A Robust Framework for Multimodal Sentiment Analysis with Noisy Labels Generated from Distributed Data Annotation

Multimodal sentiment analysis utilizes multimodal data such as text,facial expressions and voice to detect people’s attitudes.With the advent of distributed data collection and annotation,we can easily obtain and share such multimodal data.However,due to professional discrepancies among annotators and lax quality control,noisy labels might be introduced.Recent research suggests that deep neural networks(DNNs)will overfit noisy labels,leading to the poor performance of the DNNs.To address this challenging problem,we present a Multimodal Robust Meta Learning framework(MRML)for multimodal sentiment analysis to resist noisy labels and correlate distinct modalities simultaneously.Specifically,we propose a two-layer fusion net to deeply fuse different modalities and improve the quality of the multimodal data features for label correction and network training.Besides,a multiple meta-learner(label corrector)strategy is proposed to enhance the label correction approach and prevent models from overfitting to noisy labels.We conducted experiments on three popular multimodal datasets to verify the superiority of ourmethod by comparing it with four baselines.

Analysis on the Influence of Automatic Station Temperature Data on the Sequence Continuity of Historical Meteorological Data

[Objective] The research aimed to study the influence of automatic station data on the sequence continuity of historical meteorological data. [Method] Based on the temperature data which were measured by the automatic meteorological station and the corresponding artificial observation data during January-December in 2001, the monthly average, maximum and minimum temperatures in the automatic station were compared with the corresponding artificial observation temperature data in the parallel observation period by using the contrast difference and the standard deviation of difference value. The difference between the automatic station and the artificial data, the variation characteristics were understood. Meanwhile, the significance test and analysis of annual average value were carried out by the data sequence during 1990-2009. The influence of automatic station replacing the artificial observation on the sequence continuity of historical temperature data was discussed. [Result] Although the two temperature data in the parallel observation period had the certain difference, the difference was in the permitted range of automatic station difference value on average. The difference of individual month surpassed the permitted range of automatic station difference value. The significance test showed that the annual average temperature and the annual average minimum temperature which were observed in the automatic station had the difference with the historical data. It had the certain influence on the annual temperature sequence, but the difference wasn’t significant as a whole. When the automatic observation combined with the artificial observation to use, the sequence needed carry out the homogeneous test and correction. [Conclusion] The research played the important role on guaranteeing the monorail running of automatic station, optimizing the meteorological surface observation system, improving the climate sequence continuity of meteorological element and the reliability of climate statistics.

Bus transit travel time reliability evaluation based on automatic vehicle location data

In order to provide important parameters for schedule designing, decision-making bases for transit operation management and references for passengers traveling by bus, bus transit travel time reliability is analyzed and evaluated based on automatic vehicle location （AVL） data. Based on the statistical analysis of the bus transit travel time, six indices including the coefficient of variance, the width of travel time distribution, the mean commercial speed, the congestion frequency, the planning time index and the buffer time index are proposed. Moreover, a framework for evaluating bus transit travel time reliability is constructed. Finally, a case study on a certain bus route in Suzhou is conducted. Results show that the proposed evaluation index system is simple and intuitive, and it can effectively reflect the efficiency and stability of bus operations. And a distinguishing feature of bus transit travel time reliability is the temporal pattern. It varies across different time periods.

Standardized Evaluation of Camera-based Driver State Monitoring Systems

Driver state sensing technologies, such as vehicular systems, start to be widely considered by automotive manufacturers. To reduce the cost and minimize the intrusiveness towards driving, the majority of these systems rely on the in-cabin camera(s) and other optical sensors. With their great capabilities in detecting and intervening of driver distraction and inattention,these technologies may become key components in future vehicle safety and control systems. However, to the best of our knowledge,currently, there is no common standard available to objectively compare the performance of these technologies. Thus, it is imperative to develop one standardized process for evaluation purposes.In this paper, we propose one systematic and standardized evaluation process after successfully addressing three difficulties:1) defining and selecting the important influential individual and environmental factors, 2) countering the effects of individual differences and randomness in driver behaviors, and 3) building a reliable in-vehicle driver head motion tracking tool to collect ground-truth motion data. We have collected data on a large scale on a commercial driver state-sensing platform. For each subject, 30 to 40 minutes of head motion data was collected and included variables, such as lighting conditions, head/face features,and camera locations. The collected data was analyzed based on a proposed performance measure. The results show that the developed process can efficiently evaluate an individual camerabased driver state sensing product, which builds a common base for comparing the performance of different systems.

Human and Machine Vision Based Indian Race Classification Using Modified-Convolutional Neural Network

The inter-class face classification problem is more reasonable than the intra-class classification problem.To address this issue,we have carried out empirical research on classifying Indian people to their geographical regions.This work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India,referring to human vision.We have created an Automated Human Intelligence System(AHIS)to evaluate human visual capabilities.Analysis of AHIS response showed that face shape is a discriminative feature among the other facial features.We have developed a modified convolutional neural network to characterize the human vision response to improve face classification accuracy.The proposed model achieved mean F1 and Matthew Correlation Coefficient(MCC)of 0.92 and 0.84,respectively,on the validation set,outperforming the traditional Convolutional Neural Network(CNN).The CNN-Contoured Face(CNN-FC)model is developed to train contoured face images to investigate the influence of face shape.Finally,to cross-validate the accuracy of these models,the traditional CNN model is trained on the same dataset.With an accuracy of 92.98%,the Modified-CNN(M-CNN)model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems.A novel Indian regional face dataset is created for supporting this supervised classification work,and it will be available to the research community.

