequency domain. We treat the real and imaginary parts as an image, and predict a high-resolution spectrum but only keep the phase information from the viewpoint of image reconstruction. Second, we predict the magnitude information by solving the sparse signal reconstruction problem. By combining the predicted magnitude and the phase together, we can recover the high-resolution waveform. Comparison with the state-of-the-art method MfNet [1], in terms of measure metrics SNR, PESQ, and STOI, demonstrates the superior performance of our method.
Optimization of Horizontal Annular Finned Tube Under Natural Convection Heat Transfer

Abstract Free convection heat transfer over annular finned tubes was studied numerically for more than 60 different cases and verified against two experimental works. In addition, the effect of fin spacing on heat transfer was investigated. As a result, a correlation was proposed to estimate Nusselt number as a function of Rayleigh number. Moreover, it was shown that there was an optimum fin spacing for which the total heat transfer was maximized. Based on the proposed equation, an expression for optimum fin spacing was derived, which proved that the optimum Nusselt number was 0.9035. The presented equation for optimum fin spacing was a general equation in so far that it could also be used to find the optimum spacing between a series of circular disks by letting the tube diameter in the equation be zero. Moreover, a good agreement was observed between the derived equation for optimum fin spacing and the results of the scale analysis method.
High speed ultrafast laser anisotropic nanostructuring by energy deposition control via near-field enhancement

It is challenging to store the exponentially increasing amount of data in the information age. The multiplexed optical data storage with merits of high data density (hundreds of terabytes/disk), low energy consumption, and long lifetime could open a new era in data storage technology. The recent progress in ﬁve-dimensional (5D) optical data storage based on anisotropic nanostructures written by femtosecond (fs) laser pulses in transparent materials reveals its potential for real-world applications, but high writing speed and density remain a major challenge. Here, we propose a method for rapid and energy-efﬁcient writing of highly localized anisotropic nanostructures in silica glass by energy modulated megahertz-ratefspulses.Anisotropicnanovoidisinitiallygeneratedwithpulseenergyabovethemicroexplosionthresh-oldandthenelongatedtoananisotropicnanolamella-likestructureviathenear-ﬁeldenhancementeffectbylowerenergypulses,minimizingtheunwantedthermaleffectsfrommegahertz-ratefspulses.Theanisotropicnanostructuresareexploitedfor5Ddatastoragewitharateof10 6 voxels / s, corresponding to a demonstrated fast information recording of ∼ 225 kB / s and a potentially high-density data storage of ∼ 500 TB / disk. © 2021 Optical Society of America under the termsoftheOSAOpenAccessPublishingAgreement
College, career, and civic readiness: Building school communities that prepare youth to thrive as 21st century citizens

ABSTRACT In the United States, advances in information technology and globalization present new social and political terrain for citizens to navigate. Preparing well-rounded young adults who are ready to meet the demands of citizenship in the 21st century—thinking critically, communicating, collaborating, and creating—is an imperative function of education. Findings from this multiple case study of “positive outlier” schools, or those with better-than-expected graduation outcomes among youth with historically disparate rates, utilize practices that incorporate Positive Youth Development (PYD) and Deeper Learning (DL) strategies. PYD and DL facilitate students’ development of skills, abilities, and dispositions that define 21st century citizenship. Though the schools in this study were selected for their better college and career preparation as measured by graduation outcomes, educators in positive outlier schools, in contrast to typically performing schools, emphasized student preparation for citizenship along with college and career preparation. The unique features of positive outlier schools include: commitment to pluralism, ethic of shared sacrifice and responsibility, community-directed critical thinking, and democratic school governance. For these schools, the college, career, and civic readiness replaced the exclusive college and career readiness paradigm.
Inter-glacial isolation caused divergence of cold-adapted species: the case of the snow partridge

