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arXiv 2017年

作者： Liu, Shuai Lu, Mengye Liu, Gaocheng Pan, Zheng College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Key Laboratory of Data Mining and Knowledge Engineering Hohhot China

Information entropy and its extension, which are important generalization of entropy, have been applied in many research domains today. In this paper, a novel generalized relative entropy is constructed to avoid some defects of traditional relative entropy. We presented the structure of generalized relative entropy after the discussion of defects in relative entropy. Moreover, some properties of the provided generalized relative entropy is presented and proved. The provided generalized relative entropy is proved to have a finite range and is a finite distance metric. Copyright © 2017, The Authors. All rights reserved.

关键词： Defects

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Research on Government Information Sharing Model Using Blockchain Technology

Research on Government Information Sharing Model Using Block...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Yiwei Zhang Sanhong Deng Yue Zhang Jia Kong School of Information Management Nanjing University Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing China Nanjing University Nanjing China

Research Purpose: The distributed, traceable and security of blockchain technology are applicable to the construction of new government information resource models, which could eliminate the barn effect and trust in government information sharing, as well as promoting the transformation of government affairs from management to service, it is also of great significance to the sharing of government information and construction of service-oriented e-government. Propose Methods: By analyzing the current problems of government information sharing, combined with literature research, this paper proposes the theoretical framework and advantages of blockchain technology applied to government information management and sharing, expounds the blockchain-based solution, it also constructs a government information sharing model based on blockchain, and gives implementation strategies at the technical and management levels. Results and Conclusion: The government information sharing model based on the blockchain solution and the transparency of government information can be used as a research framework for information interaction analysis between the government and users. It can also promote the construction and development of information sharing for Chinese government, as well as providing unified information sharing solution at the departmental and regional levels for e-government.

关键词： Blockchain Information management Security Electronic government Information services data engineering

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Instance-adaptive Zero-shot Chain-of-Thought Prompting 38

Instance-adaptive Zero-shot Chain-of-Thought Prompting

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yuan, Xiaosong Shen, Chen Yan, Shaotian Zhang, Xiaofeng Xie, Liang Wang, Wenxiao Guan, Renchu Wang, Ying Ye, Jieping College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering MoE Jilin University China Alibaba Cloud Computing China Shanghai Jiao Tong University China College of Computer Science and Technology Zhejiang University of Technology China College of Software Zhejiang University China

Zero-shot Chain-of-Thought (CoT) prompting emerges as a simple and effective strategy for enhancing the performance of large language models (LLMs) in real-world reasoning tasks. Nonetheless, the efficacy of a singular, task-level prompt uniformly applied across the whole of instances is inherently limited since one prompt cannot be a good partner for all, a more appropriate approach should consider the interaction between the prompt and each instance meticulously. This work introduces an instance-adaptive prompting algorithm as an alternative zero-shot CoT reasoning scheme by adaptively differentiating good and bad prompts. Concretely, we first employ analysis on LLMs through the lens of information flow to detect the mechanism under zero-shot CoT reasoning, in which we discover that information flows from question to prompt and question to rationale jointly influence the reasoning results most. We notice that a better zero-shot CoT reasoning needs the prompt to obtain semantic information from the question, and then the rationale aggregates sufficient information from the question directly and via the prompt indirectly. On the contrary, lacking any of those would probably lead to a bad one. Stem from that, we further propose an instance-adaptive prompting strategy (IAP) for zero-shot CoT reasoning. Experiments conducted with LLaMA-2, LLaMA-3, and Qwen on math, logic, and commonsense reasoning tasks (e.g., GSM8k, MMLU, Causal Judgement) obtain consistent improvement, demonstrating that the instance-adaptive zero-shot CoT prompting performs better than other task-level methods with some curated prompts or sophisticated procedures, showing the significance of our findings in the zero-shot CoT reasoning mechanism. © 2024 Neural information processing systems foundation. All rights reserved.

