Best practices in supervising cognitive behavioral therapy with youth

Clinical supervision of cognitive behavioral therapy(CBT) with youth ensures better patient care and fosters trainees' professional development. However,often insufficient attention is directed toward disseminating best practices in supervision of CBT with youth. This Therapeutic Advances contribution aims to communicate the core content of supervision. Additionally, the key supervisory practices associated with CBT with youth are described. Supervisory outcomes are summarized and recommendations for supervisory practices are made.

THE MICROCOMPUTER SUPERVISING DEVICE ON A KIND OF JIG BORING MACHINE

This paper describes the design principles and functionality of a MicrocomputerSupervising Devlce(MSD)developed by us on a kind of Jig BoringMachine(JBM) Some Interference-free methods both in software and hardwareare also presented.As our MSD implemented,machining the frames of raplerlooms on acommon JBM can meet the technological requlrements suceessfully.Thedesign ideas and the circuit principles of our MSD may also be applied to othersimllar machlnes.

Design of the Hydrological Emergency Supervising Program Based on Hydrometry

According to practical hydrological emergency supervising program design,we have discovered and held a design principle with a method to get information promptly and accurately,monitor safely and conveniently,and collect information systematically,expounded the emergency hydrological monitoring program towards hydrometry,which shall be taken full analysis on the special geological environment around the site,aimed at damming body monitoring,and also the construction of hydrological emergency supervising networks in the area,the live video acquisition towards special point of the dammed barrier,then carried out monitoring measures which were suitable to contemporary economy and technology to get complete hydrological information we require. Finally,we put forward a concrete designing method for precluding disaster and reducing the loss of disaster based on the information above.

Weakly Supervised Network with Scribble-Supervised and Edge-Mask for Road Extraction from High-Resolution Remote Sensing Images

Significant advancements have been achieved in road surface extraction based on high-resolution remote sensingimage processing. Most current methods rely on fully supervised learning, which necessitates enormous humaneffort to label the image. Within this field, other research endeavors utilize weakly supervised methods. Theseapproaches aim to reduce the expenses associated with annotation by leveraging sparsely annotated data, such asscribbles. This paper presents a novel technique called a weakly supervised network using scribble-supervised andedge-mask (WSSE-net). This network is a three-branch network architecture, whereby each branch is equippedwith a distinct decoder module dedicated to road extraction tasks. One of the branches is dedicated to generatingedge masks using edge detection algorithms and optimizing road edge details. The other two branches supervise themodel’s training by employing scribble labels and spreading scribble information throughout the image. To addressthe historical flaw that created pseudo-labels that are not updated with network training, we use mixup to blendprediction results dynamically and continually update new pseudo-labels to steer network training. Our solutiondemonstrates efficient operation by simultaneously considering both edge-mask aid and dynamic pseudo-labelsupport. The studies are conducted on three separate road datasets, which consist primarily of high-resolutionremote-sensing satellite photos and drone images. The experimental findings suggest that our methodologyperforms better than advanced scribble-supervised approaches and specific traditional fully supervised methods.

Local saliency consistency-based label inference for weakly supervised salient object detection using scribble annotations

Recently,weak supervision has received growing attention in the field of salient object detection due to the convenience of labelling.However,there is a large performance gap between weakly supervised and fully supervised salient object detectors because the scribble annotation can only provide very limited foreground/background information.Therefore,an intuitive idea is to infer annotations that cover more complete object and background regions for training.To this end,a label inference strategy is proposed based on the assumption that pixels with similar colours and close positions should have consistent labels.Specifically,k-means clustering algorithm was first performed on both colours and coordinates of original annotations,and then assigned the same labels to points having similar colours with colour cluster centres and near coordinate cluster centres.Next,the same annotations for pixels with similar colours within each kernel neighbourhood was set further.Extensive experiments on six benchmarks demonstrate that our method can significantly improve the performance and achieve the state-of-the-art results.

Relational Turkish Text Classification Using Distant Supervised Entities and Relations

