Reviewerly/exHarmony huggingface dataset:
Light‐weight deep learning method for active jamming recognition based on improved MobileViT

Abstract In recent years, there has been significant research focused on identifying jamming signals using neural networks to improve identification accuracy. However, traditional networks require large‐scale datasets for training, which is challenging in small sample scenarios such as actual battlefield jamming signal data. Additionally, the large number of parameters in traditional models presents deployment challenges for devices. To address these issues, this study proposes a lightweight neural network model called MobileViT_CA, an improved version of MobileViT, for jamming recognition in small sample scenarios. MobileViT_CA adopts a coordinate attention mechanism suitable for lightweight networks, which further improves the classification performance of the network. The model converts one‐dimensional jamming signals into two‐dimensional time‐frequency distribution images through time‐frequency analysis and then uses the network for recognition. Compared with traditional neural network models, MobileViT_CA significantly reduces the network parameter volume while maintaining high recognition accuracy, accelerating network inference speed, and reducing hardware requirements. Experimental results show that MobileViT_CA performs well in small sample data. The model's fast learning ability has also been verified by using transfer learning, and the use of Grad‐CAM and t‐SNE to analyse MobileViT_CA demonstrates its advantages in feature recognition and extraction. Overall, this approach has the potential to significantly improve the reliability and effectiveness of jamming recognition systems in battlefield scenarios.
A Blockchain-Centric IoT Architecture for Effective Smart Contract-Based Management of IoT Data Communications

The exponential growth of the Internet of Things (IoT) is being witnessed nowadays in different sectors. This makes IoT data communications more complex and harder to manage. Addressing such a challenge using a centralized model is an ineffective approach and would result in security and privacy difficulties. Technologies such as blockchain provide a potential solution to enable secure and effective management of IoT data communication in a distributed and trustless manner. In this paper, a novel lightweight blockchain-centric IoT architecture is proposed to address effective IoT data communication management. It is based on an event-driven smart contract that enables manageable and trustless IoT data exchange using a simple publish/subscribe model. To maintain system complexity and overhead at a minimum, the design of the proposed system relies on a single smart contract. All the system operations that enable effective IoT data communication among the different parties of the system are defined in the smart contract. There is no direct blockchain–IoT-device interaction, making the system more useable in wide IoT deployments incorporating IoT devices with limited computing and energy resources. A practical Ethereum-based implementation of the system was developed with the ability to simulate different IoT setups. The evaluation results demonstrated the feasibility and effectiveness of the proposed architecture. Considering varying-scale and varying-density experimental setups, reliable and secure data communications were achieved with little latency and resource consumption.
Two adaptive enhancement algorithms for high gray-scale RAW infrared images based on multi-scale fusion and chromatographic remapping

As an important part of unmanned platform, the images taken by vehicle-mounted infrared camera are less affected by night, strong light and bad weather, which are widely used in the field of path planning and decision control in complex scenes. However, infrared images also have some shortcomings, such as high dynamic range, low contrast, high noise and blurred details, which make it difficult to enhance images and detect targets. In order to solve the above problems, two high grayscale infrared image enhancement algorithms were proposed in this paper. The MDDC_HDRII algorithm first proposed the idea of dehazing to solve the overexposure problem of the image, which adopted the method of multi-scale detail enhancement to improve the detail information of the image. PAEA_HDRII algorithm took the sensitivity of human vision as a reference factor, which made the image mostly in blue and red channels and enriched the detailed information of the image.Firstly, a high grayscale infrared image enhancement algorithm based on multi-scale fusion was proposed by combining the idea of debate and exposure fusion. Secondly, to solve the problems of poor visual effect and limited self-adaptation ability in the current pseudo-color ehancement algorithm, a pseudo-color enhancement algorithm for high gray-scale infrared images based on chromatographic remapping was proposed. Thirdly, qualitative and quantitative experiments were carried out by using the published FLIR data set. The results show that the two methods proposed in this paper can improve the overall quality of infrared images more effectively and highlight the details of the image. The objective image quality evaluation indexes are superior to most of the existing mainstream traditional algorithms. Then, two algorithms were used to enhance SAR images, and the experimental results show that this algorithm has good universality. Finally, the images enhanced by the two algorithms in this paper was sent to the YOLOv4 network for target detection, which improved the accuracy of target detection. The MDDC_HDRII algorithm first proposed the idea of dehazing to solve the overexposure problem of the image, which adopted the method of multi-scale detail enhancement to improve the detail information of the image. PAEA_HDRII algorithm took the sensitivity of human vision as a reference factor, which made the image mostly in blue and red channels and enriched the detailed information of the image.The algorithm can be applied in the image enhancement and target detection of vehicle-borne infrared images, SAR images, medical images and industrial film images, and provide reliable technical support for applications in related fields.
Evolutionary Echo State Network: A neuroevolutionary framework for time series prediction

