融合用户历史传播信息的微博谣言检测

随着互联网技术的发展,以微博为主的社交媒体平台上网络谣言逐渐泛滥,研究微博谣言的自动检测对维护社会稳定具有重要意义。现今主流的基于深度学习的谣言检测方法普遍存在没有充分考虑微博文本语义信息的问题,同时,过分依赖传播信息的谣言检测方法使得检测时间滞后,不能满足谣言检测的现实需求。针对以上问题,本文提出一种融合用户历史交互信息的微博谣言检测模型,不使用待检测微博的传播信息,构建并训练AbaNet(ALBERT-BiGRU-Attention)深度学习网络模型,充分考虑待检测微博和用户历史传播信息文本的文本特征和语义信息进行谣言检测。实验结果显示,本文模型具有准确率高、稳定性强的特点,并且能够在获得较高检测精度的情况下大大缩短谣言检测的时间。     

基于多传递影响力的社交媒体谣言检测方法

社交媒体谣言检测是当前研究的热点问题,现有方法多数通过获取大量用户属性学习用户特征,但不适用于谣言的早期检测,忽略了用户之间的潜在关系对信息传播的影响。提出一种基于多传递影响力的谣言检测方法,根据源微博及其对应转发(评论)之间的关系构建文本信息传播图,并通过图卷积神经网络来捕获、学习文本信息的传播特征。利用文本信息和用户传播过程中的影响力,丰富可用于谣言检测早期的检测信息。将存在转发关系的用户构成用户影响力传播图,构建一种用户节点影响力学习方法,获取用户节点影响力,以增强用户特征信息。在此基础上,将文本特征与用户特征融合以进行谣言检测,从而提升检测效果。在3个真实社交媒体数据集上的实验结果表明,该方法在谣言自动检测以及早期检测的效果都有显著提升,与目前最好的基准方法相比,在微博、Twitter15、Twitter16数据集上的正确率分别提高了2.8%、6.9%和3.4%。     

新冠疫情相关社交媒体谣言传播量化分析

新冠肺炎疫情的爆发伴随着大量的谣言在社交媒体平台传播,对网络秩序和社会稳定产生了不良影响.已有的疫情相关社交媒体谣言传播量化分析研究仅对谣言内容等单一传播要素展开分析,而忽略了构成信息传播的其他基础要素,包括传播者、受众以及传播效果等.同时,这些研究的谣言数据与真实的社交媒体谣言数据也存在分布偏差和信息缺失.因此,基于新浪微博平台对新冠疫情相关社交媒体谣言的传播展开更加全面的量化分析.具体而言,首先对谣言传播内容进行分析,包括其主题分析、涉及地区分析、事件倾向性分析以及情感分析;进一步对谣言参与用户进行分析,将参与用户分为3类:造谣者、传谣者和辟谣者,并分别对其基础属性、关注主题、个体情绪以及自网络属性进行探究;最后对谣言引发舆情进行分析,探究其情感的整体分布、与主题、关键词和地区的关系、以及情感的演变规律.该研究首次从信息传播的各个基础要素层面对疫情相关的社交媒体谣言传播展开量化分析,不仅对新冠肺炎疫情相关谣言传播有了更全面深刻的认识,同时对突发公共事件的谣言研究和谣言治理也具有十分 重要的价值.     

基于改进Transformer的社交媒体谣言检测

随着互联网的快速发展,社交媒体日益广泛而深刻地融入人们日常生活的各个方面。社交媒体逐渐成为人们彼此之间用来分享意见、见解、经验和观点的工具和平台,是人们获取分享信息、表达交流观点的主要途径。社交媒体在互联网的沃土上蓬勃发展,爆发出令人眩目的能量。由于社交媒体的开放性,用户规模庞大且来源复杂众多,容易产生各种各样的谣言虚假信息。社交媒体谣言左右着网民对事件的认识、动摇着社会的稳定。因此,如何准确高效地检测谣言成为当下亟待解决的问题。现有基于Transformer的社交媒体谣言检测模型忽略了文本位置信息。为有效提取文本位置信息,充分利用文本潜在信息,提出了一种基于改进Transformer的社交媒体谣言检测模型。该模型从相对位置和绝对位置两方面对传统Transformer进行改进：一方面采用可学习的相对位置编码捕捉文本的方向信息和距离信息;另一方面采用绝对位置编码将不同位置词语映射到不同特征空间。实验结果表明,与其他基准模型相比,所提模型在Twitter15、Twitter16和Weibo3种数据集上的准确率分别提高了0.9%、0.6%和1.4%。实验结果验证了所提的位置编码改进有效,基于...     

