Reviewerly/exHarmony huggingface dataset:
Artificial intelligence-based anomaly detection of the Assen iron deposit in South Africa using remote sensing data from the Landsat-8 Operational Land Imager

Most known mineral deposits were discovered by accident using expensive, time-consuming, and knowledge-based methods such as stream sediment geochemical data, diamond drilling, reconnaissance geochemical and geophysical surveys, and/or remote sensing. Recent years have seen a decrease in the number of newly discovered mineral deposits and a rise in demand for critical raw materials, prompting exploration geologists to seek more efficient and inventive ways for processing various data types at different phases of mineral exploration. Remote sensing is one of the most sought-after tools for early-phase mineral prospecting because of its broad coverage and low cost. Remote sensing images from satellites are publicly available and can be utilised for lithological mapping and mineral exploitation. In this study, we extend an artificial intelligence-based, unsupervised anomaly detection method to identify iron deposit occurrence using Landsat-8 Operational Land Imager (OLI) satellite imagery and machine learning. The novelty in our method includes: (1) knowledge-guided and unsupervised anomaly detection that does not assume any specific anomaly signatures; (2) detection of anomalies occurs only in the variable domain; and (3) a choice of a range of machine learning algorithms to balance between explain-ability and performance. Our new unsupervised method detects anomalies through three successive stages, namely (a) stage I – acquisition of satellite imagery, data processing and selection of bands, (b) stage II – predictive modelling and anomaly detection, and (c) stage III – construction of anomaly maps and analysis. In this study, the new method was tested over the Assen iron deposit in the Transvaal Supergroup (South Africa). It detected both the known areas of the Assen iron deposit and additional deposit occurrence features around the Assen iron mine that were not known. To summarise the anomalies in the area, principal component analysis was used on the reconstruction errors across all modelled bands. Our method enhanced the Assen deposit as an anomaly and attenuated the background, including anthropogenic structural anomalies, which resulted in substantially improved visual contrast and delineation of the iron deposit relative to the background. The results demonstrate the robustness of the proposed unsupervised anomaly detection method, and it could be useful for the delineation of mineral exploration targets. In particular, the method will be useful in areas where no data labels exist regarding the existence or specific spectral signatures of anomalies, such as mineral deposits under greenfield exploration.
Vector batch SOM algorithms for multi-view dissimilarity data

Multi-view data has become fairly important since large amounts of information are constantly generated from different sources. So far, most multi-view research on unsupervised learning has focused on clustering algorithms suitable for analyzing vector data. Instead, our study presents two batch SOM algorithms for multi-view dissimilarity data, namely a batch SOM with each cluster representative as a vector of a subset of objects (vector of set-medoids) and another batch SOM for multi-view dissimilarity data with each cluster representative as a matrix whose elements are the weights of its objects regarding the dissimilarity matrices (weighted medoids). These algorithms are suitable for clustering and visualizing multi-view dissimilarity data. In addition, they provide the relevance weights on the importance of each dissimilarity matrix (view) concerning each cluster related to the neurons in the map. Furthermore, the weight can be computed both locally, for each cluster, or globally, for the whole partition. Experiments with 12 datasets showed the importance of multi-view algorithms with respect to single-view algorithms in some contexts. Among the multi-view algorithms, the results showed the need to consider the relevance weights of the dissimilarity matrices. Moreover, an application study on the FOREST TYPE dataset found that some of the proposed methods provide better-quality maps.
Two-step phase-shifting algorithms with background removal and no background removal

• Ten two-step phase-shifting algorithms are compared. • Euclidean matrix norm of sum and difference map and fast least-squares algorithm provides the best overall performance. • Background removal method should be improved for two-step phase-shifting algorithms with background removal. • Iterative time, iterative accuracy and valid phase shift range should be improved for two-step phase-shifting algorithms with no background removal. To balance the accuracy and speed of phase reconstruction, two-step phase-shifting algorithms (TS-PSAs) have been developed. TS-PSAs can be divided into two types, one is TS-PSAs with background removal (TS-PSAs-BR), and the other is TS-PSAs with no background removal (TS-PSAs-NBR). We select 6 well-reputed TS-PSAs-BR and 4 TS-PSAs-NBR proposed by the authors of this paper for performance comparison. In order to remove the background, the Hilbert-Huang Transform (HHT) and mean intensity subtraction are used respectively. We compare 10 TS-PSAs with different fringe types, different levels of noise, different phase shifts, different fringe numbers and computational time in the simulations, and the experiments are also performed to compare them. Finally, we rank TS-PSAs according to their comparative results, and Euclidean matrix norm of sum and difference map and fast least-squares algorithm (EMNSD&FLSA) provides the best overall performance, it can achieve high accuracy and high efficiency.
Supervised Machine Learning of High Rate GNSS Velocities for Earthquake Strong Motion Signals

