Reviewerly/exHarmony huggingface dataset:
Additional Education in Digital Technologies as Competitiveness in the Labor Market

Over the past 20 years, the spread of digital technologies has accelerated and continues to evolve. In this regard, technological change is one factor that can affect the labor market, particularly the demand for competitive specialists with digital skills. The paper examines the problems of obtaining additional education in digital and information technologies to ensure the competitiveness of individuals. The author analyzes the survey results conducted by the “NAFI Research Centre,” with 1600 participants from 52 regions of Russia. The author uses empirical (analysis and generalization of the obtained results) and theoretical (survey and analytical research) research methods. During the research, the factors that influence the readiness to receive additional education are identified. The research analysis shows that working Russians are aware of the risk of possible job loss and, consequently, loss of personal competitiveness in the labor market due to the high rates of digital technology development and a lack of knowledge in this area. It is substantiated that obtaining additional education in digital technologies is not only a matter of meeting the needs of the population in digital skills but also one of the factors ensuring the competitiveness of an individual in the labor market. The author concludes that the interaction of employers, educational organizations, and the government in understanding labor market trends will make it possible to form the necessary expected digital skills among employees, motivating them to receive appropriate additional education.
SpectralMAP: Approximating Data Manifold With Spectral Decomposition

Dimensionality reduction is widely used to visualize complex high-dimensional data.This study presents a novel method for effective data visualization.Previous methods depend on local distance measurements for data manifold approximation.This leads to unreliable results when a data manifold locally oscillates because of some undesirable effects, such as noise effects.In this study, we overcome this limitation by introducing a dual approximation of a data manifold.We roughly approximate a data manifold with a neighborhood graph and prune it with a global filter.This dual scheme results in local oscillation robustness and yields effective visualization with explicit global preservation.We consider a global filter based on principal component analysis frameworks and derive it with the spectral information of the original high-dimensional data.Finally, we experiment with multiple datasets to verify our method, compare its performance to that of state-of-the-art methods, and confirm the effectiveness of our novelty and results.
Sistem Marketplace Penitipan Hewan Dengan Memanfaatkan Teknologi React Native

Keeping animals is a very fun hobby because pets can be entertainment in itself to relieve fatigue from daily activities. But there are certain times, the owner doesn't have time to take care of the pet, especially if there are tasks out of town for a few days, or other activities. Pet care is a solution to keep pets taken care of while their owners don't have time to take care of them. Animal care provides care facilities for pets that are deposited. Currently, many provide animal care services with the aim of helping facilitate the search for the best animal care services and according to needs. Developed a mobile application system in the form of an animal care marketplace using React Native technology. The purpose of making this system is so that animal owners can find animal care centers according to their wishes. There are two users who play a role in this system, namely animal owners and animal day care workers. The animal owner's user system can determine a message schedule to entrust animals to the care feature and choose the cage to be occupied. The advantage of this feature is that pet owners can also know the condition of their pets, because each officer will send information. Through testing this system, it is known that there are about 73% of users who want to use this system. This system helps people who have pets not to worry about doing care.
The effect of hashrate, transaction volume, social media and macroeconomics on Bitcoin before and during the COVID-19 pandemic

Purpose This research aims to determine the factors that affected Bitcoin price return in the period before and during the COVID-19 pandemic. Design/methodology/approach The independent variables used in this study are hashrate, transaction volume, social media and some macroeconomics variables. The data are processed using the vector error correction model (VECM) to determine the short-term and long-term relationships between variables. Findings The research shows that (1) Twitter and Gold significantly affected Bitcoin in the short term before the COVID-19 pandemic; (2) hashrate, transaction volume, Twitter and the financial stress index had a significant effect on Bitcoin in the long term before the COVID-19 pandemic; (3) the volatility index had a significant effect on Bitcoin in the short term during the COVID-19 pandemic; and (4) hashrate, transaction volume, Twitter and CHF/USD had a significant effect on Bitcoin in the long term during the COVID-19 pandemic. Research limitations/implications This research provides explanation about factors affecting Bitcoin so investors and regulators can pay more attention and prepare for the potential risks as well as to get a good understanding of market conditions for greater crypto adoption in the future. Originality/value The novelty in this study is the various factors driving the Bitcoin price were analyzed before and during the COVID-19 pandemic including the social media, as sentiment, interestingly, is being a predictive power for Bitcoin price return.
Incorrect facemask-wearing detection using image processing and deep learning

