Reviewerly/exHarmony huggingface dataset:
Prediction of Multiple Diseases Using Machine Learning Techniques

As science and medication create, authentic clinical examination is significant for early discovery, patient consideration, and public assistance. Assuming the nature of clinical data isn't completely met, examination will be decreased. Moreover, various areas show explicit attributes of certain sicknesses in the locale, which might prompt a debilitating of infection forecast. In the proposed framework, it gives AI research calculations to anticipate the different sicknesses that happen in the weakest families. It inspects the correlations made over the existence of the clinic gathered. Recuperate deficient data utilizing stowed away items to defeat fragmented data. Provincial persistent mind contamination test. Use clinic introduced and unstructured data utilizing AI calculations. It predicts sicknesses that can be brought about by mining, for example, Covid-19, persistent kidney infection and coronary illness. For our situation, neither of the current exercises in the field of enormous scope data investigation medication centers around these two kinds. Contrasted with most examination calculations, the exactness level of the proposed calculation is roughly 94.8% and is quicker than the AI infection calculation.
Usefulness of Collaborative Work in the Evaluation of Prostate Cancer from MRI

The aim of this study is to show the usefulness of collaborative work in the evaluation of prostate cancer from T2-weighted MRI using a dedicated software tool. The variability of annotations on images of the prostate gland (central and peripheral zones as well as tumour) by two independent experts was firstly evaluated, and secondly compared with a consensus between these two experts. Using a prostate MRI database, experts drew regions of interest (ROIs) corresponding to healthy prostate (peripheral and central zones) and cancer. One of the experts then drew the ROI with knowledge of the other expert's ROI. The surface area of each ROI was used to measure the Hausdorff distance and the Dice coefficient was measured from the respective contours. They were evaluated between the different experiments, taking the annotations of the second expert as the reference. The results showed that the significant differences between the two experts disappeared with collaborative work. To conclude, this study shows that collaborative work with a dedicated tool allows consensus between expertise in the evaluation of prostate cancer from T2-weighted MRI.
ESM: Selfish mining under ecological footprint

By deviating from an honest mining strategy, attackers can obtain additional rewards through mining attacks. Selfish mining is the most well-known mining attack. It includes selfish mining (SM1), optimal selfish mining (∊-optimal), and bribed selfish mining (BSM), etc. The mining models addressed in prior studies, however, are based on the Nakamoto consensus. Therefore, this paper proposes the ESM model (Selfish Mining under Ecological model). Our new model is fully compatible with fewer resource expenditure requirements. Meanwhile, we also describe a novel selfish mining attack that allows the malicious pool to gain additional revenue by using an ESM attack rather than SM1 when its power is less than 25%. Furthermore, we present an EBSM (Bribery Selfish Mining under Ecological) attack that combines ESM together with the bribery attack. As a result, under EBSM, the malicious solo miner has a chance to gain additional revenue. Finally, we undertake quantitative analyses and simulations to assess the effectiveness of our attacks.
Vision-based Deep Learning algorithm for Underwater Object Detection and Tracking

Remotely operated (ROV) and autonomous underwater (AUV) vehicles are the efficient tools that are used effectively for different ocean expedition and exploration-based missions. Underwater image processing is one of the challenging tasks in real time and post processing of the recorded underwater visuals. The challenges are due to the water turbidity, scattering, and poor visibility of the light. The acquired underwater visuals should be post-processed using image enhancement techniques for object detection. In this paper, terrestrial based deep learning-based object detection techniques and image enhancement techniques are adapted for underwater object detection in shallow waters. Simulations are carried out for known and pre-trained submerged docking station images for detection and tracking in different water quality scenarios. The proposed technique uses a transfer learning-based object detection algorithm using YOLOV4 (You Only Look Once) model with existing image enhancement techniques and the simulation results show that there is a significant increase in the accuracy of underwater docking station detection using the proposed model compared to the traditional models. The kinematics are computed for the detected object from the dynamic underwater vehicle in different scenarios.
Hybrid Electromagnetic Signal Feature Extraction