巴黎奥运周期竞技游泳运动生物力学研究进展

本文梳理了巴黎奥运周期内竞技游泳运动生物力学研究的最新进展,通过分析运动生物力学在竞技游泳中的应用,旨在揭示运动表现提升与损伤预防的关键因素,主要涵盖技术分析与优化、研究方法与设备、表现评估与强化,以及损伤预防与康复4个方面。巴黎奥运周期竞技游泳运动生物力学相关研究体现了运动生物力学在优化游泳技术动作、评估运动表现和预防运动损伤中具有重要作用。特别是最新的数据采集和分析设备,如高精度传感器、人工智能与深度学习技术的发展,使得对游泳技术的分析更加全面和精确。未来的研究应进一步结合多维度数据整合技术,通过高精度运动捕捉、流体力学测量及智能分析,深层次探索游泳技术的优化路径。

手持SLAM设备在土方测量中的应用研究

随着测绘技术的不断革新,传统的数据采集方法也在使用过程中出现了相关的弊端,无法满足现有项目的时间要求与精度要求,三维激光扫描技术的出现极大地提升了数据质量和数据采集速度,为现有项目的开展提供了数据支持。基于手持SLAM技术在土方测量中的应用与优势展开研究,通过对手持SLAM技术原理、算法和关键技术进行分析,并结合工程实例利用手持SLAM和三维激光扫描仪对土方精度进行验证。实验证明,手持SLAM技术能够在土方测量中实现高精度、高效率的数据采集和建模,为土方方量计算奠定了数据基础。

发射设备可靠性数据采集与分析研究

利用发射设备寿命周期各阶段的可靠性工作中所产生的能够反映可靠性水平与状态的各种数据,综合评估系统的可靠性水平,可以为发射设备可靠性鉴定提供依据。为了进行发射设备可靠性评估,首先,介绍了发射设备可靠性数据来源、特点、采集和分析;然后,制定了某型号发射设备可靠性评估流程,在此基础上进行发射设备可靠性评估,并根据评估结果提出了设备的改进措施;最后,提出了加强发射设备使用阶段可靠性数据采集、采用新技术进行可靠性数据采集、加强试验数据采集管理等方面的建议。

大坝安全监测数据存储及自动化处理研究与应用

目前,在水工建筑物工作性态周期性诊断和安全监测分析方面,中小型水电站普遍采用人工处理数据、单一数据源分析的方式,数据处理工作量大、效率低、准确率不高,监测结果分析整编耗时长、成本高。本研究通过水电站水工建筑物监测数据特征分析,有助于实现多电站不同业务数据的融合和报告自动整编。

大数据技术在多模态教学中的应用

阐述大数据技术在多模态教学中的应用。通过数据收集和分析,大数据技术能够提供个性化指导、实时反馈和评估等功能。多模态教学实践有更好的学习体验和成果,克服传统教学方式的不足。

实施国家义务教育英语学习质量监测,促进英语教育高质量发展

2021年,教育部印发《国家义务教育质量监测方案(2021年修订版)》,首次将英语学科纳入监测范围。2022年5月,教育部基础教育质量监测中心第一次在全国范围内实施了国家义务教育英语学习质量监测。通过本轮监测,监测中心客观掌握国家义务教育英语学习质量总体水平,全面了解影响英语教育质量的主要因素,进而推动英语教学改革,提高英语学习效率。未来,英语监测将在诊断教育问题、引领教育改革方面发挥重要作用,助力国家义务教育高质量发展。本文将从英语监测启动的意义、高质量英语监测的特征以及英语监测如何推动教育质量发展等方面,系统介绍国家义务教育英语学习质量监测。

基于云平台的煤矿数据中心研究与应用

随着智能化矿山建设的逐步推进,应用系统数量逐渐增加,数据不能够完全统一、系统缺乏统筹管理、功能重复建设等问题逐渐凸显。为此,以伊敏露天煤矿数据中心为例,详细分析了煤矿数据中心的总体架构,结合工业私有云开发平台及大数据技术建立了开放式、持续集成和部署、支持高负载弹性伸缩、满足智能化露天煤矿管理理念的数据中心,基于数据中心多源数据存储架构解决了矿山领域数据采集难、数据无法统一存储、业务系统孤岛和采地测专业无法及时便捷共享数据等问题,同时综合利用已有设备及其采集的系统数据,采用数据共享和计算分析工具实现生产情况和设备状态的实时掌控,提升了管理决策能力和手段。