From one side, Evolutionary Algorithms have enabled enormous progress over the last years in the optimization field. They have been applied to a variety of problems, including optimization of Neural Networks' architectures. On the other side, the Echo State Network (ESN) model has become increasingly popular in time series prediction, for instance when modeling chaotic sequences. The network has numerous hidden neurons forming a recurrent topology, so-called reservoir, which is fixed during the learning process. Initial reservoir design has mostly been made by human experts; as a consequence, it is prone to errors and bias, and it is a time consuming task. In this paper, we introduce an automatic general neuroevolutionary framework for ESNs, on which we develop a computational tool for evolving reservoirs, called EVOlutionary Echo State Network (EvoESN). To increase efficiency, we represent the large matrix of reservoir weights in the Fourier space, where we perform the evolutionary search strategy. This frequency space has major advantages compared with the original weight space. After updating the Fourier coefficients, we go back to the weight space and perform a conventional training phase for full setting the reservoir architecture. We analyze the evolutionary search employing genetic algorithms and particle swarm optimization, obtaining promising results with the latter over three well-known chaotic time series. The proposed framework leads fast to very good results compared with modern ESN models. Hence, this contribution positions an important family of recurrent systems in the promising neuroevolutionary domain.
FedPcf : An Integrated Federated Learning Framework with Multi-Level Prospective Correction Factor

In recent years, the issue of data privacy has attracted more and more attention. Federated learning is a practical solution to train the model while guaranteeing data privacy. It has two main characteristics: the first is that the data in the clients is usually non-IID, and the second is that the data of each client cannot be shared. However, due to the non-IID data of each client, the optimal solution of the client is often inconsistent with the global optimal solution. The non-IID data often causes the client to optimize along the local optimal direction and drift out of the global optimal solution during training. Due to the client drift problem, the server tends to converge slowly so that the overall communication efficiency of federated learning is usually limited. To improve the communication efficiency of federated learning, in this paper, we propose a new federated learning framework which integrates multi-level prospective correction factor in the training procedure of server and clients. We propose the global prospective correction factor in server aggregation to reduce model communication rounds and accelerate convergence. In client training, we introduce the local prospective correction factor to alleviate client drift. Both global and local prospective correction factors are integrated into a unified federated learning framework to further improve the communication efficiency. Extensive experiments conducted on several datasets demonstrate that our method can effectively improve the communication efficiency and is robust to different federated learning environments.
CNNs with Multi-Level Attention for Domain Generalization

In the past decade, deep convolutional neural networks have achieved significant success in image classification and ranking, finding therefore numerous applications in multimedia content retrieval. Still, these models suffer from performance degradation when neural networks are tested on out-of-distribution scenarios or on data originating from previously unseen data Domains. In the present work, we focus on this problem of Domain Generalization and propose an alternative neural network architecture for robust, out-of-distribution image classification. We attempt to produce a model that focuses on the causal features of the depicted class for robust image classification in the Domain Generalization setting. To achieve this, we propose attending to multiple-levels of information throughout a Convolutional Neural Network and leveraging the most important attributes of an image, by employing trainable attention mechanisms. To validate our method we evaluate our model on four widely accepted Domain Generalization benchmarks, where our model is able to surpass previously reported baselines in three out of four datasets and achieve the second best score in the fourth one.
A Hybrid Approach for Facial Expression Detection using Principal Component Analysis and Feature Extraction

A number of systems have been proposed to deal with the issue of automatic emotion detection from facial images. The challenges in this field of research are enormous. The face complexion of different persons belongs to different regions of the globe, different facial accessories, and different poses are further made the detection challenging. A facial expression detection system has been proposed based on a principal component analysis (PCA) approach and feature-based technique (FBT). The system considered four categories of emotions: happy, normal, surprising, and sad. Overall recognition accuracy achieved is 98.09%. The proposed approach can be further enhanced for use in the detection of other emotion categories as well.
Privacy Prevention Techniques in Cyber Physical System for Pharmaceutical Care Services

