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.

Household Waste Reduction Effect of Sorted Collection of Recyclable Waste in Japan

The purpose of this study is to analyze the household waste reduction effect of sorted collection of recyclable waste in Japan using a panel data analysis, which considers time-series and cross-section data simultaneously. Also, the study shows the effect of the type of sorted items on the quantity of household waste disposed. We used the data attained from 103 cities recorded over three years, and applied the quantity of total waste disposed, the quantity of combustible waste, the quantity of other waste （waste excluding combustible and recyclable waste）, and the quantity of combustible plus other waste as objective variables, respectively, in the models. The result suggests that when the number of sorted items is increased marginally, the quantity of household waste decreases by about 0.5%-3.3% or 1.28-4.17 grams per capita per day. In addition, it is shown that sorting out white trays is effective in reducing the quantity of combustible waste. Sorting out paper containers and packages is also effective in reducing the quantity of other waste and combustible plus other waste.

Elucidation of Latent Risk of Navigation Using an Actual Ship Behavior Analysis

In recent years, maritime transportation has played an important role in global economy development. As a result, ship traffic has become more congested. Moreover, ship navigation is susceptible to weather and environmental conditions, and in some cases, it may become dangerous. Therefore, vessels are subjected to high-risk navigation conditions. To understand the latent risk of ship navigation, this study focused on the actual ship behavior. Thus, an analysis of ship behavior was carded out using historical ship navigation based on automatic identification system data. Consequently, a dynamic analysis of the speed and encounter situation was performed. One of the main results of this work was the understanding of the latent risk involved in ships navigating the Seto Inland Sea, which is one of the most congested routes in Japan. Moreover, the risk areas were obtained, and visualized using a geographical information system. The obtained results can be applied to ensure safe navigation and the development of a safe and efficient navigation model.

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.

全院一体化重症监护管理信息系统的构建与应用

目的探讨以患者为中心的医院一体化重症监护管理信息系统的构建与应用成效。方法通过JH Framework框架搭建应用程序,采用Widget-Canvas封装功能,通过ClickOnce技术更新部署,实现监护仪、呼吸机等生命支持设备体征数据的自动采集、传输、存储、分析,实现重症患者一体化管理。结果系统应用后,重症医学科医嘱执行规范率提升至99.90%,呼吸与危重症医学科提升至99.50%(P<0.05);呼吸与危重症医学科护理文书书写合格率由98.18%上升至99.71%(P<0.05);重症医学科不良事件发生率由1.80%下降至0.56%,呼吸与危重症医学科不良事件发生率由0.32%下降至0.15%。结论该系统可以减轻医护人员手工录入、转抄工作量,降低不良事件发生率,有效规范重症电子病历文书,提高患者生命支持设备数据利用率,提升重症监护一体化和数字化管理水平。

信息采集智能车系统设计与实现

在现代社会生活中,信息采集智能车在环境监测、生态研究、地质勘探等诸多领域发挥着重要作用。该文基于ESP32微处理器设计和开发具有自动避障和语音控制功能的信息采集智能车系统,通过在智能车系统搭载多传感器信息采集模块,实现不同位置环境信息的实时采集、传输与显示。经过实际测试,验证系统功能完善,易于使用与扩展。

AIGC对传播学定量研究的意义

人工智能内容生产对于学术研究产生了巨大的影响力,从生产力的角度重塑了学术研究的范式。本文通过分析AIGC(人工智能生成内容)对传播学定量研究的影响,探寻其对于文献综述、数据收集处理和数据分析的意义。结果表明,AIGC在以上三个环节当中都能起到不同程度的功能性作用,但仍然存在一定的机器局限性,主要表现为在功能性方面有待提升,在思辨性方面不尽如人意。研究强调了人类独有思辨思维的不可替代性,并针对基于机器固定算法的主观生成提出质疑,认为这种方式可能破坏社会科学研究的客观性质。

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。

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。

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。

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。

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。

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。