Reviewerly/exHarmony huggingface dataset:
UAV Image Small Object Detection Based on RSAD Algorithm

There are many small objects in UAV images, and the object scale varies greatly. When the SSD algorithm detects them, the backbone network’s feature extraction capabilities are poor; it does not fully utilize the semantic information in the deeper feature layer, and it does not give enough consideration to the little items in the loss function, which result in serious missing object detection and low object detection accuracy. To tackle these issues, a new algorithm called RSAD (Resnet Self-Attention Detector) that takes advantage of the self-attention mechanism has been proposed. The proposed RSAD algorithm utilises the residual structure of the ResNet-50 backbone network, which is more capable of feature extraction, in order to extract deeper features from UAV image information. It then utilises the SAFM (Self-Attention Fusion Module) to reshape and concatenate the shallow and deep features of the backbone network, selectively weighted by attention units, ensuring the efficient fusion of features to provide rich semantic features for small object detection. Lastly, it introduces the Focal Loss loss function, which adjusts the corresponding parameters to enhance the contribution of small objects to the detection model. The ablation experiments show that the mAP of RSAD is 10.6% higher than that of the SSD model, with SAFM providing the highest mAP enhancement of 7.4% and ResNet-50 and Focal Loss providing 1.3% and 1.9% enhancements, respectively. The detection speed is only reduced by 3FPS, but it meets the real-time requirement. Comparison experiments show that in terms of mAP, it is far ahead of Faster R-CNN, Cascade R-CNN, RetinaNet, CenterNet, YOLOv5s, and YOLOv8n, which are the mainstream object detection models; In terms of FPS, it slightly inferior to YOLOv5s and YOLOv8n. Thus, RSAD has a good balance between detection speed and accuracy, and it can facilitate the advancement of the UAV to complete object detection tasks in different scenarios.
A comprehensive study of groundbreaking machine learning research: Analyzing highly cited and impactful publications across six decades

Machine learning (ML) has emerged as a prominent field of research in computer science and other related fields, thereby driving advancements in other domains of interest. As the field continues to evolve, it is crucial to understand the landscape of highly cited publications to identify key trends, influential authors, and significant contributions made thus far. In this paper, we present a comprehensive bibliometric analysis of highly cited ML publications. We collected a dataset consisting of the top-cited papers from reputable ML conferences and journals, covering a period of several years from 1959 to 2022. We employed various bibliometric techniques to analyze the data, including citation analysis, co-authorship analysis, keyword analysis, and publication trends. Our findings reveal the most influential papers, highly cited authors, and collaborative networks within the machine learning community. We identify popular research themes and uncover emerging topics that have recently gained significant attention. Furthermore, we examine the geographical distribution of highly cited publications, highlighting the dominance of certain countries in ML research. By shedding light on the landscape of highly cited ML publications, our study provides valuable insights for researchers, policymakers, and practitioners seeking to understand the key developments and trends in this rapidly evolving field.
Review on the development of multi- and hyperspectral remote sensing technology for exploration of copper–gold deposits

Most Copper (Cu) and Gold (Au) deposits are accompanied by hydrothermal alteration, generally related to ore-forming hydrothermal fluids. Multispectral and hyper-spectral Remote Sensing (RS) technology is essential for Cu-Au mineral prospecting. This paper provides an overview of RS data and the research situation and development tendencies of RS exploration technologies related to Cu-Au deposits. Initially, RS geologists applied the Band Ratio (BR) and Principal Component Analysis (PCA) to extract hydroxyl and iron anomalies from Landsat RS data to characterize the hydrothermal alterations associated with Cu-Au deposits. In the era of advanced spaceborne thermal emission and reflection radiometry (ASTER), clay minerals, sulfate minerals, carbonate minerals, silica minerals, and iron minerals can be identified because of three Visible and near-infrared bands (VNIR), six shortwave infrared bands (SWIR), and five Thermal Infrared bands (TIR). Alteration of mineral assemblages can be carried out to map alteration zones, which are essential for calibrating Cu-Au alteration zones, such as the typical alteration zoning characteristics of Porphyry Copper Deposits (PCD). Because of its high spectral resolution, Satellite hyperspectral RS can quantitatively map surface alteration minerals to indicate the exploration of Cu-Au deposits. RS geologists have extensively researched matching libraries and field spectra for Alteration Mineral Mapping (AMM). However, subpixel mineral information caused by low spatial resolution and atmospheric factors has an unavoidable influence on satellite hyperspectral RS AMM, and it is essential to use low-altitude unmanned aerial vehicle (UAV) hyperspectral RS. UAV hyperspectral AMM combined with geochemistry and geophysics is a means of prospecting and exploring Cu-Au deposits.
Scene Uyghur Recognition Based on Visual Prediction Enhancement