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Recovering Accurate Labeling Information from Partially Valid data for Effective Multi-Label Learning

arXiv

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arXiv 2020年

作者： Li, Ximing Wang, Yang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology China School of Computer Sci & Information Engineering Hefei University of Technology China

Partial Multi-label Learning (PML) aims to induce the multi-label predictor from datasets with noisy supervision, where each training instance is associated with several candidate labels but only partially valid. To address the noisy issue, the existing PML methods basically recover the ground-truth labels by leveraging the ground-truth confidence of the candidate label, i.e., the likelihood of a candidate label being a ground-truth one. However, they neglect the information from non-candidate labels, which potentially contributes to the ground-truth label recovery. In this paper, we propose to recover the ground-truth labels, i.e., estimating the ground-truth confidences, from the label enrichment, composed of the relevance degrees of candidate labels and irrelevance degrees of non-candidate labels. Upon this observation, we further develop a novel two-stage PML method, namely Partial MultiLabel Learning with Label Enrichment-Recovery (PML3ER), where in the first stage, it estimates the label enrichment with unconstrained label propagation, then jointly learns the ground-truth confidence and multi-label predictor given the label enrichment. Experimental results validate that PML3ER outperforms the state-of-the-art PML methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Recovery

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Discovering Reliable Information Extraction Patterns with Pre-Trained Model for Text with Writing Style

Discovering Reliable Information Extraction Patterns with Pr...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Chenyang Bu Jiacheng Liu Jiaxuan Liu Shengwei Ji Hongbin Yang Ministry of Education Key Laboratory of Knowledge Engineering with Big Data School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China School of Artificial Intelligence and Big Data Hefei University China

Large-scale pre-trained models such as GPT and BERT have demonstrated remarkable performance in information extraction tasks. However, their black-box nature poses challenges for reliability and interpretability. In contrast, rule- based extraction methods have better interpretability, but typically require domain experts to manually establish rules, limiting their generalization ability. In industry, there is often a demand for reliable knowledge extraction to reduce the time spent on manual verification of each piece of knowledge. In this paper, we explore the idea of combining GPT and symbolic-based methods to automatically discover reliable extraction patterns in text with a particular writing style. This method leverages the characteristics of high information density and similar writing patterns in text with a specific writing style to generate verifiable and reliable patterns. We conduct experiments on two datasets with a specific writing style to demonstrate its effectiveness, validating the idea of combining large models for reliable information extraction pattern discovery in the tested datasets.

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Automatic collection of the parallel corpus with little prior knowledge

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8801卷 95-106页

作者： Ma, Shutian Zhang, Chengzhi Department of Information Management Nanjing University of Science and Technology Nanjing210094 China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing University Nanjing210093 China

As an important resource for machine translation and cross-language information retrieval, collecting large-scale parallel corpus has been paid wide attention. With the development of the Internet, researchers begin to mine the parallel corpora from the multilingual websites. They use some prior knowledge like ad hoc heuristics or calculate the similarity of the webpages structure and content to find the bilingual webpages. This paper presents a method that uses the search engine and little prior knowledge about the URL patterns to get the bilingual websites from the Internet. The method is fast for its low time cost and there is no need for large-scale computation on URL pattern matching. We have collected 88 915 candidate parallel Chinese-English webpages, which average accuracy is around 90.8%. During the evaluation, the true bilingual websites that we found have high similar html structure and good quality translations. © Springer International Publishing Switzerland 2014.

关键词： Websites

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SAFL-Net: Semantic-Agnostic Feature Learning Network with Auxiliary Plugins for Image Manipulation Detection

SAFL-Net: Semantic-Agnostic Feature Learning Network with Au...

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International Conference on Computer Vision (ICCV)

作者： Zhihao Sun Haoran Jiang Danding Wang Xirong Li Juan Cao Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences School of Mathematics Science University of Chinese Academy of Sciences MoE Key Lab of Data Engineering and Knowledge Engineering Renmin University of China

Since image editing methods in real world scenarios cannot be exhausted, generalization is a core challenge for image manipulation detection, which could be severely weakened by semantically related features. In this paper we propose SAFL-Net, which constrains a feature extractor to learn semantic-agnostic features by designing specific modules with corresponding auxiliary tasks. Applying constraints directly to the features extracted by the encoder helps it learn semantic-agnostic manipulation trace features, which prevents the biases related to semantic information within the limited training data and improves generalization capabilities. The consistency of auxiliary boundary prediction task and original region prediction task is guaranteed by a feature transformation structure. Experiments on various public datasets and comparisons in multiple dimensions demonstrate that SAFL-Net is effective for image manipulation detection.

