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检索条件"任意字段=International Conference on Computer Science and Network Technology"

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Clinically Robust Polyp Segmentation: Enhanced Generalization and Perturbation Resistance

Clinically Robust Polyp Segmentation: Enhanced Generalizatio...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Shanchuan Yuan, Fang Su, Jian-Nan Gan, Min College of Computer Science and Technology Qingdao University Qingdao China College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350368741

Colonoscopy is vital for detecting colorectal polyps, which are closely linked to colorectal cancer. Accurate segmentation of polyps in colonoscopic images is essential for diagnosis and surgical planning but is challenging due to variability in polyp size, shape, and unclear boundaries. The Segment Anything Model (SAM) has shown promise in polyp segmentation but relies heavily on user-provided prompts and involves a large number of parameters, limiting its practicality in clinical settings. To address the limitations of SAM in clinical practice, we introduced Low Rank and Perturbation Segment Anything Model (LP-SAM) to improve segmentation accuracy and generalization ability while reducing the parameter count and complexity of user input. LP-SAM showed enhanced generalization and a lightweight design, making it more suitable for clinical applications where precise user input may not always be feasible. Comparative evaluations demonstrate that LP-SAM outperforms state-of-the-art methods on datasets such as CVC-ColonDB, CVC-300, and ETIS. © 2025 IEEE.

关键词： Low-rank Adaptation Perturbation Resistance polyp segmentation Segment Anything Model

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Deep neural network interpretability for image classification based on semantic compressed perception

Deep neural network interpretability for image classificatio...

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international conference on computer Research and Development (ICCRD)

作者： Xuehu Yan Yichen Wu Qingying Zhai Jinrui Zhang Yuliang Lu Jingju Liu Guozheng Yang National University of Defense Technology Hefei China Hefei University of Technology Hefei China

ISBN: (数字)9798331531881

ISBN: (纸本)9798331531898

With the rapid development of deep learning technology, as one of the core tasks in the field of computer vision, the accuracy of image classification has been significantly improved. However, as a black-box model, the decision process of deep neural network for image classification is hard to be intuitively understood and interpreted, which will lead to security risks in applications requiring high transparency and security. In this paper, we initially propose a deep neural network interpretability method for image classification based on semantic compressed perception, aiming to explore the learning and decision mechanism of deep learning models through complex network theory and semantic compressed theory. Compressed perception technology is applied to capture the semantic characteristics of image data. The local clustering coefficient and node centrality in complex network theory are utilized to find the representative sample image instance of each category in the training set, and such that we explore the interpretability of the model from a new perspective. This method is an interpretability method for the synthesis of concept-level features and image-level features, and it is speculated that a deep learning model for image classification may corresponds to an image semantic compressed method. The work in this paper not only helps to understand the internal working mechanism of a model, but also provides a certain basis for further optimizing and improving the model performance.

关键词： Deep learning Training Image coding Computational modeling Semantics Artificial neural networks Complex networks Security Research and development Image classification

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Learning Explicit Radical Representations for Zero-Shot Chinese Character Recognition 27th

Learning Explicit Radical Representations for Zero-Shot Chin...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Pan, Song-Liang Wang, Da-Han Jiang, Nanfeng Zhang, Xu-Yao Zhu, Shunzhi School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783031781186

Zero-shot Chinese character recognition (ZSCCR) aims to recognize unseen Chinese characters by learning the semantic knowledge of seen characters. Radical-based methods treat Chinese characters as combinations of radicals, recognizing characters by predicting the radicals in the images. Existing radical-based methods have a closed radical parsing process that cannot be intervened in mid-course, relying only on semantic labels for constraints. However, semantic embedding vectors are usually manually designed and lack alignment with visual features, making it extremely difficult for the model to learn and locate discriminative radical representations from visual features. This paper proposes a ZSCCR network called Learning Explicit Radical Representations (LERRNet). LERRNet introduces learnable attribute hint vectors to guide the model in locating discriminative radicals and learning explicit representations of images. Specifically, we introduce a Radical Relevance Enhanced Encoder (RREE) to enhance the correlation of local radicals by augmenting the relationships between grid regions in visual features. Guided by attribute hint vectors, LERRNet employs a Radical Representation Decoder (RRD) to locate the most relevant regions of each radical in the given image and learn explicit radical representations. Extensive experiments demonstrate that LERRNet outperforms state-of-the-art radical/stroke-based methods across three ZSCCR benchmarks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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Distributed and Cooperative Security Mechanisms for networked Applications 5th

Distributed and Cooperative Security Mechanisms for Networke...

