Twitter中重复消息的分析和处理

Twitter已经成为微博中的代表性应用,但是通过分析发现twitter上的消息（推文）有很多完全一致或相似,这对后续对推文的分析和存储都带来很大的问题。为了处理这些内容完全一致或相似的消息（推文）,针对推文特有的短文本的特点,基于规则处理完全一致的推文,采用simhash的方法来处理相似性的推文。实验采用实际抓取的240万条推文数据进行分析和处理,分别对中文和英文的推文重复情况进行了分析,实验结果发现重复的推文占总推文的10%左右。     

Identifying interesting Twitter contents using topical analysis

Social media platforms such as Twitter are becoming increasingly mainstream which provides valuable user-generated information by publishing and sharing contents. Identifying interesting and useful contents from large text-streams is a crucial issue in social media because many users struggle with information overload. Retweeting as a forwarding function plays an important role in information propagation where the retweet counts simply reflect a tweet’s popularity. However, the main reason for retweets may be limited to personal interests and satisfactions. In this paper, we use a topic identification as a proxy to understand a large number of tweets and to score the interestingness of an individual tweet based on its latent topics. Our assumption is that fascinating topics generate contents that may be of potential interest to a wide audience. We propose a novel topic model called Trend Sensitive-Latent Dirichlet Allocation (TS-LDA) that can efficiently extract latent topics from contents by modeling temporal trends on Twitter over time. The experimental results on real world data from Twitter demonstrate that our proposed method outperforms several other baseline methods.     

基于Twitter的流感疫情可视化系统

介绍了当下社交媒体的蓬勃发展以及基于社交媒体数据的多领域应用,阐述了利用Twitter数据对流感疫情监测的优势和可行性,着重论述了基于Twitter的流感疫情可视化系统的详细设计和具体实现,并讨论了系统的意义和应用方向.     

Sentiment analysis and Twitter: a game proposal

Pervasive sensing of people’s opinions is becoming critical in strategic decision processes, as it may be helpful in identifying problems and strengthening strategies. A recent research trend is to understand users’ opinions through a sentiment analysis of contents published in the Twitter platform. This approach involves two challenges: the large volume of available data and the large variety of used languages combined with the brevity of texts. The former makes manual analysis unreasonable, whereas the latter complicates any type of automatic analysis. Since sentiment analysis is a difficult process for computers, but it is quite simple for humans, in this article, we transform the sentiment analysis process into a game. Indeed, we consider the game with a purpose approach and we propose a game that involves users in classifying the polarity (e.g., positive, negative, neutral) and the sentiment (e.g., joy, surprise, sadness) of tweets. To evaluate the proposal, we used a dataset of 52,877 tweets, we developed a Web-based game, we invited people to play the game, and we validated the results through two different methods: ground-truth and manual assessment. The obtained results showed that the game approach is effective in measuring people’ sentiments and also highlighted that participants liked to play the game.     

Business engagement on Twitter: a path analysis

Social media services, such as Twitter, enable commercial businesses to participate actively in online word-of-mouth communication. In this project, we examined the potential influences of business engagement in online word-of-mouth communication on the level of consumers’ engagement and investigated the trajectories of a business’ online word-of-mouth message diffusion in the Twitter community. We used path analysis to examine 164,478 tweets from 96,725 individual Twitter users with regards to nine brands during a 5-week study period. We operationalized business engagement as the amount of online word-of-mouth messages from brand and the number of consumers the brand follows. We operationalized consumers’ engagement as the number of online word-of-mouth messages from consumers both connecting to the brand and having no connection with the brand as well as the number of consumers following the brand. We concluded that the business engagement on Twitter relates directly to consumers’ engagement with online word-of-mouth communication. In addition, retweeting, as an explicit way to show consumers’ response to business engagement, indicates that the influence only reaches consumers with a second-degree relationship to the brand and that the life cycle of a tweet is generally 1.5 to 4 hours at most. Our research has critical implications in terms of advancing the understanding of the business’s role in the online word-of-mouth communication and bringing insight to the analytics of social networks and online word-of-mouth message diffusion patterns.     

