GeoNER:Geological Named Entity Recognition with Enriched Domain Pre-Training Model and Adversarial Training

As important geological data,a geological report contains rich expert and geological knowledge,but the challenge facing current research into geological knowledge extraction and mining is how to render accurate understanding of geological reports guided by domain knowledge.While generic named entity recognition models/tools can be utilized for the processing of geoscience reports/documents,their effectiveness is hampered by a dearth of domain-specific knowledge,which in turn leads to a pronounced decline in recognition accuracy.This study summarizes six types of typical geological entities,with reference to the ontological system of geological domains and builds a high quality corpus for the task of geological named entity recognition(GNER).In addition,Geo Wo BERT-adv BGP(Geological Word-base BERTadversarial training Bi-directional Long Short-Term Memory Global Pointer)is proposed to address the issues of ambiguity,diversity and nested entities for the geological entities.The model first uses the fine-tuned word granularitybased pre-training model Geo Wo BERT(Geological Word-base BERT)and combines the text features that are extracted using the Bi LSTM(Bi-directional Long Short-Term Memory),followed by an adversarial training algorithm to improve the robustness of the model and enhance its resistance to interference,the decoding finally being performed using a global association pointer algorithm.The experimental results show that the proposed model for the constructed dataset achieves high performance and is capable of mining the rich geological information.

Contribution of the MERISE-Type Conceptual Data Model to the Construction of Monitoring and Evaluation Indicators of the Effectiveness of Training in Relation to the Needs of the Labor Market in the Republic of Congo

This study proposes the use of the MERISE conceptual data model to create indicators for monitoring and evaluating the effectiveness of vocational training in the Republic of Congo. The importance of MERISE for structuring and analyzing data is underlined, as it enables the measurement of the adequacy between training and the needs of the labor market. The innovation of the study lies in the adaptation of the MERISE model to the local context, the development of innovative indicators, and the integration of a participatory approach including all relevant stakeholders. Contextual adaptation and local innovation: The study suggests adapting MERISE to the specific context of the Republic of Congo, considering the local particularities of the labor market. Development of innovative indicators and new measurement tools: It proposes creating indicators to assess skills matching and employer satisfaction, which are crucial for evaluating the effectiveness of vocational training. Participatory approach and inclusion of stakeholders: The study emphasizes actively involving training centers, employers, and recruitment agencies in the evaluation process. This participatory approach ensures that the perspectives of all stakeholders are considered, leading to more relevant and practical outcomes. Using the MERISE model allows for: • Rigorous data structuring, organization, and standardization: Clearly defining entities and relationships facilitates data organization and standardization, crucial for effective data analysis. • Facilitation of monitoring, analysis, and relevant indicators: Developing both quantitative and qualitative indicators helps measure the effectiveness of training in relation to the labor market, allowing for a comprehensive evaluation. • Improved communication and common language: By providing a common language for different stakeholders, MERISE enhances communication and collaboration, ensuring that all parties have a shared understanding. The study’s approach and contribution to existing research lie in: • Structured theoretical and practical framework and holistic approach: The study offers a structured framework for data collection and analysis, covering both quantitative and qualitative aspects, thus providing a comprehensive view of the training system. • Reproducible methodology and international comparison: The proposed methodology can be replicated in other contexts, facilitating international comparison and the adoption of best practices. • Extension of knowledge and new perspective: By integrating a participatory approach and developing indicators adapted to local needs, the study extends existing research and offers new perspectives on vocational training evaluation.

Exploration of the Integrated Training Model for Information Technology Teachers

With the acceleration of the social information process,information awareness and information skills have become the basic qualities of every citizen.The establishment of the training mechanism for scientific and technological innovation talents from the beginning of higher education is insufficient to meet the needs of the development of the times.It is imperative to improve the training of information technology innovation talents and explore a new training model.This paper describes the general situation of the development of education in the field of information technology from a domestic and international perspective.It then analyzes the existing problems,explores new exploration models and implementation suggestions,and puts forward prospects at the end of the paper.