Now and in the future, a face mask is a very important strategy to protect people when a new contagious life threatens disease spread through the air appears. Currently, there is a serious health emergency because of the coronavirus disease 2019 (COVID-19) epidemic. The negative consequences of this pandemic need to be protected in public areas. Numerous methods are advised by the World Health Organization (WHO) to reduce infection rates and prevent depleting the available medical resources in the absence of efficient antivirals. Wearing masks is a non-pharmaceutical strategy to lessen the susceptibility to COVID-19 infection. This research aims to create a face mask identification system that is efficient and uses deep learning, which has proven to be beneficial in many real-world applications. This system has also used a transfer learning method with the MobileNetV2 model to classify people who wear face masks properly, wear face masks improperly, and are without masks. The results demonstrate that the proposed system has an accuracy of 99.4% which is higher than current systems.
A hierarchical automatic phoneme recognition model for<scp>Hindi‐Devanagari</scp>consonants using machine learning technique

Abstract A phoneme is perceptually the smallest distinct sound unit distinguished among words in a particular language. Every language has its own set of phonemes, and all the words are ordered sequences of phonemes. Therefore, phoneme recognition is essential to automatic speech recognition (ASR) systems. Phonemes of a language can be classified together using a single machine learning (ML) model through the direct classification (also known as the baseline or flat classification) approach. However, it is observed that the performance of such phoneme recognition degrades with the increase in the number of phoneme classes. The challenge is pronounced in languages with a larger number of phoneme classes, like Hindi, which has 48 phonemes. In this paper, we propose a speaker‐independent hierarchical classification approach for 33 Hindi‐Devanagari consonants/phonemes using cepstral features with ML techniques like support vector machine (SVM), random forest (RF) and fully connected deep neural network (DNN). In this hierarchical approach, a given phoneme is classified into successive subgroups until the particular phoneme class is identified. To perform the classification task, a binary or multi‐class classifier is invoked for each internal (non‐leaf) node in the hierarchy tree. Our model identified pairs of Optimal Feature Sets (based on mutual information ) and the best suitable ML classifier for each internal decision node in the hierarchy using 10‐fold cross‐validation to help in efficient classification. Our proposed hierarchical model leads to better accuracy and 57% improved performance for phoneme recognition compared with the non‐hierarchical, that is, the direct classification approach.
Towards a scalable discrete quantum generative adversarial neural network

Abstract Quantum generative adversarial networks (QGANs) have been studied in the context of quantum machine learning for several years, but there has not been yet a proposal for a fully QGAN with both, a quantum generator and discriminator. We introduce a fully QGAN intended for use with binary data. The architecture incorporates several features found in other classical and quantum machine learning models, which up to this point had not been used in conjunction. In particular, we incorporate classical noise reuploading in the generator, auxiliary qubits in the discriminator to enhance expressivity, and a direct connection between the generator and discriminator circuits, obviating the need to access the generator’s probability distribution. We show that, as separate components, the generator and discriminator perform as desired. We empirically demonstrate the expressive power of our model on both synthetic data as well as low energy states of an Ising model. Our demonstrations suggest that the model is not only capable of reproducing discrete training data, but also of potentially generalizing from it.
Fringe projection-based single-shot 3D eye tracking using deep learning and computer graphics

High-accuracy and high-speed 3D sensing technology plays an essential role in VR eye tracking as it can build a bridge to connect the user with virtual worlds. In VR eye tracking, fringe projection profilometry (FPP) can avoid dependence on scene textures and provide accurate results in near-eye scenarios; however, phase-shifting based FPP faces challenges like motion artifacts and may not meet the low-latency requirements of eye tracking tasks. On the other hand, Fourier transform profilometry can achieve single-shot 3D sensing, but the system is highly impacted by the texture variations on the eye. As a solution to the challenges above, researchers have explored deep learning-based single-shot fringe projection 3D sensing techniques. However, building a training dataset is expensive, and without abundant data the model is difficult to make generalized. In this paper, we built a virtual fringe projection system along with photorealistic face and eye models to synthesize large amounts of training data. Therefore, we can reduce the cost and enhance the generalization ability of the convolutional neural network (CNN). The training data synthesizer utilizes physically based rendering (PBR) and achieves high photorealism. We demonstrate that PBR can simulate the complex double refraction of structured light due to corneas. To train the CNN, we adopted the idea of transfer learning, where the CNN is first trained by PBR-generated data, then trained with the real data. We tested the CNN on real data, and the predicted results demonstrate that the synthesized data enhances the performance of the model and achieves around 3.722 degree gaze accuracy and 0.5363 mm pupil position error on an unfamiliar participant.
From Mobile-First to Place-First: Toward Mobile Writing Pedagogies

