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CDCAT: A Multi-Language Cross-Document Entity and Event Coreference Annotation Tool

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Tsinghua Science and Technology 2022年第3期27卷 589-598页

作者： Yang Xu Boming Xia Yueliang Wan Fan Zhang Jiabo Xu Huansheng Ning School of Computer and Communication engineering University of Science and Technology BeijingBeijing 100083China beijing engineering research center for cyberspace data analysis and applications Beijing 100083China research Institute with Run Technologies Company Ltd.Beijing 100192China School of Information engineering Xinjiang Institute of EngineeringUrumqi 830091China

A tool for the manual annotation of cross-document entity and event coreferences that helps annotators to label mention coreference relations in text is essential for the annotation of coreference corpora. To the best of our knowledge, CROss-document Main Events and entities Recognition(CROMER) is the only open-source manual annotation tool available for cross-document entity and event coreferences. However, CROMER lacks multi-language support and extensibility. Moreover, to label cross-document mention coreference relations, CROMER requires the support of another intra-document coreference annotation tool known as Content Annotation Tool, which is now unavailable. To address these problems, we introduce Cross-Document Coreference Annotation Tool(CDCAT), a new multi-language open-source manual annotation tool for cross-document entity and event coreference, which can handle different input/output formats, preprocessing functions, languages, and annotation systems. Using this new tool, annotators can label a reference relation with only two mouse clicks. Best practice analyses reveal that annotators can reach an annotation speed of 0.025 coreference relations per second on a corpus with a coreference density of 0.076 coreference relations per word. As the first multi-language open-source cross-document entity and event coreference annotation tool, CDCAT can theoretically achieve higher annotation efficiency than CROMER.

关键词： event coreference entity coreference manual annotation tool natural language processing

Electromagnetic spectrum chipless radio frequency identification:A review

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Digital Communications and Networks 2020年第3期6卷 377-388页

作者： Qingjuan Li Rana Sadaf Anwar Huansheng Ning Jun Wang Lingfeng Mao School of Computer and Communication engineering University of Science and TechnologyBeijing100083China beijing engineering research center for cyberspace data analysis and applications Beijing100083China beijing Electro-Mechanical engineering Institute Beijing100074China

Over the past few years,Radio Frequency IDentification(RFID)has stridden significantly due to its adoption in many business and daily life *** empowers end-to-end and item-by-item visibility,letting the customers or decision-makers effectively trail the personnel and resources for improved *** research and development in this area have transformed the inexpensive,miniaturized technology of RFIDs without chip *** Radio Frequency IDentification(CRFID)is a unique wireless technology based on radar principle to sense,identify,and track the *** are simplified and miniaturized tags of traditional RFID,which can widely be used in small and cheap objects/applications in our daily *** paper reviews the definition,principle,and most recent development in *** on the reported researches,the ElectroMagnetic Spectrum(EMS)method is typical,which has been concerned by many RFID ***,this paper reviews different types of EMS CRFID tag and classifies these tags based on structure design emphasizing their significant geometries in comparison with performance *** and coding methods of the EMS CRFID are also elaborated,contrasting the tag capacity and coding ***,we briefly summarize the challenges and limitations in this emerging field and look forward to the prospects of future CRFID.

关键词： CRFID Electromagnetic Tag structure Coding

dataflow Management in the Internet of Things: Sensing,Control, and Security

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Tsinghua Science and Technology 2021年第6期26卷 918-930页

作者： Dawei Wei Huansheng Ning Feifei Shi Yueliang Wan Jiabo Xu Shunkun Yang Li Zhu School of Computer and Communication engineering University of Science and Technology BeijingBeijing 100083China beijing engineering research center for cyberspace data analysis and applications Beijing 100083China Run Technologies Co. Ltd.Beijing 100192China School of Information engineering Xinjiang Institute of EngineeringUrumqi 830023China School of Reliability and Systems engineering Beihang UniversityBeijing 100191China Northern Cloud research Team Engineering University of PAPXi'an 710086China