Temporally Preserving Latent Variable Models:Offline and Online Training for Reconstruction and Interpretation of Fault Data for Gearbox Condition Monitoring

Latent variable models can effectively determine the condition of essential rotating machinery without needing labeled data.These models analyze vibration data via an unsupervised learning strategy.Temporal preservation is necessary to obtain an informative latent manifold for the fault diagnosis task.In a temporalpreserving context,two approaches exist to develop a condition-monitoring methodology:offline and online.For latent variable models,the available training modes are not different.While many traditional methods use offline training,online training can dynamically adjust the latent manifold,possibly leading to better fault signature extraction from the vibration data.This study explores online training using temporal-preserving latent variable models.Within online training,there are two main methods:one focuses on reconstructing data and the other on interpreting the data components.Both are considered to evaluate how they diagnose faults over time.Using two experimental datasets,the study confirms that models from both training modes can detect changes in machinery health and identify faults even under varying conditions.Importantly,the complementarity of offline and online models is emphasized,reassuring their versatility in fault diagnostics.Understanding the implications of the training approach and the available model formulations is crucial for further research in latent variable modelbased fault diagnostics.

Training and Implementation of Subjective Questions Scoring System Based on the Baidu Qianfan Model Platform

Leveraging the Baidu Qianfan model platform,this paper designs and implements a highly efficient and accurate scoring system for subjective questions,focusing primarily on questions in the field of computer network technology.The system enhances the foundational model by utilizing Qianfan’s training tools and integrating advanced techniques,such as supervised fine-tuning.In the data preparation phase,a comprehensive collection of subjective data related to computer network technology is gathered,cleaned,and labeled.During model training and evaluation,optimal hyperparameters and tuning strategies are applied,resulting in a model capable of scoring with high accuracy.Evaluation results demonstrate that the proposed model performs well across multiple dimensions-content,expression,and development scores-yielding results comparable to those of manual scoring.

A Study on the Work Process-Based Practical Training Model for Basic Nursing Skills in Vocational Colleges

Objective:To explore and analyze the work process-based practical training teaching model for basic nursing skills in vocational colleges and its implementation effects.Methods:A total of 82 nursing students from our school were selected for the study,which was conducted from April 2023 to April 2024.Using a random number table method,the students were divided into an observation group and a control group,each with 41 students.The control group received conventional practical training teaching,while the observation group followed the work process-based practical training model for basic nursing skills.The assessment scores and teaching satisfaction of the two groups were compared.Results:The comparison of assessment scores showed that the observation group performed significantly better than the control group(P<0.05).The comparison of teaching satisfaction also indicated that the observation group had significantly higher satisfaction than the control group(P<0.05).Conclusion:The work process-based practical training teaching model for basic nursing skills in vocational colleges can improve students’assessment scores and enhance teaching satisfaction,demonstrating its value for wider application.

Comprehensive Evaluation of Talent Training Model for Modern Rehabilitation Therapy Technology:A Case Study of School Y

The purpose of this study is to comprehensively evaluate the modern training model of rehabilitation therapy technology talents.Selecting the third-year students of the rehabilitation therapy technology program in School Y as the research subject,300 questionnaires were collected and the effective response rate was 92%.The strengths and weaknesses of the modern training model were analyzed through a mixed qualitative and quantitative research method.It was found that 68%of the students thought that the modern model had obvious advantages in practical teaching,but 42%of the students thought that it still needed to be improved in personalized teaching.This study provides an empirical basis and specific suggestions for optimizing the cultivation of rehabilitation therapy technology talents.

Low rank optimization for efficient deep learning:making a balance between compact architecture and fast training

Deep neural networks(DNNs)have achieved great success in many data processing applications.However,high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices,and it is not environmental-friendly with much power cost.In this paper,we focus on low-rank optimization for efficient deep learning techniques.In the space domain,DNNs are compressed by low rank approximation of the network parameters,which directly reduces the storage requirement with a smaller number of network parameters.In the time domain,the network parameters can be trained in a few subspaces,which enables efficient training for fast convergence.The model compression in the spatial domain is summarized into three categories as pre-train,pre-set,and compression-aware methods,respectively.With a series of integrable techniques discussed,such as sparse pruning,quantization,and entropy coding,we can ensemble them in an integration framework with lower computational complexity and storage.In addition to summary of recent technical advances,we have two findings for motivating future works.One is that the effective rank,derived from the Shannon entropy of the normalized singular values,outperforms other conventional sparse measures such as the?_1 norm for network compression.The other is a spatial and temporal balance for tensorized neural networks.For accelerating the training of tensorized neural networks,it is crucial to leverage redundancy for both model compression and subspace training.

