公共知识空间与公共文化空间的融合——中国科学院文献情报中心融入公共文化服务体系的探索

[目的/意义]图书馆作为重要的公共知识空间和公共文化空间,创新服务模式、提升公共文化服务水平责无旁贷,同时也是对国际图书馆发展趋势的积极顺应。[方法/过程]总结中国科学院文献情报中心通过设立院史馆促进公共知识空间与公共文化空间有机融合的实践经验,探索提出融合策略与服务模式。[结果/结论]图书馆基于空间融合,完善公共文化服务功能的重点在于:(1)兼顾"内融合"与"外融合"策略,提升图书馆的社会参与职能;(2)完善关联集成研究,实现资源、服务、推广等全方位融合;(3)基于空间融合,完善图书馆教育职能,构建终身教育基地;(4)融入国家公共数字文化服务建设,塑造基于大数据的文化服务新模式;(5)完善移动端服务,提升基于场景的智慧文化服务;(6)促进多元交流合作,推动文化服务资源共享;(7)完善融合模式及共享机制的研究,实现基于资源合理分权分域的共建共享、合理使用以及数据安全。     

Computing semantic similarity based on novel models of semantic representation using Wikipedia

Computing Semantic Similarity (SS) between concepts is one of the most critical issues in many domains such as Natural Language Processing and Artificial Intelligence. Over the years, several SS measurement methods have been proposed by exploiting different knowledge resources. Wikipedia provides a large domain-independent encyclopedic repository and a semantic network for computing SS between concepts. Traditional feature-based measures rely on linear combinations of different properties with two main limitations, the insufficient information and the loss of semantic information. In this paper, we propose several hybrid SS measurement approaches by using the Information Content (IC) and features of concepts, which avoid the limitations introduced above. Considering integrating discrete properties into one component, we present two models of semantic representation, called CORM and CARM. Then, we compute SS based on these models and take the IC of categories as a supplement of SS measurement. The evaluation, based on several widely used benchmarks and a benchmark developed by ourselves, sustains the intuitions with respect to human judgments. In summary, our approaches are more efficient in determining SS between concepts and have a better human correlation than previous methods such as Word2Vec and NASARI.     

A novel reasoning mechanism for multi-label text classification

The aim in multi-label text classification is to assign a set of labels to a given document. Previous classifier-chain and sequence-to-sequence models have been shown to have a powerful ability to capture label correlations. However, they rely heavily on the label order, while labels in multi-label data are essentially an unordered set. The performance of these approaches is therefore highly variable depending on the order in which the labels are arranged. To avoid being dependent on label order, we design a reasoning-based algorithm named Multi-Label Reasoner (ML-Reasoner) for multi-label classification. ML-Reasoner employs a binary classifier to predict all labels simultaneously and applies a novel iterative reasoning mechanism to effectively utilize the inter-label information, where each instance of reasoning takes the previously predicted likelihoods for all labels as additional input. This approach is able to utilize information between labels, while avoiding the issue of label-order sensitivity. Extensive experiments demonstrate that our method outperforms state-of-the art approaches on the challenging AAPD dataset. We also apply our reasoning module to a variety of strong neural-based base models and show that it is able to boost performance significantly in each case.     

Countering Youth's Negative Stereotypes of Teens Fosters Constructive Behavior

Adolescence can be a time of unconstructive behavior for many youth. This research examined if an intervention countering youth's stereotypes of teens as irresponsible fosters their constructive behavior. In two experimental intervention studies (Ns = 124 and 319) with seventh graders, stereotypes of teens as irresponsible were described as inaccurate portrayals; youth then provided their own observations of teens acting responsibly. Youth in this counterstereotyping intervention (vs. the control) held higher intentions for academic engagement and performed better on an academic task (i.e., a word-search puzzle). Over the 3 days following the intervention, their academic engagement was higher. Youth's risk taking was also reduced. Redirecting youth to see teens as responsible has the potential to provide a foundation for flourishing.     

网络对科技期刊青年编辑工作的影响

当代科技期刊在互联网的冲击下呈现网络化趋势。以编辑部实际工作为例,探讨网络条件下青年编辑工作的新方法、新途径,注重实际应用效果,以期为我国科技期刊编辑工作的顺利开展提供参考。     

晚清六十年间(1851-1911)华北地区的自然灾害

晚清六十年间(1851-1911年)华北地区(指包括北京、天津、河北、山西和内蒙古)的自然灾害包括水、旱、虫、雹等各灾,灾害的发生怵目惊心,并表现出一定的特点.其因为有自然因素和社会因素,对当时经济和社会的发展造成极大地破坏,这些也为我们今天如何对待自然灾害提供了一定的借鉴.     

网络与信息安全实验教学平台的构建

信息安全学科是一门实践性非常强的学科,许多安全技术与手段需要在实践过程中去认识和掌握,因此网络与信息安全实验教学平台的建设水平直接关系到创新型科技人才的培养质量.在构建网络与信息安全实验教学平台方面,进行了研究与实践,实验教学效果显著.     

基于自我效能的研究生创新动力研究

我国研究生群体正逐渐成为担负起建设创新型国家使命的主力军。研究生创新自我效能有助于激发研究生创新的内、外动力。研究生的创新动力来源及影响因素的调查分析表明,要从教育理念、目标、内容及方法等方面强化以激发创新动力为目标的研究生思想政治教育工作。     

促进新生代农民工市民化的若干思考

在工业化和城镇化快速发展的今天,新生代农民工既是我国城市产业工人的重要组成部分,又是统筹城乡发展的主力军。积极促进新生代农民工市民化,既符合统筹城乡发展的现实需要,又利于大量减少农民的长远发展战略,是我们科学破解城乡二元结构的最佳契入点。因此,从政策制定和社会建设两个层面入手,采取多种措施解决新生代农民工市民化问题,对实现全面小康社会具有重大的现实意义。     

支格阿龙的事迹比较

《支格阿龙》作为彝族传统文学中的优秀之作,广泛流传于彝区。从滇、黔、川三省彝族有关支格阿龙事迹的文本展示看,四川和云南的相当简略,主要表现在射日月和整治毒蛇等自然灾害方面。而在贵州彝区流传的文本,则以大量的篇幅记载了支格阿龙在统治者的领导下,消灭敌对势力,整治社会秩序的行为和过程。三省彝区的《支格阿龙》文本,反映了不同的社会生产力发展水平。川、滇操北部方言的彝族中,主要表现为与大自然的斗争,贵州彝区则表现为与社会势力的斗争。