Text classification,by automatically categorizing texts,is one of the foundational elements of natural language processing applications.This study investigates how text classification performance can be improved through the integration of entity-relation information obtained from the Wikidata(Wikipedia database)database and BERTbased pre-trained Named Entity Recognition(NER)models.Focusing on a significant challenge in the field of natural language processing(NLP),the research evaluates the potential of using entity and relational information to extract deeper meaning from texts.The adopted methodology encompasses a comprehensive approach that includes text preprocessing,entity detection,and the integration of relational information.Experiments conducted on text datasets in both Turkish and English assess the performance of various classification algorithms,such as Support Vector Machine,Logistic Regression,Deep Neural Network,and Convolutional Neural Network.The results indicate that the integration of entity-relation information can significantly enhance algorithmperformance in text classification tasks and offer new perspectives for information extraction and semantic analysis in NLP applications.Contributions of this work include the utilization of distant supervised entity-relation information in Turkish text classification,the development of a Turkish relational text classification approach,and the creation of a relational database.By demonstrating potential performance improvements through the integration of distant supervised entity-relation information into Turkish text classification,this research aims to support the effectiveness of text-based artificial intelligence(AI)tools.Additionally,it makes significant contributions to the development ofmultilingual text classification systems by adding deeper meaning to text content,thereby providing a valuable addition to current NLP studies and setting an important reference point for future research.

From Control to Empowerment:A Paradigm Shift in the Discourse of Educational Supervision

As educational reforms intensify and societal emphasis shifts towards empowerment,the traditional discourse paradigm of management and control in educational supervision faces growing challenges.This paper explores the transformation of this discourse paradigm through the lens of empowerment,analyzing its distinct characteristics,potential pathways,and effective strategies.This paper begins by reviewing the concept of empowerment and examining the current research landscape surrounding the discourse paradigm in educational supervision.Subsequently,we conduct a comparative analysis of the“control”and“empowerment”paradigms,highlighting their essential differences.This analysis illuminates the key characteristics of an empowerment-oriented approach to educational supervision,particularly its emphasis on dialogue,collaboration,participation,and,crucially,empowerment itself.Ultimately,this research advocates for a shift in educational supervision towards an empowerment-oriented discourse system.This entails a multi-pronged approach:transforming ingrained beliefs,embracing renewed pedagogical concepts,fostering methodological innovation,and optimizing existing mechanisms and strategies within educational supervision.These changes are proposed to facilitate the more effective alignment of educational supervision with the pursuit of high-quality education.

Reconstruction of lithofacies using a supervised Self-Organizing Map:Application in pseudo-wells based on a synthetic geologic cross-section

Recently,machine learning(ML)has been considered a powerful technological element of different society areas.To transform the computer into a decision maker,several sophisticated methods and algorithms are constantly created and analyzed.In geophysics,both supervised and unsupervised ML methods have dramatically contributed to the development of seismic and well-log data interpretation.In well-logging,ML algorithms are well-suited for lithologic reconstruction problems,once there is no analytical expressions for computing well-log data produced by a particular rock unit.Additionally,supervised ML methods are strongly dependent on a accurate-labeled training data-set,which is not a simple task to achieve,due to data absences or corruption.Once an adequate supervision is performed,the classification outputs tend to be more accurate than unsupervised methods.This work presents a supervised version of a Self-Organizing Map,named as SSOM,to solve a lithologic reconstruction problem from well-log data.Firstly,we go for a more controlled problem and simulate well-log data directly from an interpreted geologic cross-section.We then define two specific training data-sets composed by density(RHOB),sonic(DT),spontaneous potential(SP)and gamma-ray(GR)logs,all simulated through a Gaussian distribution function per lithology.Once the training data-set is created,we simulate a particular pseudo-well,referred to as classification well,for defining controlled tests.First one comprises a training data-set with no labeled log data of the simulated fault zone.In the second test,we intentionally improve the training data-set with the fault.To bespeak the obtained results for each test,we analyze confusion matrices,logplots,accuracy and precision.Apart from very thin layer misclassifications,the SSOM provides reasonable lithologic reconstructions,especially when the improved training data-set is considered for supervision.The set of numerical experiments shows that our SSOM is extremely well-suited for a supervised lithologic reconstruction,especially to recover lithotypes that are weakly-sampled in the training log-data.On the other hand,some misclassifications are also observed when the cortex could not group the slightly different lithologies.

Study on Independent Supervision Function of Design Assurance System

The effective operation of a design assurance system cannot be achieved without the effective performance of the independent supervision function.As one of the core functions of the design assurance system,the purpose of the independent supervision function is to ensure that the system operates within the scope of procedures and manuals.At present,the function of independent supervision is a difficult and confusing issue for various original equipment manufacturers as well as suppliers,and there is an urgent requirement to put forward relevant requirements and form relevant methods.Based on the above mentioned objective,the basic requirements of the independent supervision function of design assurance system were studied,the problems and deficiencies in the organization,staffing,and methods existing in the current independent supervision function were analyzed,the improvement suggestions and measures for the performance of the independent supervision function from the aspects of the organization,staffing,procedures,and suppliers were put forward.The present work and conclusions provide guidance and direction for the effective operation of the design assurance system.

