Reviewerly/exHarmony huggingface dataset:
Integration of ASTER-based remote sensing with petrographic studies, XRD analysis, and field surveying as a multidisciplinary approach for prospecting Cu mineralization in the Taft-Dehshir region, Iran

Taft-Dehshir region is located in the central part of the Urmia-Dokhtar Magmatic Belt (UDMB) in central Iran. Some Cu mineralizations that occurred in the UDMB are definable by surface patterns of hydrothermal alterations and thus are detectable by advanced spaceborne thermal emission and reflection radiometer (ASTER)-based remote sensing. The final product of ASTER-based remote sensing was a prepared unique map that defined the type and pattern of alterations. Then, this map was integrated with complementary petrographic studies, XRD analysis, and careful field surveys, which were meticulously prepared based on the existing literature. The results of this research show that the hydrothermal alterations around the stockworks and skarnization in carbonate host rocks caused the formation of different Cu mineralizations such as porphyry (especially in Aliabad area), porphyry-skarn (in Darre-Zereshk area), Skarn (in Khezrabad and Khut areas), and vein (in Nasrabad area) types. This research determined the 18 decisive occurrences and five promising places that occurred in Oligocene-Miocene granitoid bodies.
Digital Teaching and Training System of Vocational Colleges Based on a Cloud Platform

To effectively integrate theory and practice, teaching and training, and improve the overall quality of teaching and training, a cloud platform-based digital teaching and training system is proposed. The hardware unit of the design system includes the building unit of the cloud platform, the processor selection unit and the controller selection unit. The software module includes a teaching and training data collection and processing module, a training room management module and a database building module. Through the design of the hardware unit and software module, the running of the digital teaching and training system in vocational colleges is realized. The experimental data show that the evaluation results of the teaching and training quality are above the qualification standard after the application of the design system, which fully shows that the design system has better application performance and provides support for society to cultivate more high-skilled talent and research on the teaching and training system.
Design and Implementation of Chinese Teaching Resources Optimal Allocation Platform Based on Big Data Technology

Modern Chinese is a professional basic course for Chinese language and literature majors in colleges and universities in China, which plays an important role in the whole major. Chinese language and literature majors talk about professional innovation, professional teaching reform and curriculum integration, which can’t be separated from modern Chinese. With its many advantages, big data technology has gradually penetrated and changed all aspects of society, and has expanded from the field of science and technology to the field of education, thus having a certain impact on the university education management system. The classification mining algorithm is used to mine and analyze the data, and the rules and patterns of users’ access to curriculum resources are obtained. Collaborative filtering technology and data mining technology are used to match learners’ characteristics with online learning resources. Experiments show that compared with the traditional management platform, the optimized management platform can significantly improve the efficiency of users accessing curriculum resources and the utilization rate of curriculum resources.
Digital Transformation and Costumers Services in Emerging Countries: Loan Prediction Modeling in Modern Banking Transactions

A digital future is inevitable in today’s world. Digitalization is a necessity due to the increasing expectations for productivity and competitiveness. User experience is streamlined, and the contact between firms and their customers is accelerated, thanks to digitization. Better products and more profits are also the results of this process. Recently, the concept of digital transformation has developed as a focus for enterprises in emerging countries. Thus, human and corporate processes, as well as technology elements, are all being transformed to give better services. In this work, we present our system that predicts loan repayment or default based on customer’s digital data using classification models to save time and energy. Our system achieved promising results in terms of accuracy, AUC, and ROC which demonstrate the effectiveness of our approach.
Evaluation of the Application of YOLO Algorithm in Insulator Identification*

Insulators have a large amount of use and a large geographical span in the power grid. Conventional inspection methods mainly rely on manual experience, and it is difficult to find weak discharges on the surface of equipment. Solar blind ultraviolet imaging detection has the advantages of high sensitivity, non-contact and intuitive characterization. Based on the characteristics of dual-channel imaging of ultraviolet imagers, this paper proposes a method for identifying insulators of visible light channel images based on artificial intelligence. Based on this, discharge severity assessment of the ultraviolet channel insulator image is realized. A deep learning hardware and software research platform based on the TensorFlow and Darknet frameworks under the Linux environment was established. Based on the pictures collected in the laboratory and the field, a sample database of insulators was established and the sample pictures were marked and the YOLO network training was completed, the effects of training sample number and network depth on recognition accuracy are studied, and a matching scheme is given.
Efficient Modular Arithmetic

