Reviewerly/exHarmony huggingface dataset:
Improved Marine Predator Algorithm for Wireless Sensor Network Coverage Optimization Problem

A wireless sensor network (WSN) is a distributed network system composed of a great many sensor nodes that rely on self-organization. The random deployment of WSNs in city planning often leads to the problem of low coverage of monitoring areas. In the construction of smart cities in particular, a large number of sensor nodes need to be deployed to maintain the reception, processing, and transmission of data throughout the city. However, the uneven distribution of nodes can cause a lot of wasted resources. To solve this problem, this paper proposes a WSN coverage optimization model based on an improved marine predator algorithm (IMPA). The algorithm introduces a dynamic inertia weight adjustment strategy in the global exploration and local exploitation stages of the standard marine predator algorithm to balance the exploration and exploitation capabilities of the algorithm and improve its solution accuracy. At the same time, the improved algorithm uses a multi-elite random leading strategy to enhance the information exchange rate between population individuals and improve the algorithm’s ability to jump out of the local optimum. The optimization experiment results of 11 benchmark test functions and part of the CEC2014 test functions show that the optimization performance of the improved algorithm is significantly better than the standard marine predator algorithm and other algorithms in the literature. Finally, the improved algorithm is applied to the WSN coverage optimization problem. The simulation results demonstrate that the IMPA has a better coverage rate than other metaheuristic algorithms and other improved algorithms in the literature for solving the WSN coverage optimization problem.
Attribute-Based Access Control Meets Blockchain-Enabled Searchable Encryption: A Flexible and Privacy-Preserving Framework for Multi-User Search

Searchable encryption enables users to enjoy search services while protecting the security and privacy of their outsourced data. Blockchain-enabled searchable encryption delivers the computing processes that are executed on the server to the decentralized and transparent blockchain system, which eliminates the potential threat of malicious servers invading data. Recently, although some of the blockchain-enabled searchable encryption schemes realized that users can search freely and verify search results, unfortunately, these schemes were inefficient and costly. Motivated by this, we proposed an improved scheme that supports fine-grained access control and flexible searchable encryption. In our framework, the data owner uploads ciphertext documents and symmetric keys to cloud database and optional KMS, respectively, and manipulates the access control process and searchable encryption process through smart contracts. Finally, the experimental comparison conducted on a private Ethereum network proved the superiority of our scheme.
TGAN-AD: Transformer-Based GAN for Anomaly Detection of Time Series Data

Anomaly detection on time series data has been successfully used in power grid operation and maintenance, flow detection, fault diagnosis, and other applications. However, anomalies in time series often lack strict definitions and labels, and existing methods often suffer from the need for rigid hypotheses, the inability to handle high-dimensional data, and highly time-consuming calculation costs. Generative Adversarial Networks (GANs) can learn the distribution pattern of normal data, detecting anomalies by comparing the reconstructed normal data with the original data. However, it is difficult for GANs to extract contextual information from time series data. In this paper, we propose a new method, Transformer-based GAN for Anomaly Detection of Time Series Data (TGAN-AD), The transformer-based generators of TGAN-AD can extract contextual features of time series data to prompt the performance. TGAN-AD’s discriminator can also assist in determining abnormal data. Anomaly scores are calculated through both the generator and the discriminator. We have conducted comprehensive experiments on three public datasets. Experimental results show that our TGAN-AD has better performance in anomaly detection than the state-of-the-art anomaly detection techniques, with the highest Recall and F1 values on all datasets. Our experiments also demonstrate the high efficiency of the model and the optimal choice of hyperparameters.
Augmented reality learning environment to aid engineering students in performing practical laboratory experiments in electronics engineering

Abstract In an era where technology is being used more and more in daily life, the potential and effective use of technology is becoming increasingly vital in education. Engineering education necessitates the use of technology to assist students in understanding abstract concepts and principles. Augmented reality (AR) is a technology that can be used to create effective and engaging technology-based solutions and instructional materials. This research aims to develop an augmented reality-based learning experience to teach students about electronics engineering concepts and determine the impact of AR intervention on students' academic achievement levels, learning attitudes toward the subject, and individual attitudes toward AR technology. A quasi-experimental research design was used on 107 first-year engineering students who were grouped into control and experimental groups. The control group consisted of 53 students who learned the fundamentals of electronics using existing techniques, while experimental group consisted of 54 students who learned the same subject using an Augmented Reality-based Lab Manual. The experimental outcomes indicate that the experimental group performed better in the post-test and obtained higher academic scores compared to the control group. In addition, AR intervention has a substantial positive effect on students' learning attitudes. The study also found that students' learning attitudes towards electronics courses and their academic achievement have a significant positive relationship. Additionally, there is a correlation between a student's academic achievement and their attitude toward AR technology. As a result, students who studied using AR technology had a more optimistic perspective about the electronics course and AR technology.
Physics‐Aware Machine Learning and Adversarial Attack in Complex‐Valued Reconfigurable Diffractive All‐Optical Neural Network