Gimmicks.Emerging technologies are often susceptible to this derogatory label.A gimmick is a publicity stunt, an attention-seeking fad that masks its own uselessness through sheer novelty.Although the exact origins of the word are unknown, its etymology is often traced to the word "gimac," an anagram of "magic" referring to the tricks and mechanical devices used by magicians in the early twentieth century (Emre 2020).As such, gimmicks have long been associated with technologies and practices for duping audiences into accepting subpar (if not completely fraudulent) products and experiences.A 1985 editorial in the New York Times, for instance, criticized the impracticality of the then-nascent laptop computer, noting that it was nothing more than "a dream machine for the few" likely to only sell in "specialized niche markets" (Sandberg-Diment 1985).Granted, outdated technological speculations are low-hanging fruit.New digital technologies are often perceived as premature, and, in many cases, such technologies go on to achieve widespread adoption despite their initial reception.However, this knee-jerk reaction betrays a more pervasive cultural assumption that new technologies should seamlessly plug in to existing sociotechnical norms.Paul Dourish and Genevieve Bell lament this cultural tendency to conceptualize emerging technologies as existing solely in a "proximal future."They claim that when we imagine new technologies as being "just around the corner" we "render contemporary practice . . .irrelevant or at the least already outmoded" (Dourish and Bell 2011, 22).Consequently, this "proximate future" outlook inadvertently obfuscates, and perhaps even discourages, creative engagement with the capacity of emerging technologies to transform how we think, move, act, and compose within the world today.Composing Place begins from the premise that mobile, wearable, and spatial computing technologies are more than just the latest marketing gimmick from a perpetually "proximate" future but rather an emerging composing platform through which digital writers create and distribute place-based multimodal texts.However, as seen in the case of Google Glass, such technologies are still in a stage of cultural emergence, and the rhetorical affordances they might offer digital writers as a medium for place-based composing are still relatively unknown.As John Tinnell (2017) writes, mobile and augmented computing technologies "do not come preinstalled with literary, artistic, or rhetorical innovations" (11).As such, Composing Place forwards an approach to writing through the locative affordances of emerging mobile and augmented reality technologies.This approach, which I delineate in part II, offers a set of rhetorical design practices through which digital writers can not only better leverage the Copyrighted material, not for distribution
SBIR databases and empirical studies of the SBIR program

This chapter overviews the databases used in the subsequent chapters to describe aspects of the SBIR program. The databases are characterized not only in terms of their genesis development but also in terms of their coverage of Phase II projects. Also, the extant literature that has previously been based on portions of these data is reviewed in an effort to codify the research questions that have been asked and answered by other researchers.
Rapid development and fabrication of AR/MR displays

This paper reviews recent developments in software, materials, processes and fabrication tools that can enable a rapid transition from design, modeling and simulation to fabricating cost effective test parts and prototypes for Augmented Reality and Mixed Reality displays. Using the approaches detailed here, display systems can be laboratory and field tested in a matter of days after establishing design goals and production criteria. Robust and industry-ready Imageguides® (aka planar optical elements) produced from this short run rapid design/fabrication cycle can result in directly printed one-offs for inclusion in evaluation systems or lead directly to the production of masters for additive NIL or etch masks for subtractive processes which are required for production level quantities.
Research on deep learning video game simulation algorithm based on western wind music

Deep learning technology and graphics computing technology have made important progress in recent years, making video game technology develop. Western orchestral music based on video game technology has been significantly improved in both human–computer experience and game fun. Therefore, an optimized reinforcement learning (Deep Q Networks, DQN) video game simulation algorithm is proposed. The first is to optimize the network activation function, integrate the ReLU and Softlus functions to design a new piecewise activation function, and use it as a fully connected function of the network. Secondly, the Gabor filter is improved and replaces the traditional filter of the network. Finally, the improved filter and the game image are used to obtain the directional features, and the dimensionality reduction of the features is realized by the kernel principal component analysis (PCA) method, the network training parameters are obtained, and the game simulation is completed. In order to verify the improved DQN game simulation model, two games were selected to participate in the experiment. In the game test of the orchestra simulator, the proposed ReLU-Softplus is the best in the orchestra experience, with the highest score of 126, while the average score of ReLU, Sigmaid, Tanh and ReLU-Softplus are 59.6, 57.2, 52.6 and 64.7. Compared with the other three functions, ReLU-Softplus has a 13.2% improvement. It can be seen that ReLU-Softplus has a better experience of wind games. We still take the pipe game as the test object, adopt the improved Gabor and ReLU-Softplus, and use KPCA to reduce the dimension of the game data features. When the curvature coefficient is 0.3, the DQN model has the highest score in the game test, with a score of 159.6, and the best average score is 93.5, which is better than 73.6 without the improved Gabor. It can be seen that the improved DQN game simulation algorithm can significantly optimize the game scoring effect. The research content has important reference value for improving the experience of western wind music video games and improving the game scoring mechanism.
Virtual Sample Generation Of Hyperspectral Mineral Data

