Reviewerly/exHarmony huggingface dataset:
ShuffleFormer: An efficient shuffle meta framework for automatic modulation classification

Automatic modulation classification (AMC) plays an essential and fundamental part in wireless communication systems, which can greatly enhance the efficiency of spectrum utilization. With the rapid development of internet of things, it has been an urgent task to effectively deploy AMC method in edge devices which usually have limited computing power. Hence, there is a pressing need for a high-performance AMC method, especially in terms of high accuracy and low computation complexity in modulation recognition. Although numerous AMC methods have been proposed, it is still a challenge to develop a novel AMC method with both high recognition accuracy and limited parameters. Recently, MetaFormer, as the promising technology, has achieved outstanding performance in processing long sequences. Inspired by MetaFormer, in this paper, an efficient end-to-end deep ShuffleFormer AMC approach is proposed to realize high performance modulation recognition. In the proposed approach, firstly, the PreBlock is proposed to extract deep information from input signals, in which both the channel and temporal attention are utilized to enrich the useful features. Then, bidirectional gated recurrent unit (BiGRU) is employed to further refine the abundant information for feature extraction. Furthermore, ShuffleFormer module is proposed, which innovatively fuse shuffle blocks into MetaFormer to further enhance its both local and global feature extraction ability with less parameters. Experimental results demonstrate that the ShuffleFormer AMC approach has more superior recognition performance than other contrastive methods, i.e., at least 5.97% average accuracy improvement with almost the same number of parameters, and less 41.4% parameters with a competitive average accuracy.
Semi-Airborne UAV-TEM System Data Inversion with S-Plane Method—Case Study over Lake Baikal

The article presents the results of transient electromagnetic (TEM) prospecting surveys using an unmanned aerial system carried out at Lake Baikal, which is a unique geoelectrical setting where low-resistivity lacustrine sediments are located under a relatively isotropic water body. The task was to investigate the possibility of using a drone-based TEM survey to delineate the electrical stratigraphy of the subsurface at depths between 50 and 300 m, separated into layers and blocks. A new version of the SibGIS UAV-TEM unmanned system was used, significantly improved compared to the prototype previously described in the literature. The current switch providing bipolar current pulses connected to a grounded electrical line was the source of the electromagnetic field in the geological environment. The hexacopter carrying a measuring system consisting of 18-bit ADC and sensor—analog of 50 × 50 loop, was the receiving system. We measured survey data of 16 traverses over the Baikal going from the shore to the depths. Significant attention is being paid to a new approach to data inversion. For fast interpretation of the TEM data, we used the Sτ-method, which allows for tracing the change in the apparent longitudinal conductivity with depth. It is shown that thanks to the new sensor and current switch, the data quality has increased significantly; now, the UAV system can register sounding curves up to 1 ms. As a result, new data on the geological structure of the shelf zone of Lake Baikal were obtained. They had a good fundamental agreement with the predecessor data obtained from terrestrial measurements (from ice cover), allowing us to conclude that the UAV-TEM technology can already replace conventional ground-based electromagnetic surveys.
Dual-graph hierarchical interaction network for referring image segmentation

Referring Image Segmentation (RIS) aims to extract the object or stuff from an image according to the given natural language expression. As a representative multi-modal reasoning task, the main challenge of RIS lies in accurately understanding and aligning two types of heterogeneous data (i.e. image and text). Existing methods typically conduct this task via inexplicit cross-modal fusion toward the visual and linguistic features, which are separately extracted from different encoders and hard to capture accurate image–text alignment information due to their distinct latent representation structures. In this paper, we propose a Dual-Graph Hierarchical Interaction Network (DGHIN) to facilitate the explicit and comprehensive alignment between the image and text data. Firstly, two graphs are separately built for the initial visual and linguistic features extracted with different pre-trained encoders. By means of graph reasoning, we obtain a unified representation structure for different modalities to capture the intra-modal entities and their contexts, where each projected node incorporates the long-range dependencies into the latent representation. Then, the Hierarchical Interaction Module (HIM) is applied to the visual and linguistic graphs to extract comprehensive inter-modal correlations from the entity level and graph level, which not only capture the corresponding keywords and visual patches but also draws the whole sentence closer to the image region with the consistent context in the latent space. Extensive experiments on RefCOCO, RefCOCO+, G-Ref, and ReferIt demonstrate that the proposed DGHIN outperforms many state-of-the-art methods. Code is available at https://github.com/ZhaofengSHI/referring-DGHIN.
The Revolution of Immersive Technologies in Healthcare Research

