Reviewerly/exHarmony huggingface dataset:
Neural James-Stein Combiner for Unbiased and Biased Renderings

Unbiased rendering algorithms such as path tracing produce accurate images given a huge number of samples, but in practice, the techniques often leave visually distracting artifacts (i.e., noise) in their rendered images due to a limited time budget. A favored approach for mitigating the noise problem is applying learning-based denoisers to unbiased but noisy rendered images and suppressing the noise while preserving image details. However, such denoising techniques typically introduce a systematic error, i.e., the denoising bias, which does not decline as rapidly when increasing the sample size, unlike the other type of error, i.e., variance. It can technically lead to slow numerical convergence of the denoising techniques. We propose a new combination framework built upon the James-Stein (JS) estimator, which merges a pair of unbiased and biased rendering images, e.g., a path-traced image and its denoised result. Unlike existing post-correction techniques for image denoising, our framework helps an input denoiser have lower errors than its unbiased input without relying on accurate estimation of per-pixel denoising errors. We demonstrate that our framework based on the well-established JS theories allows us to improve the error reduction rates of state-of-the-art learning-based denoisers more robustly than recent post-denoisers.
Nonlinear flip-flop quantum walks through potential barriers

The dynamics of nonlinear flip-flop quantum walk with amplitude-dependent phase shifts with pertubing potential barrier is investigated. Through the adjustment between uniform local perturbations and a Kerr-like nonlinearity of the medium we find a rich set of dynamic profiles. We will show the existence of different Hadamard quantum walking regimes, including those with mobile soliton-like structures or self-trapped states. The latter is predominant for perturbations with amplitudes that tend to $\ensuremath{\varphi}\ensuremath{\rightarrow}\ensuremath{\pi}/2$. In this system, the qubit shows an unusual behavior as we increase the amplitudes of the potential barriers and displays a monotonic decrease in the self-trapping ${\ensuremath{\varphi}}_{c}$ with respect to the nonlinear parameter. A chaotic-like regime becomes predominant for intermediate nonlinearity values. Furthermore, we show that, by changing the quantum coins ($\ensuremath{\theta}$) a nontrivial dynamic arises, where coins close to Pauli-$X$ drives the system to a regime with predominant soliton-like structures, while the chaotic behavior are restricted to a narrow region in the $\ensuremath{\chi}\ensuremath{-}\ensuremath{\varphi}$ plane. We believe that is possible to implement and observe the proprieties of this model in an integrated photonic system.
WlCount: Geological lamination detection and counting using an image analysis approach

The manual identification and count of laminae in layered textures is a common practice in the study of geological records, which can be time consuming and carry large uncertainty for dense or disturbed lamina textures. We present here a novel image analysis approach to detect and count laminae in geoscientific imagery, called WlCount. Based on Dynamic Time Warping and Wavelet analysis, WlCount firstly aligns persistent vertical elements to increase the continuity of the lamina structure. Then, using a graphical interface, the user extracts the most significant signal frequencies and allows the automatic count of the laminae. The software, tested on a series of stalagmite cut images showing different types of laminations and a tree-ring image, provides an estimation of the laminae detection and count comparable to the manual one. WlCount presents as a useful open-source tool to help geoscientists, sensibly speeding up the lamination count process.
Sentence-level complexity in Russian: An evaluation of BERT and graph neural networks

Introduction Sentence-level complexity evaluation (SCE) can be formulated as assigning a given sentence a complexity score: either as a category, or a single value. SCE task can be treated as an intermediate step for text complexity prediction, text simplification, lexical complexity prediction, etc. What is more, robust prediction of a single sentence complexity needs much shorter text fragments than the ones typically required to robustly evaluate text complexity. Morphosyntactic and lexical features have proved their vital role as predictors in the state-of-the-art deep neural models for sentence categorization. However, a common issue is the interpretability of deep neural network results. Methods This paper presents testing and comparing several approaches to predict both absolute and relative sentence complexity in Russian. The evaluation involves Russian BERT, Transformer, SVM with features from sentence embeddings, and a graph neural network. Such a comparison is done for the first time for the Russian language. Results and discussion Pre-trained language models outperform graph neural networks, that incorporate the syntactical dependency tree of a sentence. The graph neural networks perform better than Transformer and SVM classifiers that employ sentence embeddings. Predictions of the proposed graph neural network architecture can be easily explained.
Person Tracking by Fusing Posture Data From UAV Video and Wearable Sensors