In the complex electromagnetic environment, the large number of pulse loss (data loss) and the serious overlap of signal parameter space destroy the signal regularity used by the traditional sorting method and finally make the existing signal sorting algorithm difficult to obtain satisfactory sorting effect or even completely invalid. On the basis of considering several key characteristic indexes (pulse width, carrier frequency, and arrival time), a multiparameter and five-layer mutual coupling sorting algorithm is designed. In particular, a new key feature index extraction method is proposed to study the feature vectors contained in the data and establish the corresponding mathematical model, which is finally applied to the sorting of various communication signals. The numerical results show that the five-step sorting method can be used to separate signals with serious overlap and partial missing data.
Combat network link prediction based on embedding learning

Link prediction of combat networks is of significant military value for precisely identifying the vital infrastructure of the enemy target and optimizing the operational plan of our side. Due to the profound uncertainty in the battleground circumstances, the acquired topological information of the opponent combat network always presents sparse characteristics. To solve this problem, a novel approach named network embedding based combat network link prediction (NECLP) is put forward to predict missing links of sparse combat networks. First, node embedding techniques are presented to preserve as much information of the combat network as possible using a low-dimensional space. Then, we put forward a solution algorithm to predict links between combat networks based on node embedding similarity. Last, massive experiments are carried out on a real-world combat network case to verify the validity and practicality of the proposed NECLP. This paper compares six baseline methods, and experimental results show that the NECLP has outstanding performance and substantially outperforms the baseline methods.
Medievals and Moderns in Conversation: Co-Designing Creative Futures for Underused Historic Churches in Rural Communities

For many living in rural areas, the loss of traditional community assets and increased social fragmentation are a common feature of everyday life. The empty village church is a poignant symbol of these challenges; yet, these are sites that hold considerable potential for new placemaking solutions that respond to the needs of communities today. This means looking beyond “the traditional village church” to recognise a longer history of church adaptation and resilience within the lives of communities. In this paper we ask: how can co-design, projected through a Wicked problems and Clumsy solutions lens, help imagine new futures for communities and their historic churches today? Clumsy solutions consider a plurality of different perspectives on the nature of problems and their resolution to deliver more effective solutions with broad appeal. In the search for clumsiness, we turn to ‘long history’ and ‘slow technology’ for inspiration, uncovering deeper resonance with historical communities of place and anchoring that continuity within church sites themselves. Our paper demonstrates how Wicked/Clumsy thinking can account for the challenges faced by rural communities today, bootstrap co-design activities in the development of clumsy solutions, and uncover clumsiness in long history and slow technology dimensions—together laying the foundation for new placemaking strategies.
The hydrothermal footprint of the Crixás deposit: New vectors for orogenic gold exploration in central Brazil