Aiming at the problems of Uyghur oblique deformation, character adhesion and character similarity in scene images, this paper proposes a scene Uyghur recognition model with enhanced visual prediction. First, the content-aware correction network TPS++ is used to perform feature-level correction for skewed text. Then, ABINet is used as the basic recognition network, and the U-Net structure in the vision model is improved to aggregate horizontal features, suppress multiple activation phenomena, better describe the spatial characteristics of character positions, and alleviate the problem of character adhesion. Finally, a visual masking semantic awareness (VMSA) module is added to guide the vision model to consider the language information in the visual space by masking the corresponding visual features on the attention map to obtain more accurate visual prediction. This module can not only alleviate the correction load of the language model, but also distinguish similar characters using the language information. The effectiveness of the improved method is verified by ablation experiments, and the model is compared with common scene text recognition methods and scene Uyghur recognition methods on the self-built scene Uyghur dataset.
Inverse identification of plastic anisotropy through multiple non-conventional mechanical experiments

In theory, a single non-conventional mechanical experiment generating inhomogeneous strain fields enables to inversely identify an anisotropic yield function. However, several studies have shown that it is challenging to design such a sufficiently data-rich experiment, particularly when it must be conducted on a standard uniaxial tensile machine. Instead of relying on a single non-conventional uniaxial tensile experiment, combining multiple non-conventional experiments is proposed to inversely identify an advanced anisotropic yield function. The feasibility of the proposed method is verified through the inverse identification of the Yld2000-2d yield function using synthetically generated Digital Image Correlation (DIC) data. This approach accounts for the metrological aspects of DIC while avoiding potential experimental errors and uncertainties related to the selected material model. It has been demonstrated that when a single non-conventional tensile test is combined with a non-conventional biaxial tensile test, the inversely identified anisotropy parameters are in good agreement with those found through the conventional method. This finding is contingent on maintaining an equal contribution of the strain states from each experiment to the cost function. Excluding overlapping data points and assigning proper weights to the trustworthy data based on the strain state and the level of plastic deformation is found to be key. The numerical results are experimentally validated, thereby revealing the crucial role of the adopted anisotropic yield function. Furthermore, the uncertainty associated with the inversely identified parameters based on three repetitions of the same experiment is discussed.
Double Consistency Regularization for Transformer Networks

The large-scale and deep-layer deep neural network based on the Transformer model is very powerful in sequence tasks, but it is prone to overfitting for small-scale training data. Moreover, the prediction result of the model with a small disturbance input is significantly lower than that without disturbance. In this work, we propose a double consistency regularization (DOCR) method for the end-to-end model structure, which separately constrains the output of the encoder and decoder during the training process to alleviate the above problems. Specifically, on the basis of the cross-entropy loss function, we build the mean model by integrating the model parameters of the previous rounds and measure the consistency between the models by calculating the KL divergence between the features of the encoder output and the probability distribution of the decoder output of the mean model and the base model so as to impose regularization constraints on the solution space of the model. We conducted extensive experiments on machine translation tasks, and the results show that the BLEU score increased by 2.60 on average, demonstrating the effectiveness of DOCR in improving model performance and its complementary impacts with other regularization techniques.
SONAR, a nursing activity dataset with inertial sensors

Accurate and comprehensive nursing documentation is essential to ensure quality patient care. To streamline this process, we present SONAR, a publicly available dataset of nursing activities recorded using inertial sensors in a nursing home. The dataset includes 14 sensor streams, such as acceleration and angular velocity, and 23 activities recorded by 14 caregivers using five sensors for 61.7 hours. The caregivers wore the sensors as they performed their daily tasks, allowing for continuous monitoring of their activities. We additionally provide machine learning models that recognize the nursing activities given the sensor data. In particular, we present benchmarks for three deep learning model architectures and evaluate their performance using different metrics and sensor locations. Our dataset, which can be used for research on sensor-based human activity recognition in real-world settings, has the potential to improve nursing care by providing valuable insights that can identify areas for improvement, facilitate accurate documentation, and tailor care to specific patient conditions.
Breast Ultrasound Images Augmentation and Segmentation Using GAN with Identity Block and Modified U-Net 3+

