Reviewerly/exHarmony huggingface dataset:
A New Approach to Detecting and Preventing Populations Stagnation Through Dynamic Changes in Multi-Population-Based Algorithms

Abstract In this paper, a new mechanism for detecting population stagnation based on the analysis of the local improvement of the evaluation function and the infinite impulse response filter is proposed. The purpose of this mechanism is to improve the population stagnation detection capability for various optimization scenarios, and thus to improve multi-population-based algorithms (MPBAs) performance. In addition, various other approaches have been proposed to eliminate stagnation, including approaches aimed at both improving performance and reducing the complexity of the algorithms. The developed methods were tested, among the others, for various migration topologies and various MPBAs, including the MNIA algorithm, which allows the use of many different base algorithms and thus eliminates the need to select the population-based algorithm for a given simulation problem. The simulations were performed for typical benchmark functions and control problems. The obtained results confirm the validity of the developed method.
Intra- and Inter-Modal Graph Attention Network and Contrastive Learning for SAR and Optical Image Registration

The registration of synthetic aperture radar (SAR) and optical images is challenging due to their significant radiometric and geometric differences. Recently, popular registration algorithms based on convolutional neural networks (CNNs) have been limited to extracting local features, resulting in low registration accuracy. In this article, we propose a novel intra- and inter-modal graph attention network and contrastive learning (I2M-GAN&CL) for SAR and optical image registration to solve this problem. First, the graph construction is conducted according to positional encoding (PE), local features, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbor (KNN) edges of keypoints from SAR and optical images. Second, based on the local features extracted by CNNs, an intra- and inter-modal graph attention network (I2MGAN) is designed. The I2MGAN mines context information and extracts global features shared between SAR and optical images, mitigating the influence of geometric and radiometric differences on registration results. The graph cross-attention (GCA) layer in I2MGAN extracts global features shared between the two images via message passing between nodes across graphs. Subsequently, the graph self-attention (GSA) layer in I2MGAN aggregates context information by conveying messages between nodes in one graph. Finally, a novel intra- and inter-modal contrastive learning (I2MCL) strategy is developed. This strategy conducts the contrastive learning of local and global features within and across modalities to explore feature similarity and increase the number of detected matching point pairs. Experimental results on the publicly available OS dataset demonstrate that the number of matching point pairs and registration accuracy of the proposed algorithm outperforms existing state-of-the-art algorithms.
Anthropological Comparative Analysis of CCTV Footage in a 3D Virtual Environment

The image is a particularly valuable data carrier in medical forensic and forensic analyses. One of the analyses, as mentioned above, is to assess whether a graphically captured object is the same object examined in reality. This is a complicated process due to perspective foreshortening, making it difficult to determine the scale and proportion of objects in the frame, as well as the subsequent correct reading of their actual measurements. This paper presented a method for the 3D reconstruction of silhouettes of people recorded in a photo or video, with the aim of identifying these people through subsequent comparative studies. The authors presented an algorithm for dealing with graphic evidence, using the example of the analysis of spatial correlation of the silhouette of the perpetrator of the actual event (recorded via CCTV footage) with the silhouette of the suspect (scanned in 3D in custody). In this paper, the authors posed the thesis that the isometric (devoid of perspective foreshortening) display mode that 3D platforms offer, and the animation of the figure to the desired identical poses, provides the possibility of not only obtaining linear measurements of the person but also of orthophotographic visualization of body proportions, allowing their comparison with another silhouette, which is difficult to achieve in perspective view of the studied image.
Measurement-device-independent quantum secure multiparty summation based on entanglement swapping

Abstract In this paper, we propose a measurement-device-independent (MDI) quantum secure multiparty summation protocol based on entanglement swapping. The protocol is capable of providing a secure modulo-2 summation method for n parties. Our protocol uses Bell states as the information vehicle and establishes encryption through entanglement swapping, and each party encodes the information orderly to complete the summation process through the simple single-qubit operation. In contrast to previous protocols, there is no pre-shared private key sequence and key storage process in our protocol, which helps to reduce the possibility of information leakage in transmission. Our protocol supports multiple summations by n participants, which improves quantum resource utilization. The protocol can be implemented with linear-optical devices. Furthermore, it can resist multiple attack modes including the intercept-resend attack, entangle-and-measure attack, dishonest third-party attack, and parties’ attack. Most significantly, the protocol enables to eliminate all side-channel attacks against detectors based on the MDI principle. Therefore, the protocol has advantages of high security, high efficiency, and good feasibility.
Practical Byzantine fault-tolerant consensus algorithm based on k-Nearest Neighbor

