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.

Inverse Estimation on Trigger Factors of Simultaneous Slope Failures with Purification of Training Data Sets

This paper presents an procedure for purifying training data sets （i.e., past occurrences of slope failures） for inverse estimation on unobserved trigger factors of ＂different types of simultaneous slope failures＂. Due to difficulties in pixel-by-pixel observations of trigger factors, as one of the measures, the authors had proposed an inverse analysis algorithm on trigger factors based on SEM （structural equation modeling）. Through a measurement equation, the trigger factor is inversely estimated, and a TFI （trigger factor influence） map can be also produced. As a subsequence subject, a purification procedure of training data set should be constructed to improve the accuracy of TFI map which depends on the representativeness of given training data sets of different types of slope failures. The proposed procedure resamples the matched pixels between original groups of past slope failures （i.e., surface slope failures, deep-seated slope failures, landslides） and classified three groups by K-means clustering for all pixels corresponding to those slope failures. For all cases of three types of slope failures, the improvement of success rates with respect to resampled training data sets was confirmed. As a final outcome, the differences between TFI maps produced by using original and resampled training data sets, respectively, are delineated on a DIF map （difference map） which is useful for analyzing trigger factor influence in terms of ＂risky- and safe-side assessment＂ sub-areas with respect to ＂different types of simultaneous slope failures＂.

ON THE PERFORMANCE OF DATA-DEPENDENT SUPERIMPOSED TRAINING WITHOUT CYCLIC PREFIX FOR SISO/MIMO SYSTEMS

Compared with channel estimation method based on explicit training sequences,bandwidth is saved for those methods using superimposed training sequences,while it is wasted when Cyclic Prefix(CP) is added.In previous work of McLernon,the Mean Square Error(MSE) performance of Data-Dependent Superimposed Training(DDST) without CP for Single-Input Single-Output(SISO) system was analyzed under the assumption that the data-dependent sequence matrix was a circulant matrix and not interfered by others.In fact,for the system without CP,the data-dependent sequence matrix is not circulant any more and will be interfered.This paper derives the exact expression of MSE for the system without CP and also gives its extension to Multiple-Input Multiple-Output(MIMO) system without CP.

基于Tri-training与噪声过滤的弱监督关系抽取

弱监督关系抽取利用已有关系实体对从文本集中自动获取训练数据,有效解决了训练数据不足的问题。针对弱监督训练数据存在噪声、特征不足和不平衡,导致关系抽取性能不高的问题,文中提出NF-Tri-training(Tritraining with Noise Filtering)弱监督关系抽取算法。它利用欠采样解决样本不平衡问题,基于Tri-training从未标注数据中迭代学习新的样本,提高分类器的泛化能力,采用数据编辑技术识别并移除初始训练数据和每次迭代产生的错标样本。在互动百科采集数据集上实验结果表明NF-Tri-training算法能够有效提升关系分类器的性能。

一种基于Tri-training的数据流集成分类算法

数据流分类是数据挖掘领域的重要研究任务之一,已有的数据流分类算法大多是在有标记数据集上进行训练,而实际应用领域数据流中有标记的数据数量极少。为解决这一问题,可通过人工标注的方式获取标记数据,但人工标注昂贵且耗时。考虑到未标记数据的数量极大且隐含大量信息,因此在保证精度的前提下,为利用这些未标记数据的信息,本文提出了一种基于Tri-training的数据流集成分类算法。该算法采用滑动窗口机制将数据流分块,在前k块含有未标记数据和标记数据的数据集上使用Tri-training训练基分类器,通过迭代的加权投票方式不断更新分类器直到所有未标记数据都被打上标记,并利用k个Tri-training集成模型对第k+1块数据进行预测,丢弃分类错误率高的分类器并在当前数据块上重建新分类器从而更新当前模型。在10个UCI数据集上的实验结果表明:与经典算法相比,本文提出的算法在含80%未标记数据的数据流上的分类精度有显著提高。

基于Tri-Training半监督分类算法的研究

在实际应用中,容易获取大量的未标记样本数据,而样本数据是有限的,因此,半监督分类算法成为研究者关注的热点。文中在协同训练Tri-Training算法的基础上,提出了采用两个不同的训练分类器的Simple-Tri-Training方法和对标记数据进行编辑的Edit-Tri-Training方法,给出了这三种分类方法与监督分类SVM的分类实验结果的比较和分析。实验表明,无标记数据的引入,在一定程度上提高了分类的性能;初始训练集和分类器的选取以及标记过程中数据编辑技术,都是影响半监督分类稳定性和性能的关键点。

基于Tri-Training和数据剪辑的半监督聚类算法

提出一种半监督聚类算法,该算法在用seeds集初始化聚类中心前,利用半监督分类方法Tri-training的迭代训练过程对无标记数据进行标记,并加入seeds集以扩大规模;同时,在Tri-training训练过程中结合基于最近邻规则的Depuration数据剪辑技术对seeds集扩大过程中产生的误标记噪声数据进行修正、净化,以提高seeds集质量.实验结果表明,所提出的基于Tri-training和数据剪辑的DE-Tri-training半监督聚类新算法能够有效改善seeds集对聚类中心的初始化效果,提高聚类性能.