Abstract Diffractive optical neural networks have shown promising advantages over electronic circuits for accelerating modern machine learning (ML) algorithms. However, it is challenging to achieve fully programmable all‐optical implementation and rapid hardware deployment. Here, a large‐scale, cost‐effective, complex‐valued, and reconfigurable diffractive all‐optical neural networks system in the visible range is demonstrated based on cascaded transmissive twisted nematic liquid crystal spatial light modulators. The employment of categorical reparameterization technique creates a physics‐aware training framework for the fast and accurate deployment of computer‐trained models onto optical hardware. Such a full stack of hardware and software enables not only the experimental demonstration of classifying handwritten digits in standard datasets, but also theoretical analysis and experimental verification of physics‐aware adversarial attacks onto the system, which are generated from a complex‐valued gradient‐based algorithm. The detailed adversarial robustness comparison with conventional multiple layer perceptrons and convolutional neural networks features a distinct statistical adversarial property in diffractive optical neural networks. The developed full stack of software and hardware provides new opportunities of employing diffractive optics in a variety of ML tasks and in the research on optical adversarial ML.
Analysis of Traffic Accident Based on Knowledge Graph

Traffic accident data include multidimensional dynamic and static factors such as “people, vehicles, roads, and environment” at the time of the accident, which is one of the important data sources for improving the traffic safety environment. Based on the case data of traffic accidents and the construction idea of knowledge graph, the knowledge demand, knowledge modeling, knowledge extraction, and knowledge storage of traffic accidents are analyzed in detail. Finally, the traffic accident knowledge graph is constructed. The visualization analysis of accident is carried out from four angles: accident portrait, accident classification, accident statistics, and accident correlation path. The visualized graphic network displayed by the traffic accident knowledge combines human cognition with machine cognition, which improves human’s ability to understand massive and complicated data. The theoretical system of constructing traffic accident knowledge graph has certain reference significance in the follow-up research on the analysis of massive traffic accident data.
Understanding Universal Adversarial Attack and Defense on Graph

Compared with traditional machine learning model, graph neural networks (GNNs) have distinct advantages in processing unstructured data. However, the vulnerability of GNNs cannot be ignored. Graph universal adversarial attack is a special type of attack on graph which can attack any targeted victim by flipping edges connected to anchor nodes. In this paper, we propose the forward-derivative-based graph universal adversarial attack (FDGUA). Firstly, we point out that one node as training data is sufficient to generate an effective continuous attack vector. Then we discretize the continuous attack vector based on forward derivative. FDGUA can achieve impressive attack performance that three anchor nodes can result in attack success rate higher than 80% for the dataset Cora. Moreover, we propose the first graph universal adversarial training (GUAT) to defend against universal adversarial attack. Experiments show that GUAT can effectively improve the robustness of the GNNs without degrading the accuracy of the model.
Progressive Deep Non-Negative Matrix Factorization Architecture with Graph Convolution-based Basis Image Reorganization

Deep non-negative matrix factorization is committed to using multi-layer structure to extract underlying parts-based representation. However, the basis images obtained by continuous depth factorization is too sparse, resulting in too fragmented parts reflected by the basis image. This makes the number of factorization layers limited and the underlying local feature representation is inaccurate. Therefore, we propose a novel progressive deep non-negative matrix factorization (PDNMF) architecture that adds a basis image reconstruction step to the successive basis image factorization steps. This helps the basis image in depth factorization to maintain better robustness of feature representation. In the reconstruction step, the attribute similarity graph (ASG) is constructed to describe the semantic expression ability of each basis image. With the help of the ASG, the basis image enhances its own semantic integrity through graph convolution without drastically destroying its representation. The evaluation in image recognition shows that the recognition accuracy of the proposed PDNMF improves with the increase of layers. Our method outperforms the state-of-the-art deep factorization methods in image recognition.
Does artificial intelligence aid in the detection of different types of breast cancer?

