Entity Model Research Articles

Sistem Informasi Pelayanan Anggota DPD Akli Jawa Tengah Berbasis Web

DPD AKLI Jawa Tengah , as one association engaged in the construction services of electricity, with members spread throughout Central Java, the development is still in need of a strategic plan and bring progress to the association and members. Therefore, in this paper the author will make a model of the registration certificate Web-based business entities that are expected to be one of the association's own development solutions.Member Services Information System developed by the method of approach to the development of systems with SDLC (System Development Life Cycle) results in an application that would address the needs of DPD AKLI Jawa Tengah will be a system of information services in order to enhance better service to members. In making this system, the authors use Macromedia Dreamweaver MX 2004 as a media design and PHP as the programming language used, and the MySQL database as a storage medium. The results of designing Member Services Information System can be accessed by members, assessors, and responsibility, whereby members can submit it online Business Enterprise certification. Assessors can assess more quickly and the approval of the person in charge can be done faster, so overall Business Enterprise certification process can be done more quickly and effectively.

Constraint information extraction for 3D geological modelling using a span-based joint entity and relation extraction model

Constraint information extraction for 3D geological modelling using a span-based joint entity and relation extraction model

Joint entity and relation extraction model based on directed-relation GAT oriented to Chinese patent texts

Joint entity and relation extraction model based on directed-relation GAT oriented to Chinese patent texts

Joint Entity and Relation Extraction Model Based on Inner and Outer Tensor Dot Product and Single-Table Filling

Joint relational triple extraction is a crucial step in constructing a knowledge graph from unstructured text. Recently, multiple methods have been proposed for extracting relationship triplets. Notably, end-to-end table-filling methods have garnered significant research interest due to their efficient extraction capabilities. However, existing approaches usually generate separate tables for each relationship, which neglects the global correlation between relationships and context, producing a large number of useless blank tables. This problem results in issues of redundant information and sample imbalance. To address these challenges, we propose a novel framework for joint entity and relation extraction based on a single-table filling method. This method incorporates all relationships as prompts within the text sequence and associates entity span information with relationship labels. This approach reduces the generation of redundant information and enhances the extraction capability for overlapping triplets. We utilize the internal and external multi-head tensor fusion approach to generate two sets of table feature vectors. These vectors are subsequently merged to capture a wider range of global information. Experimental results on the NYT and WebNLG datasets demonstrate the effectiveness of our proposed model, which maintains excellent performance, even in complex scenarios involving overlapping triplets.

Utilizing BERT for entity relationship extraction in Chinese medical texts

The Chinese medical sector has been somewhat lacking in knowledge graphs, a deficiency this study aims to address. By leveraging the prowess of the BERT pre-training model, a two-tier approach has been innovated that utilizes separate pre-trained encoders for both entity and relational models. These models are intricately linked: the output from the entity model seamlessly flows into the relational one, making it possible to adeptly extract entity relationships from Chinese medical texts. This research is anchored in the CMeIE dataset, sourced from the esteemed CHIP (China Health Information Processing) conference. This dataset stands as a recognized benchmark in evaluating Chinese medical texts. By harnessing this data, the methods have been rigorously tested and validated. The promising experimental results underscore the effectiveness of the approach in distilling relationships from Chinese medical literature. The implications of this research are profound. Beyond just enriching the Chinese medical domain, the boundaries of NLP technology are also being pushed. Potential applications are manifold: from constructing comprehensive Chinese medical knowledge graphs to assisting in early-stage medical diagnoses. This innovative approach not only addresses an existing gap but also sets the stage for future advancements in medical NLP.

Undergraduate students’ models of single- and multi-electron atoms

ABSTRACT Quantum physics forms the basis for exciting new technologies, including quantum computers, quantum encryption, and quantum entanglement. The advancement of science and technology highlights the importance of mastering quantum physics and its applications, not only at the college level but also as early as high school. In this multiple case study, we investigated first- and second-year undergraduate college students’ models of single and multi-electron atoms after completing a modern mechanics course, which addressed basic QM topics. The students’ models were categorised into four primary categories: the discrete entity (particle) model, hybrid model, quantum-like model, and quantum model, and subcategories for atom structure and electron attribution and motion. Despite being introduced to quantum concepts in high school and first-year undergraduate calculus-based physics classes, many students constructed incomplete, inaccurate, or incoherent models. Seven of eight students’ atomic structure models and six of eight students’ electron attribution and motion models were classified as discrete entity and hybrid models. The findings of this study reveal students’ conceptual challenges in explaining the structure of single and multiple-electron atoms and shed light on the factors contributing to the persistent difficulties in their understanding of atomic structure in the realm of quantum mechanics.

