Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities

Energy efficiency of public sector is an important issue in the context of smart cities due to the fact that buildings are the largest energy consumers, especially public buildings such as educational, health, government and other public institutions that have a large usage frequency. However, recent developments of machine learning within Big Data environment have not been exploited enough in this domain. This paper aims to answer the question of how to incorporate Big Data platform and machine learning into an intelligent system for managing energy efficiency of public sector as a substantial part of the smart city concept. Deep neural networks, Rpart regression tree and Random forest with variable reduction procedures were used to create prediction models of specific energy consumption of Croatian public sector buildings. The most accurate model was produced by Random forest method, and a comparison of important predictors extracted by all three methods has been conducted. The models could be implemented in the suggested intelligent system named MERIDA which integrates Big Data collection and predictive models of energy consumption for each energy source in public buildings, and enables their synergy into a managing platform for improving energy efficiency of the public sector within Big Data environment. The paper also discusses technological requirements for developing such a platform that could be used by public administration to plan reconstruction measures of public buildings, to reduce energy consumption and cost, as well as to connect such smart public buildings as part of smart cities. Such digital transformation of energy management can increase energy efficiency of public administration, its higher quality of service and healthier environment.     

User-aware multilingual abusive content detection in social media

Despite growing efforts to halt distasteful content on social media, multilingualism has added a new dimension to this problem. The scarcity of resources makes the challenge even greater when it comes to low-resource languages. This work focuses on providing a novel method for abusive content detection in multiple low-resource Indic languages. Our observation indicates that a post’s tendency to attract abusive comments, as well as features such as user history and social context, significantly aid in the detection of abusive content. The proposed method first learns social and text context features in two separate modules. The integrated representation from these modules is learned and used for the final prediction. To evaluate the performance of our method against different classical and state-of-the-art methods, we have performed extensive experiments on SCIDN and MACI datasets consisting of 1.5M and 665K multilingual comments, respectively. Our proposed method outperforms state-of-the-art baseline methods with an average increase of 4.08% and 9.52% in the F1-score on SCIDN and MACI datasets, respectively.     

简析工业有机热载体炉常见故障与防范措施

有机热载体锅炉是以煤、油或可燃气体为燃料,导热油为热能的载体,具有低压高温的工作特性,已被广泛应用于化工、纺织、印染、轻工、食品、造纸、建材等行业。但在实际使用中也发生一些危及安全的事故,给人民生命和财产造成了一定的损失。     

国家科技文献中心多语言信息服务研究及其应用

如何提高多语言信息服务质量已成为数字图书馆等科技信息服务领域的重要研究问题。文章首先介绍了国内外多语言信息服务相关研究,然后具体从跨语言信息检索和机器翻译两个方面介绍了国家科技文献中心多语言信息服务研究成果在国家科技文献在线服务系统中的应用。将跨语言信息检索功能和文摘翻译服务功能引入数字图书馆在线查询系统,在国内数字图书馆信息服务领域尚属探索性尝试,可以为进一步提高数字图书馆多语言信息服务质量提供经验。     

机器翻译系统融合方法及其应用探究

多机器翻译系统融合技术能够对不同机器翻译系统的输出结果有效地进行融合,产生更好的翻译性能,因此该技术成为机器翻译研究领域的一个热点问题。文章介绍了中国科学技术信息研究所（ISTIC）参加第七届全国机器翻译研讨会机器翻译评测的情况。本单位参加了英汉科技领域的机器翻译评测项目。文章阐述了本单位机器翻译系统的实现框架以及实施细节,并分析了它们在评测数据上的性能表现,最后对机器翻译系统融合方法目前的现状进行讨论,并对该系统融合方法进行总结和展望。     

基于ADAMS和BP网络的水下载体波浪能吸收效率研究

水下机器人等水下载体能量自补给问题是提高其工作效率、延长工作时间的关键技术问题。波浪能作为可再生绿色能源之一,将其作为水下载体的能量一直是国内外研究的热点内容,而能量吸收效率问题是研究的关键。提出基于惯性摆结构的波浪能吸收转换方法,建立其动力学方程,通过ADAMS获得不同确定条件下的能量输出效率,并采用BP神经网络方法进行建模,通过与ADAMS建模获取的结果进行对比,验证了训练所得模型的正确性,为进一步的分析研究奠定了基础。     

Integrated color defect detection method for polysilicon wafers using machine vision

For the typical color detects of polysilicon wafers, i.e., edge discoloration, color inaccuracy and color non-uniformity, a new integrated machine vision detection method is proposed based on an HSV color model. By transforming RGB image into three-channel HSV images, the HSV model can efficiently reduce the disturbances of complex wafer textures. A fuzzy color clustering method is used to detect edge discoloration by defining membership function for each channel image. The mean-value classi- fying method and region growing method are used to identify the other two defects, respectively. A vision detection system is developed and applied in the produc- tion of polysilicon wafers.     

“文化大革命”时期的图书馆事业：回顾与思考

我国的图书馆事业在“文化大革命”中遭受了空前的浩劫和摧残。以1971年为界,可将文革中的图书馆事业划分为两个时期。文革前期图书馆事业遭到严重破坏,文革后期图书馆事业艰难恢复。文革中编制了《中国图书馆图书分类法》,启动了《汉语主题词表》编制工作,引入了机读目录。     

An analysis of review content and reviewer variables that contribute to review helpfulness

Review helpfulness is attracting increasing attention of practitioners and academics. It helps in reducing risks and uncertainty faced by users in online shopping. This study examines uninvestigated variables by looking at not only the review characteristics but also important indicators of reviewers. Several significant review content and two reviewer variables are proposed and an effective review helpfulness prediction model is built using stochastic gradient boosting learning method. This study derived a mechanism to extract novel review content variables from review text. Six popular machine learning models and three real-life Amazon review data sets are used for analysis. Our results are robust to several product categories and along three Amazon review data sets. The results show that review content variables deliver the best performance as compared to the reviewer and state-of-the-art baseline as a standalone model. This study finds that reviewer helpfulness per day and syllables in review text strongly relates to review helpfulness. Moreover, the number of space, aux verb, drives words in review text and productivity score of a reviewer are also effective predictors of review helpfulness. The findings will help customers to write better reviews, help retailers to manage their websites intelligently and aid customers in their product purchasing decisions.