Reviewerly/exHarmony huggingface dataset:
A Breast Cancer Contour Detection With Level Sets and SVM Model

Level sets have been widely used to isolate features of breast tumors in ultrasound images. However, region-based methods always produce multiple contours. Since tumors are regularly undefined from the shadows and muscular regions in breast ultrasound images, computerized tumors location and arrangement is significantly difficult. Therefore, the authors introduce a breast cancer contour detection model using support vector machine (SVM) as a binary classification. Features of the binary SVM model were extracted from level sets and FM method (the fusion of ultrasound, elasticity, and Doppler images). The model was accurately able to predict a correct breast tumor contour from false contours which were segmented by region-based level sets. The proposed method was evaluated on 60 datasets collected by professional radiologists at the Thammasat University Hospital of Thailand. From the experimental results, the breast cancer contours were detected correctly with high accuracy. The percentage of correct detection was 93%.
Arbitrary-Scale Texture Generation From Coarse-Grained Control

Existing deep-network based texture synthesis approaches all focus on fine-grained control of texture generation by synthesizing images from exemplars. Since the networks employed by most of these methods are always tied to individual exemplar textures, a large number of individual networks have to be trained when modeling various textures. In this paper, we propose to generate textures directly from coarse-grained control or high-level guidance, such as texture categories, perceptual attributes and semantic descriptions. We fulfill the task by parsing the generation process of a texture into the three-level Bayesian hierarchical model. A coarse-grained signal first determines a distribution over Markov random fields. Then a Markov random field is used to model the distribution of the final output textures. Finally, an output texture is generated from the sampled Markov random field distribution. At the bottom level of the Bayesian hierarchy, the isotropic and ergodic characteristics of the textures favor a construction that consists of a fully convolutional network. The proposed method integrates texture creation and texture synthesis into one pipeline for real-time texture generation, and enables users to readily obtain diverse textures with arbitrary scales from high-level guidance only. Extensive experiments demonstrate that the proposed method is capable of generating plausible textures that are faithful to user-defined control, and achieving impressive texture metamorphosis by interpolation in the learned texture manifold.
HyperISO: Efficiently Searching Subgraph Containment in Hypergraphs

Searching subgraph containment, also called subgraph matching in hypergraphs, is to enumerate all the embeddings of a data hypergraph with a given query hypergraph, which plays an important role in the analysis of hypergraph-modeled applications. However, existing subgraph matching frameworks mainly focus on pairwise graphs and the existing techniques can not efficiently be applied to search subgraph containment at low costs. Therefore, this paper proposes HyperISO to efficiently search subgraph containment that consists of three parts: 1) new filtering techniques driven by exploring the properties and connections of hyperedges to reduce unpromising products for the sake of low matching costs, 2) a novel ordering strategy that is able to generate an optimized matching process by considering both the sizes of hyperedge candidates and the unmatched vertices of the hyperedges, and 3) a dual enumeration algorithm to list both the vertex and hyperedge mappings. Extensive experiments on both real and synthetic data show that HyperISO outperforms the best among the sophisticated subgraph matching frameworks and meanwhile verify the efficiency of HyperISO in various types of hypergraphs.
Double-Opportunity Estimation Via Altruism

A novel approach, based on the notion of altruism, is presented to cooperative parameter estimation in a system comprising two information-sharing agents. The underlying assumption is that the overall two-agent scheme can reach desired performance level even if only one of the agents performs satisfactorily, hence there exist two independent opportunities to estimate. The notion of altruism motivates a new definition of cooperative estimation optimality that generalizes the common definition of minimum mean squared error optimality. Fundamental equations are derived for two types of altruistic cooperative estimation problems, corresponding to heterarchical and hierarchical setups. Although these equations are, generally, hard to solve, their solution in the Gaussian case is straightforward and only entails the computation of the largest eigenvalue of the conditional covariance matrix and its corresponding eigenvector. Moreover, in the Gaussian case the performance improvement of the two altruistic cooperative estimation techniques over the conventional (egoistic) estimation approach is shown to depend on the problem's dimensionality and statistical distribution. In particular, the performance improvement grows with the dispersion of the spectrum of the conditional covariance matrix, rendering the new estimation approach especially appealing in ill-conditioned problems. The validity of the solution in the Gaussian case is illustrated numerically.
Exploring Medieval Manuscripts Writer Predictability: A Study on Scribe and Letter Identification