In response to the traditional Byzantine fault-tolerant consensus algorithm, which suffers from performance bottleneck and improper selection of master nodes when the number of nodes increases, this paper proposes a k-Nearest Neighbor based PBFT consensus algorithm, which is called K-PBFT consensus algorithm in this paper. By introducing the k-Nearest Neighbor algorithm, the idea of discarding the master node is adopted to evaluate the behaviour of nodes in consensus, dynamically update the credit value of nodes, classify the node hierarchy and find neighboring nodes for all nodes to communicate so as to complete consensus. Through comparative simulation experimental analysis, the improved algorithm can reduce the time delay and improve the consensus efficiency.
Visual Representations of Sexual Minorities in Chinese Newspapers

Same-sex sexuality remains a prevalent taboo in many cultures and societies globally. In the past decade, scholars from various fields have increasingly examined how sexual minorities (SMs) are represented. The present paper contributes to this growing research by addressing two notable gaps. First, since much of the existing research centers on Western contexts, there is limited study on the portrayal of SMs within the Chinese cultural framework. Second, past studies emphasize verbal portrayals, often sidelining the importance of visual representations. This article will report and analyze the photos gathered from two prominent Chinese newspapers, China Daily and Global Times, pertaining to SMs in mainland China. By drawing on Van Leeuwen’s visual social actor network, the article reveals that Chinese SMs most often are visually represented as victims, disguisers, warriors, and elitists. Finally, we explore the socio-cultural influences behind these representation patterns.
Improved Multi-Objective Particle Swarm Optimization Algorithm Based on Area Division With Application in Multi-UAV Task Assignment

This paper concerns the multi-UAV task assignment problem, which is solved by a multi-objective particle swarm optimization algorithm for adaptive region partitioning. Since the traditional multi-objective optimization algorithms tend to fall into local optimum solutions when dealing with optimization problems, this paper establishes an improved multi-objective particle swarm optimization (MOPSO) algorithm based on the adaptive angle area division. This paper proposes a new multi-UAV task assignment model where the threat constraint is concerned. To solve this model, the algorithm first preprocesses solution spatial information, including normalization of solutions and area division of space. Further, global optimal particle selection strategy is improved based on angle of division. In order to improve the global searching ability, some infeasible solution is used. Finally in the implementation stage of the algorithm, we set multiple nodes for the trajectory of the UAVs to increase the stability of the algorithm. The simulation experiments results demonstrate that the improved algorithm can provide a flyable solution for the UAVs and achieve better convergence and diversity.
Blockchain Technology Application in Security: A Systematic Review

Blockchain technology is a promising technology that attracts popularity among researchers. However, it was first introduced with digital currencies, particularly Bitcoin, but nowadays, it is also known as one of the most frequently used techniques for securing networks. This systematic review research identifies studies that use blockchain for their security challenges. In addition, different fields in blockchain usage, blockchain categorization type, consensus mechanism, smart contract usage, and integration with other software-based algorithms are also investigated. Our results maintain that the Internet of Things (IoT) is the main field in which blockchain provides security.
Movie Sentiment Analysis Using Data Augmentation And LSTM-Recurrent Neural Network

Research in the field of data mining, especially text-based, is growing with the discovery of new methods that have good performance. Sentiment analysis is a field of research that is still widely studied in text classification. Various approaches, including Machine Learning, have been tried in sentiment analysis research, but as the dataset grows, these methods do not produce optimal accuracy performance. The Deep Learning method is a solution that is often used by researchers to produce maximum performance on large datasets, but in some studies, if the number of datasets is limited, the training model cannot be carried out optimally. As a solution, data augmentation is an approach that can be used to increase the quantity of datasets so as to encourage better performance of a model. This study uses a sentiment analysis dataset regarding various films on Twitter, but with a limited amount of data, so a data augmentation approach is needed to increase the quantity. Text-based data augmentation techniques combined with the Deep Learning approach, namely the Recurrent Neural Network (RNN) and Long Short Term Memory Network (LSTM) methods are the approaches we propose in this study. RNN LSTM has the ability to Handling Long-Term Dependencies and Effective in Sequence-to-Sequence Tasks so that the performance of the model on the sentiment analysist will be more optimal The results show that the proposed model has succeeded in classifying the sentiment analysis dataset for film reviews by showing a model accuracy rate of 70.45%.
Quantifying the robustness of a Bayesian Belief Network in the context of Unmanned Aerial System threat prediction

