Exploring album structure for face recognition in online social networks

In this paper, we propose an album-oriented face-recognition model that exploits the album structure for face recognition in online social networks. Albums, usually associated with pictures of a small group of people at a certain event or occasion, provide vital information that can be used to effectively reduce the possible list of candidate labels. We show how this intuition can be formalized into a model that expresses a prior on how albums tend to have many pictures of a small number of people. We also show how it can be extended to include other information available in a social network. Using two real-world datasets independently drawn from Facebook, we show that this model is broadly applicable and can significantly improve recognition rates.     

Guidelines for facilitating the development of learning communities in online courses

Online learning has grown exponentially in recent years; however, dropout problem remains challenging for some online programmes. The dropout problem can be attributed to a number of reasons, with a lack of interaction between learners and the instructor constituting one of the main reasons. The lack of interaction also leads to learners' feeling of isolation. Learning communities can provide learners with an environment conducive to increased interactions and alleviate their feeling of isolation. Unfortunately, there are no clear rules that instructors can follow to help learners create learning communities. In this paper, we propose guidelines for online instructors to facilitate the development of learning communities in online courses. We first review the definition of a learning community, importance of a learning community and factors affecting the development of a learning community. Afterwards, based on a review of the existing guidelines and other relevant literature, we propose guidelines for facilitating the development of learning communities in online courses.     

PRIVACY LEAKAGE IN HEALTH SOCIAL NETWORKS

Members of health social networks may be susceptible to privacy leaks by the amount of information they leave behind. The threat to privacy increases when members of these networks reuse their pseudonyms in other social networks. The risk of re‐identifying users from such networks requires quantitative estimates to evaluate its magnitude. The estimates will enable managers and members of health social communities to take corrective measures. We introduce a new re‐identification attack, the social network attack, that takes advantage of the fact that users reuse their pseudonyms. To demonstrate the attack, we establish links between MedHelp and Twitter (two popular social networks) based on matching pseudonyms. We used Bayesian networks to model the re‐identification risk and used stylometric techniques to identify the strength of the links. On the basis of our model 7‐11. 8% of the MedHelp members in the sample population who reused their pseudonyms in Twitter were re‐identifiable compared with 1% who did not. The risk estimates were measured at the 5% risk threshold. Our model was able to re‐identify users with a sensitivity of 41% and specificity of 96%. The potential for re‐identification increases as more data is accumulated from these profiles, which makes the threat of re‐identification more serious.     

华北平原地下水资源承载力评价

华北平原地下水严重超采,并带来一系列资源环境问题,地下水资源承载力的可持续性面临严重挑战。本研究定义了地下水资源承载力的概念,提出了一种定量评价地下水承载力的新方法,并对华北平原地下水承载力进行了评价。结果表明,华北平原地区地下水资源支撑的GDP从2003年的15 608.18亿元迅速提高到2011年的37 584.9亿元,33.3%的地市位于未超采未超载区;华北平原整体及57.2%的地市均位于超采未超载区,已发生地下水超采,但通过提高用水效率,未来社会经济规模仍有发展空间;9.5%的地市位于超采超载区。最后探讨地下水资源的承载力的提高途径,主要包括增加地下水资源可利用量和提高用水效率两方面,其中提高用水效率效果更显著。     

Computational Problem Solving in Spatial Substrates -- A Cognitive Systems Engineering Approach

