Reviewerly/exHarmony huggingface dataset:
NDG-CAM: Nuclei Detection in Histopathology Images with Semantic Segmentation Networks and Grad-CAM

Nuclei identification is a fundamental task in many areas of biomedical image analysis related to computational pathology applications. Nowadays, deep learning is the primary approach by which to segment the nuclei, but accuracy is closely linked to the amount of histological ground truth data for training. In addition, it is known that most of the hematoxylin and eosin (H&E)-stained microscopy nuclei images contain complex and irregular visual characteristics. Moreover, conventional semantic segmentation architectures grounded on convolutional neural networks (CNNs) are unable to recognize distinct overlapping and clustered nuclei. To overcome these problems, we present an innovative method based on gradient-weighted class activation mapping (Grad-CAM) saliency maps for image segmentation. The proposed solution is comprised of two steps. The first is the semantic segmentation obtained by the use of a CNN; then, the detection step is based on the calculation of local maxima of the Grad-CAM analysis evaluated on the nucleus class, allowing us to determine the positions of the nuclei centroids. This approach, which we denote as NDG-CAM, has performance in line with state-of-the-art methods, especially in isolating the different nuclei instances, and can be generalized for different organs and tissues. Experimental results demonstrated a precision of 0.833, recall of 0.815 and a Dice coefficient of 0.824 on the publicly available validation set. When used in combined mode with instance segmentation architectures such as Mask R-CNN, the method manages to surpass state-of-the-art approaches, with precision of 0.838, recall of 0.934 and a Dice coefficient of 0.884. Furthermore, performance on the external, locally collected validation set, with a Dice coefficient of 0.914 for the combined model, shows the generalization capability of the implemented pipeline, which has the ability to detect nuclei not only related to tumor or normal epithelium but also to other cytotypes.
Multiuser optical image authentication platform based on sparse constraint and polar decomposition in Fresnel domain

Abstract In this paper, a new multiuser optical image encryption and authentication technique is proposed. Sparse multiplexing and polar decomposition are used in the Fresnel domain to obtain the ciphertext of an input image. To enable the multiuser platform, multiple private keys are obtained through polar decomposition during the encryption process. It will allow multiple authorized users to access the secure information simultaneously without having a key distribution problem among them. The proposed method has a large key space and is robust against several attacks, such as contamination attacks (noise and occlusion), brute force attack, plaintext attacks, and special iterative attack. A comparative analysis of the presented technique is also performed with the similar existing techniques. The numerical simulation results demonstrate the robustness and feasibility of the proposed algorithm.
Aggregating dense and attentional multi-scale feature network for salient object detection

Existing fully convolutional networks (FCNs) - based salient object detection (SOD) methods have achieved great performance by integrating diverse multi-scale context information. However, the performance of context information directly obtained by single dilated convolution has limitations because the introduction of dilated convolution with different filling rates will cause the problem of local information loss, which limits the prediction accuracy of the model. For that, in this paper, a novel Aggregating Dense and Attentional Multi-scale Feature Network (DAMFNet) is designed to generate high-quality feature representations for accurate SOD task. More specifically, we first propose a dense-depth feature exploration (DDFE) module to adequately capture the robust multi-scale and multi-receptive field context information by utilizing parallel integrated convolution (PIC) blocks and dense connections for improving the model ability of locating salient objects and refining object details. Afterwards, we develop a multi-scale channel attention enhancement (MCAE) module to further enhance the selection of the salient objects information in the feature channels by integrating multiple attentional features with diverse perspectives. The proposed DAMFNet method has been broadly evaluated on five public SOD benchmark datasets and the extensive experimental results demonstrate that our DAMFNet method has superior advantages compared to 18 state-of-the-art SOD methods under different evaluation metrics.
Footstep planning of humanoid robot in ROS environment using Generative Adversarial Networks (GANs) deep learning

This paper proposes deep learning-based footstep planning using Generative Adversarial Networks (GANs) for the indoor navigation of humanoid robots. The GAN-based architecture presents an efficient and accurate path to plan the footsteps of a humanoid robot on Robot Operating System (ROS) based architecture. During navigation, humanoid robots must understand their surroundings and be able to generate footsteps accurately. Although some algorithms that are based on sampling, such as Rapidly Exploring Random Tree (RRT*) and A*, are widely used for path planning, they fail to perform in narrow paths, especially for the footstep generation of humanoid robots. The widely growing deep learning approaches such as GANs are now producing extremely surprising results in solving real-life problems. The experiments conclude that GAN based approach performs better than conventional Dijkstra's or A* algorithms. The accuracy of the generated footsteps from the GAN-based path planner comes out to be approximately 93%.
A long article about short videos: A content analysis of U.S. academic libraries' use of TikTok

