检索结果-内蒙古大学图书馆

作者： Dong, Jichang Dai, Wei Liu, Ying Yu, Lean Wang, Jie School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Economics and Management Beijing University of Chemical Technology Beijing100029 China Department of Civil and Environmental Engineering Stanford University StanfordCA94305 United States

In this study, to address search index selection and volatility problems, we propose a learning-based search index collection method that collects the search data fraction for modeling by learning the best criteria from robust statistics. Based on the fraction of collected search index from internet search engine (***) data sources, a novel model is formulated for Chinese stock market price forecasting. We empirically test our method on the two main Chinese stock market price indexes and discover that the prediction accuracy is equivalent or superior to the benchmarks from previous studies that used alternative search index collection methods or lagged data prediction models. All prediction results outstand the importance of an effective data collection method for the robustness of forecast models and demonstrate the utility of a learning-based collection method for addressing search index collection problem, leading to a significant improvement in Chinese stock market price prediction accuracy. © 2019 World Scientific Publishing Company.

关键词： Search engines

来源：评论

学校读者我要写书评

暂无评论

Records systems and information systems: Connecting in organizations 8th

Records systems and information systems: Connecting in organ...

引用

8th International Joint Conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2016

作者： Xie, Sherry L. Fan, Guanyan Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education of China Beijing100872 China Center for Electronic Records Management Research Beijing100872 China School of Information Resource Management Renmin University of China 59 Zhongguancun Ave Haidian District Beijing100872 China

ISBN: (纸本)9783319997001

The field of Information Systems (ISs) has long been recognized, so has Enterprise Information Systems (EISs), a field close to it. Long existing also in organizations or enterprises is the field of records management, now predominantly digital records management (DRM), which shares the many goals of ISs and EISs in supporting the operation and success of organizations. While the DRM field recognized rather early in its battle to digital records challenges that the need to establish formal collaborations with the ISs and EISs professions, it is still rare to spot today discussions regarding such collaborations in the general information and communication technology (ICT) literature. To help bridge the gap, this chapter introduces to the ISs and EISs professions one of the major developments of the international DRM field, that is, the records systems elaborated by the InterPARES (International Research on Permanent and Authentic Records in Electronic Systems) project, for the purpose of invoking further discussions. © Springer Nature Switzerland AG 2019.

关键词： Information systems

来源：评论

学校读者我要写书评

暂无评论

LSTM power mid-term power load forecasting with meteorological factors 2nd

LSTM power mid-term power load forecasting with meteorologic...

引用

2nd International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2018

作者： Su, Xin Jiang, Xin-hua Zhang, Shun-miao Chen, Ming-long College of Information Science and Engineering Fujian University of Technology FuzhouFujian350108 China Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Information Science and Engineering Fujian University of Technology FuzhouFujian350118 China Fujian Key Laboratory for Automotive Electronics and Electric Drive Fujian University of Technology FuzhouFujian350118 China

ISBN: (纸本)9783030045845

In order to improve the accuracy and efficiency of mid-term power load forecasting, a mid-term power load forecasting method of long short time memory network (LSTM), which combines weather factors, is proposed. Firstly, the influence of meteorological factors affecting the power load on the mid-time power load is analyzed. Secondly, the meteorological factors are used as the input factor of the LSTM model to predict the power load. Finally, compared with the Random forest, ARIMA, GBDT, the LSTM algorithm which is not fused with meteorological factors, through the analysis of the experimental data, the LSTM fusion of meteorological factors has a better prediction effect on the mid-term load forecasting. © Springer Nature Switzerland AG 2019.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Personalizing search results using hierarchical RNN with query-aware attention

arXiv

引用

arXiv 2019年

作者： Ge, Songwei Dou, Zhicheng Jiang, Zhengbao Nie, Jian-Yun Wen, Ji-Rong School of Information Renmin University of China DIRO Université de Montréal Beijing Key Laboratory of Big Data Management and Analysis Methods National Engineering Laboratory of Big Data System Software Beijing Institute of Technology Key Laboratory of Data Engineering and Knowledge Engineering MOE

Search results personalization has become an effective way to improve the quality of search engines. Previous studies extracted information such as past clicks, user topical interests, query click entropy and so on to tailor the original ranking. However, few studies have taken into account the sequential information underlying previous queries and sessions. Intuitively, the order of issued queries is important in inferring the real user interests. And more recent sessions should provide more reliable personal signals than older sessions. In addition, the previous search history and user behaviors should influence the personalization of the current query depending on their relatedness. To implement these intuitions, in this paper we employ a hierarchical recurrent neural network to exploit such sequential information and automatically generate user profile from historical data. We propose a query-aware attention model to generate a dynamic user profile based on the input query. Significant improvement is observed in the experiment with data from a commercial search engine when compared with several traditional personalization models. Our analysis reveals that the attention model is able to attribute higher weights to more related past sessions after fine training. Copyright © 2019, The Authors. All rights reserved.

