Reviewerly/exHarmony huggingface dataset:
Explanatory pragmatism: a context-sensitive framework for explainable medical AI

Explainable artificial intelligence (XAI) is an emerging, multidisciplinary field of research that seeks to develop methods and tools for making AI systems more explainable or interpretable. XAI researchers increasingly recognise explainability as a context-, audience- and purpose-sensitive phenomenon, rather than a single well-defined property that can be directly measured and optimised. However, since there is currently no overarching definition of explainability, this poses a risk of miscommunication between the many different researchers within this multidisciplinary space. This is the problem we seek to address in this paper. We outline a framework, called Explanatory Pragmatism, which we argue has two attractive features. First, it allows us to conceptualise explainability in explicitly context-, audience- and purpose-relative terms, while retaining a unified underlying definition of explainability. Second, it makes visible any normative disagreements that may underpin conflicting claims about explainability regarding the purposes for which explanations are sought. Third, it allows us to distinguish several dimensions of AI explainability. We illustrate this framework by applying it to a case study involving a machine learning model for predicting whether patients suffering disorders of consciousness were likely to recover consciousness.
Residual Attention-Aided U-Net GAN and Multi-Instance Multilabel Classifier for Automatic Waveform Recognition of Overlapping LPI Radar Signals

Automatic waveform recognition of overlapping low probability of intercept (LPI) radar signals is an important and challenging task in electronic reconnaissance of the increasingly complicated spectrum environment. In this article, an overlapping LPI waveform recognition processing framework incorporating residual attention-aided U-net generative adversarial network (GAN) and multiinstance multilabel (MIML) classifier is proposed. This framework includes five cascade modules and can achieve satisfactory recognition performance by training with single type of signals only. First, the training signals are transformed into time–frequency images. Then, a residual attention U-net GAN (RAUGAN) with residual learning is employed to reconstruct signal images from noise-contaminated images and with the supervision of the high-quality ones. After that, an instance generation module with asymmetric convolutions generates instance representations, which are then fed into the subsequent residual attention MIML classifier (RAMIML). Finally, an adaptive threshold calibration module is implemented to obtain appropriate thresholds for the final decision. Besides, two loss functions are elaborately designed for the RAUGAN and RAMIML, respectively. Extensive experimental results validate the recognition performance of the proposed framework with a recognition accuracy of 80$\%$ at signal-to-noise ratio $>-18$ dB and show higher robustness on the power ratios and generative performance compared with other state-of-the-art methods.
Augmented Reality in AEC Education: A Case Study

Augmented Reality (AR) is a Construction 4.0 technology that is seen as a site-extension of Building Information Modelling (BIM). In addition to the practical aspect within the design and construction processes AR can be used to support teaching through visualizations and interaction. This article presents a new AR platform called “AR-supported Teaching”, applicable for both Architecture, Engineering and Construction (AEC) education and as a Construction 4.0 technology. The aim of this project is to increase the amount of AEC AR content available for education and to introduce students to the productive use of AR. During its development, special attention was paid to the needs of the AEC industry. Users can employ BIM models to create AR scenes before adding animations and annotations without requiring programming skills. The AR platform enables interaction with remote experts and is therefore also suitable for distance learning. In a pilot study, use cases were defined and students tested the usability of the applications. The results were positive and additional suggestions for improvement were made. The feedback and motivation of the students indicate that AR has a future in education, especially if enough AEC AR content and practical use cases are available. The latter also concerns the application of AR in AEC practice.
An adaptive state transition algorithm with local enhancement for global optimization

State transition algorithm (STA) is an efficient and powerful metaheuristic method for solving global optimization problems, and it has been successfully applied in many engineering fields in the past few years. However, the basic STA has weak local search capability and shows slow convergence rate and low convergence accuracy in the later search stage. In view of the above shortcomings, an adaptive state transition algorithm (ASTA) with local enhancement is proposed in this paper. Firstly, the order of using state transformation operators and the optimal parameters of the operators are considered in each iteration of ASTA, and a statistical method is employed to adaptively select the optimal transformation operator and the parameter values of the optimal operator to speed up the search process. Then, an adaptive call strategy is adopted to determine its convergence to the neighborhood of the optimal solution and to decide whether to perform the quasi-Newton operator for local enhancement. Finally, the degree to which the current solution is close to the optimal solution is judged by the information of historical solutions, and an analytical solution is quickly obtained by calling the quadratic interpolation operator. The effectiveness of the proposed ASTA is checked, through a comparison with other metaheuristic methods, on 15 benchmark functions and several real-world optimization problems. Experimental results show that ASTA has a stronger search capability than the basic STA, STA variants, and some state-of-the-art metaheuristic methods.
Fully Squeezed Multiscale Inference Network for Fast and Accurate Saliency Detection in Optical Remote-Sensing Images

