Reviewerly/exHarmony huggingface dataset:
Fully Variational Noise-Contrastive Estimation

By using the underlying theory of proper scoring rules, we design a family of noise-contrastive estimation (NCE) methods that are tractable for latent variable models. Both terms in the underlying NCE loss, the one using data samples and the one using noise samples, can be lower-bounded as in variational Bayes, therefore we call this family of losses fully variational noise-contrastive estimation. Variational autoencoders are a particular example in this family and therefore can be also understood as separating real data from synthetic samples using an appropriate classification loss. We further discuss other instances in this family of fully variational NCE objectives and indicate differences in their empirical behavior.
Camera Calibration Without Camera Access - A Robust Validation Technique for Extended PnP Methods

A challenge in image based metrology and forensics is intrinsic camera calibration when the used camera is unavailable. The unavailability raises two questions. The first question is how to find the projection model that describes the camera, and the second is to detect incorrect models. In this work, we use off-the-shelf extended PnP-methods to find the model from 2D-3D correspondences, and propose a method for model validation. The most common strategy for evaluating a projection model is comparing different models’ residual variances—however, this naive strategy cannot distinguish whether the projection model is potentially underfitted or overfitted. To this end, we model the residual errors for each correspondence, individually scale all residuals using a predicted variance and test if the new residuals are drawn from a standard normal distribution. We demonstrate the effectiveness of our proposed validation in experiments on synthetic data, simulating 2D detection and Lidar measurements. Additionally, we provide experiments using data from an actual scene and compare non-camera access and camera access calibrations. Last, we use our method to validate annotations in MegaDepth.
Analog on Digital (AoD) Theory-Based Design of Book Application for Representing Real-Life Experience

This study discusses digitization in today’s society and examines the relation be-tween digital and analog in terms of users’ content experience. As a result of our analysis, we focused on the “reality” which analog devices deliver. In this paper, we devise a design method that reflects reality, an analog feature, into digital content based on Analog on Digital (AoD) theory. The study aims to construct a book application that represents real-life experience by designing the physical sensations of reading paper books. The application represents the stains and wrinkles of paper books in the application interface, being inspired by the users’ experience with analog devices whose condition changes as they are used. On top of this, we incorporate in the application the sensations users get when tilting a book to open it and devise a method to move through pages by detecting the angle and speed of tilting the device.
PROJECT ACTIVITIES IN FORMING PROFESSIONAL COMPETENCIES OF FUTURE SPECIALISTS

The article highlights the role of project methods in teaching students of the Institute of Journalism at Borys Grinchenko Kyiv University. It is presented and described festivals, clubs and projects organised at the Department of Publishing. The aim and tasks of these events are indicated, the results and findings are analysed, the role and place of festivals, clubs, projects in the educational process are described. It is claimed that professional competencies are formed during the courses studied by students-journalists of the educational programme “Publishing and Editing”. The authors draw conclusions about the possibilities of projects in the development of professional competencies and their importance in achieving programme learning outcomes.
Modulation format recognition in a UVLC system based on reservoir computing with coordinate transformation and folding algorithm

Modulation format recognition (MFR) is one of the key technologies in adaptive optical systems and widely used in both commercial and civil applications. With the rapid development of deep learning, MFR algorithm based on neural networks (NN) has achieved impressive success. Due to the high complexity of underwater channels, to gain better performance of MFR tasks in underwater visible light communication (UVLC), the NN tend to be designed with a complex structure, which is costly in computation and hinders fast allocation and real-time processing. In this paper, we propose a lightweight and efficient method based on reservoir computing (RC), whose trainable parameters are only 0.3% of common NN-based methods. To improve the performance of RC in MFR tasks, we propose powerful feature extraction algorithms including coordinate transformation and folding algorithm. The proposed RC-based methods are implemented for six modulation formats, including OOK, 4QAM, 8QAM-DIA, 8QAM-CIR, 16APSK, and 16QAM. The experimental results show that our RC-based methods take only a few seconds for training process and under different pin voltages of LED, the accuracy for almost all exceeds 90%, and the highest is close to 100%. Analysis on how to design a well-performed RC to strike a balance between accuracy and time cost is also investigated, providing a useful guide for RC implementations in MFR.
Virtual Reality in Kindergarten

