Reviewerly/exHarmony huggingface dataset:
A Single Model Deep Learning Approach for Alzheimer’s Disease Diagnosis

Early and accurate diagnosis of Alzheimer's disease (AD) and its prodromal period mild cognitive impairment (MCI) is essential for the delayed disease progression and the improved quality of patients' life. The emerging computer-aided diagnostic methods that combine deep learning with structural magnetic resonance imaging (sMRI) have achieved encouraging results, but some of them are limit of issues such as data leakage, overfitting, and unexplainable diagnosis. In this research, we propose a novel end-to-end deep learning approach for automated diagnosis of AD. This approach has the following differences from the current approaches: (1) Convolutional Neural Network (CNN) models of different structures and capacities are evaluated systemically and the most suitable model is adopted for AD diagnosis; (2) A data augmentation strategy named Two-stage Random RandAugment (TRRA) is proposed to alleviate the overfitting issue caused by limited training data and to improve the classification performance in AD diagnosis; (3) An explainable method of Grad-CAM++ is introduced to generate the visually explainable heatmaps to make our model more transparent. Our approach has been evaluated on two publicly accessible datasets for two classification tasks of AD vs. cognitively normal (CN) and progressive MCI (pMCI) vs. stable MCI (sMCI). The experimental results indicate that our approach outperforms the state-of-the-art approaches, including those using multi-model and three-dimensional (3D) CNN methods. The resultant heatmaps from our approach also highlight the lateral ventricle and some regions of cortex, which have been proved to be affected by AD.
Convolutional-Neural-Network-Based Handwritten Character Recognition: An Approach with Massive Multisource Data

Neural networks have made big strides in image classification. Convolutional neural networks (CNN) work successfully to run neural networks on direct images. Handwritten character recognition (HCR) is now a very powerful tool to detect traffic signals, translate language, and extract information from documents, etc. Although handwritten character recognition technology is in use in the industry, present accuracy is not outstanding, which compromises both performance and usability. Thus, the character recognition technologies in use are still not very reliable and need further improvement to be extensively deployed for serious and reliable tasks. On this account, characters of the English alphabet and digit recognition are performed by proposing a custom-tailored CNN model with two different datasets of handwritten images, i.e., Kaggle and MNIST, respectively, which are lightweight but achieve higher accuracies than state-of-the-art models. The best two models from the total of twelve designed are proposed by altering hyper-parameters to observe which models provide the best accuracy for which dataset. In addition, the classification reports (CRs) of these two proposed models are extensively investigated considering the performance matrices, such as precision, recall, specificity, and F1 score, which are obtained from the developed confusion matrix (CM). To simulate a practical scenario, the dataset is kept unbalanced and three more averages for the F measurement (micro, macro, and weighted) are calculated, which facilitates better understanding of the performances of the models. The highest accuracy of 99.642% is achieved for digit recognition, with the model using ‘RMSprop’, at a learning rate of 0.001, whereas the highest detection accuracy for alphabet recognition is 99.563%, which is obtained with the proposed model using ‘ADAM’ optimizer at a learning rate of 0.00001. The macro F1 and weighted F1 scores for the best two models are 0.998, 0.997:0.992, and 0.996, respectively, for digit and alphabet recognition.
SRAS‐net: Low‐resolution chromosome image classification based on deep learning

Prenatal karyotype diagnosis is important to determine if the foetus has genetic diseases and some congenital diseases. Chromosome classification is an important part of karyotype analysis, and the task is tedious and lengthy. Chromosome classification methods based on deep learning have achieved good results, but if the quality of the chromosome image is not high, these methods cannot learn image features well, resulting in unsatisfactory classification results. Moreover, the existing methods generally have a poor effect on sex chromosome classification. Therefore, in this work, the authors propose to use a super-resolution network, Self-Attention Negative Feedback Network, and combine it with traditional neural networks to obtain an efficient chromosome classification method called SRAS-net. The method first inputs the low-resolution chromosome images into the super-resolution network to generate high-resolution chromosome images and then uses the traditional deep learning model to classify the chromosomes. To solve the problem of inaccurate sex chromosome classification, the authors also propose to use the SMOTE algorithm to generate a small number of sex chromosome samples to ensure a balanced number of samples while allowing the model to learn more sex chromosome features. Experimental results show that our method achieves 97.55% accuracy and is better than state-of-the-art methods.
Cybersecurity Challenges in Blockchain Technology: A Scoping Review

