Reviewerly/exHarmony huggingface dataset:
Application of Digital Image Processing Techniques to Detect Through-Thickness Crack in Hole Expansion Test

Advanced high-strength steels (AHSS) have become increasingly popular in the automotive industry due to their high yield and ultimate tensile strengths, enabling the production of lighter car body structures while meeting safety standards. However, they have some setbacks compared to conventional steels, such as edge cracking through sheet thickness caused by forming components with shear-cut edges. When characterizing the formability of sheet metal materials, the hole expansion test is an industry-standard method used to evaluate the stretch-flangeability of their edges. However, accurately visualizing the first cracking is usually tricky and may be subjective, often leading to inconsistent results and low reproducibility with some impact of the operator on both direct and post-processing measurements. To address these issues, a novel digital image processing method is presented to reduce operator reliance and enhance the accuracy and efficiency of the hole expansion test results. By leveraging advanced image processing algorithms, the proposed approach detects the appearance of the first edge cracks, enabling a more precise determination of the hole expansion ratio (HER). Furthermore, it provides valuable insights into the evolution of the hole diameter, allowing for a comprehensive understanding of the material behavior during the test. The proposed method was evaluated for different materials, and the corresponding HER values were compared with the traditional method.
Cracking double-blind review: Authorship attribution with deep learning

Double-blind peer review is considered a pillar of academic research because it is perceived to ensure a fair, unbiased, and fact-centered scientific discussion. Yet, experienced researchers can often correctly guess from which research group an anonymous submission originates, biasing the peer-review process. In this work, we present a transformer-based, neural-network architecture that only uses the text content and the author names in the bibliography to attribute an anonymous manuscript to an author. To train and evaluate our method, we created the largest authorship-identification dataset to date. It leverages all research papers publicly available on arXiv amounting to over 2 million manuscripts. In arXiv-subsets with up to 2,000 different authors, our method achieves an unprecedented authorship attribution accuracy, where up to 73% of papers are attributed correctly. We present a scaling analysis to highlight the applicability of the proposed method to even larger datasets when sufficient compute capabilities are more widely available to the academic community. Furthermore, we analyze the attribution accuracy in settings where the goal is to identify all authors of an anonymous manuscript. Thanks to our method, we are not only able to predict the author of an anonymous work but we also provide empirical evidence of the key aspects that make a paper attributable. We have open-sourced the necessary tools to reproduce our experiments.
Anomaly Detection in Endemic Disease Surveillance Data Using Machine Learning Techniques

Disease surveillance is used to monitor ongoing control activities, detect early outbreaks, and inform intervention priorities and policies. However, data from disease surveillance that could be used to support real-time decisionmaking remain largely underutilised. Using the Brazilian Amazon malaria surveillance dataset as a case study, in this paper we explore the potential for unsupervised anomaly detection machine learning techniques to discover signals of epidemiological interest. We found that our models were able to provide an early indication of outbreak onset, outbreak peaks, and change points in the proportion of positive malaria cases. Specifically, the sustained rise in malaria in the Brazilian Amazon in 2016 was flagged by several models. We found that no single model detected all anomalies across all health regions. Because of this, we provide the minimum number of machine learning models top-k models) to maximise the number of anomalies detected across different health regions. We discovered that the top three models that maximise the coverage of the number and types of anomalies detected across the thirteen health regions are principal component analysis, stochastic outlier selection, and the minimum covariance determinant. Anomaly detection is a potentially valuable approach to discovering patterns of epidemiological importance when confronted with a large volume of data across space and time. Our exploratory approach can be replicated for other diseases and locations to inform monitoring, timely interventions, and actions towards the goal of controlling endemic disease.
Automatic ISAR Ship Detection Using Triangle-Points Affine Transform Reconstruction Algorithm

With the capability of capturing a target’s two-dimensional information, Inverse Synthetic Aperture Radar (ISAR) imaging is widely used in Radar Automatic Target Recognition. However, changes in the ship target’s attitude can lead to the scatterers’ rotation, occlusion, and angle glint, reducing the accuracy of ISAR image recognition. To solve this problem, we proposed a Triangle Preserving level-set-assisted Triangle-Points Affine Transform Reconstruction (TP-TATR) for ISAR ship target recognition. Firstly, three geometric points as initial information were extracted from the preprocessed ISAR images based on the ship features. Combined with these points, the Triangle Preserving level-set (TP) method robustly extracted the fitting triangle of targets depending on the intrinsic structure of the ship target. Based on the extracted triangle, the TP-TATR adjusted all the ship targets from the training and test data to the same attitude, thereby alleviating the attitude sensitivity. Finally, we created templates by averaging the adjusted training data and matched the test data with the templates for recognition. Experiments based on the simulated and measured data indicate that the accuracies of the TP-TATR method are 87.70% and 90.03%, respectively, which are higher than those of the contrast algorithms and have a statistical difference. These demonstrate the effectiveness and robustness of our proposed TP-TATR method.
Arbitrary-Oriented Object Detection in Aerial Images with Dynamic Deformable Convolution and Self-Normalizing Channel Attention

