Reviewerly/exHarmony huggingface dataset:
Spatial-temporal analysis and background value determination of molybdenum concentration in basins with high molybdenum geochemical background - A case study of the upper Yi River basin

The environmental background value of the river section is important. It can be used to evaluate the effect of pollution control of the upstream of that river section, analyze the trend of environmental pollution, and assist the government to make decisions. Yi river is the main tributary of the Yellow River. In the headwaters of the Yi river, there are two very large molybdenum mines with a history of mining and smelting of many years. This area is also a region with a high molybdenum geochemical background. Using the collected regional molybdenum geochemical map, historical monitoring data, sampling data, remote sensing image, and spatial information of mineral enterprises, we found two reasons of why the molybdenum concentration is unusual in the basin. The first reason is the area is a high molybdenum region. The second reason is that the inherent solubility of molybdenum in the soil is changed due to human engineering activities. In this paper, we did a linear fitting on the soil samples and water samples collected from the natural areas and areas affected by human mining activities, and established a leaching model. By comparing the leaching capability of molybdenum in the soil of different areas, we found that the molybdenum release capability in areas affected by human mining was much higher than that in natural areas. Finally, this paper proposed a method to analyze the contribution rate of molybdenum concentration of this river section, using a combination of the leaching model and the D8 algorithm. The experimental results showed that the contribution rate of natural factors and human influence factors at the exit section of Yi River was 81.38% and 18.62% respectively. The background molybdenum concentration in this section was 0.16 mg/L.
Gated Contextual Features for Salient Object Detection

The effective extraction of local and contextual visual cues carrying information of different scales is crucial for accurate detection of the salient object(s) with varying shape, size, and location. The atrous spatial pyramid pooling (ASPP) and its dense versions are widely used for extracting contextual features for dense prediction tasks. The skip connections in densely or moderately connected ASPP directly propagate the context information from a parallel dilated convolution to the next higher rate dilated convolution to combat the “gridding issue” in àtrous convolutions. The aggregated context from several scales may dilute features belonging to small objects or confuse between the salient object and the background. To emphasize invariance features for different scale visual patterns in an image, a gate-based context extraction module is proposed in this work. Gate functions are embedded in the interbranch short connection of the proposed module. The learnable gates are deployed to decide on the relevance of the contextual information extracted at a lower scale for the next higher scale. Experimental results on salient object detection tasks demonstrate that gates are helpful to retain relevant contextual information across multiple-scales of the context-extraction module. The performance of the proposed gated contextual feature-based salient object detector is evaluated on five broadly used saliency detection benchmarks by comparing it with the other 13 state-of-the-art approaches. Experimental outcomes show that the proposed method achieves a favorable performance for various compared evaluation measures.
BFLP: An Adaptive Federated Learning Framework for Internet of Vehicles

Applications of Internet of Vehicles (IoV) make the life of human beings more intelligent and convenient. However, in the present, there are some problems in IoV, such as data silos and poor privacy preservation. To address the challenges in IoV, we propose a blockchain-based federated learning pool (BFLP) framework. BFLP allows the models to be trained without sharing raw data, and it can choose the most suitable federated learning method according to actual application scenarios. Considering the poor computing power of vehicle systems, we construct a lightweight encryption algorithm called CPC to protect privacy. To verify the proposed framework, we conducted experiments in obstacle-avoiding and traffic forecast scenarios. The results show that the proposed framework can effectively protect the user's privacy, and it is more stable and efficient compared with traditional machine learning technique. Also, we compare the CPC algorithm with other encryption algorithms. And the results show that its calculation cost is much lower compared to other symmetric encryption algorithms.
Generative Autoregressive Ensembles for Satellite Imagery Manipulation Detection

Satellite imagery is becoming increasingly accessible due to the growing number of orbiting commercial satellites. Many applications make use of such images: agricultural management, meteorological prediction, damage assessment from natural disasters, or cartography are some of the examples. Unfortunately, these images can be easily tampered and modified with image manipulation tools damaging downstream applications. Because the nature of the manipulation applied to the image is typically unknown, unsupervised methods that don't require prior knowledge of the tampering techniques used are preferred. In this paper, we use ensembles of generative autoregressive models to model the distribution of the pixels of the image in order to detect potential manipulations. We evaluate the performance of the presented approach obtaining accurate localization results compared to previously presented approaches.
Geological mapping and mineral prospectivity using remote sensing and GIS in parts of Hamissana, Northeast Sudan

