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2016 International Conference on Wireless Communication and Network engineering (WCNE2016)

作者： Tai-sheng ZENG Ji-chang WEN College of Mathematics and Computer Science Quanzhu Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University College of Engineering Quanzhou Meteorological Bureau

Real-time Information Service System for the Meteorological Screen Base on CDMA is designed, a complete set of information gathering, processing, aggregating and publishing system for weather prediction products, disaster warning information, Typhoon real-time position information, weather information every ten minutes. Real-time weather information to the grouping of the LED display live information and notice is automatically released. The system contains user classification and display management module, disaster warning information into the module, all kinds Notice entry module, real-time weather information collected the summary module as well as real-time weather information automatically release the module.

关键词： LED CDMA Information service system

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Privacy Preserving Scheme for Location Based Services Using Cryptographic Approach 2

Privacy Preserving Scheme for Location Based Services Using ...

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2nd IEEE Symposium on Privacy-Aware computing, PAC 2018

作者： Pu, Yuwen Luo, Jin Wang, Ying Hu, Chunqiang Huo, Yan Zhang, Jiong School of Big Data and Software Engineering Chongqing University Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Ningbo Branch of Agricultural Bank of China Ningbo China

ISBN: (纸本)9781538684429

Location based services pose strong security and privacy requirements but achieving privacy protection at the client side is a non-trial problem. In this paper, we propose an efficient and reliable location privacy preserving scheme using cryptographic approach. It can preserve users' privacy data by making their identity be anonymity for LBS provider and fog server. For communication, both AES with One-Time-Pad keys and IBE are employed to guarantee confidentiality and integrity of request and response data. Besides, we also provide security analysis to demonstrate that our scheme can resist internal attack, external attack and colluding attack. © 2018 IEEE.

关键词： Location based services

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DC-Instruct: An Effective Framework for Generative Multi-intent Spoken Language Understanding

DC-Instruct: An Effective Framework for Generative Multi-int...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Xing, Bowen Liao, Lizi Huang, Minlie Tsang, Ivor W. Beijing Key Laboratory of Knowledge Engineering for Materials Science School of Computer and Communication Engineering University of Science and Technology Beijing China Singapore Management University Singapore Group Tsinghua University China CFAR and IHPC Agency for Science Technology and Research Singapore College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798891761643

In the realm of multi-intent spoken language understanding, recent advancements have leveraged the potential of prompt learning frameworks. However, critical gaps exist in these frameworks: the lack of explicit modeling of dual-task dependencies and the oversight of task-specific semantic differences among utterances. To address these shortcomings, we propose DC-Instruct, a novel generative framework based on Dual-task Inter-dependent Instructions (DII) and Supervised Contrastive Instructions (SCI). Specifically, DII guides large language models (LLMs) to generate labels for one task based on the other task's labels, thereby explicitly capturing dual-task inter-dependencies. Moreover, SCI leverages utterance semantics differences by guiding LLMs to determine whether a pair of utterances share the same or similar labels. This can improve LLMs on extracting and discriminating task-specific semantics, thus enhancing their SLU reasoning abilities. Extensive experiments on public benchmark datasets show that DC-Instruct markedly outperforms current generative models and state-of-the-art methods, demonstrating its effectiveness in enhancing dialogue language understanding and reasoning. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Fast Collection of Target Tags Information for Large-Scale RFID Systems 20

Fast Collection of Target Tags Information for Large-Scale R...

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20th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Chu, Chu Fu, Ning Xu, Rui College of Computer Science Sichuan Normal University Chengdu610101 China The Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu610101 China School of Network and Communication Engineering Chengdu Technological University Chengdu611730 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798350318982

Radio Frequency Identification (RFID) technology is greatly supporting a variety of life and industry. Enhanced by sensors, RFID tags can monitor real-time information about attached objects and the environment, making efficient collection of tag information an important task. However, most works focus on collecting all tags but lack effective mechanisms to avoid interfering from irrelevant tags when the application only cares about a part of the tags in some cases. Besides, in more common scenarios with multiple RFID readers, existing works fail to effectively determine the surrounding tags for each reader, increasing the execution time. To conquer these defects, we propose a new solution called the efficient Vector-based target tag Information Collection (VIC) protocol to accelerate the collection rate. First, we introduce a composite vector to silence irrelevant tags and determine the local tags. During this process, the expected empty slots are skipped and the useless information transmitted by tags is reduced. Second, we use an allocation vector to collect tag information by resolving tag collision, thus improving the time efficiency. Finally, we provide theoretical analyses to optimize the parameter settings. Extensive experiments prove that VIC outperforms existing solutions by up to 32% increase in time efficiency. © 2023 IEEE.