Abstract High rate Global Navigation Satellite System (GNSS) processed time series capture a broad spectrum of earthquake strong motion signals, but experience regular sporadic noise that can be difficult to distinguish from true seismic signals. The range of possible seismic signal frequencies amidst a high, location‐varying noise floor makes filtering difficult to generalize. Existing methods for automatic detection rely on external inputs to mitigate false alerts, which limit their usefulness. For these reasons, geodetic seismic signal detection makes for a compelling candidate for data‐driven machine learning classification. In this study we generated high rate GNSS time differenced carrier phase (TDCP) velocity time series concurrent in space and time with expected signals from 77 earthquakes occurring over nearly 20 years. TDCP velocity processing has increased sensitivity relative to traditional geodetic displacement processing without requiring sophisticated corrections. We trained, validated and tested a random forest classifier to differentiate seismic events from noise. We find our supervised random forest classifier outperforms the existing detection methods in stand‐alone mode by combining frequency and time domain features into decision criteria. The classifier achieves a 90% true positive rate of seismic event detection within the data set of events ranging from M W 4.8–8.2, with typical detection latencies seconds behind S ‐wave arrivals. We conclude the performance of this model provides sufficient confidence to enable these valuable ground motion measurements to run in stand‐alone mode for development of edge processing, geodetic infrastructure monitoring and inclusion in operational ground motion observations and models.
Image feature selection embedded distribution differences between classes for convolutional neural network

Convolutional neural networks have achieved a great success in feature extraction and classification of images. However, some of the features extracted by convolutional neural networks are with insignificant difference between classes, which not only contribute little to image classification, but also increase the complexity of the classifier. It is important to select features that are helpful for image classification when using convolutional neural network. In view of the existence of class labels of image samples when training classifier, and motivated by the intention that these labels may also play a certain role in feature selection for image classification, we propose a feature selection approach by taking the distribution differences between classes into consideration on the basis of the features extracted by convolutional neural network. To be specific, we use the Gaussian mixture model to approximate the distribution of each feature on each subclass, and select the features significantly contribute to classification by designing a measure of distribution difference according to the numerical characteristics described by Gaussian mixture models. Further, an image classifier can be presented by redesigning the fully connected layers of the convolutional neural network based on the selected features. The proposed feature selection is adopted to image classification, and the experimental results show the effectiveness of the method.
Facial optical flow estimation via neural non-rigid registration

Abstract Optical flow estimation in human facial video, which provides 2D correspondences between adjacent frames, is a fundamental pre-processing step for many applications, like facial expression capture and recognition. However, it is quite challenging as human facial images contain large areas of similar textures, rich expressions, and large rotations. These characteristics also result in the scarcity of large, annotated real-world datasets. We propose a robust and accurate method to learn facial optical flow in a self-supervised manner. Specifically, we utilize various shape priors, including face depth, landmarks, and parsing, to guide the self-supervised learning task via a differentiable nonrigid registration framework. Extensive experiments demonstrate that our method achieves remarkable improvements for facial optical flow estimation in the presence of significant expressions and large rotations.
Joint matrix decomposition for deep convolutional neural networks compression

Deep convolutional neural networks (CNNs) with a large number of parameters require intensive computational resources, and thus are hard to be deployed in resource-constrained platforms. Decomposition-based methods, therefore, have been utilized to compress CNNs in recent years. However, since the compression factor and performance are negatively correlated, the state-of-the-art works either suffer from severe performance degradation or have relatively low compression factors. To overcome this problem, we propose to compress CNNs and alleviate performance degradation via joint matrix decomposition, which is different from existing works that compressed layers separately. The idea is inspired by the fact that there are lots of repeated modules in CNNs. By projecting weights with the same structures into the same subspace, networks can be jointly compressed with larger ranks. In particular, three joint matrix decomposition schemes are developed, and the corresponding optimization approaches based on Singular Value Decomposition are proposed. Extensive experiments are conducted across three challenging compact CNNs for different benchmark data sets to demonstrate the superior performance of our proposed algorithms. As a result, our methods can compress the size of ResNet-34 by 22× with slighter accuracy degradation compared with several state-of-the-art methods.
A Self‐adaptive Bald Eagle Search optimization algorithm with dynamic opposition‐based learning for global optimization problems