By implementing Cyber Physical Systems, pharmaceutical businesses may significantly improve healthcare, utility, safety, and environmental health (CPS). But these service enhancements and cost-cutting initiatives will make people more vulnerable and at risk. Intruders employ passive attacks to obtain access to CPSs' private information. Some privacy preservation methods have previously been made available in order to increase the security of the data held by PCPS. However, these techniques also need to be altered due to changes in the PCPS's architecture. In this study, privacy prevention techniques in CPS for health care services including prescription medicines have been evaluated. Opportunities for future research are also acknowledged, along with a number of difficulties.
Sentiment Analysis: Current State and Future Research Perspectives

With wide spread and advancement of the web as sites, informal communities, web journals, online entryways, surveys, sentiments, suggestions, appraisals, and criticism are created by essayists. This author created opinion content can be about books, individuals, lodgings, items, research, occasions, and so on. These opinions become extremely gainful for organizations, states, and people. While this content is intended to be helpful, a greater part of this essayist created content require utilizing the text mining techniques and sentimental analysis. Surveys in the web could be in millions for an item or administrations which make it hard to track and Figure out client opinions. Sentimental analysis is an arising area of exploration to remove the abstract data in source materials by applying Natural Language processing(NLP), Computational Linguistics and text examination and arrange the extremity of the assessment expressed. This paper gives a general overview about sentimental investigation or opinion mining connected with product surveys.
Gaussian kernel fuzzy c-means with width parameter computation and regularization

The conventional Gaussian kernel fuzzy c-means clustering algorithms require selecting the width hyper-parameter, which is data-dependent and fixed for the entire execution. Not only that, but these parameters are the same for every dataset variable. Therefore, the variables have the same importance in the clustering task, including irrelevant variables. This paper proposes a Gaussian kernel fuzzy c-means with kernelization of the metric and automated computation of width parameters. These width parameters change at each iteration of the algorithm and vary from each variable and from each cluster. Thus, this algorithm can re-scale the variables differently, thus highlighting those that are relevant to the clustering task. Fuzzy clustering algorithms with regularization have become popular due to their high performance in large-scale data clustering, robustness for initialization, and low computational complexity. Because the width parameters of the variables can also be controlled by entropy, this paper also proposes Gaussian kernel fuzzy c-means algorithms with kernelization of the metric and automated computation of width parameters through entropy regularization. To demonstrate their usefulness, the proposed algorithms are compared with the conventional KFCM-K algorithm and previous algorithms that automatically compute the width parameter of the Gaussian kernel.
Aviation-BERT: A Preliminary Aviation-Specific Natural Language Model

Data-driven methods form the frontier of reactive aviation safety analysis. While analysis of quantitative data from flight operations is common, text narratives of accidents and incidents have not been sufficiently mined. Among the many use cases of aviation text-data mining, automatically extracting safety concepts is probably the most important. Bidirectional Encoder Representations from Transformers (BERT) is a transformer-based large language model that is openly available and has been adapted to numerous domain-specific tasks. The present work provides a comprehensive methodology to develop domain-specific BERT model starting from the base model. A preliminary aviation domain-specific BERT model is developed in this work. This Aviation-BERT model is pre-trained from the BERT-Base model using accident and incident text narratives from the National Transportation Safety Board (NTSB) and Aviation Safety Reporting System (ASRS) using mixed-domain pre-training. Aviation-BERT is shown to outperform BERT when it comes to text-mining tasks on aviation text datasets. It is also expected to be of tremendous value in numerous downstream tasks in the analysis of aviation text corpora.
A Robust and Automated Vision-Based Human Fall Detection System Using 3D Multi-Stream CNNs with an Image Fusion Technique

Unintentional human falls, particularly in older adults, can result in severe injuries and death, and negatively impact quality of life. The World Health Organization (WHO) states that falls are a significant public health issue and the primary cause of injury-related fatalities worldwide. Injuries resulting from falls, such as broken bones, trauma, and internal injuries, can have severe consequences and can lead to a loss of mobility and independence. To address this problem, there have been suggestions to develop strategies to reduce the frequency of falls, in order to decrease healthcare costs and productivity loss. Vision-based fall detection approaches have proven their effectiveness in addressing falls on time, which can help to reduce fall injuries. This paper introduces an automated vision-based system for detecting falls and issuing instant alerts upon detection. The proposed system processes live footage from a monitoring surveillance camera by utilizing a fine-tuned human segmentation model and image fusion technique as pre-processing and classifying a set of live footage with a 3D multi-stream CNN model (4S-3DCNN). The system alerts when the sequence of the Falling of the monitored human, followed by having Fallen, takes place. The effectiveness of the system was assessed using the publicly available Le2i dataset. System validation revealed an impressive result, achieving an accuracy of 99.44%, sensitivity of 99.12%, specificity of 99.12%, and precision of 99.59%. Based on the reported results, the presented system can be a valuable tool for detecting human falls, preventing fall injury complications, and reducing healthcare and productivity loss costs.
Faster RCNN Target Detection Algorithm Integrating CBAM and FPN