In this article, a novel framework that fuses the posture data taken by a drone (or unmanned aerial vehicle, UAV) camera and the wearable sensors data recorded by smartwatches is proposed. The framework is designed for continuously tracking persons in a drone view by analyzing location-independent human posture features and correctly tagging smartwatch identities (IDs) and personal profiles to video human objects, thus conquering the former work in requiring ground markers. Person detection, ID assignment, and pose estimation are integrated into our framework to obtain recognized human postures. These recognized postures are then paired with those from the wearable sensors. Through fusing common postures, such as standing, walking, jumping, and falling down, person tracking accuracy by UAV up to 95.36% can be attained in our testing scenarios.
Clinical ultra‐high resolution CT scans enabled by using a generative adversarial network

Ultra-high resolution computed tomography (UHRCT) has shown great potential for the detection of pulmonary diseases. However, UHRCT scanning generally induces increases in scanning time and radiation exposure. Super resolution is a gradually prosperous application in CT imaging despite higher radiation dose. Recent works have proved that the convolution neural network especially the generative adversarial network (GAN) based model could generate high-resolution CT using phantom images or simulated low resolution data without extra dose. Research that used clinical CT particularly lung images are rare due to the difficulty in collecting paired dataset.To generate clinical UHRCT in lung from low resolution computed tomography (LRCT) using a GAN model.43 clinical scans with LRCT and UHRCT were collected in this study. Paired patches were selected using the structural similarity index measure (SSIM) and the peak signal-to-noise ratio (PSNR) threshold. A relativistic GAN with gradient guidance was trained to learn the mapping from LRCT to UHRCT. The performance of the proposed method was evaluated using PSNR and SSIM. A reader study with five-point Likert score (five for the worst and one for the best) is also applied to assess the proposed method in terms of general quality, diagnostic confidence, sharpness and denoise level.Experimental results show that our method got PSNR 32.60 ± 2.92 and SSIM 0.881 ± 0.057 on our clinical CT dataset, outperforming other state-of-the-art methods based on the simulated scenarios. Moreover, reader study shows that our method reached the good clinical performance in terms of general quality (1.14 ± 0.36), diagnostic confidence (1.36 ± 0.49), sharpness (1.07 ± 0.27) and high denoise level (1.29 ± 0.61) compared to other SR methods.This study demonstrated the feasibility of generating UHRCT images from LRCT without longer scanning time or increased radiation dose.
Survey on computational 3D visual optical art design

Abstract Visual arts refer to art experienced primarily through vision. 3D visual optical art is one of them. Artists use their rich imagination and experience to combine light and objects to give viewers an unforgettable visual experience. However, the design process involves much trial and error; therefore, it is often very time-consuming. This has prompted many researchers to focus on proposing various algorithms to simplify the complicated design processes and help artists quickly realize the arts in their minds. To help computer graphics researchers interested in creating 3D visual optical art, we first classify and review relevant studies, then extract a general framework for solving 3D visual optical art design problems, and finally propose possible directions for future research.
A New Range-based Breast Cancer Prediction Model Using the Bayes' Theorem and Ensemble Learning

Breast cancer prediction is essential for preventing and treating cancer. In this research, a novel breast cancer prediction model is introduced. In addition, this research aims to provide a range-based cancer score instead of binary classification results (yes or no). The Breast Cancer Surveillance Consortium dataset (BCSC) dataset is used and modified by applying a proposed probabilistic model to achieve the range-based cancer score. The suggested model analyses a sub dataset of the whole BCSC dataset, including 67632 records and 13 risk factors. Three types of statistics are acquired (general cancer and non-cancer probabilities, previous medical knowledge, and the likelihood of each risk factor given all prediction classes). The model also uses the weighting methodology to achieve the best fusion of the BCSC's risk factors. The computation of the final prediction score is done using the post probability of the weighted combination of risk factors and the three statistics acquired from the probabilistic model. This final prediction is added to the BCSC dataset, and the new version of the BCSC dataset is used to train an ensemble model consisting of 30 learners. The experiments are applied using the sub and the whole datasets (including 317880 medical records). The results indicate that the new range-based model is accurate and robust with an accuracy of 91.33%, a false rejection rate of 1.12%, and an AUC of 0.9795. The new version of the BCSC dataset can be used for further research and analysis.
An Ethics of Refusal: The Insistence of Possibles as a Speculative Pragmatic Challenge to Systemic Racism in Education

Abstract To address an ethics of refusal in higher education is to wager in the name of future possibles not already governed by the extractive politics of colonial progress and oppressive regimes of knowing and doing. In this essay, Petra Mikulan shows American pragmatism to have always been, in a certain sense, post‐Anthropocene in its condition of emergence, bound up with settler colonialism and its extractive geopolitics. However, pragmatism in its speculative trust can also help engage education in thinking of a future that does not belong to a presupposed humanity and its “nature” by dramatizing a certain insistence of educational institutions on maintaining comfort and trust in the “common good.” What is at stake in education facing planetary ecological devastation and intensified racial and social injustices, Mikulan contends, is an ethics of refusal of institutionalized learning, knowing, and doing tout court , particularly if these educational regimes continue to insist on governing what are deemed the only possible (and thus impossible) enactments of education.
SME innovation and employee creativity: The role of entrepreneurial leadership