Lower negative bounds on the static electric susceptibility of nonequilibrium cubic crystals

We use a classical, microscopic model of pointlike dipolarizable entities (a model that is standard in the case of positive polarizability) and investigate its behavior for simple cubic (sc), body-centered cubic (bcc), and face-centered cubic (fcc) crystals with one entity per primitive cell when the static polarizability of the entities is negative and the mutual electrostatic interaction between the entities is taken into account. We find that the static electric susceptibility is bounded below due to an instability towards self-polarization but negative values are possible in each case. The usual Clausius-Mossotti relation between the static polarizability and the static electric susceptibility remains valid in the case of negative parameters but is truncated at the lower bound; the value of the bound depends on the crystal structure and is always unrelated to the asymptote of the Clausius-Mossotti curve. The lower bounds of the static electric susceptibility are found to be −0.906 for sc and −1.00 for bcc and fcc. These results confirm that, although the magnitude of the static electric susceptibility does not diverge in the negative case (as it can in the positive case), the magnitudes attainable in the negative case for condensed media may, nevertheless, be many orders of magnitude greater than those predicted previously for inverted vapors and gases. This is a promising result in relation to the development of potential new technologies that exploit the phenomenon. Published by the American Physical Society 2024

APIE: An information extraction module designed based on the pipeline method

Information extraction (IE) aims to discover and extract valuable information from unstructured text. This problem can be decomposed into two subtasks: named entity recognition (NER) and relation extraction (RE). Although the IE problem has been studied for years, most work efforts focused on jointly modeling these two subtasks, either by casting them into a structured prediction framework or by performing multitask learning through shared representations. However, since the contextual representations of entity and relation models inherently capture different feature information, sharing a single encoder to capture the information required by both subtasks in the same space would harm the accuracy of the model. Recent research (Zhong and Chen, 2020) has also proved that using two separate encoders for NER and RE tasks respectively through pipeline method are effective, with the model surpassing all previous joint models in accuracy. Thus, in this paper, we design An Pipeline method Information Extraction module called APIE, APIE combines the advantages of both pipeline methods and joint methods, demonstrating higher accuracy and powerful reasoning abilities. Specifically, we design a multi-level feature NER model based on attention mechanism and a document-level RE model based on local context pooling. To demonstrate the effectiveness of our proposed approach, we conducted tests on multiple datasets. Extensive experimental results have shown that our proposed model outperforms state-of-the-art methods and improves both accuracy and reasoning abilities.

Verification and validation for large eddy simulation of the turbulent flow around an underwater entity

The objective is to analyze the errors and uncertainty in the simulation results of the complex, unsteady turbulent flow and vortices. The implicitly filtered large eddy simulation (LES) with the boundary data immersion method is used to investigate the transient turbulent flow around a fully appended underwater entity model (SUBOFF) proposed by Groves et al. [Geometric Characteristics of DARPA Suboff Models: (DTRC Model Nos. 5470 and 5471) (David Taylor Research Center, 1989)] with emphasis on verification and validation. As for the verification, the five-equation method is used to assess the LES error, including the modeling error and numerical error in the transient flow. They offset each other, leading to a small total error. As for the validation, it has been achieved in the most area when the experimental result is located at the band of ysim ± UV (ysim is the simulation result, and UV is the validation uncertainty). There is a strong relationship between the validation uncertainty and the complex vortex interaction. The validation uncertainty becomes significant, which means less accuracy of the simulation result, within the tip flow region, adverse pressure gradient region, tip vortex interacting with the boundary layer region and shear layer region. Most all of these regions occur behind the appendage and at the shape changing position of the SUBOFF.

Research on the Value Improvement Model of Private Parties as “Investor–Builder” Dual-Role Entity in Major River Green Public–Private Partnership Projects

In Public–Private Partnership (PPP) projects, the structure of the Special Purpose Vehicle (SPV) significantly impacts the value enhancement of projects. This study conducted a quantitative analysis of value enhancement in green PPP projects under single- and Dual-Role entity models and examined existing SPV private party compositions. A quantitative model was developed to enhance the value in green PPP projects through a Dual-Role “investor–builder” entity approach, comparing it with the single-role entity model. The findings indicate that in the Dual-Role entity mode, the construction party demonstrates a greater willingness to effort, resulting in shorter construction timelines and improved economic benefits for the project company. The preferred equity range for private parties escalates with the total project investment and the extent of “political support”. Nevertheless, a disproportionately high government stake in the equity is detrimental to the value enhancement in PPPs, and excessive government regulation and control should be avoided. This quantitative model serves as a decision-making criterion for selecting the SPV mode and provides an alternative approach for evaluating PPP project performance.

