Reviewerly/exHarmony huggingface dataset:
LA-ICP-MS trace element geochemistry of sphalerite: Metallogenic constraints on the Qingshuitang Pb–Zn deposit in the Qinhang Ore Belt, South China

Many hydrothermal vein-type Pb–Zn deposits were discovered in the Qinhang Ore Belt (South China), but their origin remains enigmatic due to the lack of constraints on the ore-forming process and ore-fluid physicochemical conditions. The Qingshuitang (0.426 Mt @ 3.65% Zn and 1.94% Pb) is a representative Pb–Zn deposit in the central Qinhang Ore Belt, and its mineralization comprises four stages, namely quartz-pyrite-arsenopyrite (I), quartz-galena-sphalerite (II), barite-galena-sphalerite (III), and calcite-galena-sphalerite (Ⅳ). Three generations of sphalerite were identified, namely Sp1 (Stage II), Sp2 (Stage III), and Sp3 (Stage IV). The mineralization temperature at Qingshuitang, as determined by the GGIMFis sphalerite geothermometer, is 82–204 °C (Sp1), 66–104 °C (Sp2), and 72–172 °C (Sp3), consistent with their high Ga/In (avg. 3461) but low In/Ge (<3.97) and Zn/Cd (avg. 199) ratio. These suggest that the Qingshuitang ore-forming fluid was low-temperature, with its average sulfur fugacity (fS2) evolving from Sp1(10–22.6 atm) to Sp2 (10–27.1 atm) and then to Sp3 (10–23.5 atm). Moreover, the decreasing median Mn content from Sp1 (4.86 ppm) to Sp2 (1.71 ppm) indicates an elevated oxygen fugacity (fO2) of the ore-forming fluids from Stage II to III, while the increasing median Mn content to Sp3 (2.78 ppm) reveals a subsequent fO2 drop. The low Fe–Mn–In–Co contents, low Zn/Cd (avg. 199), high Ge–Cd–Ga contents, and high Cd/Fe (avg. 1.39), Cd/Mn (721–7741), Ga/In (avg. 3469) and Ge/In (avg. 605) ratios in the Qingshuitang sphalerite resemble Mississippi Valley-type (MVT) sphalerite, but are markedly different from those in magmatic-related deposits (i.e., epithermal-, skarn-, and volcanogenic massive sulfide (VMS)-type). The Qingshuitang Pb–Zn deposit has distinct epigenetic characteristics, and similar low-temperature ore-fluids to those of MVT deposits. Therefore, we suggest that the Qingshuitang is a MVT deposit, and the physicochemical shift between Stage III and IV is probably associated with basinal brine incursion into the ore-fluids.
A hybrid approach for efficient multi‐classification of white blood cells based on transfer learning techniques and traditional machine learning methods

Abstract Diagnosed blood‐related diseases include the identification of blood samples taken from the patient. Therefore, the classification of white blood cells, also known as leukocytes, is substantial for differentiating leukemia and blood diseases. In this article, we aimed to classify four different types of white blood cells. Two different transfer learning methods are applied to achieve this goal. In the first method, AlexNet, ResNet18, and GoogleNet architectures are retrained with the fine‐tuning method using the dataset we have provided from the Kaggle and then given to classify on the softmax and SVM methods. With this method, the hybrid architecture where the ResNet18 is used with SVM achieves 99.83%.In the second method, feature transfer, the same architectures are implemented for feature extractors. First, extracted features of architectures are given to a variety of classifiers. Second, the features are taken from the architectures, and concatenated as pairs and triples, used to obtain 4 different feature sets consisting of 2000 and 3000 features. After that, these features are also subjected to the same classifiers. Finally, results are revealed by using the same classifiers after the PCA algorithm. The results illustrate that the proposed methods significantly contribute to white blood cell multi‐classification.
PatentNet: multi-label classification of patent documents using deep learning based language understanding

Abstract Patent classification is an expensive and time-consuming task that has conventionally been performed by domain experts. However, the increase in the number of filed patents and the complexity of the documents make the classification task challenging. The text used in patent documents is not always written in a way to efficiently convey knowledge. Moreover, patent classification is a multi-label classification task with a large number of labels, which makes the problem even more complicated. Hence, automating this expensive and laborious task is essential for assisting domain experts in managing patent documents, facilitating reliable search, retrieval, and further patent analysis tasks. Transfer learning and pre-trained language models have recently achieved state-of-the-art results in many Natural Language Processing tasks. In this work, we focus on investigating the effect of fine-tuning the pre-trained language models, namely, BERT, XLNet, RoBERTa, and ELECTRA, for the essential task of multi-label patent classification. We compare these models with the baseline deep-learning approaches used for patent classification. We use various word embeddings to enhance the performance of the baseline models. The publicly available USPTO-2M patent classification benchmark and M-patent datasets are used for conducting experiments. We conclude that fine-tuning the pre-trained language models on the patent text improves the multi-label patent classification performance. Our findings indicate that XLNet performs the best and achieves a new state-of-the-art classification performance with respect to precision, recall, F1 measure, as well as coverage error, and LRAP.
Visual cryptography scheme for secret color images with color QR codes