The main gold deposits of the Archaean-Paleoproterozoic Terrain of Goiás, in central Brazil, occur in the Crixás Greenstone Belt, one of the five volcanosedimentary sequences of this terrain. The Crixás orogenic gold deposit consists of several orebodies structurally controlled by low to moderate angle thrust faults, hosted in different stratigraphic units that exhibit contrasting mineralization styles and hydrothermal alteration assemblages. This study characterizes the hydrothermal footprints of the Palmeiras Structure and Structure IV, which control the orebodies hosted in the hydrothermally-altered mafic metavolcanic rocks and carbonaceous schists, respectively. We assessed the magnitude of fluid-rock interactions by integrating mineralogical, geochemical, and petrophysical data with the reflectance spectroscopy results for drill core samples for both structures. The Palmeiras Structure hydrothermal footprint is characterized by a distal halo composed of Fe-Mg chlorite + biotite ± carbonates (calcite and Fe dolomite), followed by the intermediate halo defined by epidote + carbonates (dolomite, Fe dolomite, and ankerite) ± Fe-Mg chlorite, and proximal halo composed of muscovite + Fe-enriched chlorite ± paragonite, magnetite, tourmaline, Fe dolomite, and ankerite. Additionally, the chemical pattern of the hydrothermal alteration along the Palmeiras Structure has significantly increased concentrations of alkali (K2O, Na2O, Rb, Cs, Li, and Ba), as well as Fe2O3, Al2O3, TiO2, LOI, U, W, Th, and Ta towards the gold mineralization. Also, the high magnetite concentration resulted in high magnetic susceptibility and density, with values above 1432.00×10–3 SI. In Structure IV, the proximal alteration assemblage comprises muscovite + Fe dolomite, Fe-Mg chlorite + arsenopyrite. The lithogeochemical signature is characterized by enrichment in SiO2, CaO, Na2O, K2O, P, Ba, and pathfinder elements As, W, Sb, Te, that increase towards the gold mineralization. A new potential target hosted in the hydrothermalized metabasalt was identified, whose signature is defined by the occurrence of muscovite, chlorite and enrichment in K2O, Ba, Rb, Li Cs and pathfinder elements such as As, W, Sb, Te. This study demonstrates that the integrated application of reflectance spectroscopy, petrophysics and chemical analyses provide a powerful tool for characterizing the hydrothermal footprint and determining the vectors toward gold mineralization in regional-scale mineral exploration.
A “Weighted” Geochemical Variable Classification Method Based on Latent Variables

Clustering of variables relies on relationships among them. The strength of those relationships is generally measured by the correlation coefficients between pairs of variables. This paper proposes specified variable weighted correlation coefficients and takes the clustering around latent variables (CLV) approach as an example to transform the common clustering method into a “weighted” clustering method. The aim is to eliminate factors that are unrelated to the variable that was adopted for weighting to ensure that the cluster centers are sufficiently different and have good correlations with the adopted variable. A log-transformed dataset was used to evaluate the proposed method. Three clusters were obtained under the restriction of the As element, and they represented three ore-controlling factors related to the Goldenville Formation, namely geologic features such as formation, fault contacts, and granitoid intrusions. Not only did the new cluster centers account for most of the variability related to the weighted element (As) but they also showed significant differences in spatial distributions.
DMS-SK/BLSTM-CTC Hybrid Network for Gesture/Speech Fusion and Its Application in Lunar Robot–Astronauts Interaction

In the future manned lunar exploration mission, astronauts would work with the lunar robots, which has a high requirement for human–robot interaction (HRI). As the accuracy of gesture recognition interaction does not fulfill the requirement for human–robot joint exploration missions, we propose the DMS-SK/BLSTM-CTC hybrid network to improve the performance of HRI. For gesture recognition, considering VGG-SK has low accuracy and complex architecture, we delete the fourth convolution module, optimize the last global pooling layer, introduce dilated convolution block and multiscale convolution block in VGG-SK, and get the DMS-SK-based gesture recognition sub-network. Compared with the traditional recognition methods, the accuracy and performance of DMS-SK improve. For speech recognition, considering that Bidirectional long–short-term memory unit (BLSTM) has the advantages of processing temporal information, and the Connectionist Temporal Classification (CTC) algorithm can simplify speech data preprocessing, we use BLSTM based on CTC as the speech recognition sub-network. Finally, we combine DMS-SK with BLSTM-CTC, and propose the DMS-SK/BLSTM-CTC hybrid network as the gesture/speech hybrid network. In addition, we use 10 gestures in the American Sign Language (ASL) dataset and 10 speech commands to construct the gesture/speech hybrid dataset. Experimental results show that compared with the pure gesture or pure speech networks, the recognition accuracy of the gesture-speech hybrid network improves by 2% and 12%, respectively, its accuracy reaches 97.38%, which fulfills the requirement of astronauts for HRI.
Dual Mixture Model Based CNN for Image Denoising

