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arXiv 2023年

作者： Hu, Zhilei Li, Zixuan Xu, Daozhu Bai, Long Jin, Cheng Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China State Key Laboratory of Geo-Information Engineering Xi’an710054 China Xi’an Research Institute of Surveying and Mapping Xi’an710054 China

Event Relation Extraction (ERE) aims to extract multiple kinds of relations among events in texts. However, existing methods singly categorize event relations as different classes, which are inadequately capturing the intrinsic semantics of these relations. To comprehensively understand their intrinsic semantics, in this paper, we obtain prototype representations for each type of event relation and propose a Prototype-Enhanced Matching (ProtoEM) framework for the joint extraction of multiple kinds of event relations. Specifically, ProtoEM extracts event relations in a two-step manner, i.e., prototype representing and prototype matching. In the first step, to capture the connotations of different event relations, ProtoEM utilizes examples to represent the prototypes corresponding to these relations. Subsequently, to capture the interdependence among event relations, it constructs a dependency graph for the prototypes corresponding to these relations and utilized a Graph Neural network (GNN)-based module for modeling. In the second step, it obtains the representations of new event pairs and calculates their similarity with those prototypes obtained in the first step to evaluate which types of event relations they belong to. Experimental results on the MAVEN-ERE dataset demonstrate that the proposed ProtoEM framework can effectively represent the prototypes of event relations and further obtain a significant improvement over baseline models. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning

arXiv

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arXiv 2022年

作者： Li, Zixuan Hou, Zhongni Guan, Saiping Jin, Xiaolong Peng, Weihua Bai, Long Lyu, Yajuan Li, Wei Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China Baidu Inc China

A Temporal Knowledge Graph (TKG) is a sequence of KGs with respective timestamps, which adopts quadruples in the form of (subject, relation, object, timestamp) to describe dynamic facts. TKG reasoning has facilitated many real-world applications via answering such queries as (query entity, query relation, ?, future timestamp) about future. This is actually a matching task between a query and candidate entities based on their historical structures, which reflect behavioral trends of the entities at different timestamps. In addition, recent KGs provide background knowledge of all the entities, which is also helpful for the matching. Thus, in this paper, we propose the Historical Structure Matching (HiSMatch) model. It applies two structure encoders to capture the semantic information contained in the historical structures of the query and candidate entities. Besides, it adopts another encoder to integrate the background knowledge into the model. TKG reasoning experiments on six benchmark datasets demonstrate the significant improvement of the proposed HiSMatch model, with up to 5.6% performance improvement in MRR, compared to the state-of-the-art baselines. Copyright © 2022, The Authors. All rights reserved.

关键词： Knowledge graph

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Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning

arXiv

引用

arXiv 2022年

作者： Li, Zixuan Guan, Saiping Jin, Xiaolong Peng, Weihua Lyu, Yajuan Zhu, Yong Bai, Long Li, Wei Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China Baidu Inc China

A Temporal Knowledge Graph (TKG) is a sequence of KGs corresponding to different timestamps. TKG reasoning aims to predict potential facts in the future given the historical KG sequences. One key of this task is to mine and understand evolutional patterns of facts from these sequences. The evolutional patterns are complex in two aspects, length-diversity and time-variability. Existing models for TKG reasoning focus on modeling fact sequences of a fixed length, which cannot discover complex evolutional patterns that vary in length. Furthermore, these models are all trained offline, which cannot well adapt to the changes of evolutional patterns from then on. Thus, we propose a new model, called Complex Evolutional network (CEN), which uses a length-aware Convolutional Neural network (CNN) to handle evolutional patterns of different lengths via an easy-to-difficult curriculum learning strategy. Besides, we propose to learn the model under the online setting so that it can adapt to the changes of evolutional patterns over time. Extensive experiments demonstrate that CEN obtains substantial performance improvement under both the traditional offline and the proposed online settings. Copyright © 2022, The Authors. All rights reserved.

关键词： Knowledge graph

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Crowdsourcing System for Multi-object Annotation in Surveillance Videos

Crowdsourcing System for Multi-object Annotation in Surveill...