关键词： Efficiency

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Penta-Encoder with Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation 17

Penta-Encoder with Medical Transformer for Incomplete Multim...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Yu, Guohui Wang, Lihui Chen, Qijian Wang, Li Tang, Kun Cheng, Xinyu Zhu, Yuemin Guizhou University Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China Univ Lyon Insa Lyon Cnrs Inserm Creatis Umr 5220 U1206 Lyon France

ISBN: (纸本)9798350387384

Accurate brain tumor segmentation of magnetic resonance imaging (MRI) images requires the integration of multimodal data in effective collaborative learning. However, clinical scenarios often face missing modalities, significantly impacting segmentation performance. In this paper, we propose the penta-encoder with medical transformer (Penta-MedTrans) for incomplete multimodal learning, which consists of four main components: a modality-specific encoder (MSE), a modality-dynamic encoder (MDE), a multimodal fusion module (MMFM) and a convolutional decoder. The MSE extracts unique features for each modality, while the MDE captures inter-modal semantics to address missing modalities. The MMFM establishes and aligns long-range correlations between modal-invariant features and global semantics of tumor regions. The convolutional decoder generates robust segmentation results by progressively upsampling and fusing modality-invariant features. We extensively validated our method on the BraTS 2020 and BraTS 2021 datasets for brain tumor segmentation. Results showed that our method outperforms SOTA for incomplete multimodal brain tumor segmentation across most subsets. © 2024 IEEE.

关键词： Semantic Segmentation

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Syntax-enhanced pre-trained model 59

Syntax-enhanced pre-trained model

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Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021

作者： Xu, Zenan Guo, Daya Tang, Duyu Su, Qinliang Shou, Linjun Gong, Ming Zhong, Wanjun Quan, Xiaojun Jiang, Daxin Duan, Nan School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Microsoft Research Asia Beijing China Microsoft Search Technology Center Asia Beijing China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9781954085527

We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage, so that they suffer from discrepancy between the two stages. Such a problem would lead to the necessity of having human-annotated syntactic information, which limits the application of existing methods to broader scenarios. To address this, we present a model that utilizes the syntax of text in both pre-training and fine-tuning stages. Our model is based on Transformer with a syntax-aware attention layer that considers the dependency tree of the text. We further introduce a new pre-training task of predicting the syntactic distance among tokens in the dependency tree. We evaluate the model on three downstream tasks, including relation classification, entity typing, and question answering. Results show that our model achieves state-of-the-art performance on six public benchmark datasets. We have two major findings. First, we demonstrate that infusing automatically produced syntax of text improves pre-trained models. Second, global syntactic distances among tokens bring larger performance gains compared to local head relations between contiguous tokens. © 2021 Association for Computational Linguistics

关键词： Syntactics

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Research on long and short-term social recommendation based on convolutional and gated recurrent units 29

Research on long and short-term social recommendation based ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Jia, Zihe Xue, Peng Dai, Zhiqiang Gao, Qian Zhang, Xiaomeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China

ISBN: (纸本)9798350330717

The development of the Internet has made people more closely related and has put forward higher requirements for recommendation models. Most recommendation models are studied only for the long-term interests of users. In this paper, the interaction time between the user and the item is introduced as auxiliary information in the model construction. Interaction time is used to determine users' long-term preferences and short-term preferences. In this paper, temporal features are extracted by building a convolutional gated recurrent unit with attention neural network (CNN-GRU-Attention). Firstly, for the problem of accurate feature extraction, CNN are constructed to extract higher-level and more abstract features of themselves and transform high-dimensional data into low-dimensional data;secondly, for the problem of social temporality, GRU are used to not only extract temporal information, but also effectively reduce gradient dispersion, making model convergence and training easier;finally, Graph Attention networks are used to aggregate the social relationship information of users and items respectively, which constitute the final feature representation of users and items respectively. In particular, a modified cosine similarity is used to reduce the error caused by data insensitivity when constructing the social information of the item. In this study, simulation experiments are conducted on two publicly available datasets (Epinions and Ciao), and the experimental results show that the proposed recommended model performs better than other social recommendation models, improving the evaluation metrics of MAE and RMSE by 1.06%-1.33% and 1.19%-1.37%, respectively. The effectiveness of the model innovation is proved. © 2023 IEEE.

关键词： convolutional gated recurrent unit graph attention networks neighborhood recommendation systems social networks

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A Stepwise Path Selection Scheme Based on Multiple QoS Parameters Evaluation in SDN 17th

A Stepwise Path Selection Scheme Based on Multiple QoS Param...