In Chapter 8, Efficient Modular Arithmetic, Joppe W. Bos, Thorsten Kleinjung and Dan Page discuss how to realise efficient modular arithmetic in practice on a range of platforms. They cover generic modular multiplication routines such as the popular Montgomery multiplication as well as special routines for primes of a specific shape. Moreover, especially fast methods are outlined that might produce errors with a very small probability. Such faster 'sloppy reduction' techniques are especially beneficial in cryptanalytic settings. Bos, Kleinjung and Page have all been involved in various cryptographic and cryptanalytic record-setting software implementations. In Chapter 8 they explain how to select the most efficient modular arithmetic routines for the cryptologic implementation task at hand.
A Derain System Based on GAN and Wavelet

This paper proposes an image rain removal method based on wavelet thresholding and generative adversarial networks for removing rain traces on the target image to restore the original image. This paper first uses wavelet threshold denoising to pre-process the image to remove the non-rain noise and part of the image's rain noise. After that, this paper uses the generator in the adversarial generative network to complete the rain-containing images' de-rain operation. The generators and discriminators in the adversarial generative network need to be trained in advance, and the artificially generated dataset is used for training in this paper. Finally, this paper conducts experiments on the actual rain trace image dataset and artificially generated rain trace image dataset, respectively, to verify the method's effectiveness in this paper.
Gold of Riftogenic Structures of the East Ural Uplift

Studies have been carried out to study the gold occurrences of riftogenic structures of the East Ural uplift. The article reflects the features of the geological and structural-tectonic structure of the Kirov, Anikhov and Staro-Karabutak grabens of deep occurrence. Mineralization of grabens forms a number of extended ore zones and gold-bearing nodes enriched in them, which are of the greatest industrial interest. As a result of a generalization of geological materials and gold analysis results, the nature of the distribution of gold mineralization is revealed. Significant concentrations are recorded in carbon weakly metamorphosed siltstones, shales, and fine-grained sandstones. Packs of ore-bearing rocks reach tens of meters of capacity. They noted high carbon content, reaching 7–11.2%. A specific feature of the graben deposits of the East Ural uplift is noted—the transition of the volcanogenic part of the section to carbon-terrigenous. The tuffogenic-sedimentary sequence is characterized by scattered gold mineralization without the formation of large manifestations, where the noble metal content is only several times higher than their clarks. These strata can be one of the potential sources of gold in the formation of industrial manifestations of this metal in overlying black shale formations.
Design of a Rural Idle House Resource Information Management System Based on the Ssh Framework

Aiming at the poor effect of traditional rural idle house resource information management, this study proposes a rural idle house resource information management system based on the SSH framework. To optimize the system hardware structure, integrate the mainstream SSH Framework Technology in the J2EE development platform, make the system realize more detailed layering under the SSH three-tier architecture mode, and optimize the system software function and operation process on this basis to improve the operation efficiency of the system management system. The experimental results show that the rural idle house resource information management system based on the SSH framework has high practicability in the process of practical application and can better manage massive house resource information.
Reversible data hiding in chaotic encryption domain based on odevity verification

On the premise of guaranteeing the visual effect, in order to improve the security of the image containing digital watermarking and restore the carrier image without distortion, reversible data hiding in chaotic encryption domain based on odevity verification was proposed. The original image was scrambled and encrypted by Henon mapping, and the redundancy between the pixels of the encrypted image was lost. Then, the embedding capacity of watermarking can be improved by using odevity verification, and the embedding location of watermarking can be randomly selected by using logistic mapping. When extracting the watermarking, the embedded data was judged according to the odevity of the pixel value of the embedding position of the watermarking, and the carrier image was restored nondestructively by odevity check image. The experimental results show that the peak signal-to-noise ratio (PSNR) of the original image is above 53 decibels after the image is decrypted and restored after embedding the watermarking in the encrypted domain, and the invisibility is good.
Using Convolutional Neural Network for Image Classification and Segmentation

