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检索条件"任意字段=International Symposium on Intelligence Information Processing and Trusted Computing"

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Proceedings - 2011 international symposium on intelligence information processing and trusted computing, IPTC 2011

Proceedings - 2011 International Symposium on Intelligence I...

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2011 international symposium on intelligence information processing and trusted computing, IPTC 2011

ISBN: (纸本)9780769544984

The proceedings contain 64 papers. The topics discussed include: trusted bytecode virtual machine module: towards dynamic remote attestation in cloud computing;efficient power saving adaptive routing protocol (EPSAR) for MANETs using AODV and DSDV: simulation and feasibility analysis;formal specification for byzantine fault tolerant algorithm suiting web services;integrated visual saliency based local feature selection for image retrieval;trust based secure localization in wireless sensor networks;a feature-based robust digital image watermarking scheme using image normalization and quantization;research of electronic equipment fault diagnosis algorithm based on RBF neural network;multitemporal images change detection using nonsubsampled contourlet transform and kernel fuzzy C-means clustering;and study on approach to improve the quality of veterinary medicine graduates by strengthen practical skills.

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Proceedings - 2010 international symposium on intelligence information processing and trusted computing, IPTC 2010

Proceedings - 2010 International Symposium on Intelligence I...

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2010 international symposium on intelligence information processing and trusted computing, IPTC 2010

ISBN: (纸本)9780769541969

The proceedings contain 178 papers. The topics discussed include: data hiding techniques based on wavelet-like transform and complex wavelet transforms;an improved Vegas scheme based on average round trip time;local universities of china management information system integration model design;research on the safety of university financial system in the network environment;dynamic analysis of vegetation coverage change in Miyun reservoir basin from 2000 to 2005 based on remote sensing;an image-sequence compressing algorithm based on homography transformation for unmanned aerial vehicle;a novel regression method research based on covering algorithm;power spectral analysis of wrist pulse signal in evaluating adult age;design and realization of health monitoring system on electric wheel truck;a projection pursuit based risk assessment method in mobile ad hoc networks;and design of case-based reasoning system with community detection in complexity theory.

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Overview of the NLPCC 2024 Shared Task 3: Dialogue-Level Coreference Resolution and Relation Extraction 13th

Overview of the NLPCC 2024 Shared Task 3: Dialogue-Level Cor...

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13th international Conference on Natural Language processing and Chinese computing

作者： Xiong, Yiyun Li, Fei Li, Bobo Fei, Hao Ji, Donghong Teng, Chong Wuhan Univ Sch Cyber Sci & Engn Key Lab Aerosp Informat Secur & Trusted Comp Minist Educ Wuhan Peoples R China Natl Univ Singapore Sch Comp Singapore Singapore

ISBN: (纸本)9789819794423;9789819794430

In this report, we give an overview of the shared task about dialogue-level coreference resolution and relation extraction at the 13th CCF Conference on Natural Language processing and Chinese computing (NLPCC 2024). Dialogue Relation Extraction (DRE) focuses on the relations between arguments in a conversation, which can help better understand the interaction between interlocutors. The phenomenon of coreference is widely present in natural language, especially in conversations. Dialogue Coreference Resolution (DCR) can provide more accurate contextual understanding for the DRE task. We introduce dialogue-level joint coreference resolution and relation extraction task, and evaluate the ability of models to recognize dialogue-level coreference information and extract relations using coreference information. We describe the participating systems and their results to show the up-to-date progress in the DRE and DCR tasks, providing guidelines for future research.

关键词： Dialogue Coreference Resolution Dialogue Relation Extraction NLPCC 2024

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Overview of the NLPCC 2024 Shared Task 2: Nominal Compound Chain Extraction 13th

Overview of the NLPCC 2024 Shared Task 2: Nominal Compound C...

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13th international Conference on Natural Language processing and Chinese computing

作者： Li, Bobo Fei, Hao Li, Fei Ji, Donghong Wuhan Univ Sch Cyber Sci & Engn Key Lab Aerosp Informat Secur & Trusted Comp Minist Educ Wuhan Peoples R China Natl Univ Singapore Sch Comp Singapore Singapore

ISBN: (纸本)9789819794423;9789819794430

Nominal compound chain extraction (NCCE) represents an emerging task within the domain of natural language processing, exhibiting significant potential for application in various downstream tasks, including relation extraction and summarization, etc. This task involves analyzing a document to identify and categorize all nominal compounds into distinct clusters based on semantic relatedness. Consequently, NCCE presents greater challenges than traditional coreference resolution tasks due to its reliance on semantic associations. In response to these challenges, we initiated a shared task at NLPCC 2024, attracting 15 teams to register, of which 5 submitted their final results. We introduced a baseline system to benchmark performance in this area. This paper provides a comprehensive overview of the NCCE task, detailing the task's framework, dataset characteristics, evaluation protocols, and metrics, alongside a summary of the submission results. We anticipate that the task will significantly aid in corpus construction and enhance the semantic understanding of the Chinese language.