Unmanned Aerial Systems (UASs), or drones, are becoming increasingly available to the general public. Because of this, organizations active in safety and security, such as the Dutch Armed Forces and the Dutch National Police, need to be prepared for possible UAS accidents and attacks. To that end, it is vital that the nature of the possible threat that UAS may pose is detected in a timely manner. A method that can be employed for this problem is a Bayesian Belief Network (BBN). Given the observations made of a UAS and its surroundings, a BBN is capable to determine the most likely type of threat posed by the UAS. Generally, the probabilities that are required as input for this method can be estimated from historical data if enough data are available. However, since only a small amount of data about drone incidents has been collected, expert opinion is used. This introduces uncertainty in the BBN as opinions of experts are subjective. This paper presents a means to construct a BBN for UAS threat prediction when no empirical data are available and determine the robustness of the output. The analysis is restricted specifically to NATO Class I drones (less than 150 kg) in law enforcement operations.
DeepSORT Pedestrian Tracking Algorithm based on Azimuth Estimation

In order to solve the problem that pedestrian tracking accuracy is not high and identity exchange is frequent in complex road environments, this paper proposes an improved DeepSORT target tracking algorithm combining azimuth estimation and feature moving average (FMA) feature association strategy. The algorithm uses Kalman filter algorithm to predict and update the detection results in real-time, so as to realize the classification and tracking of the same category in the video. To solve the problem of feature extraction in the tracking process, this paper adopts the FMA to extract the features of the current frame detection frame and calculate the average value of the previous frame features, so as to improve the correlation degree of features between video frames. At the same time, in order to better locate the position information of the detection frame and the prediction frame in the process of cascade matching, this paper puts forward the azimuth estimation and applies it to the cost matrix to make the detection frame match the optimal prediction. The experimental results show that MOTA, MOTP and other indexes of the proposed algorithm are greatly improved, and the frequency of identity exchange is also reduced to a certain extent.
A Improved Spatial-Reduction Attention Transformer for Medical Image Segmentation

Among various medical segmentation network models, due to the limitation of traditional CNN convolutional operations, it does not learn the dependencies of contextual features well, while Transformer is designed with sequence-to-sequence prediction, which can learn the dependencies of global features and information interactions at a distance well. In this paper, we propose a network model that combines the advantages of both Transformer and U-Net, and the Spatial-Reduction Attention mechanism is used to replace the traditional Multi-Head-Attention mechanism for high-resolution features. On the one hand, the Transformer performs image serialization encoding of the features extracted by the convolutional neural network as the input sequence of the global context. On the other hand, the Decoder performs upsampling reduction and then performs feature map fusion using jump layers for accurate segmentation. The model was evaluated on the 2017 public liver tumor segmentation dataset. The experimental results show that the model in this paper outperforms traditional image segmentation models such as FCN, V-Net and U-Net on the tumor segmentation task, with image segmentation evaluation metrics mean_dice reaching 94.26 and mean_hd95 reaching 19.59, and achieves good performance on other medical image segmentation tasks as well.
A Quality Evaluation Method of UAV Inspection Images Based on Semantic Segmentation

The widespread application of unmanned aerial vehicle (UAV) in power line inspection has resulted in the collection of a large volume of images. While many researchers focus on utilizing deep learning techniques to process these images, there has been limited research on image quality assessment. Existing studies on UAV inspection images face several challenges, including overexposure, degrading and deviant shooting angles which not capture critical components effectively. To evaluate the quality of inspection images better, firstly we definite 7 common major parts mainly shoot by UAV in power transmission lines: tower base, tower body, tower head, cross-arm, wire connection plate, insulator and wire clamp. Meanwhile, the DeepLabv3+ model is employed for semantic segmentation and utilize Densenet121 as the backbone network. Transfer learning and fine-tuning are employed to enhance the performance of the backbone network. Based on the segmented results, every segmented part is evaluated and the overall evaluation result is derived through the weighted composite calculation of evaluation result of all segmented parts. With the proposed method, the evaluation result is closer to the actual needs, focusing on the power line targets. Experimental results demonstrate the effectiveness of the proposed method in identifying UAV inspection images and this research contributes to the field of image quality assessment in UAV-based power line inspection.
Human-centered Design Investigation and Development of Chinese Young women's Daily mobility Safety