Handwriting communication is a long-established human activity that has survived into the 21st century. Accordingly, research interest in handwritten documents, both historical and modern, is significant. The way we write has changed significantly over the past few centuries. For example, texts of the Middle Ages were often written and copied by anonymous scribes. The writing of each scribe, known as his/her "scribal hand" is unique. It can be differentiated using a variety of consciously and unconsciously produced features. Distinguishing between these different scribal hands is a central focus of the humanities research field known as "paleography." Character recognition within each scribal hand has also posed an interesting challenge. Some issues make these digital processes difficult, such as paper degradation and the soiling of the manuscript page. Thus, in this paper, we propose an investigation in both perspectives, character recognition and writer identification, in medieval manuscripts to better understand the specific behaviour of two 800-year-old scribes based on their manuscripts in comparison with a modern calligrapher. The experiments demonstrated that degradation and tremor can influence the analysis of medieval handwriting documents. However, the results presented an efficient accuracy with a better accuracy rate in letter classification than in writer identification.La communication manuscrite est une longue tradition humaine qui a persisté jusqu’à nos jours, au 21e siècle. Par conséquent, l’intérêt de la recherche concernant des documents manuscrits historiques et modernes est grand. La façon dont nous écrivons a changé au cours des derniers siècles. Par exemple, des textes du Moyen Âge ont souvent été écrits et copiés par des scribes. L’écriture de chaque scribe, appelée son «&amp;nbsp;écriture scribale&amp;nbsp;» (anglais scribal hand), est unique. Nous pouvons la différencier en observant une gamme de caractéristiques produites consciemment et inconsciemment. Faire la distinction entre ces écritures scribales différentes est au centre des préoccupations du domaine de recherche de paléographie. La reconnaissance de caractères dans chacune des écritures scribales pose des défis intéressants. Certains problèmes, tels que la dégradation de papier et l’encrassement de la page manuscrite, rendent difficiles ces processus numériques. Dans cet article, nous proposons ainsi une enquête sur les deux perspectives, la reconnaissance de caractères et l’identification de scribes, dans les manuscrits médiévaux dans le but de mieux comprendre le comportement spécifique de deux scribes vivant il y a 800 ans, en se basant sur leurs manuscrits en comparaison avec un calligraphe moderne. Les expériences démontrent que la dégradation et le tremblement peuvent influer sur l’analyse des documents manuscrits médiévaux. Cependant, les résultats ont présenté une précision efficace, avec un meilleur taux de précision dans la reconnaissance de caractères que dans celui de l’identification de scribe.
Design Education in Kuzbass: Directions, Trends, Prospects: Collective Monograph / under scientific. ed. N. I. Gendina, G. S. Eliseenkov; Kemerovo State Institute of Culture. Kemerovo: KemGIK, 2022. 242 p.: Opinion Paper

The review evaluates the collective monograph “Design Education in Kuzbass: Directions, Trends, Prospects”, which was prepared at the Design Department of the Kemerovo State Institute of Culture. The monograph has been written by the following group of the authors: G. S. Eliseenkov, T. Yu. Kazarina, S. N. Kazarin, S. V. Melkova, I. V. Pashkova, N. S. Popova, S. F. Rysaeva, T. D. Rysaeva, A. A. Cherdantseva. The monograph describes the formation and development of design education in Kuzbass, considers the training of designers in the system of art educational organizations in Kuzbass, and outlines the prospects for the development of design education in the context of digitalization.
Introduction of Intermedial Analysis of a Literary Text in Literature Lessons in Senior School of the Humanities