Abstract In recent years, various geological activities and different mineral prospecting and exploration programs have been intensified along the Red Sea hills in order to elucidate the geological maps and to evaluate the mineral potentials. This study is therefore aimed at testing the viability of using remote sensing and geographic information system (GIS) techniques for geological mapping and prospecting for gold mineralization in the area. The study area is located in northeast Sudan and covers an area of about 1379 km 2 . Different digital image processing techniques were applied to Landsat 8 Operational Land Imager image in order to increase the discrimination between various lithological units and to delineate wall rock alteration which represents target zones for gold mineralization. Image sharpening was performed to enhance the spatial resolution of the images for more detailed information. Contrast stretching was applied after the various digital processing procedures to produce more interpretable images. The principal component analysis transformations yielded saturated images and resulted in more interpretable images than the original data. Several ratio images were prepared, combined together and displayed in RGB color composite ratio images. This process revealed the existence of alteration zones in the study area. These zones extend from the northeast to the southwest in the acid meta-volcanic and silica barite rocks. The enhanced satellite images were implemented in the GIS environment to facilitate the final production of the geological map at scale 1:400,000. X-ray fluorescence analyses prove that selected samples taken from the wall rock alteration zones are gold-bearing.
ILA4: Overcoming missing values in machine learning datasets – An inductive learning approach

This article introduces ILA4: A new algorithm designed to handle datasets with missing values. ILA4 is inspired by a series of ILA algorithms which also handle missing data with further enhancements. ILA4 is applied to datasets with varying completeness and also compared to other, known approaches for handling datasets with missing values. In the majority of cases, ILA4 produced favorable performance that is on a par with many established approaches for treating missing values including algorithms that are based on the Most Common Value (MCV), the Most Common Value Restricted to a Concept (MCVRC), and those that utilize the Delete strategy. ILA4 was also compared with three known algorithms namely: Logistic Regression, Naïve Bayes, and Random Forest; the accuracy obtained by ILA4 is comparable or better than the best results obtained from these three algorithms.
Intrusion-Associated Gold Systems and Multistage Metallogenic Processes in the Neoarchean Abitibi Greenstone Belt

In gold-endowed greenstone belts, ore bodies generally correspond to orogenic gold systems (OGS) formed during the main deformation stage that led to craton stabilization (syntectonic period). Most OGS deposits postdate and locally overprint magmatic-hydrothermal systems, such as Au-Cu porphyry that mostly formed during the main magmatic stage (synvolcanic period) and polymetallic intrusion-related gold systems (IRGS) of the syntectonic period. Porphyries are associated with tonalite-dominated and sanukitoid plutons, whereas most IRGS are related to alkaline magmatism. As reviewed here, most intrusion-associated mineralization in the Abitibi greenstone belt is the result of complex and local multistage metallogenic processes. A new classification is proposed that includes (1) OGS and OGS-like deposits dominated by metamorphic and magmatic fluids, respectively; (2) porphyry and IRGS that may contain gold remobilized during subsequent deformation episodes; (3) porphyry and IRGS that are overprinted by OGS. Both OGS and OGS-like deposits are associated with crustal-scale faults and display similar gold-deposition mechanisms. The main difference is that magmatic fluid input may increase the oxidation state and CO2 content of the mineralizing fluid for OGS-like deposits, while OGS are characterized by the circulation of reduced metamorphic fluids. For porphyry and IRGS, mineralizing fluids and metals have a magmatic origin. Porphyries are defined as base metal and gold-bearing deposits associated with large-volume intrusions, while IRGS are gold deposits that may display a polymetallic signature and that can be associated with small-volume syntectonic intrusions. Some porphyry, such as the Côté Gold deposit, demonstrate that magmatic systems can generate economically significant gold mineralization. In addition, many deposits display evidence of multistage processes and correspond to gold-bearing or gold-barren magmatic-hydrothermal systems overprinted by OGS or by gold-barren metamorphic fluids. In most cases, the source of gold remains debated. Whether magmatic activity was essential or marginal for fertilizing the upper crust during the Neoarchean remains a major topic for future research, and petrogenetic investigations may be paramount for distinguishing gold-endowed from barren greenstone belts.
Research on Cooperative Trajectory Planning Algorithm Based on Tractor-Trailer Wheeled Robot

The tractor-trailer wheeled robot (TTWR) is a typical modular mobile robot system.It is widely used in industry and agriculture transportation because of its simple structure, heavy load, high efficiency, strong adaptability, and flexibility.Because it is a nonlinear and underactuated system subjected to nonholonomic constraints, the complexity of the system and influence of the inner wheel difference pose great challenges to its trajectory prediction and path planning.To solve this problem, this paper proposes a trajectory prediction algorithm for an unpowered trailer for the TTWR system.In contrast to traditional single-path planning, the algorithm combines the motion characteristics of the Ackermann chassis tractor, analyzes the trailer motion law, and fully considers the lateral deviation of the trailer during the turning process.The algorithm can plan a reasonable cooperative path for the TTWR system.To verify the feasibility and effectiveness of the algorithm, the algorithm is transplanted to the TTWR intelligent experimental platform.A comparison of the simulation and experimental results shows that under the control of the preview tracking algorithm, the maximum error between the actual trajectory of the trailer and the trajectory predicted by the algorithm is generally no more than 9 cm, and the average error is 3.7 cm.This algorithm can provide an effective basis for TTWR system trajectory planning and path tracking.This algorithm is of great significance to TTWRs in warehousing logistics, freight forwarding, and skidding transportation.
Decision-making System Based on The Ontology of The Choice Problem