Non-Gaussian residual error and noise are common in the real applications, and they can be efficiently addressed by some non-quadratic fidelity terms in the classic variational method. However, they have not been well integrated to the architectures design in the convolution neural networks (CNN) based image denoising method. In this paper, we propose a deep learning approach to handle non-Gaussian residual error. Our method is developed on an universal approximation property for the probability density functions of the non-Gaussian error/noise. By considering the duality of the maximum likelihood estimation for the non-Gaussian error, an adaptive weighting strategy can be derived for image fidelity. To get a good image prior, a learnable regularizer is adopted. Solving such a problem iteratively can be unrolled as a weighted residual CNN architecture. The main advantage of our method is that the weighted residual block can well handle the non-Gaussian residual, especially for the noise with non-uniformly spatial distribution. Numerical results show that it has better performance on non-Gaussian noise (e.g. Gaussian mixture, random-valued impulse noise) removal than the related existing methods.
Attentive Part-Based Alignment Network for Vehicle Re-Identification

Vehicle Re-identification (Re-ID) has become a research hotspot along with the rapid development of video surveillance. Attention mechanisms are utilized in vehicle Re-ID networks but often miss the attention alignment across views. In this paper, we propose a novel Attentive Part-based Alignment Network (APANet) to learn robust, diverse, and discriminative features for vehicle Re-ID. To be specific, in order to enhance the discrimination of part features, two part-level alignment mechanisms are proposed in APANet, consisting of Part-level Orthogonality Loss (POL) and Part-level Attention Alignment Loss (PAAL). Furthermore, POL aims to maximize the diversity of part features via an orthogonal penalty among parts whilst PAAL learns view-invariant features by means of realizing attention alignment in a part-level fashion. Moreover, we propose a Multi-receptive-field Attention (MA) module to adopt an efficient and cost-effective pyramid structure. The pyramid structure is capable of employing more fine-grained and heterogeneous-scale spatial attention information through multi-receptive-field streams. In addition, the improved TriHard loss and Inter-group Feature Centroid Loss (IFCL) function are utilized to optimize both the inter-group and intra-group distance. Extensive experiments demonstrate the superiority of our model over multiple existing state-of-the-art approaches on two popular vehicle Re-ID benchmarks.
Exploring the Layered Structure of Containers for Design of Video Analytics Application Migration

The existing solutions to the migration of container-based applications are not suitable for live video analytics applications because these solutions can result in excessive migration time. Intuitively, it is possible to exploit the layered structure of containers to improve the migration performance, but we still need a good understanding of the characteristics of the containers related to video analytics applications to justify the intuitive idea and design a high-performance solution. In this paper, we pull 3735 representative images from Docker Hub. We analyze these images and get three main findings: (1) the images of video analytics applications have more layers and larger sizes than that of general applications; (2) we can cache images in the destination servers and reuse the same layers cached in the destination server to reduce the migration time when an image is migrated from its source server to its destination server; (3) the size of the remaining data to be transferred during migrations is still large and a high-performance migration scheme is still necessary. Based on the above findings, we propose a pipelined migration scheme to optimize migration performance. Evaluations show that pipelined migration performs significantly better than other migration schemes.
Simple Test for the Fitness for Purpose of Geological Reference Materials According to the Uncertainties Associated with Their Reference Values

A simple test is proposed for assessing whether reference material values are fit for purpose according to the uncertainties associated with these values. The approach involves comparing individual reference value uncertainties ( u RM ) with the corresponding target uncertainty ( u H ) calculated from a modified form of the Horwitz function, u H = 0.01 c 0.8495 , where c is the mass fraction of the reference value. Providing u RM &lt; u H /3, the uncertainty contribution from the reference value will cause an increase in the combined uncertainty no greater than 5.4% and the reference value can be considered fit for purpose in the calibration and validation of respective measurement results. In applying this methodology, it is convenient to calculate a quality factor (QF = 3 u RM / u H ) for each reference value, which would then be considered fit for purpose when QF &lt; 1. Examples are shown for reference materials characterised by different methodologies (NIST certification, the IAG certification protocol, GeoReM preferred values and the Geo PT programme). Calculation of the median quality factor from all quoted reference values in individual reference materials allows an overall assessment to be made of the quality of the material and the effectiveness of the methodology used for its characterisation.
2D–3D cascade network for glioma segmentation in multisequence MRI images using multiscale information