Starting from the human-centered design concept, this paper investigates the current situation of daily mobility safety for young women aged 20-30 in China, and finds that their perceptions of possible dangers in daily mobility do not match with the actual situation, and that there is a lack of standardized, systematic, and scientific daily mobility safety strategies and service guarantee systems for women. (2) Methods: Through the collection and analysis of news cases, we initially understand the safety hazards of women's daily mobility; through interviews and research, we further clarify the problems to mobility safety needs; based on the conclusions drawn from the first two types of research, we conducted a questionnaire survey to gain a more in-depth understanding. Finally, on the basis of the above human-centered design research, we clarify the design direction, summarize the user needs and complete the design practice. (3) Results: Under the guidance of the human-centered design concept, we designed an early warning product combining software and hardware including AI recognition technology, developed a set of information visualization strategies and formed a service system based on the current situation of young women's daily mobility safety. (4) Conclusions: According to the safety issues in women's daily mobility, young women need a human-centered mobility safety product from a female perspective. By combining social and government countermeasures, the product can be further improved and provide a more humane safety guarantee for young women.
Designing an AR-Based Materials Library for Higher Education: Offering a Four-Know Learning Structure for Design and Engineering Students

The development of immersive technologies can offer broader possibilities to future-oriented materials education in Design and Engineering. Augmented reality (AR) is an immersive technology that delivers a variety of information superimposed on top of the physical world via see-through glasses. Incorporating sensorial experiences provided by the physical materials library and the informative benefits of AR technology, an AR materials library comprising the Four-Know framework is proposed. Aside from material information retrieval, this library supplements sensory experience by overlapping the virtual model with specific materials in the real world. After the development of the user interface and AR functionalities, students with design and engineering backgrounds participated in a user test. The results indicated that the proposed design provided positive outcomes. In the future, this AR-based materials library is expected to serve as a design guide for the materials learning community and can be extended to other disciplines in higher education.
Immersing in A Film: A Colorization Method Based on Chinese Film Aesthetics

Colorization is the process of transforming grayscale images into vibrant and realistic ones by adding color. To enhance the aesthetics of colorization methods, this paper proposes an improved image colorization model based on the GAN generative prior, which considers the colorization task from the perspective of Chinese film style for the first time. The model follows an "encoder-generator-discriminator" architecture and incorporates a style injection block, which conditions the generation process on film styles from different classes. By using a pretrained generation model, our method can manipulate latent code for both prior generation and colorization purposes. Moreover, we have introduced a weighted layered feature loss, which is particularly effective in coloring portraits. We have collected and constructed a high-resolution image dataset called " ChineseFilm " , specifically designed to capture the unique characters of Chinese films. This dataset serves for training and testing. Experiments demonstrate the superiority of our method, especially in conveying the distinctive style of Chinese films to faded images. We believe that our work contributes to the promotion and preservation of Chinese culture.
Enhancing Efficient Global Understanding Network With CSWin Transformer for Urban Scene Images Segmentation

The global context is crucial to the semantic segmentation task of remote sensing (RS) urban scene imagery since objects have large size variations, high similarity, and mutual occlusion. However, the existing methods for extracting global context information have limitations when directly applied to very high-resolution RS images, mainly in high complexity of computation and memory consumption. To alleviate this limitation, we propose a novel Efficient Global Understanding semantic segmentation Network (EGUNet) to extract global context information efficiently for applicability to RS images. Specifically, EGUNet is a hybrid U-shaped architecture of convolutional neural networks (CNNs) and Transformer in which the encoder uses the CSWin Transformer to capture global semantic information, and the decoder uses the CNNs structure to recover local detail information. Thus, the proposed EGUNet has a powerful global extraction capability and local position information recovery capability. In addition, three effective modules are proposed to improve the segmentation accuracy to make EGUNet more applicable for urban scene image segmentation tasks. First, a feature adaptive fusion module is introduced in the decoder to improve the fusion of the deep semantics and the location detail features. Second, an adaptive atrous-spatial pyramid pooling is designed at the skip connections to enhance the multiscale understanding of high-level semantic context. Finally, we introduce a lightweight enhanced segmentation head to utilize the information from each decoder stage for segmentation. Extensive experimental results on ISPRS Vaihingen and Potsdam datasets demonstrate the exceptional segmentation accuracy of EGUNet, outperforming the state-of-the-art methods.
Radar Faint Target Detection Based on Keystone Transform

With the continuous development of new high-tech research, new challenges are posed to radar detection technology, especially for faint targets such as unmanned aircraft, which are widely used in recent years and are the key problems and hot issues in the field of radar detection. In view of the characteristics of faint targets such as unmanned aircraft, firstly, the echo sequence of faint targets is filtered out of stationary clutter by Moving Target, Indication (MTI), and then the phase reference is accumulated by correcting the distance walk based on the keystone transform method to complete the detection of targets.