The aim of the study is to substantiate the need to introduce intermedial analysis of literary texts in senior school of the humanities. In the article, the author gives the notion of intermedial analysis, reveals the specifics of studying Chekhov’s traditions in the dramaturgy of the 20th century in school with in-depth study of literature, presents the results of diagnosing the readiness of teachers to introduce intermedial analysis into the school curriculum of literature, offers methodological recommendations on the use of intermedial analysis in literature lessons in senior school. The scientific novelty of the study lies in identifying the level of readiness of modern language teachers to introduce intermedial analysis in literature lessons when studying the work of playwrights of the 20th century (B. Akunin, L. S. Petrushevskaya). As a result, the necessity of carrying out intermedial analysis at different levels of studying a literary text in literature lessons in senior school of the humanities has been proved.
Ecological Platform for Assimilating Data (EcoPAD) for Ecological Forecasting

Tremendous scientific endeavors in ecology have been driven by the goal of forecasting future ecological dynamics.This chapter introduces the webbased Ecological Platform for Assimilating Data into model (EcoPAD) to facilitate ecological forecasting.The objectives of this lecture are to understand why we need a platform like EcoPAD, the structure of the platform, and how to use EcoPAD to facilitate ecological forecasting.
Optimization of memristor based ultrasonic transducers for mesoscopic characterization of biomaterials

A nonlinear time reversal ultrasonic (US) imaging set-up coupled with memristors was used to define the concept of memosducer in order to measure locally the characterization and aging of the skin. Each memosducer consists of a ultrasonic transducer (Panametrics 2.5 MHz) with an internal electrical impedance modified by an array of BS-AF-W discrete self-directed-channel bipolar devices commercialized in 8-pin ceramics DIP packages by Knowm Inc. In order to control/program the optimized distribution of set/reset inputs applied to the Knowm Inc. memristors, a amplitude modulated chirp-coded excitation is applied to the memosducer. The typical test equipment consists of a preamplifier Juvitek TRA-02 (0.02-5MHz) connected to a computer, an VBA100-110 Acquitek amplifier, a shear wave transducer Technisonic (2.25MHz), and a longitudinal wave transducer Panametrics V155 (5MHz). Packaged chips are presented with 8 discrete memristors in 16 Pin Ceramic DIP (Dual Inline Package) with wafer batch (DM8-16DIP-BS-AF1403272-9 226,DM8-I6DIP-BSAF 1403272-9230 Tier 3, Knowm Inc, Santa Fe, USA). The experimental set-up constitutes a basis of a reservoir computing system for US nonlinear imaging of mesoscopi properties of biomaterials.
Foreground Detection Based on Superpixel and Semantic Segmentation

Foreground detection is an essential step in computer vision and video processing. Accurate foreground object extraction is crucial for subsequent high-level tasks such as target recognition and tracking. Although many foreground detection algorithms have been proposed, foreground detection in complex scenes is still a challenging problem. This paper presents a foreground detection algorithm based on superpixel and semantic segmentation. It first uses multiscale superpixel segmentation to obtain the initial foreground mask. At the same time, a semantic segmentation network is applied to separate potential foreground objects, and then use the defined rules to combine the results of superpixel and semantic segmentation to get the final foreground object. Finally, the background model is updated with the refined foreground result. Experiments on the CDNet2014 dataset demonstrate the effectiveness of the proposed algorithm, which can accurately segment foreground objects in complex scenes.
Weak Predictability of Rupture Growth Evidenced by <i>P</i> Waves: Implications for Earthquake Early Warning