Recently, salient object detection in optical remote-sensing images (RSIs) has received more and more attention. To tackle the challenges of RSIs including large-scale variation of objects, cluttered background, irregular shape of objects, and big difference in illumination, the cutting-edge convolutional neural network (CNN)-based models are proposed and have achieved an encouraging performance. However, the performance of the top-level models usually depends on the large model size and high computational cost, which limits their practical applications. To remedy the issue, we introduce a fully squeezed multiscale (FSM) module to equip the entire network. Specifically, the FSM module squeezes the feature maps from high dimension to low dimension and introduces the multiscale strategy to endow the capability of feature characterization with different receptive fields and different contexts. Based on the FSM module, we build the FSM inference network (FSMI-Net) to pop-out salient objects from optical RSIs, which is with fewer parameters and fast inference speed. Particularly, the proposed FSMI-Net only contains 3.6M parameters, and its GPU running speed is about 28 fps for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$384 \times 384$ </tex-math></inline-formula> inputs, which is superior to the existing saliency models targeting optical RSIs. Extensive comparisons are performed on two public optical RSIs datasets, and our FSMI-Net achieves comparable detection accuracy when compared with the state-of-the-art models, where our model realizes a balance between the computational cost and detection performance.
Impact of Wireless Sensor Data Mining with Hybrid Deep Learning for Human Activity Recognition

Human activity recognition is a time series classification problem that is difficult to solve (HAR). Traditional signal processing approaches and domain expertise are necessary to appropriately create features from raw data and fit a machine learning model for predicting a person’s movement. This work aims to demonstrate how a hybrid deep learning model may be used to recognize human behavior. Deep learning methodologies such as convolutional neural networks and recurrent neural networks will extract the features and achieve the classification goal. The suggested model has used wireless sensor data mining datasets to predict human activity. The model’s performance has been assessed using the confusion matrix, accuracy, training loss, and testing loss. Thus, the model has achieved greater than 96% accuracy, superior to other state-of-the-art algorithms in this field.
A cloud-based buyer-seller watermarking protocol (CB-BSWP) using semi-trusted third party for copy deterrence and privacy preserving

Nowadays, cloud computing provides a platform infrastructure for the secure dealing of digital data, but privacy and copy control are the two important issues in it over a network. Cloud data is available to the end user and requires enormous security and privacy techniques to protect the data. Moreover, the access control mechanism with encryption-based technique protects the digital rights for participants in a transaction, but they do not protect the media from being illegally redistributed and do not restrict an authorized user to reveal their secret information this is referred to as you can access but you cannot leak. This brought out a need for controlling copy deterrence and preserving the privacy of digital media over the internet. To overlook this, we proposed a cloud-based buyer-seller watermarking protocol (CB-BSWP) with the use of a semi-trusted third party for copy deterrence and privacy-preserving in the cloud environment. The suggested scheme uses 1) a privacy homomorphism cryptosystem with Diffie-Hellman key exchange algorithm to provide an encrypted domain for the secure exchange of digital media 2) adopt robust and fair watermarking techniques to ensure high imperceptibility and robustness for the watermarked images against attacks 3) two services of cloud Infrastructure as a service (IaaS) to support virtualized computing infrastructure and Watermarking as a service (WaaS) to execute the speedy process of watermarking, this process is supported by watermarking generation and signing phase (WGSP) and watermark extraction and verifying phase reported in 4th section. 4) cloud service provider (CSP) considered as a "semi-trusted" third party to reduce the burden from the trusted third party (TTP) server and provide storage for the encrypted digital media on cloud databases, this frees content owner from not having a separate storage infrastructure. The proposed scheme encrypts the digital content by using SHA-512 algorithm with key size 512-bits to ensure that it doesn't affect computational time during the process of encryption. The suggested scheme addresses the problems of piracy tracing, anonymity, tamper resistance, non-framing, customer rights problem. The role of cloud is crucial because it reduces communication overhead, provides unlimited storage, supports the watermarking process and offers a solution for the secure distribution of end-to-end security of digital content over cloud. To check the performance of the suggested CB-BSWP protocol against common image processing attacks, we have conducted experiments in which the perceptual quality of watermarked digital media was found enhanced, resulting in a robust watermark.
An Explainable Attention Network for Fine-Grained Ship Classification Using Remote-Sensing Images