Visual cryptography scheme can divide the secret image into several shares. It can be used to enhance the secure transmission of the secret image on the Internet. Most schemes cannot fully restore the secret color image and generate meaningful shares. This paper proposes two new schemes by using color XOR to solve these problems. The first proposed scheme generates meaningless shares. The second proposed scheme can generate n−1 meaningful shares and a meaningless share. Based on the first proposed scheme, n−1 shares are modified to color QR codes in the second proposed scheme. These color QR codes can be decoded by the general decoder instead of the standard decoder. All shares are performed the color XOR to restore the secret color image completely. This paper uses some experiments to test two proposed schemes. Experimental results show that the two proposed schemes are feasible.
A deep recommendation model of cross-grained sentiments of user reviews and ratings

The matrix factorization model based on user-item rating data has been widely studied and applied in recommender systems. However, data sparsity, the cold-start problem, and poor explainability have restricted its performance. Textual reviews usually contain rich information about items’ features and users’ sentiments and preferences, which can solve the problem of insufficient information from only user ratings. However, most recommendation algorithms that take sentiment analysis of review texts into account are either fine- or coarse-grained, but not both, leading to uncertain accuracy and comprehensiveness regarding user preference. This study proposes a deep learning recommendation model (i.e., DeepCGSR) that integrates textual review sentiments and the rating matrix. DeepCGSR uses the review sets of users and items as a corpus to perform cross-grained sentiment analysis by combining fine- and coarse-grained levels to extract sentiment feature vectors for users and items. Deep learning technology is used to map between the extracted feature vector and latent factor through the rating-based matrix factorization model and obtain deep, nonlinear features to predict the user's rating of an item. Iterative experiments on e-commerce datasets from Amazon show that DeepCGSR consistently outperforms the recommendation models LFM, SVD++, DeepCoNN, TOPICMF, and NARRE. Overall, comparing with other recommendation models, the DeepCGSR model demonstrated improved evaluation results by 14.113% over LFM, 13.786% over SVD++, 9.920% over TOPICMF, 5.122% over DeepCoNN, and 2.765% over NARRE. Meanwhile, the DeepCGSR has great potential in fixing the overfitting and cold-start problems. Built upon previous studies and findings, the DeepCGSR is the state of the art, moving the design and development of the recommendation algorithms forward with improved recommendation accuracy.
A new validity clustering index-based on finding new centroid positions using the mean of clustered data to determine the optimum number of clusters

Clustering, an unsupervised pattern classification method, plays an important role in identifying input dataset structures. It partitions input datasets into clusters or groups where either the optimum number of clusters is known in prior or automatically determined. In the case of automatic clustering, the performance is evaluated using a cluster validity index (CVI), which determines the optimum number of clusters in the data. From previous works, the improper cluster centroids positioning produced by clustering algorithms could reduce the performance of the validation process and performance produced by the previous state-of-the-art CVIs. In addition, those previous CVIs can only work properly with certain clustering algorithms and simple datasets structures, which their performances will reduce if they are applied to other clustering algorithms as well as more complex datasets. This study proposes an efficient CVI, namely, the validity clustering index based on finding the mean of clustered data (VCIM). The proposed approach combines the properties of the score function index and the mean to determine new cluster centroid positions. The performance of the VCIM index is compared with well-known CVIs on both artificial and real-life datasets. The obtained results on artificial datasets show that the proposed VCIM index outperforms the other CVIs in determining the true number of clusters for the five conventional clustering algorithms, namely, K-means, Fuzzy C-mean, agglomerative hierarchical average linkage clustering, variance-based differential evolution, and density peaks clustering and Particle swarm optimization (PDPC) algorithms. For the 14 real-word datasets, the proposed VCIM index correctly determined the optimum number of clusters for 11 out of 14 for the K-means clustering algorithm, 9 out of 14 for both Fuzzy clustering and agglomerative hierarchical average linkage clustering algorithms, 12 out of 14 for the variance-based differential evolution algorithm and 11 out of 14 datasets for PDPC. The obtained results using the proposed VCIM show its significance when combined with clustering algorithms and nominate its potential in various clustering applications.
Auto-Rectify Network for Unsupervised Indoor Depth Estimation