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17th EAI International Conference on Collaborative computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Liu, Lin Zhou, Jian-Tao Xing, Hai-Feng Guo, Xiao-Yong College of Computer Science Ecological Big Data Engineering Research Center of the Ministry of Education Cloud Computing and Service Software Engineering Laboratory of Inner Mongolia Autonomous Region National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Social Computing and Data Processing Key Laboratory of Inner Mongolia Autonomous Region Big Data Analysis Technology Engineering Research Center of Inner Mongolia Autonomous Region Inner Mongolia University Inner Mongolia Hohhot China College of Computer Science and Technology Inner Mongolia Normal University Inner Mongolia Hohhot China College of Computer Information and Management Inner Mongolia University of Finance and Economics Inner Mongolia Hohhot China

ISBN: (纸本)9783030926342

Nowadays, the best-effort service can not guarantee the quality of service (QoS) for all kinds of services. QoS routing is an important method to guarantee QoS requirements. It involves path selection for flows based on the current network status and the performance criteria of the service requirements. However, it is difficult for proposed solutions to obtain all the available paths owing to not fully considering all the QoS parameters of paths. In this paper, we propose SWQoS, a novel, universal, and stepwise QoS guarantee scheme based on multiple QoS parameter evaluation for selecting the available paths including preferred paths, satisfied paths and reluctant paths in SDN. The experiments show that SWQoS can select all the available paths that meet the performance criteria of the service requirements and have better QoS parameter performance compared with other path selection methods. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Quality of service

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visualizing nonlinear resonance in nanomechanical systems via single-electron tunneling

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Nano Research 2021年第4期14卷 1156-1161页

作者： Xinhe Wang Lin Cong Dong Zhu Zi Yuan Xiaoyang Lin Weisheng Zhao Zaiqiao Bai Wenjie Liang Ximing Sun Guang-Wei Deng Kaili Jiang Fert Beijing Research Institute School of Microelectronics&Beijing Advanced Innovation Centre for Big Data and Brain Computing(BDBC)Beihang UniversityBeijing 100191China State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics and Tsinghua-Foxconn Nanotechnology Research CenterTsinghua UniversityBeijing 100084China Key Laboratory of Quantum Information CASUniversity of Science and Technology of ChinaHefei 230026China Department of Physics Beijing Normal UniversityBeijing 100875China Institute of Physics Chinese Academy of SciencesBeijing 100080China Institute of Nuclear and New Energy Technology Collaborative Innovation Center of Advanced Nuclear Energy TechnologyKey Laboratory of Advanced Reactor EngineeringTsinghua UniversityBeijing 100084China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610054China

Numerous reports have elucidated the importance of mechanical resonators comprising quantum-dot-embedded carbon nanotubes(CNTs)for studying the effects of single-electron ***,there is a need to investigate the single-electron transport that drives a large amplitude into a nonlinear ***,a CNT hybrid device has been investigated,which comprises a gate-defined quantum dot that is embedded into a mechanical resonator under strong actuation *** Coulomb peak positions synchronously oscillate with the mechanical vibrations,enabling a single-electron Chopper*1 ***,the vibration amplitude of the CNT versus its frequency can be directly visualized via detecting the time-averaged single-electron tunneling *** understand this phenomenon,a general formula is derived for this time-averaged single-electron tunneling current,which agrees well with the experimental *** using this visualization method,a variety of nonlinear motions of a CNT mechanical oscillator have been directly recorded,such as Duffing nonlinearity,parametric resonance,and double-,fractional-,mixed-frequency *** approach opens up burgeoning opportunities for investigating and understanding the nonlinear motion of a nanomechanical system and its interactions with electron transport in quantum regimes.

关键词： carbon nanotube mechanical resonator quantum dot nonlinear coupling

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Improved Capon Estimator for High-Resolution DOA Estimation and Its Statistical Analysis

Improved Capon Estimator for High-Resolution DOA Estimation ...

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作者： Zuo, Weiliang Xin, Jingmin Liu, Changnong Zheng, Nanning Sano, Akira National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Xi'an710049 China Data Center China Construction Bank Beijing100033 China Keio University Department of System Design Engineering Yokohama223-8522 Japan

Despite some efforts and attempts have been made to improve the direction-of-arrival (DOA) estimation performance of the standard Capon beamformer (SCB) in array processing, rigorous statistical performance analyses of these modified Capon estimators are still lacking. This paper studies an improved Capon estimator (ICE) for estimating the DOAs of multiple uncorrelated narrowband signals, where the higher-order inverse (sample) array covariance matrix is used in the Capon-like cost function. By establishing the relationship between this nonparametric estimator and the parametric and classic subspace-based MUSIC (multiple signal classification), it is clarified that as long as the power order of the inverse covariance matrix is increased to reduce the influence of signal subspace components in the ICE, the estimation performance of the ICE becomes equivalent to that of the MUSIC regardless of the signal-to-noise ratio (SNR). Furthermore the statistical performance of the ICE is analyzed, and the large-sample mean-squared-error (MSE) expression of the estimated DOA is derived. Finally the effectiveness and the theoretical analysis of the ICE are substantiated through numerical examples, where the Cramer-Rao lower bound (CRB) is used to evaluate the validity of the derived asymptotic MSE expression. © 2014 Chinese Association of Automation.

关键词： Multiple signal classification

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