Reviewerly/exHarmony huggingface dataset:
Autoencoder-based joint image compression and encryption

Recently, learning-based methods have developed rapidly, with promising results in many areas. And deep learning models are expected to be the next-generation optimal image compression solutions. This paper proposes a new image compression and encryption framework that integrates encryption algorithms with a deep-learning compression network. Our work employs Auto-Encoder (AE) based compression network as the backbone. After extracting semantic features from the learning network, encryption layers are added to introduce end-to-end learning to image encryption. And for a higher level of visual security, the parameters of the synthesis network are replaced by a new parameter matrix based on a logistic map controlled by a secret key. The encryption key of the system is derived from the image content, which will be embedded in the deep feature vectors to save the cost of sending the key for different images. Another secret key, which will be shared among other images, is used for controlling the embedding process. And to learn the entropy model from the scrambled feature maps, an attention scheme is exploited in estimating parameters to achieve more effective compression. Experimental results and the security analysis using Kodak dataset show that the proposed scheme can achieve a sufficiently high-security level with high compression efficiency.
Non-contact sensing for anomaly detection in wind turbine blades: A focus-SVDD with complex-valued auto-encoder approach

The occurrence of manufacturing defects in wind turbine blade (WTB) production can result in significant increases in operation and maintenance costs of WTBs, reduce capacity factors of wind farms, and occasionally lead to severe and disastrous consequences.Therefore, inspection during the manufacturing process is crucial to ensure consistent fabrication of composite materials.Non-contact sensing techniques, such as Frequency Modulated Continuous Wave (FMCW) radar, are becoming increasingly popular as they offer a full view -cross sectional analysis -of these complex structures during assembly and curing.In this paper, we enhance the quality assurance of WTB manufacturing utilising FMCW radar as a non-destructive sensing modality.Additionally, a novel anomaly detection pipeline is developed that offers the following advantages: (1) We use the analytic representation of the Intermediate Frequency signal of the FMCW radar as a feature to disentangle material-specific and round-trip delay information from the received wave.(2) We propose a novel anomaly detection methodology called focus Support Vector Data Description (focus-SVDD).This methodology involves defining the limit boundaries of the dataset after removing healthy data features, thereby focusing on the attributes of anomalies.(3) The proposed method employs a complex-valued autoencoder to remove healthy features and we introduces a new activation function called Exponential Amplitude Decay (EAD).EAD takes advantage of the Rayleigh distribution, which characterises an instantaneous amplitude signal.The effectiveness of the proposed method is demonstrated through its application to collected data, where it shows superior performance compared to other state-of-the-art unsupervised anomaly detection methods.This method is expected to make a significant contribution not only to structural health monitoring but also to the field of deep complex-valued data processing and SVDD application.The code and dataset will be made publicly available.
A critical review of multi-output support vector regression

Single-output regression is a widely used statistical modeling method to predict an output based on one or more features of a datapoint. If a dataset has multiple outputs, they can be predicted independently from each other although this disregards potential correlations and thus may negatively affect the predictive performance. Therefore, multi-output regression methods predict multiple outputs simultaneously. One way to approach single-output regression is by using methods based on support vectors such as support vector regression (SVR) or least-squares SVR (LS-SVR). Based on these two, previous works have devised multi-output support vector regression methods. In this review, we introduce a unified notation to summarize the single-output support vector regression methods SVR and LS-SVR as well as state-of-the-art multi-output support vector regression methods. Furthermore, we implemented a workflow for subject- and record-wise bootstrapping and nested cross-validation experiments, which we used for an exhaustive evaluation of all single- and multi-output support vector regression methods on synthetic and non-synthetic datasets. Although the reviewed papers claim that their multi-output methods improve regression performance, we find that none of them outperform both single-output methods SVR and LS-SVR for various reasons. Due to these results, we reflected about the general concept of support vector regression and then concluded that support vector regression methods do not appear to be suitable for the task of multi-output regression.
ELTS-Net: An enhanced liver tumor segmentation network with augmented receptive field and global contextual information