Generalized filtering algorithms for infeasibility analysis

We present generalized filtering algorithms for debugging linear, mixed integer and nonlinear infeasible programs. Given a set of constraints that are infeasible or inconsistent, we give algorithms to identify a minimal subset of these constraints that are inconsistent. The algorithms combine existing filtering algorithms with a binary-search based divide-and-conquer approach to improve search speed. We give computational results to show the speed of the algorithms on various problem types.     

Brain-inspired algorithms for retinal image analysis

Retinal image analysis is a challenging problem due to the precise quantification required and the huge numbers of images produced in screening programs. This paper describes a series of innovative brain-inspired algorithms for automated retinal image analysis, recently developed for the RetinaCheck project, a large-scale screening program for diabetic retinopathy and other retinal diseases in Northeast China. The paper discusses the theory of orientation scores, inspired by cortical multi-orientation pinwheel structures, and presents applications for automated quality assessment, optic nerve head detection, crossing-preserving enhancement and segmentation of retinal vasculature, arterio-venous ratio, fractal dimension, and vessel tortuosity and bifurcations. Many of these algorithms outperform state-of-the-art techniques. The methods are currently validated in collaborating hospitals, with a rich accompanying base of metadata, to phenotype and validate the quantitative algorithms for optimal classification power.     

商业智能系统的应用

随着我国社会主义经济的迅猛发展,我国的科技实力也在不断的增强,智能系统也得到了广泛的应用,在所有的商业领域都是如此,因为智能系统可以从大量的数据中提取出有价值的信息,为企业未来的发展方向作出指导,本文主要论述了智能系统的感念、发展现状以及最终的发展方向.     

商务智能系统

商务智能系统是第三代企业信息系统，本文阐述了商务智能系统结构，以及商务智能系统的核心技术。     

Towards computational discourse analysis: A methodology for mining Twitter backchanneling conversations

In this paper we present a methodology to analyze and visualize streams of Social Media messages and apply it to a case in which Twitter is used as a backchannel, i.e. as a communication medium through which participants follow an event in the real world as it unfolds. Unlike other methods based on social networks or theories of information diffusion, we do not assume proximity or a pre-existing social structure to model content generation and diffusion by distributed users; instead we refer to concepts and theories from discourse psychology and conversational analysis to track online interaction and discover how people collectively make sense of novel events through micro-blogging. In particular, the proposed methodology extracts concept maps from twitter streams and uses a mix of sentiment and topological metrics computed over the extracted concept maps to build visual devices and display the conversational flow represented as a trajectory through time of automatically extracted topics. We evaluated the proposed method through data collected from the analysis of Twitter users’ reactions to the March 2015 Apple Keynote during which the company announced the official launch of several new products.     

水面舰艇智能辅助决策系统研究

海军集团是联合作战的组成部分,该文以红蓝双方海上舰艇编队对抗为作战想定背景,以实时辅助指挥引导水面舰艇的作战行动为目的,采用面向分布式人工智能的理论技术方法,进行水面舰艇智能辅助决策系统研究,通过建立作战智能体作为MAS系统应用智能体的新概念,根据联合作战水面舰艇指挥系统的总体结构、作战信息流程和功能模块划分,给出了水面舰艇智能辅助决策MAS系统的Agent组成,Agent主体结构,Agent功能分类,系统的运行流程,最后提出和建立了作战指挥控制系统的MAS体系结构模板,构造了水面舰艇智能辅助决策形式化结构模型和构成框架,为解决联合作战水面舰艇作战指挥控制决策问题提出了一种新的思路和方法。     