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International Conference on Big data Computing and Communications (BIGCOM)

作者： Zheng Zhang Zixin Zhao Lan Zhang Xiangyang Li LINKE Lab University of Science and Technology of China School of Data Science University of Science and Technology of China School of Computer Science and Technology CAS Key Laboratory of Wireless-optical Communications University of Science and Technology of China

The collection and labeling of data is a labor-intensive task and this has given rise to a large market for data crowdsourcing transactions. While there are many publicly available video datasets, task-specific data is still scarce and requires Customized annotation services are required. Even with many excellent auxiliary models and tools, video annotation is still a lengthy and time-consuming task. To address these challenges, this paper provides a new and effective annotation method in which the annotator no longer just provides annotations, but also plays the role of a reviewer to review the annotation results of other annotators. This method focuses on surveillance video data, in addition, it also supports adding additional custom tasks (e.g., action tagging, person relationship recognition, video summarization, etc.). And in this paper we mainly consider the additional custom temporal action annotation task. In this paper, we develop rules for filtering frames or segments that need to be re-labeled based on the temporal information of the model inference results and rely on the correlation between target and time to determine the task relevance, and asynchronously assign the task to different annotators for and dynamically portray the ability of the annotators while annotation is in progress, so as to allocate tasks to achieve annotation and mutual review of annotators. We have experimentally demonstrated that this method can reduce costs and improve labeling accuracy.

关键词： Crowdsourcing Costs Correlation Annotations Surveillance Manuals Tagging

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Communication is Verification: IoT Identifier System Using Device Fingerprints

Communication is Verification: IoT Identifier System Using D...

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International Conference on Big data Computing and Communications (BIGCOM)

作者： Yuan Ji Tian Ye Xiang-Yang Li LINKE Lab University of Science and Technology of China School of Data Science University of Science and Technology of China CAS Key Laboratory of Wireless-optical Communications School of Computer Science and Technology University of Science and Technology of China

With the gradual development of the Internet of Things, people rely more and more on the Internet of Things devices to assist their daily lives. The permissions of IoT devices make it easy for them to rescue or destroy our lives. This paper proposes a new scalable IoT security framework (ISDF), which uses the unforgeable physical layer characteristics of IoT devices as the verification basis to identify illegal devices. This framework combined with the identifier generation algorithm to generate a unique trusted identifier. Under the verification of more than a dozen devices such as bluetooth, wifi, and voice, the identifiers we generate have high robustness and stability, the accuracy of verification is more than 93%. Compared with existing solutions, our framework has the advantages of supporting multimodality and high verification success rate.

关键词： Bluetooth Systems architecture Speech recognition Fingerprint recognition Physical layer Robustness Internet of Things

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A commentary of Digital contact tracing in MIT technology Review 2021

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Fundamental Research 2021年第6期1卷 838-839页

作者： Xueqi Cheng CAS Key Laboratory of Network Data Science and Technology Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China

In 2020,the COVID-19 pandemic has brought“digital contact tracing”to the forefront of public *** the context of COVID-19,technology has offered public health investigators a new capability for locating infected individuals,i.e.,digital contact *** this technology,investigators were able to track the location of patients without relying on their memory,which alleviated disease surveillance *** practical application of this technology is known as“Exposure Notification.”Developers were able to complete the creation and operation of this digital contact tracing system within a few weeks,and they made the code open-source to ensure that Apple and Android users worldwide could utilize it.

关键词： Developer Android individual

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Learning Hybrid Negative Probability Model for Weakly-Supervised Whole Slide Image Recognition

Learning Hybrid Negative Probability Model for Weakly-Superv...

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International Conference on Acoustics, Speech, and Signal Processing (IcasSP)

作者： Yining Qiu Yuxi Li Jiafu Wu Zhenye Gan Mingmin Chi Yabiao Wang Chengjie Wang Pei Wang School of Computer Science Shanghai Key Laboratory of Data Science Fudan University China Tencent Youtu Lab China Zhongshan PoolNet Technology Co. Ltd Zhongshan Fudan Joint Innovation Center China Zhejiang University China NAOC CAS China

Classifying an entire Whole Slide Image (WSI) in a single forward pass is challenging due to its vast resolution. Consequently, current effort on WSI classification resorts to multiple instance learning (MIL), using patch-wise instances to predict categories under image-wise supervision. However, recent MIL methods usually follow implicit instance selection strategy and ignore the effect from inherent patch category imbalances. In a statistical sense, negative patches dominate in WSIs and provide sufficient samples for accurate density estimation. Therefore, in this paper, we learn from anomaly detection and propose a deep MIL framework which learns a hybrid negative probability model to bootstrap discovery of potential positive lesion. We associate attention-based MIL approach with a regularization loss function to explicitly improve patch selection process in positive images. Experiments conducted on benchmarks of WSI recognition demonstrate that our method brings significant improvement to classic attention-based MIL baseline and achieves state-of-the-art performance.

