Reviewerly/exHarmony huggingface dataset:
Application of Ant Colony Optimization Algorithm Based on Triangle Inequality Principle and Partition Method Strategy in Robot Path Planning

Path planning is an important area of mobile robot research, and the ant colony optimization algorithm is essential for analyzing path planning. However, the current ant colony optimization algorithm applied to the path planning of mobile robots still has some limitations, including early blind search, slow convergence speed, and more turns. To overcome these problems, an improved ant colony optimization algorithm is proposed in this paper. In the improved algorithm, we introduce the idea of triangle inequality and a pseudo-random state transfer strategy to enhance the guidance of target points and improve the search efficiency and quality of the algorithm. In addition, we propose a pheromone update strategy based on the partition method with upper and lower limits on the pheromone concentration. This can not only improve the global search capability and convergence speed of the algorithm but also avoid the premature and stagnation phenomenon of the algorithm during the search. To prevent the ants from getting into a deadlock state, we introduce a backtracking mechanism to enable the ants to explore the solution space better. Finally, to verify the effectiveness of the proposed algorithm, the algorithm is compared with 11 existing methods for solving the robot path planning problem, including several ACO variants and two commonly used algorithms (A* algorithm and Dijkstra algorithm), and the experimental results show that the improved ACO algorithm can plan paths with faster convergence, shorter path lengths, and higher smoothness. Specifically, the algorithm produces the shortest path length with a standard deviation of zero while ensuring the most rapid convergence and the highest smoothness in the case of the shortest path in four different grid environments. These experimental results demonstrate the effectiveness of the proposed algorithm in path planning.
Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review

With the advent of the fourth industrial revolution, data-driven decision making has also become an integral part of decision making. At the same time, deep learning is one of the core technologies of the fourth industrial revolution that have become vital in decision making. However, in the era of epidemics and big data, the volume of data has increased dramatically while the sources have become progressively more complex, making data distribution highly susceptible to change. These situations can easily lead to concept drift, which directly affects the effectiveness of prediction models. How to cope with such complex situations and make timely and accurate decisions from multiple perspectives is a challenging research issue. To address this challenge, we summarize concept drift adaptation methods under the deep learning framework, which is beneficial to help decision makers make better decisions and analyze the causes of concept drift. First, we provide an overall introduction to concept drift, including the definition, causes, types, and process of concept drift adaptation methods under the deep learning framework. Second, we summarize concept drift adaptation methods in terms of discriminative learning, generative learning, hybrid learning, and others. For each aspect, we elaborate on the update modes, detection modes, and adaptation drift types of concept drift adaptation methods. In addition, we briefly describe the characteristics and application fields of deep learning algorithms using concept drift adaptation methods. Finally, we summarize common datasets and evaluation metrics and present future directions.
Federated Reinforcement Learning in IoT: Applications, Opportunities and Open Challenges

The internet of things (IoT) represents a disruptive concept that has been changing society in several ways. There have been several successful applications of IoT in the industry. For example, in transportation systems, the novel internet of vehicles (IoV) concept has enabled new research directions and automation solutions. Moreover, reinforcement learning (RL), federated learning (FL), and federated reinforcement learning (FRL) have demonstrated remarkable success in solving complex problems in different applications. In recent years, new solutions have been developed based on this combined framework (i.e., federated reinforcement learning). Conversely, there is a lack of analysis concerning IoT applications and a standard view of challenges and future directions of the current FRL landscape. Thereupon, the main goal of this research is to present a literature review of federated reinforcement learning (FRL) applications in IoT from multiple perspectives. We focus on analyzing applications in multiple areas (e.g., security, sustainability and efficiency, vehicular solutions, and industrial services) to highlight existing solutions, their characteristics, and research gaps. Additionally, we identify key short- and long-term challenges leading to new opportunities in the field. This research intends to picture the current FRL ecosystem in IoT to foster the development of new solutions based on existing challenges.
Meet-in-the-Filter and Dynamic Counting with Applications to Speck

We propose a new cryptanalytic tool for differential cryptanalysis, called meet-in-the-filter (MiF). It is suitable for ciphers with a slow or incomplete diffusion layer such as the ones based on Addition-Rotation-XOR (ARX). The MiF technique uses a meet-in-the-middle matching to construct differential trails connecting the differential’s output and the ciphertext difference. The proposed trails are used in the key recovery procedure, reducing time complexity and allowing flexible time-data trade-offs. In addition, we show how to combine MiF with a dynamic counting technique for key recovery. We illustrate MiF in practice by reporting improved attacks on the ARX -based family of block ciphers Speck. We improve the time complexities of the best known attacks up to 15 rounds of Speck 32 and 20 rounds of Speck 64/128. Notably, our new attack on 11 rounds of Speck 32 has practical analysis and data complexities of $$2^{24.66}$$ and $$2^{26.70}$$ respectively, and was experimentally verified, recovering the master key in a matter of seconds.
Smart Flood Detection with AI and Blockchain Integration in Saudi Arabia Using Drones

