Reviewerly/exHarmony huggingface dataset:
Gaussian Sampling Guided Differential Evolution Based on Elites for Global Optimization

Mutation takes a vital part in assisting differential evolution (DE) to achieve satisfactory per-formance. The most crucial factor for a good mutation scheme is to mutate individuals dispersedly but with fast convergence to optimal regions. With this purpose, this paper designs a novel mutation approach, termed as “DE/current-to-gselite/1”, by utilizing the Gaussian distribution to sample guiding exemplars around elites in the population to evolve individuals. Accordingly, a Gaussian sampling guided differential evolution (GSGDE) is devised to hopefully tackle optimization problems effectively. With the assistance of the Gauss-ian distribution, GSGDE mutates distinct individuals with very different guiding exemplars. Hence, high mutation diversity is expectedly maintained, which leads to that individuals could traverse the problem space in diverse directions. Thanks to the narrow sampling range of the Gaussian distribution, the generated guiding exemplars are likely better and thus individuals in the population are anticipated to move towards optimal regions fast. This is of great profit for fast convergence to high-quality solutions. Further, a dynamic param-eter adjustment strategy is proposed to dynamically regulate the number of elites. Hereafter, GSGDE gradu-ally shifts from concentrating on exploring problem space to focusing on exploiting found optimal areas. Cooperated with an existing adaptive parameter strategy, GSGDE is anticipated to strike a good balance between exploitation and exploration to traverse the problem space and hence likely obtain satisfactory per-formance. Experiments have been extensively carried out on the latest CEC2014 and CEC2017 problem suites with three settings of the dimensionality. Experimental results substantiate that GSGDE has a good scalability and attains highly competitive performance with or even significantly superior performance to 11 latest and representative DE methods. Particularly, its superiority becomes more and more significant as the dimensionality increases.
TS-CNN: A Three-Tier Self-Interpretable CNN for Multi-Region Medical Image Classification

Medical image classification is critical, where reliability and transparency are crucial for the safe and accurate diagnosis of diseases. Deep Convolutional Neural Networks (DCNNs) are widely used in medical image classification due to their high performance. However, they are often considered black-boxes because they offer little insight into decision-making. Therefore, improving the interpretability of DCNNs is crucial for their adoption in medical diagnoses. This paper proposes a novel three-tier self-interpretable DCNN (TS-CNN) architecture for multi-region medical image classification, which improves classification performance while being inherently interpretable. The proposed TS-CNN architecture is well-suited for medical images with multiple regions, such as images with scattered and randomly shaped lesions. The proposed architecture has three branches: a global branch that learns the relevant patterns from the raw input image; an attention branch that selects the important regions and discards the irrelevant parts for the local branch to learn; and a fusion branch that distills knowledge from both the global and local branches for classification. The proposed architecture is flexible in terms of the backbone CNNs used for classification and post-hoc interpretability methods used for attention capture.We demonstrate the flexibility and generalization of the architecture through a series of experiments involving multiple state-of-the-artCNN architectures such as DenseNet-121, Inception, Xception, and ResNet-50 as the global/local branches, each paired with GradCAM and Saliency maps as attention modules. The proposed architecture outperformed the backbone model in classification tasks on two datasets: a custom-made blob dataset and a publicly available skin lesion PAD-UFES-20 dataset, demonstrating its potential for improving accuracy in medical image classification tasks. The code related to this work can be found at: https://github.com/sikha2552/TS-CNN-A-Three-Tier-Self-Interpretable-CNN-for-Medical-Image-Classification-Empowered-with-Post-hoc.git.
PSNet: Towards Efficient Image Restoration With Self-Attention

Image restoration aims to recover a clean image from various degradations, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.,</i> haze, snow, and blur, playing an important role in robot vision, autonomous vehicles, and medical imaging. Recently, the use of Transformer has witnessed a significant improvement in multifarious image restoration tasks. However, despite a few remedies to reduce the quadratic complexity of self-attention, these approaches are still impractical for real-world applications, which need high efficiency and speed. To ameliorate this issue, we propose an efficient framework for image restoration based on self-attention. To this end, we combine the strengths of patch-based and strip-based self-attention units to improve efficiency. More specifically, we apply self-attention of different operation scales to features of different resolutions, i.e., we adopt a relatively smaller region for self-attention on high-resolution features while a larger region for low-restoration features. In addition, instead of using global self-attention in each partitioned region, we leverage a strip-based version for low complexity. To further improve efficiency, we insert our design into a U-shaped CNN network to establish our framework, dubbed PSNet. Extensive experiments demonstrate that our network receives state-of-the-art performance on five representative image restoration tasks with low computational complexity and high speed, including single-image defocus deblurring, image dehazing, image motion deblurring, image desnowing, and image denoising.
The Use of Blockchain Technology in Education: A Comprehensive Review and Future Prospects

