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LSBert: Lexical Simplification Based on BERT

IEEE/ACM Transactions on Audio Speech and Language Processing 2021年 29卷 3064-3076页

作者： Qiang, Jipeng Li, Yun Zhu, Yi Yuan, Yunhao Shi, Yang Wu, Xindong Department of Computer Science Jiangsu Yangzhou China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Anhui Hefei China Mininglamp Academy of Sciences Minininglamp Beijing 100864 China

Lexical simplification (LS) aims at replacing complex words with simpler alternatives. LS commonly consists of three main steps: complex word identification, substitute generation, and substitute ranking. Existing LS methods focus on the contextual information of the complex word in the last step (substitute ranking). However, they miss out the following two facts: (1) The word complexity of a polysemous word is very closely related to its context;(2) The step of substitute generation regardless of the context will inevitably produce a large number of spurious candidates. Therefore, we propose a novel LS system LSBert based on pretrained language model BERT to address the aforementioned issues, which is capable of making use of the wider context when both identifying the words in need of simplification and generating substitute candidates for the complex words. Specifically, LSBert consists of a network for complex word identification by fine-tuning BERT and a network for substitute generation based on BERT. Experimental results show that LSBert performs well in both complex word identification and substitute generation, achieving state-of-the-art results in three benchmarks. © 2014 IEEE.

关键词： BERT Complex word identification Lexical simplification Sentence simplification

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Unifying Large Language Models and knowledge Graphs: A Roadmap

arXiv

引用

arXiv 2023年

作者： Pan, Shirui Luo, Linhao Wang, Yufei Chen, Chen Wang, Jiapu Wu, Xindong Griffith University QLD Australia The Department of Data Science and AI Monash University Melbourne Australia The Nanyang Technological University Singapore The Faculty of Information Technology Beijing University of Technology Beijing China The Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei University of Technology Hefei China The Research Center for Knowledge Engineering Zhejiang Lab Hangzhou China

Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the field of natural language processing and artificial intelligence, due to their emergent ability and generalizability. However, LLMs are black-box models, which often fall short of capturing and accessing factual knowledge. In contrast, knowledge Graphs (KGs), Wikipedia and Huapu for example, are structured knowledge models that explicitly store rich factual knowledge. KGs can enhance LLMs by providing external knowledge for inference and interpretability. Meanwhile, KGs are difficult to construct and evolve by nature, which challenges the existing methods in KGs to generate new facts and represent unseen knowledge. Therefore, it is complementary to unify LLMs and KGs together and simultaneously leverage their advantages. In this article, we present a forward-looking roadmap for the unification of LLMs and KGs. Our roadmap consists of three general frameworks, namely, 1) KG-enhanced LLMs, which incorporate KGs during the pre-training and inference phases of LLMs, or for the purpose of enhancing understanding of the knowledge learned by LLMs;2) LLM-augmented KGs, that leverage LLMs for different KG tasks such as embedding, completion, construction, graph-to-text generation, and question answering;and 3) Synergized LLMs + KGs, in which LLMs and KGs play equal roles and work in a mutually beneficial way to enhance both LLMs and KGs for bidirectional reasoning driven by both data and knowledge. We review and summarize existing efforts within these three frameworks in our roadmap and pinpoint their future research directions. Copyright © 2023, The Authors. All rights reserved.

关键词： knowledge graph

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EABC: Energy-aware Centrality-based Caching for Named data Networking in the IoT

EABC: Energy-aware Centrality-based Caching for Named Data N...