Global warming and climate change are responsible for many disasters. Floods pose a serious risk and require immediate management and strategies for optimal response times. Technology can respond in place of humans in emergencies by providing information. As one of these emerging artificial intelligence (AI) technologies, drones are controlled in their amended systems by unmanned aerial vehicles (UAVs). In this study, we propose a secure method of flood detection in Saudi Arabia using a Flood Detection Secure System (FDSS) based on deep active learning (DeepAL) based classification model in federated learning to minimize communication costs and maximize global learning accuracy. We use blockchain-based federated learning and partially homomorphic encryption (PHE) for privacy protection and stochastic gradient descent (SGD) to share optimal solutions. InterPlanetary File System (IPFS) addresses issues with limited block storage and issues posed by high gradients of information transmitted in blockchains. In addition to enhancing security, FDSS can prevent malicious users from compromising or altering data. Utilizing images and IoT data, FDSS can train local models that detect and monitor floods. A homomorphic encryption technique is used to encrypt each locally trained model and gradient to achieve ciphertext-level model aggregation and model filtering, which ensures that the local models can be verified while maintaining privacy. The proposed FDSS enabled us to estimate the flooded areas and track the rapid changes in dam water levels to gauge the flood threat. The proposed methodology is straightforward, easily adaptable, and offers recommendations for Saudi Arabian decision-makers and local administrators to address the growing danger of flooding. This study concludes with a discussion of the proposed method and its challenges in managing floods in remote regions using artificial intelligence and blockchain technology.
Automatic Deep Sparse Multi-Trial Vector-based Differential Evolution clustering with manifold learning and incremental technique

Most deep clustering methods despite utilizing complex networks to learn better from data, use a shallow clustering method. These methods have difficulty in finding good clusters due to the lack of ability to handle between local search and global search to prevent premature convergence. In other words, they do not consider different aspects of the search and it causes them to get stuck in the local optimum. In addition, the majority of existing deep clustering approaches perform clustering with the knowledge of the number of clusters, which is not practical in most real scenarios where such information is not available. To address these problems, this paper presents a novel automatic deep sparse clustering approach based on an evolutionary algorithm called Multi-Trial Vector-based Differential Evolution (MTDE). Sparse auto-encoder is first applied to extract embedded features. Manifold learning is then adopted to obtain representation and extract the spatial structure of features. Afterward, MTDE clustering is performed without prior information on the number of clusters to find the optimal clustering solution. The proposed approach was evaluated on various datasets, including images and time-series. The results demonstrate that the proposed method improved MTDE by 18.94% on average and compared to the most recent deep clustering algorithms, is consistently among the top three in the majority of datasets.
The State-of-art Applications of NLP: Evidence from ChatGPT

Contemporarily, CharGPT, one of the latest applications of natural language processing (NLP) based on a popular deep learning model developed by OpenAI, has attracted tremendous attention of researchers. On this basis, this study takes NLP as an example to provide a basic introduction to the development history and application of NLP, and lists some models in the development process. These are valuable knowledge that is still used in OpenAI applications. Before in-depth study of the components and models of ChatGPT, this paper also mentioned the basic introduction of chat robots. Accorind to the analysis, it is shown that people are increasingly interested in the use of ChatGPT in various applications, including language translation and question answering. However, it also lists several limitations of ChatGPT, e.g., its tendency to generate biased or inappropriate responses. The future prospects of ChatGPT or similar products are also mentioned later. Moreover, the main findings of the paper and proposes potential avenues for future research are presented. Overall, these results shed light on guiding further exploration of NLP.S.
High-Accurate Quantitative Phase Imaging Based on the Transport of Intensity Equation and Wavelet Transform