ABSTRACT The time-evolving growth behavior during the early phase of an earthquake rupture and when distinguishable features arise for events of different sizes are critical issues for real-time seismology. These issues are related to the key concept of rupture predictability, which directly determines the warning time and the potential of earthquake early warning systems. P waves carry the temporal information of rupture development, which provides us with an important clue to investigate and understand the source physical process in the early stage of the rupture. In this article, for 36 shallow, moderate- to large-magnitude earthquakes (5.0≤Mw≤9.1) that occurred in Japan and China, we systematically assess and quantify the temporal evolution of the P-wave velocity amplitude (V-env) and the characteristic period (τc). The initial growth rate and the secondary evolution parameters are extracted to investigate the main features of the early rupture behavior. Our results show that the V-env exhibits distinguishable growth features after a similar steep rise affected by propagation attenuation for earthquakes of different sizes. Near the distinguishable points, the bell-shaped fluctuations are generally observed in the τc curves, and the fluctuation amplitude is positively correlated with the final magnitude. The distinct evolution of P waves radiated from earthquakes of different magnitudes occurs earlier than one-half of the rupture duration, which constitutes new evidence supporting the weak predictability of ruptures. Finally, we discuss the implications for the physical source mechanism and earthquake early warning.
Improvement of Segmentation Efficiency in Mammogram Images Using Dual-ROI Method

Mammogram segmentation utilizing multi-region of intrigue is a standout amongst the most rising exploration territory in the medical image analysis. The steps engaged with the research are grouped into two kinds: 1) segmentation of mammogram images and 2) extraction of texture features from mammogram images. To overcome these difficulties, a compelling technique is proposed in this paper that comprises of three phases. In the principal arrangement, mammogram images from INbreast database are selected and improved utilizing Laplacian filtering. At that point, the pre-processed mammogram images are utilized for segmentation utilizing modified adaptively regularized kernel-based fuzzy C means (M-ARKFCM). After segmentation, statistical texture FE is connected for recognizing the patterns of cancer and non-cancer regions in mammogram images. Finally, the experimental outcome demonstrated that the proposed approach enhanced the segmentation efficiency by methods of statistical parameters contrasted with the existing operating procedures.
Automated Localization and Segmentation of Mononuclear Cell Aggregates in Kidney Histological Images Using Deep Learning

Background: Allograft rejection is a major concern in kidney transplantation.Inflammatory processes in patients with kidney allografts involve various patterns of immune cell recruitment and distributions.Lymphoid aggregates (LAs) are commonly observed in patients with kidney allografts and their presence and localization may correlate with severity of acute rejection.Alongside with other markers of inflammation, LAs assessment is currently performed by pathologists manually in a qualitative way, which is both time consuming and far from precise.Here we present the first automated method of identifying LAs and measuring their densities in whole slide images of transplant kidney biopsies.Methods: We trained a deep convolutional neural network based on U-Net on 44 core needle kidney biopsy slides, monitoring loss on a validation set (n=7 slides).The model was subsequently tested on a hold-out set (n=10 slides).Findings: We found that the coarse pattern of LAs localization agrees between the annotations and predictions, which is reflected by high correlation between the annotated and predicted fraction of LAs area per slide (Pearson R of 0.9756).Furthermore, the network achieves an auROC of 97.78 ± 0.93% and an IoU score of 69.72 ± 6.24 % per LA-containing slide in the test set.Interpretation: Our study demonstrates that a deep convolutional neural network can accurately identify lymphoid aggregates in digitized histological slides of kidney.This study presents a first automatic DL-based approach for quantifying inflammation marks in allograft kidney, which can greatly improve precision and speed of assessment of allograft kidney biopsies when implemented as a part of computer-aided diagnosis system.
Lucky imaging method based on wavelet analysis

ABSTRACT Lucky imaging is able to reconstruct high-resolution images effectively from astronomical images affected by atmospheric turbulence. Its image reconstruction process can be carried out in the spatial domain or in the Fourier domain. This paper extend the lucky imaging technique to the wavelet domain, and proposes a lucky imaging method based on wavelet analysis. First, the short-exposure astronomical images are decomposed into four subbands by using the bior3.7 wavelet basis. Then, for the low-frequency subbands, the components with higher wavelet coefficients are extracted in a given selection ratio for fusion. For the high-frequency subbands in the horizontal, vertical, and diagonal directions, taking the whole subband as a unit, the subbands with smaller sums of wavelet coefficients are extracted for stacking in a given selection ratio. Finally, an inverse wavelet transform is performed on the four fused subbands to reconstruct the resultant image. Experimental results show that the proposed algorithm can better extract the details hidden in the original short-exposure astronomical images and obtain a higher-resolution image, and that it has a good image restoration effect.
Performance Analysis of Different Type PV Module for 3kW Residential Roof Top PV System using PVSyst Simulation tool