基于评论异常度的新浪微博谣言识别方法

以微博为代表的社交媒体在为公众提供信息共享平台的同时, 也为谣言提供了可乘之机.开展微博中谣言的识别和清理方法研究, 对维护社会的安全稳定有着重要的现实意义.本文针对新浪微博平台中谣言识别的问题, 提出了一种基于评论异常度的微博谣言识别方法.首先采用D-S理论实现微博评论异常度的计算方法; 然后利用评论异常度与微博的内容特征、传播特征、用户特征对微博进行抽象表示; 最后再利用SVM (Support vector machine)构建一个基于评论异常度的谣言识别模型, 实现对新浪微博中谣言微博的识别.实验表明, 本文提出的谣言识别模型对新浪微博中谣言识别具有较好的效果, 谣言微博识别的F1值达到了96.2 %, 相较于现有文献的最好结果提高了1.3 %.     

一种新的考虑注意力机制的微博谣言检测模型

随着社交媒体的迅速发展,谣言通过社交媒体迅速传播,识别社交媒体网络上的谣言是社交网络研究中一个至关重要的问题.本文提出了一种新的考虑注意力机制的微博谣言检测模型,考虑到卷积神经网络(CNN)提取到的特征对输出结果影响力问题,在经典的文本卷积神经网络(Text CNN)上引入了注意力机制,通过CNN中的卷积层学习微博窗口的特征表示,再根据每个特征表示对输出结果的影响力不同通过注意力机制赋予不同的权重来进行谣言事件的检测.研究结果表明,本文提出的微博谣言检测模型准确率达到了96.8%,并且在召回率和F1值上也有提升,即本文提出的新的微博谣言检测模型具有更好的谣言识别能力.     

融合多模态信息的社交网络谣言检测方法

随着社交网络平台的发展,社交网络已经成为人们获取信息的重要来源。然而社交网络的便利性也导致了虚假谣言的快速传播。与纯文本的谣言相比,带有多媒体信息的网络谣言更容易误导用户以及被传播,因此对多模态的网络谣言检测在现实生活中有着重要意义。研究者们已提出若干多模态的网络谣言检测方法,但这些方法都没有充分挖掘出视觉特征和融合文本与视觉的联合表征特征。为弥补这些不足,提出了一个基于深度学习的端到端的多模态融合网络。该网络首先抽取出图片中各个兴趣区域的视觉特征,然后使用多头注意力机制将文本和视觉特征进行更新与融合,最后将这些特征进行基于注意力机制的拼接以用于社交网络多模态谣言检测。在推特和微博公开数据集上进行对比实验,结果表明,所提方法在推特数据集上F1值有13.4%的提升,在微博数据集上F1值有1.6%的提升。     

基于多任务学习的网络谣言检测研究

当今,社交媒体已经成为获取信息的主要来源。通过社交媒体平台,谣言以前所未有的速度传播,传播给全球受众,并使用户和社会面临巨大风险,尽早发现并阻止谣言是极其重要的。基于深度学习的方法在谣言检测中表现出很好的性能。但是,很多研究方法都是单任务学习,而且缺少外部知识对内容的检查,因此从知识增强角度出发,将谣言检测和主题领域分类这两个相关的任务结合起来进行多任务学习,以补充微博帖子的语义表示,提升谣言检测性能。     

基于微博重复发送的垃圾用户甄别

针对微博平台上的垃圾用户甄别问题,本文提出了基于微博重复发送行为的垃圾用户行为建模和甄别算法。在真实微博垃圾用户数据分析的基础上,本建模方法综合考虑了微博垃圾用户的行为信息、社交网络信息和文本信息,从不同的角度对垃圾用户进行了分析和建模。在真实数据集上的实验证明了方法的有效性,并且对模型中若干参数进行了优化,同时也分析了垃圾用户行为信息、社交网络信息和文本信息对模型的影响程度。     

基于改进主题模型的微博短文本情感分析研究

信息时代,社交媒体发展异常迅速,微博、微信等社交平台受到了广大网友的喜爱和关注。微博社交平台发布的信息文本相对较短,传播速度快,更新速度快,越来越多的网民在微博平台上表达个人意见和想法。但是,微博短文本的信息较为分散,上下文内容联系不紧密,分析微博短文本情感态度的过程中常常存在一定阻碍和问题。基于此,重点研究了基于改进主题模型的微博短文本情感分析,希望可以深入挖掘质量更高的情感主题。     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Designing economic np control charts: A programmed simulation approach

This paper presents control charts models and the necessary simulation software for the location of economic values of the control parameters. The simulation program is written in FORTRAN, requires only 10K of main storage, and can run on most mini and micro computers. Two models are presented - one describes the process when it is operating at full capacity and the other when the process is operating under capacity. The models allow the product quality to deteriorate to a further level before an existing out-of-control state is detected, and they can also be used in situations where no prior knowledge exists of the out-of-control causes and the resulting proportion defectives.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

How do children do mathematics with LOGO?

Abstract  This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired understanding of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development.