TikTok, a social media platform that focuses on short form videos, has gained a high level of popularity, particularly among young adults. Because libraries are always looking for ways to engage with their patrons, the popularity among this demographic provides an opportunity for college and university libraries to do so. This study analyzed and categorized the videos within 15 academic library TikTok accounts in order to answer two questions: 1) How do academic libraries use TikTok? and 2) Is there a correlation between content category and user interaction on academic library TikTok accounts? The results show that humanizing or fun videos were most utilized by academic libraries, followed by videos including or highlighting collections and spaces. Results also demonstrated that humanizing and fun videos received the highest amount of user engagement. This differs from previous studies on other types of social media wherein news and announcements or events were often the dominant categories.
The High-Precision Detection Method for Insulators’ Self-Explosion Defect Based on the Unmanned Aerial Vehicle with Improved Lightweight ECA-YOLOX-Tiny Model

Aiming at the application of the overhead transmission line insulator patrol inspection requirements based on the unmanned aerial vehicle (UAV), a lightweight ECA-YOLOX-Tiny model is proposed by embedding the efficient channel attention (ECA) module into the lightweight YOLOX-Tiny model. Some measures of data augmentation, input image resolution improvement and adaptive cosine annealing learning rate are used to improve the target detection accuracy. The data of the standard China power line insulator dataset (CPLID) are used to train and verify the model. Through a longitudinal comparison before and after the model improved, and a cross-sectional comparison with other similar models, the advantages of the proposed model are verified in terms of multi-target identification for normal insulators, localization for small target defect areas, and the parameters required for calculation. Finally, the comparative analysis between the proposed ECA-YOLOX-Tiny model and YOLOV4-Tiny model is given by introducing the visualization method of class activation mapping (CAM). The comparative results show that the ECA-YOLOX-Tiny model is more accurate in locating the self-explosion areas of defective insulators, and has a higher response rate for decision areas and some special backgrounds, such as the overlapping small target insulators, the insulators obscured by tower poles, or the insulators with high-similarity backgrounds.
Decentralized and Self-Sovereign Identity in the Era of Blockchain: A Survey

With the emergence of concepts such as Metaverse and Web 3.0, digital identity plays a very important role as one of its infrastructures. The traditional digital identity model is no longer suitable for the requirements of the digital economy to some extent at this stage. The traditional centralized identity management system has many drawbacks. For example, the owner of the digital identity does not actually control his own identity, and there is a risk of easy disclosure and theft of identity information. Blockchain technology has the characteristics of decentralization, tamper-proof, traceability, etc., which can effectively solve the problems of centralized digital identity. After analyzing and summarizing the evolution trend of digital identity from centralization to decentralization, this paper focuses on the Self-Sovereign Identity (SSI) based on blockchain. Based on the analysis and comparison of various SSI implementation schemes, the development difficulties of blockchain digital identity are summarized and the future development direction is pointed out.
Adversarial Deep Learning: A Survey on Adversarial Attacks and Defense Mechanisms on Image Classification

The popularity of adapting deep neural networks (DNNs) in solving hard problems has increased substantially.Specifically, in the field of computer vision, DNNs are becoming a core element in developing many image and video classification and recognition applications.However, DNNs are vulnerable to adversarial attacks, in which, given a well-trained image classification model, a malicious input can be crafted by adding mere perturbations to misclassify the image.This phenomena raise many security concerns in utilizing DNNs in critical life applications which attracts the attention of academic and industry researchers.As a result, multiple studies have proposed discussing novel attacks that can compromise the integrity of state-of-the-art image classification neural networks.The raise of these attacks urges the research community to explore countermeasure methods to mitigate these attacks and increase the reliability of adapting DDNs in different major applications.Hence, various defense strategies have been proposed to protect DNNs against adversarial attacks.In this paper, we thoroughly review the most recent and state-of-the-art adversarial attack methods by providing an in-depth analysis and explanation of the working process of these attacks.In our review, we focus on explaining the mathematical concepts and terminologies of the adversarial attacks, which provide a comprehensive and solid survey to the research community.Additionally, we provide a comprehensive review of the most recent defense mechanisms and discuss their effectiveness in defending DNNs against adversarial attacks.Finally, we highlight the current challenges and open issues in this field as well as future research directions.
Semi-supervised medical image classification with adaptive threshold pseudo-labeling and unreliable sample contrastive loss

