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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Huang, Yuyang Dong, Shiwen Wang, Dandan Wan, Chunling Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai China Bio-X Institutes Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders Ministry of Education Shanghai Jiao Tong University Shanghai200030 China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai200240 China

ISBN: (纸本)9781665468190

Niacin skin-flushing test is a promising method for fast and objective diagnosis of schizophrenia and affective disorder. Compared to healthy controls, the patients have attenuated flushing extent and reduced flushing rate. The traditional analysis of niacin test relies on manually evaluated flushing extent, while flushing patterns sh own in the sk in test images have not yet been considered. To exploit the potential of raw images for diagnosis and avoid subjective and time-consuming estimation of the flushing levels, we propose a CNN-LSTM hybrid model to learn the time-series of images captured in the niacin skin-flushing test. Moreover, an attention layer is introduced to enhance the model performance and interpretability. We collect a data set of 796 participants, and the experimental results show that the new model outperforms the traditional 4-point scaled measurement and conventional deep learning methods by large margins. Besides, the attention scores yielded by the model reveals key time points during the flushing reaction. © 2022 IEEE.

关键词： Time series

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LSBert: Lexical Simplification Based on BERT

引用

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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NEW BOOLEAN SATISFIABILITY PROBLEM HEURISTIC STRATEGY: MINIMAL POSITIVE NEGATIVE PRODUCT STRATEGY

arXiv

引用

arXiv 2023年

作者： Qun, Zhao Xintao, Wang Menghui, Yang School of Information Resource Management Renmin University of China Beijing100872 China Institute of Economics Chinese Academy of Social Sciences Beijing100872 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing100872 China

This study presents a novel heuristic algorithm called the "Minimal Positive Negative Product Strategy" to guide the CDCL algorithm in solving the Boolean satisfiability problem. It provides a mathematical explanation for the superiority of this algorithm over widely used heuristics such as the Dynamic Largest Individual Sum (DLIS) and the Variable State Independent Decaying Sum (VSIDS). Experimental results further confirm the effectiveness of this heuristic strategy in *** Codes 03 Copyright © 2023, The Authors. All rights reserved.

关键词： Heuristic algorithms

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Investigation on the Electrical Characteristics of Vacuum Diode with Nanoscale Channel 24

Investigation on the Electrical Characteristics of Vacuum Di...

引用

24th International Vacuum Electronics Conference, IVEC 2023

作者： Tang, Wenhua Hou, Zhenfei Gan, Lu Wu, Shengli Liu, Yiwei Wang, Xiao Shen, Zhihua Xi'An Jiaotong University Key Lab. for Physical Electronics and Devices of the Ministry of Education China Hunan Institute of Advanced Sensing and Information Technology Xiangtan University China School of Electronic Information and Artificial Intelligence Shanxi University of Science and Technology China School of Electronics and Information Engineering Nantong Vocational University China

ISBN: (纸本)9781665473217

A vacuum nano-diode with nanoscale channel formed from the square-hole was proposed and prepared via traditional semiconductor lithography and dry etching fabrication techniques in this paper. A thin dielectric layer with a thickness of about 20 nm corresponds to the channel length, this helps the diode operate in atmospheric environments. The effects of different vacuum channel apertures on the electrical properties of vacuum diodes were explored experimentally, and the reasons for the influence in electrical properties were elab.rated in detail. The emission mechanism and working principle of the diode were analyzed through Fowler-Nordheim theory. Besides, the breakdown ability and emission current stability of vacuum diodes with different apertures were investigated by continuously voltage increasing test. © 2023 IEEE.

关键词： Nanotechnology

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An Improved data-Driven Adaptive Dynamic Programming Approach for Cooperative Games 42

An Improved Data-Driven Adaptive Dynamic Programming Approac...

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42nd Chinese Control Conference, CCC 2023

作者： Wang, Yi-Min Sun, Tao Ma, Song He, Lei-Ming Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China Tsinghua University Department of Automation Beijing100084 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China Chinese Aeronautical Establishment Beijing100029 China

ISBN: (纸本)9789887581543

In this paper, we consider a continuous system cooperative dynamic game problem with two players. Through the introduction of dynamic programming concept, a Hamilton-jacobi-Belman equation for two players is designed. By introducing detection noises and using the basis function approximation method, a data-driven adaptive dynamic programming method is proposed and an iterative equation is obtained. Then, by introducing two detection noises and using the basis function approximation method, an iterative equation is obtained. It is note-worthy that we can obtain the Nash equilibrium solution of the cooperative game. Finally, a numerical simulation example is given to demonstrate the effectiveness of the proposed adaptive dynamic programming approach. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Dynamic programming

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Unifying Large Language Models and knowledge Graphs: A Roadmap

arXiv

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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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Multi-source Multi-lab.l Feature Selection

Multi-source Multi-label Feature Selection

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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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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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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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