论面向未来的公共图书馆包容性发展

本文提出了面向未来的公共图书馆包容性发展命题,分析了包容性增长与包容性发展的概念,论述了后全面小康时期公共图书馆实施包容性发展战略需要秉持的优质均衡、全程智能和协调共享的发展理念;阐述了公共图书馆包容性发展的路径图,提出了进一步将国家文化制度优势转化为文化治理效能、进一步发挥国家图书馆和省市公共图书馆的统筹协调功能、进一步把握新科技革命群体迭代的发展机遇等具体举措。     

空间的建构与认同——刘三姐文化深层底蕴的探讨之二

作为壮民族的文化代表之一的刘三姐文化之所以经久不衰,是与壮民族的文化空间建构与文化认同分不开的.刘三姐山歌文化现象的深层底蕴其实就是壮民族及其地区文化空间建构和认同的巨大隐喻,她的形成与流传过程就是壮族独特的"他者文化空间"、"身体文化空间"、"公共空间"、"认同空间"、审美及其他空间的建构和认同的历史过程.     

粤港澳大湾区建设与港澳发展新机遇

横琴已经进入了共商共建共管共享的新时代。随着《横琴粤澳深度合作区建设总体方案》的颁布,横琴粤澳深度合作区形成共商共建共管共享的新体制,告别了联席会议式的松散型合作,走向了制度化、规范化的紧密型合作。横琴粤澳深度合作区的共管体制,形成了“领导层、决策层和执行层”层次鲜明的制度架构,蕴含着“多层政府、共同治理”的制度特性,体现了“归并澳门特别行政区进入横琴管理体制”“相互调适的多层治理”以及“政府和治理的融合”的制度创新。横琴共管体制面临着跨区治理、跨境治理和跨制治理的挑战,未来横琴粤澳深度合作区要不断创新和完善共管体制,提升中央政府的统筹协调能力和粤澳地方政府的合作治理能力。     

Semantic text classification: A survey of past and recent advances

Automatic text classification is the task of organizing documents into pre-determined classes, generally using machine learning algorithms. Generally speaking, it is one of the most important methods to organize and make use of the gigantic amounts of information that exist in unstructured textual format. Text classification is a widely studied research area of language processing and text mining. In traditional text classification, a document is represented as a bag of words where the words in other words terms are cut from their finer context i.e. their location in a sentence or in a document. Only the broader context of document is used with some type of term frequency information in the vector space. Consequently, semantics of words that can be inferred from the finer context of its location in a sentence and its relations with neighboring words are usually ignored. However, meaning of words, semantic connections between words, documents and even classes are obviously important since methods that capture semantics generally reach better classification performances. Several surveys have been published to analyze diverse approaches for the traditional text classification methods. Most of these surveys cover application of different semantic term relatedness methods in text classification up to a certain degree. However, they do not specifically target semantic text classification algorithms and their advantages over the traditional text classification. In order to fill this gap, we undertake a comprehensive discussion of semantic text classification vs. traditional text classification. This survey explores the past and recent advancements in semantic text classification and attempts to organize existing approaches under five fundamental categories; domain knowledge-based approaches, corpus-based approaches, deep learning based approaches, word/character sequence enhanced approaches and linguistic enriched approaches. Furthermore, this survey highlights the advantages of semantic text classification algorithms over the traditional text classification algorithms.     

中文科技政策文本分类：增强的TextCNN视角*

近年尽管针对中文本文分类的研究成果不少，但基于深度学习对中文政策等长文本进行自动分类的研究还不多见。为此，借鉴和拓展传统的数据增强方法，提出集成新时代人民日报分词语料库（NEPD）、简单数据增强（EDA）算法、word2vec和文本卷积神经网络（TextCNN）的NEWT新型计算框架；实证部分，基于中国地方政府发布的科技政策文本进行算法校验。实验结果显示，在取词长度分别为500、750和1 000词的情况下，应用NEWT算法对中文科技政策文本进行分类的效果优于RCNN、Bi-LSTM和CapsNet等传统深度学习模型，F1值的平均提升比例超过13%；同时，NEWT在较短取词长度下能够实现全文输入的近似效果，可以部分改善传统深度学习模型在中文长文本自动分类任务中的计算效率。     

Novel groundtruth transformations for the aesthetic assessment problem

Aesthetic assessment evaluates the quality of a given image using subjective annotations, commonly user ratings, as a knowledge base. Rating complexity is usually relaxed in state-of-the-art works by employing a binary high/low quality label computed from the mean value of rating votes. Nevertheless, this approach introduces uncertainty to average-quality images, which may affect the performance of machine learning models trained from annotated data.In this work, we present a novel approach to aesthetic assessment based on redefining the rating-based groundtruths present in most datasets. Our intent is twofold: to reduce the rating uncertainty and to automatically group them into clusters reflecting high and low quality patterns, thus avoiding an arbitrary threshold like 5 in 1–10 ratings. The experimentation uses the well-known AVA dataset, which consists of more than 255,000 images, and we train several CNN models to test our new groundtruths against the baseline ones. The results show that our approach achieves significant performance gains, between 3% and 9% more balanced accuracy than the baseline groundtruths.     

From statistical methods to deep learning,automatic keyphrase prediction: A survey

Keyphrase prediction aims to generate phrases (keyphrases) that highly summarizes a given document. Recently, researchers have conducted in-depth studies on this task from various perspectives. In this paper, we comprehensively summarize representative studies from the perspectives of dominant models, datasets and evaluation metrics. Our work analyzes up to 167 previous works, achieving greater coverage of this task than previous surveys. Particularly, we focus highly on deep learning-based keyphrase prediction, which attracts increasing attention of this task in recent years. Afterwards, we conduct several groups of experiments to carefully compare representative models. To the best of our knowledge, our work is the first attempt to compare these models using the identical commonly-used datasets and evaluation metric, facilitating in-depth analyses of their disadvantages and advantages. Finally, we discuss the possible research directions of this task in the future.     

A novel framework for deep knowledge tracing via gating-controlled forgetting and learning mechanisms

In this paper, we propose a framework called Gating-controlled Forgetting and Learning mechanisms for Deep Knowledge Tracing (GFLDKT for short). In GFLDKT, two gating-controlled mechanisms are designed to model explicitly forgetting and learning behaviors in students’ learning process. With the designed gating-controlled mechanisms, both the interaction records and students’ different backgrounds are combined effectively for tracing the dynamic changes of students’ mastery of knowledge concepts. Results from extensive experiments demonstrate that the proposed framework outperforms the state-of-the-art models on the KT task. In addition, the ablation study shows that designed forgetting and learning mechanisms contribute clearly to the performance improvement of GFLDKT.     

基于检验检测-服务质量-长短期记忆网络-情感分析模型的检验检测服务质量评价研究

为促进检验检测业服务质量提升，以检验检测（IT）服务质量评级和用户服务需求为切入点，采用基于长短期记忆网络（LSTM）的深度学习方法，设计由有形性、可靠性、响应性、安全性和移情性5个维度构成的评价体系，通过检验检测-服务质量-长短期记忆网络-情感分析模型（IT-QoS-LSTM-SA）对检验检测服务机构服务质量（QoS）进行评价与反馈，并利用7万多条相关文本数据进行实证。结果显示：LSTM模型在检验检测用户评论分类中的准确率达到了85.24%；根据情感分析（SA）计算得出检验检测服务质量的总评分为0.491 6，处于满意和非常满意程度之间。由此可以直观地看出检验检测服务质量在各项评价指标上的优劣程度。