Creativity in non‐routine jobs: The role of transformational leadership and organizational identification

An increasing number of individuals work in jobs with little standardization and repetition, that is, with high levels of job non‐routinization. At the same time, demands for creativity are high, which raises the question of how employees can use job non‐routinization to develop creativity. Acknowledging the importance of social processes for creativity, we propose that transformational leaders raise feelings of organizational identification in followers and that this form of identification then helps individuals to develop creativity in jobs with little routinization. This is because organizational members evaluate and promote those ideas as more creative, which are in line with a shared understanding of creativity within the organization. To investigate these relationships, we calculated a mediated moderation model with 173 leader–follower dyads from China. Results confirm our hypotheses that transformational leadership moderates the relationship between job non‐routinization on employee creativity through organizational identification. We conclude that raising feelings of social identity is a key task for leaders today, especially when working in uncertain and fast developing environments with little repetition and the constant need to develop creative ideas.     

众筹项目推荐:面向隐式反馈的深度学习协同过滤

目前,在推荐系统研究中,用户的隐式反馈,以及极度稀疏的数据,已成为影响协同过滤推荐效果的主要问题.针对这一现象,本文提出了深度学习协同过滤算法,先利用卷积神经网络,对用户-项目矩阵的隐层特征进行学习,再结合协同过滤,对用户-项目的交互信息进行建模,并将两种特征融合预测推荐列表.以众筹平台的数据为实验对象,比较模型中各参数对推荐效果的影响,并设计与基线方法的对比实验.实验结果表明:均匀采集负反馈,并在一定卷积层数的网络中,数据稀疏度越高,效果越好;对比基线方法,本文提出的算法在公开数据集(Yahoo!Movie)上取得了最好的推荐结果.本文提出的算法有助于提高众筹平台的融资成功率,同时也丰富了推荐系统的研究体系.     

非传统信息安全下的搜索引擎

搜索引擎已成为当今人们上网获取信息所不可缺少的一种手段,在得到自己所需要信息的同时也在暴露着自己的行踪,从而引出了保护个人隐私的大讨论。本文利用社会工程学原理从非传统信息安全角度分析,以通过QQ号入手,利用现有的搜索引擎的例子,来揭示搜索引擎在非传统信息安全下带来的危害,阐明社会工程学原理对非传统信息安全的重大影响,并提出相应的防范对策。     

An improved bacterial foraging algorithm for combined static/dynamic environmental economic dispatch

Economic dispatch is carried out at the energy control center to find out the optimal output of thermal generating units such that power balance criterion is met, unit operating limits are satisfied and the fuel cost is minimized. With growing environmental awareness and strict government regulations throughout the world, it has become essential to optimize not only the total fuel cost but also the harmful emissions, both, under static as well as dynamic conditions. The static environment economic dispatch finds the optimal output of generating units for a fixed load demand at a given time, while the dynamic environmental economic dispatch schedules the output of online generators with changing power demands over a certain time period (normally one day) so as to minimize these two conflicting objectives, simultaneously. In this paper, the price penalty factor approach is employed for simultaneous minimization of cost and emission. The generator ramp rate constraints, non-convex and discontinuous nature of cost function and the large number of generators in practical power plants, make this problem very difficult to solve. Here, a fuzzy ranking approach is employed to identify the solution which offers the best compromise between cost and emission objectives.     

社保系统个人信息隐私保护算法

针对社会保障信息系统中公民的个人信息隐私保护越来越困难的现状,介绍了社保系统的隐私保护难题包括如何保护信息安全和如何界定隐私信息2个方面,分析了隐私信息保护的2个根源性问题在于个人信息的不当采集和不当使用,在此基础上提出了利用转换原始数据的方式保护隐私信息的算法,阻止了信息拥有者对隐私信息直接或间接的获取,为社保系统中个人信息隐私保护的实现提供了一定的研究思路。     