Blockchain technology has the potential to revolutionize the education system by making it more secure, transparent, efficient, and accessible. This paper provides a comprehensive review of the use of blockchain technology in education, discussing the potential benefits and challenges of this technology.The paper begins by providing an overview of blockchain technology, discussing its key features and how it works. It then discusses the potential benefits of using blockchain technology in education, such as the ability to store educational records securely, track student progress accurately, and provide micro-credentials. The paper also discusses the challenges of using blockchain technology in education, such as the need for technical expertise and the lack of regulatory clarity.The paper concludes by discussing the future prospects of blockchain technology in education. It argues that blockchain has the potential to revolutionize the education system, but that further research and development is needed to overcome the challenges of this technology.
Virtual Microscopy Goes Global: The Images Are Virtual and the Problems Are Real

For the last two centuries, the scholarly education of histology and pathology has been based on technology, initially on the availability of low-cost, high-quality light microscopes, and more recently on the introduction of computers and e-learning approaches to biomedical education. Consequently, virtual microscopy (VM) is replacing glass slides and the traditional light microscope as the main instruments of instruction in histology and pathology laboratories. However, as with most educational changes, there are advantages and disadvantages associated with a new technology. The use of VM for the teaching of histology and pathology requires an extensive infrastructure and the availability of computing devices to all learners, both posing a considerable financial strain on schools and students. Furthermore, there may be valid reasons for practicing healthcare professionals to maintain competency in using light microscopes. In addition, some educators may be reluctant to embrace new technologies. These are some of the reasons why the introduction of VM as an integral part of histology and pathology instruction has been globally uneven. This paper compares the teaching of histology and pathology using traditional or VM in five different countries and their adjacent regions, representing developed, as well as developing areas of the globe. We identify general and local roadblocks to the introduction of this still-emerging didactic technology and outline solutions for overcoming these barriers.
Robust Visual Object Tracking Based on Feature Channel Weighting and Game Theory

Although the discriminative correlation filter- (DCF)-based tracker improves tracking performance, some object representation issues can still be further optimized. On the one hand, the DCF tracker’s deep convolutional features contain many noisy channels, and assigning the same weights to multiple channels cannot distinguish the importance of different channels. On the other hand, a simple weighted fusion approach cannot fully utilize the benefits of different feature types. We propose a visual object tracking algorithm based on adaptive channel weighting and feature game fusion to solve these problems. In this study, an adaptive channel weighting strategy is designed to assign suitable weights to each channel based on the average energy ratio of the target and background regions in the feature channels and prune the channels with low weights to improve feature robustness and reduce computational complexity. Simultaneously, the game theory concept is introduced in the multifeature fusion. The handcrafted features are combined with shallow and deep convolutional features according to feature complementarity. Then, the two combined features are seen as two sides of the game, continuously gamed during the tracking process to generate a feature model with a higher representation capacity. Extensive experiments are conducted on four mainstream visual tracking benchmark datasets, including OTB2015, VOT2018, LaSOT, and UAV123. The experimental results show that the proposed algorithm performs outstandingly compared to the state-of-the-art trackers.
Innovative Assessment Using Smart Glasses In Further Education: HDI Considerations

Exploring the possibilities for innovative assessment in the use of wearable technology in Further Education (FE), this chapter discusses outcomes from a recent research project that used smart glasses within an FE college in England. The FE college has 6,000 students and is situated in a deprived area in the West Midlands, where digital poverty is an issue. Smart glasses offer potential benefits for students to see things live and increase their engagement but there is also an initial cost, development time for users, and Human Data Interaction and privacy issues to be considered. The main aim of the project was to determine the overall effectiveness of the smart glasses for assessment purposes in each of the subject areas in this postdigital context. Findings indicated that smart glasses could be a valuable tool to facilitate a more flexible and collaborative approach to assessment practices in FE. However, it is necessary to consider how sustained or immersive use of smart glasses may affect the behaviour of students in the long term. Data generation, collection and analysis must be carried out accurately, safely and in accordance with the ethical considerations of the HDI framework.
The Datafication of Education in England: A Children’s Rights-Based Approach to Human Data Interaction Theory