Abstract Deciphering the role of climatic oscillations in species divergence helps us understand the mechanisms that shape global biodiversity. The cold-adapted species may have expanded their distribution with the development of glaciers during glacial period. With the retreat of glaciers, these species were discontinuously distributed in the high-altitude mountains and isolated by geographical barriers. However, the study that focuses on the speciation process of cold-adapted species is scant. To fill this gap, we combined population genetic data and ecological niche models (ENMs) to explore divergence process of snow partridge (Lerwa lerwa). Lerwa lerwa is a cold-adapted bird that is distributed from 4,000 to 5,500 m. We found 2 genetic populations within L. lerwa, and they diverged from each other at about 0.40–0.44 million years ago (inter-glacial period after Zhongliangan glaciation). The ENMs suggested that L. lerwa expanded to the low elevations of the Himalayas and Hengduan mountains during glacial period, whereas it contracted to the high elevations, southern of Himalayas, and Hengduan mountains during inter-glacial periods. Effective population size trajectory also suggested that L. lerwa expanded its population size during the glacial period. Consistent with our expectation, the results support that inter-glacial isolation contributed to the divergence of cold-adapted L. lerwa on Qinghai-Tibetan Plateau. This study deepens our understanding of how climatic oscillations have driven divergence process of cold-adapted Phasianidae species distributed on mountains.
Knowledge Development Trajectories of the Radio Frequency Identification Domain: An Academic Study Based on Citation and Main Paths Analysis

The study collected papers on radio frequency identification (RFID) applications from an academic database to explore the topic’s development trajectory and predict future development trends. Overall, 3820 papers were collected, and citation networks were established on the basis of the literature references. Main path analysis was performed on the networks to determine the development trajectory of RFID applications. After clustering into groups, the results are twenty clusters, and six clusters with citation counts of more than 200 were obtained. Cluster and word cloud analyses were conducted, and the main research themes were identified: RFID applications in supply chain management, antenna design, collision prevention protocols, privacy and safety, tag sensors, and localization systems. Text mining was performed on the titles and abstracts of the papers to identify frequent keywords and topics of interest to researchers. Finally, statistical analysis of papers published in the previous 4 years revealed RFID applications in construction, aquaculture, and experimentation are less frequently discussed themes. This study provides planning directions for industry, and the findings serve as a reference for business domain. The integrated analysis successfully determined the trajectory of RFID-based technological development and applications as well as forecast the direction of future research.
An end‐to‐end framework for the detection of mathematical expressions in scientific document images

The detection of mathematical expressions is a prerequisite step for the digitisation of scientific documents. Many different multistage approaches have been proposed for the detection of expressions in document images, that is, page segmentation and expression detection. However, the detection accuracy of such methods still needs improvement owing to errors in the page segmentation of complex documents. This paper presents an end‐to‐end framework for mathematical expression detection in scientific document images without requiring optical character recognition (OCR) or document analysis techniques applied in conventional methods. The novelty of this paper is twofold. First, because document images are usually in binary form, the direct use of these images, which lack texture information as input for detection networks, may lead to an incorrect detection. Therefore, we propose the application of a distance transform to obtain a discriminating and meaningful representation of mathematical expressions in document images. Second, the transformed images are fed into the faster region with a convolutional neural network (Faster R‐CNN) optimized to improve the accuracy of the detection. The proposed framework was tested on two benchmark data sets (Marmot and GTDB). Compared with the original Faster R‐CNN, the proposed network improves the accuracies of detection of isolated and inline expressions by 5.09% and 3.40%, respectfully, on the Marmot data set, whereas those on the GTDB data set are improved by 4.04% and 4.55%. A performance comparison with conventional methods shows the effectiveness of the proposed method.
COVID-19 Death Occurrences, Pandemic Fatigue, and Well-Being

Abstract Fifteen months since the pandemic started, the Philippines continues its struggle to control COVID-19 cases and deaths. This cross-sectional study, with 1,467 respondents, examined the predictive role of occurrences of COVID-19 related deaths within social circles to pandemic fatigue and selected well-being outcomes. Findings suggest that personally knowing someone who died from COVID-19 significantly increases pandemic fatigue, subjective well-being, and life satisfaction.
Import trade liberalization and individual happiness: evidence from chinese general social survey 2010-2015