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Simple and Deep Graph Convolutional Networks

arXiv

引用

arXiv 2020年

作者： Chen, Ming Wei, Zhewei Huang, Zengfeng Ding, Bolin Li, Yaliang School of Information Renmin University of China China Gaoling School of Articial Intelligence Renmin University of China China Beijing Key Lab of Big Data Management and Analysis Methods China MOE Key Lab of Data Engineering and Knowledge Engineering China School of Data Science Fudan University China Alibaba Group

Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-structured data. Recently, GCNs and subsequent variants have shown superior performance in various application areas on real-world datasets. Despite their success, most of the current GCN models are shallow, due to the over-smoothing problem. In this paper, we study the problem of designing and analyzing deep graph convolutional networks. We propose the GCNII, an extension of the vanilla GCN model with two simple yet effective techniques: Initial residual and Identity mapping. We provide theoretical and empirical evidence that the two techniques effectively relieves the problem of over-smoothing. Our experiments show that the deep GCNII model outperforms the state-of-the-art methods on various semi- and full-supervised tasks. Code is available at https://***/chennnM/GCNII. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Deep learning-based large-scale named entity recognition for anatomical region of mammalian brain

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Quantitative Biology 2022年第3期10卷 253-263页

作者： Xiaokang Chai Yachao Di Zhao Feng Yue Guan Guoqing Zhang Anan Li Qingming Luo Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for OptoelectronicsMoE Key Laboratory for Biomedical PhotonicsHuazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Biomedical Engineering of Hainan Province School of Biomedical EngineeringHainan UniversityHaikou 570228China CAS Key Laboratory of Computational Biology Bio-Med Big Data CenterShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesShanghai 200031China Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging Chinese Academy of Medical SciencesHUST-Suzhou Institute for BrainsmaticsJITRISuzhou 215123China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200031China

Background:Images of anatomical regions and neuron type distribution,as well as their related literature are valuable assets for neuroscience *** are vital evidence and vehicles in discovering new phenomena and knowledge refinement through image and text big *** knowledge acquired from image data generally echoes with the literature accumulated over the *** knowledge within the literature can provide a comprehensive context for a deeper understanding of the image ***,it is quite a challenge to manually identify the related literature and summarize the neuroscience knowledge in the large-scale ***,neuroscientists are in dire need of an automated method to extract neuroscience knowledge from large-scale ***:A proposed deep learning model named BioBERT-CRF extracts brain region entities from the WhiteText *** model takes advantage of BioBERT and CRF to predict entity labels while ***:The proposed deep learning model demonstrated comparable performance against or even outperforms the previous models on the WhiteText *** BioBERT-CRF model has achieved the best average precision,recall,and F1 score of 81.3%,84.0%,and 82.6%,*** used the BioBERT-CRF model to predict brain region entities in a large-scale PubMed abstract dataset and used a rule-based method to normalize all brain region entities to three neuroscience ***:Our work shows that the BioBERT-CRF model can be well-suited for brain region entity *** rankings of different brain region entities by their appearance in the large-scale corpus indicate the anatomical regions that researchers are most concerned about.

关键词： brain region entity extraction literature mining WhiteText deep learning

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Maritime Unmanned Rescue System-of-Systems Design based on Attention Transformer

Maritime Unmanned Rescue System-of-Systems Design based on A...

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2024 IEEE International Conference on Unmanned Systems, ICUS 2024

作者： Ding, Wei Ming, Zhenjun Wang, Guoxin Yan, Yan van Beek, Anton Zhan, Hongfei School of Mechanical Engineering Beijing Institute of Technology Beijing China Yangtze Delta Region Academy of Beijing Institute of Technology Zhejiang China Key Laboratory of Industry Knowledge & Data Fusion Technology and Application Ministry of Industry and Information Technology Beijing China School of Mechanical and Materials Engineering University College Dublin Dublin Ireland Shanghai Aircraft Design and Research Institute Shanghai China

ISBN: (纸本)9798350384185

When natural or man-made disasters occur at sea, a maritime unmanned rescue system-of-systems (MURSoSs), as an important guarantee for the safety of people's lives and property, has a rapid response to emergency rescue services. In the design process of MURSoSs, it is often faced with problems such as unexplainable mechanisms and inaccurate modeling due to the characteristics of multi-level coupling and irregular emergence. A design method of MURSoSs based on an attention Transformer is proposed. First, this paper decomposes the MURSoSs into a multi-level organizational structure composed of individual performance, group structure, and overall effectiveness. Second, the maritime unmanned rescue simulation environment is constructed, to obtain the multi-level evolution data of group structure and overall effectiveness under different individual performance, and the attention Transformer is used to mine the functional correlation relationship between levels. Finally, the experimental simulation prediction is carried out by using this function relationship. Taking the design of MURSoSs as an example, the results show that the constructed model accurately quantifies the correlation relationship between levels and effectively reveal the internal logic of the emergence process of maritime rescue. © 2024 IEEE.

关键词： Disasters

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