关键词： Search engines

来源：评论

学校读者我要写书评

暂无评论

A deep belief network-based method to identify proteomic risk markers for Alzheimer’s disease

arXiv

引用

arXiv 2020年

作者： An, Ning Jin, Liuqi Ding, Huitong Yang, Jiaoyun Yuan, Jing Key Laboratory of Knowledge Engineering Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Neurology Peking Union Medical College Hospital Chinese Academy of Medical Sciences Beijing China

While a large body of research has formally identified apolipoprotein E (APOE) as a major genetic risk marker for Alzheimer’s disease, accumulating evidence supports the notion that other risk markers may exist. The traditional Alzheimer’s-specific signature analysis methods, however, have not been able to make full use of rich protein expression data, especially the interaction between attributes. This paper develops a novel feature selection method to identify pathogenic factors of Alzheimer’s disease using the proteomic and clinical data. This approach has taken the weights of network nodes as the importance order of signaling protein expression values. After generating and evaluating the candidate subset, the method helps to select an optimal subset of proteins that achieved an accuracy greater than 90%, which is superior to traditional machine learning methods for clinical Alzheimer’s disease diagnosis. Besides identifying a proteomic risk marker and further reinforce the link between metabolic risk factors and Alzheimer’s disease, this paper also suggests that apidonectin-linked pathways are a possible therapeutic drug target. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

A watermarking method for printed QR code based on module expansion 14th

A watermarking method for printed QR code based on module ex...

引用

14th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2018

作者： Weng, Cai-Jie Pan, Jeng-Shyang Liu, Shi-Jian Wang, Mei-Jin School of Information Science and Engineering Fujian University of Technology Fuzhou China Key Laboratory of Big Data Mining and Applications of Fujian Province Fujian University of Technology Fuzhou China Department of Information Management Chaoyang University of Technology Taichung Taiwan

ISBN: (纸本)9783030037444

With the development of the Internet and smart terminals, Quick Response (QR) code and its related applications become increasingly popular. Though there are plenty of advantages for the usage of QR code, but its security issue is always a problem that can not be overlooked. Aiming for embedding message into QR code, a Module Expansion (ME) based method is proposed in this paper. The core idea of ME is to expand a module to its neighbor if these two modules are in different colors. Experimental results show that (1) our capacity for embedding is about half of that for encoding of the same QR code, which is much higher comparing to the state-of-the-art methods, (2) for the QR codes of versions under 20, the average time cost for embedding and extraction is around 51 ms and 59 ms respectively, (3) the success ratio of message extraction under noise attack and print-and-scan process are both higher than 80%. The proposed method can be used in QR code applications such as the anti-counterfeiting for encoded message of a printed QR code, and others for which message sharing is needed. © Springer Nature Switzerland AG 2019.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

A resource usage prediction-based energy-aware scheduling algorithm for instance-intensive cloud workflows 14th

A resource usage prediction-based energy-aware scheduling al...

引用

14th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Wang, Zhibin Wen, Yiping Zhang, Yu Chen, Jinjun Cao, Buqing School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Key Laboratory of Knowledge Processing and Networked Manufacturing Hunan University of Science and Technology Xiangtan China Swinburne Data Science Research Institute Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9783030129804

The applications of instance-intensive workflow are widely used in e-commerce, advanced manufacturing, etc. However, existing studies normally do not consider the problem of reducing energy consumption by utilizing the characters of instance-intensive workflow applications. This paper presents a resource usage Prediction-based Energy-Aware scheduling algorithm, named PEA. Technically, this method improves the energy efficiency of instance-intensive cloud workflow by predicting resources utilization and the strategies of batch processing and load balancing. The efficiency and effectiveness of the proposed algorithm are validated by extensive experiments. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Scheduling algorithms

来源：评论

学校读者我要写书评

暂无评论

MDPL-net: Multi-layer Dictionary Learning Network with Added Skip Dense Connections

MDPL-net: Multi-layer Dictionary Learning Network with Added...