Single-View depth estimation using the CNNs trained from unlabelled videos has shown significant promise. However, excellent results have mostly been obtained in street-scene driving scenarios, and such methods often fail in other settings, particularly indoor videos taken by handheld devices. In this work, we establish that the complex ego-motions exhibited in handheld settings are a critical obstacle for learning depth. Our fundamental analysis suggests that the rotation behaves as noise during training, as opposed to the translation (baseline) which provides supervision signals. To address the challenge, we propose a data pre-processing method that rectifies training images by removing their relative rotations for effective learning. The significantly improved performance validates our motivation. Towards end-to-end learning without requiring pre-processing, we propose an Auto-Rectify Network with novel loss functions, which can automatically learn to rectify images during training. Consequently, our results outperform the previous unsupervised SOTA method by a large margin on the challenging NYUv2 dataset. We also demonstrate the generalization of our trained model in ScanNet and Make3D, and the universality of our proposed learning method on 7-Scenes and KITTI datasets.
Toward Sustainable Serverless Computing

Although serverless computing generally involves executing short-lived “functions,” the increasing migration to this computing paradigm requires careful consideration of energy and power requirements. serverless computing is also viewed as an economically-driven computational approach, often influenced by the cost of computation, as users are charged for per-subsecond use of computational resources rather than the coarse-grained charging that is common with virtual machines and containers. To ensure that the startup times of serverless functions do not discourage their use, resource providers need to keep these functions hot, often by passing in synthetic data. We describe the real power consumption characteristics of serverless, based on execution traces reported in the literature, and describe potential strategies (some adopted from existing VM and container-based approaches) that can be used to reduce the energy overheads of serverless execution. Our analysis is, purposefully, biased toward the use of machine learning workloads because: (1) workloads are increasingly being used widely across different applications; (2) functions that implement machine learning algorithms can range in complexity from long-running (deep learning) versus short-running (inference only), enabling us to consider serverless across a variety of possible execution behaviors. The general findings are easily translatable to other domains.
A survey on federated learning in data mining

Abstract Data mining is a process to extract unknown, hidden, and potentially useful information from data. But the problem of data island makes it arduous for people to collect and analyze scattered data, and there is also a privacy security issue when mining data. A collaboratively decentralized approach called federated learning unites multiple participants to generate a shareable global optimal model and keeps privacy‐sensitive data on local devices, which may bring great hope to us for solving the problems of decentralized data and privacy protection. Though federated learning has been widely used, few systematic studies have been conducted on the subject of federated learning in data mining. Hence, different from prior reviews in this field, we make a comprehensive summary and provide a novel taxonomy of the application of federated learning in data mining. This article starts by providing a thorough description of the relevant definitions and concepts, followed by an in‐depth investigation on the challenges faced by federated learning. In this context, we elaborate four taxonomies of major applications of federated learning in data mining, including education, healthcare, IoT, and intelligent transportation, and discuss them comprehensively. Finally, we discuss four promising research directions for further research, that is, privacy enhancement, improvement of communication efficiency, heterogeneous system processing, and reducing economic costs. This article is categorized under: Technologies &gt; Machine Learning Fundamental Concepts of Data and Knowledge &gt; Big Data Mining Technologies &gt; Artificial Intelligence
Fuzzy clustering decomposition of genetic algorithm-based instance selection for regression problems

Data selection, which includes feature and instance selection, is often an important step in building prediction systems. Genetic algorithms (GA) frequently allow finding better solutions than classical methods in many areas. This is also true for the instance selection task. The main difficulties and challenges in GA-based instance selection are high computational complexity and decreasing performance with the dataset size growth. This is caused by the fact that each instance is encoded in one chromosome position. Hence bigger datasets result in longer chromosomes. The main contribution of this paper addresses the above problems in a three-step process. In the first step the dataset is divided into several consistent regions by fuzzy clustering. Then GA-based instance selection is performed independently within each cluster. Finally ensemble voting provides seamless aggregation of the partial results from the overlapping clusters. This improves dataset exploitation by more localized search and also takes the advantage of ensemble methods. This method significantly improves the predictive model performance and data reduction in comparison to instance selection performed on the whole training dataset.
EPNet++: Cascade Bi-Directional Fusion for Multi-Modal 3D Object Detection

