检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Ma, Zhengyi Dou, Zhicheng Zhu, Yutao Zhong, Hanxun Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China School of Information Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Key Laboratory of Data Engineering and Knowledge Engineering MOE Université de Montréal MontréalQC Canada

Personalized chatbots focus on endowing chatbots with a consistent personality to behave like real users, give more informative responses, and further act as personal assistants. Existing personalized approaches tried to incorporate several text descriptions as explicit user profiles. However, the acquisition of such explicit profiles is expensive and time-consuming, thus being impractical for large-scale real-world applications. Moreover, the restricted predefined profile neglects the language behavior of a real user and cannot be automatically updated together with the change of user interests. In this paper, we propose to learn implicit user profiles automatically from large-scale user dialogue history for building personalized chatbots. Specifically, leveraging the benefits of Transformer on language understanding, we train a personalized language model to construct a general user profile from the user's historical responses. To highlight the relevant historical responses to the input post, we further establish a key-value memory network of historical post-response pairs, and build a dynamic post-aware user profile. The dynamic profile mainly describes what and howthe user has responded to similar posts in history. To explicitly utilize users' frequently used words, we design a personalized decoder to fuse two decoding strategies, including generating a word from the generic vocabulary and copying one word from the user's personalized vocabulary. Experiments on two real-world datasets show the significant improvement of our model compared with existing methods. Our code is available at https://***/zhengyima/DHAP. Copyright © 2021, The Authors. All rights reserved.

关键词： User profile

来源：评论

学校读者我要写书评

暂无评论

Collaborative innovation community capacity building for electronic records security management 17

Collaborative innovation community capacity building for ele...

引用

17th International Conference on Intellectual Capital, knowledge Management and Organisational Learning, ICICKM 2020

作者： Wang, Lili An, Xiaomi Xu, Jicang Huang, Jie Guo, Mingjun Hu, Jufang School of Information Resource Management Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing China Renmin University of China Libraries Beijing China

ISBN: (纸本)9781912764723

With more and more governmental business going digital and collaborative to the best of whole government value and public value, electronic records security management (ERSM) faces great challenges. There is a growing understanding that standardization is the key to the successful ERSM, when governmental business requires cross-boundary collaboration. However, previous studies of collaborative innovation community capacity building (CICCB) on ERSM are mainly from the perspective of archival study to the best of long term preservation of electronic archives,with little concerns about needs of different interested parties. Few studies have been taken from interdisciplinary perspective and integrated use of international standards of business continuity management, records management, knowledge management and information security management to the best value of knowledge sharing and continuity of business, records, knowledge and security service. Based on prior findings and the integrated use of ISO management systems standards (ISO/IEC 27001, 2013;ISO 15489-1, 2016;ISO 30401, 2018;ISO 22301, 2019;ISO 30301, 2019), this study analyses the motivation mechanisms of CICCB for ERSM, then develops an extended conceptual framework for CICCB, including cognitive collaboration which means shared knowledge and values on ERSM for all types of interested parties that enable continual improvement;interested parties collaboration which means human interactions on shared rules for community practice on ERSM that enable improved transparency and accountability;process collaboration which means shared standards, guidelines and models for whole process control that enable electronic records controllable with characteristics of authenticity, reliability, integrity, usability, confidentiality and availability;technical collaboration which means shared solutions that enable electronic records systems reliable, secure, compliant, comprehensive, systematic. The above collaborations need com

关键词： knowledge management

来源：评论

学校读者我要写书评

暂无评论

Static and Dynamic Connectivity Structure of White-Matter Functional Network across the Adult Lifespan

Research Square

引用

Research Square 2024年

作者： LinLi, Zeqiang Hu, Kang Guo, Qingdong Guo, Shuixia School of Mathematics and Statistics Guangdong University of Foreign Studies Guangzhou510004 China MOE-LCSM School of Mathematics and Statistics Hunan Normal University Changsha410006 China School of Information Engineering Wuhan Business University Wuhan430056 China School of Mathematical Sciences Xiamen University Xiamen361005 China Key Laboratory of Applied Statistics and Data Science Hunan Normal University College of Hunan Province Changsha410006 China