Cloudless-Training:基于serverless的高效跨地域分布式ML训练框架

跨地域分布式机器学习(ML)训练能够联合多区域的云资源协作训练,可满足许多新兴ML场景(比如大型模型训练、联邦学习)的训练需求。但其训练效率仍受2方面挑战的制约。首先,多区域云资源缺乏有效的弹性调度,这会影响训练的资源利用率和性能;其次,模型跨地域同步需要在广域网(WAN)上高频通信,受WAN的低带宽和高波动的影响,会产生巨大通信开销。本文提出Cloudless-Training,从3个方面实现高效的跨地域分布式ML训练。首先,它基于serverless计算模式实现,使用控制层和训练执行层的2层架构,支持多云区域的弹性调度和通信。其次,它提供一种弹性调度策略,根据可用云资源的异构性和训练数据集的分布自适应地部署训练工作流。最后,它提供了2种高效的跨云同步策略,包括基于梯度累积的异步随机梯度下降(ASGD-GA)和跨云参数服务器(PS)间的模型平均(MA)。Cloudless-Training是基于OpenFaaS实现的,并被部署在腾讯云上评估,实验结果表明Cloudless-Training可显著地提高跨地域分布式ML训练的资源利用率(训练成本降低了9.2%~24.0%)和同步效率(训练速度最多比基线快1.7倍),并能保证模型的收敛精度。

Disturbance rejection tube model predictive levitation control of maglev trains

Magnetic levitation control technology plays a significant role in maglev trains.Designing a controller for the levitation system is challenging due to the strong nonlinearity,open-loop instability,and the need for fast response and security.In this paper,we propose a Disturbance-Observe-based Tube Model Predictive Levitation Control(DO-TMPLC)scheme combined with a feedback linearization strategy for the levitation system.The proposed strategy incorporates state constraints and control input constraints,i.e.,the air gap,the vertical velocity,and the current applied to the coil.A feedback linearization strategy is used to cancel the nonlinearity of the tracking error system.Then,a disturbance observer is implemented to actively compensate for disturbances while a TMPLC controller is employed to alleviate the remaining disturbances.Furthermore,we analyze the recursive feasibility and input-to-state stability of the closed-loop system.The simulation results indicate the efficacy of the proposed control strategy.

Robust Parameter Identification Method of Adhesion Model for Heavy Haul Trains

A robust parameter identification method based on Kiencke model was proposed to solve the problem of the parameter identification accuracy being affected by the rail environment change and noise interference for heavy-duty trains. Firstly, a Kiencke stick-creep identification model was constructed, and the parameter identification task was transformed into a quadratic programming problem. Secondly, an iterative algorithm was constructed to solve the problem, into which a time-varying forgetting factor was added to track the change of the rail environment, and to solve the uncertainty problem of the wheel-rail environment. The Granger causality test was adopted to detect the interference, and then the weights of the current data were redistributed to solve the problem of noise interference in parameter identification. Finally, simulations were carried out and the results showed that the proposed method could track the change of the track environment in time, reduce the noise interference in the identification process, and effectively identify the adhesion performance parameters.

Comparative Analysis of Machine Learning Models for PDF Malware Detection:Evaluating Different Training and Testing Criteria

The proliferation of maliciously coded documents as file transfers increase has led to a rise in sophisticated attacks.Portable Document Format(PDF)files have emerged as a major attack vector for malware due to their adaptability and wide usage.Detecting malware in PDF files is challenging due to its ability to include various harmful elements such as embedded scripts,exploits,and malicious URLs.This paper presents a comparative analysis of machine learning(ML)techniques,including Naive Bayes(NB),K-Nearest Neighbor(KNN),Average One Dependency Estimator(A1DE),RandomForest(RF),and SupportVectorMachine(SVM)forPDFmalware detection.The study utilizes a dataset obtained from the Canadian Institute for Cyber-security and employs different testing criteria,namely percentage splitting and 10-fold cross-validation.The performance of the techniques is evaluated using F1-score,precision,recall,and accuracy measures.The results indicate that KNNoutperforms other models,achieving an accuracy of 99.8599%using 10-fold cross-validation.The findings highlight the effectiveness of ML models in accurately detecting PDF malware and provide insights for developing robust systems to protect against malicious activities.

