Reviewerly/exHarmony huggingface dataset:
Entity recognition of railway signal equipment fault information based on RoBERTa-wwm and deep learning integration

&lt;abstract&gt; &lt;p&gt;The operation and maintenance of railway signal systems create a significant and complex quantity of text data about faults. Aiming at the problems of fuzzy entity boundaries and low accuracy of entity recognition in the field of railway signal equipment faults, this paper provides a method for entity recognition of railway signal equipment fault information based on RoBERTa-wwm and deep learning integration. First, the model utilizes the RoBERTa-wwm pretrained language model to get the word vector of text sequences. Second, a parallel network consisting of a BiLSTM and a CNN is constructed to obtain the context feature information and the local attention information, respectively. Third, the feature vectors output from BiLSTM and CNN are combined and fed into MHA, focusing on extracting key feature information and mining the connection between different features. Finally, the label sequences with constraint relationships are outputted in CRF to complete the entity recognition task. The experimental analysis is carried out with fault text of railway signal equipment in the past ten years, and the experimental results show that the model has a higher evaluation index compared with the traditional model on this dataset, in which the precision, recall and F&lt;sub&gt;1&lt;/sub&gt; value are 93.25%, 92.45%, and 92.85%, respectively.&lt;/p&gt; &lt;/abstract&gt;
Entity relationship extraction from Chinese electronic medical records based on feature augmentation and cascade binary tagging framework

&lt;abstract&gt; &lt;p&gt;Extracting entity relations from unstructured Chinese electronic medical records is an important task in medical information extraction. However, Chinese electronic medical records mostly have document-level volumes, and existing models are either unable to handle long text sequences or exhibit poor performance. This paper proposes a neural network based on feature augmentation and cascade binary tagging framework. First, we utilize a pre-trained model to tokenize the original text and obtain word embedding vectors. Second, the word vectors are fed into the feature augmentation network and fused with the original features and position features. Finally, the cascade binary tagging decoder generates the results. In the current work, we built a Chinese document-level electronic medical record dataset named VSCMeD, which contains 595 real electronic medical records from vascular surgery patients. The experimental results show that the model achieves a precision of 87.82% and recall of 88.47%. It is also verified on another Chinese medical dataset CMeIE-V2 that the model achieves a precision of 54.51% and recall of 48.63%.&lt;/p&gt; &lt;/abstract&gt;
Knowledge graph completion method for hydraulic engineering coupled with spatial transformation and an attention mechanism

&lt;abstract&gt; &lt;p&gt;In response to the limited capability of extracting semantic information in knowledge graph completion methods, we propose a model that combines spatial transformation and attention mechanisms (STAM) for knowledge graph embedding. Firstly, spatial transformation is applied to reorganize entity embeddings and relation embeddings, enabling increased interaction between entities and relations while preserving shallow information. Next, a two-dimensional convolutional neural network is utilized to extract complex latent information among entity relations. Simultaneously, a multi-scale channel attention mechanism is constructed to enhance the capture of local detailed features and global semantic features. Finally, the surface-level shallow information and latent information are fused to obtain feature embeddings with richer semantic expression. The link prediction results on the public datasets WN18RR, FB15K237 and Kinship demonstrate that STAM achieved improvements of 8.8%, 10.5% and 6.9% in the mean reciprocal rank (MRR) evaluation metric compared to ConvE, for the respective datasets. Furthermore, in the link prediction experiments on the hydraulic engineering dataset, STAM achieves better experimental results in terms of MRR, Hits@1, Hits@3 and Hits@10 evaluation metrics, demonstrating the effectiveness of the model in the task of hydraulic engineering knowledge graph completion.&lt;/p&gt; &lt;/abstract&gt;
PREDIKSI PENJUALAN KOPI PADA PT. KOPI CAP LESUNG LUBUKLINGGAU MENGGUNAKAN METODE SINGLE MOVING AVERAGE (SMA)

The problem in this study is that it is difficult for PT. Kopi Cap Lesung to determine sales in the next period, because no one has predicted using the Single Moving Average (SMA). This study uses data collection methods by observing and recording directly at the research site (observation), conducting direct questions and answers to sources (interviews) and documentation by reading previous guidelines. The results of this study are the values ​​obtained for the next 3 years using the Single Moving Average method and for the error value MAD (Mean Absolute Deviation) Error Value of 25.280434782608694, Mean Square Error (MSE) Error Value of 10843.610677083334, and Error Mean Absolute Percentage Error (MAPE) of 5.638958229765874% less than 10% which obtained an accuracy rate of 95% which is implemented in the Python Programming Languagethat is google collab. which means the value is very good for predicting coffee sales. Keywords: Single Moving Average, Prediction, Python
Secure Content Based Image Retrieval System Using Deep Learning

