Reviewerly/exHarmony huggingface dataset:
Metal source of gold deposits in the <scp>Jiaoxibei</scp> area, <scp>Eastern China</scp>: Insights from trace element analysis of gold and pyrite

Abstract The metal source of gold deposits in the Jiaoxibei area, eastern China, has been investigated by many researchers, but no consensus has been reached so far. In this study, three typical gold deposits, the Xinli, Jiaojia, and Dayingezhuang deposits, were selected for trace element analysis of gold and pyrite to constrain the metal source. Pyrite from these three deposits has similar morphological and compositional characteristics, and can be divided into three types: Py1 with euhedral to subhedral textures, Py2 with subhedral to anhedral textures with micro‐fractures, and Py3 with subhedral to anhedral textures and intergrowing polymetallic sulfides. Among them, Py2 and Py3 were formed in the main ore‐forming stage and they are the dominant host minerals of visible gold. In these deposits, visible gold occurs mainly in micro‐fractures or as inclusions in Py2 and Py3. Most of the pyrite has extremely low concentration of invisible gold, indicating that visible gold in the Jiaoxibei district is not a product of the remobilization of invisible gold from earlier pyrite. Both Py2 and Py3 are characterized by low Co concentration of &lt;100 ppm and Co/Ni ratio of &lt;1, which are similar to those of pyrite in sedimentary rocks. Therefore, ore‐forming metals of these gold deposits in the Jiaoxibei district may originate mainly from a sediment‐related metal source.
Features of Evaluation of Complex Objects with Stochastic Parameters

This article is devoted to the development and study of a parametric model of the dynamics of the results of measurements of biomedical indicators and its use for unilateral classification of local and global disorders in functional models of the dynamic equilibrium of the biological state. Improvement of existing methods for estimating the accuracy of measurements, the sequence of results of which is modeled by locally non-stationary time series. This modeling is based on the basic development of a probabilistic model of time-distributed measurements taking into account the stochastic influence of factors of heterogeneity of experimental conditions and biological instability of the studied object and analysis of additive and multiplicative elements of the probabilistic model responsible for its global and local changes. This approach allows to determine the values of F-statistics that are sensitive to violations of the dynamics of biological equilibrium and information properties of the elements of the probabilistic model.
Selective Pseudo-Label Clustering

Deep neural networks (DNNs) offer a means of addressing the challenging task of clustering high-dimensional data. DNNs can extract useful features, and so produce a lower dimensional representation, which is more amenable to clustering techniques. As clustering is typically performed in a purely unsupervised setting, where no training labels are available, the question then arises as to how the DNN feature extractor can be trained. The most accurate existing approaches combine the training of the DNN with the clustering objective, so that information from the clustering process can be used to update the DNN to produce better features for clustering. One problem with this approach is that these “pseudo-labels” produced by the clustering algorithm are noisy, and any errors that they contain will hurt the training of the DNN. In this paper, we propose selective pseudo-label clustering, which uses only the most confident pseudo-labels for training the DNN. We formally prove the performance gains under certain conditions. Applied to the task of image clustering, the new approach achieves a state-of-the-art performance on three popular image datasets.
Serverless Deployment of a Voice-Bot for Visually Impaired

Serverless Computing is an upcoming paradigm for the development and deployment of cloud applications. Rather than directly provisioning cloud infrastructure, developers write their short, ephemeral functions to be executed on a serverless platform. This simplified programming model relieves its developers from the burden of server management tasks like allocation, scaling and de-allocation of resources. Resources are typically allocated and charged in proportion to the number of times a function call is made. Monetary profits of serverless architectures greatly depend on the function execution behavior and application workloads volumes. Out of few relevant case studies, certain applications are exemplar and suit perfectly to serverless frameworks—like voice bot and chatbot applications. Recently, chatbots are becoming popular in all varieties of business applications as they can handle multiple users’ requests at a time and reduce per service cost. In this paper, we have investigated a case study of “Feed-O-Back—A Teachers Feedback Voice bot for visually impaired students’’ and we also observed its implementation in serverless frameworks. Feed-O-Back Bot is a web application powered by conversational AI and enabled with natural language processing to collect the feedback from the students. Since this bot is used intermittently and applicable to few students only with special needs, reserving a dedicated cloud virtual machine instance will not be economically justifiable. We opted for Google Cloud Platform (GCP) and compared pricing of dedicated cloud instances (Infrastructure-as-a-Service model—IaaS) to serverless architectures. We evaluated that Google Cloud Function (FaaS model) is more cost-efficient and proven to be economical than Google Compute Engine (IaaS model). We have described the details of voice bot implementation using Dialogflow conversation service deployed on Cloud Function for Firebase.
On Stricter Reachable Repetitiveness Measures