The liver is one of the organs with the highest incidence rate in the human body, and late-stage liver cancer is basically incurable. Therefore, early diagnosis and lesion location of liver cancer are of important clinical value. This study proposes an enhanced network architecture ELTS-Net based on the 3D U-Net model, to address the limitations of conventional image segmentation methods and the underutilization of image spatial features by the 2D U-Net network structure. ELTS-Net expands upon the original network by incorporating dilated convolutions to increase the receptive field of the convolutional kernel. Additionally, an attention residual module, comprising an attention mechanism and residual connections, replaces the original convolutional module, serving as the primary components of the encoder and decoder. This design enables the network to capture contextual information globally in both channel and spatial dimensions. Furthermore, deep supervision modules are integrated between different levels of the decoder network, providing additional feedback from deeper intermediate layers. This constrains the network weights to the target regions and optimizing segmentation results. Evaluation on the LiTS2017 dataset shows improvements in evaluation metrics for liver and tumor segmentation tasks compared to the baseline 3D U-Net model, achieving 95.2% liver segmentation accuracy and 71.9% tumor segmentation accuracy, with accuracy improvements of 0.9% and 3.1% respectively. The experimental results validate the superior segmentation performance of ELTS-Net compared to other comparison models, offering valuable guidance for clinical diagnosis and treatment.
Explainable deep learning for automatic rock classification

As deep learning (DL) gains popularity for its ability to make accurate predictions in various fields, its applications in geosciences are also on the rise. Many studies focus on achieving high accuracy in DL models by selecting models, developing more complex architectures, and tuning hyperparameters. However, the interpretability of these models, or the ability to understand how they make their predictions, is less frequently discussed. To address the challenge of high accuracy but low interpretability of DL models in geosciences, we study rock classification from thin-section photomicrographs of six types of sedimentary rocks, including quartz arenite, feldspathic arenite, lithic arenite, siltstone, oolitic packstone, and dolomite. These rocks' characteristic framework grains and grain textures are their distinguishing features, such as the rounded or oval ooids in oolitic packstone. We first train regular DL models, such as ResNet-50, on these photomicrographs and achieve an accuracy of over 0.94. However, these models make classifications based on features like cracks, cements, and scale bars, which are irrelevant for distinguishing sedimentary rocks in real-world applications. We then propose an attention-based dual network incorporating both global (overall photomicrograph) and local (distinguishing framework grains) features to address this issue. Our proposed model achieves not only high accuracy (0.99) but also provides interpretable feature extractions. Our study highlights the need to consider interpretability and geological knowledge in developing DL models, in addition to aiming for high accuracy.
CalD3r and MenD3s: Spontaneous 3D facial expression databases

In the last couple of decades, the research on 3D facial expression recognition has been fostered by the creation of tailored databases containing prototypical expressions of different individuals and by the advances in cost effective acquisition technologies. Though, most of the currently available databases consist of exaggerated facial expressions, due to the imitation principle which they rely on. This makes these databases only partially employable for real world applications such as human-computer interaction for smart products and environments, health, and industry 4.0, as algorithms learn on these ‘inflated’ data which do not respond to ecological validity requirements. In this work, we present two novel 2D + 3D spontaneous facial expression databases of young adults with different geographical origin, in which emotions have been evoked thanks to affective images of the acknowledged IAPS and GAPED databases, and verified with participants’ self-reports. To the best of our knowledge, these are the first three-dimensional facial databases with emotions elicited by validated affective stimuli.
Certificate-based authenticated encryption with keyword search: Enhanced security model and a concrete construction for Internet of Things

The Internet of Things (IoT) devices produce a humongous amount of data frequently stored on cloud servers, and as a result, cryptographic techniques to guarantee the privacy of outsourced data while preserving search ability on servers are becoming indispensable research topics. A prominent example of such topics is the concept of public key authenticated encryption with keyword search (PAEKS). In spite of the large number of PAEKS schemes in the literature, most existing schemes exhibit issues in certificate management, key escrow, or even key distribution. To address these issues, recently, PAEKS schemes in the certificate-based setting have gained attention. To the best of our knowledge, there exist only two Certificate-Based Authenticated Encryption with Keyword Search (CBAEKS) schemes, both presented with rather weak security models based on single keyword challenges. In this paper, we propose an enhanced security model for CBAEKS which captures notions of multi-ciphertext and multi-trapdoor indistinguishability, then proceed to devise a concrete instantiation for a CBAEKS scheme and formally prove its security under our enhanced model. Furthermore, we prove that the existing CBAEKS schemes are insecure under the enhanced security model. Comparisons with related schemes in the literature are also provided to demonstrate that the enhanced security is achieved at some affordable costs.
Artificial intelligence and machine learning applications in the Spanish nuclear field

