Reviewerly/exHarmony huggingface dataset:
A reconnaissance study for tracing and ordering new mineralisation zones using integrated remote sensing, GIS, and aeromagnetic techniques, west Allaqi-Heiani-Suture,Egypt

Reducing expenses is a critical aspect of natural resource explorations, especially in the reconnaissance stage. Accordingly, GIS algorithms using the free remote sensing data were used to rank the probable mineralised zones before any future implementations of the high-cost drilling and/or detailed geophysical programmes. Geological and tectonic settings, and being a historical mineral resource were motivations to choose the Allaqi-Heiani Suture (AHS), Upper Egypt, as a study area. Remote sensing data were processed for tracing the probably mineralised alteration zones, determining fractures and dominating structural systems, and mapping different geological units. Various alteration minerals including hydroxyls–carbonates, ferrous and ferric iron oxides were detected over different rock units using band ratio (BR) and Crosta techniques. Different geological units were distinguished using false colour composite (FCC) images. Moreover, an intense fractured system, demonstrating the area, was revealed by tracing lineaments using informative, spatially enhanced remote-sensed images. Fractures within the altered rocks represent 35% of the total fracturing system in the AHS to indicate a structural system controlling mineralisation occurrences. The processing of magnetic data suggested a deep/buried nature of these remotely detected lineaments/structures. Reduced to pole (RTP) and tilt derivative (TDR) maps detected the buried magnetic sources, whereas zero contours of TDR, Euler deconvolution, analytic signal and source parameter imaging (SPI) techniques traced buried contacts/structures and estimated their depths. A GIS strategy depending on the intensity of the fracturing system and the presence of more than one type of alteration was applied to potential-rank the prospective mineralised occurrences over AHS. The correlation between ranked zones with the old/known mines showed encouraging evaluation.
Secure post‐quantum group key exchange: Implementing a solution based on Kyber

Abstract Quantum computing poses fascinating challenges for current cryptography, threatening the security of many schemes and protocols widely used today. To adapt to this reality, the U.S. National Institute for Standards and Technology (NIST) is currently running a standardization process in search of post‐quantum (classical, yet resistant to quantum attacks ) cryptographic tools, focusing on signature schemes and key encapsulation mechanisms. Many of the competing proposals also include designs for two‐party key exchange, which can be combined in different ways to fit scenarios involving parties, that is, yielding group key exchange protocols. However, very few implementations of such group protocols are available to practitioners, which face a non‐trivial challenge when deciding how to implement a protocol for establishing secure group sessions in this new post‐quantum scenario. With this in mind, the authors report on the implementation of a secure post‐quantum group key exchange protocol in the so‐called Quantum Random Oracle Model . The protocol decided to implement is based on a KEM called Kyber , which is one of the finalists of the NIST competition. Not only this group construction is the only one available in the literature using a NIST finalist, but also, among all post‐quantum designs the authors are aware of, it uses this strongest security model (as, e.g. in other proposals, the adversarial interaction with the hash functions of the system is assumed to be exclusively classical). Furthermore, experimental evidence is provided supporting this choice in terms of performance, even if the number of involved entities is large (up to 2000). All data and code are publicly available.
Double Branch Rebalanced Network for Class Imbalance Learning in Process Fault Diagnosis

In the process of industrial production, the equipment is operating at normal conditions for most of the time, failures are generally rare. Therefore, the real-world data set collected from industrial processes always have skewed distributions; the data in normal conditions are more than that in fault conditions, which result in a class imbalance (CI) problem. Skewed data distributions will affect the ability of feature learning of the monitoring model. To overcome the CI problem, in this paper, a method named double branch rebalanced network (DBRN) is proposed. DBRN is a two-stage method, which learns two decision boundaries by designed convolutional neural network branches. First, in the resampling branch, a novel resampling method is proposed to rebalance the classes from the data-level. Second, a cost adaptive reweighting strategy is developed in the reweighting branch to rebalance the cost of each class. Finally, a fusing learning strategy is designed to fuse these two branches. To describe the proposed method, the experiment-based Tennessee Eastman (TE) data set is constructed with different imbalance ratios. The experiment results show that the proposed DBRN method achieves better performance in CI fault diagnosis.
Wavelet detail perception network for single image super-resolution