Children's emotions, dispositions, and actions are influenced by their immediate surroundings.By "physical environment," we mean the layout and design of institutional spaces like schools and lecture halls.Children learn the most during their time in a kindergarten setting, which is why the past decade has seen a number of modifications in the design of kindergartens as a result of shifts in both architecture and pedagogy.While architects produce designs in accordance with architectural theories and building standards (the "theoretical approach"), psychologists think that design should support local programme and philosophy (the "pedagogical approach").This research will provide a novel strategy for improving kindergarten facilities, with the development of client-based design as a third strategy to integrate the first two.Therefore, kids were included in the preliminary stages of the project's design and development, representing a more advanced rung on hart's ladder.They were shown two different kindergarten designs in a virtual reality (VR) setting, namely a CAVE (Cave Automated Virtual Environment), and given the opportunity to provide feedback on the proposed layout and paint colours.The findings confirmed that CAVE is a powerful visualization tool that is well-suited to the abilities and visual perception of young learners.
Probability Logics for Reasoning About Quantum Observations

In this paper, we present two families of probability logics (denoted QLP and QLPORT ) suitable for reasoning about quantum observations. Assume that a means “O = a”. The notion of measuring of an observable O can be expressed using formulas of the form ??a which intuitively means “if we measure O we obtain a”. In that way, instead of non-distributive structures (i.e., non-distributive lattices), it is possible to relay on classical logic extended with the corresponding modal laws for the modal logic B. We consider probability formulas of the form CSz1,?1;...;zm,?m??a related to an observable O and a possible world (vector) w: if a is an eigenvalue of O, w1, . . . , wm form a base of a closed subspace of the considered Hilbert space which corresponds to eigenvalue a, and if w is a linear combination of the basis vectors such that w = c1·w1+. . .+cm·wm for some ci ? C, then ?c1 - z1? = ?1, . . . , ?cm - zm? = ?m, and the probability of obtaining a while measuring O in the state w is equal to Sm i=1?ci?2. Formulas are interpreted in reflexive and symmetric Kripke models equipped with probability distributions over families of subsets of possible worlds that are orthocomplemented lattices, while for QLPORT also satisfy ortomodularity. We give infinitary axiomatizations, prove the corresponding soundness and strong completeness theorems, and also decidability for QLP-logics.
An Intensional Formalization of Generic Statements

A statement is generic if it expresses a generalization about the members of a kind, as in, ’Pear trees blossom in May,’ or, ’Birds lay egg’. In classical logic, generic statements are formalized as universally quantified conditionals: ’For all x, if ..., then ....’ We want to argue that such a logical interpretation fails to capture the intensional character of generic statements because it cannot express the generic statement as a simple proposition in Aristotle’s sense, i.e., a proposition containing only one single predicate. On the contrary, we’ll show that lambda abstraction and combinatory logic can help us transform the classical, non-simple and extensional expression of generic statements into a new, simple and intensional formalization, through the introduction of an operator that we will call ALL*. We will show that this new operator allows for the possibility of a single predication, e.g. fly(), because it builds, out of a concept like ’bird’, a concrete universal, e.g. ’birds’, upon which the single predicate can be applied to authentically formalize a generic statement, e.g. ’birds fly’.
REAL-TIME PLOTTING OF BIOMEDICAL SIGNALS IN APPLICATIONS UTILIZING WINDOWS PRESENTATION FOUNDATION TECHNOLOGY

Abstract: Utilization of personal computers in medicine requires strong emphasis on software performance. Contemporary programming languages give engineers great flexibility in developing applications. Since these technologies are more and more complex, there is a need to analyze methods of achieving desired results taking into account time requirements. This paper focuses mainly on development of plotting module using Windows Presentation Foundation library, which is one of the newest tool for software developers. Aspects of plotter creation for technologically limited screen are covered and conclusions are summarized giving recommendations regarding the topic. General methods of drawing signal on a raster surface are also discussed. Since medical signals in monitoring range consist of frequencies up to about 100 Hz, one has to take into account aspects of signal scaling in order to not to make its important parts invisible. The main problem is that personal computer screen, e.g. a monitor, is purely digital in nature; actually it is the set of pixels (consisting of three components). Because resolution is limited, the signal must be limited in frequency to the range which can be shown at a given plotting speed. This paper describes these aspects of signal plotting as well as the method for virtually increasing pixel resolution by using particular components of a pixel. This paper is a summary giving a general idea of the capabilities of the above mentioned WPF technology and the basis for creating such programs using methods such as: WriteableBitmap method, DrawingVisual method, Canvas Method type 1, Canvas Method type 2.
Leveraging synthetic data for robust gesture recognition