One of the most prevalent diseases affecting women in recent years is breast cancer. Early breast cancer detection can help in the treatment, lower the infection risk, and worsen the results. This paper presents a hybrid approach for augmentation and segmenting breast cancer. The framework contains two main stages: augmentation and segmentation of ultrasound images. The augmentation of the ultrasounds is applied using generative adversarial networks (GAN) with nonlinear identity block, label smoothing, and a new loss function. The segmentation of the ultrasounds applied a modified U-Net 3+. The hybrid approach achieves efficient results in the segmentation and augmentation steps compared with the other available methods for the same task. The modified version of the GAN with the nonlinear identity block overcomes different types of modified GAN in the ultrasound augmentation process, such as speckle GAN, UltraGAN, and deep convolutional GAN. The modified U-Net 3+ also overcomes the different architectures of U-Nets in the segmentation process. The GAN with nonlinear identity blocks achieved an inception score of 14.32 and a Fréchet inception distance of 41.86 in the augmenting process. The GAN with identity achieves a smaller value in Fréchet inception distance (FID) and a bigger value in inception score; these results prove the model's efficiency compared with other versions of GAN in the augmentation process. The modified U-Net 3+ architecture achieved a Dice Score of 95.49% and an Accuracy of 95.67%.
Search for Ways to Develop Regional Book Culture

The objective of the article is to give an overview of the materials of the Public Hearings on the problems of books and reading «The Reader: Global Challenges and Personal Stories» held on March 24, 2023 in Novosibirsk in the frames of the IV International Scientific and Practical Conference “Homo legens in the Past and Present”. They brought together about 30 representatives of the government and administration, book theorists and practitioners, heads of higher educational institutions, representatives of the library community, and public organizations. There were identified three directions for the development of the regional infrastructure for reading: the first one – related to the organization of regional studies of books and reading, search for tools to improve the quality of reading; the second – associated with the development of publishing (book, magazine) and bookselling practices, growth of their influence/ impact on the socio-cultural environment; the third direction – defining libraries as centers for organizing reading and formulating the problems of training library specialists. Resulting the Public Hearings, the resolution was prepared for the heads of legislative and executive authorities of the region with proposals for the development of the regional infrastructure for reading. The general outcome was the realization of the need to provide comprehensive support to all actors of book culture (writers, artists, publishers, critics, literary scholars, journalists and the media, bookstores, libraries and readers) and to strengthen partnerships, state, public and business initiatives in all areas of book business.
BDBRC: A Chinese military entity recognition model combining context contribution and residual dilatation convolutional networks

Military information is gradually overloaded due to the diversity of sources and the exponential growth in quantity, which greatly affects the accuracy of intelligence personnel in extracting and analyzing military information. The modern warfare approach has also evolved from the traditional physical domain to the cognitive domain, and competing for advantages in the cognitive domain has become a key objective of combat. Therefore, constructing domain knowledge graphs and mining the relationships between data play an important role in cognitive domain analysis. In this paper, we propose a convolutional network recognition method based on improved two-layer bi-directional BiLSTM networks named the BERT-α BiLSTMs-RECNN-CRF (BDBRC) model. For the difficulties of military entities generally having long names and low extraction accuracy, as well as the existence of a large number of composite entities that are difficult to recognize, an improved two-layer BiLSTM model is devised first. In view of the fact that the BiLSTMs model always extracts features equally in long-distance text sequences without actually considering the different influences of different sentence contexts, contribution factor a is added to extract the contribution of the above and below to the target entity in different sentences respectively. Then, aiming at the strong problem of the domain of military news texts and the high level of inter-entity ambiguity, we propose a method that utilizes a modified convolutional network (RECNN) for partial feature extraction and jointly with a modified two-layer BiLSTM network for entity recognition. The experiment on the self-constructed dataset shows that the F1 value of the model proposed in this paper reaches 93.18%, and the F1 value, P, and H of our model are all better than the baseline model, which verifies the performance of the model. At the same time, we use public data sets MSRA and CLUB2020, and the experimental results show that the model proposed in this paper also has a good performance in the public data set, verifying the universality of the model. It can provide methodological support for the construction of the military knowledge graph.
Conservative network for offline reinforcement learning

Offline reinforcement learning (RL) aims to learn policies from static datasets. The value overestimation of out-of-distribution (OOD) actions makes it difficult to directly apply general RL methods in the offline setting. To overcome this problem, many works focus on estimating the value function conservatively or pessimistically. However, existing methods require additional OOD sampling or uncertainty estimation to underestimate OOD values, making them complex and vulnerable to hyperparameters. Is it possible to design a specific value function that can automatically be conservative on OOD samples? In this study, we reveal the anti-conservation property of the widely used ReLU network under certain conditions and explain the reason theoretically. Based on the analysis of the ReLU network, we propose a novel neural network architecture that pushes down the value of those samples far away from the datasets; we call this kind of new architecture the Conservative Network (ConsNet). Based on ConsNet, a new offline RL algorithm with simple implementation and high performance is proposed. Since we can obtain additional conservation from the ConsNet itself, by integrating the ConsNet into several existing offline RL methods, we find that it can significantly improve the performance or reduce the original algorithm complexity. With its simplicity and superiority, we hope that ConsNet could be a new fundamental network architecture for offline RL.
A deep local-temporal architecture with attention for lightweight human activity recognition