ABSTRACT The happiness of citizens has long been the focus of all national governments. From a perspective of import trade liberalization, this paper uses 2010–2015 Chinese General Social Survey (CGSS) data to analyse whether import trade liberalization can improve national happiness and through which channels. The results show that import trade liberalization can significantly improve individual happiness and promote the happiness of citizens in the central region. When individuals are high-educated or high-income ones, trade liberalization can make them come out of the feelings of unhappiness more quickly. The trade liberalization can consolidate the happiness foundation of rural residents and improve the feelings of very happy of the urban ones. Further mechanism analysis demonstrates that import trade liberalization can improve individual happiness by enhancing individual fitness and marketization of the provinces where the individual is settled.
The focus, function and framing of feedback information: linguistic and content analysis of in-text feedback comments

Abstract In-text comments, in the form of annotations on students’ work, are a form of feedback information that should guide students to take action. Both the focus of the in-text comments, and the ways in which they are linguistically communicated, have potential to impact upon the way in which they are perceived by students. This study reports on an analysis of 2101 in-text comments added by markers to 60 summative essays from two disciplines. The majority of comments, regardless of the grade awarded, were found to be directed at the task performance, rather than relating to the level of process or self-regulation. Work awarded higher grades received fewer annotations; these essays were found to include more feedback comments expressing a positive tone, with limited opportunities for informing further development. Work awarded lower grades mainly received corrective comments, as well as comments characterised by interrogative language and words expressing risk. It is argued that the linguistic style may influence engagement with in-text comments, impacting upon students’ affective and emotional states, and their level of cognitive engagement with the feedback information. Recommendations for markers’ practices are identified, to facilitate the opportunities for engagement and action that in-text comments might afford.
Late Reverberant Spectral Variance Estimation for Single-Channel Dereverberation Using Adaptive Parameter Estimator

The estimation of the late reverberant spectral variance (LRSV) is of paramount importance in most reverberation suppression algorithms. This letter proposes an improved single-channel LRSV estimator based on Habets LRSV estimator by using an adaptive parameter estimator. Instead of estimating the direct-to-reverberation ratio (DRR), the proposed LRSV estimator directly estimates the parameter κ in a generalized statistical model since the experimental results show that even the κ calculated using measured ground truth DRR may not be the optimal parameter for the LRSV estimator. Experimental results using synthetic reverberant signals demonstrate the superiority of the proposed estimator to conventional approaches.
Excitation Source Feature Based Dialect Identification in Ao - A Low Resource Language

Ao is an under-resourced Tibeto-Burman tonal language spoken in Nagaland, India. There are three distinct dialects of the language, namely, Chungli, Mongsen and Changki. The ob-jective of dialect identiﬁcation is to identify one dialect from the other within the same language family. The goal of this study is to ascertain the potential of excitation source features for automatic dialect identiﬁcation in Ao. In this direction, Integrated Linear Prediction Residual (ILPR), an approximate representation of source signal, is explored. The log Mel spectrogram of ILPR ( S Ext ) signal is used to exploit the time-frequency characteristics of the excitation source. This work proposes attention based CNN-BiGRU architecture for automatic dialect identiﬁcation tasks. Additionally, log Mel spectrogram ( S V T ), extracted from the pre-emphasized speech signal, is used as a baseline method. The S V T contains the vocal-tract characteristics of the speech signal. A signiﬁcant performance improvement of (nearly) 6% accuracy is observed when the excitation source feature ( S Ext ) is combined with the vocal tract representation ( S V T ). To analyse the effect of segment duration, dialect identiﬁcation performance is reported for three different durations, viz., 1 sec, 3 sec and 6 sec. The effect of gender in dialect identiﬁcation task for Ao is also studied in this work.
Attribute Embedding: Learning Hierarchical Representations of Product Attributes from Consumer Reviews

