Reviewerly/exHarmony huggingface dataset:
Evolutionary neural architecture search based on evaluation correction and functional units

Neural architecture search (NAS) has been a great success in the automated design of deep neural networks. However, neural architecture search using evolutionary algorithms is challenging due to the diverse structure of neural networks and the difficulty in performance evaluation. To this end, this paper proposes an evolutionary neural architecture search algorithm (called EF-ENAS) based on evaluation corrections and functional units. First, a mating selection operation based on evaluation correction is developed, which can help EF-ENAS discriminate high-performance network architectures and reduce the harmful effects of low fidelity accuracy evaluation methods. Then, a functional unit-based network architecture crossover operation is designed, which divides the neural network into different functional units for crossover and protects valuable network architectures from destruction. Finally, the idea of species protection is introduced into the traditional environmental selection operation and a species protection-based environmental selection operation is designed, which can improve the diversity of network architectures in a population. The EF-ENAS is tested on ten benchmark datasets with varying complexities. In addition, the proposed algorithm is compared with 44 state-of-the-art algorithms, including DARTS, EvoCNN, CNN-GA, AE-CNN, etc. The experimental results show that the proposed algorithm1 can automatically design neural networks and perform better.
Automatic language analysis identifies and predicts schizophrenia in first-episode of psychosis

Automated language analysis of speech has been shown to distinguish healthy control (HC) vs chronic schizophrenia (SZ) groups, yet the predictive power on first-episode psychosis patients (FEP) and the generalization to non-English speakers remain unclear. We performed a cross-sectional and longitudinal (18 months) automated language analysis in 133 Spanish-speaking subjects from three groups: healthy control or HC (n = 49), FEP (n = 40), and chronic SZ (n = 44). Interviews were manually transcribed, and the analysis included 30 language features (4 verbal fluency; 20 verbal productivity; 6 semantic coherence). Our cross-sectional analysis showed that using the top ten ranked and decorrelated language features, an automated HC vs SZ classification achieved 85.9% accuracy. In our longitudinal analysis, 28 FEP patients were diagnosed with SZ at the end of the study. Here, combining demographics, PANSS, and language information, the prediction accuracy reached 77.5% mainly driven by semantic coherence information. Overall, we showed that language features from Spanish-speaking clinical interviews can distinguish HC vs chronic SZ, and predict SZ diagnosis in FEP patients.
The role of artificial intelligence in effective business operations during COVID-19

Purpose The pressure to survive in a highly competitive market by using artificial intelligence (AI) has further demonstrated the need for automation in business operations during a crisis, such as COVID-19. Prior research finds managers' mixed perceptions about the use of technology in business, which underscores the need to better understand their perceptions of adopting AI for automation in business operations during COVID-19. Based on social exchange theory, the authors investigated managers' perceptions of using AI in business for effective operations during the COVID-19 pandemic. Design/methodology/approach This study collected data through a survey conducted in China ( N = 429) and ran structural equation modeling to examine the proposed research model and structural relationships using Smart PLS software. Findings The results show that using AI in supply chain management, inventory management, business models, and budgeting are positively associated with managers' satisfaction. Further, the relationship between managers' satisfaction and effective business operations was found to be positively significant. In addition, the findings suggest that top management support and the working environment have moderating effects on the relationship between managers' satisfaction and effective business operations. Practical implications The results of this study can guide firms to adopt an AI usage policy and execution strategy, according to managers' perceptions and psychological responses to AI. Social implications The study can be used to manage the behavior of managers within organizations. This will ultimately improve society's perception of the employment of AI in business operations. Originality/value The study's outcomes provide valuable insights into business management and information systems with AI application as a business response to any crisis in the future.
The impact of synthetic text generation for sentiment analysis using GAN based models

Data imbalance in datasets is a common issue where the number of instances in one or more categories far exceeds the others, so is the case with the educational domain. Collecting feedback on a course on a large scale and the lack of publicly available datasets in this domain limits models’ performance, especially for deep neural network based models which are data hungry. A model trained on such an imbalanced dataset would naturally favor the majority class. However, the minority class could be critical for decision-making in prediction systems, and therefore it is usually desirable to train a model with equally high class-level accuracy. This paper addresses the data imbalance issue for the sentiment analysis of users’ opinions task on two educational feedback datasets utilizing synthetic text generation deep learning models. Two state-of-the-art text generation GAN models namely CatGAN and SentiGAN, are employed for synthesizing text used to balance the highly imbalanced datasets in this study. Particular emphasis is given to the diversity of synthetically generated samples for populating minority classes. Experimental results on highly imbalanced datasets show significant improvement in models’ performance on CR23K and CR100K after balancing with synthetic data for the sentiment classification task.
Student’s Health Exercise Recognition Tool for E-Learning Education