This paper investigates Content-Based Image Retrieval (CBIR) using an ensemble of three cutting-edge deep learning architectures: Xception, MobileNet, and Inception. This ensemble approach demonstrated exceptional retrieval accuracy, with Xception and Inception models achieving an accuracy of 92.375%, precision and recall of 93% and 92% respectively, and an F1-score of 92%. The MobileNet model also showed strong performance, with an accuracy of 87.125%, precision and recall of 88% and 87%, and an F1-score of 87%.Beyond mere retrieval accuracy, the study places a significant emphasis on the security of the image database. A dual-layer encryption method was employed, integrating visual cryptography with the Advanced Encryption Standard (AES) to ensure robust protection of sensitive data. This approach guarantees efficient image retrieval based on content while securing the data against potential breaches.The results underscore the efficiency of the ensemble model in balancing high retrieval accuracy with stringent security measures. This balance is particularly relevant for applications in digital libraries, historical research, fingerprint identification, and crime prevention. The paper’s findings advocate for the critical need to integrate strong security protocols in future CBIR systems, ensuring optimal performance without compromising data security.
A Study on the Geological Characteristics and Metallogenic Laws of the Xilahe Copper Deposit in Yuanjiang County

The Xilahe copper deposit in Yuanjiang County, Yunnan Province, China, is a highly concerned copper mining area. The geological characteristics, structural characteristics, distribution of magmatic rocks, ore body characteristics, metamorphism and wall rock alteration, as well as the ore-forming horizon and mineralization characteristics of the deposit are described and analyzed in this article, revealing its mineralization laws. The strata of the ore deposit is mainly composed of the upper section of the Fei weihe Formation, and the structural characteristics show the development of multiple faults in the mining area, including magmatic rocks such as diabase. The characteristics of the ore body indicate the presence of five copper ore bodies, mainly sulfide ore, and the main metal mineral is Fool's Gold. Metamorphism and wall rock alteration are manifested as silicification, carbonization, and pyritization. The mineralization pattern is influenced by various geological factors, including geological structure, magnetic action, hydrothermal activity, and time factors. The deposit belongs to a sedimentary metamorphic copper deposit. Comprehensive analysis shows that the copper deposits in the study area have great potential for ore exploration, which has important theoretical and practical significance for subsequent exploration and application of ore exploration methods.
Geological characteristics and genetic types of antimony deposits in the Weishan Yongping ore concentration area of Yunnan Province

The Weishan Yongping ore concentration area is located at the southern end of the "Jubao Basin" in the Lanping Basin, and is part of the Lanping-Simao mineralization belt. It is an important antimony ore belt in Yunnan Province. This article analyzes the geological characteristics of antimony deposits in the ore concentration area from the perspectives of regional geology and typical deposit characteristics, explores their genetic types, and provides a basis for the exploration and development of antimony resources in the area.
A Study on The Tectonic Control and Metallogenic Regularity of The Sanjiang Sanjiang Metallogenic Belt Cobalt Deposit in Yunnan Province

The Sanjiang region of Yunnan is rich in minerals, especially known for gold, copper, lead zinc, and iron ore deposits. There are many genetic types of ore deposits, and most of the ore deposits (points) and anomalies are controlled by the northwest north-south structural belt. The regional tectonic activity is strong and the metallogenic conditions are superior. In this paper, the structural ore-controlling effect and metallogenic regularity of Sanjiang Metallogenic Belt cobalt deposit in Yunnan Province are further studied. The mineralization of the ore belt is most closely related to the Caledonian submarine hydrothermal sedimentation, and after mineralization, it was obviously transformed by Indosinian metamorphism and hydrothermal superposition. Some scholars have carried out relevant research on the geological and geochemical characteristics and genesis of the deposit, and think that the deposit is stratabound-reformed, jet sedimentary, hydrothermal sedimentary-tectonic reformed, etc. However, the chemical properties of ductile rocks are inactive and the permeability is poor, which makes the ore-forming fluid difficult to lose, which is beneficial to the full water-rock reaction between minerals in hydrothermal solution and surrounding rocks flowing through carbonate rocks, resulting in a large number of minerals precipitation and enrichment. When the soft and hard rocks alternate with each other, it is beneficial to form bedding detachment structure to become ore storage space, and it is also beneficial to the precipitation of minerals along the chemical interface and the enrichment of minerals.
Case Material: “Patricia”