Abstract The complexity of the decision-making process for experts in various topics is in the problem of initial processing of unstructured information. This problem of Big data requires the latest solutions that provide a structural reflection and integrative use of information descriptions under consideration by experts. It takes the process of information and analytical evaluation and decision-making beyond one subject area and raises questions about the ontological consideration of the problem itself. The ontological principles of such consideration are based on multiple hyperproperties of information for the realization of the categories of integration, systematicity and continuity, as well as implementation of structures and their functionality reflection and transformation. For this, there is the ontology of the problem of rational choice based on means of a ranking of alternatives on a set of indicators. It provides the creation of a system designed to solve the ranking problem based on the ontological model of the subject area. Hyperproperties of ontological representation of large amounts of information ensure the implementation of meta-tasks of its analysis, its structuring, synthesis, and support the process of rational choice. Such an approach creates the conditions for optimal processing of a Big Data.
Neural Network Oriented RONI Prediction for Embedding Process with Hex Code Encryption in DICOM Images

Advancements in digital era leads to several innovations which helps in sharing and remote accessing of clinical DICOM image format. While utilizing the updated facilities of health care innovations, one key issue is to confirm that the image falsification induced because of the watermarking procedure does not compromise the image analysis value. As watermarking process mainly focuses on the hiding of data into to the image in order to provide confidentiality and integrity to the sensitive information. Hence there is a need to recheck the quality of image after the process has been completed. For this case, the technique of reversible watermarking is emerged to confirm the quality of image at the end user side. This technique is a significant procedure in the applications which are requiring high quality image like medical sector. In this exploration effort, a novel image reversible watermarking is proposed called CNN oriented Region of Non-Interest (RONI) identification method in diagnostic imaging. This work is having two phases, 1) to implant a watermark in Region of the Non-Interest (RONI) and 2) for the adaptive derivation in quality factor using neural network system. The liver cancer image is taken as an input image in which the cancerous area is the Region of the Interest (ROI) and the remaining area forms the RONI. The performance metrics are Mean Square Error(MSE), Peak Signal to Noise Ratio(PSNR), Entropy(E) and Correlation Coefficient(CC). The outcome validates that the projected reversible watermarking procedure does not destroy the quality of image as the watermark is implanted only in region of non-interest and is resistive to threats.
Design and simulation of a 1-GWp solar photovoltaic power station in Sudan

Abstract Developing nations have a critical need to increase electricity supply. Sudan has much unrealized potential for generating solar energy, particularly in the northern region. This research study focuses on designing a 1-GW solar power station in northern Sudan using the PVsyst7.0 software program. To determine the appropriate location for the solar-energy station, 14 criteria were evaluated. This process is generic and suitable for use in any other country. The method for conducting cash-flow estimates and return on investment is illustrated in the economic evaluation. The city of Dongola, the capital of the northern state, was selected because of its high annual irradiance on a horizontal surface at ~2333.2 kWh/m2. The simulation results show that the annual optimum tilt angle of inclination for photovoltaic (PV) modules is 30°, the energy production is 1 979 259 MWh/yr and the average annual performance rate is 0.810. In addition, the electric power consumption per capita in Sudan is 269 kWh/yr, so the proposed solar power plant with 1 979 259 MWh/yr can provide energy to 7.4 million people per year annually and reduce carbon emissions by ~18 million tons of carbon dioxide per year. Economic calculations show that the levelized cost of electricity (LCOE) is $0.06/kWh, the discounted payback period is ~11 years and the net present value is $635 291 000. As a result, the proposed grid-connected PV solar plant is considered economically, technically and environmentally feasible in Sudan.
Hybrid features prediction model of movie quality using Multi-machine learning techniques for effective business resource planning