Abstract Background On mammography many cancers may be missed even in retrospect either due to the breast density, the small size of the tumor or the subtle signs of cancer that are imperceptible. We aimed to compare the sensitivity of artificial intelligence (AI) to that of digital mammography in the detection of different types of breast cancer. Also, the sensitivity of AI in picking up the different breast cancer morphologies namely mass, pathological calcifications, asymmetry, and distortion was assessed. Tissue biopsy and pathology were used as the standard reference. The study included 123 female patients with 134 proved carcinoma. All patients underwent digital mammogram (DM) examination scanned with artificial intelligence algorithm. Results AI achieved higher sensitivity than mammography in detecting malignant breast lesions. The sensitivity of AI was 96.6%, and false negative rate was 3.4%, while mammography sensitivity was 87.3% and false negative rate 12.7%. Our study showed AI performed better than mammography in detecting ductal carcinoma in situ and invasive lobular carcinoma with sensitivity (100% and 96.6%) vs (88.9% and 82.2%) respectively. AI was more sensitive to detect cancers presented with suspicious mass 95.2% vs 75%, suspicious calcifications 100% vs 86.5% and asymmetry and distortion 100% vs 84.6%, than mammography. Conclusions AI showed potential values to overcome mammographic limitations in the detection of breast cancer even those with challenging morphology as invasive lobular carcinoma, ductal carcinoma in situ, tubular carcinoma and micropapillary carcinoma.
CIE-LSCP: color image encryption scheme based on the lifting scheme and cross-component permutation

Abstract Encryption of meaningful images into unidentifiable noise-like images can improve the security of images during storage and transmission. In this paper, a novel color image encryption method based on the lifting scheme and cross-component permutation (CIE-LSCP) is introduced. First, original image is divided into red, green and blue components, and then the three components are processed by a preprocessing strategy based on the lifting scheme (PSLS) to change the statistical distribution of the pixels. Second, a block-based cross-component permutation based on index vectors (BCPIV) is presented to divide three preprocessed components into blocks and perform the cross-component permutation operation on them, and three random matrices are utilized to determine the target component, target block and target pixel position of the current pixel to be moved, respectively. Subsequently, a multi stochastic diffusion based on random sequences (MSDRS) is developed to alter the pixel values of the permutated components, and finally a ciphertext image is gotten by merging the three components. Moreover, the SHA256 hash values of the plaintext image are used to obtain the initial parameters of the chaotic system, and the obtained chaotic sequences are applied in the image encryption process. Wherein the generated random sequences are highly reliant on the plaintext image, making the encryption scheme resistant to both known-plaintext attacks and chosen-plaintext attacks. Experimental results demonstrate that the proposed scheme has good security and effectiveness and can be applied for secure transmission of digital images over the Internet.
MiniNet: Dense squeeze with depthwise separable convolutions for image classification in resource-constrained autonomous systems

In recent years, artificial intelligence (AI) has been developed vigorously, and a great number of AI autonomous applications have been proposed. However, how to decrease computations and shorten training time with high accuracy under the limited hardware resource is a vital issue. In this paper, on the basis of MobileNet architecture, the dense squeeze with depthwise separable convolutions model is proposed, viz. MiniNet. MiniNet utilizes depthwise and pointwise convolutions, and is composed of the dense connection technique and the Squeeze-and-Excitation operations. The proposed MiniNet model is implemented and experimented with Keras. In experimental results, MiniNet is compared with three existing models, i.e., DenseNet, MobileNet, and SE-Inception-Resnet-v1. To validate that the proposed MiniNet model is provided with less computation and shorter training time, two types as well as large and small datasets are used. The experimental results showed that the proposed MiniNet model significantly reduces the number of parameters and shortens training time efficiently. MiniNet is superior to other models in terms of the lowest parameters, shortest training time, and highest accuracy when the dataset is small, especially.
Misleading attention and classification: An adversarial attack to fool object detection models in the real world

• We propose an adversarial attack called MACA to fool state-of-the-art detectors. • The MACA realises a universal attack that can transfer among different networks. • We introduce simulated disturbances for the robustness and naturalness of attacking. • We use 3D human models for experiments to demonstrate the effectiveness of MACA. • We accomplish real-world adversarial attacks through trained adversarial patches. Object detection is a hot topic in computer vision (CV), and it has many applications in various security fields. However, many works have demonstrated that neural network-based object detection is vulnerable to adversarial attacks. In this paper, we study adversarial attacks on object detectors in the real world and propose a new adversarial attack called Misleading Attention and Classification Attack (MACA), which can generate adversarial patches to mislead the object detectors. Specifically, we propose a new scheme to generate adversarial patches to fool the object detector. Our scheme restricts the noise of the adversarial patches and aims to ensure that the generated adversarial patches are visually similar to natural images. The attack simulates the complex external physical environment and the 3D transformations of non-rigid objects to increase the robustness of adversarial patches. We attack the up-to-date object detectors (e.g., Yolo-V5), and we prove that our technique has strong transferability among different detectors. Extensive experiments show that it is feasible to transfer the digital adversarial patches to the real world while maintaining the transferability of adversarial patches among different models and the success rate of adversarial attacks.
Structure enhanced deep clustering network via a weighted neighbourhood auto-encoder

