科技时代下中国当代婚姻观的影响因素

婚姻家庭生活是人类生活中必不可少的部分,是社会的基本细胞。本文结合时代特点就中国当代婚姻观的影响因素从西方文化观念的冲击、传统婚姻道德的碰撞、现代社会思潮的影响及当代文化建设的滞后等方面进行了分析探讨,力图构建和谐的符合时代发展需求的正确的婚姻观。     

基于SEOA算法的抽水蓄能梯级水库调度研究

针对含混合式抽水蓄能电站的梯级水库发电调度的维数灾问题,在考虑历史径流序列随机性和非线性特点的基础上,建立含混合式抽水蓄能电站的梯级水库长期优化调度模型。文中以调度期内混合式抽水蓄能电站梯级水库发电量最大为目标函数,引入改进社会情态优化(SEOA)算法编程求解。仿真结果表明,新方法在混合式抽水蓄能电站梯级水库发电优化调度问题中既能减少传统算法的冗余计算,又可有效地结合决策者的主观偏好和利益倾向。对比动态规划算法,新方法在较短时间内得到年发电量为66.96×10 ⁸ kWh的高质量解。     

Privacy-preserving photo sharing framework cross different social network

With rapid developments of digital photography and social networks,users of photo-sharing-supported social networking applications can easily forward photos across different social networks at the cost of their growing privacy concerns.To address this problem,a privacy-preserving photo sharing framework was proposed,which could apply to extended control and privacy invasion tracing.In extended control scheme,the following users on a dissemination chain was restrained by each user’s privacy policy.Then several privacy areas of photos were encrypted and the access control polices were bound to the uploaded photos,so that any privacy areas on the photos could be hidden away from unwanted viewers even across different social networks.On this basis,the behaviors of users were record by tracing scheme of privacy invasion,the integrality of records was protected by using nested signature algorithm.The correctness,security and performance of overhead of the scheme are then thoroughly analyzed and evaluated via detailed simulations.     

ED-SWE: Event detection based on scoring and word embedding in online social networks for the internet of people

Online social media networks are gaining attention worldwide, with an increasing number of people relying on them to connect, communicate and share their daily pertinent event-related information. Event detection is now increasingly leveraging online social networks for highlighting events happening around the world via the Internet of People. In this paper, a novel Event Detection model based on Scoring and Word Embedding (ED-SWE) is proposed for discovering key events from a large volume of data streams of tweets and for generating an event summary using keywords and top-k tweets. The proposed ED-SWE model can distill high-quality tweets, reduce the negative impact of the advent of spam, and identify latent events in the data streams automatically. Moreover, a word embedding algorithm is used to learn a real-valued vector representation for a predefined fixed-sized vocabulary from a corpus of Twitter data. In order to further improve the performance of the Expectation-Maximization (EM) iteration algorithm, a novel initialization method based on the authority values of the tweets is also proposed in this paper to detect live events efficiently and precisely. Finally, a novel automatic identification method based on the cosine measure is used to automatically evaluate whether a given topic can form a live event. Experiments conducted on a real-world dataset demonstrate that the ED-SWE model exhibits better efficiency and accuracy than several state-of-art event detection models.     

Predicting the Lifespan and Retweet Times of Tweets Based on Multiple Feature Analysis

In social network services, such as Facebook, Google+, Twitter, and certain postings attract more people than others. In this paper, we propose a novel method for predicting the lifespan and retweet times of tweets, the latter being a proxy for measuring the popularity of a tweet. We extract information from retweet graphs, such as posting times; and social, local, and content features, so as to construct prediction knowledge bases. Tweets with a similar topic, retweet pattern, and properties are sequentially extracted from the knowledge base and then used to make a prediction. To evaluate the performance of our model, we collected tweets on Twitter from June 2012 to October 2012. We compared our model with conventional models according to the prediction goal. For the lifespan prediction of a tweet, our model can reduce the time tolerance of a tweet lifespan by about four hours, compared with conventional models. In terms of prediction of the retweet times, our model achieved a significantly outstanding precision of about 50%, which is much higher than two of the conventional models showing a precision of around 30% and 20%, respectively.     

基于主成分分析的防空导弹武器综合评价

以防空导弹综合评价为例,应用SPSS软件,验证了主成分分析在武器装备综合评价中的正确性和有效性.结果表明,应用主成分分析进行综合评价,充分利用了武器装备指标数据;依据指标间相关程度,自动生成权重,避免了人为因素干扰;简化了武器装备的综合评价工作,能够为武器装备论证及采办提供科学的依据.     

TSL:predicting popularity of Facebook content based on tie strength

The rapid development of online social networks leads to an explosion of information,however,there are great differences in the popularity of different messages,and accurate prediction is always a great difficulty is the current study.Popularity prediction of online content aims to predict the popularity in the future based on its early diffusion status.Existing models for popularity prediction were mostly based on discovering network features or fitting the equation into a varying time function that the accuracy of current popularity prediction model was not high enough.Therefore,with the help of the weak ties theory in sociology,the concept of tie strength was introduced and a multilinear regression equation was constructed combined with the early popularity.A TSL model to predict the popularity of Facebook’s well-known pages was proposed.The main contribution of this article was to solve the problem and few or no work based on sociology.A high linear correlation between the proportion of faithful fans was existed in Facebook homepage with frequent shares in the early and the future popularity.Compared with other baseline models,an experimental study of Facebook (including 1.54 million shares) illustrates the effectiveness of the proposed TSL model,and the performance is better than the existing similar methods.     

信息时代下创新团队社会网络探讨

伴随着信息技术的不断发展,现今社会,任何个人和组织都不可能脱离社会网络而独立存在。本文从社会网络的概念、影响因素、维度划分以及测量量表出发,探讨了创新团队社会网络,以期丰富社会网络的研究。     

认知无线电系统中基于量子粒子群算法的PHY层及MAC层决策优化

认知无线电技术是下一代通信发展的方向和趋势,同时也是目前通信研究中的热点问题。针对认知无线电系统中,在某一信道条件下其通信系统的物理层（PHY）和媒体链路层（MAC）的基于多个目标来实现优化的问题,基于量子粒子群算法的基本思想,把量子粒子群算法引入认知无线电系统中,并对该算法进行适当的改进,最后基于WSGA控制对电台优化问题进行了仿真,通过仿真证明了其有效性。