Single image super-resolution (SR) is an important topic in computer vision because of its ability to generate high-resolution (HR) images. Traditional SR methods do not pay attention to high-frequency detail perception in the reconstruction process, resulting in unrealistic high-frequency details of images. To address the problem of over-smoothing of details, a novel wavelet detail perception network (WDPNet) is proposed in this study. Different from traditional SR methods that directly restore high-resolution images, the proposed WDPNet decomposes images into low-frequency and high-frequency sub-images by wavelet transform and then uses different models to train these sub-images. Moreover, low-frequency structures are also provided to the high-frequency model to further recover and enhance high-frequency details through the proposed low-to-high information delivery (L2HID) and detail perception enhancement (DPE) mechanisms. Finally, the low-frequency and high-frequency models are fused and weighted to different degrees to enhance image details further. Compared with the state-of-the-art methods, the experimental results show that the proposed WDPNet achieves better performance and visual results in image detail perception.
An efficient fault-tolerant distributed Bayesian filter based on conservative fusion

This paper proposes a fault-tolerant distributed Bayesian filter for multi-sensor state estimation using a peer-to-peer sensor network with incoherent local estimates problems. The proposed approach uses a Gaussian mixture rather than a single Gaussian distribution to represent the fusion result, which can effectively reduce the negative impact of corrupted local estimates on the fusion results. The resulting filter performs Bayesian recursion via Gaussian mixture. To accommodate a heterogeneous sensor network, we develop a novel arithmetic average fusion employing a set of covariance-dependent weighting coefficients, where the fusion error covariance is effectively reduced in the case of fusing information with different qualities. For intersensor communication, a partial flooding scheme is investigated, in which only valid-likely Gaussian components are disseminated and fused between neighbor sensors. Theoretically, it is shown that under reasonable assumptions, the presented fault-tolerant distributed estimator can guarantee local stability with the exponentially bounded estimation error in the mean square. The effectiveness and superiority of our approach are validated through both simulation and experiment scenarios.
Latent Features Embedded Dynamic Graph Evolution Deep Clustering Network

As a typical unsupervised machine learning task, clustering is always a hot research topic. Motivated by deep learning approaches, deep clustering has become prevalent in recent years, and achieves appealing performance. Most of current deep clustering methods focus on learning a new discriminative representation to enhance separability of original data, i.e., autoencoder, multilayer perceptrons and deep belief networks. However, the structure information which is very important in unsupervised learning, attracts little attention in previous deep feature representation learning clustering works. In this paper, a dynamic graph evolution based graph convolutional network (DGE-GCN) is introduced for clustering task, in which the data structural information and learned latent features are integrated into a unified network for deep clustering. Instead of using a fixed graph during the graph convolution process, we design a dynamic graph evolution strategy to refine the initial graph which could be not accurate. In addition, the latent representations learned by an autoencoder are embedded for refining the graph in a layer-wise manner. In such a manner, the latent features can help improve the graph structure, while the refined graph can in turn constrain the autoencoder to learn more discriminative features. In order to unify the graph convolutional network branch and autoencoder branch, a dual self-supervised mechanism is designed to guide the parameter learning of the whole network architecture. Comprehensive experiments demonstrate that the proposed network consistently performs better than several state-of-the-art methods on various benchmark datasets.
Social interaction model enhanced with speculation stage for human trajectory prediction

Accurate human trajectory prediction is still challenging due to the complicated interactions with surroundings. A Spatio-Temporal Graph Convolution Neural Network based Social Interaction Model (STGCNN-SIM) is proposed to address this challenge. In addition to historical trajectory information, the presented method employs the speculated trajectories in the future to extract social interactive features and model interaction behaviors. Three social interactive features are extracted explicitly from the observed and speculated trajectories: (1) the relative distance, (2) the angle between the velocity vectors of two interacting partners, and (3) the angles between the velocity vectors of interacting partners and the distance vector. STGCNN-SIM utilizes these social interactive features to model interactions with surroundings in the historical and speculated stages. Then an attention mechanism is adopted to improve the model by focusing on more relevant features. Experimental results on three public datasets demonstrate that STGCNN-SIM achieves higher accuracy and stability than the state-of-the-art methods.
How close is a quad mesh to a polycube?

We compute the shortest sequence of local connectivity modifications that transform a genus 0 quad mesh to a polycube. The modification operations are (dual) loop preserving and thus, we are restricted to quad meshes where loops don't self-intersect and two loops intersect at most twice. The intersection patterns of the loops are encoded in a simplicial complex, which we call loop complex. To formulate the modification search over the loop complex, we characterise polycubes combinatorially and determine dependencies between modifications. We show that the full task can be encoded as a mixed-integer problem that is solved by a commodity MIP-solver. We demonstrate the practical feasibility by a number of examples with varying complexity.
A versatile mathematical function for generating stable and chaotic systems: A data encryption application