Background. The maturation and ageing of the human brain involve intricate biological processes that result in complex changes in brain structure and function. Although the effects of aging on functional connectivity within gray matter (GM) regions have been extensively studied, the investigation of white matter (WM) functional changes remains limited. Methods. In this study, our aim was to investigate age-related trajectory in WM functional dynamics using resting-state functional magnetic resonance imaging (rs-fMRI) data from a large lifespan sample of 494 individuals. First, GM and WM functional networks (FNs) were identified by k-means clustering. Next, we performed static and dynamic analysis of WM functional network connectivity (FNC) to explore age effect on WM-FNs. Furthermore, we investigated recurrent patterns of dynamic FNC. Finally, we conducted several validation analyses to ensure replicability. Results. We identified 9 reliable WM and 12 GM FNs. The findings revealed age-related effects on WM FNC strength and WM-GM FNC dynamics, primarily including linear positive and U-shaped age trajectories in static FNC strength, as well as linear negative and inverted U-shaped age trajectories in FNC temporal variability. Additionally, we identified three distinct brain states with significant age-related differences. The aforementioned findings were largely replicated in the validation analysis. Conclusion. High integration and low temporal variability in WN-GM FNC may reflect a less efficient network system in older adults. These findings enhance our understanding of brain aging processes and provide insights into the trajectory of WM functional dynamics during normal ageing. © 2024, CC BY.

关键词： Trajectories

来源：评论

学校读者我要写书评

暂无评论

A Survey on Fairness in Large Language Models

arXiv

引用

arXiv 2023年

作者： Li, Yingji Du, Mengnan Song, Rui Wang, Xin Wang, Ying College of Computer Science and Technology Jilin University Changchun130012 China Department of Data Science New Jersey Institute of Technology Newark United States School of Artificial Intelligence Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun130012 China

Large Language Models (LLMs) have shown powerful performance and development prospects and are widely deployed in the real world. However, LLMs can capture social biases from unprocessed training data and propagate the biases to downstream tasks. Unfair LLM systems have undesirable social impacts and potential harms. In this paper, we provide a comprehensive review of related research on fairness in LLMs. Considering the influence of parameter magnitude and training paradigm on research strategy, we divide existing fairness research into oriented to medium-sized LLMs under pre-training and fine-tuning paradigms and oriented to large-sized LLMs under prompting paradigms. First, for medium-sized LLMs, we introduce evaluation metrics and debiasing methods from the perspectives of intrinsic bias and extrinsic bias, respectively. Then, for large-sized LLMs, we introduce recent fairness research, including fairness evaluation, reasons for bias, and debiasing methods. Finally, we discuss and provide insight on the challenges and future directions for the development of fairness in LLMs. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Sparse Training data-Based Hyperspectral Image Super Resolution Via ANFIS Interpolation

Sparse Training Data-Based Hyperspectral Image Super Resolut...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Jing Yang Changjing Shang Lu Chen Pan Su Qiang Shen School of Automation and Software Engineering Shanxi University Taiyuan China Department of Computer Science Aberystwyth University Aberystwyth UK Insti. of Big Data Science & Industry Shanxi University Taiyuan China Department of Computer Hebei Key Laboratory of Knowledge Computing for Energy & Power Baoding North China Electric Power University China

Hyperspectral image super resolution aims to improve the spatial resolution of given hyperspectral images, which has become a highly attractive topic in the field of image processing. Existing techniques typically focus on super resolution with sufficient training data. However, restricted by data acquisition conditions, certain hyperspectral images or band images are very different to obtain, resulted in insufficient training data. In order to solve this problem, a new hyperspectral image super resolution method is proposed in this paper in an effort to conduct the super resolution task over insufficient (sparse) training data, by applying the recently introduced ANFIS (Adaptive Network-based Fuzzy Inference System) interpolation method. Particularly, the training dataset is divided into several subsets. For the subsets with sufficient training data, the relevant ANFIS models are trained using standard ANFIS learning algorithm, while for the subsets with sparse training data, the corresponding ANFIS models are interpolated through the use of ANFIS interpolation. Experimental results indicate that compared with the methods using sufficient training data, the proposed method can achieve very similar result, showing its effectiveness for situations where only sparse training data is available.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Pre-training for ad-hoc retrieval: Hyperlink is also you need

arXiv

引用

arXiv 2021年

作者： Ma, Zhengyi Dou, Zhicheng Xu, Wei Zhang, Xinyu Jiang, Hao Cao, Zhao Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China Beijing China Distributed and Parallel Software Lab Huawei Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China