This chapter begins with an exploration of what the concept of a post-digital society includes, the position of education within this, and its relevance for children and their rights. Exploring the notion of a children's rights-based approach, the chapter seeks to unpick some of the complexities in this, before considering the three facets of Human Data Interaction theory; legibility, agency and negotiability. The key relevance here becomes the human-centred approach of HDI placing the human, or for this chapter's purpose the child, at the centre of any future frameworks and decision making. Literature concerning the dataveillance and datafication of children within education in England is then explored before the presentation and discussion of a case study drawing on the author's first-hand experiences. Ultimately, it is questioned what message is promoted by cognitive authorities such as teachers and government departments when they favour data generation that rejects the agency and negotiability aspects of HDI theory. The danger here is that developments in the postdigital society replicate old models of working, instead of informing new ways of being.
Differential Evolution with Group-Based Competitive Control Parameter Setting for Numerical Optimization

Differential evolution (DE) is one of the most popular and widely used optimizers among the community of evolutionary computation. Despite numerous works having been conducted on the improvement of DE performance, there are still some defects, such as premature convergence and stagnation. In order to alleviate them, this paper presents a novel DE variant by designing a new mutation operator (named “DE/current-to-pbest_id/1”) and a new control parameter setting. In the new operator, the fitness value of the individual is adopted to determine the chosen scope of its guider among the population. Meanwhile, a group-based competitive control parameter setting is presented to ensure the various search potentials of the population and the adaptivity of the algorithm. In this setting, the whole population is randomly divided into multiple equivalent groups, the control parameters for each group are independently generated based on its location information, and the worst location information among all groups is competitively updated with the current successful parameters. Moreover, a piecewise population size reduction mechanism is further devised to enhance the exploration and exploitation of the algorithm at the early and later evolution stages, respectively. Differing from the previous DE versions, the proposed method adaptively adjusts the search capability of each individual, simultaneously utilizes multiple pieces of successful parameter information to generate the control parameters, and has different speeds to reduce the population size at different search stages. Then it could achieve the well trade-off of exploration and exploitation. Finally, the performance of the proposed algorithm is measured by comparing with five well-known DE variants and five typical non-DE algorithms on the IEEE CEC 2017 test suite. Numerical results show that the proposed method is a more promising optimizer.
Post-Quantum Authentication in the MQTT Protocol

Message Queue Telemetry Transport (MQTT) is a common communication protocol used in the Internet of Things (IoT). MQTT is a simple, lightweight messaging protocol used to establish communication between multiple devices relying on the publish–subscribe model. However, the protocol does not provide authentication, and most proposals to incorporate it lose their lightweight feature and do not consider the future risk of quantum attacks. IoT devices are generally resource-constrained, and postquantum cryptography is often more computationally resource-intensive compared to current cryptographic standards, adding to the complexity of the transition. In this paper, we use the postquantum digital signature scheme CRYSTALS-Dilithium to provide authentication for MQTT and determine what the CPU, memory and disk usage are when doing so. We further investigate another possibility to provide authentication when using MQTT, namely a key encapsulation mechanism (KEM) trick proposed in 2020 for transport level security (TLS). Such a trick is claimed to save up to 90% in CPU cycles. We use the postquantum KEM scheme CRYSTALS-KYBER and compare the resulting CPU, memory and disk usages with traditional authentication. We found that the use of KEM for authentication resulted in a speed increase of 25 ms, a saving of 71%. There were some extra costs for memory but this is minimal enough to be acceptable for most IoT devices.
Using online machine translation in international scholarly writing and publishing: A longitudinal case of a Chinese engineering scholar

Abstract Scholars who use English as an additional language (EAL) worldwide are under increasing pressure to write and publish in English due to the pervasive publish‐or‐perish culture and the growing dominance of English in academia. While previous studies have focused either on textual differences between EAL scholars' writing and native writers', or explored the difficulties EAL scholars meet and strategies they employ in the process, this paper examines a longitudinal case of a Chinese engineering scholar using online machine translation (MT) to support his academic writing and publishing in English. Drawing from the technology acceptance model as the theoretical background, this study shows that online MT can be a crucial support for scholars with limited English proficiency in the process of preparing a manuscript, submitting it and communicating with editors and reviewers. Frequently occurring problems in machine‐translated academic texts include improper use of technical terms, use of informal language and the inclusion of grammatical mistakes. However, in the last decade, online MT has made great improvements in academic translation. We believe these findings can empower EAL scholars to effectively use online MT in academic English writing and inspire educators to help novice scholars mobilize various high‐tech tools such as online MT to support their writing and international publishing.
An Improved Lightweight Dense Pedestrian Detection Algorithm