Effective communication and control of a team of humans and robots is critical for a number DoD operations and scenarios. In an ideal case, humans would communicate with the robot teammates using nonverbal cues (i.e., gestures) that work reliably in a variety of austere environments and from different vantage points. A major challenge is that traditional gesture recognition algorithms using deep learning methods require large amounts of data to achieve robust performance across a variety of conditions. Our approach focuses on reducing the need for “hard-to-acquire” real data by using synthetically generated gestures in combination with synthetic-to-real domain adaptation techniques. We also apply the algorithms to improve the robustness and accuracy of gesture recognition from shifts in viewpoints (i.e., air to ground). Our approach leverages the soon-to-be released dataset called Robot Control Gestures (RoCoG-v2), consisting of corresponding real and synthetic videos from ground and aerial viewpoints. We first demonstrate real-time performance of the algorithm running on low-SWAP, edge hardware. Next, we demonstrate the ability to accurately classify gestures from different viewpoints with varying backgrounds representative of DoD environments. Finally, we show the ability to use the inferred gestures to control a team of Boston Dynamic Spot robots. This is accomplished using inferred gestures to control the formation of the robot team as well as to coordinate the robot’s behavior. Our expectation is that the domain adaptation techniques will significantly reduce the need for real-world data and improve gesture recognition robustness and accuracy using synthetic data.
High spectral covariation between frequency channels contributes to clear speech intelligibility

Speech signals are acoustically redundant, which could explain why sentence intelligibility is fairly robust even when sentences are acoustically degraded. We investigated the contributions to sentence intelligibility of clear speech redundancy encoded as patterns of spectrotemporal covariation between frequency channels. Participants (N = 16) transcribed 120 clear-speech English sentences acoustically degraded to 5, 8, or 15 frequency bands derived from an ERB-scaled filter bank. Before the acoustic degradation, each sentence was expressed as a linear combination of principal component eigenvectors representing different patterns of covariation between channels. Half of the sentences preserved the channels providing larger score magnitudes for the eigenvector accounting for more spectral covariance (high-covariance condition). These channels represented the spectral covariation patterns that were more dominant in each sentence. The other half of the sentences preserved the bands conveying larger score magnitudes for the eigenvector accounting forless spectral covariance (low-covariance condition). These bands represented the spectral covariation patterns that were less dominant. Participants yielded significantly better transcription accuracy in the high-covariance condition (mixed-effects, ps &amp;lt; 0.0021). Critically, accuracy in this condition was higher than 56% on average for as few as 5 bands. These findings indicate that clear speech intelligibility is supported by patterns of spectral covariation between frequency bands.
Clear speech improves word segmentation in quiet and in noise: Evidence from visual-world eye-tracking

Listener-oriented hyperarticulated clear speech facilitates linguistic processing and cognitive functioning associated with speech perception under various listening conditions. Using the visual-world eye-tracking paradigm, we investigated whether clear speech also aids speech segmentation, or the discovery of word boundaries, and examined the dynamic time course of its effect. Native American English speakers (N = 77) heard sentences in which the target word (e.g., ham) was temporarily ambiguous with a longer unintended competitor (e.g., hamster) across a word boundary (e.g., She saw the ham starting…) while viewing images depicting the target, competitor, and unrelated distractors. Clear and conversational sentences were presented in quiet or in speech-shaped noise at +3 dB signal-to-noise ratio. Analysis of eye fixations to the images over time revealed that compared with conversational speech, clear speech facilitated the disambiguation of the target from the competitor even before the disambiguation point was reached. The facilitation was found in both listening conditions but was relatively delayed in noise. These findings suggest that speaking clearly improves word segmentation and reduces lexical competition especially in optimal listening conditions. The speech segmentation facilitation may partly underlie the clear speech benefits observed for other signal-dependent and relatively signal-independent linguistic and cognitive processes.
Hybrid recommendation model based on multi-head attention mechanism and cross network fusion

The combination of low and high order features in the recommendation system is crucial to the predicted click through rate. This paper designs an Attention Deep Cross Attention Recognition Machine (ADCAFM). Traditional recommendation models only use attention factor decomposers and deep cross networks to extract low and high order features, but the diversity of deep cross networks mining user interests is weak. Therefore, this paper extracts the feature depth of different subspaces by integrating the multi head attention mechanism to solve the problem of user interest diversity in deep cross network mining; Finally, the low and high order combined features are effectively fused and recommended together. Through experimental comparison on Criteo and Movielens-100K data sets, the AUC index is used for evaluation. Compared with the benchmark model, the AUC index is 1.85% and 1.55% higher.
E-OrbF: a robust image feature matching algorithm

