Reviewerly/exHarmony huggingface dataset:
Adversarial Patch Attacks on Deep-Learning-Based Face Recognition Systems Using Generative Adversarial Networks

Deep learning technology has developed rapidly in recent years and has been successfully applied in many fields, including face recognition. Face recognition is used in many scenarios nowadays, including security control systems, access control management, health and safety management, employee attendance monitoring, automatic border control, and face scan payment. However, deep learning models are vulnerable to adversarial attacks conducted by perturbing probe images to generate adversarial examples, or using adversarial patches to generate well-designed perturbations in specific regions of the image. Most previous studies on adversarial attacks assume that the attacker hacks into the system and knows the architecture and parameters behind the deep learning model. In other words, the attacked model is a white box. However, this scenario is unrepresentative of most real-world adversarial attacks. Consequently, the present study assumes the face recognition system to be a black box, over which the attacker has no control. A Generative Adversarial Network method is proposed for generating adversarial patches to carry out dodging and impersonation attacks on the targeted face recognition system. The experimental results show that the proposed method yields a higher attack success rate than previous works.
Utilizing an adaptive window rolling median methodology for time series anomaly detection

With the rise of industry 4.0 and the amount of data gathered from sensors, anomaly detection has become an extremely important task. Due to the variety of anomalies, a universal approach is not yet possible, therefore, many methods to identify abnormal behaviours on data have been researched. In this paper, a new anomaly detection algorithm is proposed that spots abnormal data points in time series using a rolling median with an adaptive sliding window. The window changes based on two methods, F1 based and T-test. F1 method tries to make the F1 score have only an upward trend, while T-test recognizes trends in time series and adjusts the window accordingly. For the evaluation, two well - known benchmark datasets were employed. Moreover, the proposed algorithm was also tested on a dataset consisting of real industrial machinery sensor observations coming from a furniture manufacturer. In the two benchmark datasets mentioned, the two variants are compared with an ensemble of 7 models. The results indicate that the proposed method achieves, in the most cases, better F1 score compared to the benchmarks.
Causality-Aware Convolutional Neural Networks for Advanced Image Classification and Generation

Smart manufacturing uses emerging deep learning models, and particularly Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), for different industrial diagnostics tasks, e.g., classification, detection, recognition, prediction, synthetic data generation, security, etc., on the basis of image data. In spite of being efficient for these objectives, the majority of current deep learning models lack interpretability and explainability. They can discover features hidden within input data together with their mutual co-occurrence. However, they are weak at discovering and making explicit hidden causalities between the features, which could be the reason behind the particular diagnoses. In this paper, we suggest Causality-Aware CNNs (CA-CNNs) and Causality-Aware GANs (CA-GANs) to address the issue of learning hidden causalities within images. The core architecture includes an additional layer of neurons (after the last convolution-pooling and just before the dense layers), which learns pairwise conditional probabilities (aka causality estimates) for the features. Computations for these neurons are driven by the adaptive Lehmer mean function. Learned causalities are merged with the features during flattening and (via fully connected layers) influence the classification outcomes. Such causality estimates can be done for the mixed inputs where images are combined with other data. We argue that CA-CNNs not only improve the classification performance of normal CNNs but also open additional opportunities for the explainability of the models’ outcomes. We consider as an additional advantage for CA-CNNs (if used as a discriminator within CA-GANs) the possibility to generate realistically looking images with respect to the causalities.
HyperRS: Hypernetwork-Based Recommender System for the User Cold-Start Problem

Meta-learning has been proven to be effective for the cold-start problem of recommender systems. Many meta-learning recommender systems that are designed for the user cold-start problem are gradient-based. They use a global parameter learned from existing users to initialize the recommender system parameter for new users that provides a personalized recommendation with limited user-item interactions. Many systems require users’ demographic information to learn the global parameter. This requirement raises privacy concerns, and demographic information is not always available. In addition, some of the gradient-based systems need dozens or even hundreds of user-item interactions from existing users to learn the global parameter. This requirement is difficult to satisfy in specific scenarios in which active users and their item interactions are rare. Moreover, gradient-based meta-learning systems rarely capture user preferences over different item attributes, as updating the corresponding weights requires many optimization steps. We proposed HyperRS, a Hypernetwork-based Recommender System, for the user cold-start problem. Our system does not rely on demographic information to provide personalized recommendations. In our system, a hypernetwork generates all weights in the underlying recommender system. The hypernetwork enables the weights to adapt quickly to capture user interest in both item attributes and the contents of the item attributes. The experimental results show that our method outperforms several state-of-the-art meta-learning recommender systems for the user cold-start problem.
Semi-Global Stereo Matching Algorithm Based on Multi-Scale Information Fusion