The ability to perform spatial tasks is crucial for everyday life and of great importance to cognitive agents such as humans, animals, and autonomous robots. A common artificial intelligence approach to accomplish spatial tasks is to represent spatial configurations and tasks in form of detailed knowledge about various aspects of space and time. Suitable algorithms then use the representations to compute solutions to spatial problems. In comparison, natural embodied and situated agents often solve spatial tasks without detailed knowledge about geometric, topological, or mechanical laws; they directly relate actions to effects that are due to spatio-temporal affordances in their bodies and environments. Accordingly, we propose a paradigm that makes the spatio-temporal substrate an integral part of the engine that drives spatial problem solving. We argue that spatial and temporal structures in body and environment can substantially support (and even replace) reasoning effort in computational processes: physical manipulation and perception in spatial environments substitute formal computation. While the approach is well known – for example, we employ diagrams as spatial substrate for geometric problem solving and maps for wayfinding – the underlying principle has not been systematically investigated or formally analyzed as a paradigm of cognitive processing. Topology, distance, and orientation constraints are all integrated and interdependent in truly 2- or 3-dimensional space. Exploiting this fact may not only help overcome the need for acquiring detailed knowledge about the interrelationships between different aspects of space; it also can point to a way of avoiding exploding computational complexity that occurs when we deal with these aspects of space in complex real-world scenarios. Our approach employs affordance-based object-level problem solving to complement knowledge-level formal approaches. We will assess strengths and weaknesses of the new cognitive systems paradigm.     

Perception of own and robot engagement in human–robot interactions and their dependence on robotics knowledge

Communication between socially assistive robots and humans might be facilitated by intuitively understandable mechanisms. To investigate the effects of some key nonverbal gestures on a human’s own engagement and robot engagement experienced by humans, participants read a series of instructions to a robot that responded with nods, blinks, changes in gaze direction, or a combination of these. Unbeknown to the participants, the robot had no form of speech processing or gesture recognition, but simply measured speech volume levels, responding with gestures whenever a lull in sound was detected. As measured by visual analogue scales, engagement of participants was not differentially affected by the different responses of the robot. However, their perception of the robot’s engagement in the task, its likability and its understanding of the instructions depended on the gesture presented, with nodding being the most effective response. Participants who self-reported greater robotics knowledge reported higher overall engagement and greater success at developing a relationship with the robot. However, self-reported robotics knowledge did not differentially affect the impact of robot gestures. This suggests that greater familiarity with robotics may help to maximise positive experiences for humans involved in human–robot interactions without affecting the impact of the type of signal sent by the robot.     

环境保护与汽车涂装

叙述了与汽车涂装相关的国际环保法规及汽车涂装的公害,重点介绍了汽车用环保型涂料、5R活动和3C1B涂装技术等环保达标措施.     

Research on the measure method of complaint theme influence on online social network

Consumer complaints on online social network quickly become online groups complaints through many people’s aggregation and looking on, interaction and word-of-mouth communication. Therefore, assessing and managing online complain influence has become a new problem for enterprise to listen to and manage online group complaints. This paper analyzed the complaint information feature of consumer group on online social network, from three-dimensional perspective of complaint text’s quality, transmission timeliness and user interaction degree. We built the influence measure model of online complaint theme based on entropy weight model by monitoring and analyzing real-time the static and dynamic properties of complaint information, explored the measure method of complaint theme influence, employed empirical method to verify the validity and provided scientific decision-making tools and methods for enterprise listening to and managing online group complains.     

隐目标回归算法设计研究

对常用的回归方法进行研究.此类方法虽然几何解释明确、易于求解,但均须事先确定(或假定)变量间的因果关系,再考虑建模,在实际应用中,对于很难确定变量的因果关系的问题,如物联网数据分析,上述方法就会失效.为此,提出一种无需假定因变量的隐目标回归方法.该方法易于核化,可以推广到非线性回归问题.通过人工数据和国际标准数据集上的实验验证了所提算法的有效性.     

基于信息流动分析的动态社区发现方法

随着社交网络和微博等互联网应用的逐渐流行,其用户规模在迅速膨胀.在这些大规模网络中,社区发现可以为个性化服务推荐和产品推广提供重要依据.不同于传统的网络,这些新型网络的节点之间除了拓扑结构外,还进行频繁的信息交互.信息流动使得这些网络具有方向性和动态性等特征.传统的社区发现方法由于没有考虑到这些新的特征,并不适用于这些新型网络.在传染病动力学理论的基础上,从节点间信息流动的角度,提出一种动态社区发现方法.该方法通过对信息流动的分析来发现联系紧密、兴趣相近的节点集合,以实现动态的社区发现.在真实数据集上的实验结果表明：相对于传统的社区发现方法,所提出的方法能够更准确地发现社区,并且更能体现网络中社区的动态变化.