Identification of earth surface features or useful resources even at inaccessible locations for humans made possible with the help of spatial-spectral information provided by remote sensing data especially hyperspectral images. At present deep learning (DL) based models have become a best choice to address the issues and provide better solutions in many fields including remote sensing data analysis. Due to very subtle differences exhibited by mineral signatures and also lack of sufficient training samples, geo-science and remote sensing research community has not much explored the DL techniques in analyzing mineral data. Therefore, in this paper, a virtual sample generation technique using vector rotation is proposed to increase the mineral data for training DL models. The proposed virtual sample generation technique is explored on a spectral library of 5 minerals that is formed from Cuprite scene, a benchmark mineral data set. The quality of the mineral samples generated are assessed using visual inspection as well as a relative spectral discriminating power of target minerals with respect to non-target or remaining minerals. Results show that mineral samples generated by the proposed virtual sample generation technique are not only qualitative in nature but also helpful or encouraging in exploration of DL in mineral data analysis.
A Novel Approach for SAR to Optical Image Registration using Deep Learning

Image registration is one of the essential pre-processing steps in remote sensing data applications. Since Synthetic Aperture Radar (SAR) and optical images give complementary information, they are used simultaneously for several applications. However, they differ in their geometric and radiometric properties; hence, SAR to optical image registration is challenging. An image registration approach based on Conditional Generative Adversarial Network (cGAN) to synthesize optical-like SAR images and Fast Fourier Transform (FFT) based correlation is proposed in this work. cGAN does the image translation of the SAR patch and generates a corresponding optical-like patch. The optical-like SAR image is used as a reference to register the optical image. Sentinel-l SAR and Sentine1-2 optical patch pairs are used for this work. When registered with optical-like patches using FFTbased correlation, optical patches could give a sub-pixel accuracy.
Machine learning-based mapping of pyroxene on global lunar surface

The advancement of remote sensing technology has created a gargantuan dataset with high spatial and spectral resolution enabling the use of Machine Learning (ML) methods for analysis. The Moon Mineralogy Mapper (M3) imaging spectrometer onboard the Chandrayaan-1 mission captured hyperspectral data of the lunar surface within the wavelength of 430-3000 nm in 85 contiguous hyperspectral bands. In this study, the distribution of pyroxene on the global lunar surface is mapped with M3 hyperspectral data using the support vector machine classifier (SVM) and reflectance spectra as a feature, and the result was compared with the previous mapping result. This study demonstrates that the use of machine learning is an efficient way for mapping of minerals using hyperspectral data on the lunar surface, and this technique can be used to map minerals of other planetary bodies as well in the future.
Enhanced Efficient Image Dehazing for Onboard Satellite Imagery Processing

Satellite imagery is often composed of diverse terrains like forest, desert, snow and exhibits haze, fog, thin clouds which require dehazing in order to make them analysisready. Onboard processing of satellite imagery requires the algorithm's parameters to be fine-tuned depending on the type of terrain encountered. From the atmospheric light scattering model, the estimation of atmospheric light and transmission map is performed in single image dehazing method. This paper focuses on tuning an existing method the “Efficient Image Dehazing with Boundary Constraints and Contextual Regularization method for satellite imagery”. A new image quality assessment method is introduced to enable fine-tuning the exponent of the algorithm. With the onset of onboard processing requirements, parallel implementation and faster image processing methods are explored for small run-times.
RGBT tracking using randomly projected CNN features

Integrating multiple complementary features has been proved to be an effective way for improving tracking results. In this paper, we exploit how to perform robust visual tracking in challenging situations by adaptively integrating information from RGB and thermal videos. Specifically, convolutional neural network (CNN) representation with random projection is proposed to depict RGB and thermal images, respectively. Furthermore, an adaptive fusion strategy based on a period of time is developed. We jointly optimize the reliable weights of different modalities. In addition, we explore the random projection to CNN features. Extensive experiments against other state-of-the-art methods demonstrate the effectiveness of the proposed method. Through analyzing quantitative tracking results, we provide basic insights in RGB and thermal data tracking.