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IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Xingyun He Hu Liu Wuyungerile Li Alvin Valera Winston K.G. Seah Inner Mongolia Key Lab. of Wireless Networking & Mobile Computing Engineering Research Center of Ecological Big Data Ministry of Education School of Computer Science Inner Mongolia University Hohhot China Wireless Networks Research Group School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (数字)9798350394665

ISBN: (纸本)9798350394672

Named data Networking (NDN) is an information-centric internet architecture that delivers packets based on the name of the content in the packet. A key component of NDN is the caching strategy designed to reduce total network latency and load on content producers. To improve the speed and reliability of web content delivery, existing caching strategies typically cache content on a large number of intermediate nodes, which incur significant energy consumption and memory overhead. However, in Internet of Things (IoT) scenarios, memory and energy of nodes are scarce resources. Therefore, in NDN-based IoT applications, traditional caching strategies can cause node failures due to energy depletion, which can significantly reduce the network operational lifetime as well as create problems that caching is supposed to solve. In this paper, a caching strategy based on node centrality and energy availab.lity, called Energy-aware Approximate Betweenness Centrality (EABC) is proposed for NDN-based IoT. EABC uses a topology-based heuristic to cache data content on nodes with high centrality and makes caching decisions based on the remaining energy of the nodes. We evaluate EABC using simulations based on ndnSIM in different topologies and compare it with several existing NDN caching strategies. The results show that EABC performs better in different types of network topologies, reduces the average transmission delay of data and balances the energy consumption of highly central nodes, thus extending the network lifetime.

关键词： Wireless communication Energy consumption Network topology Memory management Topology Delays Internet of Things

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From knowledge Graph Development to Serving Industrial knowledge Automation:A Review 41

From Knowledge Graph Development to Serving Industrial Knowl...

引用

第41届中国控制会议

作者： Guangxuan Song Dongmei Fu Dawei Zhang Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical EngineeringUniversity of Science and Technology Beijing Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical EngineeringUniversity of Science and Technology Beijing Shunde Graduate School of University of Science and Technology Beijing Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology Beijing National Materials Corrosion and Protection Data Center University of Science and Technology Beijing

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

knowledge is essential for the management,scheduling,and operation of all industrial *** automation refers to the automation of knowledge-based work,which is a disruptive technology of the *** in the era of big data,KG inherits the good interpretability of traditional symbolism system approaches and becomes one of the feasible and important directions to realize industrial knowledge *** paper begins with the origin of KGs,introduces the characteristics of KGs,and reviews the state of the art of KG through the cases of KG constructed and applied in general and vertical *** paper also focuses on the research progress of KG applications in industry,by identifying the current challenges of KG in industrial applications,and highlighting the substantial potential of KG to serve knowledge automation.

关键词： knowledge knowledge Graph(KG) knowledge Automation Symbolism Industry

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Multi-source Multi-lab.l Feature Selection

Multi-source Multi-label Feature Selection

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Xiulan Yuan Xuegang Hu Peipei Li Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei City China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei City China

Feature selection for multi-source multi-lab.l data has attracted much attention, because there are a lot of scenarios that produce multiple source data with multi-lab.ls in the real-world applications, which aggravates the problems of dimensional disaster and the lab.l skewness. However, most of existing multi-source feature selection methods miss the multi-lab.l issue while existing multi-lab.l feature selection methods can not select the optimal feature set in the multi-source environment. Motivated by this, we propose a novel feature selection method, called MSMLFS. To be specific, the Inf-FS algorithm is firstly introduced to handle multi-lab.l feature selection for each data source, which considers the lab.l weight in the feature selection. Secondly, the over-sampling mechanism and the inter-source feature fusion method are used to handle the lab.l skewness of multi-lab.l data and the feature selection in multiple sources respectively. Finally, extensive experiments conducted on synthetic and real-world multi-source multi-lab.l data sets demonstrate that the proposed method outperforms several state-of-the-art multi-source or multi-lab.l feature selection methods.