Being the generation of chaos through a practical function still a current challenge, here we present a new function called Tukmuky Jinma’any-Function (TJF), highly programable, that generates chaos for practical applications. TJF provides complex behaviour with easy implementation, generating either periodic, quasi-periodic, weak, or strong chaotic trajectories. As an example of its applicability, we use this function as the entropy source of a symmetric encryption algorithm, using an image bitstream as test data with outstanding results, overcoming the drawbacks of previous chaos-based cryptosystems; e.g. the encryption uses strong keys and does not present gaps in its key space.
Rethinking low-cost microscopy workflow: Image enhancement using deep based Extended Depth of Field methods

Microscopic techniques in low-to-middle income countries are constrained by the lack of adequate equipment and trained operators. Since light microscopy delivers crucial methods for the diagnosis and screening of numerous diseases, several efforts have been made by the scientific community to develop low-cost devices such as 3D-printed portable microscopes. Nevertheless, these devices present some drawbacks that directly affect image quality: the capture of the samples is done via mobile phones; more affordable lenses are usually used, leading to poorer physical properties and images with lower depth of field; misalignments in the microscopic set-up regarding optical, mechanical, and illumination components are frequent, causing image distortions such as chromatic aberrations. This work investigates several pre-processing methods to tackle the presented issues and proposed a new workflow for low-cost microscopy. Additionally, two new deep learning models based on Convolutional Neural Networks are also proposed (EDoF-CNN-Fast and EDoF-CNN-Pairwise) to generate Extended Depth of Field (EDoF) images, and compared against state-of-the-art approaches. The models were tested using two different datasets of cytology microscopic images: public Cervix93 and a new dataset that has been made publicly available containing images captured with μSmartScope. Experimental results demonstrate that the proposed workflow can achieve state-of-the-art performance when generating EDoF images from low-cost microscopes.
Less is not more: We need rich datasets to explore

Traditional datacenter analysis is based on high-level, coarse-grained metrics. This obscures our vision of datacenter behavior, as we do not observe the full picture nor subtleties that might make up these high-level, coarse metrics. There is room for operational improvement based on fine-grained temporal and spatial, low-level metric data. We leverage in this work one of the (rare) public datasets providing fine-grained information on datacenter operations, with over 60 billion measurements captured in 15-second intervals. We show evidence that fine-grained information reveals new operational aspects, that the different metrics cannot be derived from one another (and thus need to be captured), and that many low-level metrics, gathered frequently are key to understanding datacenter operations. We propose a holistic analysis for datacenter operations, providing statistical characterization of node and workload aspects. Our analysis reveals both generic and machine learning-specific aspects, summarized in over 30 observations, providing deep insight into this dataset and the originating cluster. We give actionable insights, surprising findings, and exemplify how our observations support performance-engineering tasks such as workload prediction and long-term datacenter design.
A fuzzy scheduler for MAS applied to object tracking

Intelligent industry and Internet of Things (IoT) rely on sensors to monitor the surroundings and allow information exchange. More sensors deployed in an environment offer additional benefits, such as increased availability and more accurate observations, as they generate information from different perspectives. Nonetheless, dealing with large amounts of data and multiple input sources to deduct unified information often requires considerable processing, communication, and energy resources. To address such a problem, this work examines intelligent agents that cooperate to generate a unified output in a Multi-Agent System (MAS) environment. The proposed approach presents a fuzzy scheduler that evaluates the processing and accuracy level to determine the relevance of each agent by comparing its information with the unified output. The proposal orchestrates when an agent can have its processing suspended. Our evaluations demonstrate that this proposal improves service level metrics, such as the rate of video frames processed per second for an increasing number of agents. We consider three experimental setups using tracking agents. First, we evaluate the threshold and how long an agent should remain idle when its observations are irrelevant. The second experiment considers an increasing number of agents. Agent scheduling achieves a frame processing rate 12.5 times faster while suffering a 6% accuracy loss for 20 agents. The third experiment focuses on using different tracking algorithms simultaneously. In this case, compared to when no agent scheduling is applied, there is a 23.9 fold increase in the frame processing rate while losing some accuracy, limited to around 6%.
A local-global shape characterization scheme using quadratic Bezier triangle aiding retrieval