The pervasiveness of the smart Internet of Things(IoTs) enables many electric sensors and devices to be connected and generates a large amount of dataflow. Compared with traditional big data, the streaming dataflow is faced with representative challenges, such as high speed, strong variability, rough continuity, and demanding timeliness, which pose severe tests of its efficient management. In this paper, we provide an overall review of IoT dataflow management. We first analyze the key challenges faced with IoT dataflow and initially overview the related techniques in dataflow management, spanning dataflow sensing, mining, control, security, privacy protection,etc. Then, we illustrate and compare representative tools or platforms for IoT dataflow management. In addition,promising application scenarios, such as smart cities, smart transportation, and smart manufacturing, are elaborated,which will provide significant guidance for further research. The management of IoT dataflow is also an important area, which merits in-depth discussions and further study.

关键词： Internet of Things(IoTs) dataflow security privacy management

Robot and its living space:A roadmap for robot development based on the view of living space

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Digital Communications and Networks 2021年第4期7卷 505-517页

作者： Xiaofeng Cai Huansheng Ning Sahraoui Dhelim Rongjuan Zhou Tao Zhang Yang Xu Yueliang Wan School of Computer and Communication engineering University of Science and Technology Beijing Beijing 100083China beijing engineering research center for cyberspace data analysis and applications Beijing 100083China School of Foreign Studies University of Science and Technology Beijing Beijing 100083China Key Lab of Information Network Security Ministry of Public SecurityChina Run Technologies Co. LidBeijing China

Robot’s living space Human-robot relationship Robot humanization Hybrid intelligence Human-robot integrated society Robots,as the creation of humans,became an irreplaceable component in human *** the advancement of technologies,robots have become more and more intelligent and have been widely used in many fields,such as disease diagnosis,customer services,healthcare for the older people,and so *** robots made our lives much more convenient than ever before,they also brought many potential risks and challenges in technology,security,and *** better understand the development of robots,we proposed a concept of a robot’s living space and analyzed the role of robots in our *** this paper,we focus on setting a theoretical framework of the robot’s living space to further understand the human-robot *** research in this paper contains three central ***,we interpret the concept of the robot’s living space and the functions of each ***,we analyze and summarize the relative technologies which support robots living well in each ***,we provide advice and improvement measures based on a discussion of potential problems caused by the developments of *** the trend of robots humanization and human-robot society integration,we should seriously consider how to collaborate with intelligent robots to achieve hybrid *** build a harmonious human-robot integrated society,studying the robot’s living space and its relationship with humans is the prerequisite and roadmap.

关键词： Robot's living space Human-robot relationship Robot humanization Hybrid intelligence Human-robot integrated society

Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication engineering University of Science and Technology BeijingBeijing100083China beijing engineering research center for cyberspace data analysis and applications Beijing100083China Faculty of Informatics engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

Snapshot boosting: a fast ensemble framework for deep neural networks

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Science China(Information Sciences) 2020年第1期63卷 77-88页

作者： Wentao ZHANG Jiawei JIANG Yingxia SHAO Bin CUI center for data Science Peking University National engineering Laboratory for Big data analysis and applications Department of Computer Science beijing Key Lab of Intelligent Telecommunications Software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications Key Lab of High Confidence Software Technologies Department of Computer SciencePeking University

Boosting has been proven to be effective in improving the generalization of machine learning models in many fields. It is capable of getting high-diversity base learners and getting an accurate ensemble model by combining a sufficient number of weak learners. However, it is rarely used in deep learning due to the high training budget of the neural network. Another method named snapshot ensemble can significantly reduce the training budget, but it is hard to balance the tradeoff between training costs and diversity. Inspired by the ideas of snapshot ensemble and boosting, we propose a method named snapshot boosting. A series of operations are performed to get many base models with high diversity and accuracy, such as the use of the validation set, the boosting-based training framework, and the effective ensemble strategy. Last, we evaluate our method on the computer vision(CV) and the natural language processing(NLP) tasks, and the results show that snapshot boosting can get a more balanced trade-off between training expenses and ensemble accuracy than other well-known ensemble methods.