Designing pre-training objectives that more closely resemble the downstream tasks for pre-trained language models can lead to better performance at the fine-tuning stage, especially in the ad-hoc retrieval area. Existing pre-training approaches tailored for IR tried to incorporate weak supervised signals, such as query-likelihood based sampling, to construct pseudo query-document pairs from the raw textual corpus. However, these signals rely heavily on the sampling method. For example, the query likelihood model may lead to much noise in the constructed pre-training data. In this paper, we propose to leverage the large-scale hyperlinks and anchor texts to pre-train the language model for ad-hoc retrieval. Since the anchor texts are created by webmasters and can usually summarize the target document, it can help to build more accurate and reliable pre-training samples than a specific algorithm. Considering different views of the downstream ad-hoc retrieval, we devise four pre-training tasks based on the hyperlinks. We then pre-train the Transformer model to predict the pair-wise preference, jointly with the Masked Language Model objective. Experimental results on two large-scale ad-hoc retrieval datasets show the significant improvement of our model compared with the existing methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Hypertext systems

来源：评论

学校读者我要写书评

暂无评论

UAV Swarm-enabled Collaborative Post-disaster Communications in Low Altitude Economy via a Two-stage Optimization Approach

arXiv

引用

arXiv 2025年

作者： Zheng, Xiaoya Sun, Geng Li, Jiahui Wang, Jiacheng Wu, Qingqing Niyato, Dusit Jamalipour, Abbas College of Computer Science d Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University 639798 Singapore College of Computing and ta Science Nanyang Technological University 639798 Singapore Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Electrical and Computer Engiering The University of Sydney SydneyNSW2006 Australia

The low-altitude economy (LAE), as a new economic paradigm, plays an indispensable role in cargo transportation, healthcare, infrastructure inspection, and especially post-disaster communication. Specifically, unmanned aerial vehicles (UAVs), as one of the core technologies of the LAE, can be deployed to provide communication coverage, facilitate data collection, and relay data for trapped users, thereby significantly enhancing the efficiency of post-disaster response efforts. However, conventional UAV self-organizing networks exhibit low reliability in long-range cases due to their limited onboard energy and transmit ability. Therefore, in this paper, we design an efficient and robust UAV-swarm enabled collaborative self-organizing network to facilitate post-disaster communications. Specifically, a ground device transmits data to UAV swarms, which then use collaborative beamforming (CB) technique to form virtual antenna arrays and relay the data to a remote access point (AP) efficiently. Then, we formulate a rescue-oriented post-disaster transmission rate maximization optimization problem (RPTRMOP), aimed at maximizing the transmission rate of the whole network. Given the challenges of solving the formulated RPTRMOP by using traditional algorithms, we propose a two-stage optimization approach to address it. In the first stage, the optimal traffic routing and the theoretical upper bound on the transmission rate of the network are derived. In the second stage, we transform the formulated RPTRMOP into a variant named V-RPTRMOP based on the obtained optimal traffic routing, aimed at rendering the actual transmission rate closely approaches its theoretical upper bound by optimizing the excitation current weight and the placement of each participating UAV via a diffusion model-enabled particle swarm optimization (DM-PSO) algorithm. Simulation results show the effectiveness of the proposed two-stage optimization approach in improving the transmission rate of the construct

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

data Stream Classification Based on Extreme Learning Machine: A Review

引用

Big data Research 2022年 30卷

作者： Zheng, Xiulin Li, Peipei Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology Hefei 230601 China Mininglamp Academy of Sciences Mininglamp Technology Beijing 100084 China