关键词：

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Recurring Drift Detection and Model Selection-Based Ensemble Classification for data Streams with Unlab.led data

引用

New Generation Computing 2021年第2期39卷 341-376页

作者： Li, Peipei Wu, Man He, Junhong Hu, Xuegang Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei 230009 China School of Computer Science and Information Engineering Hefei University of Technology Hefei 230009 China CloudWalk Technology Co. Ltd. Guangzhou China

data stream classification is widely popular in the field of network monitoring, sensor network and electronic commerce, etc. However, in the real-world applications, recurring concept drifting and lab.l missing in data streams seriously aggravate the difficulty on the classification solutions. And this challenge has received little attention from the research community. Motivated by this, we propose a new ensemble classification approach based on the recurring concept drifting detection and model selection for data streams with unlab.led data. First, we build an ensemble model based on the classifiers and clusters. To improve the classification accuracy, we use the ensemble model to predict each data chunk and partition clusters according to the distribution of predicted class lab.ls. Second, we adopt a new concept drifting detection method based on the divergence of concept distributions between adjoining data chunks to distinguish recurring concept drifts. All historical new concepts will be maintained. Meanwhile, we introduce the time-stamp-based weights for base models in the ensemble model. In the selection of the base model, we consider the time-stamp-based weight and the divergence between concept distributions simultaneously. Finally, extensive experiments conducted on four benchmark data sets show that our approach can quickly adapt to data streams with recurring concept drifts, and improve the classification accuracy compared to several state-of-the-art classification algorithms for data streams with concept drifts and unlab.led data. © 2021, Ohmsha, Ltd. and Springer Japan KK, part of Springer Nature.

关键词： data stream classification Ensemble learning Recurring concept drift Unlab.led data

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Parametric Analysis of the Static Behavior for Stainless-Steel Beam-Column Connections 2

Parametric Analysis of the Static Behavior for Stainless-Ste...

引用

2022 2nd International Conference on Fluid and Chemical engineering, ICFCE 2022

作者： Qiao, Xueliang Wang, Yuanqing Jia, Lianguang Zhang, Tianxiong Qiao, Guangyu The Third Construction Engineering Company Ltd of China Construction Second Engineering Bureau Beijing100070 China Tsinghua University Department of Civil Engineering Key Lab. of Civil Eng. Safety and Durability of China Education Ministry Beijing100084 China School of Civil Engineering Shenyang Jianzhu University Shenyang110168 China School of Civil Engineering Tianjin University Tianjin300072 China

To study the influence of geometric parameters on flexural capacity and stiffness of beam-column connections using stain-steel S31608 and get the key influence parameters, on the basis of test data, using the finite element analysis software ABAQUS, parameter analysis on three main geometrical parameters of flexural capacity and stiffness of stain-steel beam-column connections was conducted. The analysis results show that when flange thickness of I-shaped bars increases, bearing capacity and initial stiffness of the joints increase with large amplitude;when slipping coefficient increases, bearing capacity and initial stiffness of the joints increase with small amplitude;when weld ac-cess hole changes, bearing capacity increases and initial stiffness increase with small amplitude, but can decrease stress concentrate phenomenon;when "Dog-bone"is used, the position of plastic hinge appearance will out-shift, but bearing capacity and initial stiffness decrease with large amplitude. Using the weld access hole of FEME350 and increasing the flange thickness of I-shaped bars is a good way to improve the static behavior of stain-steel beam-column connections. © Published under licence by IOP Publishing Ltd.

关键词： Finite element method

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Semi-Supervised Image Captioning Considering Wasserstein Graph Matching

arXiv

引用

arXiv 2024年

作者： Yang, Yang The Nanjing University of Science and Technology Nanjing210094 China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China Ministry of Education State Key Lab. for Novel Software Technology Nanjing University China

Image captioning can automatically generate captions for the given images, and the key challenge is to learn a mapping function from visual features to natural language features. Existing approaches are mostly supervised ones, i.e., each image has a corresponding sentence in the training set. However, considering that describing images always requires a huge of manpower, we usually have limited amount of described images (i.e., image-text pairs) and a large number of undescribed images in real-world applications. Thereby, a dilemma is the "Semi-Supervised Image Captioning". To solve this problem, we propose a novel Semi-Supervised Image Captioning method considering Wasserstein Graph Matching (SSIC-WGM), which turns to adopt the raw image inputs to supervise the generated sentences. Different from traditional single modal semi-supervised methods, the difficulty of semi-supervised cross-modal learning lies in constructing intermediately comparable information among heterogeneous modalities. In this paper, SSIC-WGM adopts the successful scene graphs as intermediate information, and constrains the generated sentences from two aspects: 1) inter-modal consistency. SSIC-WGM constructs the scene graphs of the raw image and generated sentence respectively, then employs the wasserstein distance to better measure the similarity between region embeddings of different graphs. 2) intra-modal consistency. SSIC-WGM takes the data augmentation techniques for the raw images, then constrains the consistency among augmented images and generated sentences. Consequently, SSIC-WGM combines the cross-modal pseudo supervision and structure invariant measure for efficiently using the undescribed images, and learns more reasonable mapping function. Experiments show that our method can outperform state-of-the-art comparison methods on the MS-COCO "Karpathy" offline test split, under different complex semi-supervised scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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Chinese Idiom Paraphrasing

