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Edge optimization detection network for camouflaged targets

Edge optimization detection network for camouflaged targets

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Natural language processing (ICNLP), international conference on

作者： Hao Yuan Haibo Ge Wenhao He Chaofeng Huang School of Electrical Engineering Xi’an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

In order to solve the problem of incomplete spatial information and blurred target boundary in traditional camouflaged target detection (COD), an edge optimization detection network for camouflaged target is proposed. Texture highlight module (thM), Global scale matching module (GSMM), Target boundary exploration module (TBEM) and Boundary inference module (BIM). thM first uses the sensing field to cover the entire multi-stage features extracted by the Res2Net-50 backbone network to realize the global understanding of the visual scene, and then adopts the parallel multi-branch mixed convolution mode to amplify the difference between the target and the background. GSMM encodes the features at the top of the network in channel and spatial dimensions, and can extract the features of different sizes of camouflaged targets from multiple angles. TBEM uses self-attention mechanism to combine lower-level features with higher-level features to deal with the dependency between different boundary pixels in multi-target camouflage. the BIM module uses the boundary guidance factor provided by the TBEM module to guide the inference of the real target and refine the edge details from the features fused by the Attentional Feature Fusion module (AFFM). Quantitative and qualitative experiments were carried out on the public benchmark data set, and the experimental results proved that the proposed algorithm still outperforms the other 8 most advanced models under four objective evaluation indicators.

关键词： Visualization Image edge detection Semantics Object detection Feature extraction Inference algorithms Natural language processing Sensors Reliability Optimization

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KGAT-MRC: A Knowledge Graph and Attention Mechanism-Based Model for Machine Reading Comprehension

KGAT-MRC: A Knowledge Graph and Attention Mechanism-Based Mo...

引用

international conference on Communications, information System and Computer Engineering (CISCE)

作者： Miaomiao Zhou Dongsheng Wang Yue Feng Sha Liu Department of Computer Science Jiangsu University of Science and Technology Zhenjiang China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

Machine Reading Comprehension (MRC) has garnered significant attention in the field of natural language processing. While the maritime domain lacks openly available MRC task models and corresponding datasets, resulting in a deficiency within existing models to provide accurate answers tailored to the maritime domain. In response to these issues, our work uses hand annotation and data augmentation to create the OurShipSet machine reading comprehension dataset, which is tailored for the maritime industry. Additionally, we propose the KGAT-MRC model, which integrates relevant knowledge triplets from the domain to enhance the understanding of text semantics. In order to mitigate the issue of noise introduced by incorporating knowledge triplets, this paper embeds the most relevant knowledge triplets to the questions, ensuring that the impact of noisy questions on the model is minimized through computation-based selection. Experimental results demonstrate that the proposed approach achieves an EM (Exact Match) score of 70.69% and an F1 score of 87.94%, representing improvements of 4.22% and 2.47% respectively over existing methods.

关键词： Knowledge engineering Computational modeling Semantics Noise Knowledge graphs Data models Natural language processing

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Investigating the Dynamic Decision Mechanisms of Users' Relevance Judgment for information Retrieval via Log Analysis 15th

Investigating the Dynamic Decision Mechanisms of Users' Rele...

引用

15th Pacific Rim international conference on Artificial Intelligence (PRICAI) / 15th Pacific Rim Knowledge Acquisition Workshop (PKAW)

作者： Su, Yi Li, Jingfei Song, Dawei Zhang, Pengqing Zhang, Yazhou Tianjin Univ Sch Comp Sci & Technol 135 Yaguan Rd Tianjin 300350 Peoples R China Coordinat Ctr China Natl Comp Network Emergency Response Tech Team 3A Yumin Rd Beijing 100020 Peoples R China Beijing Inst Technol Sch Comp Sci & Technol 5 South Zhongguancun St Beijing 100081 Peoples R China Open Univ Sch Comp & Commun Walton Hall Milton Keynes MK7 6AA Bucks England

ISBN: (纸本)9783319973043;9783319973036

Measuring relevance of documents with respect to a user's query is at the heart of information retrieval ( IR), where the user's relevance judgment criteria have been recognized as multi-dimensional. A set of relevance dimensions that are considered as critical factors in document relevance judgment have been investigated, such as topicality, novelty, and reliability. However, most existing work focuses on individual relevance dimensions, yet neglecting how different dimensions would interact with each other to influence the overall relevance judgment in real-world search scenarios. this paper aims at an initial step to fill the gap. Specifically, we divide 7 relevance dimensions in an enriched Multidimensional User Relevance Model ( MURM) into three categories according to three main requirements for document relevance, i.e., document content requirement, document quality requirement and personalization requirement. We then exploit the Learning to Rank framework to conduct document ranking experiments on a query log dataset from a prominent search engine. the experimental results indicate the existence of an order effect between different dimensions, and suggest that considering different dimensions across categories in different orders for document relevance judgment could lead to distinct search results. Our findings provide valuable insights to build more intelligent and user-centric information retrieval systems, and potentially benefit other natural language processing tasks that involve decision making from multiple perspectives.