引用

IEEE International Conference on data mining (ICDM)

作者： Zhao Zhang Yulin Sun Zheng Zhang Yang Wang Lin Wu Meng Wang School of Computer Science and Technology Soochow University Suzhou China Ministry of Education Key Laboratory of Knowledge Engineering with Big Data School of Computer Science and Information Engineering Hefei University of Technology Hefei China Bio-Computing Research Center Harbin Institute of Technology (Shenzhen) Shenzhen China

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Dictionary learning (DL) is powerful for representation learning, while it fails to capture the deep hierarchical information hidden in data. In this paper, we propose a new generalized end-to-end mulita-layer representation learning architecture referred to as Multi-layer Dictionary Pair Learning Network (MDPL-net) for the deep sparse and hierarchical representation of images. To enable MDPL-net to conduct accurate classification, MDPL-net clearly integrates the skip connection end-to-end network and multi-layer deep sparse dictionary learning into a unified architecture. The representation learning module has several hidden DL blocks, where each hidden DL block has a dictionary pair learning (DPL) layer, a batch-norm layer and an activation function layer, and the DL blocks are connected in a feed-forward manner. To further improve the information flow and maintain the privileged features between different DL blocks, a novel skip dense connectivity pattern is deployed between hidden DL blocks, which can obtain more stable and discriminative features. The DPL layer jointly formulates the discriminative synthesis dictionary and analysis dictionary by minimizing reconstruction error within each batch over the feature maps from front layers. Extensive results on benchmark databases demonstrate the effectiveness of MDPL-net for discriminative representation and robust image classification.

关键词： Dictionaries databases Conferences Machine learning data mining Image reconstruction Image classification

来源：评论

学校读者我要写书评

暂无评论

Distribution Pattern of Soil Organic Carbon and Its Regional Humification Constant in the Coastal Monsoon Region of Eastern China

Research Square

引用

Research Square 2021年

作者： Liu, Shutian Xie, Xiansheng Wang, Xiaochuan Feng, Xinxin Hou, Xianda Wang, Shuojin Lin, keyu Huang, Mei Jia, Shugang Hou, Yanlin Dou, Sen Guangxi Geographical Indication Crops Research Centre of Big Data Mining and Experimental Engineering Technology Nanning Normal University Nanning530001 China Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation Nanning Normal University Nanning530001 China School of Geography and Planning Nanning Normal University Nanning530001 China College of Resource and Environmental Science Jilin Agricultural University Changchun130118 China

Soils are an important pool for storing organic carbon. Soil organic carbon (SOC) content is generally considered as an important indicator to evaluate farmland soil quality. The loss of SOC causes soil degradation and reduces the sustainability of farmland. In order to reveal the distribution pattern of SOC in the coastal monsoon area of eastern China, and to clarify the macro dominant factors of SOC accumulation caused by temperature and precipitation, this paper analyzed the distribution pattern of SOC in the coastal monsoon area of eastern China by using the SOC data collected from the national soil testing and formula fertilization data set, and discussed the effects of temperature and precipitation on SOC content. According to the provincial administrative divisions, the distribution of SOC in the coastal monsoon areas of eastern China from Heilongjiang Province to Hainan Province was calculated. According to the annual average temperature 20.95℃, annual average precipitation 0~400 mm, 400~800 mm, > 800 mm, the study area was divided into different regions, and the effects of annual average temperature and annual average precipitation on SOC content were studied. In the region with annual average temperature less than 10.18℃, the temperature had a negative correlation with the accumulation of organic carbon, and the ratio of precipitation and temperature had a positive correlation with the accumulation of SOC;In the region of 10.18℃~20.95℃, the annual average temperature and annual average precipitation had a significant positive correlation, and the ratio of precipitation and temperature had a positive correlation with the accumulation of SOC;In the region >20.95℃, the temperature had a negative correlation with the accumulation of organic carbon, and the ratio of precipitation and temperature was not related to the accumulation of SOC. In the range of 0 ~ 400 mm of annual average precipitation, the temperature had a positive correlation with the accumulation

关键词： Temperature

来源：评论

学校读者我要写书评

暂无评论

On the security of a three factor remote user authentication scheme using fuzzy extractor 14th

On the security of a three factor remote user authentication...

引用

14th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2018

作者： Chen, Chien-Ming Huang, Yanyu Deng, Xiaoting Wu, Tsu-Yang Shenzhen China College of Computer Science and Engineering Shandong University of Technology Shandong China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou 350118 China Fujian University of Technology Fuzhou 350118 China

ISBN: (纸本)9783030037444

A secure authenticated key exchange protocol is an important key to establish a secure wireless communication. Various research have been conducted to study the efficiency and security of these authenticated key exchange protocol. A recent work by Giri et al. proposed a three factor remote user authentication scheme using Fuzzy Extractor for single server environment. However we found that their protocol is still vulnerable against an online password guessing attack. We also found that their protocol does not provide the perfect forward secrecy. To solve such problems, we propose a simple but effective improvement. © Springer Nature Switzerland AG 2019.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：