Structural deep clustering involves the use of neural networks for fusing semantic and structural representations for clustering tasks, and it has been receiving increasing attention. In some pioneering works, auto-encoder (AE)-specific representations were integrated with a graph convolutional network (GCN)-specific representation by delivering semantic information to the GCN module layer-by-layer. Although promising performance has been achieved in various applications, we observed that a vital aspect was overlooked in these works: the structural information may vanish in the learning process because of the over-smoothing problem of the GCN module, leading to non-representative features and, thus, deteriorating clustering performance. In this study, we address this issue by proposing a structure enhanced deep clustering network. The GCN-specific structural data representation is enhanced and supervised by its structural information. Specifically, the GCN-specific structural data representation is strengthened during the learning process by combining it with a structure enhanced semantic (SES) representation. A novel structure enhanced AE, named the weighted neighbourhood AE (wNAE), is employed to learn the SES representation for each data sample. Finally, we design a joint supervision strategy to uniformly guide the simultaneous learning of the wNAE and GCN modules and the clustering assignment. Experimental results for different datasets empirically validate the importance of semantic and neighbour-wise structure learning.
A hybrid deep learning approach with genetic and coral reefs metaheuristics for enhanced defect detection in software

Early detection and correction of software defects is essential in the software development process. In the production stage, software with defects negatively impacts on operational costs and ultimately affects customer satisfaction. Although different approaches exist to predict software defects, two essential factors are timely and accurate detection. This paper presents a hybrid Deep Neural Network model for enhanced prediction of software bugs . Different Nature-Inspired Algorithms have been applied to improve the exploration of the hyperparameter solution space to optimize the Deep Neural Network architecture. Experimental investigations have been conducted using NASA dataset to predict software defects and evaluation measures like accuracy, computational time and F1 score have been used for performance comparison. The approach based on the combination of Genetic and Coral Reef metaheuristics outperformed all other models, achieving highest accuracy of around 96% and average F1-score of 0.92.
A modified probabilistic roadmap algorithm for efficient mobile robot path planning

Aiming to address the shortcomings of conventional algorithms in mobile robot path planning, such as long paths, random sampling and high collision risk, a novel probabilistic roadmap algorithm, or node reduction-based search algorithm, is proposed. A decision-making strategy is developed to identify the suitable node, which depends on the distance error to the nearby obstacles and goal and the Gaussian cost function, which improves path efficiency by eliminating unwanted nodes. Then, an optimal path is selected based on the weight of the edges. The comparative analysis is conducted in five different test cases with differing complexity. Different performance parameters are measured to validate the effectiveness of the proposed algorithm. The outcomes acquired from the different test cases indicate that the proposed algorithm outperforms the state-of-the-art algorithms, with a maximum improvement of 13.51% in path length, 66.82% in execution time, 28.5% in smoothness and 38.05% in collision risk value.
Prediction of dyslexia severity levels from fixation and saccadic eye movement using machine learning

Dyslexia is a neurological disorder that causes poor reading and comprehension skills. Dyslexics experience problems in understanding phonemes of languages hence they show less ability to relate letters to form words and sentences. Dyslexia is not directly related to vision but it has been observed that most dyslexics have a magnocellular deficit which causes abnormal eye movements. From the literature, it has been observed that atypical eye movement could be a strong indicator of dyslexia. This research work identifies the severity levels of dyslexia from fixations and saccade eye movement events based on eye gaze points. Fixations and saccades are computed using a velocity threshold algorithm. Features related to fixations and saccades are used to divide dyslexics into high and low based on their severity levels. Unsupervised learning approach K-Means clustering is adopted to group the dyslexics into high and low groups. Cluster evaluation measures such as Silhouette analysis, Elbow Method, and Davies Bouldin Index are used to find the optimal value of K. The quality of the clusters is further reinforced by Multivariate analysis of variance (MANOVA) analysis. Data in the clusters are visualized to understand the data distribution and range of different features. K-means clustering gave two clusters – high and low dyslexics. The Silhouette score was high and the Davies Bouldin Index was low showing that the clusters are of good quality. MANOVA analysis has given a p-value less than 0.01 which further reinforces the quality of clusters to be good. The data in clusters are visualized, analyzed, and compared to existing eye movement findings on dyslexia in literature. It has been observed that features such as fixation duration, number of fixations, saccade duration, and number of saccades act as key indicators to identify the severity levels of dyslexia. The characteristics of data in the clusters generated using these features match with the characteristics of existing eye movement findings, hence the data value ranges can be used to identify the severity levels of dyslexia.