In the digital era, many technologies such as artificial intelligence (AI), computer information systems, Internet of Things (IoT), Industry 4.0, immersive technologies, 3D printing, etc. are being adopted to facilitate operations, provide better management, and enhance workflow and working efficiency. As such, digital health technology and management are the key topics that are attracting wide attention, since it is important both in enhancing efficiency and safety. In fact, most of the healthcare and medical care tasks cannot be replaced entirely by computers. The training of healthcare workers and medical practitioners still remains important. Immersive technologies including virtual Reality (VR), augmented reality (AR), and mixed reality (MR) are widely adopted in numerous industrial and training applications. VR provides a fully immersive experience for the trainees, while AR and MR provide interactive stimulation while maintaining attention in the physical world. Despite the types of immersive technologies used for training, healthcare training, and medical simulation are key components of digital health technology. Nevertheless, in many cases, a trainee’s acceptance and behavioural training in participating in immersive training are still uncertain. Understanding their acceptance and behaviour is important not only in developing effective simulated training but also in enhancing their autonomy and motivation in participation. To this end, we also introduce some of the research models that are commonly used to support health and medical training and simulation.
What makes accidents severe! explainable analytics framework with parameter optimization

Most analytics models are built on complex internal learning processes and calculations, which might be unintuitive, opaque, and incomprehensible to humans. Analytics-based decisions must be transparent and intuitive to foster greater human acceptability and confidence in analytics. Explainable analytics models are transparent models in which the primary factors and weights that lead to a prediction can be explained. Typical AI models are non-transparent or opaque models, in which even the designers cannot explain how their models arrive at a specific decision. These transparent models help decision-makers understand their judgments and build trust in analytics. This study introduces an innovative, comprehensive model that fuses descriptive, predictive, and prescriptive analytics, offering a fresh perspective on car accident severity. Our methodological contribution lies in the application of advanced techniques to address data-related challenges, optimize feature selection, develop predictive models, and fine-tune parameters. Importantly, we also incorporate model-agnostic interpretation techniques, further enhancing the transparency and interpretability of our model, and separate explanations from models (i.e., model-agnostic interpretation techniques). Our findings should provide novel insights for a domain expert to understand accident severity. The explainable analytics approach suggested in this study supplements non-transparent machine learning prediction models, particularly optimized ensemble models. Our model's end product is a comprehensible representation of crash severity factors. To obtain a more trustworthy assessment of accident severity, this model may be supplemented with insurance data, medical data such as blood work and pulse rate, and previous medical history.
Saturation-Based Airlight Color Restoration of Hazy Images

Typically, images captured in adverse weather conditions such as haze or smog exhibit light gray or white color on screen; therefore, existing hazy image restoration studies have performed dehazing under the same assumption. However, hazy images captured under actual weather conditions tend to change color because of various environmental factors such as dust, chemical substances, sea, and lighting. Color-shifted hazy images have hindered accurate color perception of the images, and due to the dark haze color, they have worsened visibility compared to conventional hazy images. Therefore, various color correction-based dehazing algorithms have recently been implemented to restore colorcast images. However, existing color restoration studies are limited in that they struggle to distinguish between haze and objects, particularly when haze veils and images have a similar color or when objects with a high saturation value occupy a significant portion of the scene, resulting in overly grayish images and distorted colors. Therefore, we propose a saturation-based dehazing method that extracts only the hue of the cast airlight and preserves the information of the object. First, the proposed color correction method uses a dominant color extraction method for the clustering of CIELAB(LAB) color images and then assigns area scores to the classified clusters. Sorting of the airlight areas is performed using the area score, and gray world-based white balance is performed by extracting the hue of the area. Finally, the saturation of the restored image is used to separate and process the distant objects and airlight, and dehazing is performed by applying a weighting value to the depth map based on the average luminance. Our color restoration method prevents excessive gray tone and color distortion. In particular, the proposed dehazing method improves upon existing issues where near-field information is lost and noise is introduced in the far field as visibility improves.
AA-LMM: Robust Accuracy-Aware Linear Mixture Model for Remote Sensing Image Registration

Remote sensing image registration has been widely applied in military and civilian fields, such as target recognition, visual navigation and change detection. The dynamic changes in the sensing environment and sensors bring differences to feature point detection in amount and quality, which is still a common and intractable challenge for feature-based registration approaches. With such multiple perturbations, the extracted feature points representing the same physical location in space may have different location accuracy. Most existing matching methods focus on recovering the optimal feature correspondences while they ignore the diversities of different points in position, which easily brings the model into a bad local extrema, especially when existing with the outliers and noises. In this paper, we present a novel accuracy-aware registration model for remote sensing. A soft weighting is designed for each sample to preferentially select more reliable sample points. To better estimate the transformation between input images, an optimal sparse approximation is applied to approach the transformation by multiple iterations, which effectively reduces the computation complexity and also improves the accuracy of approximation. Experimental results show that the proposed method outperforms the state-of-the-art approaches in both matching accuracy and correct matches.
Additive margin networks with adaptive feature recalibration and its applications in Brain Stroke CT Image classification