Semi-supervised learning (SSL) may employ unlabeled data to improve model performance, which has great significance in medical imaging tasks. However, pseudo-labeling-based semi-supervised approaches suffer from two problems in medical image datasets: (1) the models' predictions are biased toward the majority class in imbalanced datasets, and (2) discarding unlabeled data with confidence below the thresholds results in the loss of useful information. To solve these issues, we propose a novel SSL framework, FullMatch, which improves the model's performance by utilizing all unlabeled data. Specifically, we propose adaptive threshold pseudo-labeling (ATPL), a method for generating pseudo-labels based on the model's current learning status. ATPL dynamically adjusts the thresholds for each class during the training process, which can generate more pseudo-labels for classes with learning difficulties, thus alleviating the problem of data imbalance. Unlike existing semi-supervised methods based on pseudo-labeling, we do not discard unlabeled data with confidence below the thresholds. We propose an unreliable sample contrastive loss (USCL) to leverage useful information from unlabeled data with confidence below the thresholds by learning the similarities and differences between sample features. To evaluate the performance of the proposed method, we conducted experiments on the ISIC 2018 skin lesion classification dataset and the blood cell classification dataset. The experimental results show that our method outperforms the state-of-the-art SSL methods.
Improved Harris hawks optimization for non-convex function optimization and design optimization problems

Harris hawks optimization (HHO) is a nature-inspired algorithm. It has the advantages of very few parameters, a simple structure, fast convergence and strong local search capability. The main drawback of the Harris hawks optimization is that it can easily fall into a local optimum. To solve this problem, a novel mutant strategy based on Brownian motion is proposed to combine with the original HHO. This mutant strategy is driven by exploiting the randomness of Brownian motion and does not require location information between populations and user-set parameters. As a result, it can guide the algorithm to better jump out of the local optimum trap. To verify the performance of the proposed algorithm, numerical experiments are carried out to compare the proposed algorithm with heuristic optimization algorithms for 54 non-convex functions and two classic engineering design problems. The results show that our algorithm not only escapes the local optimum trap, but also has better robustness and convergence.
Explainable machine learning rapid approach to evaluate coal ash content based on X-ray fluorescence

As one of the most important indexes of coal quality, accurate and rapid prediction of ash content is urgent and important significance for the coal processing industry. In this work, combined with Shapley Additive exPlanations (SHAP), a machine learning model has been developed to predict the ash content of coal samples based on composition data of XRF analysis. Among the many supervised regression learning algorithms, Poly, RFR, XGBoost, and DNN are used in this predictive model to overcome the ash content prediction research gap. The input parameters were the elements content and ash contents of the coal samples. To evaluate the proposed method, a dataset of XRF data was constructed, containing 217 sets of element content with different ash content labels. Specifically, the dataset is divided into a training set and test set in the proportion 8:2, and RandomizedSearchCV is used to optimize hyperparameters during model training. Experimental results show that the RFR model produced a superior prediction performance over other models (the RMSE, MAE and R2 were 1.3278, 0.9339 and 0.9916, respectively). The contribution and role of each element to the ash prediction model are explained and investigated. Moreover, as a result of SHAP interpretation, the nine most important elements (Al, S, Si, Fe, Ca, Ti, K, Sr and Zr) has the greatest contribution to model performance. The case of this paper suggests that interpreted machine learning models and XRF data are good alternatives to ash content prediction.
Realization of optical logic gates using on-chip diffractive optical neural networks

Optical computing is highly desired as a potential strategy for circumventing the performance limitations of semiconductor-based electronic devices and circuits. Optical logic gates are considered as fundamental building blocks for optical computation and they enable logic functions to be performed extremely quickly without the generation of heat and crosstalk. Here, we discuss the design of a multi-functional optical logic gate based on an on-chip diffractive optical neural network that can perform AND, NOT and OR logic operations at the wavelength of 1.55 µm. The wavelength-independent operation of the multi-functional logic gate at seven wavelengths (over a bandwidth of 60 nm) is also studied which paves the way for wavelength division multiplexed parallel computation. This simple, highly-integrable, low-loss, energy-efficient and broadband optical logic gate provides a path for the development of high-speed on-chip nanophotonic processors for future optical computing applications.
DFMKE: A dual fusion multi-modal knowledge graph embedding framework for entity alignment