Human Activity Recognition (HAR) is an essential area of pervasive computing deployed in numerous fields. In order to seamlessly capture human activities, various inertial sensors embedded in wearable devices have been used to generate enormous amounts of signals, which are multidimensional time series of state changes. Therefore, the signals must be divided into windows for feature extraction. Deep learning (DL) methods have recently been used to automatically extract local and temporal features from signals obtained using wearable sensors. Likewise, multiple input deep learning architectures have been proposed to improve the quality of learned features in wearable sensor HAR. However, these architectures are often designed to extract local and temporal features on a single pipeline, which affects feature representation quality. Also, such models are always parameter-heavy due to the number of weights involved in the architecture. Since resources (CPU, battery, and memory) of end devices are limited, it is crucial to propose lightweight deep architectures for easy deployment of activity recognition models on end devices. To contribute, this paper presents a new deep parallel architecture named DLT, based on pipeline concatenation. Each pipeline consists of two sub-pipelines, where the first sub-pipeline learns local features in the current window using 1D-CNN, and the second sub-pipeline learns temporal features using Bi-LSTM and LSTMs before concatenating the feature maps and integrating channel attention. By doing this, the proposed DLT model fully harnessed the capabilities of CNN and RNN equally in capturing more discriminative features from wearable sensor signals while increasing responsiveness to essential features. Also, the size of the model is reduced by adding a lightweight module to the top of the architecture, thereby ensuring the proposed DLT architecture is lightweight. Experiments on two publicly available datasets showed that the proposed architecture achieved an accuracy of 98.52% on PAMAP2 and 97.90% on WISDM datasets, outperforming existing models with few model parameters.
Knowledge sharing in the era of Covid-19: a bibliometric analysis using scopus and web-of-science (WoS)

Purpose This study aims to conduct a bibliometric analysis of knowledge sharing during COVID-19 and highlight prominent contributors, diverse trends and themes followed with provisions of future research avenues. Design/methodology/approach The study through scientific procedures and rationales for systematic literature reviews framework analyses 148 peer-reviewed journal publications and conference proceedings indexed in Scopus and WoS databases from 2020 to 2022. It uses general statistics and diverse bibliometric techniques, including co-occurrence analysis for trend and cluster identification in the literature. Findings The findings reveal an exponential annual growth rate of 150% in the domain, highlighting the global research focus. With regards to domain contribution, the Journal of Knowledge Management and China leads with ten publications in their respective categories. The co-occurrence analysis further highlights four diverse clusters in the domain, which are further discussed in detail. The study highlights significant contributions from developed economies, thus providing scope for future research from developing or transitioning economies in the Middle East, Central Asia or Africa. The study concludes by presenting the elementary role of knowledge sharing in response to external crises. Originality/value The interest in the knowledge sharing domain has grown exponentially during the COVID-19 pandemic. This research is the first bibliometric analysis with comprehensive and rigorous analytic techniques to unearth critical developments and insights for a holistic understanding.
A multimodal smartwatch-based interaction concept for immersive environments

Augmented and Virtual Reality (AR/VR) environments require user interaction concepts beyond the traditional mouse-and-keyboard setup for seated desktop computer usage. Although advanced input modalities such as hand or gaze tracking have been developed, they have yet to be widely adopted in available hardware. Modern smartwatches have been shown to provide a powerful and intuitive means of input, thereby overcoming the limitation of the current AR/VR headsets. They typically offer a set of interesting input modalities, such as a touchscreen, rotary buttons, and an Inertial Measurement Unit (IMU), which can be used for mid-air gesture recognition. Compared to other input devices, they have the benefit that they are hands-free as soon as the user stops interacting since they are attached to the wrist. As many concepts have been proposed, comparative evaluations of their effectiveness and user-friendliness are still rare. In this paper, we evaluate the usability of two commonly found approaches for using a smartwatch as an interaction device, specifically in immersive environments provided by AR/VR HMDs: using the physical inputs of the watch (touchscreen, rotary buttons) or mid-air gestures. We conducted a user study with 20 participants, where they tested both of the interaction methods, and we compared them in their usability and performance. Based on a prototypical AR application, we evaluated the performance and user experience of these two smartwatch-based interaction concepts. We have found that the input using a touchscreen and buttons was generally favored by the participants and led to shorter task completion times.