Abstract In this paper, an additive margin network with adaptive feature recalibration, AMNets, for handling a wide range of visual tasks, is proposed. The new AMNets consists of three improvements on convolutional neural networks: (1) squeeze‐and‐excitation mechanism was embedded into the the residual block of single layer to improve the performance of the classification, (2) softmax loss was replaced by the additive margin softmax loss to minimize intra‐class variance and maximize inter‐class variance, and (3) Adam optimizer was exploited to update iteratively the network parameters. Experiments on the Brain Stroke CT Image Dataset show that our additive margin network is quite effective to improve state‐of‐the‐art algorithms.
Enhancing the SVD compression losslessly

Orthonormality is the foundation of a number of matrix decomposition methods. For example, Singular Value Decomposition (SVD) implements the compression by factoring a matrix with orthonormal parts and is pervasively utilized in various fields. Orthonormality, however, inherently includes constraints that would induce redundant information, preventing SVD from deeper compression and even making it frustrated as the data fidelity is strictly required. In this paper, we theoretically prove that these redundances resulted by orthonormality can be completely eliminated in a lossless manner. An enhanced version of SVD, namely E-SVD, is accordingly established to losslessly and quickly release constraints and recover the orthonormal parts in SVD. According to our theory, advantages of E-SVD over SVD become increasingly evident with the rising requirement of data fidelity. In particular, E-SVD will reduce 25% storage units as SVD reaches its limitation and fails to compress data. Empirical evidences from remote sensing and internet of things justify our theory by demonstrating the consistent compression superiority of E-SVD over SVD, and the additional calculation cost of E-SVD is of the same magnitude with SVD. Digital image compression experiment with typical size 375 × 500 also shows that E-SVD needs the least storage space as compared to alternative compression solutions when the rank is sufficient to maintain a good visual quality. The presented theory sheds insightful lights on the constraint solution in orthonormal matrices and E-SVD, guaranteed by which will profoundly enhance the SVD-based compression in the context of explosive growth in both data acquisition and fidelity levels.
LRTransDet: A Real-Time SAR Ship-Detection Network with Lightweight ViT and Multi-Scale Feature Fusion

In recent years, significant strides have been made in the field of synthetic aperture radar (SAR) ship detection through the application of deep learning techniques. These advanced methods have substantially improved the accuracy of ship detection. Nonetheless, SAR images present distinct challenges, including complex backgrounds, small ship targets, and noise interference, thereby rendering the detectors particularly demanding. In this paper, we introduce LRTransDet, a real-time SAR ship detector. LRTransDet leverages a lightweight vision transformer (ViT) and a multi-scale feature fusion neck to address these challenges effectively. First, our model implements a lightweight backbone that combines convolutional neural networks (CNNs) and transformers, thus enabling it to simultaneously capture both local and global features from input SAR images. Moreover, we boost the model’s efficiency by incorporating the faster weighted feature fusion (Faster-WF2) module and coordinate attention (CA) mechanism within the feature fusion neck. These components optimize computational resources while maintaining the model’s performance. To overcome the challenge of detecting small ship targets in SAR images, we refine the original loss function and use the normalized Wasserstein distance (NWD) metric and the intersection over union (IoU) scheme. This combination improves the detector’s ability to efficiently detect small targets. To prove the performance of our proposed model, we conducted experiments on four challenging datasets (the SSDD, the SAR-Ship Dataset, the HRSID, and the LS-SSDD-v1.0). The results demonstrate that our model surpasses both general object detectors and state-of-the-art SAR ship detectors in terms of detection accuracy (97.8% on the SSDD and 93.9% on the HRSID) and speed (74.6 FPS on the SSDD and 75.8 FPS on the HRSID), all while demanding 3.07 M parameters. Additionally, we conducted a series of ablation experiments to illustrate the impact of the EfficientViT, the Faster-WF2 module, the CA mechanism, and the NWD metric on multi-scale feature fusion and detection performance.
An Improved Approach for Human Movement Gesture Recognition Based on Grey Wolf Optimization and 3D Reconstruction

