Reviewerly/exHarmony huggingface dataset:
Physical Exercise Improves Academic Performance: Based on CNKI Meta-Analysis Evidence

Background. It has long been a major source of debate in academic circles whether physical activity has a positive or negative effect on academic performance. Although this subject has been covered in several studies at various levels, only a small number of researchers have examined and supported the data found in the literature. Methods. A meta-analysis is used to thoroughly examine the literature on physical exercise and academic performance included in the China National Knowledge Infrastructure (CNKI) database. After three rounds of searching, screening, and according to the criteria of literature inclusion, 13 valid documents are obtained for analysis with 90 final indicators. Results. (1) Physical exercise has a significant effect on academic performance; (2) physical exercise has a significant effect on single-subject performance, and there are no group differences in academic performance between different subjects; and (3) different exercise intensities have a significant effect on academic performance and significant differences in academic performance. Conclusions. (1) Physical exercise can improve academic performance and should increase students’ physical education participation; (2) physical exercise in different disciplines should hold physical education as equal to other cultural subjects; and (3) sports exercise with different sports intensities has different benefits for academic performance, and sports should be scientifically designed to achieve the maximum effect on academic performance.
RDPNet: a single-path lightweight CNN with re-parameterization for CPU-type edge devices

Abstract Deep convolutional neural networks have produced excellent results when utilized for image classification tasks, and they are being applied in a growing number of contexts. Model inference on edge devices is challenging due to the unending complicated structures needed to improve performance, which adds a significant computing burden.According to recent research, the often utilized residual structure in models does not support model inference. The idea of structural reparameterization is put out to address this shortcoming. The RepVGG produced with this method is a high-performance, quick-inference single-path network. Even after reparameterization, the model still needs GPUs and other specialized computing libraries to accelerate inference, however this still has a limit on how quickly the model can infer at the edge. We construct RDPNet using depthwise separable convolution and structural reparameterization to further reduce model size and accelerate inference. When utilizing an Intel CPU, this is a straightforward network that may be utilized for inference. For re-parameterization, we specifically adopt Depthwise separable convolution as the basic convolution form. Create a multi-branch model for training on the training side, and then simplify it into a single-branch model that the edge devices can easily infer. Research demonstrates that compared to alternative lightweight networks that can attain SOTA performance, RDPNet offers a superior trade-off between accuracy and latency.
Special Issue: Rethinking educational practices and responsibilities in the light of digitalisation

• Young people grow up in a digitised world. • Educators and educational researchers need to be equipped to support young people. • This special issue employs various research designs. • It aims at unravelling the relationships between digital technologies and their significance for education.
Common belief multi-agent reinforcement learning based on variational recurrent models

The tacit cooperation among human teams benefits from the fact that consensus can be reached on a task through common belief. Similar to human social groups, agents in distributed learning systems can also rely on common belief to achieve cooperation under the condition of limited communication. In this paper, we show the role of common belief among agents in completing cooperative tasks, by proposing the Common Belief Multi-Agent (CBMA) reinforcement learning method. CBMA is a novel value-based method that infers the belief between agents with a variational model and models the environment with a variational recurrent neural network. We validate CBMA on two grid-world games as well as the StarCraft II micromanagement benchmark. Experimental results show that the learned common belief by CBMA can improve performance in both discrete and continuous state settings.
A new hybrid optimizer for stochastic optimization acceleration of deep neural networks: Dynamical system perspective

Stochastic optimization acceleration is extremely significant and challenging for deep neural networks (DNNs). In recent years, several novel proportional-integral–differential-based (PID-based) optimizers have been proposed to speed up the optimization by alleviating the oscillation behavior of stochastic gradient descent with momentum (SGD-M), yet lacked theoretical analysis. Along this line of research, this paper adopts dynamical system theory to design a new hybrid optimizer and present theoretical analysis. Firstly, it is found that DNN optimization is equivalent to a discrete time dynamical system. Building upon the equivalence, high order augmented dynamical system viewpoint is utilized to design a PI-like optimizer for ensuring high accuracy, which is more stable than SGD-M. Then, hybrid dynamical system viewpoint is employed to improve the PI-like optimizer as a new hybrid form for suppressing oscillation and accelerating optimization. Lyapunov method, Taylor series, matrix theory and equilibrium are combined to theoretically investigate the convergence and the oscillation of loss function, showing that the proposed hybrid optimizer can alleviate oscillation, boost optimization speed, and maintain high accuracy. In theoretical analyses, explicit conditions of hyper-parameters that guarantee training stability are calculated and presented, practically guiding the adjustment of hyper-parameters and promoting the application of hybrid optimizer. Experiments are presented on three commonly used benchmark datasets, i.e., MNIST, CIFAR10 and CIFAR100, demonstrating that the hybrid optimizer obtains up to 42% acceleration with competitive accuracy relative to state-of-the-art optimizers. In short, this paper not only presents a new hybrid optimizer for accelerating optimization, but also provides a novel, theoretical and systematic perspective to find and analyze new optimizer for DNNs.
Clustering-based fusion for medical information retrieval