基于Tri-Training算法的数据编辑技术

Tri-Training是一种半监督学习算法,在少量标记数据下,通过三个不同的分类器,从未标记样本中采样并标记新的训练数据,作为各分类器训练数据的有效补充。但由于错误标记样本的存在,引入了噪音数据,降低了分类的性能。论文在Tri-Training算法中分别采用DE-KNN,DE-BKNN和DE-NED三种数据编辑技术,识别移除误标记的数据。通过对六组UCI数据集的实验,分析结果表明,编辑技术的引入是有效的,三种方法的使用在一定程度上提升了Tri-Training算法的分类性能,尤其是DE-NED方法更为显著。

基于自适应数据剪辑策略的Tri-training算法

Tri-training能有效利用无标记样例提高泛化能力.针对Tri-training迭代中无标记样例常被错误标记而形成训练集噪声,导致性能不稳定的缺点,文中提出ADE-Tri-training(Tri-training with Adaptive Data Editing)新算法.它不仅利用Remove Only剪辑操作对每次迭代可能产生的误标记样例识别并移除,更重要的是采用自适应策略来确定Remove Only触发与抑制的恰当时机.文中证明,PAC理论下自适应策略中一系列判别充分条件可同时确保新训练集规模迭代增大和新假设分类错误率迭代降低更多.UCI数据集上实验结果表明:ADE-Tri-training具有更好的分类泛化性能和健壮性.

Blockchain for Education:Verification and Management of Lifelong Learning Data

In recent years,blockchain technology has been applied in the educational domain because of its salient advantages,i.e.,transparency,decentralization,and immutability.Available systems typically use public blockchain networks such as Ethereum and Bitcoin to store learning results.However,the cost of writing data on these networks is significant,making educational institutions limit data sent to the target network,typically containing only hash codes of the issued certificates.In this paper,we present a system based on a private blockchain network for lifelong learning data authentication and management named B4E(Blockchain For Education).B4E stores not only certificates but also learners’training data such as transcripts and educational programs in order to create a complete record of the lifelong education of each user and verify certificates that they have obtained.As a result,B4E can address two types of fake certificates,i.e.,certificates printed by unlawful organizations and certificates issued by educational institutions for learners who have not met the training requirements.In addition,B4E is designed to allow all participants to easily deploy software packages to manage,share,and check stored information without depending on a single point of access.As such,the system enhances the transparency and reliability of the stored data.Our experiments show that B4E meets expectations for deployment in reality.

用于在线产品评论质量分析的Co-training算法

在线评论广泛存在于电子商务网站平台,其中包含着客户对产品的评价及偏好.高效分析在线评论数据并满足客户需求,对许多谋求立足于竞争激烈的国际化市场的企业来说至关重要.但因在线评论的质量不一,使得如何分析在线评论的质量成为一项重要工作.从两个方面提取特征对在线评论进行描述,并构建了一种Co-training算法来判断评论的质量.通过对比实验验证了该算法相对于单一分类算法的优势.

基于多视图Tri-Training的微博用户性别判断

互联网技术不断发展,新浪微博作为公开的网络社交平台拥有庞大的活跃用户.然而由于用户数量庞大,且个人信息并不一定真实,造成训练样本打标困难.本文采用了一种多视图tri-training的方法,构建三个不同的视图,利用这些视图中少量已打标样本和未打标样本不断重复互相训练三个不同的分类器,最后集成这三个分类器实现用户性别判断.本文用真实用户数据进行实验,发现和单一视图分类器相比,使用多视图tri-training学习训练后的分类器准确性更好,且需要打标的样本更少.

基于改进Tri-Training算法的大数据保险业客户分类研究

保险行业正处于比较快速的发展阶段,为了能够盈利,构建良好的客户关系是非常关键的,可利用改进TriTraining算法对大数据保险业客户进行分类.首先确定保险业客户细分的指标;其次分析改进Tri-Training分类算法的基本理论;再次设计基于改进Tri-Training算法的大数据保险业客户分类流程;最后进行大数据保险业客户的分类实例研究,研究结果表明改进Tri-Training算法能够有效地提升保险业客户分类的精度.