Objects in aerial images often have arbitrary orientations and variable shapes and sizes. As a result, accurate and robust object detection in aerial images is a challenging problem. In this paper, an arbitrary-oriented object detection method for aerial images, based on Dynamic Deformable Convolution (DDC) and Self-normalizing Channel Attention Mechanism (SCAM), is proposed; this method uses ReResNet-50 as the backbone network to extract rotation-equivariant features. First, DDC is proposed as a replacement for the conventional convolution operation in the Convolutional Neural Network (CNN) in order to cope with various shapes, sizes and arbitrary orientations of the objects. Second, SCAM embedded into the high layer of ReResNet-50, which allows the network to enhance the important feature channels and suppress the irrelevant ones. Finally, Rotation Regions of Interest (RRoI) are generated based on a Region Proposal Network (RPN) and a RoI Transformer (RT), and the RoI-wise classification and bounding box regression are realized by Rotation-invariant RoI Align (RiRoI Align). The proposed method is comprehensively evaluated on three publicly available benchmark datasets. The mean Average Precision (mAP) can reach 80.91%, 92.73% and 94.1% on DOTA-v1.0, DOTA-v1.5 and HRSC2016 datasets, respectively. The experimental results show that, when compared with the state-of-the-arts methods, the proposed method can achieve superior detection accuracy.
FAIR-Checker: supporting digital resource findability and reuse with Knowledge Graphs and Semantic Web standards

The current rise of Open Science and Reproducibility in the Life Sciences requires the creation of rich, machine-actionable metadata in order to better share and reuse biological digital resources such as datasets, bioinformatics tools, training materials, etc. For this purpose, FAIR principles have been defined for both data and metadata and adopted by large communities, leading to the definition of specific metrics. However, automatic FAIRness assessment is still difficult because computational evaluations frequently require technical expertise and can be time-consuming. As a first step to address these issues, we propose FAIR-Checker, a web-based tool to assess the FAIRness of metadata presented by digital resources. FAIR-Checker offers two main facets: a "Check" module providing a thorough metadata evaluation and recommendations, and an "Inspect" module which assists users in improving metadata quality and therefore the FAIRness of their resource. FAIR-Checker leverages Semantic Web standards and technologies such as SPARQL queries and SHACL constraints to automatically assess FAIR metrics. Users are notified of missing, necessary, or recommended metadata for various resource categories. We evaluate FAIR-Checker in the context of improving the FAIRification of individual resources, through better metadata, as well as analyzing the FAIRness of more than 25 thousand bioinformatics software descriptions.
Domain adaptive semantic segmentation by optimal transport

Scene segmentation is widely used in autonomous driving for environmental perception. Semantic scene segmentation (3S) has gained considerable attention owing to its rich semantic information. It assigns labels to the pixels in an image, thereby enabling automatic image labeling. Current approaches are based mainly on convolutional neural networks (CNN), however, they rely on numerous labels. Therefore, the use of a small amount of labeled data to achieve semantic segmentation has become increasingly important. In this study, we developed a domain adaptation (DA) framework based on optimal transport (OT) and an attention mechanism to address this issue. Specifically, we first generated the output space via a CNN owing to its superior of feature representation. Second, we utilized OT to achieve a more robust alignment of the source and target domains in the output space, where the OT plan defined a well attention mechanism to improve the adaptation of the model. In particular, the OT reduced the number of network parameters and made the network more interpretable. Third, to better describe the multiscale properties of the features, we constructed a multiscale segmentation network to perform domain adaptation. Finally, to verify the performance of the proposed method, we conducted an experiment to compare the proposed method with three benchmark and four SOTA methods using three scene datasets. The mean intersection-over-union (mIOU) was significantly improved, and visualization results under multiple domain adaptation scenarios also show that the proposed method performed better than semantic segmentation methods.
Polymetallic mineralization hosted in the Neogene sedimentary strata of the Algerian Tellian Range: A comprehensive overview