Semi-global matching (SGM) has been widely used in binocular vision. In spite of its good efficiency, SGM still has difficulties in dealing with low-texture regions. In this paper, an SGM algorithm based on multi-scale information fusion (MSIF), named SGM-MSIF, is proposed by combining multi-path cost aggregation and cross-scale cost aggregation (CSCA). Firstly, the stereo pairs at different scales are obtained by Gaussian pyramid down-sampling. The initial matching cost volumes at different scales are computed by combining census transform and color information. Then, the multi-path cost aggregation in SGM is introduced into the cost aggregation at each scale and the aggregated cost volumes are fused by CSCA. Thirdly, the disparity map is optimized by internal left-right consistency check and median filter. Finally, experiments are conducted on Middlebury datasets to evaluate the proposed algorithm. Experimental results show that the average error matching rate (EMR) of the proposed SGM-MSIF algorithm reduced by 1.96% compared with SGM. Compared with classical cross-scale stereo matching algorithm, the average EMR of SGM-MSIF algorithm reduced by 0.92%, while the processing efficiency increased by 58.7%. In terms of overall performance, the proposed algorithm outperforms the classic SGM and CSCA algorithms. It can achieve high matching accuracy and high processing efficiency for binocular vision applications, especially for those with low-texture regions.
Mining Mobile Network Fraudsters with Augmented Graph Neural Networks

With the rapid evolution of mobile communication networks, the number of subscribers and their communication practices is increasing dramatically worldwide. However, fraudsters are also sniffing out the benefits. Detecting fraudsters from the massive volume of call detail records (CDR) in mobile communication networks has become an important yet challenging topic. Fortunately, Graph neural network (GNN) brings new possibilities for telecom fraud detection. However, the presence of the graph imbalance and GNN oversmoothing problems makes fraudster detection unsatisfactory. To address these problems, we propose a new fraud detector. First, we transform the user features with the help of a multilayer perceptron. Then, a reinforcement learning-based neighbor sampling strategy is designed to balance the number of neighbors of different classes of users. Next, we perform user feature aggregation using GNN. Finally, we innovatively treat the above augmented GNN as weak classifier and integrate multiple weak classifiers using the AdaBoost algorithm. A balanced focal loss function is also used to monitor the model training error. Extensive experiments are conducted on two open real-world telecom fraud datasets, and the results show that the proposed method is significantly effective for the graph imbalance problem and the oversmoothing problem in telecom fraud detection.
Image encryption scheme based on a controlled zigzag transform and bit-level encryption under the quantum walk

With the rapid development of science and technology and network technology, the study of information security has become a hot spot, and image encryption has potential value in this regard. In this paper, an image encryption scheme based on controlled zigzag transform and bit-level encryption under the quantum walk environment is proposed. First, the parameters of the alternating quantum walk are obtained using the SHA-256 method, and the probability matrix of the quantum distribution on the two-dimensional lattice is obtained by multiple walk measurements; second, the spatial dislocation and bit-level dislocation of the image are realized by performing controlled zigzag dislocation and three-dimensional tesseract-like rotational dislocation on the color image; finally, after preprocessing the probability matrix of the quantum distribution, the matrix is bitwise with the dislocated image to achieve the encryption protection of image information. The effectiveness of the encryption scheme is verified by simulation experiments, and the scheme has a significant encryption effect. Compared with other encryption schemes, this scheme has better key sensitivity and dislocation effect, which provides a new approach to the field of image quantum encryption.
An Image Processing Framework for Breast Cancer Detection Using Multi-View Mammographic Images

Breast cancer is the leading cause of cancer death in women. The early phase of breast cancer is asymptomatic, without any signs or symptoms. The earlier breast cancer can be detected, the greater chance of cure. Early detection using screening mammography is a common step for detecting the presence of breast cancer. Many studies of computer-based using breast cancer detection have been done previously. However, the detection process for craniocaudal (CC) view and mediolateral oblique (MLO) view angles were done separately. This study aims to improve the detection performance for breast cancer diagnosis with CC and MLO view analysis. An image processing framework for multi-view screening was used to improve the diagnostic results rather than single-view. Image enhancement, segmentation, and feature extraction are all part of the framework provided in this study. The stages of image quality improvement are very important because the contrast of mammographic images is relatively low, so it often overlaps between cancer tissue and normal tissue. Texture-based segmentation utilizing the first-order local entropy approach was used to segment the images. The value of the radius and the region of probable cancer were calculated using the findings of feature extraction. The results of this study show the accuracy of breast cancer detection using CC and MLO views were 88.0% and 80.5% respectively. The proposed framework was useful in the diagnosis of breast cancer, that the detection results and features help clinicians in making treatment.
Applications Review of Hassanat Distance Metric