Blockchain technology (BCT) is an emerging technology. Cybersecurity challenges in BCT are being explored to add greater value to business processes and reshape business operations. This scoping review paper was aimed at exploring the current literature’s scope and categorizing various types of cybersecurity challenges in BCT. Databases such as Elsevier, ResearchGate, IEEE, ScienceDirect, and ABI/INFORM Collection (ProQuest) were searched using a combination of terms, and after rigorous screening, 51 research studies were found relevant. Data coding was performed following a framework proposed for scoping review. After careful analysis, thirty different types of cybersecurity challenges in BCT were categorized into six standardized classes. Our results show that most of the studies disclose cybersecurity challenges in BCT generally without pointing to any specific industry sector, and to a very little extent, few papers reveal cybersecurity challenges in BCT related to specific industry sectors. Also, prior studies barely investigated the strategies to minimize cybersecurity challenges in BCT. Based on gap identification, future research avenues were proposed for scholars.
Comparison of different machine learning methods for estimating compressive strength of mortars

Compressive strength and ultrasonic pulse velocity (UPV) are two of the most preferred technics for determining both mechanical properties and microstructural characteristics of cement-based materials. Since the most important mechanical property of concrete compared to many other properties is its compressive strength, it is of great importance to predict and determine the compressive strength. In the current investigation, the compressive strength and ultrasonic pulse velocities of twelve (12) different cement mortars containing different amounts of fly ash and nano calcite were experimentally obtained for the curing ages of 1, 3, 7, 28 and 90 days. By using the experimentally obtained data, compressive strength was estimated by the regression methods developed using the extreme learning machine (ELM), support vector machine (SVM) and group method of data handling (GMDH). In order to compare both experimental and predicted results and also to measure their performance, coefficient of determination (R2) and mean squared error (MSE) were utilized. Two different methods were made for each regression method. These were; Method 1: Estimation of compressive strength values without including UPV test results and Method 2: Estimation of compressive strength values including UPV results. Thus, the relationship between (i) the compressive strength and UPV experimental results and (ii) the estimation results of the regression models were determined and then compared. In this study, the best test performances (namely, for R2/MSE) were found as 0.917/20.239 with the ELM model in the Method 1; 0.982/3.882 with the SVM model in the Method 2. According to these results, it was revealed that the selected regression models achieved high success in the estimation of compressive strength.
A spatial optimization approach to increase the accuracy of rooftop solar energy assessments

Recent years have seen a substantial increase in energy produced by renewable sources. The International Energy Agency (IEA) expects a large portion of future growth in renewable energy to come from solar, especially rooftop photovoltaic (PV) systems. Studies focused on estimating rooftop solar energy potential generally use the total area available for PV installation as determined by solar irradiance availability. This process can lead to substantial over- or under-estimation of energy estimates. Only a few studies have incorporated the spatial layout of PV panels in the solar energy generation estimates, and none have simultaneously considered PV panel size, orientation, and rooftop structure. We address this limitation with a new spatially explicit optimization framework to enhance the accuracy of rooftop solar energy assessments. We consider both the roof's structural configuration and the shape and size of the panels in a novel maximum cover spatial optimization model. After applying the framework to three different types of rooftops (flat roof, pitched roof, and complex roof), we find that conventional methods can lead to a nearly 60% overestimation of energy potential compared to the optimized panel layout. Our work illustrates the importance of considering panel size and rooftop characteristics and offers a mechanism for designing more efficient rooftop PV systems.
Causal GraphSAGE: A robust graph method for classification based on causal sampling

GraphSAGE is a widely-used graph neural network for classification, which generates node embeddings in two steps: sampling and aggregation. In this paper, we introduce causal inference into the GraphSAGE sampling stage, and propose Causal GraphSAGE (C-GraphSAGE) to improve the robustness of the classifier. In C-GraphSAGE, we use causal bootstrapping to obtain a weighting between the target node's neighbors and their label. Then, these weights are used to resample the node's neighbors to enforce the robustness of the sampling stage. Finally, an aggregation function is trained to integrate the features of the selected neighbors to obtain the embedding of the target node. Experimental results on the Cora, Pubmed, and Citeseer citation datasets show that the classification performance of C-GraphSAGE is equivalent to that of GraphSAGE, GCN, GAT, and RL-GraphSAGE in the case of no perturbation, and outperforms these as the perturbation ratio increases.
A Comprehensive Review of Recent Deep Learning Techniques for Human Activity Recognition