关键词：

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TOWARDS GENERALIZABLE GRAPH CONTRASTIVE LEARNING: AN INFORMATION THEORY PERSPECTIVE

arXiv

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arXiv 2022年

作者： Yuan, Yige Xu, Bingbing Shen, Huawei Cao, Qi Cen, Keting Zheng, Wen Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China CAS Key Laboratory of Network Data Science and Technology Institute of Com- puting Technology Chinese Academy of Sciences Beijing China

Graph contrastive learning (GCL) emerges as the most representative approach for graph representation learning, which leverages the principle of maximizing mutual information (InfoMax) to learn node representations applied in downstream tasks. To explore better generalization from GCL to downstream tasks, previous methods heuristically define data augmentation or pretext tasks. However, the generalization ability of GCL and its theoretical principle are still less reported. In this paper, we first propose a metric named GCL-GE for GCL generalization ability. Considering the intractability of the metric due to the agnostic downstream task, we theoretically prove a mutual information upper bound for it from an information-theoretic perspective. Guided by the bound, we design a GCL framework named InfoAdv with enhanced generalization ability, which jointly optimizes the generalization metric and InfoMax to strike the right balance between pretext task fitting and the generalization ability on downstream tasks. We empirically validate our theoretical findings on a number of representative benchmarks, and experimental results demonstrate that our model achieves state-of-the-art performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Information theory

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Unleashing the Power of LLMs in Dense Retrieval with Query Likelihood Modeling

arXiv

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arXiv 2025年

作者： Zhang, Hengran Bi, Keping Guo, Jiafeng Sun, Xiaojie Liu, Shihao Shi, Daiting Yin, Dawei Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS University of Chinese Academy of Sciences Beijing China Baidu Inc Beijing China

Dense retrieval is a crucial task in Information Retrieval (IR) and is the foundation for downstream tasks such as re-ranking. Recently, large language models (LLMs) have shown compelling semantic understanding capabilities and are appealing to researchers studying dense retrieval. LLMs, as decoder-style generative models, are competent at language generation while falling short on modeling global information due to the lack of attention to tokens afterward. Inspired by the classical word-based language modeling approach for IR, i.e., the query likelihood (QL) model, we seek to sufficiently utilize LLMs’ generative ability by QL maximization. However, instead of ranking documents with QL estimation, we introduce an auxiliary task of QL maximization to yield a better backbone for contrastively learning a discriminative retriever. We name our model as LLM-QL. To condense global document semantics to a single vector during QL modeling, LLM-QL has two major components, Attention Stop (AS) and Input Corruption (IC). AS stops the attention of predictive tokens to previous tokens until the ending token of the document. IC masks a portion of tokens in the input documents during prediction. Experiments on MSMARCO show that LLM-QL can achieve significantly better performance than other LLM-based retrievers and using QL estimated by LLM-QL for ranking outperforms word-based QL by a large margin. Our code can be found at https://***/Trustworthy-Information-Access/llm-ql. Copyright © 2025, The Authors. All rights reserved.

关键词： Online searching

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Few-Shot Stance Detection via Target-Aware Prompt Distillation

arXiv

引用

arXiv 2022年

作者： Jiang, Yan Gao, Jinhua Shen, Huawei Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Cas Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Stance detection aims to identify whether the author of a text is in favor of, against, or neutral to a given target. The main challenge of this task comes two-fold: few-shot learning resulting from the varying targets and the lack of contextual information of the targets. Existing works mainly focus on solving the second issue by designing attention-based models or introducing noisy external knowledge, while the first issue remains under-explored. In this paper, inspired by the potential capability of pre-trained language models (PLMs) serving as knowledge bases and few-shot learners, we propose to introduce prompt-based fine-tuning for stance detection. PLMs can provide essential contextual information for the targets and enable few-shot learning via prompts. Considering the crucial role of the target in stance detection task, we design target-aware prompts and propose a novel verbalizer. Instead of mapping each label to a concrete word, our verbalizer maps each label to a vector and picks the label that best captures the correlation between the stance and the target. Moreover, to alleviate the possible defect of dealing with varying targets with a single hand-crafted prompt, we propose to distill the information learned from multiple prompts. Experimental results show the superior performance of our proposed model in both full-data and few-shot scenarios. © 2022, CC BY.

关键词： Distillation

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