Journal of Analytical PsychologyEarly View Clinical Commentary Case Material: “Patricia” First published: 27 December 2023 https://doi.org/10.1111/1468-5922.12966Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
Synthesis of Vision and Language: Multifaceted Image Captioning Application

The rapid advancement in image captioning has been a pivotal area of research, aiming to mimic human-like understanding of visual content. This paper presents an innovative approach that integrates attention mechanisms and object features into an image captioning model. Leveraging the Flickr8k dataset, this research explores the fusion of these components to enhance image comprehension and caption generation. Furthermore, the study showcases the implementation of this model in a user-friendly application using FASTAPI and ReactJS, offering text-to-speech translation in multiple languages. The findings underscore the efficacy of this approach in advancing image captioning technology. This tutorial outlines the construction of an image caption generator, employing Convolutional Neural Network (CNN) for image feature extraction and Long Short-Term Memory Network (LSTM) for Natural Language Processing (NLP). Keywords—Convolutional Neural Networks, Long Short Term Memory, Attention Mechanism, Transformer Architecture, Vision Transformers, Transfer Learning, Multimodal fusion, Deep Learning Models, Pre-Trained Models, Image Processing Techniques
Squeezed States Generation in a Three-Mode System of Nonlinear Quantum Oscillators

Three identical, interacting with each other nonlinear oscillators are considered. In addition, they are also externally driven by a coherent field of constant amplitude. The possibility of generating two-mode squeezed states is analyzed in this system. The two-mode principal squeeze variance is used to study the properties of the squeezed states of the system. The time evolution of this variance is analyzed, as well as the effect of the strength of the interaction between the oscillators and the damping strength on the generation of squeezed states.
A Privacy-aware Framework for Assessing and Recommending Short Video Advertisement

With the increasing number of short video platforms, short video advertisements have gained significant importance as a means of dissemination. Given the usual protection of user data, the efficient recommendation of high-quality short video advertisements to the public without relying on additional information becomes an increasingly important issue. To solve this challenge, we put forward a framework for assessing the quality of short videos, utilizing only visual and audio features extracted from the videos as input. In our framework, we employ pre-trained models to extract the features above. To make better use of these two modalities, we also incorporate an attention module and a modality fusion to further improve the performance of our framework. In the end, we use a large number of short video advertisements to evaluate our proposed framework. The experimental results demonstrate that the accuracy of this method can reach 70.45%, which exhibits a considerable improvement compared to the baseline (50.00%).
A Critical Review on Hazard Analysis and Risk Assessment for Development of Wearable Medical Devices

Many patients suffer from variable chronic illness. In the past, the patients are required to carry out health check in half year or yearly interval. However, the result is only to reflect the instantaneous health index, for example the glucose level in blood. The wearable medical device is a good solution to the medical doctors to understand the patient’s continuous health status. This paper aims to review the different hazard analysis and risk assessment approaches for developing the wearable medical devices. In the beginning, the need for wearable medical devices is discussed, Secondly, the typical product development cycle is reviewed, Thirdly, different hazard analysis and risk assessment approaches are reviewed and summarized. Finally, a conclusion is made to recommend to the developers.
State Management Against Two-message Attacks in Hash-Based Post Quantum Signatures for Large IoT Sensor Networks Using Blockchain

With the rise of quantum computers, quantum-safe digital signatures have been invented to ensure the security and verifiability of communication. Hash-based one-time signatures are one of the candidates to ensure unforgeability against the improved processing power. This signature algorithm requires tracking of secret keys to ensure one-time usage, called key state. In an Internet of Things(IoT) environment, failure to maintain key state due to network failures, hardware/software failures, implementation flaws, and improper access is possible. Consequences of failure to manage key state results in the degradation of the system’s security, opening up opportunities for attackers to forge signatures. Solutions to this however are limited due to the scale and resource constraints of IoT devices. Hence, this paper proposes a solution named, Blockchain-based Key State Management to ensure the key state is maintained without affecting resources and scalability heavily. The solution utilizes blockchain and smart contracts to maintain key states by leveraging blockchain protocols to ensure key states remain updated and un-tampered. The paper also contains a qualitative survey comparing other alternative solutions for preventing key reuse to highlight the importance of the proposed solution.
ANALYSIS OF CALIBRATION ERRORS IN AUTOMOBILE CONTROL TELEVISION SYSTEM