Medicine is a fast-moving field, and the number of medical publications has increased rapidly over recent years. How to find relevant information from this vast pool of research effectively and efficiently has therefore become highly challenges. Previous studies have demonstrated that data fusion can improve search performance if properly utilized. However, in most cases effectiveness is the only concern and efficiency is not considered. A fusion-based system is by nature more complicated and expensive computationally than other retrieval models such as BM25, because many component retrieval systems and an extra layer of fusion are required. The number of component retrieval systems involved is an important indicator of complexity of the fusion-based system. We aim to select the optimal k-subset of component retrieval systems for any given number k, to optimize both fusion performance and reduce the cost of data fusion. A clustering-based approach is proposed. First all the candidates are divided into clusters by the Chameleon clustering algorithm, then representatives from every cluster are chosen by Sequential Forward Selection for fusion. Evaluated with two datasets from TREC, the proposed method performs more effectively than the other baseline methods including the state-of-the-art subset selection method significantly. When either of the two typical fusion methods is used, an improvement rate of over 10% is observed for both measures Mean Average Precision and Recall-level Precision, and an improvement rate of over 5% is observed for both measures Precision at 10 document level and Mean Reciprocal Rank.
Nonclassical properties of a non-degenerate parametric amplifier

Nonclassical properties of electromagnetic radiation propagating through a non-degenerate parametric amplifier (NDPA) are investigated. A complete quantum mechanical description is being used to describe the system. Under rotating wave approximation, the exact analytical solutions of the Heisenberg’s equations of motion for signal and idler modes are used to investigate the existence of various nonclassical properties of the radiation fields. These nonclassical properties include squeezing, intermodal entanglement and antibunching. The analytical results confirm the presence of these nonclassical properties for both the input composite number and coherent states. In addition, mixed mode squeezing is observed for input pair coherent states in both the quadratures. The analytical results are compared with their numerical counterpart obtained by QuTiP. The perfect matching of these results justifies the correctness of the analytical results.
Thinking like a bricoleur: New forms of rigor in research on information experience

Research on information behavior, practice, and experience focuses on complex topics embedded in multiple circles of context. LIS researchers in these areas often adopt interpretive, participatory, or critical methods. Yet, they just as often are ambivalent to measurements of rigor, or they unintentionally use positivist criteria to measure quality. Each of these decisions is indicative of a deep-seated assumption about the nature of objectivity and subjectivity. Bricolage offers researchers a way to think about research outside of this assumption: to understand the object of inquiry as it really is, not as the researcher frames it. Thinking like a bricoleur, researchers circumvent exact research protocols and cultivate difference by combining methods, methodologies, theories, and philosophical positions in creative ways. Bricolage already has a toehold in LIS research, but the diverse topics and methods that researchers bring to studies of information behavior, practice, and experience call for greater engagement with it.
Reasoning about general preference relations

Preference relations are at the heart of many fundamental concepts in artificial intelligence, ranging from utility comparisons, to defeat among strategies and relative plausibility among states, just to mention a few. Reasoning about such relations has been the object of extensive research and a wealth of formalisms exist to express and reason about them. One such formalism is conditional logic, which focuses on reasoning about the “best” alternatives according to a given preference relation. A “best” alternative is normally interpreted as an alternative that is either maximal (no other alternative is preferred to it) or optimal (it is at least as preferred as all other alternatives). And the preference relation is normally assumed to satisfy strong requirements (typically transitivity and some kind of well-foundedness assumption). Here, we generalize this existing literature in two ways. Firstly, in addition to maximality and optimality, we consider two other interpretations of “best”, which we call unmatchedness and acceptability. Secondly, we do not inherently require the preference relation to satisfy any constraints. Instead, we allow the relation to satisfy any combination of transitivity, totality and anti-symmetry. This allows us to model a wide range of situations, including cases where the lack of constraints stems from a modeled agent being irrational (for example, an agent might have preferences that are neither transitive nor total nor anti-symmetric) or from the interaction of perfectly rational agents (for example, a defeat relation among strategies in a game might be anti-symmetric but not total or transitive). For each interpretation of “best” (maximal, optimal, unmatched or acceptable) and each combination of constraints (transitivity, totality and/or anti-symmetry), we study the sets of valid inferences. Specifically, in all but one case we introduce a sound and strongly complete axiomatization, and in the one remaining case we show that no such axiomatization exists.
Graph neural networks meet with distributed graph partitioners and reconciliations

