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Short Review of Intention Mining in Social Crisis Management through Automatic Technologies 35

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Short Review of Intention Mining in Social Crisis Management...

35th International Conference on Software Engineering and Knowledge Engineering, SEKE 2023

作者： Wang, Xinzhi Li, Mengyue Yao, Yige Li, Zhennan Liu, Yi Zhang, Hui School of Computer Engineering and Science Shanghai University Shanghai China Public Order School Peopleś Public Security University of China Beijing China Key Laboratory of City Integrated Emergency Response Science Tsinghua University Beijing China

In the current social environment, social crisis events occur frequently with significant impacts. Group intention mining through automatic technologies for managing social crises has gained extensive attention. This paper presents an overview of research on group intention mining in social crisis events, covering three areas: knowledge graph inference, intention attribution, and risk management. Knowledge graph inference facilitates the detection of group intention in crisis events. It is supported by the construction of crisis knowledge graphs, which organize crisis elements and inter-element relations into structured semantic knowledge. The interpretable semantics in the crisis knowledge graphs enables attribution of intention. Group intention mining consists of intention detection and intention attribution, serving the risk management of social crisis events. To gain insights into the process of group intention mining in social crises, the Covid-19 event is selected as a case study. Finally, the paper proposes future research directions to solve the limitations of existing intention mining methods in social crises. © 2023 Knowledge Systems Institute Graduate school. All rights reserved.

关键词： Risk management

SEGMENT+: Long Text Processing with Short-Context Language Models

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arXiv 2024年

作者： Shi, Wei Li, Shuang Yu, Kerun Chen, Jinglei Liang, Zujie Wu, Xinhui Qian, Yuxi Wei, Feng Zheng, Bo Liang, Jiaqing Chen, Jiangjie Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Columbia University United States MYbank Ant Group China School of Data Science Fudan University China

There is a growing interest in expanding the input capacity of language models (LMs) across various domains. However, simply increasing the context window does not guarantee robust performance across diverse long-input processing tasks, such as understanding extensive documents and extracting detailed information from lengthy and noisy data. In response, we introduce SEGMENT+, a general framework that enables LMs to handle extended inputs within limited context windows efficiently. SEGMENT+ utilizes structured notes and a filtering module to manage information flow, resulting in a system that is both controllable and interpretable. Our extensive experiments across various model sizes, focusing on longdocument question-answering and Needle-in-a- Haystack tasks, demonstrate the effectiveness of SEGMENT+ in improving performance. © 2024, CC BY-SA.

关键词： data handling

Towards the Identifiability and Explainability for Personalized Learner Modeling: An Inductive Paradigm 24

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Towards the Identifiability and Explainability for Personali...

33rd ACM Web Conference, WWW 2024

作者： Li, Jiatong Liu, Qi Wang, Fei Liu, Jiayu Huang, Zhenya Yao, Fangzhou Zhu, Linbo Su, Yu University of Science and Technology of China Anhui Province Key Laboratory of Big Data Analysis and Application State Key Laboratory of Cognitive Intelligence Hefei China University of Science and Technology of China School of Computer Science and Technology Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei China Hefei Normal University Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei China

ISBN: (纸本)9798400701719

Personalized learner modeling using cognitive diagnosis (CD), which aims to model learners' cognitive states by diagnosing learner traits from behavioral data, is a fundamental yet significant task in many web learning services. Existing cognitive diagnosis models (CDMs) follow theproficiency-response paradigm that views learner traits and question parameters as trainable embeddings and learns them through learner performance prediction. However, we notice that this paradigm leads to the inevitable non-identifiability and explainability overfitting problem, which is harmful to the quantification of learners' cognitive states and the quality of web learning services. To address these problems, we propose an identifiable cognitive diagnosis framework (ID-CDF) based on a novelresponse-proficiency-response paradigm inspired by encoder-decoder models. Specifically, we first devise the diagnostic module of ID-CDF, which leverages inductive learning to eliminate randomness in optimization to guarantee identifiability and captures the monotonicity between overall response data distribution and cognitive states to prevent explainability overfitting. Next, we propose a flexible predictive module for ID-CDF to ensure diagnosis preciseness. We further present an implementation of ID-CDF, i.e., ID-CDM, to illustrate its usability. Extensive experiments on four real-world datasets with different characteristics demonstrate that ID-CDF can effectively address the problems without loss of diagnosis preciseness. Our code is available at https://***/CSLiJT/ID-CDF. © 2024 ACM.

关键词： Learning systems

Multi-objective multi-population Genetic Programming for feature selection and classification to high-dimensional data

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Multi-objective multi-population Genetic Programming for fea...