基于Tri-training GPR的半监督软测量建模方法

集成学习因通过构建并结合多个学习器,常获得比单一学习器显著优越的泛化能力。但是在标记数据比例较少时,建立高性能的集成学习软测量模型依然是个挑战。针对这一个问题,提出一种基于半监督集成学习的软测量建模方法——Tri-training GPR模型。该建模策略充分发挥了半监督学习的优势,减轻建模过程对标记样本数据的需求,在低数据标签率下,仍能通过对无标记数据进行筛选从而扩充可用于建模的有标记样本数据集,并进一步结合半监督学习和集成学习的优势,提出一种新的选择高置信度样本的思路。将所提方法应用于青霉素发酵和脱丁烷塔过程,建立青霉素和丁烷浓度预测软测量模型,与传统的建模方法相比获得了更优的预测结果,验证了模型的有效性。

Research on the Evaluation of VBSE Practical Training Teaching Effectiveness Based on CIPP Model at X University

Based on the CIPP(context,input,process,and product)model,this paper constructs an index system suitable for evaluating the teaching effect of VBSE(virtual business social environment)practical training from four aspects:context evaluation,input evaluation,process evaluation,and product evaluation.Taking the students participating in VBSE practical training in X university as the survey population,381 valid sample data were obtained through an online questionnaire survey,and the index weights were determined by factor analysis method.The score value of the VBSE practical training teaching effect was calculated based on the evaluation mean value of three indexes.The results showed that the context evaluation score was 1.56,the input evaluation score was 1.54,the process evaluation score was 1.51,and the product evaluation score was 1.48.Subsequently,this paper put forward some countermeasures from the aspects of optimizing course arrangement,improving hardware facilities,and enhancing team cooperation to provide a guideline for improving the effect of VBSE practical training.

Discriminative tone model training and optimal integration for Mandarin speech recognition

Two discriminative methods for solving tone problems in Mandarin speech recognition are presented. First, discriminative training on the HMM （hidden Markov model） based tone models is proposed. Then an integration technique of tone models into a large vocabulary continuous speech recognition system is presented. Discriminative model weight training based on minimum phone error criteria is adopted aiming at optimal integration of the tone models. The extended Baum Welch algorithm is applied to find the model-dependent weights to scale the acoustic scores and tone scores. Experimental results show that tone recognition rates and continuous speech recognition accuracy can be improved by the discriminatively trained tone model. Performance of a large vocabulary continuous Mandarin speech recognition system can be further enhanced by the discriminatively trained weight combinations due to a better interpolation of the given models.

The Training Model and Development Route of Market-oriented Professional Farmers

To cultivate new professional farmers is a key way for rural labor development, resolving existing problems such as how to farming. It is notable that government and market take advantages in training of new professional farmers. Therefore, it is necessary to guarantee government and market playing the roles. The research explored market-oriented farmer training model and the characteristics and investigated training routes for new professional farmers.

基于ADDIE Training Model的高职英语课程教学实践与探索

加快发展现代高等职业教育,既有利于缓解当前就业压力,也是解决高技能人才短缺的战略之举。现代职场对有英语应用能力要求的岗位越来越多,一些专业技能强的学生由于无法满足用人单位在英语应用能力上的要求而与理想岗位失之交臂的情况时有发生。为改变这样的现状、提高学生的英语实际应用能力,开展基于ADDIE Training Model的高职英语课程教学实践与应用研究,并将试验效果与02O教学活动和传统的"讲授型"教学活动效果进行对比分析。