We propose a high accurate quantitative phase imaging (QPI) method by using transport of intensity equation (TIE) and wavelet transform (WT). TIE provides a simple and fast method for QPI, but its accuracy is always limited due to the nonlinear error and noise problems caused by the defocus distance. To improve the accuracy of the phase recovered by TIE, WT is introduced to combine with TIE under several defocus distances. With the help of the multiresolution characteristics of the WT, effective information can be extracted from the phases retrieved with TIE at different defocus distances. As a result, a more accurate phase can be obtained by fusing this effective information. Moreover, to extend the applicability of our method, the problems of phase discrepancy and phase singularity in TIE are discussed and solved with an iterative WT-TIE algorithm. Numerical simulations and experiments with various types of phase maps are presented to comprehensively demonstrate the accuracy and effectiveness of our method.
Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge

This work presents a mitosis detection method with only one vanilla Convolutional Neural Network (CNN). Our method consists of two steps: given an image, we first apply a CNN using a sliding window technique to extract patches that have mitoses; we then calculate each extracted patch's class activation map to obtain the mitosis's precise location. To increase the model performance on high-domain-variance pathology images, we train the CNN with a data augmentation pipeline, a noise-tolerant loss that copes with unlabeled images, and a multi-rounded active learning strategy. In the MIDOG 2022 challenge, our approach, with an EfficientNet-b3 CNN model, achieved an overall F1 score of 0.7323 in the preliminary test phase, and 0.6847 in the final test phase (task 1). Our approach sheds light on the broader applicability of class activation maps for object detections in pathology images.
Joint Holistic and Masked Face Recognition

With the widespread use of face masks due to the COVID-19 pandemic, accurate masked face recognition has become more crucial than ever. While several studies have investigated masked face recognition using convolutional neural networks (CNNs), there is a paucity of research exploring the use of plain Vision Transformers (ViTs) for this task. Unlike ViT models used in image classification, object detection, and semantic segmentation, the model trained by modern face recognition losses struggles to converge when trained from scratch. To this end, this paper initializes the model parameters via a proxy task of patch reconstruction and observes that the ViT backbone exhibits improved training stability with satisfactory performance for face recognition. Beyond the training stability, two strategies based on prompts are proposed to integrate holistic and masked face recognition in a single framework, namely FaceT. Along with popular holistic face recognition benchmarks, several open-sourced masked face recognition benchmarks are collected for evaluation. Our extensive experiments demonstrate that the proposed FaceT performs on par or better than state-of-the-art CNNs on both holistic and masked face recognition benchmarks. Codes will be made available at https://github.com/zyainfal/Joint-Holistic-and-Masked-Face-Recognition.
Interacting-Enhancing Feature Transformer for Cross-Modal Remote-Sensing Image and Text Retrieval

Cross-modal remote sensing image-text retrieval (CMRSITR) is a challenging topic in the remote sensing (RS) community. It has gained growing attention because it can be flexibly used in many practical applications. In the current deep era, with the help of deep convolutional neural networks (DCNNs), many successful CMRSITR methods have been proposed. Most of them first learn valuable features from RS images and texts respectively. Then, the obtained visual and textual features are mapped into a common space for the final retrieval. The above operations are feasible, however, two difficulties are still to be solved. One is that the semantics within the visual and textual features are misaligned due to the independent learning manner. The other one is that the deep links between RS images and texts cannot be fully explored by simple common space mapping. To overcome the above challenges, we propose a new model named interacting-enhancing feature transformer (IEFT) for CMRSITR, which regards the RS images and texts as a whole. First, a simple feature embedding module (FEM) is developed to map images and texts into the visual and textual feature spaces. Second, an information interacting-enhancing module (IIEM) is designed to simultaneously model the inner relationships between RS images and texts and enhance the visual features. IIEM consists of three feature interacting-enhancing (FIE) blocks, each of which contains an inter-modality relationship interacting (IMRI) sub-block and a visual feature enhancing (VFE) sub-block. The duty of IMRI is to exploit the hidden relations between cross-modal data, while the responsibility of VFE is to improve the visual features. By combining them, semantic bias can be mitigated, and the complex contents of RS images can be studied. Finally, the retrieval module (RM) is constructed to generate the matching scores for deciding the search results. Extensive experiments are conducted on four public RS data sets. The positive results demonstrate that our IEFT can achieve superior retrieval performance compared with many existing methods. Our source codes are available at https://github.com/TangXu-Group/Cross-modal-remote-sensing-image-and-text-retrieval-models/tree/main/IEFT.
Environment Semantics Aided Wireless Communications: A Case Study of mmWave Beam Prediction and Blockage Prediction