The size b of the smallest bidirectional macro scheme, which is arguably the most general copy-paste scheme to generate a given sequence, is considered to be the strictest reachable measure of repetitiveness. It is strictly lower-bounded by measures like \(\gamma \) and \(\delta \), which are known or believed to be unreachable and to capture the entropy of repetitiveness. In this paper we study another sequence generation mechanism, namely compositions of a morphism. We show that these form another plausible mechanism to characterize repetitive sequences and define NU-systems, which combine such a mechanism with macro schemes. We show that the size \(\nu \le b\) of the smallest NU-system is reachable and can be \(o(\delta )\) for some string families, thereby implying that the limit of compressibility of repetitive sequences can be even smaller than previously thought. We also derive several other results characterizing \(\nu \).
Seasonal Relevance in E-Commerce Search

Seasonality is an important dimension for relevance in e-commerce search. For example, a query jacket has a different set of relevant documents in winter than summer. For an optimal user experience, the e-commerce search engines should incorporate seasonality in product search. In this paper, we formally introduce the concept of seasonal relevance, define it and quantify using data from a major e-commerce store. In our analyses, we find 39% queries are highly seasonally relevant to the time of search and would benefit from handling seasonality in ranking. We propose LogSR and VelSR features to capture product seasonality using state-of-the-art neural models based on self-attention. Comprehensive offline and online experiments over large datasets show the efficacy of our methods to model seasonal relevance. The online A/B test on 784 MM queries shows the treatment with seasonal relevance features results in 2.20% higher purchases and better customer experience overall.
Improving the fidelity of CT image colorization based on pseudo-intensity model and tumor metabolism enhancement

Subject to the principle of imaging, most medical images are gray-scale images. Human eyes are more sensitive to color images compared to gray-scale images. The state-of-the-art medical image colorization results are unnatural and unrealistic, especially in some organs, such as the lung field. We propose a CT image colorization network that consists of a pseudo-intensity model, tumor metabolic enhancement, and MemoPainter-cGAN colorization network. First, the distributions of both the density of CT images and the intensity of anatomical images are analyzed with the aim of building a pseudo-intensity model. Then, the PET images, which are sensitive to tumor metabolism, are used to highlight the tumor regions. Finally, the MemoPainter-cGAN is used to generate colorized anatomical images. Our experiment verified that the mean structural similarity between the colorized images and the original color images is 0.995, which indicates that the colorized image maintains the features of the original images enormously. The average image information entropy is 6.62, which is 13.4% higher than that of the images before metabolism enhancement and colorization. It indicates that the image fidelity is significantly improved. Our method can generate vivid and fresh anatomical images based on prior knowledge of tissue or organ intensity. The colorized PET/CT images with abundant anatomical knowledge and high sensitivity of metabolic information provide radiologists with access to a new modality that offers additional reference information.
An Improved Version of the SIPO Algorithm with Fast Convergence Speed

According to the utilization of evolutionary algorithms such as the Inclined Planes System Optimization (IPO) algorithm in various fields of engineering science, attempts to improve the performance of these algorithms have always been considered. The standard version of IPO algorithm is based on the dynamic movement of objects on inclined planes without friction. There is also a simplified version of this algorithm to reduce the complexity of equations called Simplified Inclined Planes System Optimization (SIPO). The efficiency of the standard version of IPO and the simplified IPO version (SIPO) is proven in solving many optimization problems. However, due to the strong exploitation, this algorithm suffers from weak exploration. Hence, this leads to low convergence rates and in real applications can get stuck in the local optimum. In this paper, a uniform mutation operator is applied to the SIPO algorithm to make more effective exploration of the latent information contained in the population of solutions. The measurements of the proposed method is first taken on 10 known standard benchmark functions. Then, the performance is compared to the results obtained from the standard IPO algorithm and the SIPO version. Also, the proposed method is utilized in training Adaptive Neuro Fuzzy Inference System (ANFIS) classifier and the results obtained from using IPO, SIPO, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms in the training of this classifier are compared. The results indicate that the proposed algorithm, in both the experiments of standard benchmark functions and in the training of ANFIS classifier, is able to achieve better performance in comparison to the mentioned methods and has a faster convergence speed.
Recent Progress, Emerging Techniques, and Future Research Prospects of Bangla Machine Translation: A Systematic Review