Purpose. In the electronics industry, stibium (Sb) is an important element used in the development of silicon-based devices. The metallogenic district of Guelma, known for its polymetallic antimony mineralization, is not currently exploited. The research purpose is to characterize the minerals and gang formations of antimony elements required as dopants during the growth of monocrystalline silicon. Methods. The research methods include detailed geological mapping and sampling, XRD mineralogical identification, XRF geo-chemical analysis, and atomic absorption spectrophotometry. Findings. The results obtained prove that Sb mineralization is the youngest in the North-East of Algeria, since it is hosted in the dolomitized lacustrine limestone of the Mio-Plio-Quaternary. The mineralization has been deposited by the replacement and filling open spaces. A supergene alteration of the Nadorite mineral has led to the antimony sulfides appearance. Originality. Unique mineral species of the world have been identified: Nadorite in Jebel Nador, Flajolotite in Jebel Heimel, valentinite in Jebel Senza, senarmontite in Jebel Hammimat, and cervantite near Aïn Kerma. The Ham-man N’Baïls deposits still contain significant reserves of Zn, Sb, and Pb. Practical implications. With the help of this study, we have answered questions related to the origin of the mineralizing fluids responsible for the formation of deposits. In addition to the epigenetic nature of the mineralization with the formation of unique and rare minerals throughout the world, the presence of gold in this region has been proven by chemical analysis.
Facilitating the communication with deaf people: Building a largest Saudi sign language dataset

Recently, several countries have been trying hard to facilitate the integration of disabled people into their societies by ensuring equal opportunities through ease of access to social services, daily human necessities, and the labor market. Deafness is considered one of the major disabilities separating the deaf from their society. To integrate the deaf fully into society, a two-way mode of communication is required: one from the deaf to the hearing people, and the other from the hearing to the deaf. Communication from the hearing person to the deaf is generally easy and can be done through speech recognition and text-to-sign representations, but communication from the deaf to the hearing is somewhat difficult and requires a sign recognition module that recognizes the sign motions from the deaf and translates it to a text; following this, a speech synthesis module will translate this text to speech. To build the sign recognition module, a sign language dataset is required. This paper contributes to the literature by introducing a comprehensive survey of 17 Arabic sign language datasets and by developing a well-organized framework that is used to build a sign language dataset. This paper also contributes to the literature by creating the largest Saudi Sign Language (SSL) database—the King Saud University Saudi-SSL (KSU-SSL data-base)—with 293 signs, 33 signers, 145035 samples, and 10 domains (healthcare, common, alpha-bets, verbs, pronouns and adverbs, numbers, days, kings, family, and regions). This paper also contributes to the literature by introducing a convolutional graph neural network (CGCN) architecture for sign language recognition and applying the proposed architecture to the built KSU-SSL database. The architecture is made up of a small number of separable 3DGCN layers, and is augmented with a spatial attention mechanism. This study is a part of the project that aims to develop a two-way communication system for Saudi deaf individuals.
Hepatocellular carcinoma histopathological images grading with a novel attention-sharing hybrid network based on multi-feature fusion

Throughout history until today, hepatocellular carcinoma (HCC) remains one of the most serious illnesses worldwide due to its high mortality rates. One of the most essential steps to diagnose HCC and its differentiated degree is histopathological image analysis, which is implemented by experienced pathologists. However, this method is time-consuming and difficult for young pathologists to implement. Nowadays, few researchers have proposed approaches based on computer-aided systems to automatically grade HCC due to the complexity of hepatocellular carcinoma histopathological images. Therefore, this paper proposes a novel dual channel attention-sharing hybrid network (DCAH-Net) based on multi-feature fusion to automatically grade HCC histopathological images to assist pathologists in diagnosing HCC. To solve the problem that large scale histopathological images cannot be utilized to train networks directly, patch-based method is utilized in this work to divide large scale image into patches. To effectively extract local and global features, convolutional neural networks and transformers are deployed in the DCAH-Net. A novel attention-sharing mechanism containing a simplified outlook attention and a feature projection block is proposed in DCAH-Net to provide detailed information for both channels. The performance of DCAH-Net compared with existing methods achieves the highest accuracy, macro recall, macro precision and macro F1-score of 88.36%, 82.14%, 83.00% and 82.30% on HCC histopathological dataset. In addition, LC25000 and BreakHis dataset are utilized to validate the generalization ability of DCAH-Net. The results obtained show that DCAH-Net has great potential in other medical classification tasks.
ConvFormer: parameter reduction in transformer models for 3D human pose estimation by leveraging dynamic multi-headed convolutional attention

Recently, fully-transformer architectures have replaced the defacto convolutional architecture for the 3D human pose estimation task. In this paper, we propose ConvFormer, a novel convolutional transformer that leverages a new dynamic multi-headed convolutional self-attention mechanism for monocular 3D human pose estimation. We designed a spatial and temporal convolutional transformer to comprehensively model human joint relations within individual frames and globally across the motion sequence. Moreover, we introduce a novel notion of temporal joints profile for our temporal ConvFormer that fuses complete temporal information immediately for a local neighborhood of joint features. We have quantitatively and qualitatively validated our method on three common benchmark datasets: Human3.6 M, MPI-INF-3DHP, and HumanEva. Extensive experiments have been conducted to identify the optimal hyper-parameter set. These experiments demonstrated that we achieved a significant parameter reduction relative to prior transformer models while attaining State-of-the-Art (SOTA) or near SOTA on all three datasets. Additionally, we achieved SOTA for Protocol III on H36M for both GT and CPN detection inputs. Finally, we obtained SOTA on all three metrics for the MPI-INF-3DHP dataset and for all three subjects on HumanEva under Protocol II.
Inducing consumers’ self-disclosure through the fit between Chatbot’s interaction styles and regulatory focus