Graph neural networks (GNNs) have shown great success in various applications. As real-world graphs are large, training GNNs in distributed systems is desirable. In current training schemes, their edge partitioning strategies have a strong impact on the performance of GNNs for the unbalanced influence of high-degree nodes and the damaged neighbor integrity of low-degree nodes. Meanwhile, a lack of reconciliations of different local models leads to converging up and down across workers. In this work, we design DEPR, a suitable framework for distributed GNN training. We propose a degree-sensitive edge partitioning with influence-balancing and locality-preserving to adapt distributed GNNs training by following an owner-compute rule (each partition performs all the computations involving data that it owns). And then knowledge distillation and contrastive learning are used to reconcile the fusion of local models and boost convergence. We show in extensive empirical experiments on the node classification task of three large-scale graph datasets (Reddit, Amazon, and OGB-Products) that DEPR achieves 2x speedup of convergence and get absolute up 3.97 performance improvement of F1-micro score compared to DistDGL.
A New Block Based Non-Blind Hybrid Color Image Watermarking Approach Using Lifting Scheme and Chaotic Encryption Based on Arnold Cat Map

Online platforms became preferred mode of communication due to advancement in communication technology. Sharing of digital documents over online communication medium grown exponentially and thus demanded a secure, robust and transparent watermarking technique for authenticity of digital media and copyright protection. This research study proposes a robust and secure non-blind SVD-LWT watermarking technique. Color images are employed instead of gray scale images and Y channel of YCbCr color model is utilized to embed secret digital information. The selected color model is in accordance with the human visual system and Y channel is ideal for data hiding. Two level LWT, SVD is used and diagonal matrix of Y channel of host (cover) and watermark image along with scaling factor (α) is used to embed digital data. Block based and chaotic image encryption transform are used for image scrambling. The performances of presented watermarking scheme evaluated with the aid of fidelity parameters namely MSE, PSNR, SSIM and NCC.
Polarization-based optical characterization for color texture analysis and segmentation

Texture characterization is very useful for automatic analysis of object surface images for a plethora of applications in medicine, agriculture, industry or remote sensing. Various texture characterization techniques exist, from the classical Haralick descriptors, Gabor filters, local binary patterns to automatically-extracted features using machine learning models. We propose a new hand-crafted texture characterization technique, based on light polarization property, by deploying a circular polarization filter (rotated from 0° to 360° in steps of 10°) in the image acquisition process. The hypothesis is that different materials and surfaces will exhibit different polarization signatures defined as pixel values variation as a function of polarization angle. Such polarization signature is able to locally characterize texture as a consequence of light reflections captured in every pixel due to the texture intrinsic variations. We show the usefulness of our approach for surface/material classification for the purpose of color image segmentation of natural outdoor scenes.
Short Video Recommendation Algorithm Incorporating Temporal Contextual Information and User Context

With the popularity of 5G and the rapid development of mobile terminals, an endless stream of short video software exists.Browsing short-form mobile video in fragmented time has become the mainstream of user's life.Hence, designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their requirements.Nevertheless, the explosive growth of data leads to the low efficiency of the algorithm, which fails to distill users' points of interest on one hand effectively.On the other hand, integrating user preferences and the content of items urgently intensify the requirements for platform recommendation.In this paper, we propose a collaborative filtering algorithm, integrating time context information and user context, which pours attention into expanding and discovering user interest.In the first place, we introduce the temporal context information into the typical collaborative filtering algorithm, and leverage the popularity penalty function to weight the similarity between recommended short videos and the historical short videos.There remains one more point.We also introduce the user situation into the traditional collaborative filtering recommendation algorithm, considering the context information of users in the generation recommendation stage, and weight the recommended short-form videos of candidates.At last, a diverse approach is used to generate a Top-K recommendation list for users.And through a case study, we illustrate the accuracy and diversity of the proposed method.
Ensemble Classifier Design Based on Perturbation Binary Salp Swarm Algorithm for Classification