Due to the limited memory and computing resources in the real application of target detection, the method is challenging to implement on mobile and embedded devices. In order to achieve the balance between detection accuracy and speed in pedestrian-intensive scenes, an improved lightweight dense pedestrian detection algorithm GS-YOLOv5 (GhostNet GSConv- SIoU) is proposed in this paper. In the Backbone section, GhostNet is used to replace the original CSPDarknet53 network structure, reducing the number of parameters and computation. The CBL module is replaced with GSConv in the Head section, and the CSP module is replaced with VoV-GSCSP. The SloU loss function is used to replace the original IoU loss function to improve the prediction box overlap problem in dense scenes. The model parameters are reduced by 40% and the calculation amount is reduced by 64% without losing the average accuracy, and the detection accuracy is improved by 0.5%. The experimental results show that the GS-YOLOv5 can detect pedestrians more effectively under limited hardware conditions to cope with dense pedestrian scenes, and it is suitable for the online real-time detection of pedestrians.
Research on a Coal Seam Gas Content Prediction Method Based on an Improved Extreme Learning Machine

With the rapid advancement of artificial neural network (ANN) algorithms, many researchers have applied these methods to mine gas prediction and achieved numerous research achievements. It is of great significance to study methods that can accurately predict the gas content for the prevention of gas disasters in mining areas. In order to enhance the accuracy, stability, and generalization capability of the gas content prediction model, the GASA-KELM prediction model was established using the GASA algorithm to improve the KELM initial parameter assignment method, and the prediction model based on BPNN and SVM was established under the same conditions. The experimental results show that the GASA-BPNN model failed to achieve the desired outcome within 800 iterations. On the other hand, the GASA-SVM and GASA-KELM models accomplished the goal in significantly fewer iterations, taking only 673 and 487 iterations, respectively. Moreover, the overall average relative errors of the cross-validated gas content predictions were 15.74%, 13.85%, and 9.87% for the three models, respectively. Furthermore, the total average variance of the test set was 3.99, 2.76, and 2.05 for the GASA-BPNN, GASA-SVM, and GASA-KELM models, respectively. As a result, compared with other ANN models, the GASA-KELM model demonstrates higher accuracy, stronger prediction stability, and generalization ability in the practical application. This novel model provides a basis for accurately predicting gas content and proposing effective regional gas management measures.
Measurement Technologies of Light Field Camera: An Overview

Visual measurement methods are extensively used in various fields, such as aerospace, biomedicine, agricultural production, and social life, owing to their advantages of high speed, high accuracy, and non-contact. However, traditional camera-based measurement systems, relying on the pinhole imaging model, face challenges in achieving three-dimensional measurements using a single camera by one shot. Moreover, traditional visual systems struggle to meet the requirements of high precision, efficiency, and compact size simultaneously. With the development of light field theory, the light field camera has garnered significant attention as a novel measurement method. Due to its special structure, the light field camera enables high-precision three-dimensional measurements with a single camera through only one shot. This paper presents a comprehensive overview of light field camera measurement technologies, including the imaging principles, calibration methods, reconstruction algorithms, and measurement applications. Additionally, we explored future research directions and the potential application prospects of the light field camera.
Raindrop-Removal Image Translation Using Target-Mask Network with Attention Module

Image processing plays a crucial role in improving the performance of models in various fields such as autonomous driving, surveillance cameras, and multimedia. However, capturing ideal images under favorable lighting conditions is not always feasible, particularly in challenging weather conditions such as rain, fog, or snow, which can impede object recognition. This study aims to address this issue by focusing on generating clean images by restoring raindrop-deteriorated images. Our proposed model comprises a raindrop-mask network and a raindrop-removal network. The raindrop-mask network is based on U-Net architecture, which learns the location, shape, and brightness of raindrops. The rain-removal network is a generative adversarial network based on U-Net and comprises two attention modules: the raindrop-mask module and the residual convolution block module. These modules are employed to locate raindrop areas and restore the affected regions. Multiple loss functions are utilized to enhance model performance. The image-quality assessment metrics of proposed method, such as SSIM, PSNR, CEIQ, NIQE, FID, and LPIPS scores, are 0.832, 26.165, 3.351, 2.224, 20.837, and 0.059, respectively. Comparative evaluations against state-of-the-art models demonstrate the superiority of our proposed model based on qualitative and quantitative results.