Numerous machine learning methods depend on distance measures. In both supervised and unsupervised learning, these distance measures are primarily employed to assess the degree of similarity between data points. One of the most recent distance metrics that comes highly recommended by several academics for its improved performance in machine learning applications is the so-called Hassanat distance. In this paper, we review different applications that used this distance, improve its proof as being a distance metric, and simplify its formula to speed up the process. We found that this distance performs better when the data contains noise, outliers, and in particular different scale, and therefore, we recommend using it in some real-world machine learning applications.
New closed form estimators for a bivariate gamma distribution

AbstractMotivated by Zhao, Jang and Kim [Journal of Multivariate Analysis, 191, 2022, article number 105109], we propose new closed form estimators for a bivariate gamma distribution. The new estimators are simpler. In addition, they can have smaller asymptotic variances and smaller asymptotic covariances compared to Zhao et al.'s estimators and method of moments estimators. The new estimators can also perform better in real-data applications.Keywords: Covariancedigamma functiontrigamma functionvariance AcknowledgmentsThe authors would like to thank the Editor for careful reading and comments which greatly improved the paper. SN wrote the numerical sections of the paper. VN wrote the rest of the paper. All authors gave explicit consent to publish this manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData are listed in the paper.
Fragile Watermarking for Tamper Localization and Self-Recovery Based on AMBTC and VQ

Digital images have unique features that include being both easily transmittable over the Internet and being easy to tamper. With the advancement of digital processing techniques and an increasing number of valuable digital images being transmitted via the Internet, image authentication has been made more crucial than ever. In this paper, we present an image authentication scheme with tamper localization and self-recovery using fragile watermarking. We embed the fragile watermarks consisting of the authentication code and the recovery information onto the image to verify its integrity. The proposed fragile watermarking scheme can authenticate the image without accessing the original image, localizing the modifications as well as verifying the integrity, and even reconstructing the tampered regions. We use an AMBTC compressed code as the authentication code to minimize the distortion introduced by embedding. To reduce the blocking effect that occurs in the reconstructed image, a VQ compressed code is applied instead of the average intensity as the recovery information. Several representative test images and 200 different test images were randomly selected from BOWS to examine the performance of the proposed scheme. Experimental results confirm that the proposed scheme can effectively resist a cutting attack and a copy-paste attack while retaining the high accuracy of tamper localization. The average TPR and average FTP rate were around 97% and 0.12%, respectively, while maintaining the image quality of the watermarked image and restoring the image at up to 48 dB and 39.28 dB, respectively.
A semi-automatic data integration process of heterogeneous databases

One of the most difficult issues today, is the integration of data from various sources. Thus, it arises the need of automatic Data Integration (DI) methods. However, in the literature there are fully automatic or semi-automatic DI techniques, but they require the involvement of IT-experts with specific domain skills. In this paper we present a novel DI methodology for which it is not required the involvement of IT-experts; in this methodology syntactically/semantically similar entities present in the sources are merged, by exploiting an information retrieval technique, a clustering method and a trained neural network. Although the suggested process is completely automated, we planned some interactions with the Company Manager, a figure who is not required to have IT-skills, but whose only contribution will be to define limits and tolerance thresholds during the DI process, based on the interests of the company. The validity of the proposed approach showed an integration accuracy between 99%−100%.
An IoT Based Traffic Management System Using Drone and AI

Management of ground traffic on both urban streets and highways in a smart city setting requires collecting a huge amount of logistical data. Accessing real-time information of the traffic is essential in the event of an emergency. It requires the traffic control center to regularly monitor flows of vehicles and take suitable actions to reduce traffic jams. Several tiny devices are needed to collect and transmit real-time data from different locations. However, the bandwidth and power consumption of each device is very limited. Therefore, it is essential to utilize an efficient algorithm which reduces the bandwidth, as well as power consumption. In this paper, an efficient method is proposed to reduce the transmission bandwidth while keeping the quality of the videos acceptable for image processing on the server end. To evaluate the performance of the algorithm, a framework to monitor and control the traffic on highways is developed. This framework uses a drone to fly over the traffic to capture the logistical information, and then send real-time video to the server. An object detection algorithm empowered by artificial intelligence (AI) is implemented on the cloud server that detects the number of type of vehicles, and accordingly makes decisions to manage traffic flow.
Oppositional Cat Swarm Optimization-Based Feature Selection Approach for Credit Card Fraud Detection