Abstract Bald Eagle Search optimization (BES) is introduced recently, which mimics the bald eagles' hunting and food searching behaviour. The capability of a BES algorithm is enhanced in this paper by avoiding local optima stagnation and premature convergence problems. The BES algorithm is modified to enhance the performance of the algorithm and the modified algorithm is called the Self Adaptive Bald Eagle Search (SABES) algorithm. Dynamic‐opposite Learning (DOL) method is invoked in the initialization phase to increase the population diversity and convergence speed. To find better global solutions, the exploitation capability of the SBES algorithm is enhanced by considering the dynamic‐opposite solutions. In addition, the algorithmic parameter values of the BES algorithm have been determined using the linear and non‐linear time‐varying adaption strategy to create a balance between the search abilities which promotes the overall performance of the algorithm. The performance of the SABES algorithm is validated by comparing the results of 50 benchmark functions and CEC2017 functions with different erstwhile algorithms. The proposed algorithm achieves the best results in 80% of the benchmark functions, whereas the BES only gets the best results in 56% of functions. For the CEC2017, the SABES algorithm achieves optimal results for 20 functions which are highest in comparison to state‐of‐the‐art algorithms. The feasibility and effectiveness of the proposed algorithm are checked using the 15 CEC2020 competition real‐world single‐objective constrained optimization problems. The SABES achieves a 100% success rate and 100% feasibility rate in comparison to other well‐regarded algorithms. The statistical significance of the algorithm has been proved using Friedman's mean rank and Wilcoxon sign rank test.
Photonic reservoir computing for nonlinear equalization of 64-QAM signals with a Kramers–Kronig receiver

Abstract Photonic reservoirs are machine learning based systems that boast energy efficiency and speediness. Thus they can be deployed as optical processors in fiber communication systems to aid or replace digital signal equalization. In this paper, we simulate the use of a passive photonic reservoir to target nonlinearity-induced errors originating from self-phase modulation in the fiber and from the nonlinear response of the modulator. A 64-level quadrature-amplitude modulated signal is directly detected using the recently proposed Kramers–Kronig (KK) receiver. We train the readout weights by backpropagating through the receiver pipeline, thereby providing extra nonlinearity. Statistically computed bit error rates for fiber lengths of up to 100 km fall below 1 × 10 −3 bit error rate, outperforming an optical feed-forward equalizer as a linear benchmark. This can find applications in inter-datacenter communications that benefit from the hardware simplicity of a KK receiver and the low power and low latency processing of a photonic reservoir.
Explanation sets: A general framework for machine learning explainability

Explainable Machine Learning (ML) is an emerging field of Artificial Intelligence that has gained popularity in the last decade. It focuses on explaining ML models and their predictions, enabling people to understand the rationale behind them. Counterfactuals and semifactuals are two instances of Explainable ML techniques that explain model predictions using other observations. These techniques are based on the comparison between the observation to be explained and another one. In counterfactuals, their prediction is different, and in semifactuals, it is the same. Both techniques have been studied in the Social Sciences and Explainable ML communities, and they have different use cases and properties. In this paper, the Explanation Set framework, an approach that unifies counterfactuals and semifactuals, is introduced. Explanation Sets are example-based explanations defined in a neighborhood where most observations satisfy a grouping measure. The neighborhood allows defining and combining restrictions. The grouping measure determines if the explanations are counterfactuals (dissimilarity) or semifactuals (similarity). Besides providing a unified framework, the major strength of the proposal is to extend these explanations to other tasks such as regression by using an appropriate grouping measure. The proposal is validated in a regression and classification task using several neighborhoods and grouping measures.
A survey of continuous subgraph matching for dynamic graphs

Abstract With the rapid development of information technologies, multi-source heterogeneous data has become an open problem, and the data is usually modeled as graphs since the graph structure is able to encode complex relationships among entities. However, in practical applications, such as network security analysis and public opinion analysis over social networks, the structure and the content of graph data are constantly evolving. Therefore, the ability to continuously monitor and detect interesting patterns on massive and dynamic graphs in real-time is crucial for many applications. Recently, a large group of excellent research works has also emerged. Nevertheless, these studies focus on different updates of graphs and apply different subgraph matching algorithms; thus, it is desirable to review these works comprehensively and give a thorough overview. In this paper, we systematically investigate the existing continuous subgraph matching techniques from the aspects of key techniques, representative algorithms, and performance evaluation. Furthermore, the typical applications and challenges of continuous subgraph matching over dynamic graphs, as well as the future development trends, are summarized and prospected.
Revocable attribute-based encryption from standard lattices