A digital twin-based adaptive optimization approach applied to waste heat recovery in green steel production: Development and experimental investigation

Renewable-dominated power grids will require industry to run their processes in accordance with the availability of energy. At the same time, digitalization introduces new possibilities to leverage the untapped optimization potential to provide this flexibility. Mathematical optimization methods such as mixed-integer linear programming (MILP) are widely used to predict optimal operation plans for industrial systems. MILP models are difficult to adapt, but the viability of the predicted plans relies on accurate underlying models of the actual behavior. New automation paradigms, such as the digital twin (DT), can overcome these current drawbacks. In this work, we present the implementation and experimental evaluation of several micro-services on a standardized five-dimensional DT platform that automate MILP model adaption and operation optimization. These micro-services guarantee that, (1) deviations between the physical entity and its virtual entity models are detected, (2) the models are adapted accordingly, (3) subsequently linearized to suit the MILP approach and (4) used for live operational optimization. These novel services and DT workflows that orchestrate them were experimentally tested with a packed bed thermal energy storage (PBTES) test rig that acts as a physical entity. A waste heat recovery use case in steel production is used as the evaluation scenario. While the model error of a static simulation model would increase to 60% over 7 days of operation, the model error remains well below 25% as a result of successful model adaption. The prediction error of the optimization model remains in a typical magnitude of 10 to 20% during the evaluation period, despite the degradation of the PBTES power.

Stealthy dynamic backdoor attack against neural networks for image classification

Deep Neural Networks (DNNs) are vulnerable to backdoor attacks, which are external entity model providers that can inject backdoors into the network for illegal purposes. Current attack methods rely on manipulated images with embedded triggers to infiltrate carrier networks, but they suffer from detectable distortions and limited integration into neural networks. To delve further into the potential vulnerabilities of these models, this study introduces an innovative backdoor attack strategy, leveraging deep learning steganography through a Generative Adversarial Network (GAN). Our approach utilizes steganography to create manipulated images, capitalizing on the unique sensitivity of neural networks to minute perturbations. The network is then trained de novo with these manipulated images, creating a backdoor-infused model. Fundamentally, our method harnesses the pronounced sensitivity of DNNs to nuanced alterations. Experimental outcomes substantiate that our backdoor can be effectively integrated into the models, yielding high attack success rates. Notably, it also adeptly circumvents both contemporary state-of-the-art defense mechanisms and human inspection. Our source code is publicly accessible at https://github.com/DLAIResearch/NNSDB.

Analysis of the construction and reform path of preschool education professional practice courses in the context of computer big data

Abstract This paper first uses the big data adaptive learning system to facilitate students’ mastery of knowledge points, improve their learning performance, and stimulate their interest in learning. Then, the practical curriculum of preschool education is realized through curriculum facilities, curriculum objectives, curriculum content, curriculum organization and implementation, and curriculum evaluation. Next, the QFD entity model for quality assurance of preschool education personnel training is established. The realistic basis for the construction of the model is also elaborated, and the conceptual framework of the QFD model for quality assurance of higher education personnel training is proposed and comprehensively analyzed. The results show that the cooperative group learning approach is more favored by students, up to 64.44% in the practical courses. The number of graduates who chose to work in early education and other industries increased by 10%. It shows that improving students’ personal comprehensive quality can adapt to society’s comprehensive demand for professionals.

Research on Multirelational Entity Modeling based on Knowledge Graph Representation Learning

Background: A research concern revolves around as to what can make the representation of entities and relationships fully integrate the structural information of the knowledge atlas to solve the entity modeling capability in complex relationships. World knowledge can be organized into a structured knowledge network by mining entity and relationship information in real texts. In order to apply the rich structured information in the knowledge map to downstream applications, it is particularly important to express and learn the knowledge map. In the knowledge atlas with expanding scale and more diversified knowledge sources, there are many types of relationships with complex types. The frequency of a single relationship in all triples is further reduced, which increases the difficulty of relational reasoning. Thus, this study aimed to improve the accuracy of relational reasoning and entity reasoning in complex relational models. Methods: For the multi-relational knowledge map, CTransR based on the TransE model and TransR model adopts the idea of piecewise linear regression to cluster the potential relationships between head and tail entities, and establishes a vector representation for each cluster separately, so that the same relationship represented by different clusters still has a certain degree of similarity. Results: The CTransR model carried out knowledge reasoning experiments in the open dataset, and achieved good performance. Conclusion: The CTransR model is highly effective and progressive for complex relationships. In this experiment, we have evaluated the model, including link prediction, triad classification, and text relationship extraction. The results show that the CTransR model has achieved significant improvement.