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5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Kamalraj, R. Singh, Vaishali Bhambu, Pawan Wadhwa, Bhawna Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Engineering Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819780426

Cutting-edge applications rely heavily on conversation via PC networks to provide improved functionality, multiplied interactivity, and more to get admission to sources. Cozy verbal exchange and gain control access should be enforced to shield information and different assets. Distributed and cooperative protection mechanisms enable exceptional components of a networked utility to collaborate for you to get the right of entry to resources responsibly, proportion records securely, and authenticate customers. Such mechanisms are regularly designed to offer automated authorization and authentication, secure conversation channels, and a cozy records garage. In addition, they usually include additives, which have users, roles, and permissions to assist in controlling access. Utility developers and IT directors can ensure that networked packages remain cozy no matter the ever-converting safety landscape by adopting allotted and cooperative safety mechanisms. Distributed and Cooperative security Mechanisms (DCSM) are community security mechanisms designed to provide a layered method to defend in opposition to cyber-attacks on networked applications. The use of DCSM permits a shift from traditional networks to an agile and resilient system structure that can react speedily to a capacity chance, mitigating the damaging effect of malicious attacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computer viruses

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SMEVCA: Stable Matching-based EV Charging Assignment in Subscription-Based Models 25

SMEVCA: Stable Matching-based EV Charging Assignment in Subs...

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26th international conference on Distributed Computing and networking, ICDCN 2025

作者： Khanda, Arindam Satpathy, Anurag Vangala, Anusha Das, Sajal K. Missouri University of Science and Technology RollaMO United States

ISBN: (纸本)9798400710629

The rapid shift from internal combustion engine vehicles to battery-powered electric vehicles (EVs) presents considerable challenges, such as limited charging points (CPs), unpredictable wait times for charging, and difficulty in selecting appropriate CPs for EVs. To address these challenges, we propose a novel end-to-end framework, called Stable Matching based EV Charging Assignment (SMEVCA) that efficiently assigns charge-seeking EVs to CPs with the assistance of roadside units (RSUs). The proposed framework operates within a subscription-based model, ensuring that the subscribed EVs complete their charging within a predefined time limit enforced by a service level agreement (SLA). The framework SMEVCA employs a stable, fast, and efficient EV-CP assignment formulated as a one-to-many matching game with preferences. The matching process identifies the preferred coalition (a subset of EVs assigned to the CPs) using two strategies: (1) Preferred Coalition Greedy (PCG) that offers an efficient, locally optimal heuristic solution;and (2) Preferred Coalition Dynamic (PCD) that is more computation-intensive but delivers a globally optimal coalition. Extensive simulations reveal that PCG and PCD achieve a gain of 14.6% and 20.8% over random elimination for in-network charge transferred with only 3% and 0.1% EVs unserved within the RSUs vicinity. © 2025 Copyright held by the owner/author(s).

关键词： Dynamic programming

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ChainSafari: A General and Efficient Blockchain Verifiable Query Scheme with Real-Time Synchronization 24th

ChainSafari: A General and Efficient Blockchain Verifiable ...

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xu, Yuwei Dai, Shengjiang Zeng, Junyu Cao, Jie He, Ran Xiang, Qiao School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Engineering Research Center of Blockchain Application Supervision and Management Nanjing China Department of Electrical and Computer Engineering Queen’s University Kingston Canada School of Informatics Xiamen University Xiamen China

ISBN: (纸本)9789819615414

With the rapid advancement of blockchain technology, its utilization has become widespread across various domains, resulting in the accumulation of significant volumes of valuable data. This trend has created an increasing demand for efficient querying of blockchain data. However, the inherent structure of blockchain leads to low querying efficiency and limited support for complex queries. Therefore, there is a trend toward migrating data to off-chain storage for querying purposes. The untrustworthy nature of the off-chain environment necessitates data validation. However, existing validation schemes incur high costs and fail to ensure the timely updating of the off-chain data. To address the challenges above, we propose ChainSafari, a general, efficient, and real-time synchronized scheme for verifiable querying on the blockchain. In our scheme, we design a general data model for storing transactions of various blockchains and develop an efficient authenticated data structure (ADS) based on this data model, enabling efficient verifiable queries for multiple data types in parallel. Furthermore, we propose a multi-party synchronization detection and recovery protocol to ensure the consistency of on-chain and off-chain data. Finally, we implement a prototype system of ChainSafari and conduct extensive experiments. The experimental results demonstrate the superior query throughput and enhanced data validation efficiency of ChainSafari compared to existing schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Structured Query Language

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IoT-Supported Indoor Navigation System Using Machine Learning Technique 12th

IoT-Supported Indoor Navigation System Using Machine Learnin...