Machine Learning and Artificial Intelligence techniques are increasingly applied in the nuclear field. In this way, Spanish companies and research institutions have been developing different projects in the field of nuclear energy applying such techniques. A brief overview of some of these projects is presented, which cover areas as diverse as nuclear data, nuclear fuel manufacturing, simulation codes, fuel performance monitoring, nuclear inspection, and operation training. Among the techniques used to develop them are deep neural networks for building surrogate models, convolutional neural networks for computer vision, Bayesian networks and Gaussian processes for predictive models, and speech recognition with large language models.
DUIANet: A double layer U-Net image hiding method based on improved Inception module and attention mechanism

Image hiding is the process of hiding secret image into cover image, and revealing secret image from steganographic image. However, the quality of generated images and the ability to resist steganalysis detection of steganographic image can be further improved. We propose a novel double-layer U-Net image hiding method based on improved Inception and attention mechanism. The secret image undergoes Haar wavelet transform, hiding network hides the wavelet sub-band of secret image into cover image. We use improved Inception while cascading convolution and attention mechanisms to better extract and fuse information, resulting in high-quality steganographic image. In the revealing stage, Attention mechanism can help networks extract more features about secret image, then we using inverse Haar wavelet transform to revealing high-quality secret image. The experimental results show that the method can obtain high quality images and have good security. When hiding multiple images, the method still has good performance.
Batch image encryption using cross image permutation and diffusion

Chaotic encryption mostly has been proposed to protect a single image or batch images of identical size. Typically, it splices these images into a huge one, and later performs batch image encryption. Yet, this solution may not be feasible given arbitrary-size images, which could not be directly connected one by one. To address it, we propose an arbitrary-size encryption scheme via chaos, for efficiently protecting batch/multiple images at one go. Our scheme supports to automatically read size of images, and then perform cross image permutation and diffusion. The cross image permutation is generic, which could output each permutation with equal expectation. We newly propose a bi-direction cross image diffusion, which has superior performance with only 1 round operation to spread the encryption influence to all images. At last, we conduct extensive experiments and security analyses. The experimental results show that our algorithm can mask the statistical information and resist differential attacks, with metrics such as information entropy, correlation coefficients, NPCR, and UACI closely approximating their theoretical values, namely 8, 0, 99.609%, and 33.464%, respectively. Our code is available at [https://github.com/TcSong/CrossImgEncryption].
DCCNMF: Deep Complementary and Consensus Non-negative Matrix Factorization for multi-view clustering

Deep non-negative matrix factorization-based methods have recently been explored in multi-view clustering due to their ability to deal with complex non-linear data. Although a few methods exist that learn both complementary and consensus information simultaneously by adding new regularizations or adjusting hyperparameters without providing a solid architecture, they do not fully exploit this information. This paper proposes a novel method called Deep Complementary and Consensus Non-negative Matrix Factorization (DCCNMF) that combines the strengths of Non-negative Matrix Factorization and Coupled Non-negative Matrix Factorization using a novel architecture to simultaneously learn both complementary and consensus information present in multi-view data. Two manifold regularization terms, namely complementary manifold and consensus manifold are introduced to preserve the view-specific and view-shared geometric structures during the dimensionality reduction from higher to lower order. Further, smoothness regularization is employed to achieve distinct low-order representations by maximizing the cosine similarity among data points with similar orientations and minimizing it among data points with dissimilar orientations. DCCNMF and benchmark methods are evaluated on several real-world datasets and the results demonstrate that DCCNMF significantly outperforms state-of-the-art multi-view learning approaches.
En-DeepONet: An enrichment approach for enhancing the expressivity of neural operators with applications to seismology

The Eikonal equation plays a central role in seismic wave propagation and hypocenter localization, a crucial aspect of efficient earthquake early warning systems. Despite recent progress, real-time earthquake localization remains challenging due to the need to learn a generalizable Eikonal operator. We introduce a novel deep learning architecture, Enriched-DeepONet (En-DeepONet), addressing the limitations of current operator learning models in dealing with moving-solution operators. Leveraging addition and subtraction operations and a novel ‘root’ network, En-DeepONet is particularly suitable for learning such operators and achieves up to four orders of magnitude improved accuracy without increased training cost. We demonstrate the effectiveness of En-DeepONet in earthquake localization under variable velocity and arrival time conditions. Our results indicate that En-DeepONet paves the way for real-time hypocenter localization for velocity models of practical interest. The proposed method represents a significant advancement in operator learning that is applicable to a gamut of scientific problems, including those in seismology, fracture mechanics, and phase-field problems.
An electronic medical record access control model based on intuitionistic fuzzy trust

