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.

Co-seismic fault geometry and slip distribution of the 26 December 2004, giant Sumatra–Andaman earthquake constrained by GPS, coral reef, and remote sensing data

We analyze co-seismic displacement field of the 26 December 2004, giant Sumatra–Andaman earthquake derived from Global Position System observations,geological vertical measurement of coral head, and pivot line observed through remote sensing. Using the co-seismic displacement field and AK135 spherical layered Earth model, we invert co-seismic slip distribution along the seismic fault. We also search the best fault geometry model to fit the observed data. Assuming that the dip angle linearly increases in downward direction, the postfit residual variation of the inversed geometry model with dip angles linearly changing along fault strike are plotted. The geometry model with local minimum misfits is the one with dip angle linearly increasing along strike from 4.3oin top southernmost patch to 4.5oin top northernmost path and dip angle linearly increased. By using the fault shape and geodetic co-seismic data, we estimate the slip distribution on the curved fault. Our result shows that the earthquake ruptured ＊200-km width down to a depth of about 60 km.0.5–12.5 m of thrust slip is resolved with the largest slip centered around the central section of the rupture zone78N–108N in latitude. The estimated seismic moment is8.2 9 1022 N m, which is larger than estimation from the centroid moment magnitude（4.0 9 1022 N m）, and smaller than estimation from normal-mode oscillation data modeling（1.0 9 1023 N m）.

Comparative Study and Spatial-Temporal Distribution of Regolith and Rock Geochemical Data from Xingmeng-North China

1 Introduction Geochemical mapping at national and continental scales continues to present challenges worldwide due to variations in geologic and geotectonic units.Use of the proper sampling media can provide rich information on

An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

It is crucial,while using healthcare data,to assess the advantages of data privacy against the possible drawbacks.Data from several sources must be combined for use in many data mining applications.The medical practitioner may use the results of association rule mining performed on this aggregated data to better personalize patient care and implement preventive measures.Historically,numerous heuristics(e.g.,greedy search)and metaheuristics-based techniques(e.g.,evolutionary algorithm)have been created for the positive association rule in privacy preserving data mining(PPDM).When it comes to connecting seemingly unrelated diseases and drugs,negative association rules may be more informative than their positive counterparts.It is well-known that during negative association rules mining,a large number of uninteresting rules are formed,making this a difficult problem to tackle.In this research,we offer an adaptive method for negative association rule mining in vertically partitioned healthcare datasets that respects users’privacy.The applied approach dynamically determines the transactions to be interrupted for information hiding,as opposed to predefining them.This study introduces a novel method for addressing the problem of negative association rules in healthcare data mining,one that is based on the Tabu-genetic optimization paradigm.Tabu search is advantageous since it removes a huge number of unnecessary rules and item sets.Experiments using benchmark healthcare datasets prove that the discussed scheme outperforms state-of-the-art solutions in terms of decreasing side effects and data distortions,as measured by the indicator of hiding failure.

Remote Control for the HT-7 Distributed Data Acquisition System

HT-7 is the first superconducting tokamak device for fusion research in China. Many experiments have been done in the machine since 1994, and lots of satisfactory results have been achieved in the fusion research field on HT-7 tokamak [1]. With the development of fusion research, remote control of experiment becomes more and more important to improve experimental efficiency and expand research results. This paper will describe a RCS (Remote Control System), the combined model of Browser/Server and Client/Server, based on Internet of HT-7 distributed data acquisition system (HT7DAS). By means of RCS, authorized users all over the world can control and configure HT7DAS remotely. The RCS is designed to improve the flexibility, opening, reliability and efficiency of HT7DAS. In the paper, the whole process of design along with implementation of the system and some key items are discussed in detail. The System has been successfully operated during HT-7 experiment in 2002 campaign period.