Abstract The purpose of this article is to discuss a performance comparison of solar panel between mono crystalline, poly crystalline and HIT photovoltaic for roof top solar photovoltaic (PV) system. Residential PV power generation is a growing fast nowadays due to technology transfer success to everyone while also serving as a renewable energy source. Correct selection of PV modules possible to get a fast return of investment while wrong selection is vice versa. Three type of PV panel was applying in 3.12kW rooftop PV design in Selangor, Malaysia residential this work for comparison analysis. To make a fair comparison, the PV module analyse with same location, weather, orientation and losses. Simulation was performed fully under PVSyst software and looking at performance ratio system, a yearly balanced, effective output energy, daily output generation, system output generation, and yearly system losses. In this article the performance of three different types of PV panels is demonstrated. The HIT PV module shows the highest yearly performance ratio up to 81 % compare to mono and poly type. The normalized production with standardized variable to evaluate the PV output including collection losses, device losses and energy generated show the HIT type produces lowest losses while highest output. The performance of HIT PV module was good compared to PV module mono and poly type; this is due to the lower losses and high output produce significantly. The PV system’s payback period can be shortened, and the PV system can perform better, if the PV panels are more efficient.
Power Controller for Dual-axis Solar Tracking System using PID

Abstract Solar energy is one of the rich and unlimited energy sources in Malaysia which can generate power instead of the burning of fossil fuels which can cause air pollution. In this study, a dual-axis solar tracking system is proposed to improve the harvested power. To encourage the maximum output power is produced, PID controller is used. Its function is to achieve the reference power where the angle of the horizontal and vertical axes is considered. The solar radiation is studied from 7 a.m. (0700) to 5 p.m. (1700) according to the habit of solar altitude angle and the azimuth angle in Perlis, Malaysia. There is two PID controllers are developed for the orientation and inclination axis of the solar tracker. The output power between the dual-axis and single-axis of solar tracking system are compared through the simulation in MATLAB Simulink. In conclusion, the dual-axis solar tracker has a better performance than the single-axis solar tracker due to the higher degree of freedom to follow the motion of sun. This maximum power can keep going to the reference power of the photovoltaic module during the greatest radiation of sun depending on the solar altitude and azimuth angle.
Slice-to-voxel reconstruction of porous media using style-based generative adversarial networks

Abstract Prediction of microstructure properties is of great significance for porous media, and the use of limited two-dimensional data to synthesize three-dimensional (3D) structural samples is an important support for this task. Although generative adversarial network technology has been successfully migrated to this scenario, its applicability is limited by the development of deep network technology. In this work, we use a more advanced StyleGAN architecture to accomplish this task and show that this method can achieve or even exceed the reconstruction effect of the previous voxel-to-voxel method.
Range‐free disparity estimation with self‐adaptive dual‐matching

Abstract Depth estimation from stereo images is an important task in computer vision. Despite of the great contributions that are made in this field, most matching‐based methods still face the limitations brought by a pre‐set‐fixed disparity range. Stereo matching is reconsidered using a specially designed dual‐matching method with a cross‐attention mechanism, which liberates the algorithm from manually pre‐specified disparity ranges and the performance is guaranteed without re‐training when the camera rig varies. Moreover, to tackle the mismatches on edges and details, an exquisite module is designed based on left‐right consistency, which further refines the estimated disparity map. The efficient multi‐scale aggregation is done with both 2D and 3D convolutional layers and the proposed method is proved to be competitive and effective by experiments conducted under popular benchmarks.