我国国家情报监督体系构建研究

涉及公权力运用的国家情报工作需要制约和监督。将我国国家情报监督置于党和国家监督体系进行阐释,为推进情报治理体系和治理能力现代化提供智力支持。采用思辨分析和比较研究方法,梳理我国国家情报监督的体系构成,分析我国国家情报监督机制存在的问题,并提出完善路径。我国国家情报监督体系由党内监督、国家机关监督和社会监督构成,但国家情报监督机制仍存在国家情报权力配置的科学性有待加强、国家情报工作监督制约机制尚不完善、国家情报权力运行缺乏透明度等问题。我国国家情报监督的完善应以科学配置国家情报权力,明晰国家情报权力的边界、完善国家情报工作监督制约机制、坚持国家情报工作公开与秘密相结合原则,健全国家情报工作适度公开制度为基本路径。

Edge-Federated Self-Supervised Communication Optimization Framework Based on Sparsification and Quantization Compression

The federated self-supervised framework is a distributed machine learning method that combines federated learning and self-supervised learning, which can effectively solve the problem of traditional federated learning being difficult to process large-scale unlabeled data. The existing federated self-supervision framework has problems with low communication efficiency and high communication delay between clients and central servers. Therefore, we added edge servers to the federated self-supervision framework to reduce the pressure on the central server caused by frequent communication between both ends. A communication compression scheme using gradient quantization and sparsification was proposed to optimize the communication of the entire framework, and the algorithm of the sparse communication compression module was improved. Experiments have proved that the learning rate changes of the improved sparse communication compression module are smoother and more stable. Our communication compression scheme effectively reduced the overall communication overhead.

基于MVBCN-FLW的中文法律文书命名实体识别

中文法律文书命名实体识别是智慧司法的基础性任务。目前的中文法律文书命名实体识别研究中已经取得一些成果,但其中大部分方法依赖于已标注的法律语料而未有效利用未标注的法律语料,且不能深入获取法律文书的特征。针对上述问题,提出一种中文法律文书命名实体识别框架。该框架使用基于双向编码器的转换器模型来学习中文法律文书的向量表示,并使用能够融合法律术语特征的双向长短时记忆网络语言模型来捕捉法律文书序列的上下文特征向量。该框架将中文法律文书的向量表示和上下文特征向量进行融合,融合后的特征向量被输入到一个由双向门控循环单元、自注意力机制和条件随机场组成的模块中进行训练。此外,为了使框架在缺少已标注的法律语料时也能得到更加充分的训练,使用未标注的法律语料进行自训练,生成新标注的法律语料并将其与初始标注的法律语料合并,通过进行迭代训练来提升框架性能。实验结果表明,该框架优于其他基于主流神经网络的命名实体识别模型。

Decentralized Semi-Supervised Learning for Stochastic Configuration Networks Based on the Mean Teacher Method

The aim of this paper is to broaden the application of Stochastic Configuration Network (SCN) in the semi-supervised domain by utilizing common unlabeled data in daily life. It can enhance the classification accuracy of decentralized SCN algorithms while effectively protecting user privacy. To this end, we propose a decentralized semi-supervised learning algorithm for SCN, called DMT-SCN, which introduces teacher and student models by combining the idea of consistency regularization to improve the response speed of model iterations. In order to reduce the possible negative impact of unsupervised data on the model, we purposely change the way of adding noise to the unlabeled data. Simulation results show that the algorithm can effectively utilize unlabeled data to improve the classification accuracy of SCN training and is robust under different ground simulation environments.

安徽省普通话水平测试员队伍的现状及发展策略

普通话水平测试是推广普及国家通用语言文字、提高普及质量的重要措施和重要途径之一。测试员在确保普通话水平测试的有效性和公正性方面发挥着重要作用。测试员的素质、操作能力,标准统一,公正性,应试人情绪调节、反馈技巧,以及自我发展是确保普通话水平测试高效、准确、公平的关键。科学的选拔机制,持续的培训、交流,开展语言文化活动,奖惩分明,可以提升测试员的专业能力,保障测试质量,从而更好地服务于普通话推广和语言文字工作。测试员需要不断更新知识,提高专业技能,与时俱进,同时,保持高尚的职业道德,以适应不断变化的职业挑战。

修改《监察法》的原理、原则与建言

与时俱进修改《监察法》,蕴含着具有“发生学”意义的政理、哲理和法理。其政理为“保持自我革命引领社会革命高度自觉”,其哲理为“党风廉政建设和反腐败斗争永远在路上”,其法理为“以良法引领推进腐败治理能力现代化”。修改《监察法》的基本原则,应坚持以政治为统领、以人民为本位、以问题为导向、以系统为理路。新修《监察法》充分吸收了学术界研究成果和实务部门探索出的成功经验,体现了惩治腐败与保障人权相结合、程序公正与实体公正并重的法治原则,增强了监察全覆盖的有效性和反腐败工作的实效性。其进一步完善可通过未来修改《监察法实施条例》的方式,从党的领导、纪监一体、监司衔接、行贿惩治等方面入手。