Statecharts for Distributed Product Data Management System Modelling

Product data management (PDM) has been accepted as an important tool for the manufacturing industries. In recent years, more and mor e researches have been conducted in the development of PDM. Their research area s include system design, integration of object-oriented technology, data distri bution, collaborative and distributed manufacturing working environment, secur ity, and web-based integration. However, there are limitations on their rese arches. In particular, they cannot cater for PDM in distributed manufacturing e nvironment. This is especially true in South China, where many Hong Kong (HK) ma nufacturers have moved their production plants to different locations in Pearl R iver Delta for cost reduction. However, they retain their main offices in HK. Development of PDM system is inherently complex. Product related data cover prod uct name, product part number (product identification), drawings, material speci fications, dimension requirement, quality specification, test result, log size, production schedules, product data version and date of release, special tooling (e.g. jig and fixture), mould design, project engineering in charge, cost spread sheets, while process data includes engineering release, engineering change info rmation management, and other workflow related to the process information. Accor ding to Cornelissen et al., the contemporary PDM system should contains manageme nt functions in structure, retrieval, release, change, and workflow. In system design, development and implementation, a formal specification is nece ssary. However, there is no formal representation model for PDM system. Theref ore a graphical representation model is constructed to express the various scena rios of interactions between users and the PDM system. Statechart is then used to model the operations of PDM system, Fig.1. Statechart model bridges the curr ent gap between requirements, scenarios, and the initial design specifications o f PDM system. After properly analyzing the PDM system, a new distributed PDM (DPDM) system is proposed. Both graphical representation and statechart models are constructed f or the new DPDM system, Fig.2. New product data of DPDM and new system function s are then investigated to support product information flow in the new distribut ed environment. It is found that statecharts allow formal representations to capture the informa tion and control flows of both PDM and DPDM. In particular, statechart offers a dditional expressive power, when compared to conventional state transition diagr am, in terms of hierarchy, concurrency, history, and timing for DPDM behavioral modeling.

A Data Mining Algorithm Based on Distributed Decision-Tree in Grid Computing Environments

Recently, researches on distributed data mining by making use of grid are in trend. This paper introduces a data mining algorithm by means of distributed decision-tree,which has taken the advantage of conveniences and services supplied by the computing platform-grid,and can perform a data mining of distributed classification on grid.

Geographical distribution of hypocentral depths of Chinese earthquakes

The hypocentral depths of more than 200 Chinese earthquakes, of magnitudes from M 8.6 to M 3.0, are calculated from macroseismic data carried on earthquake catalogs, by using the formula for macroseismic hypocentral depths and the formula for general solution of macroseismic hypocentral depths. The results are plotted on maps to show their geographical distribution. It can be seen that most Chinese earthquakes are shollow ones. Of the 200 earthquakes calculated, 162(81.0%) hypocenters are shallower than 9 km, of which 111 (55.5%) hypocenters are shallower than 5 km. Such shallow earthquakes are mostly distributed in the provinces near to the North South Earthquake Belt, while the rest are scattered in the other provinces(except Zhejiang province). Earthquakes of medium(between 10 and 20 km) depth are relatively few (32 in number, 15.0%); they are distributed along the North South Earthquake Belt, and the western part of Xinjiang Uygur Autonomous Region and in provinces Shaanxi, Shanxi and Shandong (along the Tanlu Fracture Zone, crossing the sea to northeast China). Deep earthquakes are rare, being scattered in south Yunnan and the east end of Inner Mongolia Uygur Autonomous Region.

新能源电力系统灵活性供需量化及分布鲁棒优化调度

电网中新能源渗透率的提升导致电力系统在局部时段灵活性严重不足。针对现有处理电力系统灵活性和供需不确定性过于保守或冒险的问题,提出一种数据驱动的分布鲁棒优化调度模型。首先,考虑风光出力的时空相关性,基于Copula理论构建出力集合。结合场景法与区间法对电力系统灵活性需求进行量化,引入灵活调节因子表征各类资源参与灵活性调节的能力,建立灵活性供需平衡约束。其次,考虑电动汽车等需求侧资源的灵活性供给能力,以灵活性资源运行成本与电网灵活性缺额惩罚成本最优作为目标函数,建立数据驱动的两阶段分布鲁棒模型。为降低保守性,采用综合范数对其概率分布进行约束,减小了灵活性需求出现极端情况的概率。针对两阶段鲁棒模型求解问题,利用零和博弈思想将模型解耦为主问题和子问题,采用列与约束生成算法进行迭代求解。最后,仿真算例表明,所提模型相比于传统不确定性模型对提高电力系统灵活性裕度与经济性具有积极作用。