井涌井漏监测系统设计

在石油勘探开发中,尤其随着欠平衡钻井的推广应用,通过钻井液池相应参数的监测,可指导钻井作业快速、安全、有效地进行。系统采用友好的人机界面,通过对钻井液池出入口密度、温度、电导率、硫化氢气体含量及钻井液总体积的实时测量。可有效地监测各参数的变化,判断可能出现的井漏,井喷等异常工况,并具有报警及历史数据查询等功能。系统宜于井队配备,可与综合录井仪系统无缝连接,实现数据共享;更适于无综合录井仪系统情况下,井队独自完成对井涌井漏的监测。     

Methods and algorithms for automatic text analysis

This is a review of methods for the automated analysis of texts. The features of algorithms and programs that are used at the morphological, lexical, syntactical, and discursive levels of a language system are described.     

Hybrid genetic algorithms for structural reliability analysis

The traditional genetic algorithms (GA) involve step-by-step numerical iterations for searching the minimum reliability index of a structural system, and therefore require a relatively long computation time. In practice the size of a design problem can be very large, the limit state functions are usually implicit in terms of the random variables. When using the traditional genetic algorithms, one can encounter problems with the immense effort required in coding ones own finite element code (or for integration with other commercial finite element software) when using the traditional genetic algorithms. For convenient practical applications of the GA in engineering, two new GA methods, namely, a hybrid GA method consisting of artificial neural network (ANN) and a hybrid GA method consisting of ANN and Monte Carlo simulation with importance sampling are proposed in the present study. A distinctive feature of these proposed methods is the introduction of an explicit approximate limit state function. The explicit formulation of the approximate limit state function is derived by using the parameters of the ANN model. By introducing the derived approximate limit state function, the failure probability can be easily calculated, practically when the limit state functions are not explicitly known. These proposed methods are investigated and their accuracy and efficiency are demonstrated using numerical examples. Finally, some important parameters in these proposed methods are also discussed.     

Stability-based PAC-Bayes analysis for multi-view learning algorithms

Multi-view learning exploits structural constraints among multiple views to effectively learn from data. Although it has made great methodological achievements in recent years, the current generalization theory is still insufficient to prove the merit of multi-view learning. This paper blends stability into multi-view PAC-Bayes analysis to explore the generalization performance and effectiveness of multi-view learning algorithms. We propose a novel view-consistency regularization to produce an informative prior that helps to obtain a stability-based multi-view bound. Furthermore, we derive an upper bound on the stability coefficient that is involved in the PAC-Bayes bound of multi-view regularization algorithms for the purpose of computation, taking the multi-view support vector machine as an example. Experiments provide strong evidence on the advantageous generalization bounds of multi-view learning over single-view learning. We also explore strengths and weaknesses of the proposed stability-based bound compared with previous non-stability multi-view bounds experimentally.     

On the analysis of algorithms for trees

The analysis of many algorithms concerning trees requires the enumeration of families of nodes of a given height in a set of given trees. The aim of this article is to present a theorem that reduces such an enumeration, under certain conditions of regularity, to the simplest or most well known enumeration of a particular family of trees. Several examples of its application are given.     

Spanish sentiment analysis in Twitter at the TASS workshop

This paper describes a support vector machine-based approach to different tasks related to sentiment analysis in Twitter for Spanish. We focus on parameter optimization of the models and the combination of several models by means of voting techniques. We evaluate the proposed approach in all the tasks that were defined in the five editions of the TASS workshop, between 2012 and 2016. TASS has become a framework for sentiment analysis tasks that are focused on the Spanish language. We describe our participation in this competition and the results achieved, and then we provide an analysis of and comparison with the best approaches of the teams who participated in all the tasks defined in the TASS workshops. To our knowledge, our results exceed those published to date in the sentiment analysis tasks of the TASS workshops.     

视频智能交通系统

文章提出了基于视频的智能交通监控系统在一个城市中运用的系统方案,详细介绍了车道级功能模块的实现,同时讨论了车辆属性自动识别的算法、系统软件设计的要点。该系统的应用可极大地提高城市交通管理的效率,具有很好的应用前景。