Hand gesture recognition (HGR) is one of the significant tasks in the field of human–computer interaction. Gesture recognition is a technology for identifying human hand movements and postures that has extensive applications in areas such as human–computer interaction, intelligent control, and virtual reality. Recently, accurate and robust machine learning methods have been introduced for real-world applications. On the other hand, accurate techniques typically utilize various modalities generated from RGB input sequences, including optical flow. However, this approach impacts real-time performance due to its substantial computational overhead. This article aims to introduce a robust and effective approach to HGR. We utilize two publicly available benchmark datasets. Initially, we performed preprocessing steps, including denoising, foreground extraction, and hand detection via associated component techniques. Next, hand segmentation is done to detect landmarks. Further, we utilized three multi-fused features, including geometric features, 3D point modeling and reconstruction, and angular point features. Finally, grey wolf optimization served useful feature of artificial neural networks for hand gesture classification. The experimental results have shown that the proposed HGR achieved significant recognition of 89.92% and 89.76% over IPN hand and Jester datasets, respectively.
Comparative analysis of various Evolutionary Algorithms: Past three decades

INTRODUCTION: The Evolutionary algorithms created back in 1953, have gone through various phases of development over the years. It has been put to use to solve various problems in different domains including complex problems such as the infamous problem of Travelling Salesperson (TSP).OBJECTIVES: The main objective of this research is to find out the advancements in Evolutionary algorithms and to check whether it is still relevant in 2023.METHODS: To give an overview of the related concepts, subdomains, pros, and cons, the historical and recent developments are discussed and critiqued to provide insights into the results and a better conception of the trends in the domain. RESULTS: For a better perception of the development of evolutionary algorithms over the years, decade-wise trend analysis has been done for the past three decades.CONCLUSION: Scope of research in the domain is ever expanding and to name a few EAs for Data mining, Hybrid EAs are still under development.
Automated Sensitivity Analysis for Probabilistic Loops

We present an exact approach to analyze and quantify the sensitivity of higher moments of probabilistic loops with symbolic parameters, polynomial arithmetic and potentially uncountable state spaces. Our approach integrates methods from symbolic computation, probability theory, and static analysis in order to automatically capture sensitivity information about probabilistic loops. Sensitivity information allows us to formally establish how value distributions of probabilistic loop variables influence the functional behavior of loops, which can in particular be helpful when choosing values of loop variables in order to ensure efficient/expected computations. Our work uses algebraic techniques to model higher moments of loop variables via linear recurrence equations and introduce the notion of sensitivity recurrences. We show that sensitivity recurrences precisely model loop sensitivities, even in cases where the moments of loop variables do not satisfy a system of linear recurrences. As such, we enlarge the class of probabilistic loops for which sensitivity analysis was so far feasible. We demonstrate the success of our approach while analyzing the sensitivities of probabilistic loops.
Verb-driven machine reading comprehension with dual-graph neural network

Logical reasoning of context is vital for reading comprehension, which requires to explore the logical relationship through sentence structure. However, previous methods of logical symbols and graph-based models do not make full explore the relationships among entities. In this paper, we present a verb-driven dual-graph network (VDGN) that utilizes core verbs of sentences to model the inter-sentence relationship by the ability of verbs to express linguistic context and the shortest dependency path to model the relationship between entities of intra-sentence. We construct a context graph and a query graph respectively through the above method. In order to predict the answer correctly, our framework fuses information from the context graph and the query graph applying a bi-directional attention mechanism on graph data. We evaluate our approach on two public logical reasoning machine reading comprehension(MRC) datasets: ReClor and LogiQA. Experiments on representative benchmark datasets demonstrate the effectiveness of our approach.
Sentiment Analysis of Chinese Product Reviews Based on Fusion of DUAL-Channel BiLSTM and Self-Attention

Product reviews provide crucial information for both consumers and businesses, offering insights needed before purchasing a product or service. However, existing sentiment analysis methods, especially for Chinese language, struggle to effectively capture contextual information due to the complex semantics, multiple sentiment polarities, and long-term dependencies between words. In this paper, we propose a sentiment classification method based on the BiLSTM algorithm to address these challenges in natural language processing. Self-Attention-CNN BiLSTM (SAC-BiLSTM) leverages dual channels to extract features from both character-level embeddings and word-level embeddings. It combines BiLSTM and Self-Attention mechanisms for feature extraction and weight allocation, aiming to overcome the limitations in mining contextual information. Experiments were conducted on the onlineshopping10cats dataset, which is a standard corpus of e-commerce shopping reviews available in the ChineseNlpCorpus 2018. The experimental results demonstrate the effectiveness of our proposed algorithm, with Recall, Precision, and F1 scores reaching 0.9409, 0.9369, and 0.9404, respectively.