2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Li, Qiaoman Gao, Xiaoying Meng, Wenyang Ma, Jianbin Hebei Agricultural University College of Information Science and Technology Baoding China Victoria University of Wellington School of Engineering and Computer Science Wellington New Zealand College of Information Science and Technology Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural University Baoding China

ISBN: (纸本)9798400704956

Classification for high-dimensional data is a challenging task due to a great number of redundant and irrelevant features. Genetic Programming (GP) has its built-in feature selection characteristics and is suitable for processing high-dimensional data. However, if the number of features in GP individual is not restricted, the bloat phenomenon will occur, which affects the generalization of the training model. Moreover, GP is easy to fall into local search in the evolutionary process. In this paper, a multi-objective multi-population GP classifier construction method is proposed, which uses a multipopulation co-evolution strategy to divide the evolving population into the main and auxiliary populations with different evolutionary strategies and different evaluation criteria, and employs a multiobjective strategy to ensure classification performance and restrict the number of features at the same time. Experiments on seven high-dimensional datasets show that our proposed multi-population co-evolution strategy and multi-objective strategy are all effective to improve the classification performance of GP classifiers. Comparisons with three state-of-the-art GP classifier construction methods show that our proposed methods achieve better or comparable classification performance on most cases. © 2024 held by the owner/author(s).

关键词： Spatio-temporal data

Is There a One-Model-Fits-All Approach to Information Extraction? Revisiting Task Definition Biases

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arXiv 2024年

作者： Huang, Wenhao He, Qianyu Li, Zhixu Liang, Jiaqing Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China School of Data Science Fudan University China Fudan-Aishu Cognitive Intelligence Joint Research Center China

Definition bias is a negative phenomenon that can mislead models. Definition bias in information extraction appears not only across datasets from different domains but also within datasets sharing the same domain. We identify two types of definition bias in IE: bias among information extraction datasets and bias between information extraction datasets and instruction tuning datasets. To systematically investigate definition bias, we conduct three probing experiments to quantitatively analyze it and discover the limitations of unified information extraction and large language models in solving definition bias. To mitigate definition bias in information extraction, we propose a multi-stage framework consisting of definition bias measurement, bias-aware fine-tuning, and task-specific bias mitigation. Experimental results demonstrate the effectiveness of our framework in addressing definition bias. © 2024, CC BY.

关键词： Information retrieval

Towards Adaptive Neighborhood for Advancing Temporal Interaction Graph Modeling

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arXiv 2024年

作者： Zhang, Siwei Chen, Xi Xiong, Yun Wu, Xixi Zhang, Yao Fu, Yongrui Zhao, Yinglong Zhang, Jiawei Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Ant Group Shanghai China IFM Lab Department of Computer Science University of California DavisCA United States

Temporal Graph Networks (TGNs) have demonstrated their remarkable performance in modeling temporal interaction graphs. These works can generate temporal node representations by encoding the surrounding neighborhoods for the target node. However, an inherent limitation of existing TGNs is their reliance on fixed, handcrafted rules for neighborhood encoding, overlooking the necessity for an adaptive and learnable neighborhood that can accommodate both personalization and temporal evolution across different timestamps. In this paper, we aim to enhance existing TGNs by introducing an adaptive neighborhood encoding mechanism. We present SEAN (Selective Encoding for Adaptive Neighborhood), a flexible plug-and-play model that can be seamlessly integrated with existing TGNs, effectively boosting their performance. To achieve this, we decompose the adaptive neighborhood encoding process into two phases: (i) representative neighbor selection, and (ii) temporal-aware neighborhood information aggregation. Specifically, we propose the Representative Neighbor Selector component, which automatically pinpoints the most important neighbors for the target node. It offers a tailored understanding of each node’s unique surrounding context, facilitating personalization. Subsequently, we propose a Temporal-aware Aggregator, which synthesizes neighborhood aggregation by selectively determining the utilization of aggregation routes and decaying the outdated information, allowing our model to adaptively leverage both the contextually significant and current information during aggregation. We conduct extensive experiments by integrating SEAN into three representative TGNs, evaluating their performance on four public datasets and one financial benchmark dataset introduced in this paper. The results demonstrate that SEAN consistently leads to performance improvements across all models, achieving SOTA performance and exceptional robustness. © 2024, CC BY-SA.