Recently, fusing the LiDAR point cloud and camera image to improve the performance and robustness of 3D object detection has received more and more attention, as these two modalities naturally possess strong complementarity. In this paper, we propose EPNet++ for multi-modal 3D object detection by introducing a novel Cascade Bi-directional Fusion (CB-Fusion) module and a Multi-Modal Consistency (MC) loss. More concretely, the proposed CB-Fusion module enhances point features with plentiful semantic information absorbed from the image features in a cascade bi-directional interaction fusion manner, leading to more powerful and discriminative feature representations. The MC loss explicitly guarantees the consistency between predicted scores from two modalities to obtain more comprehensive and reliable confidence scores. The experimental results on the KITTI, JRDB and SUN-RGBD datasets demonstrate the superiority of EPNet++ over the state-of-the-art methods. Besides, we emphasize a critical but easily overlooked problem, which is to explore the performance and robustness of a 3D detector in a sparser scene. Extensive experiments present that EPNet++ outperforms the existing SOTA methods with remarkable margins in highly sparse point cloud cases, which might be an available direction to reduce the expensive cost of LiDAR sensors.
The similarity of Elicitation Software Requirements Specification in Student Learning Applications of SMKN7 Baleendah Based on Use Case Diagrams Using Text Mining

This study uses a Use Case Description (UCD) based approach to measure the similarity of software requirements in the development of learning applications for students of SMKN 7 Baleendah. The similarity was measured based on Functional Requirements and UCD. The use of the Use Case in this study is used predominantly as the basic model of Artifacts. In general, this study aims to determine the suitability of User Requirements with the software to be built through the similarity between the results of requirements elicitation activities and the software modeling. This similarity activity is carried out through a Text Mining approach in the form of Text Pre-Processing activities on elicitation results, which are then compared with Unified Modeling Language (UML) artifacts at UCD. The specific objectives of this research have the following activities: eliciting requirements, making Use Case Diagrams and Description, clarifying between UCD and SRS software models, determining the similarity of elicitation results, validating and reliability using Gwet AC1. The results of the similarity measurement get the highest value of 0.948683. The kappa index obtained is 0.8831398, where the proportion of agreements obtained is "Almost Perfect."
MDFM: Multi-Decision Fusing Model for Few-Shot Learning

In recent years, researchers pay growing attention to the few-shot learning (FSL) task to address the data-scarce problem. A standard FSL framework is composed of two components: i) Pre-train. Employ the base data to generate a CNN-based feature extraction model (FEM). ii) Meta-test. Apply the trained FEM to the novel data (category is different from base data) to acquire the feature embeddings and recognize them. Although researchers have made remarkable breakthroughs in FSL, there still exists a fundamental problem. Since the trained FEM with base data usually cannot adapt to the novel class flawlessly, the novel data’s feature may lead to the distribution shift problem. To address this challenge, we hypothesize that even if most of the decisions based on different FEMs are viewed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">weak decisions</i> , which are not available for all classes, they still perform decent in some specific categories. Inspired by this assumption, we propose a novel method Multi-Decision Fusing Model (MDFM), which comprehensively considers the decisions based on multiple FEMs to enhance the efficacy and robustness of the model. MDFM is a simple, flexible, non-parametric method that can directly apply to the existing FEMs. Besides, we extend the proposed MDFM to two FSL settings (e.g., supervised and semi-supervised settings). We evaluate the proposed method on five benchmark datasets and achieve significant improvements of 3.4%-7.3% compared with state-of-the-arts.
Global-Local Interplay in Semantic Alignment for Few-Shot Learning

Few-shot learning aims to recognize novel classes from only a few labeled training examples. Aligning semantically relevant local regions has shown promise in effectively comparing a query image with support images. However, global information is usually overlooked in the existing approaches, resulting in a higher possibility of learning semantics unrelated to the global information. To address this issue, we propose a Global-Local Interplay Metric Learning (GLIML) framework to employ the interplay between global features and local features to guide semantic alignment. We first design a Global-Local Information Concurrent Learning (GLICL) module to extract both global features and local features and perform global-local interplay. We then design a Global-Local Information Cross-Covariance Estimator (GLICCE) to learn the similarity on the global-local interplay, in contrast to the current practice where only local features are considered. Visualizations show that the global-local interplay decreases (1) the weights placed on the semantics that are irrelevant to the global information and (2) the variability of the learned features within every class in the feature space. Quantitative experiments on three benchmark datasets demonstrate that GLIML achieves state-of-the-art performance while maintaining high efficiency.
Problems Of Algorithmization Of Control Of Complex Systems Based On Functioning Tables In Dynamic Control Systems

This article describes how to effectively solve plan-ning problems from the point of view of operational efficiency of production process management. Therefore, the main task of this management task is the task of scheduling. In the conditions of discrete single production, the management of the organization of production should consist in the management of internal factors of the organizational environment of the production enterprise. The main issues of production systems management are issues related to achieving the main goal of the functioning of the production unit.