Entity alignment is critical for multiple knowledge graphs (KGs) integration. Although researchers have made significant efforts to explore the relational embeddings between different KGs, existing approaches may not describe multi-modal knowledge well in some tasks, e.g., entity alignment. In this paper, we propose DFMKE, a dual fusion multi-modal knowledge graph embedding framework, to address entity alignment. We first devise an early fusion method for fusing features of multi-modal entity representations of a KG. Simultaneously, multiple representations of various types of knowledge are generated independently by various techniques and fused by a low-rank multi-modal late fusion method. Finally, the outputs of early and late fusion methods are combined using a dual fusion scheme. DFMKE provides an ultimate fusion solution by leveraging the advantages of early and late fusion methods. Extensive experiments on two public datasets show that the DFMKE outperforms state-of-the-art methods by a significant margin achieving at least 10% more regard to Hits@n and MRR metrics. • A dual multi-modal knowledge graph embedding framework called DFMKE is proposed. • A late fusion method using modality-specifc low-rank factors is proposed. • A combination strategy for early and late fusion modules is proposed.
Modeling the Knowledge of Users in an Augmented Reality-Based Learning Environment Using Fuzzy Logic

Augmented Reality has been an important part of engineering education, and more specific in the field of spatial skills training. Based on the literature review, desktop or mobile spatial ability training applications already developed, lack personalization as they do not identify students’ learning styles, and the activities delivered to them are the same complexity for all. In view of the above, this paper presents PARSAT, a developed mobile augmented reality spatial ability training system, which converts students’ knowledge to fuzzy weights, and delivers adaptive learning activities to students’ spatial skills. The innovation of the developed system is that PARSAT is a student-centered system, delivering adaptive domain knowledge content, using the technology of fuzzy weights in a rule-based decision-making module. As a testbed of this research, the developed system has been incorporated in the laboratory course deliveries of a computer-aided-design university course, and has been evaluated by the students during the 2021–22 winter semester.
Blockchain as enabling factor for implementing RFID and IoT technologies in VMI: a simulation on the Parmigiano Reggiano supply chain

Abstract Blockchain has recently been associated to Supply Chain Management to solve several problems and change operations management processes. The study proposes to analyse three different scenarios of the Parmigiano Reggiano supply chain considering blockchain technology as an enabler for the use of other technologies such as RFID and the Internet of Things (IoT) and for the exploitation of the Vendor Managed Inventory (VMI) strategy. The study is based on the evaluation of three agent-based simulation scenarios, a traditional "as is" scenario, a second "to be" scenario implementing emerging technologies including blockchain, and a third “to be” scenario that combines the second one with the VMI optimization strategy. The results show how the combined adoption of these technologies improves the procurement process and customer satisfaction. Findings highlight the impacts that the different scenarios have on the supply chain operations in a quantitative way and allows to evaluate the changes in supply chain processes. By employing emerging technologies, order management activities are more automated and time to order and lead time order preparation are reduced. However, to achieve these performances, other data capture tools such as RFID and IoT are needed. Finally, the introduction of the VMI strategy, when enabled by blockchain technology, improves the procurement performances and significantly reduces unfilled orders.
ClarifyNet: A high-pass and low-pass filtering based CNN for single image dehazing

Dehazing refers to removing the haze and restoring the details from hazy images. In this paper, we propose ClarifyNet, a novel, end-to-end trainable, convolutional neural network architecture for single image dehazing. We note that a high-pass filter detects sharp edges, texture, and other fine details in the image, whereas a low-pass filter detects color and contrast information. Based on this observation, our key idea is to train ClarifyNet on ground-truth haze-free images, low-pass filtered images, and high-pass filtered images. Based on this observation, we present a shared-encoder multi-decoder model ClarifyNet which employs interconnected parallelization. While training, ground-truth haze-free images, low-pass filtered images, and high-pass filtered images undergo multi-stage filter fusion and attention. By utilizing a weighted loss function composed of SSIM loss and L1 loss, we extract and propagate complementary features. We comprehensively evaluate ClarifyNet on I-HAZE, O-HAZE, Dense-Haze, NH-HAZE, SOTS-Indoor, SOTS-Outdoor, HSTS, and Middlebury datasets. We use PSNR and SSIM metrics and compare the results with previous works. For most datasets, ClarifyNet provides the highest scores. On using EfficientNet-B6 as the backbone, ClarifyNet has 18 M parameters (model size of ∼71 MB) and a throughput of 8 frames-per-second while processing images of size 2048 × 1024.
Optimize performance of a diffractive neural network by controlling the Fresnel number

To achieve better performance of a diffractive deep neural network, increasing its spatial complexity (neurons and layers) is commonly used. Subject to physical laws of optical diffraction, a deeper diffractive neural network (DNN) would be more difficult to implement, and the development of DNN is limited. In this work, we found controlling the Fresnel number can increase DNN’s capability of expression and its spatial complexity is even less. DNN with only one phase modulation layer was proposed and experimentally realized at 515 nm. With the optimal Fresnel number, the single-layer DNN reached a maximum accuracy of 97.08% in the handwritten digits recognition task.