Image segmentation and Image Classification are two fundamental tasks in computer vision. In this thesis, a novel segmentation algorithm based on a deformable model and robust estimation is introduced to produce reliable segmentation results. The algorithm is extended to handle touching objects and partially occluded image segmentation. Although current conventional image classification methods have been widely applied to realistic problems, there are some issues with their implementation, including unsatisfactory results, poor classification accuracy, and a lack of adaptive capacity. This approach has been used to isolate the two processes of image feature extraction and classification into two stages. The deep learning model possesses a strong learning capability, which enables it to incorporate the feature extraction and classification processes, thereby improving the image classification accuracy. This thesis explores various machine learning methods to improve the model's performance. The primary objective is to discover the accuracy of the various networks on the datasets and to evaluate the consistency of each of these deep learning predictions. Nonetheless, there are limitations to this approach: first, it is difficult to perform accurate approximation in the advanced model. The second point is that the deep learning model comes with poor accuracy in its classifier. So, this paper introduces the idea of using different datasets and models of the deep learning network and comprehensively utilizes it to determine the best test accuracy for the images. In this paper, a deep neural network primarily based on Keras and TensorFlow is deployed using python. The two datasets are used to compare to determine which has the maximum accurate and fine time for processing. And a VGG-16 model method based on the optimized kernel function is proposed to replace the classifier in the deep learning model. The experimental results show that the proposed method not only has higher average accuracy than other mainstream methods but also can be good adapted to various image databases. Compared with other deep learning methods, it can better solve the problems of complex function approximation and poor classifier effectiveness, thus further improving image classification accuracy. Keywords: Image Classification, Deep Learning, TensorFlow DOI: 10.7176/CEIS/13-1-03 Publication date: January 31 st 2022
Inside-Out: Testing, Implementation, and Communication of Experience Elements

Being or becoming a customer experience-centric company is an ongoing challenge. The processes towards greater customer experience never come to an end, but should be lived in the daily operative business. Before and after implementing ideas, managers should test them and evaluate whether they contribute to the company’s goals. Testing may sound simple at first glance. However, generating valid results requires methodological know-how. In this chapter, we show which criteria should be considered in order to reach valid conclusions via testing.
The Model for Pneumothorax Knowledge Extraction Based on Dependency Syntactic Analysis

As the frontier application technology of artificial intelligence in the medical field, medical knowledge mapping plays an important role in assisting medical staff in the diagnosis and treatment of diseases. In this paper, pneumothorax is selected as the research object, and the method of natural language processing is applied to study the knowledge extraction model involved in the construction of knowledge map. Firstly, based on the principle of dependency syntax, the grammatical relationship of pneumothorax corpus is analyzed, and the dependency syntax tree is constructed. Secondly, based on the special text features of pneumothorax corpus, a knowledge extraction model based on Text Syntactic Structure and dependency representation is proposed, and the predicate is used as the core term to extract pneumothorax triples. Finally, based on the standardized knowledge representation defined by experts, the automatically extracted triples are normalized and presented in the form of pneumothorax knowledge map.
1pPA5 - What does multiple scattering tell us about rheology?

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Research on Embedded Speech Recognition System Based on Artificial Intelligence

Aiming at the problems of large space occupation, high power consumption, and low recognition accuracy of current speech recognition systems, an embedded speech recognition system based on artificial intelligence is studied. The hardware part of the system is designed with an SPCE061A single-chip microcomputer as the core, and the software part of language recognition is designed on the system hardware architecture. By collecting and filtering the features of voice samples, a classifier template for voice recognition is established. After improving the naive Bayes model in artificial intelligence, the model is used to complete language recognition processing. The system test results show that the language recognition accuracy rate of the studied system is higher than 95%, the hardware resource consumption is low, and it has good performance.
Homology Detection of Malicious Codes Based on a Fuzzy Graph Neural Network

Malicious code detection and homology analysis has been a hot research field in malicious code analysis. The API call graph extracted from malicious code can express the behavior information of malicious code effectively, but the efficiency of malicious code analysis based on graph structure is low because of the high algorithm complexity of solving subgraph isomorphism. To solve these problems, a new method of malicious code homology detection based on a fuzzy graph neural network is proposed. First, the malicious code features are selected and summarized, then the malicious code features are extracted, and finally the malicious code features are compared. Static analysis is used to analyze malicious code, extract its API call graph, and then process the API call graph to make it match the input of the convolutional neural network. With the help of the online code analysis platform, the platform adopts a sandbox mechanism, integrates more than 90 kinds of antivirus software, and analyzes the samples uploaded by users, which can give malicious evaluations. Experimental results show that the accuracy rate of malicious code homology analysis is 93%, and the accuracy rate of malicious code detection is 96%.
Peer Review Declaration Form Guidance