Due to the recently increased requirements of e-learning systems, multiple educational institutes such as kindergarten have transformed their learning towards virtual education.Automated student health exercise is a difficult task but an important one due to the physical education needs especially in young learners.The proposed system focuses on the necessary implementation of student health exercise recognition (SHER) using a modified Quaternion-based filter for inertial data refining and data fusion as the pre-processing steps.Further, cleansed data has been segmented using an overlapping windowing approach followed by patterns identification in the form of static and kinematic signal patterns.Furthermore, these patterns have been utilized to extract cues for both patterned signals, which are further optimized using Fisher's linear discriminant analysis (FLDA) technique.Finally, the physical exercise activities have been categorized using extended Kalman filter (EKF)-based neural networks.This system can be implemented in multiple educational establishments including intelligent training systems, virtual mentors, smart simulations, and interactive learning management methods.
Retrieving High-Dimensional Quantum Steering from a Noisy Environment with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>N</mml:mi></mml:math> Measurement Settings

One of the most often implied benefits of high-dimensional (HD) quantum systems is to lead to stronger forms of correlations, featuring increased robustness to noise. Here, we experimentally demonstrate the n-setting linear HD quantum steering criterion. We verify the large violation of the steering inequalities without full-state tomography. The lower bound of the violation is 2.24±0.01 in 11 dimensions, exceeding the bound (V<2) of two-setting criteria. Hence, a higher strength of steering has been revealed. Moreover, we demonstrate the method for enhancing the noise robustness without increasing dimension, alternatively, by increasing measurement settings. Using the entanglement in 11 dimensions, we experimentally retrieve steering nonlocality with 63.4±1.4% isotropic noise fraction, surpassing the 50% limitation of two-setting criteria. Our Letter offers the potential for practical one-sided device-independent quantum information processing that tolerates the noisy environment, lossy detection, and transcends the present transmission distance limitation.
Weakly Supervised Violence Detection in Surveillance Video

Automatic violence detection in video surveillance is essential for social and personal security. Monitoring the large number of surveillance cameras used in public and private areas is challenging for human operators. The manual nature of this task significantly increases the possibility of ignoring important events due to human limitations when paying attention to multiple targets at a time. Researchers have proposed several methods to detect violent events automatically to overcome this problem. So far, most previous studies have focused only on classifying short clips without performing spatial localization. In this work, we tackle this problem by proposing a weakly supervised method to detect spatially and temporarily violent actions in surveillance videos using only video-level labels. The proposed method follows a Fast-RCNN style architecture, that has been temporally extended. First, we generate spatiotemporal proposals (action tubes) leveraging pre-trained person detectors, motion appearance (dynamic images), and tracking algorithms. Then, given an input video and the action proposals, we extract spatiotemporal features using deep neural networks. Finally, a classifier based on multiple-instance learning is trained to label each action tube as violent or non-violent. We obtain similar results to the state of the art in three public databases Hockey Fight, RLVSD, and RWF-2000, achieving an accuracy of 97.3%, 92.88%, 88.7%, respectively.
Inverse design of a nano-photonic wavelength demultiplexer with a deep neural network approach

In this paper, we propose a pre-trained-combined neural network (PTCN) as a comprehensive solution to the inverse design of an integrated photonic circuit. By utilizing both the initially pre-trained inverse and forward model with a joint training process, our PTCN model shows remarkable tolerance to the quantity and quality of the training data. As a proof of concept demonstration, the inverse design of a wavelength demultiplexer is used to verify the effectiveness of the PTCN model. The correlation coefficient of the prediction by the presented PTCN model remains greater than 0.974 even when the size of training data is decreased to 17%. The experimental results show a good agreement with predictions, and demonstrate a wavelength demultiplexer with an ultra-compact footprint, a high transmission efficiency with a transmission loss of -2dB, a low reflection of -10dB, and low crosstalk around -7dB simultaneously.
Digital Transformation of Education and Challenges of the 21st Century