Many daily applications are generating massive amount of data in the form of stream at an ever higher speed, such as medical data, clicking stream, internet record and banking transaction, etc. In contrast to the traditional static data, data streams are of some inherent properties, to name a few, infinite length, concept drift, multiple labels and concept evolution. Among all the data mining tasks, classification is one of the basic topics in data stream mining and has gained more and more attentions among different research communities. Extreme Learning Machine (ELM) has drawn much interests in data classification due to its high efficiency, universal approximation capability, generalization ability, and simplicity, which have greatly inspired the development of many ELM-based algorithms and their applications during the past decades. In this paper, we mainly provide a comprehensive review on ELM theoretical research and its variants in data stream classification, and categorize these algorithms from different perspectives. Firstly, we briefly introduce the basic principles of ELM and its characteristics. Secondly, we give an overview of different ELM variants to address the particular issues of data stream classification. Thirdly, we present an overview of different strategies to optimize the ELM, which have further improved the stability, accuracy and generalization ability of ELM, and briefly introduce some practical applications of ELM in data stream classification. Finally, we conduct several groups of experiments to compare the performance of ELM based models addressing the focused issues. Also, the open issues and prospects of ELM models used for stream classification are discussed, which are worthwhile to be further studied in the future. © 2022

关键词： Classification data stream Extreme learning machine

来源：评论

学校读者我要写书评

暂无评论

Mandari: Multi-Modal Temporal knowledge Graph-aware Sub-graph Embedding for Next-POI Recommendation

Mandari: Multi-Modal Temporal Knowledge Graph-aware Sub-grap...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoqian Liu Xiuyun Li Yuan Cao Fan Zhang Xiongnan Jin Jinpeng Chen School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China The Technology Innovation Center of Cultural Tourism Big Data of Hebei Province Chengde China Hebei Normal University for Nationalities Chengde China Knowledge Discovery and Data Mining Research Center Zhejiang Lab Hangzhou China

Next-POI recommendation aims to explore from user check-in sequence to predict the next possible location to be visited. Existing methods are often difficult to model the implicit association of multi-modal data with user choices. Moreover, traditional methods struggle to fully explore the variation of user preferences at variable time intervals. To tackle these limitations, we propose a Multi-Modal Temporal knowledge Graph-aware Sub-graph Embedding approach (Mandari). We first construct a novel Multi-Modal Temporal knowledge Graph. Based on the proposed knowledge graph, we integrate multi-modal information and leverage the graph attention network to calculate sub-graph prediction probability. Next, we implement a temporal knowledge mining method to model the segmentation and periodicity of user check-in and obtain temporal prediction probability. Finally, we fuse temporal prediction probability with the previous sub-graph prediction probability to obtain the final result. Extensive experiments demonstrate that our approach outperforms existing state-of-the-art methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Reliable flight performance assessment of multirotor based on interacting multiple model particle filter and health degree

引用

Chinese Journal of Aeronautics 2019年第2期32卷 444-453页

作者： Zhiyao ZHAO Peng YAO Xiaoyi WANG Jiping XU Li WANG Jiabin YU School of Computer and Information Engineering Beijing Technology and Business University Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University College of Engineering Ocean University of China

Multirotor has been applied to many military and civilian mission scenarios. From the perspective of reliability, it is difficult to ensure that multirotors do not generate hardware and software failures or performance anomalies during the flight process. These failures and anomalies may result in mission interruptions, crashes, and even threats to the lives and property of human ***, the study of flight reliability problems of multirotors is conductive to the development of the drone industry and has theoretical significance and engineering value. This paper proposes a reliable flight performance assessment method of multirotors based on an Interacting Multiple Model Particle Filter(IMMPF) algorithm and health degree as the performance indicator. First, the multirotor is modeled by the Stochastic Hybrid System(SHS) model, and the problem of reliable flight performance assessment is formulated. In order to solve the problem, the IMMPF algorithm is presented to estimate the real-time probability distribution of hybrid state of the established SHS-based multirotor model, since it can decrease estimation errors compared with the standard interacting multiple model algorithm based on extended Kalman filter. Then, the reliable flight performance is assessed with health degree based on the estimation result. Finally, a case study of a multirotor suffering from sensor anomalies is presented to validate the effectiveness of the proposed method.

关键词： Health degree Interacting multiple model Multirotor Particle filter Reliability and safety Reliable flight performance Unmanned aerial vehicles

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：