关键词： ensemble learning deep learning boosting neural network snapshot ensemble

Caution About Truncation-By-Death in Clinical Trial Statistical analysis:A Lesson from Remdesivir

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China CDC weekly 2021年第25期3卷 538-540,I0001页

作者： Yuhao Deng Xiao-Hua Zhou School of Mathematical Sciences Peking UniversityBeijingChina beijing International center for Mathematical research Peking UniversityBeijingChina Department of Biostatistics School of Public HealthPeking UniversityBeijingChina National engineering Lab for Big data analysis and applications Peking UniversityBeijingChina

In an effort to combat coronavirus disease 2019(COVID-19)caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),clinicians across the globe have been working tirelessly to find effective *** 2020,inspir... 详细信息

关键词： acute respiratory

Fast Solution Method for the Large-scale Unit Commitment Problem with Long-term Storage

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Chinese Journal of Electrical engineering 2023年第3期9卷 39-49页

作者： Bo Li Chunjie Qin Ruotao Yu Wei Dai Mengjun Shen Ziming Ma Jianxiao Wang School of Electrical engineering Guangxi UniversityNanning 530004China State Grid Sichuan Electric Power Company Chengdu 610041China National Power Dispatching and Control center State Grid Corporation of ChinaBeijing 100031China National engineering Laboratory for Big data analysis and applications(Peking University) Beijing 100871China

Long-term storage(LTS)can provide various services to address seasonal fluctuations in variable renewable energy by reducing energy ***,long-term unit commitment(UC)with LTS involves mixed-integer programming with large-scale coupling constraints between consecutive intervals(state-of-charge(SOC)constraint of LTS,ramping rate,and minimum up/down time constraints of thermal units),resulting in a significant computational ***,an iterative-based fast solution method is proposed to solve the long-term UC with ***,the UC with coupling constraints is split into several sub problems that can be solved in ***,the solutions of the sub problems are adjusted to obtain a feasible solution that satisfies the coupling ***,a decoupling method for long-term time-series coupling constraints is proposed to determine the global optimization of the SOC of the *** price-arbitrage model of the LTS determines the SOC boundary of the LTS for each sub ***,the sub problem with the SOC boundary of the LTS is iteratively solved *** proposed method was verified using a modified IEEE 24-bus *** results showed that the computation time of the unit combination problem can be reduced by 97.8%,with a relative error of 3.62%.

关键词： Constraint splitting long-term storage mix-integer programming unit commitment

Stage Division and Pattern Discovery of Complex Patient Care Processes

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Journal of Systems Science & Complexity 2017年第5期30卷 1136-1159页

作者： WANG Tingyan TIAN Xin YU Ming QI Xin YANG Lan Health Care Services research center Department of Industrial EngineeringTsinghua UniversityBeijing 100084China research center on Fictitious Economy and data Science Chinese Academy of SciencesBeijing 100190China School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Health Care Serv-ices research center Department of Industrial EngineeringTsinghua UniversityBeijing 100084China

This paper studies the design of a clinical pathway that defines medical service activities within each stage of a patient care process. Much prior research has developed clinicM process models that consider the trajectory of services occurring in a care process, by using data mining techniques on process execution logs. A novel approach that provides a more efficient way of clinical pathway design is introduced in this paper. Based on the strategy of TEI@I methodology, the proposed approach integrates statistical methods, optimization techniques and data mining. With the preprocessed data, a complex care process is subsequently divided into several medical stages, and then the patterns of each stage are identified, and thus a clinical pathway is developed. Finally, the proposed method is applied to the real world, using archival data derived from a hospital in beijing, about three diseases that involve various departments, with an average of 300 samples for each disease. The results of real- world applications demonstrate that the proposed method can automatically and efficiently facilitate clinical pathways design. The main contributions to the field in this paper include （a） a new application of TEI@I methodology in healthcare domain, （b） a novel method for complex processes analysis, （c） tangible evidence of automatic clinical pathways design in the real world.

关键词： Clinical pathway frequent pattern mining stage division TEI@I virtual business.