关键词： information retrieval User relevance dimensions Order effect

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Relation Extraction Based on Relation Label Constraints

Relation Extraction Based on Relation Label Constraints

引用

international conference on Computer and Communications (ICCC)

作者： Kaihong Lin Kehua Miao Wenxing Hong Chaoyi Yuan Automation Xiamen University Xia men China North China Institute of Computing Technology Beijing China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

Knowledge graphs have a significant role in promoting natural language processing tasks, and they have received substantial attention. the relation extractor is a key step in the construction of a knowledge graph, so it is important to improve its performance. However, previous works are mainly based on the pipeline method, which rarely address the problem of overlapping triplets. In addition, the literature does not consider models in which the correlation between relation pairs is addressed, which limits their accuracy. In this paper, we propose a new model called Relation Extraction Based On Relation Label Constraints(RRC) that is based on relation matrix constraints. the subject is extracted in our model in the first step; then, the relation and object are extracted based on the subject information. Each relation is regarded as a vector to assist in the extraction of the relation and object; the vector is used to consider the correlation between the relation vectors. this is used as a constraint to optimize the relation vector. Experiments on two public datasets, NYT and webNLG, show that this method can perform well.

关键词： Correlation Computational modeling Pipelines Data models Natural language processing Data mining Task analysis

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Improvement of Timing Synchronization Algorithm for FBMC-OQAM System Based on Training Sequence

Improvement of Timing Synchronization Algorithm for FBMC-OQA...

引用

Natural language processing (ICNLP), international conference on

作者： Xihai Xie Ruichao Ning School of Communications and Information Engineering Xi’an University of Posts & Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

In the context of 5G with high rate transmission, the filter bank multicarrier-offset orthogonal amplitude modulation (FBMC-OQAM) technique has attracted much attention as an alternative to OFDM technology because the FBMC system reduces the wastage of spectral resources and has better time-frequency focusing characteristics, as well as better robustness to channel frequency selective fading. However, the inter-symbol overlap and interference caused by this modulation can make timing synchronization difficult. To address this problem, this paper designs a special training sequence based on existing synchronization algorithms, which exploits the conjugate symmetry property of the FBMC system for timing synchronization, and consists of four symbols, the first two of which are computed to have conjugate symmetry in the time domain; and the last two of which are zero-valued. the length of the correlation sequence is enlarged compared to the traditional algorithm, and the number of symbols used for training is increased. theoretical analysis and simulation results show that the proposed algorithm has higher timing accuracy and better synchronization performance.

关键词： Training Time-frequency analysis Analytical models Accuracy OFDM Simulation Symbols Interference Synchronization Time-domain analysis

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An Energy-Efficient Architecture of Approximate Softmax Functions for Transformer in Edge Computing

An Energy-Efficient Architecture of Approximate Softmax Func...

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Electronics and Electrical Engineering technology (EEET), international conference on

作者： Suhang Li Bo Yin Hao Zhang Information Science and Engineering Ocean University of China Qingdao China

ISBN: (数字)9798350395594

ISBN: (纸本)9798350395600

Recently, Transformer models have provided better accuracy than traditional models in fields such as computer vision (CV) and natural language processing (NLP). However, compared with traditional convolutional neural networks (CNNs), a large number of softmax function calculations in Transformer models involve expensive exponential and division calculations, resulting in huge consumption of computing and storage resources and waste of power consumption, which makes Transformer networks unable to be effectively applied to edge computing. To solve this problem, in this paper an approximate softmax computation architecture is proposed. Compared with the classic base-e softmax function or the base-2 softmax function proposed in recent years, our proposed design uses simpler shift and fixed-point addition operations to replace complex exponential and division operations, and thus significantly reduces the resource consumption while maintaining a high computation accuracy. the proposed architecture can effectively accelerate the Softmax computation of Transformer models in edge computing. Experimental results show that for 16-bit Softmax computation, the proposed design can achieve less resource occupation and energy consumption. Vision transformer(ViT) models are also used to verify the effectiveness of the proposed method in real transformer models.

关键词： Computer vision Accuracy Power demand Computational modeling Computer architecture Transformers Natural language processing

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An Enhanced BLSTIP Dialogue Research Platform

An Enhanced BLSTIP Dialogue Research Platform

引用

6～(th) international conference on Spoken language processing

作者： Qiru Zhou Antoine Saad Shereiff Abdou Dialogue Systems Research Department Bell Labs Lucent Technologies 600 Mountain Ave. Murray Hill NJ 07974 USA Dialogue Systems Research Department Bell Labs Lucent Technologies 600 Mountain Ave. Murray Hill NJ 07974 USA Dialogue Systems Research Department Bell Labs Lucent Technologies 600 Mountain Ave. Murray Hill NJ 07974 USA

this paper presents some recent enhancement to Bell Labs Speech technology Integration Platform (BLSTIP), a common platform to integrate Bell Lab's speech, telephony, Internet, and dialogue technologies for spoken and multi-modal dialogue system research and prototyping. Last year, we introduced BLSTIP to our partners as a speech technology platform for collaborative research and new application development. BLSTIP software is packaged as a single, network downloadable installation file. It supports a variety of speech applications such as natural language call routing/steering for call centers, natural language information system, messaging system with voice user interface, speaker verification, speech application trial and data collection, etc. As an enhancement to BLSTIP, we also designed a VoiceXML (Voice eXtensible Markup language) integration infrastructure to study emerging web hosted speech applications such as voice portal, multimodal internet access, and wireless internet access.