Human action recognition is an important field in computer vision that has attracted remarkable attention from researchers. This survey aims to provide a comprehensive overview of recent human action recognition approaches based on deep learning using RGB video data. Our work divides recent deep learning-based methods into five different categories to provide a comprehensive overview for researchers who are interested in this field of computer vision. Moreover, a pure-transformer architecture (convolution-free) has outperformed its convolutional counterparts in many fields of computer vision recently. Our work also provides recent convolution-free-based methods which replaced convolution networks with the transformer networks that achieved state-of-the-art results on many human action recognition datasets. Firstly, we discuss proposed methods based on a 2D convolutional neural network. Then, methods based on a recurrent neural network which is used to capture motion information are discussed. 3D convolutional neural network-based methods are used in many recent approaches to capture both spatial and temporal information in videos. However, with long action videos, multistream approaches with different streams to encode different features are reviewed. We also compare the performance of recently proposed methods on four popular benchmark datasets. We review 26 benchmark datasets for human action recognition. Some potential research directions are discussed to conclude this survey.
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results

This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video. In this challenge, we proposed the LDV 2.0 dataset, which includes the LDV dataset (240 videos) and 95 additional videos. This challenge includes three tracks. Track 1 aims at enhancing the videos compressed by HEVC at a fixed QP. Track 2 and Track 3 target both the super-resolution and quality enhancement of HEVC compressed video. They require x2 and x4 super-resolution, respectively. The three tracks totally attract more than 600 registrations. In the test phase, 8 teams, 8 teams and 12 teams submitted the final results to Tracks 1, 2 and 3, respectively. The proposed methods and solutions gauge the state-of-the-art of super-resolution and quality enhancement of compressed video. The proposed LDV 2.0 dataset is available at https://github.com/RenYang-home/LDV_dataset. The homepage of this challenge (including open-sourced codes) is at https://github.com/RenYang-home/NTIRE22_VEnh_SR.
Adversarial Attack and Defense: A Survey

In recent years, artificial intelligence technology represented by deep learning has achieved remarkable results in image recognition, semantic analysis, natural language processing and other fields. In particular, deep neural networks have been widely used in different security-sensitive tasks. Fields, such as facial payment, smart medical and autonomous driving, which accelerate the construction of smart cities. Meanwhile, in order to fully unleash the potential of edge big data, there is an urgent need to push the AI frontier to the network edge. Edge AI, the combination of artificial intelligence and edge computing, supports the deployment of deep learning algorithms to edge devices that generate data, and has become a key driver of smart city development. However, the latest research shows that deep neural networks are vulnerable to attacks from adversarial example and output wrong results. This type of attack is called adversarial attack, which greatly limits the promotion of deep neural networks in tasks with extremely high security requirements. Due to the influence of adversarial attacks, researchers have also begun to pay attention to the research in the field of adversarial defense. In the game process of adversarial attacks and defense technologies, both attack and defense technologies have been developed rapidly. This article first introduces the principles and characteristics of adversarial attacks, and summarizes and analyzes the adversarial example generation methods in recent years. Then, it introduces the adversarial example defense technology in detail from the three directions of model, data, and additional network. Finally, combined with the current status of adversarial example generation and defense technology development, put forward challenges and prospects in this field.
Extended Reality and Internet of Things for Hyper-Connected Metaverse Environments

The Metaverse encompasses technologies related to the internet, virtual and augmented reality, and other domains toward smart interfaces that are hyper-connected, immersive, and engaging. However, Metaverse applications face inherent disconnects between virtual and physical components and interfaces. This work explores how an Extended Metaverse framework can be used to increase the seamless integration of interoperable agents between virtual and physical environments. It contributes an early theory and practice toward the synthesis of virtual and physical smart environments anticipating future designs and their potential for connected experiences.
Path Planning of Unmanned Aerial Vehicles (UAVs) in Windy Environments

Path planning of unmanned aerial vehicles (UAVs) is one of the vital components that supports their autonomy and deployment ability in real-world applications. Few path-planning techniques have been thoroughly considered for multirotor UAVs for pursuing ground moving targets (GMTs) with variable speed and direction. Furthermore, most path-planning techniques are generally devised without taking into consideration wind disturbances; as a result, they are less suitable for real-world applications as the wind effect usually causes the UAV to drift and tilt from its original course, impacting the mission’s main objective of having an adequate non-deviant camera aim point and steady coverage over the GMT. This paper presents a novel UAV path-planning technique, based on the artificial potential field (APF) for following GMTs in windy environments, to provide steady and continuous coverage over the GMT, by proposing a new modified attractive force to enhance the UAV’s sensitivity to wind speed and direction. The modified wind resistance attractive force function accommodates for any small variation of relative displacement caused by wind leading the UAV to drift in a certain direction. This enables the UAV to maintain its position by tilting (i.e., changing its roll and pitch angles) against the wind to retain the camera aim point on the GMT. The proposed path-planning technique is hardware-independent, does not require an anemometer for measuring wind speed and direction, and can be adopted for all types of multirotor UAVs equipped with basic sensors and an autopilot flight controller. The proposed path-planning technique was evaluated in a Gazebo-supported PX4-SITL and a robot operating system (ROS) for various simulation scenarios. Its performance demonstrated superiority in handling wind disturbances and showed high suitability for deployment in real-world applications.
Cluster-oriented instance selection for classification problems

