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检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

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Fine Tuning Swin Transformer Based Pretrained Model for Microscopic Fungi Images Classification 11

Fine Tuning Swin Transformer Based Pretrained Model for Micr...

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11th international conference on Computer, Control, Informatics and its Applications, IC3INA 2024

作者： Anggara, Muchamad Galih Hindasyah, Ahmad Amda, Julfa Muhammad Waskita, A.A. Graduate Program of Informatics Engineering Universitas Pamulang Pamulang Indonesia Research Center for Computing National Research and Innovation Agency Cibinong Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798331542313

Accurate image classification of microscopic pathogenic fungi is a challenge in the field of mycology. This study proposes a Swin Transformer-based approach to automate the identification process. The Swin Transformer model, known for its ability to capture complex spatial interactions, is adapted from ImageNet’s pre-trained model. The adaptation process is focused on fine-tuning the final classification layer to match the 5 classes of fungi present in the dataset. This classification layer is replaced by a new fully-connected layer that has the number of output according to the number of fungal classes. The weights on these layers are randomly initialized and trained in conjunction with several final convolutional layers. In addition, regularization techniques such as dropouts are applied to prevent overfitting. Adaptation using 5-fold cross-validation was carried out to find the best fold with hyperparameters including AdamW optimizer with a learning rate of 10−3, StepLR scheduler, batch size of 128 images, and a total of 9 epochs / iterations. After that, fine-tuning is carried out on the best fold with hyperparameters including AdamW optimizer with a learning rate of 10−5, StepLR scheduler, batch size of 32 images, and a total of 100 epochs / iterations. The final result showed excellent performance, with accuracy, precision, recall and an average F1-score of 95%. These results show the potential of the Swin Transformer in becoming an effective tool for mycologists. © 2024 ieee.

关键词： Deep learning

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Heuristic Structures of cognitive Aerospace Remote Sensing Radars 3

Heuristic Structures of Cognitive Aerospace Remote Sensing R...

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3rd ieee international conference on System Analysis and Intelligent computing, SAIC 2022

作者： Volosyuk, Valeriy Zhyla, Simeon Pavlikov, Vladimir Vlasenko, Dmytro Inkarbaieva, Olha Kolesnikov, Denys National Aerospace University 'Kharkiv Aviation Institute' Aerospace Radio-Electronic Systems Department Kharkiv Ukraine

ISBN: (数字)9798350396744

ISBN: (纸本)9798350396744

cognitive radars are the most complex in structure and the most flexible radio engineering systems in terms of tasks to be solved. Much attention is paid to their construction and improvement because of their high potential. In this paper, it is proposed to apply the technology of cognitive dynamic systems to airborne synthetic aperture radars (SAR). An approach to optimizing the structure of onboard cognitive radars using the theory of nonlinear filtering is proposed. Heuristically, summarizing the experience of the authors in the synthesis of optimal algorithms for the functioning of onboard multichannel SAR and the existing results of optimization of tracking systems, several structures of onboard cognitive remote sensing radars are proposed. © 2022 ieee.

关键词： Synthetic aperture radar

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SERNet-T: A Smart Contract Multi-label Vulnerability Detection Model Based on SE Attention and One-Dimensional Residual Network

SERNet-T: A Smart Contract Multi-label Vulnerability Detecti...

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cognitive computing and complex data (ICCD), international conference on the

作者： Linjiu Guo Wenyin Yang Luobin Hu Li Ma Zepeng Wu Zejian Lu Foshan University Foshan China Guangdong Strong Metal Technology Co. Ltd Foshan China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

The development of blockchain technology has expanded from its initial application in digital cryptocurrencies to a wider range of fields, especially smart contracts. However, security issues related to smart contracts remain a pressing challenge. Existing methods for detecting vulnerabilities in smart contracts have limitations, such as focusing only on specific types of vulnerabilities, limited computational resources, and high false positive rates. To address these issues, this paper proposes a smart contract multi-label vulnerability detection model based on SE attention and one-dimensional residual networks(SERNet-T). This network not only emphasizes functionality but also integrates improved SEBlock residual blocks and Transformer-encoder modules, which can efficiently extract complex features from smart contract bytecode and capture long-term dependencies. The SEBlock residual blocks in SERNet-T significantly enhance feature channel dependency, refine inter-channel responses, and improve the processing capability of critical information. Experimental results show that our method achieves 75.56% accuracy and 86.46% micro-average F1 score on public datasets, improving by 2.1% and 2.8 % compared to existing methods, respectively. Additionally, SERNet-T reduces the number of parameters by more than 20%.

关键词： Computational modeling Smart contracts Focusing Pressing Feature extraction Transformers data models Blockchains Security Residual neural networks

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Enhancing Knowledge Selection with data Processing Based on Multiple Turns of Dialog in Knowledge-Grounded Open-Domain Conversations

Enhancing Knowledge Selection with Data Processing Based on ...