关键词： service An Enhanced BLSTIP Dialogue Research Platform session

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DESIGN OF ROBUST SUBTRACTIVE BEAMFORMER FOR NOISY SPEECH RECOGNITION

DESIGN OF ROBUST SUBTRACTIVE BEAMFORMER FOR NOISY SPEECH REC...

引用

6～(th) international conference on Spoken language processing

作者： Mitsunori Mizuma(?hi Masato Nkagi Satoshi Nakamura ATR Spoken Language Translation Research Laboratories 2-2-2 Hikaridai. Seika-(?h(?. Souraku-gun. Kyoto 619-0288. JAPAN. School of Information Science. Japan Advanced Insti(?ute of Science and Technology. (? Asahidai. (? atsunokuchi. Nomi-gun. Ishikawa 923-1292. JAPAN. ATR Spoken Language Translation Research Laboratories 2-2-2 Hikaridai. Seika-(?h(?. Souraku-gun. Kyoto 619-0288. JAPAN.

there is a big demand for noise reduetion to enhance ASR robustuess. A great variety of noise reduction methods have been proposed. but almost none of them can reduce non-stationary noises. the authers have proposed an algorithm that can redu(?)(?) noises that former methods, such as adaptive beamfor(?)ers. find difflicult to deal with. In this paper, the authors verify the proposed method as a front-end for ASR by phoneme recognition tests. Its feasibility is also discussed under reverberaut environments.

关键词： SOI DESIGN OF ROBUST SUBTRACTIVE BEAMFORMER FOR NOISY SPEECH RECOGNITION

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Analyzing information Diffusion in Ego-centric Twitter Social Network

Analyzing Information Diffusion in Ego-centric Twitter Socia...

引用

international conference on Signal processing and Communication (ICSC)

作者： Ankit Kumar Dipansha Chhabra Bhavya Mendiratta Adwitiya Sinha Department of Computer Science and Engineering Jaypee Institute of Science and Technology Noida India

ISBN: (数字)9781728154930

ISBN: (纸本)9781728154947

the ever dynamic world of social network is progressively about innovation and this headway in advancements has been an amalgamation of several advantages and disadvantages. the most quickly developing segment of the web is the social media life. the simplicity of correspondence with the individuals living far with the rise of social media life is deserving of acclaim. these applications are presently even broadly utilized for advancing organizations or occasions. Speaking of social media, Twitter has emerged out to be the most influential social media platform. therefore, it has common possibility to be the premise of future ego-centric networks and information exchange frameworks, notwithstanding novel types of internet administrations misusing the properties of human social connections. In this respect, our research focuses on study of information flow within ego-centric network with real data set extracted from Twitter user handle. Our proposed model performs high-level soft analysis via community detection techniques based on network homophily. this is followed by applying of various diffusion models to study information diffusion rate in ego network.

关键词： Twitter Data mining Mathematical model Image edge detection Time complexity Integrated circuit modeling

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Practical applications of intelligent systems :Porceedings of the sixth international conference on intelligent systems and knowledge engineering, Shanghai, China, Dec 2011 (ISKE2011) /

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丛书名： Advances in Intelligent and Soft Computing

2011年

作者： international conference on intelligent systems and knowledge engineering(6th :2011 :Shanghai China)editors Yinglin Wang and Tianrui Li. Wang Yinglin. Li Tianrui.

ISBN: (纸本)9783642256578

Proceedings oftheSixthinternational conference on Intelligent System and Knowledge Engineering presents selected papers from the conference ISKE 2011, held December 15-17 in Shanghai, China. this proceedings doesnt only examine original research and approaches in the broad areas of intelligent systems and knowledge engineering, but also present new methodologies and practices in intelligent computing paradigms. the book introduces the current scientific and technical advances in the fields of artificial intelligence, machine learning, pattern recognition, data mining, information retrieval, knowledge-based systems, knowledge representation and reasoning, multi-agent systems, natural-language processing, etc. Furthermore, new computing methodologies are presented, including cloud computing, service computing and pervasive computing with traditional intelligent methods. the proceedings will be beneficial for both researchers and practitioners who want to utilize intelligent methods in their specific research fields. Dr. Yinglin Wang is a professor at the Department of Computer Science and Engineering, Shanghai Jiao Tong University, China; Dr. Tianrui Li is a professor at the School of information Science and technology, Southwest Jiaotong University, China.

关键词： Intellegent Systems Knowledge engineering

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