Electronic Medical Records (EMR), as the core data of medical big data, has improved the efficiency of medical services in the process of sharing and use, but a series of data privacy leakage problems have emerged. Access control technology can ensure users' legitimate and appropriate access to data resources, but traditional access control has many limitations such as static and coarse-grained, which is difficult to meet in the dynamic and complex healthcare environment. We create a model to solve the privacy leakage problem of healthcare big data. The model utilizes the Latent Delicacy Allocation (LDA) topic model to quickly search documents and extract the topic probability distributions between the target patient and other patients' electronic medical records, which improves the efficiency of medical record document analysis; Calculating physician access similarity, we quantify physician access behavior to more accurately describe physician access behavior in the form of data. We combined the intuitionistic fuzzy theory with the trust evaluation method to create a novel trust evaluation method-- Intuitionistic Fuzzy Trust Evaluation Method, which can reflect the uncertainty characteristics that exist in the physician's diagnostic and treatment process when quantifying the trust of the access by using this method, and it is more flexible and practical, which enhances the accuracy of the quantification of the trust. Improving the traditional trust aggregation method by adding a time window and time decay function, the optimized method improves the accuracy of trust aggregation, and the experimental results are more in line with the actual law. We created a new intuitionistic fuzzy trust access control model using a relative entropy-based intuitionistic fuzzy clustering algorithm and a reward and punishment mechanism to achieve adaptive and dynamic access control for physician access behavior. Experiments show that the access control model based on intuitionistic fuzzy trust proposed in this article achieves 95% correctness in identifying user access behaviors and 94.29% correctness in identifying excessive user access behaviors. Experiments have proved that this model has obvious advantages over other models in the process of privacy protection of electronic medical record systems and has certain control effects.
Rationalizing Graph Neural Networks with Data Augmentation

Graph rationales are representative subgraph structures that best explain and support the graph neural network (GNN) predictions. Graph rationalization involves the joint identification of these subgraphs during GNN training, resulting in improved interpretability and generalization. GNN is widely used for node-level tasks such as paper classification and graph-level tasks such as molecular property prediction. However, on both levels, little attention has been given to GNN rationalization and the lack of training examples makes it difficult to identify the optimal graph rationales. In this work, we address the problem by proposing a unified data augmentation framework with two novel operations on environment subgraphs to rationalize GNN prediction. We define the environment subgraph as the remaining subgraph after rationale identification and separation. The framework efficiently performs rationale–environment separation in the representation space for a node’s neighborhood graph or a graph’s complete structure to avoid the high complexity of explicit graph decoding and encoding. We conduct experiments on 17 datasets spanning node classification, graph classification, and graph regression. Results demonstrate that our framework is effective and efficient in rationalizing and enhancing GNNs for different levels of tasks on graphs.
An Improved Heterogeneous Comprehensive Learning Symbiotic Organism Search for Optimization Problems

The Symbiotic Organism Search (SOS) algorithm, and most of its variants, exhibit the same search behaviour. That is, each organism or individual in the ecosystem applies the same update rules according to 3 phases: mutualism, commensalism, and parasitism. By applying this strategy, an organism is capable of crippling the search effort of another organism. To overcome this problem, this paper proposes a Heterogeneous Comprehensive Learning SOS (HCLSOS) which divides the population into two distinct subpopulations namely: the exploration and exploitation subpopulations. HCLSOS allows each organism to follow one of two search behaviour according to into its subpopulation: whether to explore or exploit. This addresses the problem of balancing exploration and exploitation in SOS. The proposed algorithm employs a comprehensive learning strategy for the exploration group to generate a new type of mutual vector called the multispecies mutual vector capable of preserving organisms' diversity and discouraging premature convergence. Information exchange between the two groups is unidirectional and managed through a random elite learning strategy. Through this collaboration, HCLSOS can effectively evolve organisms to explore the search space and properly exploit the discovered optimal regions. The study tested HCLSOS on 23 benchmark functions, CEC2014, CEC2017, and the recent CEC 2022 test suites. The outcome of HCLSOS is compared with 15 state-of-the-art algorithms, and the results obtained showed that HCLSOS can attain competitive or even better results. Furthermore, we applied HCLSOS to solve 3 constrained engineering problems and to design an efficient frequency control of a two-area islanded microgrid system.