Credit card fraud has drastically increased in recent times due to the advancements in e-commerce systems and communication technology. Falsified credit card transactions affect the financial status of the companies as well as clients regularly and fraudsters incessantly try to develop new approaches to commit frauds. The recognition of credit card fraud is essential to sustain the trustworthiness of e-payments. Therefore, it is highly needed to design effective and accurate credit card fraud detection (CCFD) techniques. The recently developed machine learning (ML) and deep learning (DL) can be employed for CCFD because of the characteristics of building an effective model to identify fraudulent transactions. In this view, this study presents a novel oppositional cat swarm optimization-based feature selection model with a deep learning model for CCFD, called the OCSODL-CCFD technique. The major intention of the OCSODL-CCFD technique is to detect and classify fraudulent transactions using credit cards. The OCSODL-CCFD technique derives a new OCSO-based feature selection algorithm to choose an optimal subset of features. Besides, the chaotic krill herd algorithm (CKHA) with the bidirectional gated recurrent unit (BiGRU) model is applied for the classification of credit card frauds, in which the hyperparameter tuning of the BiGRU model is performed using the CKHA. To demonstrate the supreme outcomes of the OCSODL-CCFD model, a wide range of simulation analyses were carried out. The extensive comparative analysis highlighted the better outcomes of the OCSODL-CCFD model over the compared ones based on several evaluation metrics.
A meta-level analysis of online anomaly detectors

Real-time detection of anomalies in streaming data is receiving increasing attention as it allows us to raise alerts, predict faults, and detect intrusions or threats across industries. Yet, little attention has been given to compare the effectiveness and efficiency of anomaly detectors for streaming data (i.e., of online algorithms). In this paper, we present a qualitative, synthetic overview of major online detectors from different algorithmic families (i.e., distance, density, tree or projection based) and highlight their main ideas for constructing, updating and testing detection models. Then, we provide a thorough analysis of the results of a quantitative experimental evaluation of online detection algorithms along with their offline counterparts. The behavior of the detectors is correlated with the characteristics of different datasets (i.e., meta-features), thereby providing a meta-level analysis of their performance. Our study addresses several missing insights from the literature such as (a) how reliable are detectors against a random classifier and what dataset characteristics make them perform randomly; (b) to what extent online detectors approximate the performance of offline counterparts; (c) which sketch strategy and update primitives of detectors are best to detect anomalies visible only within a feature subspace of a dataset; (d) what are the trade-offs between the effectiveness and the efficiency of detectors belonging to different algorithmic families; (e) which specific characteristics of datasets yield an online algorithm to outperform all others.
Color Image Edge Detection Method Based on the Improved Whale Optimization Algorithm

A high level of computation is required for edge detection in color images captured by unmanned aerial vehicles (UAVs) to address issues, such as noise, distortion, and information loss. Thus, an edge detection method for UAV-captured color images based on the improved whale optimization algorithm (WOA) is proposed in this study. In this method, the color image pixels are represented by quaternions, and the global random position variables and information exchange mechanism are introduced into the random walk foraging formula of the WOA. Further, a random disturbance factor is also introduced into the predator-prey formula of the spiral bubble net. The proposed improved WOA is then used to obtain the preliminary edge of the UAV-captured color image. An edge-point classification method using the radius of the shortest distance between the whale and the current global optimum in each iteration is presented to enhance a preliminary edge. The experimental results show that the proposed edge detection method has the advantages of strong denoising, fast speed, and good quality.
AMNet: Adaptive multi-level network for deformable registration of 3D brain MR images

Three-dimensional (3D) deformable image registration is a fundamental technique in medical image analysis tasks. Although it has been extensively investigated, current deep-learning-based registration models may face the challenges posed by deformations with various degrees of complexity. This paper proposes an adaptive multi-level registration network (AMNet) to retain the continuity of the deformation field and to achieve high-performance registration for 3D brain MR images. First, we design a lightweight registration network with an adaptive growth strategy to learn deformation field from multi-level wavelet sub-bands, which facilitates both global and local optimization and achieves registration with high performance. Second, our AMNet is designed for image-wise registration, which adapts the local importance of a region in accordance with the complexity degrees of its deformation, and thereafter improves the registration efficiency and maintains the continuity of the deformation field. Experimental results from five publicly-available brain MR datasets and a synthetic brain MR dataset show that our method achieves superior performance against state-of-the-art medical image registration approaches.