arXiv

引用

arXiv 2022年

作者： Qiang, Jipeng Li, Yang Zhang, Chaowei Li, Yun Yuan, Yunhao Zhu, Yi Wu, Xindong School of Information Engineering Yangzhou University Jiangsu Yangzhou China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Anhui Hefei China

Idioms, are a kind of idiomatic expression in Chinese, most of which consist of four Chinese characters. Due to the properties of non-compositionality and metaphorical meaning, Chinese Idioms are hard to be understood by children and non-native speakers. This study proposes a novel task, denoted as Chinese Idiom Paraphrasing (CIP). CIP aims to rephrase idioms-included sentences to non-idiomatic ones under the premise of preserving the original sentence’s meaning. Since the sentences without idioms are easier handled by Chinese NLP systems, CIP can be used to pre-process Chinese datasets, thereby facilitating and improving the performance of Chinese NLP tasks, e.g., machine translation system, Chinese idiom cloze, and Chinese idiom embeddings. In this study, CIP task is treated as a special paraphrase generation task. To circumvent difficulties in acquiring annotations, we first establish a large-scale CIP dataset based on human and machine collab.ration, which consists of 115,530 sentence pairs. We further deploy three baselines and two novel CIP approaches to deal with CIP problems. The results show that the proposed methods have better performances than the baselines based on the established CIP dataset. © 2022, CC BY.

关键词： Natural language processing systems

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Research on Lightweight Small Object Detection Method with Fusion Attention Mechanism

Research on Lightweight Small Object Detection Method with F...

引用

2023 China Automation Congress, CAC 2023

作者： Dai, Haowen Liu, Bingyou Wan, Guoyang Qi, Jingjing Wan, Luanfei School of Electrical Engineering Anhui Polytechnic University Wuhu China Anhui Polytechnic University The Key Lab. of Advanced Perception and Intelligent Control of High-End Equipment Ministry of Education Wuhu China Anhui Dar Intelligent Control System Co. Ltd. Wuhu China Wuhu Institute of Technology Wuhu China

ISBN: (纸本)9798350303759

In the task of small object detection, due to the relatively small size of the objects, complex situations such as occlusion and overlap may arise in high-density scenes. These factors can significantly increase the issues of missed detections and false detections in object detection tasks. To address the aforementioned issues, this paper proposes a lightweight small object detection model, YOLOv5s-MGC. Firstly, we introduce an improved Mobilenetv3 network to replace the backbone network of YOLOv5s, aiming to improve the network's capability for effective feature extraction. Secondly, we incorporate the GSConv module into the feature fusion network to reduce model parameters and strengthen the network's ability in fusing feature information. Finally, we have integrated Convolutional Block Attention Module (CBAM) at the forefront of the detection heads, achieving the fusion of fine-grained features. We assessed our approach on the openly accessible dataset 'SHWD' and contrasted the results with the baseline YOLOv5s and various SOTA object detection models. Precisely, our method attains an accuracy of 91.1%, exceeding the baseline module by 1.6%. Furthermore, the module size is reduced to 1.97MB and it achieves a 52.4 FPS. In comparison to other prevailing algorithms, YOLOv5s-MGC enables efficient and accurate small object detection. © 2023 IEEE.

关键词： Object detection

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