Entrepreneurial leadership and innovation are needed for small and medium enterprises (SMEs) to survive and grow. Entrepreneurial leaders must unlock and use their employees’ creativity to innovate. Moreover, they should foster creativity, risk-taking, and teamwork to secure business growth and success. Therefore, this study analyzes the link between entrepreneurial leadership, creativity, and innovation among SMEs. The aim is to identify the traits that lead to entrepreneurial leadership which helps in fostering creativity and innovation among SMEs in Saudi Arabia.The study is based on primary data from employees, managers, and owners of SMEs in the capital region of the Kingdom of Saudi Arabia. Two hundred and eighty-one responses were collected. However, only two hundred and forty-eight responses were left for the final analysis. The results of the study indicate that entrepreneurial leadership positively influences organizational innovation and employee creativity among SMEs, where the coefficient was found to be significant. Next, no mediation relationships involving creativity were noted between entrepreneurial leadership and organizational innovation. However, entrepreneurial leadership has been identified as a critical driver of innovation and employee creativity in SMEs. Furthermore, the study suggests that SMEs need to foster a culture of innovation to unleash employee creativity. Finally, the study can have important implications for practitioners and academic scholars. AcknowledgmentThis project was supported by the Deanship of Scientific Research at Prince Sattam Bin Abdulaziz University under the research project (PSAU-2022/01/20421).
Influence of Empowering and Ethical Leadership on Employees’ Job Satisfaction, Performance, and Organization Commitment

Unethical behavior in public and private organizations raises concerns among stakeholders about ethical stand in the workplace. This study aimed to investigate the effect of empowering and ethical leadership on employees' job satisfaction, performance, and organizational commitment. A total of 650 employees were identified from 25 companies in Sulawesi Selatan district, Indonesia. Data were collected through survey study, and Structural Equation Modeling (SEM) was used to test the hypotheses. We found that empowered and ethical leaders positively influenced employees' job satisfaction which also bridged the predictor variables to employees' job performance and organizational commitment. Job satisfaction also played a crucial role in mediating the relationship between predictor variables (e.g., empowering and ethical leadership) and employees' job satisfaction and organizational commitment. Following the call for determinants of empowering and ethical behavior, this study provides insights for managers to foster ethical behavior at the workplace.
Acute Myeloid Leukemia Multi-classification using Enhanced Few-shot Learning Technique

Acute Myeloid Leukemia (AML) is a form of the condition that is fatal and has a high mortality rate. It is characterised by abnormal cells growing rapidly inside the human body. The conventional method for detecting AML seems to be examining the blood sample manually under a microscope, which is a manual and cumbersome task that also requires well-trained medical expertise for efficient identification. On the other hand, considering medical diagnosis, the capacity to classify medical images faster and accurate is essential. The classification of medical images my currently be accomplished using a range of methodologies including Machine Learning (ML), Deep Learning (DL) and Transfer Learning (RL). While these approaches are effective for large datasets, they can take a while and~not ideal for small datasets. In recent years, advances in Deep Convolutional Neural Networks (DCNN) have made it possible and produce more accurate and promising outcome while processing a~medical image. However, the paradigm that DCNN~use for training includes a large number of annotations in order to prevent overfitting and produce promising results. Obtaining large-scale semantic annotations in clinical operations might be problematic in some cases, particularly biological expertise knowledge is needed. It is also regular occurrence in scenarios where only a small number of annotated classes are accessible in some circumstances. At this context, in order overcome the drawback of traditional approach a framework has been developed which comprises of Enhanced Few-Shot Learning Technique integrated Base Classifier (Feature Encoder)-EFLTBC. The proposed model has built using base classifier and meta-learning block, and it optimized the better results. To diagnose AML, the doctor must count the number of white blood cells and red blood cells and see if there are any abnormal health conditions in that using a microscope. However, obtaining an accurate result takes time and effort. To address these issues, the proposed Novel AML detection model employing is used in this study. Base classifier utilizing ResNet-18 pretrained model and meta learning block has computed using the average feature of every samples. Also, the dataset that we used consisting of three classes includes Normal monocytes, Abnormal monocytes, Lymphocyte and Experimental results outperform various existing deep learning technique with the accuracy of 97%, recall of 96.55% F1-Score of 96.65% and precision of 96.60.
PointScatter: Point Set Representation for Tubular Structure Extraction