Multiple classifier system exhibits strong classification capacity compared with single classifiers, but they require significant computational resources.Selective ensemble system aims to attain equivalent or better classification accuracy with fewer classifiers.However, current methods fail to identify precise solutions for constructing an ensemble classifier.In this study, we propose an ensemble classifier design technique based on the perturbation binary salp swarm algorithm (ECDPB).Considering that extreme learning machines (ELMs) have rapid learning rates and good generalization ability, they can serve as the basic classifier for creating multiple candidates while using fewer computational resources.Meanwhile, we introduce a combined diversity measure by taking the complementarity and accuracy of ELMs into account; it is used to identify the ELMs that have good diversity and low error.In addition, we propose an ECDPB with powerful optimizing ability; it is employed to find the optimal subset of ELMs.The selected ELMs can then be used to form an ensemble classifier.Experiments on 10 benchmark datasets have been conducted, and the results demonstrate that the proposed ECDPB delivers superior classification capacity when compared with alternative methods.
Facial Expression Recognition Based on Multi-Channel Attention Residual燦etwork

For the problems of complex model structure and too many training parameters in facial expression recognition algorithms, we proposed a residual network structure with a multi-headed channel attention (MCA) module.The migration learning algorithm is used to pre-train the convolutional layer parameters and mitigate the overfitting caused by the insufficient number of training samples.The designed MCA module is integrated into the ResNet18 backbone network.The attention mechanism highlights important information and suppresses irrelevant information by assigning different coefficients or weights, and the multi-head structure focuses more on the local features of the pictures, which improves the efficiency of facial expression recognition.Experimental results demonstrate that the model proposed in this paper achieves excellent recognition results in Fer2013, CK+ and Jaffe datasets, with accuracy rates of 72.7%, 98.8% and 93.33%, respectively.
Driver Assistant System using YOLO V3 and VGGNET

The ability to observe and comprehend both things that remain static and being non static around the vehicle in a variety of driving and climatic circumstance is the fundamental criteria for autonomous vehicles and advanced systems that assist in driving. Convolutional neural networks (CNN) have the potential to fulfil the current promise of delivering safe driving assistance in modern automobiles. In this study, the performance of YOLO-based software and VGGNet-based architecture for traffic sign detection that is augmented with a CNN is compared, because the detection should be real-time and quick for the driving to be safe, the networks used in this article have only been trained to recognise and classify items such as traffic signs and lights. Detected traffic signs are then forwarded to CNN, where they are classified into one of 43 categories. We exhibit a great degree of classification confidence by correctly recognizing more than 98.9 percent (YOLO) and 96 percent (VGGNet) of evaluated signs in a multitude of environments.
Tunicate swarm-based grey wolf algorithm for fetal heart chamber segmentation and classification: a heuristic-based optimal feature selection concept

Ultrasound image quality management and assessment are an important stage in clinical diagnosis. This operation is often carried out manually, which has several issues, including reliance on the operator’s experience, lengthy labor, and considerable intra-observer variance. As a result, automatic quality evaluation of Ultrasound images is particularly desirable in medical applications. This research work plans to perform the fetal heart chamber segmentation and classification using the novel intelligent technology named as hybrid optimization algorithm Tunicate Swarm-based Grey Wolf Algorithm (TS-GWA). Initially, the US fetal images data is collected and data undergoes the preprocessing using the total variation technique. From the preprocessed images, the optimal features are extracted using the TF-IDF approach. Then, Segmentation is processed on optimally selected features using Spatially Regularized Discriminative Correlation Filters (SRDCF) method. In the final step, the classification of fetal images is done using the Modified Long Short-Term Memory (MLSTM) Network. The fitness function behind the optimal feature selection as well as the hidden neuron optimization of MLSTM is the maximization of PSNR and minimization of MSE. The PSNR value is improved from 3.1 to 9.8 in the proposed method and accuracy of the proposed classification algorithm is improved from 1.9 to 12.13 compared to other existing techniques. The generalization ability and the adaptability of proposed TS-GWA method are described by conducting the various performance analysis. Extensive performance result shows that proposed intelligent techniques performs better than the existing segmentation methods.