Integrated Use of Existing Global Land Cover Datasets for Producing a New Global Land Cover Dataset with a Higher Accuracy: A Case Study in Eurasia

It has been commonly acknowledged that the current global mapping projects have encountered the accuracy challenge. By conducting a comparison among the four existing global land cover datasets (MODIS LC, GLC2000, GLCNMO and GLOBCOVER), it has been identified that certain areas’ accuracy has dragged down the overall accuracy of these global land cover datasets. In this paper, those areas have been defined as the “unreliable area”. This study has recollected the training data from the “unreliable area” within the above four mentioned datasets and reclassified the “unreliable area” by using two supervised classifications. The final result has shown that compared with any existing datasets, a relatively higher accuracy has been able to achieve.

基于Train2Vec的消防培训数据分类

为提高消防培训效果,提出一种基于深度神经网络的对混合现实消防培训数据分类的模型。通过Train2Vec将受训人员在混合现实火灾场景中进行消防培训时所产生的过程数据、生理数据构建为多维培训向量;通过对生成的多维培训向量进行相似度求解,实现将受训人员进行分类的功能,即归为专业消防人员、应对火灾经验丰富人员、未经历过火灾心理素质良好人员和未经历过火灾心理素质较差人员中的某类,为其生成个性化的应对火灾建议和消防培训任务,达到提升培训效果的目的。实验结果表明,该Train2Vec模型预测的身份信息的准确率为92.00%,可以实现受训人员的正确分类,从而实现个性化消防培训。

大语言模型“数据为王”:训练数据的价值、迷思与数字传播的未来挑战

伴随着ChatGPT的问世和流行,关于生成式人工智能的意涵和影响迅速成为学界和业界的关注焦点。在这场由大语言模型引领的非监督性深度学习浪潮中,一个核心议题就是训练数据。对训练数据的规模和质量的追求,演绎了“万模大战”形势下的“数据为王”法则。而在训练数据的价值、功能和误读的背后,是对数据概念的改写、对数据可供性的迷信和对数据所有权的争夺。训练数据的具体架构和内部机制引发了智能传播生态的重建和信息生产秩序的重构,在这一变革之中也蕴藏着大语言模型时代的数字危机,其具体体现为蒸馏式传播的偏见再生产、过滤式传播的信息保守化和随机性传播的意义之消散。大语言模型及其训练数据急需破除规模迷思,着重思考如何让数据切实成为社会技术系统的一部分。

生成式人工智能训练数据的风险与治理研究

生成式人工智能技术目前正处于蓬勃发展时期,但其在训练数据收集和处理时,潜在的合规、泄露和偏见等风险时刻威胁着生成式人工智能技术的健康发展、扰乱社会秩序,成为生成式人工智能治理必须解决的问题。我国目前对生成式人工智能训练数据风险治理既有政策支持,又有法律保障。在这些基础上,行政机关可以建立生成式人工智能基础模型信息库,预防训练数据合规风险;也可以开发、使用合成数据,扩大训练数据来源,规避训练数据泄露风险;还可以通过适当细化训练数据审核制度和逐步实施生成式人工智能企业数据信用制度的方式,规范生成式人工智能训练数据使用行为,降低风险发生概率。

Classifying Unstructured Text Using Structured Training Instances and an Ensemble of Classifiers

Typical supervised classification techniques require training instances similar to the values that need to be classified. This research proposes a methodology that can utilize training instances found in a different format. The benefit of this approach is that it allows the use of traditional classification techniques, without the need to hand-tag training instances if the information exists in other data sources. The proposed approach is presented through a practical classification application. The evaluation results show that the approach is viable, and that the segmentation of classifiers can greatly improve accuracy.

新质生产力与数字新农人培育:耦合逻辑、现实困境与破解策略

新质生产力是创新起主导作用,符合新发展理念的先进生产力质态,呈现以生产要素创新配置为基本内涵,数据作为关键要素进入生产函数;彰显人的本质力量,劳动者是核心影响因素和主体动力源;以科技创新为驱动力,新产业新模式新业态是其主要表现形态等新特征。基于新质生产力、数字新农人与职业培训之间耦合逻辑分析发现,数字新农人是新质生产力形成和发展的关键变量,而新质生产力与职业培训双向驱动助力数字新农人培育。基于职业培训在数字新农人培育中存在的承载数字新农人培育的职业培训体系不健全;承接数字新农人培育的培训机构效能低下;承担数字新农人培育的主体参与度低等诸多现实困境。构建新质生产力语境下的数字新农人培育体系需要从构建发达的职业培训供给体系,打通制约数字新农人培育的堵点;分类设计“数字+N型农民”培育方案,实施数字新农人差异化精准培训;以数字技术嵌融职业培训,营建数字新农人培育教学实践场景;顶层设计数字新农人培育工程,助推职业培训配套制度和政策的创新供给等方面实施。