More training instances could lead to better classification accuracy . However, accuracy could also degrade if more training instances mean further noises and outliers. Additional training instances arguably need additional computational resources in future data mining operations. Instance selection algorithms identify subsets of training instances that could desirably increase accuracy or at least do not decrease accuracy significantly. There exist many instance selection algorithms, but no single algorithm, in general, dominates the others. Moreover, existing instance selection algorithms do not allow direct controlling of the instance selection rate. In this paper, we present a simple and generic cluster-oriented instance selection algorithm for classification problems. Our proposed algorithm runs an unsupervised K Means Clustering algorithm on the training instances and with a given selection rate, selects instances from the centers and the borders of the clusters. On 24 benchmark classification problems, when very similar percentages of instances are selected by various instance selection algorithms, K Nearest Neighbours classifiers achieve more than 2%–3% better accuracy when using instances selected by our proposed method than when using those selected by other state-of-the-art generic instance selection algorithms.
MDA-Unet: A Multi-Scale Dilated Attention U-Net for Medical Image Segmentation

The advanced development of deep learning methods has recently made significant improvements in medical image segmentation. Encoder–decoder networks, such as U-Net, have addressed some of the challenges in medical image segmentation with an outstanding performance, which has promoted them to be the most dominating deep learning architecture in this domain. Despite their outstanding performance, we argue that they still lack some aspects. First, there is incompatibility in U-Net’s skip connection between the encoder and decoder features due to the semantic gap between low-processed encoder features and highly processed decoder features, which adversely affects the final prediction. Second, it lacks capturing multi-scale context information and ignores the contribution of all semantic information through the segmentation process. Therefore, we propose a model named MDA-Unet, a novel multi-scale deep learning segmentation model. MDA-Unet improves upon U-Net and enhances its performance in segmenting medical images with variability in the shape and size of the region of interest. The model is integrated with a multi-scale spatial attention module, where spatial attention maps are derived from a hybrid hierarchical dilated convolution module that captures multi-scale context information. To ease the training process and reduce the gradient vanishing problem, residual blocks are deployed instead of the basic U-net blocks. Through a channel attention mechanism, the high-level decoder features are used to guide the low-level encoder features to promote the selection of meaningful context information, thus ensuring effective fusion. We evaluated our model on 2 different datasets: a lung dataset of 2628 axial CT images and an echocardiographic dataset of 2000 images, each with its own challenges. Our model has achieved a significant gain in performance with a slight increase in the number of trainable parameters in comparison with the basic U-Net model, providing a dice score of 98.3% on the lung dataset and 96.7% on the echocardiographic dataset, where the basic U-Net has achieved 94.2% on the lung dataset and 93.9% on the echocardiographic dataset.
The effects of gender and personality of robot assistants on customers’ acceptance of their service

Abstract The Covid-19 pandemic has stimulated the use of social robots in front-office services. However, some initial applications yielded disappointing results, as managers were unaware of the level of development of the robots’ artificial intelligence systems. This study proposes to adapt the Almere model to estimate the technological acceptance of service robots, which express their gender and personality, whilst assisting consumers. A 2 × 2 (two genders vs. two personalities) between-subjects experiment was conducted with 219 participants. Model estimation with Structural Equation Modelling confirmed seven out of eight hypotheses, and all four scenarios were estimated with Ordinary Least Squares, showing that robot gender and personality affected their technological acceptance.
Block by block: A blockchain-based peer-to-peer business transaction for international trade

Heterogeneity and complicated processes, risk of information leakage, and higher costs are some of the challenges that stem from third-party involvement in business transactions. This study proposes a novel mechanism to address the shortcomings of third-party-dependent transactions in the context of international trade. Moreover, we provide business process modeling, deployed in a business transaction scenario, to furnish a deeper perspective on the working of the mechanism based on Business Process Model and Notation (BPMN) 2.0 standards and guidelines. By analyzing and identifying blockchain roles and capabilities, this study proposes a blockchain technology-based letter of credit (BTLC), which is a mechanism providing letters of credit (LCs) that incorporate the benefits of blockchain and smart contracts.