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检索条件"丛书名=Lecture Notes in Computer Science"

共 300226 条记录，以下是1161-1170 订阅

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Foreground-Background Partitioning and Feature Fusion for Weakly Supervised Fine-Grained Image Recognition 7th

Foreground-Background Partitioning and Feature Fusion for We...

引用

7th Chinese Conference on Pattern Recognition and computer Vision

作者： Liu, Chenglin Li, Jiuliang Chen, Yanmin Luo, Jun Zhou, Mengyao Yang, Jian Li, Zhenfei Huazhong Agr Univ Coll Informat Wuhan 430070 Peoples R China

ISBN: (纸本)9789819785018;9789819785025

Fine-grained image recognition (FGIR) aims to distinguish visual objects belonging to different subclasses within the same category. Existing methods mainly focus on identifying discriminative regions and extracting the most prominent features. However, this approach leads to scale imbalance between the foreground and background of an image. And it tends to focus on extracting features from salient foreground regions while neglecting valuable information present in the background. To address these two challenges, we propose a weakly supervised foreground-background partitioning and feature fusion framework. Specifically, a foreground-background image partition module is employed to separate the foreground and background regions to resolve the scale imbalance in image. We incorporate a feature similarity calculation module to weigh the foreground and background features. To leverage the background information while capturing discriminative regions, we introduce a selective mask feature module. Comprehensive experiments on four popular and competitive datasets demonstrated the superiority of the proposed method in comparison with the state-of-the-art methods.

关键词： Fine-grained image recognition Foreground-background partitioning Scale imbalance

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Dual-Task Feedback Learning for Tongue Detection via Super-Resolution Integration 31st

Dual-Task Feedback Learning for Tongue Detection via Super-R...

引用

31st International Conference on Multimedia Modeling

作者： Sun, Ying Wei, Meiyi Chen, Gang Wuhan Univ Minist Educ Key Lab Aerosp Informat Secur & Trusted Comp Sch Cyber Sci & Engn Wuhan Peoples R China

ISBN: (纸本)9789819620531;9789819620548

Traditional Chinese Medicine (TCM) has gained prominence in clinical practice, with tongue diagnosis, a key technique, now being integrated with Artificial Intelligence (AI) to achieve more objective and quantifiable results, thereby mitigating reliance on subjective judgment. However, challenges such as poor lighting conditions and limited imaging equipment often compromise image clarity, complicating tongue detection and identification. To address these issues, we propose a DualTask Feedback Learning (DTFL) framework, designed to enhance tongue detection in patient images by improving image quality. In our approach, Super-Resolution (SR) serves as a preliminary task preceding Tongue Detection (TD), enabling the TD network to process high-quality images for more accurate results. To further improve the interaction between SR and TD tasks, we incorporate Feature Alignment (FA) loss, which establishes a feedback connection that allows the SR network to acquire task-specific knowledge from the TD network. Additionally, we introduce a quality fusion augmentation and alternate training strategy to address potential challenges associated with FA loss during training. To the best of our knowledge, we are the first to integrate SR into TD. Experiments demonstrate that DTFL significantly improves performance by generating SR images that are optimally suited for TD.

关键词： Traditional Chinese Medicine Tongue Detection Super-Resolution Artificial Intelligence Multi-Task Learning

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Unifying Weak Memory Verification Using Potentials 26th

Unifying Weak Memory Verification Using Potentials

引用

26th International Symposium on Formal Methods (FM)

作者： Bargmann, Lara Dongol, Brijesh Wehrheim, Heike Carl von Ossietzky Univ Oldenburg Oldenburg Germany Univ Surrey Guildford Surrey England

ISBN: (纸本)9783031711619;9783031711626

Concurrency verification for weak memory models is inherently complex. Several deductive techniques based on proof calculi have recently been developed, but these are typically tailored towards a single memory model through specialised assertions and associated proof rules. In this paper, we propose an extension to the logic Piccolo to generalise reasoning across different memory models. Piccolo is interpreted on the semantic domain of thread potentials. By deriving potentials from weak memory model states, we can define the validity of Piccolo formulae for multiple memory models. We moreover propose unified proof rules for verification on top of Piccolo. Once (a set of) such rules has been shown to be sound with respect to a memory model MM, all correctness proofs employing this rule set are valid for MM. We exemplify our approach on the memory models SC, TSO and SRA using the standard litmus tests Message-Passing and IRIW.

关键词： Memory architecture

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Efficient and Dependency-Aware Placement of Serverless Functions on Edge Infrastructures 22nd

Efficient and Dependency-Aware Placement of Serverless Funct...