On the following page you will find the declaration form. • Please answer each question. • You should submit the form along with the rest of your submission files. • The deadline is the submission date written in your publishing agreement. All conference organisers/editors are required to declare details about their peer review. We will published the information you provide as part of your proceedings. Peer review declaration All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. Type of peer review: Single-blind / Double-blind / Triple-blind / Open / Other (please describe) Double-blind • Conference submission management system: http://conference.tts.or.th • Number of submissions received: 95 • Number of submissions sent for review: 47 • Number of submissions accepted: 43 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100): 45% • Average number of reviews per paper: 2 • Total number of reviewers involved: 72 • Any additional info on review process: - • Contact person for queries: Name : Prof.Dr.Jakrapong Kaewkhao Affiliation: Nakhon Pathom Rajabhat University Email: jakrapong@webmail.npru.ac.th
Prospects for the Development of Higher Education in the Conditions of Practice-Oriented Student Training

The most important feature of modern higher education is its innovative orientation based on the intellectual level of the teaching staff, as well as information and digital educational services which predetermine the need for constant updating and increment of new knowledge. The problem of providing a practice-oriented approach in obtaining higher education has been actively studied in the works of domestic and foreign authors for more than 10 years. Purpose of the research: identifying the features of the development of higher education and determining measures to improve the quality of practice-oriented training of students in conditions of limited financial resources. The methodological basis of the study was the determinism of the process of providing educational services and their financial support, as well as the methods of systemic and structural-functional approaches. In the case of strengthening the practice-oriented training of graduates of higher educational institutions, an increase in the quality of mastering professional competence, obtaining practical skills and experience necessary for starting practical activity is expected. Achievement of these results will ensure the growth of competitiveness not only of graduates, but also of universities.
A Topic Based Method to Classify the Question Clarity in CQA Networks

Better results would be obtained by distinguishing the clarity of questions as well as increasing their quality. This improvement in clarity may improve the output of a search engine when it encounters the query. Furthermore, it can lead to getting the correct answer to a question when asked in CQAs. In this regard, thousands of different questions are posted daily in CQAs, making these questions and their answers one of the world's most valuable information sources. Nonetheless, most of the questions posted in these forums do not result in proper answers, with one of the most important reasons being a lack of clarity in the questions. This paper addresses one of the most important issues in this field, which is classifying questions based on their clarity. For this purpose, a feature vector based on clustering approaches and obtaining similar questions is designed uniquely for each question based on the total data provided in this field. Following that, the questions are classified based on their clarity using a machine learning classification model. Furthermore, we investigated and reported our new approach using other related approaches in this field in the following step. What we describe as an accomplishment in this paper is the high separability of the questions based on the feature vector extracted by different clusters, which has a much higher performance when compared to other proposed textual classification methods.
Development of Multi-User Synchronous VR System and its Performance Analysis

With the improvement of the efficiency of information and network technology, virtual reality has gradually developed in the direction of multi-user synchronization in network systems. In general, virtual reality systems have high computing costs, so performance analysis is one of the most important considerations for system execution when developing multi-user synchronous VR systems. In order to explore the execution performance of a multi-user synchronous VR system, this study uses Unity, Steam VR, Mirror Networking, to develop a multi-user synchronous VR system based on client-server architecture. In the efficiency analysis, VR systems pay attention to the ability of real-time. Therefore, this study discusses the execution performance of each client-end VR system and analyzes the required synchronous execution data with FPS.
An Abnormal Event Detection Method for Substation Monitoring

Aiming at the complexity of Substation Scene and the serious interference of electromagnetic noise and natural climate on foreground target detection, an intelligent identification system and method for abnormal event detection oriented to Substation Scene optimization are proposed. Combined with the substation monitoring objects and equipment, the abnormal condition monitoring strategy is designed. Based on the system architecture, combined with the improved Gaussian mixture background model and wavelet change principle, the abnormal target events are extracted and denoised. The spatiotemporal gradient model is used to improve the efficiency of anomaly target feature extraction, and the hidden Markov model is used to monitor the anomaly of surveillance video. Through field experiments and application verification, it is proved that the proposed method has higher accuracy and better detection effect than the traditional Gaussian mixture model method.