With rising concerns among consumers over the sharing of personal information in the digital era, it is increasingly difficult to collect abundant and timely consumer data for modern online marketing practices. This paper suggests that well-designed AI agents (e.g. chatbots) can play a vital role in online marketing strategy by enhancing consumers' willingness to self-disclosure personal information. More specifically, based on the stereotype content model (SCM) and regulatory focus theory, we examined how the interaction effect between the chatbot interaction style and individual regulatory focus influences consumers' willingness to self-disclosure. The results from four studies (N = 1075) indicate that chatbots with warm interaction styles lead to a stronger willingness to self-disclosure for promotion-focused consumers than those who are prevention-focused, while chatbots with a competent interaction style lead to a stronger willingness to self-disclosure for prevention-focused consumers than those who are promotion-focused. Further, consumers' trust in chatbots acts as a psychological mechanism in the disclosure induction process. This paper contributes to AI agent-related research by providing insights into how to enhance the positive effect of consumer-chatbot interaction and by offering suggestions for future research.
Fast RF-UIC: A fast unsupervised image captioning model

For visually impaired individuals, image captioning is a crucial task that utilizes deep learning models to recognize an image and generate a descriptive sentence, enabling them to understand the content of the image through words. However, the existing image captioning model needs a lot of manual annotation. Fortunately, the emergence of unsupervised methods provides a new approach to image captioning. Our proposed model Fast RF-UIC achieves unsupervised functionality through the designed Pre-trainer. Compared with the existing pre-trained model, the Pre-trainer has a faster and shorter training cycle. The R2-Inception-V4 model is designed as an encoder that fuse the Res2Net structure with Inception-V4 to obtain more image features. Bi-FGRU is designed as the decoder, which the FReLU activation function is used to improve the character representation ability from two-dimensional space. Furthermore, we expanded the corpus used in existing unsupervised image captioning and included additional captions for common objects, effectively enhancing the model’s generalization ability. Through experiments, Fast RF-UIC achieved higher scores than existing unsupervised image captioning methods on several text evaluation metrics such as BLUE, ROUGE, and CIDEr.
RADAM: Texture recognition through randomized aggregated encoding of deep activation maps

Texture analysis is a classical yet challenging task in computer vision for which deep neural networks are actively being applied. Most approaches are based on building feature aggregation modules around a pre-trained backbone and then fine-tuning the new architecture on specific texture recognition tasks. Here we propose a new method named Random encoding of Aggregated Deep Activation Maps (RADAM) which extracts rich texture representations without ever changing the backbone. The technique consists of encoding the output at different depths of a pre-trained deep convolutional network using a Randomized Autoencoder (RAE). The RAE is trained locally to each image using a closed-form solution, and its decoder weights are used to compose a 1-dimensional texture representation that is fed into a linear SVM. This means that no fine-tuning or backpropagation is needed for the backbone. We explore RADAM on several texture benchmarks and achieve state-of-the-art results with different computational budgets. Our results suggest that pre-trained backbones may not require additional fine-tuning for texture recognition if their learned representations are better encoded.
Neurodynamic optimization approaches with finite/fixed-time convergence for absolute value equations

This paper proposes three novel accelerated inverse-free neurodynamic approaches to solve absolute value equations (AVEs). The first two are finite-time converging approaches and the third one is a fixed-time converging approach. It is shown that the proposed first two neurodynamic approaches converge to the solution of the concerned AVEs in a finite-time while, under some mild conditions, the third one converges to the solution in a fixed-time. It is also shown that the settling time for the proposed fixed-time converging approach has an uniform upper bound for all initial conditions, while the settling times for the proposed finite-time converging approaches are dependent on initial conditions. The proposed neurodynamic approaches have the advantage that they are all robust against bounded vanishing perturbations. The theoretical results are validated by means of a numerical example and an application in boundary value problems.
Symbolic Perception Risk in Autonomous Driving

We develop a novel framework to assess the risk of misperception in a traffic sign classification task in the presence of exogenous noise. We consider the problem in an autonomous driving setting, where visual input quality gradually improves due to improved resolution, and less noise since the distance to traffic signs decreases. Using the estimated perception statistics obtained using the standard classification algorithms, we aim to quantify the risk of misperception to mitigate the effects of imperfect visual observation. By exploring perception outputs, their expected high-level actions, and potential costs, we show the closed-form representation of the conditional value-at-risk (CVaR) of misperception. Several case studies support the effectiveness of our proposed methodology.