Advances in space-based ocean surveillance systems have improved the detection of objects from high-quality remote-sensing big data. Previous studies mainly focused on finding and recognizing objects based on deep learning and statistical frameworks. Studies have not fully explored the transparent and reasonable decision-making process for the final predicted results, which is vital for civil and military applications. An explainable attention network for fine-grained ship image classification is proposed in the present study to bridge this gap. The present study seeks to increase attention to objects’ discriminative parts and explore intrinsic relationships between multiple attention parts and predicted outcomes. Several causal multi-attention maps are generated by combining the multi-head attention mechanism and structural causal model. The convolutional filters in the last layer of the network are divided into several groups, and each group is designed to express specific semantic information under supervision of the filter loss function. The results show which parts of the objects are adopted as the key factors for the network to make the final predicted outcome. In the training process, the network is designed to rapidly focus on the salient feature of objects and played a role in guiding other parts of the network to improve the explainable capability of the network without affecting the discrimination power or compromising the classification accuracy. Extensive experiments based on two public datasets show that the network is highly effective as indicated by high classification accuracy and explainable ability.
U/Pb geochronology of fossil fish dentine from Romualdo Formation, Araripe Basin, northeast of Brazil

Absolute dating of sedimentary rocks and fossils from the Mesozoic are still rare, mainly due to the difficulties arising from diagenetic changes. Diagenesis promotes changes in the primary structure and chemical composition of biomineralization, which should remain unchanged from the time of deposition and fossilization. Three bioapatite teeth extracted from a jaw fragment of Cladocyclus gardneri from the Romualdo Formation, Araripe Basin, Northeastern Brazil were dated by the U/Pb method in order to obtain an absolute age. The rocks that contain this fossil are dated to Aptian by biostratigraphy of palynomorphs (Sergipea variverrucata biozone, Petrobras P-270), ostracods (Damonella grandiensis biozone, Petrobras) and from the international biozoning of planktonic foraminifera Microhedbergella miniglobularis. The teeth were mounted in 25 mm epoxy resin rounds, imaged by optical microscope and SEM and dated with a Nu Instruments AttoM single-collector ICP-MS coupled to an NWR193nm UC (Ultra Compact) ArF excimer laser system. A Discordia U–Pb date obtained from three separate Cladocyclus gardneri dentine is 110.5 ± 7.4 Ma (2s, MSWD = 2.6), consistent with the biostratigraphic age. This date was based on 155 integrations of line scan analyses (spot size of 30 μm, scan moved at 2 μm per second/100 μm in total length). 238U/206Pb ratios that differed from the mean value by more than 2SD for each tooth were excluded based on the likelihood they reflect U-redistribution during fossilization/post-fossilization fluid alteration. Remaining variations in 238U/206Pb and 207Pb/206Pb ratios along the Discordia reflect variable contributions from common Pb, both within and between the three teeth. They reflect primary U/Pb compositions given that they date the expected age of fossilization.
Uncertainties associated with GAN-generated datasets in high energy physics

Recently, Generative Adversarial Networks (GANs) trained on samples of traditionally simulated collider events have been proposed as a way of generating larger simulated datasets at a reduced computational cost. In this paper we point out that data generated by a GAN cannot statistically be better than the data it was trained on, and critically examine the applicability of GANs in various situations, including a) for replacing the entire Monte Carlo pipeline or parts of it, and b) to produce datasets for usage in highly sensitive analyses or sub-optimal ones. We present our arguments using information theoretic demonstrations, a toy example, as well as in the form of a formal statement, and identify some potential valid uses of GANs in collider simulations.
Deep Neural Network-Based SQL Injection Detection Method

Among the network security problems, SQL injection is a common and challenging network attack means, which can cause inestimable loop-breaking and loss to the database, and how to detect SQL injection statements is one of the current research hotspots. Based on the data characteristics of SQL statements, a deep neural network-based SQL injection detection model and algorithm are built. The core method is to convert the data into word vector form by word pause method, then form a sparse matrix and pass it into the model for training, build a multihidden layer deep neural network model containing ReLU function, optimize the traditional loss function, and introduce Dropout method to improve the generalization ability of this model. The accuracy of the final model is maintained at over 96%. By comparing the experimental results with traditional machine learning algorithms and LSTM algorithms, the proposed algorithm effectively solves the problems of overfitting in machine learning and the need for manual screening to extract features, which greatly improves the accuracy of SQL injection detection.
Character-level word encoding deep learning model for combating cyber threats in phishing URL detection