太行肉牛育种指数构成分析

对应太行肉牛育种实践,综合分析太行肉牛新品种选育的目标性状以及各性状在总性能指数构成中的权重。太行肉牛新品种选育的总性能指数由体型结构、适应性、生长发育、繁殖力、泌乳力及牛肉指数等六大目标性状构成,权重分别为16%、18%、25%、25%、6%和10%。同时描述了各目标性状的具体度量与数据采集方法。以期业界同仁共同探讨、批评指正,合力推进太行肉牛的育种进程。

3D扫描技术在活塞顶零件逆向设计中的应用

运用3D扫描设备修复失效活塞顶。首先,运用3D扫描仪3DScanGO Laser对失效零件进行高精度数据采集。应用Geomagic Wrap软件对扫描数据进行点云及三角面片优化处理。通过GeomagicDesignX软件重构失效活塞顶参数化模型。最后,应用PolyWorks检测软件对模型进行精度检测与分析。

智能化分析在整合优化多台站综合监测系统精准告警推送中的应用

传统各个台站独立运维模式下的告警,都在其内部监控室单独电脑上呈现。在如今智能化、全面化运维模式整合的大背景下,为了整合各个台站统一运维管理,建立总体监看调度各台站技术工作,出现故障告警时,应用智能化分析,设立全局告警整合模块来统一整合优化推送各层级各台站告警信息。在原有监控系统的基础上,引入智能化分析模块,建立多台站综合监测系统,将所有告警信息采集、分析、整合优化,最后精准推送分发给需要的台站和技术员。

深基坑自动化监测技术的应用研究

随着基坑开挖深度的逐步增加,对基坑施工过程中的主体结构和周边环境的变形数据要求也越来越高,传统人工监测作业效率低,难以实现基坑安全态势实时监测。本次研究通过详细阐述基坑支撑应力、地下水位、墙体测斜及三维形变的自动化监测原理、方法及产品设备,将其应用到某深基坑项目中去,并采用人工监测方法对自动化监测数据进行分析检验,同时与施工进度及工况进行结合,验证了基坑自动化监测系统的技术可行性及监测成果的可靠性,为基坑动态变形数据的获取提供了新的技术方法。

经济统计与碳中和目标的协同推进策略

文章旨在探讨经济统计在碳中和目标实现过程中的作用,并提出协同推进策略,以实现经济发展与达成碳减排目标的双赢局面。首先,从经济统计与碳中和目标的关系入手,分析二者的定义、作用和关联性。其次,阐述经济统计在实现碳中和目标过程中的具体作用,包括数据收集与分析、监测与评估以及政策制定与决策支持等方面。随后,提出经济统计与碳中和目标的协同推进策略,包括数据整合与标准化、制定有效的政策措施以及提供准确的信息与指导。最后,以国际经验与启示为结语,探讨发达国家和新兴经济体在碳中和目标实现过程中的经验教训,以期为中国经济与碳减排协同推进提供一定的启示与借鉴。

数字媒体大数据处理与分析平台的设计与应用

随着数字媒体的快速发展,产生了大量包括文本、图片、视频和音频等多种形式的数据。有效的大数据处理与分析对理解用户行为、优化内容推荐、提升广告效果等具有重要意义。设计包括数据采集、数据存储、数据处理和数据分析等关键模块的数字媒体大数据处理与分析平台,详细描述各模块的功能和实现方法,基于Scrapy框架实现数据采集,利用MapReduce模型对图像数据进行清洗,基于Hive仓库设计数据分析方法。该平台能够高效地处理与分析大规模的数字媒体数据,符合不同类型的数字媒体数据处理需求。

400 km/h高速综合检测列车研制

高速综合检测列车是保障高速铁路安全运营的高端核心技术装备,该检测列车以“复兴号”智能动车组为载运平台,集成400 km/h的轨道几何、车辆动态响应、接触网、通信信号、线路环境等检测系统。在400 km/h高速综合检测列车研制中,开发了检测集中控制和数据服务平台,实现检测作业的自动化管理和数据自动归集;设计了基于时空二元基准的检测系统同步架构,实现病害缺陷的多源融合精确定位和多专业检测数据的动态精确关联分析;对检测系统和载运动车组平台进行一体化设计,提升更高运行速度条件下列车检测的可靠性。经试验验证,400 km/h高速综合检测列车可满足高速铁路基础设施最高450 km/h等速、等工况条件下的检测要求。

核电力学分析集成系统的应用研究

当前核电项目存在数量多、进度紧、要求高等特点,传统的核电力学分析流程与工作模式,越来越难以满足工程的进度需求。为了实现更高效、更高质、更协同的设计研发与制造模式,提高核电项目中力学分析的工作效率、降低人因失误、强化协同设计能力以及加强数据管理应用,该文提出并研究建立一套核电力学分析集成系统,该系统以核电工程力学强度评估方法及分析过程为基础,以自动化过程为核心,以智能数据应用为驱动,以数字集成平台为依托,能够对上下游的结构化数据进行解析和传递,可完成设备、管道的一体化自动分析流程,并实现数据管理和关联统计等功能。该系统应用于核电项目的过程中,在保证设计安全和质量的同时,也将进一步推动核工程领域中力学分析环节的程序化、信息化、智能化发展。