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12th international conference on Recent Trends in Computing, ICRTC 2024

作者： Christy Jeba Malar, A. Deva Priya, M. Bhuvana, S. Vidhya, K. Sudharsan, S. Suhita, S. Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819788354

Indoor Navigation System (INS) supports seamless movement of objects within confined spaces in smart environments. In this paper, a novel INS that relies on ESP32-based Received Signal Strength Indication (RSSI) measurements for precise estimation of distance using K-Nearest Neighbour (K-NN) algorithm is proposed. The system architecture includes three ESP32 nodes functioning as clients, and ESP8266 combined with ESP32 acting as a server. To overcome the limitations of Global Positioning System (GPS) within an indoor environment, Machine Learning (ML)-based prediction models are employed to estimate distance between nodes and predict user locations along with the distance between source and destination. This approach not only addresses the challenges posed by GPS in indoor settings, but also shows the potential of ESP32 devices and predictive algorithms for enhancing navigation accuracy and reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Global positioning system

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An Evaluation of Hashing and Other Applied Cryptography Algorithms 5th

An Evaluation of Hashing and Other Applied Cryptography Algo...

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5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Mishra, Awakash Haripriya Sharma, Apurva Thomas, Jobin S. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

In the modern digital world, cryptography is critical in protecting personal statistics. Most of the cryptography algorithms used to ease data, hashing, and other implemented cryptography algorithms are extensively used. Hashing refers to transforming plaintext or every other form of statistics into unreadable ciphertext. This type of encryption is used primarily to store passwords, authentication records, and other touchy records. Other applied cryptography algorithms include symmetric encryption, uneven encryption, and key exchanges. In this paper, an assessment of hashing and different implemented cryptography algorithms is conducted. The evaluation will be conscious of each of those algorithms’ distinctive traits, strengths, weaknesses, and safety concerns. The review also appears in the scenarios wherein one-of-a-kind algorithms can be carried out. It is followed by comparing the one-of-a-kind algorithms about their overall performance, protection, and comfort. Numerous pointers are also discussed regarding the application of these algorithms. Finally, the paper concludes with a standard assessment of the multiple algorithms and their suitability in one-of-a-kind contexts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

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Universal Sign Language Recognition System Using Gesture Description Generation and Large Language Model 18th

Universal Sign Language Recognition System Using Gesture Des...

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18th international conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Podder, Kanchon Kanti Zhang, Jian Wang, Lingyan Department of Electrical and Computer Engineering Kennesaw State University MariettaGA30060 United States Department of Information Technology Kennesaw State University MariettaGA30060 United States Department of Computer Science Kennesaw State University MariettaGA30060 United States

ISBN: (纸本)9783031714696

Sign language is a priceless means of communication for deaf and hard-of-hearing people to fully enable them to participate in society and interact with others. This study introduces a novel universal sign language system that uses the Gesture-script to generate a detailed description of gestures in videos, which involve continuous movement of hands, arms, heads, and body language. Subsequently, we input this description into a Large Language Model (LLM) to interpret sign language. We deployed a few-shot prompting technique for LLM, enabling it to precisely transfer the sign videos into corresponding sentences in natural language. Furthermore, the Few-shot prompting technique enables our system to interpret multiple types of sign language without pre-training or fine-tuning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Audition

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E-URES 2.0: Efficient User-Centric Residual-Echo Suppression with a Lightweight Neural network

E-URES 2.0: Efficient User-Centric Residual-Echo Suppression...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ivry, Amir Cohen, Israel Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering Technion - Israel Institute of Technology Haifa3200003 Israel

ISBN: (纸本)9798350368741

We recently introduced the Efficient User-centric Residual-Echo Suppression (E-URES) framework, which significantly reduces the floating-point operations per second (FLOPS) required during inference by 90% compared to the URES framework. The E-URES operates based on a user-operating point (UOP) defined by two key metrics: the residual echo suppression level (RESL) and the desired-speech maintained level (DSML) that the user anticipates from the output signal of a residual echo suppression (RES) system. In the first stage, an ensemble of 101 branches is employed, where each branch has two cascaded neural networks: a preliminary RES system with a design parameter, which varies between branches and balances the RESL and DSML of its RES systems' prediction, and a subsequent UOP estimator. In the second stage, a neural network uses available acoustic signals and the UOP to predict which three branches achieve the highest acoustic echo cancellation mean opinion score (AECMOS) within a specified UOP-error tolerance. Then, costly AECMOS calculations are performed only for these selected branches. Despite this efficiency mechanism, the E-URES can apply real-time inference only with dedicated and expensive hardware, limiting its wide adoption. Here, we present E-URES 2.0, which focuses on reducing the computational costs of E-URES in its first stage. A lightweight neural network preprocesses available acoustic signals and the UOP to track a subset of the 101 design parameters that their branches produce the most accurate UOP estimations in their outcomes. Only these branches are calculated during inference and continue to the AECMOS estimation stage. With 60 hours of data, we show that with a negligible performance drop on average, the E-URES 2.0 can reduce 87% of the branches and 61% of the FLOPS of the E-URES and can achieve real-time inference with standard, affordable hardware. © 2025 IEEE.

关键词： AECMOS computational efficiency deep learning Residual-echo suppression user-centric

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