The purpose of the article is to analyze the topical problems of the digital transformation of education as a process of changing its goals, meanings, content and technologies and to find ways to solve them. The authors emphasize the primacy of pedagogical innovations and the auxiliary role of information technologies providing the technological basis for the creation and application of new pedagogical practices. The analysis of changes in the educational process in the context of the expansion of the digital educational space and the possibilities of distance technologies leads to the conclusion that the use of the traditional one-way monologue transmission of knowledge on modern digital platforms is ineffective. The paper proposes the methodology based on the self-study underpinned by the mankind achievements, the creation of student’s own system of knowledge, when he gives his meaning to the world and changes him/herself, developing his/her creative abilities. This methodology has been proven to work and tested in practice. The article presents an experience of the Belarusian State University (hereinafter – BSU) in the development and implementation of the concept of creative education, the deployment of a large-scale program for the introduction of distance technologies. The practical steps for the implementation of this program are described, such as the development of a new educational and methodological literature, the creation of an interuniversity portal “Methodology, content, and practice of creative education”, the implementation of the full-time distance training program “Technologies of heuristic education in higher education: teaching methods through discovery” and the project “Online Learning Workshop”. The main quantitative indicators of the effectiveness of the digital transformation of the educational process at BSU are analyzed. The authors describe the information infrastructure of BSU, which ensures the implementation of innovative pedagogical technologies: telecommunications infrastructure and a “cloud” data center, an educational process management system integrated with corporate systems and services, its own video conferencing system, an integrated automated information system developed by the university.
Robotic Telekinesis: Learning a Robotic Hand Imitator by Watching Humans on YouTube

We build a system that enables any human to control a robot hand and arm, simply by demonstrating motions with their own hand.The robot observes the human operator via a single RGB camera and imitates their actions in real-time.Human hands and robot hands differ in shape, size, and joint structure, and performing this translation from a single uncalibrated camera is a highly underconstrained problem.Moreover, the retargeted trajectories must effectively execute tasks on a physical robot, which requires them to be temporally smooth and free of selfcollisions.Our key insight is that while paired human-robot correspondence data is expensive to collect, the internet contains a massive corpus of rich and diverse human hand videos.We leverage this data to train a system that understands human hands and retargets a human video stream into a robot hand-arm trajectory that is smooth, swift, safe, and semantically similar to the guiding demonstration.We demonstrate that it enables previously untrained people to teleoperate a robot on various dexterous manipulation tasks.Our low-cost, glove-free, markerfree remote teleoperation system makes robot teaching more accessible and we hope that it can aid robots in learning to act autonomously in the real world.Video demos can be found at: https://robotic-telekinesis.github.io
Ore-controlling factors as the basis for singling out the prospective areas within the Syrymbet rare-metal deposit, Northern Kazakhstan

Purpose is prediction of the prospective areas within the Syrymbet deposit basing on the systematization and complementing of the ore-controlling factors of ore localization and predictive-prospecting criteria. Methods. The methodology of carrying out the research in terms of the indicated problem involves complex application of empiric, theoretical, and logical techniques of a general-purpose method of scientific knowledge. To model the ore bodies and distribution of ore content in them in the 3D format, the computer modelling (GIS-Micromine) methods were used; 2D modelling of a temperature field of the ore-bearing Syrymbet mass involved methods of mathematical modelling (Maple 10). The main conclusions were drawn by means of analysis and synthesis of the results obtained by the indicated methods. Findings.. The ore-controlling factors of the localization of rare metal ores were systematized and complemented by synthesizing the results of the analysis of 3D models of the deposit under consideration with the visualization of ore component contents within the ore bodies and their 2D sections in terms of the survey profiles where spatial distribution of tin content in the vertical section of this deposit was obtained. The factors were also updated by analyzing the thermodynamic conditions of the formation of ore stockworks where temperature conditions of the enclosing media in the area of ore formation were considered along with the intervals of the ore deposition temperatures. Originality. The results of scientific studies (computer and mathematical modelling) based on the empiric geological data helped solve not only the problems of ore formation theory but also the practical tasks concerning the improvement of methods for predicting the prospective areas within the deposit. Practical implications. The methodology of scientific study dealing with systematization of ore-controlling factors can be also used for other endogenous ore objects.
Generative Adaptive Convolutions for Real-World Noisy Image Denoising