Machine Translation (MT), the way of translating texts or documents from a source language to a target language automatically without human intervention, has gained popularity in the growing information technology-based era of globalization.Bangla is a major language, and several MT studies with different tools and techniques have been investigated in the last two decades.Considering the importance of the Bangla language and its prospects in MT studies, this study provides a comprehensive review of existing Bangla MT studies to meet the timely demand.Specifically, at first, the basic ideas of different MT methods (Rule-based, Example-based, Statistical, Neural, and Hybrid) and performance measures of MT are presented as a background study of the present review.Then an overview of the Bangla language and a brief description of the available Bangla-English corpora are provided.Next, a description of the existing Bangla MT studies is provided categorically following the common strategic fashion to create a valuable reference for current researchers in the field that is also suitable for nonexpert users.The achieved performances of individual methods are also compared in a tabular form.Finally, a number of future research prospects are revealed from the studies, encouraging researchers and practitioners to develop a better and comprehensive Bangla MT system.
A Novel Deep Learning ArCAR System for Arabic Text Recognition with Character-Level Representation

AI-based text classification is a process to classify Arabic contents into their categories. With the increasing number of Arabic texts in our social life, traditional machine learning approaches are facing different challenges due to the complexity of the morphology and the delicate variation of the Arabic language. This work proposes a model to represent and recognize Arabic text at the character level based on the capability of a deep convolutional neural network (CNN). This system was validated using five-fold cross-validation tests for Arabic text document classification. We have used our proposed system to evaluate Arabic text. The ArCAR system shows its capability to classify Arabic text in character-level. For document classification, the ArCAR system achieves the best performance using the AlKhaleej-balance dataset in terms of accuracy equal to 97.76%. The proposed ArCAR seems to provide a practical solution for accurate Arabic text representation, both for understanding and as a classifications system.
Viewpoint-Switching Delay of Multi-viewpoint 360 VR in a Bandwidth-limited Environment

The transmission of multi-view or free-view 360VR contents, which selects the viewer's viewpoint and experiences 360VR contents, consumes a very large bandwidth. In a cellular environment in which multiple users exist, only a limited bandwidth can be allocated to a specific user, and thus transmission quality is inevitably deteriorated or transmission delay occurs. In the case of multi-viewpoint 360VR content, if the viewer changes the position of the viewpoint, the contents of the corresponding position are transmitted. In a bandwidth-limited communication environment, a large transmission delay occurs when the viewpoint is moved, resulting in a very poor user experience. In this paper, in order to reduce the transmission delay when attempting to change the viewpoint in a multi-view 360 video, a transmission method of appropriately pre-loading the video of the surrounding location is considered. In addition, the delay performance according to the viewpoint change was analyzed in a practical 5G network environment. In order to lower the viewpoint switching delay below a certain level, an appropriate prediction technique for the user movement pattern is required, and the decrease in the delay in accordance with the accuracy is presented through an experiment.
Topic-to-Essay Generation with Comprehensive Knowledge Enhancement

Generating high-quality and diverse essays with a set of topics is a challenging task in natural language generation. Since several given topics only provide limited source information, utilizing various topic-related knowledge is essential for improving essay generation performance. However, previous works cannot sufficiently use that knowledge to facilitate the generation procedure. This paper aims to improve essay generation by extracting information from both internal and external knowledge. Thus, a topic-to-essay generation model with comprehensive knowledge enhancement, named TEGKE, is proposed. For internal knowledge enhancement, both topics and related essays are fed to a teacher network as source information. Then, informative features would be obtained from the teacher network and transferred to a student network which only takes topics as input but provides comparable information compared with the teacher network. For external knowledge enhancement, a topic knowledge graph encoder is proposed. Unlike the previous works only using the nearest neighbors of topics in the commonsense base, our topic knowledge graph encoder could exploit more structural and semantic information of the commonsense knowledge graph to facilitate essay generation. Moreover, the adversarial training based on the Wasserstein distance is proposed to improve generation quality. Experimental results demonstrate that TEGKE could achieve state-of-the-art performance on both automatic and human evaluation.
Feature-guided dictionary learning for patch-and-group sparse representations in single image deraining

Single image deraining as a fundamental task in computer vision is important in improving the visual quality of images and videos. In this paper, we propose a feature-guided dictionary learning method for patch-and-group sparse representation in single image deraining. Specifically, we develop an external dictionary that learns the generic feature representation from a lot of natural images using Gaussian mixture models (GMMs), and a novel strategy is designed to learn an internal dictionary using the given rainy image guiding by the generic features. The external dictionary and the internal dictionary are adaptively tailored to produce a coherent dictionary, which can extend the representation abilities of the learned dictionary for the group-based sparse representation. Moreover, we present a novel patch-and-group sparse representation framework to reconstruct an image tending to be free of rain by simultaneously combining the local sparsity and the nonlocal self-similarity property. This framework can integrate their advantages of the two representative methods capable of capturing more effective features for the single image deraining. The results of experiments on both the synthetic and the real-world rainy images demonstrate that the proposed method delivers more favorable visual effects and superior quality results, and it outperforms several other state-of-the-art methods.
The number of solutions of diagonal cubic equations over finite fields