引用

22nd International Conference on Service Oriented Computing

作者： Baresi, Luciano Quattrocchi, Giovanni Ticongolo, Inacio Gaspar Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy Univ Eduardo Mondlane Dept Matemat & Informat Maputo Mozambique

ISBN: (纸本)9789819608041;9789819608058

Serverless computing is a promising paradigm for deploying and managing applications on edge infrastructures. It provides small granularity and high flexibility by decomposing applications into lightweight functions. Although this modularity facilitates efficient resource allocation and function placement on edge nodes, complex dependencies among functions pose significant challenges to their effective management. Existing research has explored various optimization techniques for serverless computing platforms, but dependency-aware function placement remains an open challenge. In this paper, we propose PLUTO, an efficient solution for the placement of serverless functions that supports complex dependencies. First, we present an optimal non-linear formulation of the placement problem. Then, we introduce a heuristic approach, derived from the optimal formulation, that ensures efficiency as the number of functions increases. An extensive empirical evaluation against state-of-the-art solutions shows that PLUTO significantly reduces the overall delay and memory consumption by up to 85% and 78%, respectively.

关键词： Serverless functions Edge computing Function dependencies Placement

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A Process Algebraic View of In/Out Prisoners 12th

A Process Algebraic View of In/Out Prisoners

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12th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation

作者： Bruni, Roberto Univ Pisa Comp Sci Dept Pisa Italy

ISBN: (纸本)9783031737084;9783031737091

This contribution reflects on a shared teaching experience with Rocco De Nicola that unfolded from 2006 to 2009 at the IMT School for Advanced Studies in Lucca, Italy. During this period, we co-taught a course on formal methods based on process algebras, including Milner's Calculus of Communicating Systems (CCS). While the course underwent yearly changes in nomenclature and content, it was there that we started using a logical puzzle known as the "50 prisoners puzzle" as a pedagogical tool for CCS modelling and analysis. By bridging theoretical abstractions with concrete problem solving scenarios, we empowered students to grasp the intricacies of process algebras while fostering critical thinking and problem-solving skills. Subsequently I revisited this very same example in subsequent MSc level courses, which I teach at the University of Pisa.

关键词： CCS Bisimulation Hennessy-Milner logic CAAL

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AFSNet: Adaptive Feature Suppression Network for Remote Sensing Image Change Detection 7th

AFSNet: Adaptive Feature Suppression Network for Remote Sens...

引用

7th Chinese Conference on Pattern Recognition and computer Vision

作者： Li, Yang Wang, Liejun Xinjiang Univ Sch Comp Sci & Technol Urumqi Peoples R China

ISBN: (纸本)9789819785018;9789819785025

Recently, deep learning has made great progress in remote sensing change detection. However, some interference information are involved in bitemporal images which causes the algorithms to be affected by pseudo-changes in the background, such as shooting angle, seasonal turnover, and illumination intensity. Some researchers try to use the attention mechanism to solve this issue. To date, the existing attention methods explore incompletely the potentiality of feature suppression. Unlike existing spatial attention methods, we hope to obtain the interested features while removing some irrelevant-task features. From this perspective, we propose a new change detection architecture, i.e. adaptive feature suppression network (AFSNet), which includes two core components: adaptive feature suppression attention (AFSA) module and spatial and channel feature fusion (SCFF) strategy. We carefully design the AFSA inspired by soft threshold function, and it only uses 10 parameters to suppress interference information. Specifically, we remove spatial irrelevant information in the calculated process of soft threshold function and introduce a set of scaling factors to restrain redundant channel features. SCFF is an effective feature fusion strategy, and it utilizes simultaneously learnable addition and concatenation operations to aggregate better bitemporal features. Compared with some state-of-the-art (SOTA) methods on two challenging remote sensing change detection datasets, ASFNet can achieve superior performance. The code will be publicly available at https://***/tlyslll/AFSNet.

关键词： Change detection attention mechanisms adaptive feature suppression attention dual-branch feature fusion

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An Ontology-Lexicon-Driven Approach for Refining Sentiment Analysis Processes 25th

An Ontology-Lexicon-Driven Approach for Refining Sentiment A...

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25th International Conference on Intelligent Data Engineering and Automated Learning

作者： Toldy, Francisco Belo, Orlando Univ Minho ALGORITMI Res Ctr LASI P-4710059 Braga Portugal

ISBN: (纸本)9783031777370;9783031777387

The high number of sentiment analysis systems and applications developed over the last few years provided companies with very sophisticated analysis tools, allowing them to establish preferences, trends and patterns of customer behavior. This is quite important for companies intending to change their way of being, promoting work actions aimed at specific customer segments, to obtain business advantages and improve their image and performance in the market in which they work. In this paper, we present and describe a sentiment analysis system that combine techniques based on ontologies and domain lexicons, to provide relevant indicators to support the evaluation of the degree of user satisfaction and know the influence of each ontological element incorporated in opinion texts in sentiment classification.