关键词： Nominal Compound Chain Extraction Coreference Resolution information Extraction

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Generative Dialogue Sentiment and Act Recognition with Feature Denoising and Set Prediction 13th

Generative Dialogue Sentiment and Act Recognition with Featu...

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13th international Conference on Natural Language processing and Chinese computing

作者： Liu, Jiahui Li, Bobo Li, Zhuang Chai, Yuyang Li, Fei Teng, Chong Ji, Donghong Wuhan Univ Sch Cyber Sci & Engn Key Lab Aerosp Informat Secur & Trusted Comp Minist Educ Wuhan Peoples R China Monash Univ Fac Informat Technol Melbourne Vic Australia

ISBN: (纸本)9789819794423;9789819794430

Joint dialog sentiment classification and act recognition focus on the simultaneous identification of sentiment and act categories of every utterance from conversations. Current classification-based methods often rely on complex structures to model label dependencies, which can be sensitive to noisy features in dialogue contexts. In light of this, we introduce an end-to-end generative framework, termed Sentiment and Act T5 (SAT5), specifically designed to address this joint task. Our SAT5 framework simply leverages the inherent ability of generative models to adequately model label dependencies without additional structures. Meanwhile, considering the order sensitivity issues in generative models, a set loss mechanism is further incorporated to eliminate these concerns effectively. Additionally, we develop a graph-based feature optimization strategy grounded in the information bottleneck principle, aimed at minimizing the impact of noisy features. Experiments on two public datasets demonstrate that our SAT5 framework significantly outperforms previous models. In-depth analysis further validates the efficacy and rationality of our set loss mechanism and feature optimization strategy.

关键词： Generative Model Sentiment Analysis Dialogue Understanding Feature Denoising Set Prediction

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SHGNN: A Substructure-Aware and High Expressive Graph Neural Network for Graph Classification 5th

SHGNN: A Substructure-Aware and High Expressive Graph Neural...

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5th international Conference on Neural computing for Advanced Applications (NCAA)

作者： Sun, Jinyong Dong, Zhiwei Sun, Zhigang Wen, Yimin Zhao, Xiang Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin 541004 Peoples R China Guangxi Normal Univ Key Lab Educ Blockchain & Intelligent Technol Minist Educ Guilin 541004 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China Guilin Univ Elect Technol Key Lab Cognit Radio & Informat Proc Guilin 541004 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

Graph Neural Networks (GNNs), with their remarkable capabilities in learning graph features, have achieved success in graph classification tasks. However, the Message Passing GNNs (MP-GNNs), sharing the feature aggregation mechanism with the 1-Weisfeiler-Leman (1-WL) graph isomorphism test, suffer from limited expressiveness in downstream tasks, such as graph classification. To enhance the expressive power of GNNs, we propose a substructure-aware and high expressive graph neural network model. Firstly, Ego substructures are extracted from a graph and fed into a MP-GNN to obtain substructure-level features. Then, a count-sensitive neighborhood hashing method is introduced to encode the structural characteristics of Ego substructures, which are then injected into the corresponding substructure-level features to enhance the model's expressiveness. Next, an Ego-substructure-aware Transformer is utilized to extract global features from the graph, which use a token to fuse the interaction information among graphs' nodes, thereby generating graph-level feature vector. Finally, we apply a Multi-Layer Perceptron (MLP) followed by a softmax layer to classify graphs. Theoretical analysis demonstrates that the proposed SHGNN ismore powerful than 1-WL test. Experimental results show that, compared to the state-of-the-art graph deep learning methods, our model not only outperforms 1-WL in expressiveness, but also achieves higher classification accuracy.

关键词： graph classification graph neural networks substructure-aware neighborhood hashing Transformer

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31st international symposium on String processing and information Retrieval, SPIRE 2024

31st International Symposium on String Processing and Inform...

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31st international symposium on String processing and information Retrieval, SPIRE 2024

ISBN: (纸本)9783031721991

The proceedings contain 26 papers. The special focus in this conference is on String processing and information Retrieval. The topics include: Indexing Finite-State Automata Using Forward-Stable Partitions;burst Edit Distance;generalization of Repetitiveness Measures for Two-Dimensional Strings;on computing the Smallest Suffixient Set;revisiting the Folklore Algorithm for Random Access to Grammar-Compressed Strings;logarithmic-Time Internal Pattern Matching Queries in Compressed and Dynamic Texts;bounded-Ratio Gapped String Indexing;simultaneously Building and Reconciling a Synteny Tree;quantum Algorithms for Longest Common Substring with a Gap;online Computation of String Net Frequency;on the Number of Non-equivalent Parameterized Squares in a String;another Virtue of Wavelet Forests;all-Pairs Suffix-Prefix on Dynamic Set of Strings;adaptive Dynamic Bitvectors;compressed Graph Representations for Evaluating Regular Path Queries;greedy Conjecture for the Shortest Common Superstring Problem and Its Strengthenings;faster Computation of Chinese Frequent Strings and Their Net Frequencies;faster Algorithms for Ranking/Unranking Bordered and Unbordered Words;computing String Covers in Sublinear Time;LZ78 Substring Compression with CDAWGs;2d Side-Sharing Tandems with Mismatches;faster and Simpler Online/Sliding Rightmost Lempel-Ziv Factorizations;Space-Efficient SLP Encoding for O(log N)-Time Random Access;simple Linear-Time Repetition Factorization.