Let Fq be a finite field of q=pk elements. For any z∈Fq, let An(z) and Bn(z) denote the number of solutions of the equations x13+x23+⋯+xn3=z and x13+x23+⋯+xn3+zxn+13=0 respectively. Recently, using the generator of Fq⁎, Hong and Zhu gave the generating functions ∑n=1∞An(z)xn and ∑n=1∞Bn(z)xn. In this paper, we give the generating functions ∑n=1∞An(z)xn and ∑n=1∞Bn(z)xn immediately by the coefficient z. Moreover, we gave the formulas of the number of solutions of equation a1x13+a2x23+a3x33=0 and our formulas are immediately determined by the coefficients a1,a2 and a3. These extend and improve earlier results.
A Self-Supervised Learning Framework for Sequential Recommendation

Sequential recommendation that aims to predict user preference with historical user interactions becomes one of the most popular tasks in the recommendation area. The existing methods concentrated on user's sequential features among exposed items have achieved good performance. However, they only rely on single item prediction optimization to learn data representation, which ignores the association between context data and sequence data. In this paper, we propose a novel self-supervised learning based sequential recommendation network (SSLRN), which contrastively learns data correlation to promote data representation of users and items. We design two auxiliary contrastive learning tasks to regularize user and item representation based on mutual information maximization (MIM). In particular, the item contrastive learning captures sequential contrast feature with sequence-item MIM, and the user contrastive learning regularizes user latent representation with user-item MIM. We evaluate our model on five real-world datasets and the experimental results show that the proposed framework significantly and consistently outperforms state-of-the-art sequential recommendation techniques.
Detection of Helmet Wearing Based on Improved Yolo v3

Aiming at the problem of helmet recognition in complex working environment, an improved Yolo v3 algorithm is proposed. The backbone network of the improved Yolo v3 algorithm is the darknet-53 network. The size is changed by changing the size of the convolution kernel, and the feature selection is performed by using the full convolution method. The k-means++ clustering algorithm is used to optimize the selection method of the anchor box to solve the problem of unstable initial centroid. GIoU is used as the bounding box coordinate regression loss in the design of the loss function, which solves the scale-sensitive problem of mean square error loss. In addition, Focal Loss is introduced to reduce the weight of simple background classes, so that the algorithm model is more focused on target detection. Finally, the algorithm is applied to the helmet wearing detection task. The experimental results show that all the evaluation indexes of the improved algorithm model perform well and meet the accuracy requirements of practical application detection.
Explaining Effects of Transformational Leadership on Teachers’ Cooperative Professional Development through Structural Equation Model and Phantom Model Approach

This study aims to explore strategies for promoting Korean teachers’ cooperative professional development in the context of Education for Sustainable Development (ESD). To this end, this study aimed to provide empirical evidence that may help in explaining the mechanism through which transformative leadership affects cooperative professional development. Based on research purposes, the author constructed a serial multiple mediation model that incorporates self-efficacy and attitudes as an underlying mechanism that explains transformational leaders’ positive impact on cooperative professional development related to ESD. A total of 203 valid cases were used to test the hypothesized model. Furthermore, the author constructed structural equation modeling (SEM) and phantom models specifying the specific indirect effects of transformative leadership on cooperative professional development. In addition, bias-corrected bootstrapped point estimates for the specific indirect effects were calculated. The data were analyzed using SPSS Ver. 25.0 and AMOS Ver. 26.0. The findings strongly supported predictive links among transformational leadership, self-efficacy in ESD teachers’ attitudes toward ESD, and cooperative professional development. Therefore, findings provided greater insight into transformational leadership and ESD research and revealed mechanisms through which transformational leadership works in the ESD contexts. Finally, the major findings were discussed to encourage teachers to participate in cooperative professional development.
Local linear embedding algorithm of mutual neighborhood based on multi-information fusion metric

Local linear embedding (LLE) algorithm is an effective tool, which mines low-dimensional features in high-dimensional space. However, the local region and inner structure directly affect the performance of the LLE algorithm. To address this problem, the LLE algorithm of mutual neighborhood by employing multi-information fusion metric (MIFM-MNLLE) is proposed. First, the Euclidean distance and cosine similarity method are combined to evaluate the similary among samples, by which the accuracy of selected neighbors can be improved. Subsequently, the idea of mutual neighbor structure is utilized to construct the neighbor and the mutual neighbor graph of the sample to describe the internal structure of the data set. Finally, the coefficient of LLE is rectified according to the mutual neighborhood relationship between the sample and its neighbors, so as to extract significant features effectively. Extensive experimental results show that the proposed method has better performance compared with the existing methods on bearing datasets.