关键词： Sentiment Analysis Opinion Analysis Ontologies Lexical Classification Data Preparation Tagging Texts Marketing Applications

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Mission: COMANND Conceptualizing an AI Assistant for Decision-Making 10th

Mission: COMANND Conceptualizing an AI Assistant for Decisio...

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10th International Conference on Modelling and Simulation for Autonomous Systems

作者： Hofer, Peter Theresian Mil Acad A-2700 Wiener Neustadt Austria

ISBN: (纸本)9783031713965;9783031713972

Future military command will be challenged by a quadruplicity of urbanization, digitization and artificial intelligence, and mission command. It seems therefore important to ask, how to improve decision-making within this framework. Mission: COMANND (Comprehensive Operational Memory And Neural Network Deliberation) conceptualizes an AI decision support capable of rapidly integrating and visualizing the urban operational environment, providing in-depth knowledge and analysis combining key information on infrastructure, tacit knowledge, and socio-cultural aspects.

关键词： Mission command artificial intelligence decision-making

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Hierarchical Decoder with Parallel Transformer and CNN for Medical Image Segmentation 7th

Hierarchical Decoder with Parallel Transformer and CNN for M...

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7th Chinese Conference on Pattern Recognition and computer Vision

作者： Li, Shijie Gong, Yu Xiang, Qingyuan Li, Zheng Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Sichuan Univ Tianfu Engn Oriented Numercial Simulat & Software Chengdu 610207 Peoples R China

ISBN: (纸本)9789819784950;9789819784967

With the success of Transformers, hybrid Transformer and CNN methods gain considerable popularity in medical image segmentation. These methods utilize a hybrid architecture that combines Transformers and CNNs to fuse global and local information, supplemented by a pyramid structure to facilitate multi-scale interaction. However, they encounter two primary limitations: (i) Transformer struggle to capture complete global information due to the sliding window nature of the convolutional operator, and (ii) the pyramid structure within single decoder fails to provide sufficient multi-scale interaction necessary for restoring detailed features at higher levels. In this paper, we introduce the Hierarchical Decoder with Parallel Transformer and CNN (HiPar), a novel architecture designed to address these limitations. Firstly, we present a parallel structure of Transformer and CNN to maximize the capture of both global and local features. Subsequently, we propose a hierarchical decoder to model multi-scale information and progressively restore spatial details. Additionally, we incorporate lightweight components to enhance the efficiency of feature representation. Extensive experiments demonstrate that our HiPar achieves state-of-the-art results on three popular medical image segmentation benchmarks: Synapse, ACDC and GlaS.

关键词： Medical image segmentation Hierarchical decoder Attention mechanism

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Don't Use it Twice! Solving Relaxed Linear Equivalence Problems 30th

Don't Use it Twice! Solving Relaxed Linear Equivalence Probl...

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30th International Conference on the Theory and Application of Cryptology and Information Security

作者： Budroni, Alessandro Chi-Dominguez, Jesus-Javier D'Alconzo, Giuseppe Di Scala, Antonio J. Kulkarni, Mukul Technol Innovat Inst Cryptog Res Ctr Abu Dhabi U Arab Emirates Polytech Univ Turin Dept Math Sci Turin Italy

ISBN: (纸本)9789819609437;9789819609444

The Linear Code Equivalence (LCE) Problem has received increased attention in recent years due to its applicability in constructing efficient digital signatures. Notably, the LESS signature scheme based on LCE is under consideration for the NIST post-quantum standardization process, along with the MEDS signature scheme that relies on an extension of LCE to the rank metric, namely the Matrix Code Equivalence (MCE) Problem. Building upon these developments, a family of signatures with additional properties, including linkable ring, group, and threshold signatures, has been proposed. These novel constructions introduce relaxed versions of LCE (and MCE), wherein multiple samples share the same secret equivalence. Despite their significance, these variations have often lacked a thorough security analysis, being assumed to be as challenging as their original counterparts. Addressing this gap, our work delves into the sample complexity of LCE and MCE-precisely, the sufficient number of samples required for efficient recovery of the shared secret equivalence. Our findings reveal, for instance, that one should not use the same secret twice in the LCE setting since this enables a polynomial time (and memory) algorithm to retrieve the secret. Consequently, our results unveil the insecurity of two advanced signatures based on variants of the LCE Problem.

关键词： Algebraic Attack Code Equivalence Group Actions Cryptanalysis Post-quantum Cryptography

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