A contrastive learning framework for safety information extraction in construction

Typically named entity recognition (NER) and relation extraction (RE) from safety documentation (e.g., accident reports) adopt a pipeline processing approach whereby tasks are split into two sub-tasks. As a result, error propagation occurs between components, and useful information from one task may go unexploited by the other. Additionally, training sets to perform NER and RE from safety documentation are often limited and context-specific. Thus, our research addresses the following question: How can we accurately identify entities and extract relations from safety documentation using limited training sets? This paper utilizes ‘contrastive learning’ to tackle our research question. It proposes a contrastive learning-based cascade binary tagging framework (CasRel) to automatically and synchronously extract entities and relations from safety documents. A five-fold cross-validation process is used to validate the effectiveness and feasibility of our approach. Results from the validation process achieve an average precision of 77.8%, recall of 58.7%, and F1-score of 66.9%, outperforming CasRel with an increase of about 10% in precision, 5% in recall, and 7% in F1-score. Thus, our approach can accurately recognize entities and extract relations from safety documentation. The contributions of our study are twofold: (1) an improved unified model is developed to recognize and extract the entity and relation from safety documents to reduce error propagation and improve its accuracy; and (2) the concept of ‘contrastive learning’ is introduced in the design of the joint entity and relation extraction model with limited training sets.

ER-LAC: Span-Based Joint Entity and Relation Extraction Model with Multi-Level Lexical and Attention on Context Features

In recent years, joint entity–relation extraction (ERE) models have become a hot research topic in natural language processing (NLP). Several studies have proposed a span-based ERE framework, which utilizes simple span embeddings for entity and relation classification. This framework addresses the issues of overlap and error propagation that were present in previous entity–relation extraction models. However, span-based models overlook the influence of lexical information on the semantic representation of the span and fail to consider relations with a strong intrinsic connection between span pairs. To tackle these aforementioned issues, we present a new ERE model called ER-LAC (Span-based Joint Entity and Relation Extraction Model with Multi-level Lexical and Attention on Context Features). This model is designed with multi-granularity lexical features to enhance the semantic representation of spans, and a transformer classifier is employed to capture the internal connections between span pairs, thereby improving the performance of relational classification. To demonstrate the effectiveness of the proposed model, ablation experiments were conducted on the CoNLL04 dataset. The proposed model was also compared with other models on three datasets, showcasing its computational efficiency. The results indicate that the introduced lexical features and classifier enhance the F1 score for entity extraction by 0.84% to 2.04% and improve the F1 score for relationship classification by 0.96% to 2.26% when compared to the previous state-of-the-art (SOTA) model and the baseline SpERT model, respectively.

Research on Fault Prevention and Maintenance System of Automatic Substation Primary Equipment Based on Decision Tree Algorithm

Abstract Electric power enterprises are developing rapidly in the era of big data information digitization. At this stage, the total number of substations is gradually increasing, the structure of the power engineering system is slowly becoming complicated, and the video monitoring system instantly collects a lot and contains a lot of noisy data information, which affects the power supply system’s access to effective data information and fault detection. To prevent the above phenomenon. This paper selects a decision tree algorithm to obtain and analyze meaningful operation-confirming information from a large amount of data information, and then can quickly and confirm the diagnosis of common fault machines and equipment in substations, reduce the running time of common fault machines, and improve the safety and reliability of primary equipment in substations with automation technology. The paper describes the basic concept of big data mining common algorithm and its data mining algorithm in the automation technology substation primary equipment fault detection, selected the typical alarm signal to start the analysis, and categorization and collocation solution. A decision tree algorithm entity model is built, several classical decision tree algorithms are described, and their data analysis is carried out for each attribute, and then the decision tree algorithm is improved. According to build the decision tree algorithm according to improve the decision tree algorithm under the fuzzy set base theory, mainly by expertise in the four on cannot identify the association, rough set and up close and down close, similar and membership relationship, expertise concise to optimize the calculation method. And the common ID3, C4.5, and CRAT algorithm of each property is compared and analyzed, and the results show that: compared with C4.5 and ID3, the boosted optimization algorithm has higher classification accuracy and can model rate more quickly. The research in this paper can quickly diagnose the automation technology substation primary equipment and fault phenomena, and its establishment of the whole process is easy, the scope of application is relatively high, and it has wide applicability.