基于数据驱动分布鲁棒优化的梯级水光蓄联合优化调度

多种可再生能源互补联合发电技术因其独特优越性正在成为“双碳”背景下电力系统优选供电方案之一,而其不确定性复杂耦合特性下的互补联合调度问题越来越受到人们关注。针对不确定性优化调度问题,本文引入能较好平衡不确定性及鲁棒性的数据驱动分布鲁棒优化理论(data-driven DRO),提出了一种新的基于数据驱动DRO梯级水光蓄联合发电系统协同优化调度方法。首先,考虑系统互补经济调度成本建立两阶段调度模型,制定各电站日前出力调度计划;然后,引入综合范数约束限定概率置信区间,并考虑最恶劣分布下的实时运行调整成本,获取日前调度计划的最优调整方案,日调度计划和调度调整方案形成最优调度计划;最后,本方法采用MP-SP框架,引入CCG算法展开两阶段协同求解。为验证所提方法的性能,引入四川示范区实际运行数据,开展了有效性验证、性能对比分析、计算效率仿真验证等。结果表明:本调度方法的有效性在数据规模、置信度水平两个维度得到了验证;对于SO、RO及本方法鲁棒性及经济性等性能指标的对比,本方法可获得高于SO的鲁棒性及高于RO的经济性;将本调度方法与概率性时序生产模拟方法的计算耗时进行对比,该方法实现了相同计算精度的较高计算效率。基于两阶段调度模型及循环迭代求解的DRO梯级水光蓄联合优化调度方法实现了协同调度结果经济性与保守性的均衡,其高效性能得到验证,为多种可再生能源互补协同调度提供了新思路。

A Two-Phase Paradigm for Joint Entity-Relation Extraction

An exhaustive study has been conducted to investigate span-based models for the joint entity and relation extraction task.However,these models sample a large number of negative entities and negative relations during the model training,which are essential but result in grossly imbalanced data distributions and in turn cause suboptimal model performance.In order to address the above issues,we propose a two-phase paradigm for the span-based joint entity and relation extraction,which involves classifying the entities and relations in the first phase,and predicting the types of these entities and relations in the second phase.The two-phase paradigm enables our model to significantly reduce the data distribution gap,including the gap between negative entities and other entities,aswell as the gap between negative relations and other relations.In addition,we make the first attempt at combining entity type and entity distance as global features,which has proven effective,especially for the relation extraction.Experimental results on several datasets demonstrate that the span-based joint extraction model augmented with the two-phase paradigm and the global features consistently outperforms previous state-ofthe-art span-based models for the joint extraction task,establishing a new standard benchmark.Qualitative and quantitative analyses further validate the effectiveness the proposed paradigm and the global features.

基于联邦学习的网络化ICU呼吸机和镇静剂管理方法

医疗物联网设备的激增和丰富的医疗数据为智慧医疗提供了新的可能。重症监护室(ICU)的病人依靠众多医疗边缘设备来持续监测管理患者的健康状况。在ICU常见的治疗干预措施中,有创机械通气和镇静剂的注射多用于维持患者的呼吸功能,提高治疗质量,而现有的治疗干预措施很大程度上依赖于医生的判断。文中提出了一种基于联邦学习的临床辅助决策方法——MFed,可以基于网络化ICU分布式协作学习最佳干预政策。该方法应用基于差分隐私的联邦学习方法,打破了医疗数据隐私方面的限制以及医疗数据孤岛的窘境;用分布鲁棒优化确保最坏情况下的性能并结合伪孪生网络实现自适应地滤除噪声数据。最后,在现实ICU数据集上的实验表明,与其他最先进的基线相比,所提方法的准确率提高了36.75%。