Sales, product design, and engineering teams benefit immensely from better understanding customer perspectives. How do customers combine a product's technical specifications (i.e., engineered attributes) to form abstract product benefits (i.e., meta-attributes)? To address this question, the authors use machine learning and natural language processing to develop a methodological framework that extracts a hierarchy of product attributes based on contextual information of how attributes are expressed in consumer reviews. The attribute hierarchy reveals linkages between engineered attributes and meta-attributes within a product category, enabling flexible sentiment analysis that can identify how consumers receive meta-attributes, and which engineered attributes are main drivers. The framework can guide managers to monitor only portions of review content that are relevant to specific attributes of interest. Moreover, managers can compare products within and between brands, where different names and attribute combinations are often associated with similar benefits. The authors apply the framework to the tablet computer category to generate dashboards and perceptual maps and provide validations of the attribute hierarchy using both primary and secondary data. Resultant insights allow the exploration of substantive questions, such as how Apple improved successive generations of iPads and why Hewlett-Packard and Toshiba discontinued their tablet product lines.
Simultaneous Retrieval of Soil, Leaf, and Canopy Parameters from Sentinel-3 OLCI and SLSTR Multi-spectral Top-of-Canopy Reflectances

Multi- and hyper-spectral, multi-angular top-of-canopy reflectance data call for an efficient retrieval system which can improve the retrieval of standard canopy parameters (as albedo, LAI, fAPAR), and exploit the information to retrieve additional parameters (e.g. leaf pigments). Furthermore consistency between the retrieved parameters and quantification of uncertainties are required for many applications. % (2) methods We present a retrieval system for canopy and sub-canopy parameters (OptiSAIL), which is based on a model comprising SAIL, PROSPECT-D (leaf properties), TARTES (snow properties), a soil model (BRDF, moisture), and a cloud contamination model. The inversion is gradient based and uses codes % created by Automatic Differentiation. The full per pixel covariance-matrix of the retrieved parameters is computed. For this demonstration, single observation data from the Sentinel-3 SY_2_SYN (synergy) product is used. The results are compared with the MODIS 4-day LAI/fPAR product and PhenoCam site photography. OptiSAIL produces generally consistent and credible results, at least matching the quality of the technically quite different MODIS product. For most of the sites, the PhenoCam images support the OptiSAIL retrievals. The system is computationally efficient with a rate of 150 pixel per second (7 millisecond per pixel) for a single thread on a current desktop CPU using observations on 26 bands. Not all of the model parameters are well determined in all situations. Significant correlations between the parameters are found, which can change sign and magnitude over time. OptiSAIL appears to meet the design goals, puts real-time processing with this kind of system into reach, seamlessly extends to hyper-spectral and multi-sensor retrievals, and promises to be a good platform for sensitivity studies. The incorporated cloud and snow detection adds to the robustness of the system.
Ultra‐short‐term photovoltaic power forecasting of multifeature based on hybrid deep learning

The output power of photovoltaic power has randomness and volatility, which poses new challenges to the peak shaving and dispatching of the power system. Therefore, the accurate prediction of photovoltaic power output is an effective way to maintain the security and stability of the power grid. In this article, a hybrid prediction model based on improved convolutional neural network and bidirectional gated recurrent unit is proposed to predict PV output power. Firstly, the concept of time relevance is introduced to fuse this feature with weather characteristics as the input of the prediction model. Secondly, the time series generated adversarial neural network is applied to the field of photovoltaic power generation prediction for the first time to enhance the dataset obtained. Thirdly, the model proposed in this article is trained and compared with other models under different characteristics, different weather conditions, and different forecast time steps. The experimental results show that the hybrid deep learning model proposed in this article has better prediction performance. Under different characteristics, the model proposed in this article has the highest prediction accuracy. After data enhancement, the prediction accuracy of each model has been improved by 1% to 3%. Among them, the prediction accuracy of the model proposed in this article is 0.977 and 0.960, respectively. Under different weather conditions and different time steps, the proposed model has the best stability. Under cloudy and rainy weather, the prediction accuracy of the proposed model only decreases by about 6%. In addition, the prediction accuracy of the proposed model only decreases by approximately 1.5% to 4% from 15 to 180 minutes.