MHlinker: Research on a Joint Extraction Method of Fault Entity Relationship for Mine Hoist

Triplet extraction is the key technology to automatically construct knowledge graphs. Extracting the triplet of mechanical equipment fault relationships is of great significance in constructing the fault diagnosis of a mine hoist. The pipeline triple extraction method will bring problems such as error accumulation and information redundancy. The existing joint learning methods cannot be applied to fault texts with more overlapping relationships, ignoring the particularity of professional knowledge in the field of complex mechanical equipment faults. Therefore, based on the Chinese pre-trained language model BERT Whole Word Masking (BERT-wwm), this paper proposes a joint entity and relation extraction model MHlinker (Mine Hoist linker, MHlinker) for the mine hoist fault field. This method uses BERT-wwm as the underlying encoder. In the entity recognition stage, the classification matrix is constructed using the multi-head extraction paradigm, which effectively solves the problem of entity nesting. The results show that this method enhances the model’s ability to extract fault relationships as a whole. When the small-scale manually labeled mine hoist fault text data set is tested, the extraction effect of entities and relationships is significantly improved compared with several baseline models.

A Joint Entity and Relation Extraction Model based on Efficient Sampling and Explicit Interaction

Joint entity and relation extraction (RE) construct a framework for unifying entity recognition and relationship extraction, and the approach can exploit the dependencies between the two tasks to improve the performance of the task. However, the existing tasks still have the following two problems. First, when the model extracts entity information, the boundary is blurred. Secondly, there are mostly implicit interactions between modules, that is, the interactive information is hidden inside the model, and the implicit interactions are often insufficient in the degree of interaction and lack of interpretability. To this end, this study proposes a joint entity and relation extraction model (ESEI) based on E fficient S ampling and E xplicit I nteraction. We innovatively divide negative samples into sentences based on whether they overlap with positive samples, which improves the model’s ability to extract entity word boundary information by controlling the sampling ratio. In order to increase the explicit interaction ability between the models, we introduce a heterogeneous graph neural network (GNN) into the model, which will serve as a bridge linking the entity recognition module and the relation extraction module, and enhance the interaction between the modules through information transfer. Our method substantially improves the model’s discriminative power on entity extraction tasks and enhances the interaction between relation extraction tasks and entity extraction tasks. Experiments show that the method is effective, we validate our method on four datasets, and for joint entity and relation extraction, our model improves the F1 score on multiple datasets.

Exploring the Practice of Cultivating Business English Talents from the Perspective of Artificial Intelligence

Abstract In the context of the development tendency of artificial intelligence, the paper describes the opportunities and challenges artificial intelligence brings to business English talent training, innovates and reforms innovative business negotiation talent training methods, and develops independent innovation according to the future development trend of artificial intelligence. The paper discusses the practical activities of training business English talents using artificial intelligence. It mainly focuses on the indicators of building the entity model of business English talents training through artificial intelligence technical RBF ne ural network, grasping the genetic algorithm from four aspects such as the number of gender chromosomes, integral function formula calculation, genetic algorithm, RBF neural network parameters to optimize the entity model and genetic algorithm, and optimizing the limitation of RBF neural network according to the genetic algorithm. The RBF neural network entity model with the genetic algorithm is used to implement detailed analysis and scientific research exploration of the business English talent training evaluation system. The database deviation index and precision index values under the two entity models were implemented for comparison operation. The experimental results show that under the deviation index, the RBF neural network entity model, according to the genetic algorithm, converges quickly and longer than the RBF entity model. Regarding the precision index, the genetic algorithm RBF neural network has a higher precision than 0.94 for every 60 teams, while the RBF model has a precision of 0.84. This research allows for a more accurate analysis of the strengths and weaknesses of school students in business English. Teachers need to improve their academic performance and quality based on their strengths and weaknesses to facilitate the shaping of more excellent business English talents for China. It is important to explore the way of training business English talents under artificial intelligence to accelerate the innovative development of English education and training in China and the trend of English development.