This paper explores the point set representation for tubular structure extraction tasks. Compared with the traditional mask representation, the point set representation enjoys its flexibility and representation ability, which would not be restricted by the fixed grid as the mask. Inspired by this, we propose PointScatter, an alternative to the segmentation models for the tubular structure extraction task. PointScatter splits the image into scatter regions and parallelly predicts points for each scatter region. We further propose the greedy-based region-wise bipartite matching algorithm to train the network end-to-end and efficiently. We benchmark the PointScatter on four public tubular datasets, and the extensive experiments on tubular structure segmentation and centerline extraction task demonstrate the effectiveness of our approach.Code is available at https://github.com/zhangzhao2022/pointscatter .
An Efficient Hybrid LSTM-CNN and CNN-LSTM with GloVe for Text Multi-class Sentiment Classification in Gender Violence

Gender-based violence is a public health issue that needs high concern to eliminate discrimination and violence against women and girls.Several cases are through the offline organization and the respective online platform.However, many victims share their experiences and stories on social media platforms.Twitter is one of the methods for locating and identifying gender-based violence based on its type.This paper proposed a hybrid Long Short-Term Memory (LSTM) and Convolution Neural Network CNN with GloVe to perform multiclassification of gender violence.Intimate partner violence, harassment, rape, femicide, sex trafficking, forced marriage, forced abortion, and online violence against women are e eight gender violence keyword for data extraction from Twitter text data.Next is data cleaning to remove unnecessary information.Normalization converts data into a structure the machine can recognize as model input.The evaluation considers cross-entropy loss parameters, learning rate, an optimizer, and epochs.LSTM+GloVe vector embedding outperforms all other methods.CNN-LSTM+Glove and LSTM-CNN+GloVe achieved 0.98 for test accuracy, 0.95 for precision, 0.94 for recall, and 0.95 for the f1-score.The findings can help the public and relevant agencies differentiate and categorize different types of gender violence through text.With this effort, the government can use as one of the mechanisms that indirectly can support monitoring of the current situation of gender violence.
Multi-Level Progressive Learning for Unsupervised Vehicle Re-Identification

Vehicle re-identification (ReID) technology has played a more and more important role in Intelligent Transport System (ITS), which aims at searching the same query vehicle identity from a large amount of gallery datasets under different non-overlapping camera views. Current related researches mainly focus on discriminative feature mining of vehicle images and train the model in a fully supervised manner which highly relies on the manual annotations of training data. However, it is labor-consuming and impractical to generate the annotation for each sample image in real-word applications especially for those large-scale transport systems with tons of surveillance data. To this point, we propose in this paper a multi-level progressive learning (MLPL) method for unsupervised vehicle ReID, which gives a good performance by only utilizing the unlabeled target domain images. We firstly introduce a multi-branch architecture to explore the vehicle representations in different level, which consists of one branch for global feature and two branches for local feature learning. A density-based clustering method is employed to generate pseudo labels. Combining with the unique model, we propose a novel re-clustering method to better mine the labels with high reliability. Then a dynamic progressive contrast learning (DPCL) strategy is carefully designed to train the network based on these clustered labels. DPCL could dynamically adjust the training process to maximally strengthen the multi-level feature learning. Moreover, we further propose a self-adaptive loss balance method to automatically compute the weights of different losses during each training iteration. Comprehensive experiments are conducted on several mainstream evaluation datasets, including VeRi776, VehicleID and CityFlowV2-ReID. Compared to other existed unsupervised methods, our approach achieves the new state-of-the-art performance.
An Ethereum-Based Image Copyright Authentication Scheme

Decentralization and on-chain data Immutability, two of the primary characteristics of blockchain, drive the further technological development as well as effectively reduce the cooperation cost in present era of artificial intelligence. In this paper, an image copyright authentication scheme is proposed on the basis of Ethereum to avoid the watermarking embedding or third-party organizations’ certification in traditional image copyright authentication mechanism. Firstly, image summary descriptions are calculated through the Secure Hash Algorithm 3(SHA-3) and stored subsequently in blockchain by means of smart contracts. Then, the ownership of the image is certified by timestamp on the blockchain. Besides, with the consideration of multiple and repeated transactions of image copyright, a new image copyright authentication traceable model is proposed to enable the image authentication and traceability by identifying every user-held timestamp from the very beginning when the image is generated to the last deal. Finally, our proposed scheme has been realized on Ganache, a fast and convenient Ethereum construction platform where the smart contracts have been tested and modified for many times. Simulation results show that the Ethereum based image copyright authentication scheme proposed in this paper is capable of effectively protecting the copyright of the image and tracking the source on the blockchain.