选择性集成算法分类与比较

选择性集成是当前机器学习领域的研究热点之一。由于选择性集成属于NP"难"问题,人们多利用启发式方法将选择性集成转化为其他问题来求得近似最优解,因为各种算法的出发点和描述角度各不相同,现有的大量选择性集成算法显得繁杂而没有规律。为便于研究人员迅速了解和应用本领域的最新进展,本文根据选择过程中核心策略的特征将选择性集成算法分为四类,即迭代优化法、排名法、分簇法、模式挖掘法;然后利用UCI数据库的20个常用数据集,从预测性能、选择时间、结果集成分类器大小三个方面对这些典型算法进行了实验比较;最后总结了各类方法的优缺点,并展望了选择性集成的未来研究重点。     

面向老年人的社会化网络服务平台研究

针对目前已有社会化网络服务对社会影响的研究更侧重于青少年,对老年人群体研究相对较少的情况,本文研究了面向老年人的社会化网络服务平台,介绍了平台的体系结构,阐述了社交系统、数据分析和信息服务。此平台利用面向老年人的社交网络服务系统获得用户行为数据,在数据集成、时空数据仓库存储的基础上,通过建立老年人社会网络行为的数学模型,运用统计学方法进行建模分析,分析结果可为多层次人员提供不同的信息服务。     

A power fluctuation evaluation method of PV plants based on RankBoost ranking

Fluctuation evaluation is an important task in promoting the accommodation of photovoltaic (PV) power generation. This paper proposes an evaluation method to quantify the power fluctuation of PV plants. This consists of an index system and a ranking method based on the RankBoost algorithm. Eleven indices are devised and included in the index system to fully cover diverse fluctuation features. By handling missing and invalid data effectively, the ranking method fuses multiple indices automatically and provides a systematic and comprehensive comparison of power fluctuation. Simulation results based on power data from six PV plants indicate that the evaluation list obtained by the RankBoost ranking method is better represented and more comprehensive than that derived by the equal weight method.     

Oxytocin Signaling as a Target to Block Social Defeat-Induced Increases in Drug Abuse Reward

There is huge scientific interest in the neuropeptide oxytocin (OXT) due to its putative capacity to modulate a wide spectrum of physiological and cognitive processes including motivation, learning, emotion, and the stress response. The present review seeks to increase the understanding of the role of OXT in an individual’s vulnerability or resilience with regard to developing a substance use disorder. It places specific attention on the role of social stress as a risk factor of addiction, and explores the hypothesis that OXT constitutes a homeostatic response to stress that buffers against its negative impact. For this purpose, the review summarizes preclinical and clinical literature regarding the effects of OXT in different stages of the addiction cycle. The current literature affirms that a well-functioning oxytocinergic system has protective effects such as the modulation of the initial response to drugs of abuse, the attenuation of the development of dependence, the blunting of drug reinstatement and a general anti-stress effect. However, this system is dysregulated if there is continuous drug use or chronic exposure to stress. In this context, OXT is emerging as a promising pharmacotherapy to restore its natural beneficial effects in the organism and to help rebalance the functions of the addicted brain.     

Graph-Based Dimensionality Reduction for Hyperspectral Imagery: A Review

Hyperspectral image(HSI) contains a wealth of spectral information, which makes fine classification of ground objects possible. In the meanwhile, overly redundant information in HSI brings many challenges. Specifically, the lack of training samples and the high computational cost are the inevitable obstacles in the design of classifier. In order to solve these problems, dimensionality reduction is usually adopted. Recently, graph-based dimensionality reduction has become a hot topic. In this paper, the graph-based methods for HSI dimensionality reduction are summarized from the following aspects. 1) The traditional graph-based methods employ Euclidean distance to explore the local information of samples in spectral feature space. 2) The dimensionality-reduction methods based on sparse or collaborative representation regard the sparse or collaborative coefficients as graph weights to effectively reduce reconstruction errors and represent most important information of HSI in the dictionary. 3) Improved methods based on sparse or collaborative graph have made great progress by considering global low-rank information, local intra-class information and spatial information. In order to compare typical techniques, three real HSI datasets were used to carry out relevant experiments, and then the experimental results were analysed and discussed.Finally, the future development of this research field is prospected.