Recently, deep learning techniques are soaring and have shown dramatic improvements in real-world noisy image denoising. However, the statistics of real noise generally vary with different camera sensors and in-camera signal processing pipelines. This will induce problems of most deep denoisers for the overfitting or degrading performance due to the noise discrepancy between the training and test sets. To remedy this issue, we propose a novel flexible and adaptive denoising network, coined as FADNet. Our FADNet is equipped with a plane dynamic filter module, which generates weight filters with flexibility that can adapt to the specific input and thereby impedes the FADNet from overfitting to the training data. Specifically, we exploit the advantage of the spatial and channel attention, and utilize this to devise a decoupling filter generation scheme. The generated filters are conditioned on the input and collaboratively applied to the decoded features for representation capability enhancement. We additionally introduce the Fourier transform and its inverse to guide the predicted weight filters to adapt to the noisy input with respect to the image contents. Experimental results demonstrate the superior denoising performances of the proposed FADNet versus the state-of-the-art. In contrast to the existing deep denoisers, our FADNet is not only flexible and efficient, but also exhibits a compelling generalization capability, enjoying tremendous potential for practical usage.
Reliability Exploration with Self-Ensemble Learning for Domain Adaptive Person Re-identification

Person re-identifcation (Re-ID) based on unsupervised domain adaptation (UDA) aims to transfer the pre-trained model from one labeled source domain to an unlabeled target domain. Existing methods tackle this problem by using clustering methods to generate pseudo labels. However, pseudo labels produced by these techniques may be unstable and noisy, substantially deteriorating models’ performance. In this paper, we propose a Reliability Exploration with Self-ensemble Learning (RESL) framework for domain adaptive person ReID. First, to increase the feature diversity, multiple branches are presented to extract features from different data augmentations. Taking the temporally average model as a mean teacher model, online label refning is conducted by using its dynamic ensemble predictions from different branches as soft labels. Second, to combat the adverse effects of unreliable samples in clusters, sample reliability is estimated by evaluating the consistency of different clusters’ results, followed by selecting reliable instances for training and re-weighting sample contribution within Re-ID losses. A contrastive loss is also utilized with cluster-level memory features which are updated by the mean feature. The experiments demonstrate that our method can signifcantly surpass the state-of-the-art performance on the unsupervised domain adaptive person ReID.
A Survey on Symmetrical Neural Network Architectures and Applications

A number of modern techniques for neural network training and recognition enhancement are based on their structures’ symmetry. Such approaches demonstrate impressive results, both for recognition practice, and for understanding of data transformation processes in various feature spaces. This survey examines symmetrical neural network architectures—Siamese and triplet. Among a wide range of tasks having various mathematical formulation areas, especially effective applications of symmetrical neural network architectures are revealed. We systematize and compare different architectures of symmetrical neural networks, identify genetic relationships between significant studies of different authors’ groups, and discuss opportunities to improve the element base of such neural networks. Our survey builds bridges between a large number of isolated studies with significant practical results in the considered area of knowledge, so that the presented survey acquires additional relevance.
Are updatable learned indexes ready?

Recently, numerous promising results have shown that updatable learned indexes can perform better than traditional indexes with much lower memory space consumption. But it is unknown how these learned indexes compare against each other and against the traditional ones under realistic workloads with changing data distributions and concurrency levels. This makes practitioners still wary about how these new indexes would actually behave in practice. To fill this gap, this paper conducts the first comprehensive evaluation on updatable learned indexes. Our evaluation uses ten real datasets and various workloads to challenge learned indexes in three aspects: performance, memory space efficiency and robustness. Based on the results, we give a series of takeaways that can guide the future development and deployment of learned indexes.
Segmentation and Classification of White Blood Cells Using the UNet

In the bone marrow, plasma cells are made up of B lymphocytes and are a type of WBC. These plasma cells produce antibodies that help to keep bacteria and viruses at bay, thus preventing inflammation. This presents a major challenge for segmenting blood cells, since numerous image processing methods are used before segmentation to enhance image quality. White blood cells can be analyzed by a pathologist with the aid of computer software to identify blood diseases accurately and early. This study proposes a novel model that uses the ResNet and UNet networks to extract features and then segments leukocytes from blood samples. Based on the experimental results, this model appears to perform well, which suggests it is an appropriate tool for the analysis of hematology data. By evaluating the model using three datasets consisting of three different types of WBC, a cross-validation technique was applied to assess it based on the publicly available dataset. The overall segmentation accuracy of the proposed model was around 96%, which proved that the model was better than previous approaches, such as DeepLabV3+ and ResNet-50.