Spectral clustering eigenvector selection of hyperspectral image based on the coincidence degree of data distribution

Spectral clustering is a well-regarded subspace clustering algorithm that exhibits outstanding performance in hyperspectral image classification through eigenvalue decomposition of the Laplacian matrix.However,its classification accuracy is severely limited by the selected eigenvectors,and the commonly used eigenvectors not only fail to guarantee the inclusion of detailed discriminative information,but also have high computational complexity.To address these challenges,we proposed an intuitive eigenvector selection method based on the coincidence degree of data distribution(CDES).First,the clustering result of improved k-means,which can well reflect the spatial distribution of various types was used as the reference map.Then,the adjusted Rand index and adjusted mutual information were calculated to assess the data distribution consistency between each eigenvector and the reference map.Finally,the eigenvectors with high coincidence degrees were selected for clustering.A case study on hyperspectral mineral mapping demonstrated that the mapping accuracies of CDES are approximately 56.3%,15.5%,and 10.5%higher than those of the commonly used top,high entropy,and high relevance eigenvectors,and CDES can save more than 99%of the eigenvector selection time.Especially,due to the unsupervised nature of k-means,CDES provides a novel solution for autonomous feature selection of hyperspectral images.

基于最优决策树的多能系统快速鲁棒优化调度

新能源渗透率的持续增长造成了多能系统快速协调调度的巨大挑战,包括调度结果过于保守以及日内调度低效等问题。为此,提出了一种基于最优决策树分布式鲁棒优化的多能系统协调快速调度方法,所构建模型考虑电网日内经济调度,引入基于范数约束的概率分布置信集精准描述新能源的不确定性,防止调度结果过于保守。同时,根据新能源日内运行数据,分别通过可解释的最优分类树和最优回归树算法,优化日内机组启停状态和出力水平的初始决策量,解决日内鲁棒调度的低效问题。在四川某地区电网的验证结果表明,该模型可在兼顾调度成本和鲁棒性的同时,实现水风光多能系统的日内快速协调调度。

考虑风电统计特性挖掘的分布鲁棒优化调度方法

提出一种基于数据驱动分布鲁棒优化的电力系统调度方法,以应对考虑风电不确定性的电力系统运行问题。首先,挖掘风电数据统计信息,提出了基于主成分分析与核密度估计的风电概率分布模糊集构建方法,刻画风电随机性以及不同风电机组出力之间的空间相关性;其次,针对含风电的调度问题,建立考虑概率分布模糊集的两阶段分布鲁棒优化问题;然后,将分布鲁棒优化问题变换为其等价可解形式,采用仿射策略以及对偶原理将其转化为线性规划问题进行求解,并且提出了基于样本外测试的概率分布模糊集范围参数选择策略,以保证调度方案的可靠性与经济性。采用6节点与IEEE 118节点系统进行仿真分析,并将所提出的分布鲁棒优化方法与不考虑随机变量相关性的分布鲁棒优化方法以及传统随机、鲁棒优化方法进行对比,以验证所提方法的有效性。

基于分布鲁棒机会约束的充电运营商参与调峰市场投标策略

电动汽车作为一类重要的新型可控负荷资源,具有为电网提供调控服务的潜力。然而电动汽车的调节能力受其出行安排的影响,既呈现出一定的规律性,也具有不确定性。首先,提出一种电动汽车调节能力模型,该模型可以统一考虑数据结构不同的公共充电站与专用充电站,进而采用数据驱动的分布鲁棒机会约束来描述调节能力的不确定性。考虑聚合了多个不同类型充电站的充电运营商参与调峰辅助服务市场的日前投标阶段,提出一种双层投标-分配模型,使得运营商对外可以控制风险,对内可以灵活调度资源。基于实际数据的仿真结果表明,分布鲁棒机会约束描述的调节能力可以适应电动汽车出行规律的随机变化,所提出的投标-分配策略能够灵活地权衡运营商的经济效益与违约风险。