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Graph Contrastive Learning with Decoupled Augmentation

Graph Contrastive Learning with Decoupled Augmentation

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2025 IEEE international Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Gao, Shihao Li, Caoshuo Mao, Cunli Zhang, Xulong Qu, Xiaoyang Jin, Taisong Wang, Jianzong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University China Faculty of Information Engineering and Automation Kunming University of Science and Technology China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Anyang Normal University China Ping An Technology Co. Ltd. Shenzhen China

ISBN: (纸本)9798350368741

Graph contrastive learning based on augmentation strategies has recently demonstrated remarkable performance. Existing methods typically jointly leverage attribute and structural augmentations to generate graph views, learning data invariance information through contrasting sample pairs. However, this joint approach may deviate from the expectation of semantically similar before and after augmentation. The propagation of attribute information in graphs usually occurs through their structure, meaning that structural and attribute augmentations can interfere with each other and potentially distort the graph's semantics. To address this, we propose a decoupled augmentation framework for graph contrastive learning, which eliminates the mutual interference between the two levels of augmentation while fully exploring graph information. Specifically, our framework employs separate encoders to learn data invariance under different augmentation levels, and it considers the positive gains generated between these levels. Experimental results on five public datasets show that the proposed method is more competitive than state-of-the-art approaches. © 2025 IEEE.

关键词： Contrastive learning Graph representation Self-supervised learning

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Anaheim: Architecture and Algorithms for processing Fully Homomorphic Encryption in Memory 31

Anaheim: Architecture and Algorithms for Processing Fully Ho...

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31st IEEE international symposium on High Performance Computer Architecture, HPCA 2025

作者： Kim, Jongmin Yun, Sungmin Ji, Hyesung Choi, Wonseok Kim, Sangpyo Ahn, Jung Ho Seoul National University Korea Republic of

ISBN: (纸本)9798331506476

Fully homomorphic encryption (FHE) is a promising solution for privacy-preserving cloud computing as it enables computations on sensitive data without any risk of data leakage. Despite the significant attention FHE has received, substantial computation and memory demands make it hardly practical for real-world applications. We propose a readily available and practical hardware solution to tackle this problem by using GPUs. GPUs have adequate computational and memory resources to handle complex operations in FHE, including number-theoretic transform (NTT), on which most prior work has deeply focused. However, through detailed analyses, we discover that the performance bottleneck on GPUs is primarily due to simpler element-wise operations, which are limited by off-chip memory (DRAM) bandwidth. Motivated by these observations, we develop Anaheim, a processing-in-memory (PIM) architecture for FHE. We develop optimized FHE execution flows and an end-toend software framework for using PIM with GPUs. Also, we design a versatile PIM unit that handles various modular integer arithmetic PIM instructions, along with an efficient data mapping and associated PIM execution algorithms that minimize data access overhead by leveraging the internal structure of DRAM. Our concerted efforts substantially enhance the performance and energy efficiency of various FHE workloads on GPUs. © 2025 IEEE.

关键词： information leakage

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PRPFD: Polynomial Resampling Particle Filter Based Deconvolution Approach for Resting-State BOLD-fMRI Data Analysis 24

PRPFD: Polynomial Resampling Particle Filter Based Deconvolu...

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5th international symposium on Artificial intelligence for Medicine Science, ISAIMS 2024

作者： Luo, Yunying Zeng, Weiming Key Laboratory for Digital Imaging Processing and Intelligent Computing School of Information Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9798400717826

Functional Magnetic Resonance Imaging (fMRI) stands out in brain science research due to its non-invasive, non-intrusive, radiation-free, high spatial resolution, and precise localization advantages. However, the BOLD-fMRI signals it generates exhibit a several-second delay compared to neural activity, making it unable to reflect brain neural activity directly. This discrepancy may lead to differences from the actual situation. Therefore, this study aims to enhance the deconvolution of fMRI by refining existing methods to reconstruct the brain’s neural signals more faithfully. Building upon the foundation of particle filtering deconvolution, this paper introduced a novel approach to deconvolving resting-state fMRI data: the polynomial resampling particle filtering deconvolution (PRPFD). They adapted the particle distribution better to match the actual state through weight computation and resampling. We performed a simulation-based experiment to validate the feasibility of our approach. Besides, we validated the performance of our method using group data, focusing on both effective connectivity and functional connectivity, providing strong evidence for the usability and stability of our approach, and compared our method with other deconvolution techniques, demonstrating improvements in accuracy and computational efficiency. © 2024 Copyright held by the owner/author(s).

关键词： Polynomials

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