Shape characterization plays a highly prominent role in retrieval and relies extremely upon descriptors inbuilt with lightweight operations and compaction qualities. However, realizing such a simple and robust shape descriptor capable of dealing with noise, variations in brightness, and deformations pose a significant challenge. In this regard, a simple and effective shape descriptor using the Quadratic Bezier Triangle (QBT) targeting shape matching and retrieval is presented in this work. The mechanism commences with triangular tessellation of each image followed by determining their side-wise intensity differences that are then mapped to QBT vertices to yield the order-wise control points. The maxima of the resulting points are transformed into the binary equivalent of the given shape that is compacted into octal values. Later, these localized values are globally transformed into shape histograms to yield the QBT-based Feature (QBTF) descriptor representing the given input. QBTFs performance is exhaustively analyzed in terms of Bulls Eye Retrieval (BER) score and classification accuracy using the public datasets namely Kimia-99, MPEG-7 CE-1 part B, PHOS, and Tari-1000. Relative BER investigations witnessed across diverse datasets reveal a consistent and improved score of 93% achieved by this scheme over its peers. Mathematical analysis of invariance, noise resilience, and the complexities involved in realizing QBTF, establish the robustness and suitability of this scheme for real-time shape description.
An impartial trimming algorithm for robust circle fitting

Accurate circle fitting can be seriously compromised by the occurrence of even few anomalous points. Then, it is proposed to resort to a robust fitting strategy based on the idea of impartial trimming. Malicious data are supposed to be deleted, whereas estimation only relies on a set of genuine observations. The procedure is impartial in that trimmed points are not decided in advance but they are detected simultaneously to parameters estimation, according to an iterative algorithm: in each step a fixed proportion of the data is trimmed after sorting their geometric distances from the current fitted circle in non decreasing order. A reweighting step is also considered to improve the quality of the fit and make it less dependent on the selected trimming level. The global robustness properties of the method are established. The finite sample behavior of the proposed estimator has been investigated according to some numerical studies and real data examples.
Protection Efficiency Assessment Method Key Infrastructures in Distributed Ledger Technology

Against the backdrop of the tense economic situation in the world, the dependence of payment systems on political beliefs and the will of their administrators, the sanctions burden, the forced use of electronic financial services during the pandemic, the constant presence of the financial and economic system under the threat of cyber-attacks, the emergence of new schemes for legalizing income received by criminal means, there is an objective need to improve financial and economic processes. One of the areas of improvement is the development of distributed ledger technology in the economic sphere. At the same time, the active development and implementation of distributed ledger technology in the economies of countries creates new unsolved problems. One such task is to evaluate the security of the key infrastructure handled by such systems. Within the framework of the article, the methodology for assessing the compliance of information security is adapted in relation to assessing the effectiveness of protecting the key infrastructure in distributed registries. To achieve this goal, a methodology for assessing the compliance of information protection with GOST R 57580.2 was considered, and adaptation was carried out in relation to the technology of distributed registries, taking into account its features. In conclusion, a methodology for evaluating the effectiveness of protecting the key infrastructure within the framework of distributed ledger technology is proposed, requirements for the calculation of complex indicators and assessments are formulated, and conclusions are drawn about the appropriateness of applying the proposed methodology.
Global video object segmentation with spatial constraint module

Abstract We present a lightweight and efficient semi-supervised video object segmentation network based on the space-time memory framework. To some extent, our method solves the two difficulties encountered in traditional video object segmentation: one is that the single frame calculation time is too long, and the other is that the current frame’s segmentation should use more information from past frames. The algorithm uses a global context (GC) module to achieve high-performance, real-time segmentation. The GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real time. Moreover, the prediction mask of the previous frame is helpful for the segmentation of the current frame, so we input it into a spatial constraint module (SCM), which constrains the areas of segments in the current frame. The SCM effectively alleviates mismatching of similar targets yet consumes few additional resources. We added a refinement module to the decoder to improve boundary segmentation. Our model achieves state-of-the-art results on various datasets, scoring 80.1% on YouTube-VOS 2018 and a $${\cal J}{\rm{\&amp; }}{\cal F}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>J</mml:mi> <mml:mtext>&amp;</mml:mtext> <mml:mi>F</mml:mi> </mml:mrow> </mml:math> score of 78.0% on DAVIS 2017, while taking 0.05 s per frame on the DAVIS 2016 validation dataset.
Algorithm of Reconstruction 3D Model by Two of Its Images

The article deals with the problem of calculating cloud of points of 3D model by its images obtained by two and more cameras. Algorithm of this reconstruction is based on calculation coordinates of each points by its projection from two cameras using fact that projection of ray joining center one of these camera doesn’t contain projection more one point on other camera. Such situation can be achieved by small motion of cameras. Also the problem of reconstruction 3D model by two its images is solved in the article. We consider parameterized geometric models and reconstruction problem reduce to fitting vertices of 3D model to calculated cloud of points. To solve this issue we use calculation the vectors of central moments of vertices of 3D model and cloud of points and minimization of difference between them. In addition we present algorithm of cleaning extra points which are erroneously calculated. This algorithm is based on geometric hashing of the calculated cloud of points in 3D box using images from three and more cameras.