Quality prediction plays an essential role in the business outcome of the product. Due to the business interest of the concept, it has extensively been studied in the last few years. Advancement in machine learning (ML) techniques and with the advent of robust and sophisticated ML algorithms, it is required to analyze the factors influencing the success of the movies. This paper presents a hybrid features prediction model based on pre-released and social media data features using multiple ML techniques to predict the quality of the pre-released movies for effective business resource planning. This study aims to integrate pre-released and social media data features to form a hybrid features-based movie quality prediction (MQP) model. The proposed model comprises of two different experimental models; (i) predict movies quality using the original set of features and (ii) develop a subset of features based on principle component analysis technique to predict movies success class. This work employ and implement different ML-based classification models, such as Decision Tree (DT), Support Vector Machines with the linear and quadratic kernel (L-SVM and Q-SVM), Logistic Regression (LR), Bagged Tree (BT) and Boosted Tree (BOT), to predict the quality of the movies. Different performance measures are utilized to evaluate the performance of the proposed ML-based classification models, such as Accuracy (AC), Precision (PR), Recall (RE), and F-Measure (FM). The experimental results reveal that BT and BOT classifiers performed accurately and produced high accuracy compared to other classifiers, such as DT, LR, LSVM, and Q-SVM. The BT and BOT classifiers achieved an accuracy of 90.1% and 89.7%, which shows an efficiency of the proposed MQP model compared to other state-of-art- techniques. The proposed work is also compared with existing prediction models, and experimental results indicate that the proposed MQP model performed slightly better compared to other models. The experimental results will help the movies industry to formulate business resources effectively, such as investment, number of screens, and release date planning, etc.
Self-Supervised Keypoint Detection Based on Multi-Layer Random Forest Regressor

This paper proposes a keypoint regressor (KeyReg), which consists of multi-layer random forest (MRF) regressor and single random forest (SRF) classifier modules. To increase the keypoints’ repeatability, the MRF regressor is applied to multi-scale images in a shared rules manner, and keypoints predicted at each scale are given a confidence score through the SRF for reliability measurement. Each candidate point is detected as the final keypoint through a non-maxima suppression process based on a confidence score. The MRF structure of KeyReg is designed to maintain a coarse-to-fine structure by varying the number of nodes per layer. In addition, the accuracy of the matching can be improved by removing less confidential keypoints through the continuous SRF classifier. KeyReg is the first approach to apply an MRF to the keypoint regression and is designed to run on a CPU rather than a GPU compared to DNN-based approaches. KeyReg training was conducted using COCO, and positive and negative examples were automatically obtained under a self-supervised learning method between the original image and a warped image. The proposed KeyReg showed superior performance in terms of repeatability, the accuracy of the homography, mean matching accuracy (MMA), and localization errors on HPatches dataset compared to state-of-the-art methods.
Weighted Pseudo Labeled Data and Mutual Learning for Semi-Supervised Classification

In this article, a semi-supervised classification algorithm that is based on weighted pseudo labeled data and mutual learning is proposed.The purpose of our method is to improve the classification performance of semi-supervised learning models and rectify the incorrect pseudo labels in the process of training.Specifically, the algorithm is built with a deep convolutional neural network and an ensemble learning model.First, output smearing is employed to construct different training sets and perform model initialization.The pseudo labels of unlabeled data are inferred by network predictions.Second, based on selection and weighting strategies for pseudo labeled data, pseudo labeled data with high confidence are selected and added to the real labeled training set.Accordingly, the model is retrained on the weighted pseudo labeled data.Last, a mutual learning strategy is applied to enhance the prediction consistency among classifiers.Furthermore, diversity fine tuning and mutual learning are performed alternately to determine the optimal balance between diversity and consistency, which consequently improves the accuracy of the pseudo label predictions.The experimental results on three benchmark datasets, namely, MNIST, CIFAR10 and SVHN, demonstrate that the proposed method effectively rectifies the incorrect pseudo labels.Notably, the method achieves the best performance compared with state-of-the-art semi-supervised classification methods.
Multiway Cluster Robust Double/Debiased Machine Learning

Harold D. Chianga*, Kengo Katob, Yukun Mac & Yuya Sasakica Department of Economics, University of Wisconsin-Madison, Madison, WIb Department of Statistics and Data Science, Cornell University, Ithaca, NYc Department of Economics, Vanderbilt University, Nashville, TN
Blockchain-Based Agriculture Assistance

The working system of agricultural management is very important in today's world. However, growers, farmers, and sellers are physically dispersed to choose their pesticides and to manage both data and information. As a result, the production of crops and trust between the consumer and producer decreases regularly. In this paper, we propose an overall structure of agricultural maintenance system to work efficiently and properly based on blockchain technology in order to solve the problems facing by the farmers to choose the best pesticides on their own. The information recorded in these management operations includes farmer's problems and other suggestions. Using blockchain techniques and methods to the field of agricultural not only improves schema, domain, and application of blockchain but also supports the farmers to choose the suitable pesticides and helps in increasing the trusted network among different stakeholders around agricultural environment.