To improve the real-time performance and robustness of traditional feature matching algorithms, an improved image feature matching algorithm E-OrbF based on ORB and FREAK is proposed. In E-OrbF, the original FAST feature points in ORB algorithm are distributed unevenly and redundant. The strategy of subregion and local threshold is adopted to improve the uniform distribution and stability of feature points. Then simplify the sampling mode of FREAK algorithm and design a new feature descriptor. While improving the matching speed, the sampling point pairs are further filtered to improve the matching accuracy. Finally, combine RANSAC matching algorithm to eliminate mismatches and reduce the rate of mismatches. The experimental results show that the algorithm has good real-time performance, while under the conditions of perspective transformation, rotation scale, complex illumination and blur. Both of them can well complete feature detection and feature matching and improve the robustness of existing methods. The algorithm can be applied to the fusion of virtual and real scenes on mobile terminals, and the average visual frame rate reaches 30 FPS, meeting the real-time requirements.
Adaptive fusion of multi-exposure images based on perceptron model

Abstract Multi-exposure image fusion as a technical means to bridge the dynamic range gap between real scenes and image acquisition devices, which makes the fused images better quality and more realistic and vivid simulation of real scenes, has been widely concerned by scholars from various countries. In order to improve the adaptive fusion effect of multi-exposure images, this paper proposes a fusion algorithm based on multilayer perceptron (MLP) based on the perceptron model and verifies the feasibility of the algorithm by the peak signal-to-noise ratio (PSNR), correlation coefficient (PCC), structural similarity (SSMI) and HDR-VDR-2, an evaluation index of HDR image quality. Comparison with other algorithms revealed that the average PSNR of the MLP algorithm improved by 4.43% over the Ma algorithm, 7.88% over the Vanmail algorithm, 10.30% over the FMMR algorithm, 11.19% over the PMF algorithm, and 11.19% over the PMF algorithm. For PCC, the MLP algorithm improves by 20.14%, 17.46%, 2.31%, 11.24%, and 15.36% over the other algorithms in that order. For SSMI, the MLP algorithm improved by 16.99%, 8.96%, 17.17%, 14.41%, and 4.85% over the other algorithms, in that order. For HDR-VDR-2, the MLP algorithm improved by 3.02%, 2.79%, 6.84%, 4.90%, and 6.55% over the other algorithms, in that order. The results show that the MLP algorithm can avoid image artifacts while retaining more details. The MLP-based adaptive fusion method is a step further in the theoretical study of multi-exposure image fusion, which is of great significance for subsequent research and practical application by related technology vendors.
Research on the identification of common faults of agricultural machinery based on vibration characteristics

Abstract In recent years, along with the rapid growth of the number of agricultural machinery and equipment in China, the problems of high failure rate and low operational efficiency of agricultural machinery have gradually emerged. Therefore, how to diagnose and monitor the common faults of agricultural machinery in real time has become a hot spot for research on common faults of agricultural machinery. Based on the current technical requirements of fault diagnosis and monitoring of common faults of agricultural machinery, this paper designs and implements a vibration feature model based on the GDBT algorithm with the common faults of agricultural machinery as the monitoring object, and shows that the corresponding values of the fault size at 0.1 mm are equal to 8.1m/s 2 , and the corresponding effective values at the size of 2 mm are 34, 19 m/s 2 . And the vibration waveform is particularly similar to the trend of the fault, with the minimum values of 13 and 110 m/s 2 , respectively, both at the fault size of 0.5 mm.
A study on the aesthetic characteristics of ethnic music based on radial basis neural network model

Abstract To explore the aesthetic features of folk music, this paper takes the radial basis neural network model as the basis and takes the zither piece “Flowing Water” and “Spring River Flower and Moonlight Night” as the examples, and unearths and analyzes the examples to understand the harmonious beauty, unique beauty and contextual beauty under the aesthetic characteristics. The results show that among the harmonious beauty, the average percentage of the characteristic vocabulary tranquility of the zither piece “Flowing Water” is 80.34% after learning by the radial-based neural network model, and the remaining percentages are 69.56%, 85.35%, 80.27% and 86.30% respectively, which shows that the radial-based neural network-based model can well resolve the characteristic vocabulary in the music through training and learning This shows that the radial basis neural network-based model can, through training and learning, well parse out the characteristic words in the music, and then explore the aesthetic features of the music. From the viewpoint of unique beauty, the musicalization of poetry and the poetization of music have been the main themes from ancient times to the present, with changes of 838% increase and 74.91% decrease, respectively, which also shows that the current degree of our attention to national music is not as much as that of the Tang, Song and Yuan dynasties, and we should strengthen the excavation of national music and create a unique humanistic environment. From the perspective of the beauty of the context, 67.81% of those who listened to “Spring River Moon Night Song” thought that the rhythm and context were full, and 79.48% thought that the theme melody and performance method were eye-catching, but there were still voices of dissatisfaction. Therefore, with the radial basis neural network model, we can intuitively see the aesthetic characteristics of folk music, and we can improve folk music according to its shortcomings so that folk music can be further renewed through a new interpretation.