Attribute-based encryption (ABE) is an attractive extension of public key encryption, which provides fine-grained and role-based access to encrypted data. In its key-policy flavor, the secret key is associated with an access policy and the ciphertext is marked with a set of attributes. In many practical applications, and in order to address scenarios where users become malicious or their secret keys are compromised, it is necessary to design an efficient revocation mechanism for ABE. However, prior works on revocable key-policy ABE schemes are based on classical number-theoretic assumptions, which are vulnerable to quantum attacks. In this work, we propose the first revocable key-policy ABE scheme that offers an efficient revocation mechanism while maintaining fine-grained access control to encrypted data. Our scheme is based on the learning with errors (LWE) problem, which is widely believed to be quantum-resistant. Our scheme supports polynomial-depth policy function and has short secret keys, where the size of the keys depends only on the depth of the supported policy function. Furthermore, we prove that our scheme satisfies selective revocation list security in the standard model under the LWE assumption.
TFUN: Trilinear Fusion Network for Ternary Image-Text Retrieval

Recently, a particular type of image-text retrieval task named Ternary Image-Text Retrieval (TITR) has drawn increasing attention. In this task, the total inputs of query and target consist of three components, rather than two inputs in the widely-studied retrieval case. The typical TITR scenarios include recipe retrieval (e.g., ingredients text, instructions text and food images) and fashion search (e.g., original images, text and modified images). A few recently proposed TITR methods mainly focus on learning the semantic correlations of two modality data by projecting them to the same embedding space to capture the alignment between image and text modalities. Nevertheless, two limitations still exist in these methods: 1) the underlying difference between data in the same modality (e.g., ingredients and instructions) is neglected; and 2) the trilinear interaction among the three inputs is implicitly captured. To this end, we propose a novel fusion framework named Trilinear FUsion Network (TFUN) to utilize high-level associations between these three inputs simultaneously and learn an accurate cross-modal similarity function via bi-directional triplet loss explicitly, which is generic for the TITR task. To reduce the model complexity, we introduce the advanced method of tensor decomposition to ensure computational efficiency and accessibility. We also develop a three-stage hard triplet sampling scheme to ensure fast convergence. Extensive experiments on three large-scale TITR datasets Recipe1M, Fashion200k and FashionIQ demonstrate the superiority of our proposed TFUN model compared to the state-of-the-art approaches. The implementation code and additional instructions are provided at https://github.com/CFM-MSG/Code_TFUN.
Multimodal Interface for Human–Robot Collaboration

Human–robot collaboration (HRC) is one of the key aspects of Industry 4.0 (I4.0) and requires intuitive modalities for humans to communicate seamlessly with robots, such as speech, touch, or bodily gestures. However, utilizing these modalities is usually not enough to ensure a good user experience and a consideration of the human factors. Therefore, this paper presents a software component, Multi-Modal Offline and Online Programming (M2O2P), which considers such characteristics and establishes a communication channel with a robot with predefined yet configurable hand gestures. The solution was evaluated within a smart factory use case in the Smart Human Oriented Platform for Connected Factories (SHOP4CF) EU project. The evaluation focused on the effects of the gesture personalization on the perceived workload of the users using NASA-TLX and the usability of the component. The results of the study showed that the personalization of the gestures reduced the physical and mental workload and was preferred by the participants, while overall the workload of the tasks did not significantly differ. Furthermore, the high system usability scale (SUS) score of the application, with a mean of 79.25, indicates the overall usability of the component. Additionally, the gesture recognition accuracy of M2O2P was measured as 99.05%, which is similar to the results of state-of-the-art applications.
Collaborative Defense-GAN for protecting adversarial attacks on classification system

With rapid progress and significant successes in a wide domain of applications, deep learning has been extensively employed for solving complex problems. However, performance of deep learning has been vulnerable to well-designed samples, called adversarial samples. These samples are carefully designed to deceive the deep learning models without human perception. Therefore, vulnerability to adversarial attacks becomes one of the major concerns in life-critical applications of deep learning. In this paper, a novel approach to counter adversarial samples is proposed to strengthen the robustness of a deep learning model. The strategy is to filter the perturbation noise in adversarial samples prior to prediction. The proposed defense framework is based on DiscoGANs to discover the relation between attacker and defender characteristics. Attacker models are created to generate the adversarial samples from the training data, while the defender model is trained to reconstruct original samples from the adversarial samples. These two frameworks are trained to compete with each other in an alternating manner. The experimental results on different attack models are compared with popular defense mechanisms on three benchmark datasets. Our proposed method shows promising results and can improve the robustness on both white-box and black-box attacks including the computation time.