Camera calibration is a critical method for 3D measurement applications based on 2D images. During calibration, internal camera parameters such as principal point and focal length are determined. The principal point is the intersection of the camera's optical axis and the image plane, while the focal length is measured from the lens center to the principal point.This article theoretically investigates calibration errors in two reference planes considering various conditions, such as shifts in the reference plane and changes in focal lengths. Errors in determining the coordinates of reference points in images were used to evaluate these calibration errors.The error in estimating the focal length and principal point is formulated using analytical equations, and the results are compared with simulation outcomes to validate the effectiveness of the proposed formulas. The research reveals that calibration errors arise due to shifts in the reference plane and changes in focal lengths. The obtained results are crucial for enhancing camera calibration methods and improving the accuracy of 3D measurements based on 2D images.This article extends the understanding of camera calibration errors and provides new theoretical approaches for their assessment. The research findings may be beneficial for scientists and practitioners working in the fields of computer vision, image processing, and 3D measurements.Further research in this area may contribute to the development of new calibration methods and approaches that will improve the performance of automotive systems and ensure high accuracy and reliability in the future.
Automation of digital elevation model analyzing when solving problems of economic development of karst territories

The combination of geographic information systems (GIS) and remote sensing data analysis tools will make it possible&#x0D; to assess in a short time and with high accuracy the suitability of an area for the construction of land or linear structures in the&#x0D; areas where dangerous geological processes are developed. In this article we use the example of karst area in Kisherstky district&#x0D; of Perm region as an example to carry out the karts hazard zoning of the territory according to geomorphological indicators by&#x0D; analyzing ALOS-DEM digital elevation model (DEM). Patterns in the distribution of karst forms in the study area were evaluated&#x0D; depending on the dissection of the relief, its slope and distance from the watercourses. Based on the patterns, an integral map&#x0D; was constructed. The approach allows to do zoning the territory with respect to the studied danger geological process in a short&#x0D; time and with high accuracy, which is due to the use of patterns of forms distribution in the analysis.
A Stable Vision Transformer for Out-of-Distribution Generalization

Vision Transformer (ViT) has achieved amazing results in many visual applications where training and testing instances are drawn from the independent and identical distribution (I.I.D.). The performance will drop drastically when the distribution of testing instances is different from that of training ones in real open environments. To tackle this challenge, we propose a Stable Vision Transformer (SViT) for out-of-distribution (OOD) generalization. In particular, the SViT weights the samples to eliminate spurious correlations of token features in Vision Transformer and finally boosts the performance for OOD generalization. According to the structure and feature extraction characteristics of the ViT models, we design two forms of learning sample weights: SViT(C) and SViT(T). To demonstrate the effectiveness of two forms of SViT for OOD generalization, we conduct extensive experiments on the popular PACS and OfficeHome datasets and compare them with SOTA methods. The experimental results demonstrate the effectiveness of SViT(C) and SViT(T) for various OOD generalization tasks.
The Determinants of Risky Cybersecurity Behaviour: A Case Study Among Employees in Water Sector in Malaysia

Objective: Cybersecurity threats are a growing concern around the world. Research found that the weakest element in the cybersecurity chain is that of the human. The use of security technologies failed to address the problem in instances where employees engage in activities that place themselves and the company at risk. Hence, the role human factors play in cybersecurity is crucial. The present study aims to examine the effects of information security issues awareness, top management support, leadership, information security policy and cybersecurity awareness training on risky cybersecurity behaviour among employees in water sector.&#x0D; &#x0D; Method: The quantitative method was applied in this study. The data were collected from 425 employees from four water companies located in northern states of Malaysia. The respondents are selected using disproportionate stratified random sampling technique. The survey was conducted using questionnaire. PLS-SEM was used to test the proposed hypotheses.&#x0D; &#x0D; Results: The results show that security issues awareness and top management support are negatively related to risky cybersecurity behaviour. This suggest that the greater security issues awareness and top management support, lower the tendency to engage in risky cybersecurity behaviour among employees. Contrary to the prediction, cybersecurity awareness training was found to be positively related with risky cybersecurity behaviour.&#x0D; &#x0D; Conclusions: The findings of the study have several theoretical and practical implications. Security issues awareness and top management support are important factors to avoid threat of cyber-attacks. By ensuring cybersecurity, water security is stored hence the well-being of people is taken care since water is a fundamental need of human lives. The stability and security of the country also can be maintained with secure and sustainable water resiliency. Finally, the economic losses due to cyber-attacks can be reduced.