Endoscopic submucosal dissection training with pig models in a Western country

AIM:To test a strategy for endoscopic submucosal dissection(ESD) training in animal models designed to overcome the initial learning curve.METHODS:ESD was attempted in ex vivo and in vivo pig models.Thirty ESD procedures were attempted in the esophagus(n=9) or the stomach(n=21).The ex vivo model was used until initial competence was achieved.In the in vivo model,several ESD procedures were performed in up to 3 sessions.The following variables were analyzed:specimen size,complete and en bloc resection rate,time for circumferential incision,time for submucosal dissection,total ESD duration,and complications.RESULTS:Complete resection was achieved in 28 cases(en bloc 27);2 could not be completed(one perforation,one technical diff iculty).The mean ± SD time for circumferential incision was 36.2±16.8 min(range:8-87 min),and the mean±SD time for submucosal dissection was 45.1±35.7 min(range:9-196 min).The mean±SD size of the resected specimens was 45.2±17.8 mm.The mean±SD total resection time was signif icantly increased for the gastric cases performed in the f irst half of the study(n=13) than in the second half(n=8)(98.9±62.4 min vs 61.7±17.6 min,P=0.04),although the specimen size did not differ.CONCLUSION:Training in animal models could help endoscopists overcome the learning curve before starting ESD in humans.

Interactive Training Model of TRIZ for Mechanical Engineers in China

Abstract： Innovation is a process of taking an original idea and converting it into a business value, in which the engineers face some inventive problems which can be solved hardly by experience. TRIZ, as a new theory for companies in China, provides both conceptual and procedural knowledge for finding and solving inventive problems. Because the government plays a leading role in the diffusion of TRIZ, too many companies from different industries are waiting to be trained, but the quantity of the trainers mastering TRIZ is incompatible with that requirement. In this context, to improve the training effect, an interactive training model of TRIZ for the mechanical engineers in China is developed and the implementation in the form of training classes is carried out. The training process is divided into 6 phases as follows： selecting engineers, training stage-l, finding problems, training stage-2, finding solutions and summing up. The government, TRIZ institutions and companies to join the programs interact during the process. The government initiates and monitors a project in form of a training class of TRIZ and selects companies to join the programs. Each selected companies choose a few engineers to join the class and supervises the training result. The TRIZ institutions design the training courses and carry out training curriculum. With the beginning of the class, an effective communication channel is established by means of interview, discussion face to face, E-mail, QQ and so on. After two years training practices, the results show that innovative abilities of the engineers to join and pass the final examinations increased distinctly, and most of companies joined the training class have taken congnizance of the power of TRIZ for product innovation. This research proposes an interactive training model of TRIZ for mechanical engineers in China to expedite the knowledge diffusion of TRIZ.

Models to predict injury, physical fitness failure and attrition in recruit training: a retrospective cohort study

Background:Attrition rate in new army recruits is higher than in incumbent troops.In the current study,we identified the risk factors for attrition due to injuries and physical fitness failure in recruit training.A variety of predictive models were attempted.Methods:This retrospective cohort included 19,769 Army soldiers of the Australian Defence Force receiving recruit training during a period from 2006 to 2011.Among them,7692 reserve soldiers received a 28-day training course,and the remaining 12,077 full-time soldiers received an 80-day training course.Retrieved data included anthropometric measures,course-specific variables,injury,and physical fitness failure.Multivariate regression was used to develop a variety of models to predict the rate of attrition due to injuries and physical fitness failure.The area under the receiver operating characteristic curve was used to compare the performance of the models.Results:In the overall analysis that included both the 28-day and 80-day courses,the incidence of injury of any type was 27.8%.The 80-day course had a higher rate of injury if calculated per course(34.3%vs.17.6%in the 28-day course),but lower number of injuries per person-year(1.56 vs.2.29).Fitness test failure rate was significantly higher in the 28-day course(30.0%vs.12.1%).The overall attrition rate was 5.2%and 5.0%in the 28-day and 80-day courses,respectively.Stress fracture was common in the 80-day course(n=44)and rare in the 28-day course(n=1).The areas under the receiver operating characteristic curves for the course-specific predictive models were relatively low(ranging from 0.51 to 0.69),consistent with"failed"to"poor"predictive accuracy.The course-combined models performed somewhat better than the course-specific models,with two models having AUC of 0.70 and 0.78,which are considered"fair"predictive accuracy.Conclusion:Attrition rate was similar between 28-day and 80-day courses.In comparison to the 80-day full course,the 28-day course had a lower rate of injury but a higher number of injuries per person-year and of fitness test failure.These findings suggest fitness level at the commencement of training is a critically important factor to consider when designing the course curriculum,particularly short courses.