Similarities and differences of city-size distributions in three main urban agglomerations of China from 1992 to 2015： A comparative study based on nighttime light data

Comparing the city-size distribution at the urban agglomeration（UA） scale is important for understanding the processes of urban development. However, comparative studies of city-size distribution among China＇s three largest UAs, the Beijing-Tianjin-Hebei agglomeration（BTHA）, the Yangtze River Delta agglomeration（YRDA）, and the Pearl River Delta agglomeration（PRDA）, remain inadequate due to the limitation of data availability. Therefore, using urban data derived from time-series nighttime light data, the common characteristics and distinctive features of city-size distribution among the three UAs from 1992 to 2015 were compared by the Pareto regression and the rank clock method. We identified two common features. First, the city-size distribution became more even. The Pareto exponents increased by 0.17, 0.12, and 0.01 in the YRDA, BTHA, and PRDA, respectively. Second, the average ranks of small cities ascended, being 0.55, 0.08 and 0.04 in the three UAs, respectively. However, the average ranks of large and medium cities in the three UAs experienced different trajectories, which are closely related to the similarities and differences in the driving forces for the development of UAs. Place-based measures are encouraged to promote a coordinated development among cities of differing sizes in the three UAs.

Burden Distribution Calculation of Bell-Less Top of Blast Furnace Based on Multi-Radar Data

The burden distribution real-time estimation problem of multi-loop charging based on the real multi-radar data is resolved.Firstly , an iterative algorithm is introduced to calculate the radial coordinate of the pile-top.Then , based on the multi-radar data , the burden profile is estimated by a cubic-curve equation at the end of the multi-loop charging.Furthermore , the burden profile before the next multi-loop charging is calculated based on multi-radar data by considering the impact of burden descent.On the basis of these burden profiles , a more accurate thickness ratio of ore to coke （ RO/C ） at the radial direction of blast furnace can be obtained.Finally , an example is given to calculate the burden profiles and RO/C by using the real multi-radar data sampled from Baosteel , which shows the effectiveness of the method introduced.

不确定性环境下数据驱动的电力系统优化调度方法综述

随着可再生能源大规模并网,电力系统运行的不确定性显著增加,考虑不确定性因素的调度方法逐渐得到重视。另一方面,建立准确的不确定性因素模型是求解优化问题的前提与关键,可再生能源出力具有复杂的不确定性,其发电数据为随机调度提供了科学的数据支撑。文中总结了不确定性环境下数据驱动的电力系统调度的理论方法以及应用场景。首先,总结了传统随机优化调度中数据驱动的随机变量建模方法。其次,介绍了鲁棒优化调度中数据驱动的不确定性集合建模方法。然后,针对随机优化中不确定性因素建模不准确以及鲁棒优化结果较为保守的问题,重点阐述了基于数据驱动分布鲁棒的电力系统优化调度理论与方法,梳理了随机变量的概率分布模糊集构建方法和分布鲁棒优化的模型构建及求解算法。最后,对数据驱动的电力系统调度未来的研究工作进行了展望。

不确定环境下含云计算数据中心的电网韧性增强调度

为解决飓风来临前路径不确定时输电线路随机故障等带来的难题,提出了适用于含云计算数据中心的电网韧性增强日前调度策略,并将其构建为两阶段风险规避的分布鲁棒优化问题。以飓风对输电线路的时空影响为出发点,采用蒙特卡洛模拟获得飓风路径不确定时线路的离散故障集合,并构建基于L1距离度量的分布鲁棒模糊集合。然后,在日前调度中,对机组和数据中心进行优化以平衡经济性和电网韧性,并采用追索问题量化其对日间调度的影响,形成两阶段优化问题。随后,对优化问题进行确定性转换与解耦求解。最后,以含4个数据中心的IEEE-RTS系统为测试算例,验证了所提韧性增强策略应对模糊不确定性的有效性。