A cyber threat is generally a malicious activity that damages or steals data, or something that disrupts digital life. Such threats include viruses, security breaches, DoS attacks, and data theft. Phishing is a type of cyber threat whereby the attackers mimic a genuine URL or a webpage and steal user data, 21% fall into the phishing category. The novel approach of using the character-level encoding of URLs is introduced. Unlike word-level encoding, the use of character-level encoding decreases the discrete workspace and can be effective even in an energy-constrained environment. The experimental results of comparisons to other state-of-the-art methods demonstrate that the proposed method achieved 98.12% of true positive instances. Moreover, Conclusions: An experimental evaluation was performed to demonstrate the efficiency, and it was observed that the accuracy reached an all-time high of 98.13%. the experiments prove that the proposed method can operate efficiently even in energy-saving modes of phishing detection systems.
Adversarial Graph Contrastive Learning with Information Regularization

Contrastive learning is an effective unsupervised method in graph representation learning. Recently, the data augmentation based contrastive learning method has been extended from images to graphs. However, most prior works are directly adapted from the models designed for images. Unlike the data augmentation on images, the data augmentation on graphs is far less intuitive and much harder to provide high-quality contrastive samples, which are the key to the performance of contrastive learning models. This leaves much space for improvement over the existing graph contrastive learning frameworks. In this work, by introducing an adversarial graph view and an information regularizer, we propose a simple but effective method, Adversarial Graph Contrastive Learning (ArieL), to extract informative contrastive samples within a reasonable constraint. It consistently outperforms the current graph contrastive learning methods in the node classification task over various real-world datasets and further improves the robustness of graph contrastive learning.
Graph Neural Networks with Dynamic and Static Representations for Social Recommendation

Recommender systems based on graph neural networks receive increasing research interest due to their excellent ability to learn a variety of side information including social networks. However, previous works usually focus on modeling users, not much attention is paid to items. Moreover, the possible changes in the attraction of items over time, which is like the dynamic interest of users are rarely considered, and neither do the correlations among items. To overcome these limitations, this paper proposes graph neural networks with dynamic and static representations for social recommendation (GNN-DSR), which considers both dynamic and static representations of users and items and incorporates their relational influence. GNN-DSR models the short-term dynamic and long-term static interactional representations of the user's interest and the item's attraction, respectively. The attention mechanism is used to aggregate the social influence of users on the target user and the correlative items' influence on a given item. The final latent factors of user and item are combined to make a prediction. Experiments on three real-world recommender system datasets validate the effectiveness of GNN-DSR.
Model Stealing Attacks Against Inductive Graph Neural Networks

Many real-world data come in the form of graphs. Graph neural networks (GNNs), a new family of machine learning (ML) models, have been proposed to fully leverage graph data to build powerful applications. In particular, the inductive GNNs, which can generalize to unseen data, become mainstream in this direction. Machine learning models have shown great potential in various tasks and have been deployed in many real-world scenarios. To train a good model, a large amount of data as well as computational resources are needed, leading to valuable intellectual property. Previous research has shown that ML models are prone to model stealing attacks, which aim to steal the functionality of the target models. However, most of them focus on the models trained with images and texts. On the other hand, little attention has been paid to models trained with graph data, i.e., GNNs. In this paper, we fill the gap by proposing the first model stealing attacks against inductive GNNs. We systematically define the threat model and propose six attacks based on the adversary’s background knowledge and the responses of the target models. Our evaluation on six benchmark datasets shows that the proposed model stealing attacks against GNNs achieve promising performance. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Reservoir Computing with Spin Waves in a Skyrmion Crystal

The application of spintronics to physical reservoir computing has great potential, but development still suffers from inevitable technical complications in nanofabrication. This numerical study considers spin waves excited in a self-organized skyrmion lattice under a magnetic field in a chiral magnet---which would not require advanced manufacturing in practice---to make progress on the problem. Such a skyrmion lattice offers great levels of generalizability, memory capacity, and nonlinearity, fulfilling the fundamental requirements of reservoir computing. The results will promote engineering solutions to pave the way toward reliable, energy-conserving reservoir computing.
Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning

Non-exemplar class-incremental learning is to recognize both the old and new classes when old class samples cannot be saved. It is a challenging task since representation optimization and feature retention can only be achieved under supervision from new classes. To address this problem, we propose a novel self-sustaining representation expansion scheme. Our scheme consists of a structure reorganization strategy that fuses main-branch expansion and side-branch updating to maintain the old features, and a main-branch distillation scheme to transfer the invariant knowledge. Furthermore, a prototype selection mechanism is proposed to enhance the discrimination between the old and new classes by selectively incorporating new samples into the distillation process. Extensive experiments on three benchmarks demonstrate significant incremental performance, outperforming the state-of-the-art methods by a margin of 3%, 3% and 6%, respectively.