贸易物流中大数据监管探究

大数据是数据量大、类型多样、处理速度快的数据集合,大数据是能够洞察一切的互联网之眸。将大数据新技术融入到贸易物流监管体系中,可以对物流各个环节进行全方位、细致的监管,极大地提高贸易物流监管的效率与执行力,特别是大数据能够提供全面的贸易和物流管理大视图,有利于提升贸易和物流行业的透明度。可以通过构建大数据监管的理论基础,优化大数据监管的流程,积极培育大数据监管人才等,来进一步优化大数据监管。

唐前期监察考课制度安排与护民政策考量

唐朝前期,通过皇帝颁诏、均田制、户婚律等立法制度规制,唐王朝确立实施了保护广大均田农户农民的劝农务本国家政策,以根本上维护王朝国家政权统治。为了切实落实这一国策,唐王朝还对京司及地方诸官司施政履职和官吏政绩制定了四项监察考课制度,具体有朝廷御史台监察制度、分道遣使巡察制度、州司对县司和县司内部监察考课制度、尚书省吏部总负责功过行能考课制度。这四项监察考课制度以律令诏敕等作出规制安排,各有侧重,相互配合,通过纠弹施政违法和黜陟官吏,根本目的也是维护国家政权统治,但其中包含着保护广大均田农户农民的明确政策考量,在实际上有益于农户农民,应当予以肯定。当然,由于唐朝帝制封建等级性阶级社会本质使然,也不能过高评价其实际益处。

省级监察委员会立法权问题的法治省思

伴随着国家监察体制改革持续向纵深推进,在国家监察委员会已然获得监察法规制定权的背景下,是否应当赋予省级监察委员会以监察规章制定权便成为一个亟待回应的理论和实践问题,且形成了“肯定论”和“否定论”两种不同的观点。立法权限的转移、立法效率的考量、观照地方实际、立法中的“配备”惯例、监察机关系统纵向上的领导关系、纪检监察联合发文的需要等支持理由,为主张省级监察委员会应当享有监察规章制定权的观点提供了充分论证。赋予省级监察委员会以监察规章制定权,具有理论、制度和实践上的正当性。基于监察法治原理,赋予省级监察委员会以监察规章制定权,既需要从实体层面提供解决方案,又需要从程序层面进行程序设计,还需要构建监察规章的双重备案审查机制,从而确保地方监察工作在法治轨道上运行。

基于CNN和Transformer交叉教学的半监督医学图像分割

由于医学图像分割领域缺乏高质量的标注数据,半监督学习方法在医学图像语义分割任务中受到高度重视.为了充分利用卷积神经网络(Convolutional Neural Network,CNN)和Transformer在半监督学习中的优势,本文提出一种基于CNN与Transformer交叉教学的半监督医学图像分割方法.该方法将经典的深度协同训练从一致性正则化简化为交叉教学,利用循环伪标签方案使两个网络的预测差异转换为无监督损失,以鼓励两个网络具有一致的低熵预测.所提方法在ISIC 2018数据集上进行实验,在采用20%的标注比例时,Dice系数和Jaccard系数分别达到87.25%和79.17%,相比于监督U-Net++的训练结果分别提升了2.89%和3.53%,并且优于目前主流的半监督学习方法,验证了所提方法在半监督医学图像分割上的有效性和泛化性.

数字检察的逻辑、风险及其治理

在数字中国战略引领下,检察机关主动推行数字检察改革,期冀提升法律监督质效,以充分彰显其作为法律监督机关的职能定位。司法实践中,数字检察改革主要遵循“运动式治理”的推广逻辑、监督职权拓展的权力逻辑、技术赋能监督的效率逻辑。三重逻辑加持下的数字检察改革虽取得一定成效,但也因此面临三重风险:改革推广失序风险、监督权力失序风险与监督程序失序风险。为克服这些风险,保障数字检察改革的正当性和稳定性,需要以正当程序为基本改革理念,制定具体的数字检察改革方略。具体而言,在改革推广层面,数字检察应摆脱“运动式治理”思路,明确数字检察组织架构和法律监督模型竞赛推行机制;在权力拓展层面,应明晰数字检察的正当边界和协商沟通机制;在监督程序层面,应探索法律监督案件的案件化办理程序与建构技术性正当程序规则。