In the process of image shooting, due to the influence of angle, distance, complex scenes, illumination intensity, and other factors, small targets and occluded targets will inevitably appear in the image. These targets have few effective pixels, few features, and no obvious features, which makes it difficult to extract their effective features and easily leads to false detection, missed detection, and repeated detection, thus affecting the performance of target detection models. To solve this problem, an improved faster region convolutional neural network (RCNN) algorithm integrating the convolutional block attention module (CBAM) and feature pyramid network (FPN) (CF-RCNN) is proposed to improve the detection and recognition accuracy of small-sized, occluded, or truncated objects in complex scenes. Firstly, it incorporates the CBAM attention mechanism in the feature extraction network in combination with the information filtered by spatial and channel attention modules, focusing on local efficient information of the feature image, which improves the detection ability in the face of obscured or truncated objects. Secondly, it introduces the FPN feature pyramid structure, and links high-level and bottom-level feature data to obtain high-resolution and strong semantic data to enhance the detection effect for small-sized objects. Finally, it optimizes non-maximum suppression (NMS) to compensate for the shortcomings of conventional NMS that mistakenly eliminates overlapping detection frames. The experimental results show that the mean average precision (MAP) of target detection of the improved algorithm on PASCAL VOC2012 public datasets is improved to 76.2%, which is 13.9 percentage points higher than those of the commonly used Faster RCNN and other algorithms. It is better than the commonly used small-sample target detection algorithm.
How real will the metaverse be? Exploring the spatial impact of virtual worlds

In this paper, we perform a preliminary analysis of the technologies, firms and industries that may be affected by the possible futures of the metaverse, attempting to derive some hypotheses on the spatial effects of this process.We distinguish between two possible evolutive scenariosthe 'metaverse shaped by reality view' and the 'metaverse shaping reality view'and factors affecting them, deriving implications for public policy planning.The first scenario presents relatively traditional core policy challenges: ensuring homogeneous availability of network infrastructures as well as the skills indispensable to catch the new technological opportunities at the local level, accompanying the reallocation of factors of production associated to disruption and addressing inequalities.In the second, the main challenge is more radical: to ensure that desirable features are incorporated in the emerging virtual worlds from the start.
VOLVQAD: An MPEG V-PCC Volumetric Video Quality Assessment Dataset

We present VOLVQAD, a volumetric video quality assessment dataset consisting 7,680 ratings on 376 video sequences from 120 participants. The volumetric video sequences are first encoded with MPEG V-PCC using 4 different avatar models and 16 quality variations, and then rendered into test videos for quality assessment using 2 different background colors and 16 different quality switching patterns. The dataset is useful for researchers who wish to understand the impact of volumetric video compression on subjective quality. Analysis of the collected data are also presented in this paper.
Vegvisir: A testing framework for HTTP/3 media streaming

Assessing media streaming performance traditionally requires the presence of reproducible network conditions and a heterogeneous dataset of media materials. Setting up such experiments represents a complex challenge in itself. This challenge becomes even more complex when we consider the new QUIC transport protocol, which has many tunable features, yet is difficult to analyze due to its inherent encrypted nature. In this paper, we introduce Vegvisir, which aims to solve these aforementioned challenges by providing an open-source automated testing framework for orchestrating media streaming experiments over HTTP/3. We describe how users can steer the behavior of Vegvisir through its configuration system. We provide a high-level overview of its inner workings and its broad applicability by describing two use cases: one covering sizeable experiments spanning multiple days and another covering HAS evaluation scenarios.
Casting a BAIT for offline and online source-free domain adaptation

We address the source-free domain adaptation (SFDA) problem, where only the source model is available during adaptation to the target domain. We consider two settings: the offline setting where all target data can be visited multiple times (epochs) to arrive at a prediction for each target sample, and the online setting where the target data needs to be directly classified upon arrival. Inspired by diverse classifier based domain adaptation methods, in this paper we introduce a second classifier, but with another classifier head fixed. When adapting to the target domain, the additional classifier initialized from source classifier is expected to find misclassified features. Next, when updating the feature extractor, those features will be pushed towards the right side of the source decision boundary, thus achieving source-free domain adaptation. Experimental results show that the proposed method achieves competitive results for offline SFDA on several benchmark datasets compared with existing DA and SFDA methods, and our method surpasses by a large margin other SFDA methods under online source-free domain adaptation setting.