In this paper, we propose an environment semantics aided wireless communication framework to reduce the transmission latency and improve the transmission reliability, where semantic information is extracted from environment image data, selectively encoded based on its task-relevance, and then fused to make decisions for channel related tasks. As a case study, we develop an environment semantics aided <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">network architecture</i> for mmWave communication systems, which is composed of a semantic feature extraction network, a feature selection algorithm, a task-oriented encoder, and a decision network. With images taken from street cameras and user’s identification information as the inputs, the environment semantics aided network architecture is trained to predict the optimal beam index and the blockage state for the base station. It is seen that without pilot training or costly beam scans, the environment semantics aided network architecture can realize extremely efficient beam prediction and timely blockage prediction, thus meeting requirements for ultra-reliable and low-latency communications (URLLCs). Simulation results demonstrate that compared with existing works, the proposed environment semantics aided network architecture can reduce system overheads such as storage space and computational cost while achieving satisfactory prediction accuracy and protecting user privacy.
DCS-CTN: Subtle Gesture Recognition Based on TD-CNN-Transformer via Millimeter-Wave Radar

Gesture recognition has been a hot research topic in human–computer interaction, since contactless gesture recognition will provide increasing applications in many fields. Millimeter-wave (mmWave) radar well serves this technology because of its high accuracy, easy integration, and strong anti-jamming ability in moving object detection. However, it is still challenging to meet the requirement of high precision in subtle gesture recognition based on traditional methods via point cloud or Range-Doppler heat map of mmWave radar. Considering the raw data from mmWave radar with more information, such as phase, we propose a system that uses the constructed mmWave radar data cube sequence and timedistributed-CNN-transformer network (CTN), called DCS-CTN system, to get higher hand gesture recognition accuracy. In this system, we introduce a time-distributed wrapper (TD) and convolutional neural network (CNN) to extract local features of the data cube sequence, a position encoder to retain time information of the sequence, and a transformer network to get global features of the sequence. The experiments results show that this system can achieve hand gesture recognition accuracy of 99.75%, which is significantly higher than the other traditional approaches.
MLP-UNet: Glomerulus Segmentation

Glomerulus segmentation in kidney tissue segments is a crucial nephropathology process used to diagnose renal diseases effectively.This study proposes a novel and robust application of MLP (Multi-Layer Perceptron) based architectures for the segmentation of glomeruli in PAS (Periodic AcidSchiff) stained whole renal images for effective diagnosis of renal diseases.For the segmentation challenge, the proposed unique solution uses MLP-UNet (Multi-Layer Perceptron U-Net), a novel design that evades using conventional convolution and self-attention mechanisms.Additionally, the study compares various approaches, including U-Net, and for the first time, trains the TransUNet model on the kidney WSI (Whole Slide Image) dataset.Dice Score and Dice Loss were used for training these models as the metric and loss function.Results showed that MLP-based architectures provide comparable results (89.96%) to pre-trained architectures like TransUNet (90.58%) with effectively 20% lesser parameters and no pre-training, and also produce superior Dice scores across the 5-fold cross-validation training and learn more quickly than conventional U-Net architectures.
Decoding Contextual Factors Differentiating Adolescents’ High, Average, and Low Digital Reading Performance Through Machine-Learning Methods

The prevalence of information and communication technologies (ICTs) has brought about profound changes in the field of reading, resulting in a large and rapidly growing number of young digital readers.The study intends to identify key contextual factors that synergistically differentiate high and low performers, high and average performers, and low and average performers in digital reading, through the utilization of machine learning methods, namely, support vector machine (SVM) and SVM recursive feature elimination (SVM-RFE).Additionally, the Shapley additive explanations (SHAP) method was applied to augment the machine learning models and detect the features impact on the final output.The latest-released Programme for International Student Assessment (PISA) reading data were analyzed, and the samples included 276,269 15-year-old students from 38 Organization for Economic Co-operation and Development (OECD) countries.The results show that an optimal feature set of contextual factors at the school, classroom, and student levels in the high-low model, high-average model and lowaverage model boast high accuracy.Compared with averageperforming students, high-performing students spend more time reading emails and are associated with high-quality teaching that incorporates digital literacy, and low-performing students are characterized by a lack of interest in ICT use and are more susceptible to the abuse of ICT resources, classroom disorder and discrimination at school.The use of machine learning algorithms for pairwise comparisons provides new perspectives for personalized digital reading education, and the evaluation of the effect of every factor using SHAP method offers a clear view for educational researchers.This study sheds light on factors that may contribute to the development of students' digital reading literacy and the practice of adopting an individualized approach to digital reading pedagogy for educators and instructors.