关键词： Graph neural networks

Measurement of atmospheric nanoparticles:Bridging the gap between gas-phase molecules and larger particles

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Journal of Environmental sciences 2023年第1期123卷 183-202页

作者： Chao Peng Chenjuan Deng Ting Lei Jun Zheng Jun Zhao Dongbin Wang Zhijun Wu Lin Wang Yan Chen Mingyuan Liu Jingkun Jiang Anpei Ye Maofa Ge Weigang Wang State Key Laboratory for Structural Chemistry of Unstable and Stable Species Beijing National Laboratory for Molecular Sciences(BNLMS)CAS Research/Education Center for Excellence in Molecular SciencesInstitute of ChemistryChinese Academy of SciencesBeijing 100190China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China School of Environment Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China School of Atmospheric Sciences Sun Yat-sen UniversityZhuhaiGuangdong 519082China State Key Joint Laboratory of Environmental Simulation and Pollution Control College of Environmental Sciences and EngineeringPeking UniversityBeijing 100871China Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention(LAP3) Department of Environmental Science&EngineeringFudan UniversityShanghai 200433China Key Laboratory for the Physics and Chemistry of Nanodevices Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China University of Chinese Academy of Sciences Beijing 100049China

Atmospheric nanoparticles are crucial components contributing to fine particulate matter(PM_(2.5)),and therefore have significant effects on visibility,climate,and human *** to the unique role of atmospheric nanoparticles during the evolution process from gas-phase molecules to larger particles,a number of sophisticated experimental techniques have been developed and employed for online monitoring and characterization of the physical and chemical properties of atmospheric nanoparticles,helping us to better understand the formation and growth of new *** this paper,we firstly review these state-of-the-art techniques for investigating the formation and growth of atmospheric nanoparticles(e.g.,the gas-phase precursor species,molecular clusters,physicochemical properties,and chemical composition).Secondly,we present findings from recent field studies on the formation and growth of atmospheric nanoparticles,utilizing several advanced ***-more,perspectives are proposed for technique development and improvements in measuring atmospheric nanoparticles.

关键词： Nanoparticle Measurement techniques Size distributions Physical properties Chemical composition

Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution Supplementary Material

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IEEE Transactions on Image Processing 2025年 34卷 3013-3024页

作者： Yuan, Jiang Ma, Ji Wang, Bo Hu, Weiming North China Electric Power University School of Control and Computer Engineering Beijing102206 China CASIA State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China

Implicit degradation modeling-based blind super-resolution (SR) has attracted more increasing attention in the community due to its excellent generalization to complex degradation scenarios and wide application range. How to extract more discriminative degradation representations and fully adapt them to specific image features is the key to this task. In this paper, we propose a new Content-decoupled Contrastive Learning-based blind image super-resolution (CdCL) framework following the typical blind SR pipeline. This framework introduces negative-free contrastive learning technique for the first time to model the implicit degradation representation, in which a new cyclic shift sampling strategy is designed to ensure decoupling between content features and degradation features from the data perspective, thereby improving the purity and discriminability of the learned implicit degradation space. In addition, we propose a detail-aware implicit degradation adapting module that can better adapt degradation representations to specific LR features by enhancing the basic adaptation unit's perception of image details, significantly reducing the overall SR model complexity. Extensive experiments on synthetic and real data show that our method achieves highly competitive quantitative and qualitative results in various degradation settings while obviously reducing parameters and computational costs, validating the feasibility of designing practical and lightweight blind SR tools. Codes and models will be available at https://***/Fieldhunter/CdCL. © 1992-2012 IEEE.

关键词： Depression Anxiety disorders Face recognition Schizophrenia Visualization Training data collection Uncertainty Facial features Emotion recognition

Past Meets Present: Creating Historical Analogy with Large Language Models

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arXiv 2024年

作者： Li, Nianqi Yuan, Siyu Chen, Jiangjie Liang, Jiaqing Wei, Feng Liang, Zujie Yang, Deqing Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China School of Data Science Fudan University China ByteDance Inc. China MYbank Ant Group China

Historical analogies, which compare known past events with contemporary but unfamiliar events, are important abilities that help people make decisions and understand the world. However, research in applied history suggests that people have difficulty finding appropriate analogies. And previous studies in the AI community have also overlooked historical analogies. To fill this gap, in this paper, we focus on the historical analogy acquisition task, which aims to acquire analogous historical events for a given event. We explore retrieval and generation methods for acquiring historical analogies based on different large language models (LLMs). Furthermore, we propose a self-reflection method to mitigate hallucinations and stereotypes when LLMs generate historical analogies. Through human evaluations and our specially designed automatic multidimensional assessment, we find that LLMs generally have a good potential for historical analogies. And the performance of the models can be further improved by using our self-reflection method. © 2024, CC BY-NC-SA.

关键词： History