Glioma segmentation is an important procedure for the treatment plan and follow-up evaluation of patients with glioma. UNet-based networks are widely used in medical image segmentation tasks and have achieved state-of-the-art performance. However, context information along the third dimension is ignored in 2D convolutions, whereas difference between z-axis and in-plane resolutions is large in 3D convolutions. Moreover, an original UNet structure cannot capture fine details because of the reduced resolution of feature maps near bottleneck layers.To address these issues, a novel 2D-3D cascade network with multiscale information module is proposed for the multiclass segmentation of gliomas in multisequence MRI images. First, a 2D network is applied to fully exploit potential intra-slice features. A variational autoencoder module is incorporated into 2D DenseUNet to regularize a shared encoder, extract useful information, and represent glioma heterogeneity. Second, we integrated 3D DenseUNet with the 2D network in cascade mode to extract useful inter-slice features and alleviate the influence of large difference between z-axis and in-plane resolutions. Moreover, a multiscale information module is used in the 2D and 3D networks to further capture the fine details of gliomas. Finally, the whole 2D-3D cascade network is trained in an end-to-end manner, where the intra-slice and inter-slice features are fused and optimized jointly to take full advantage of 3D image information.Our method is evaluated on publicly available and clinical datasets and achieves competitive performance in these two datasets.These results indicate that the proposed method may be a useful tool for glioma segmentation.
Total and Net-Directional Connectedness of Cryptocurrencies During the Pre- and Post-COVID-19 Pandemic

This paper presents how volatility propagates through the cryptocurrency market. Our paper provides evidence for volatility connectedness on cryptocurrencies. The different econometric techniques, including the stochastic volatility (SVOL) model and time-varying parameter VAR models using a quasi-Bayesian local likelihood (QBLL), are applied to measure the volatility of the cryptocurrency market. Using high-frequency, intra-day data of the largest cryptocurrencies over 2018–2021, we detect the great volatility of the cryptocurrency market are the beginning of 2019, the beginning of 2020, and throughout the year of 2021. The total connectedness values suggest that the cryptocurrency market becomes volatile as the new strains of the COVID-19 appear at the end of 2021. However, by using directional connectedness, we reveal that there are negative and positive spillovers from a specific cryptocurrency to other cryptocurrencies. The great fluctuations in the period before the COVID-19 health crisis stem from the positive resonance (symmetric) between the volatility of each cryptocurrency, while this health crisis leads to substantially positive and negative spillovers (asymmetric) of cryptocurrencies, and this makes market volatility weaker than it actually is.
Learning-free, divide and conquer text-line extraction algorithm for printed Arabic text with diacritics

The extraction of text lines from document images is a critical step in optical character recognition. It is still considered an open document analysis problem. The presence of numerous font variations, diacritics, overlapping, and touching text-lines presents a challenge to algorithms designed for machine-printed text. In this paper, we present a simple and robust text-line extraction algorithm for printed Arabic text. The presented method is divided into two stages: preprocessing and text-line extraction. It extracts text-lines efficiently, even in small font sizes, by utilizing baselines, projection profiles, and a top-down divide and conquer technique. The presented method is fast and efficient when dealing with non-uniform inter-line spacing and the text-line overlapping problem. A set of experiments were conducted on the collected dataset. The experiments revealed that the proposed method outperforms two baseline approaches, with an average error rate of 3% on Arabic text without diacritics and 11% on Arabic text with diacritics. Furthermore, the experiments demonstrate that the proposed algorithm has a simple computational running time, with an average running time of 0.087 s per document image.