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international conference on Big data and Smart computing (BIGCOMP)

作者： Eojin Joo Do Kyung Lee Junyoung Youn Joo-Won Sung Ho-Jin Choi KAIST School of Computing Daejeon South Korea KT AI2XL Seoul South Korea

In NLG, recent research in Knowledge-Grounded Text Generation aims to refine sentence specificity and naturalness. When generating texts, considering multiple turns within a conversation is considered important because it allows models to generate sentences that reflect the context of the conversation. Addressing open-domain conversations, determining the optimal conversation history for training knowledge selection models lacks prior exploration. This study aims to improve KGTG models to effectively handle complex utterances by progressively incorporating more turns. This finding offers a foundational direction for boosting knowledge selection models in text generation.

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TRRRACED – Towards Reproducible, Replicable and Reusable Affective computing Experiments and data

TRRRACED – Towards Reproducible, Replicable and Reusable Af...

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international conference on cognitive Infocommunications (CogInfoCom)

作者： Paul Schreiber Florian Grensing Maria Maleshkova Department of Data Engineering Helmut-Schmidt Universität Hamburg Germany

ISBN: (数字)9798350378245

ISBN: (纸本)9798350378252

We present TRRRACED – a framework that aims to support Reproducible, Replicable and Reusable Affective computing Experiments and data. To the best of our knowledge, TRRRACED is the first framework aiming to provide standardised guidelines that facilitate structural unity of experiments and data in the domain of AC research. To this end, state-of-the-art studies considering physiological signals in relation to affective states were examined regarding (i) stimuli used, (ii) experimental setup and (iii) self-reports. Based on this analysis we employ reproducibility, replicability, and reusability as criteria to develop our framework. We propose a protocol for conducting experiments to enhance replicability, guidelines for annotating data, and a sample dataset.

关键词： Affective computing Protocols Reproducibility of results Physiology Replicability Guidelines

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Study on Surface Crack Closure Behavior of Ship Hull Plate Structures under Constant and Variable Amplitude Low-Cycle Fatigue Loading

Study on Surface Crack Closure Behavior of Ship Hull Plate S...

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cognitive computing and complex data (ICCD), international conference on the

作者： Wenlong Rong JunLin Deng Shourong Lai Haoyue Ming Chuanao Deng School of Mechanical and Marine Engineering Beibu Gulf University Guangxi China Technical Center Technology Center China Shipbuilding Group Changjiang Technology Co. Ltd. Chongqing China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Based on in-depth analysis of crack closure theory and finite element simulation, this paper analyzes the crack closure behavior under constant and variable amplitude loading, and determines how the stress ratio and elliptical shape factor affect the closure process of the crack surface. Under constant and variable amplitude loading, the stress-strain response at the crack tip tends to be stable with the increase of cycles; however, after the introduction of single-peak overload, the stress-strain response at the crack tip changes significantly, and the crack closure effect is significantly enhanced during the unloading process, reflecting that single-peak overload has a decisive effect on crack closure behavior and the final crack growth rate. The relationship between crack growth rate and opening load for different combinations of overload and stress ratios reveals the complexity of crack closure behavior under overload conditions. These findings not only illustrate the protective effect of crack closure on crack growth, but also provide new understanding for predicting crack growth behavior under different overload conditions.

关键词： Shape Loading Fatigue Finite element analysis complexity theory Surface cracks Marine vehicles cognitive systems Stress Surface treatment

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A cognitive computing Tool for Differential Diagnosis between Metastases and High Grade Gliomas

A Cognitive Computing Tool for Differential Diagnosis betwee...

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ieee Canada international Humanitarian Technology conference (IHTC)

作者： Camilla Russo Paolo Maresca Alfredo Marinelli Department of Neuroscience Azienda Ospedaliera di Rilievo Nazionale Santobono Pausilipon Napoli Italy DIETI (Dipartimento di Ingegneria Elettrica e Delle Tecnologie Dell’Informazione) Università di Napoli Federico II Napoli Italy Dipartimento di Medicina Clinica e Chirurgia Università di Napoli Federico II Napoli Italy

ISBN: (数字)9798350354645

ISBN: (纸本)9798350354652

This paper describes the research carried out in the 3 years of the PhD in Information and Communication Technology for Health (ICTH) on the topic of cognitive computing Systems for Neuroncology at the Department of Electrical Engineering and Information Technologies (DIETI) of the University of Naples "Federico II". The aim of the work is to design an innovative computational system based on cognitive computing technologies for the analysis of data derived from magnetic resonance images (MRI) in patients with brain tumors of unknown origin. To do this, the open source deep learning framework based on PyTorch for medical data called FuseMedML was used, the main objective is to obtain a supervised binary classification model that allows the timely identification of the two most common malignant brain tumors of adulthood, which require different therapeutic strategies and clinical-radiological monitoring.

关键词： Deep learning Training Accuracy Magnetic resonance imaging Brain tumors Surgery Feature extraction data models cognitive systems Medical diagnostic imaging

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cognitive Oracles: Provable on-chain assessment through Validity Machine Learning

Cognitive Oracles: Provable on-chain assessment through Vali...

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ieee Annual conference on Pervasive computing and Communications Workshops (PerCom)

作者： Marco Esposito Francesco Bruschi Manuel Montecalvo Donatella Sciuto Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Politecnico di Milano Milan Italy

Blockchains offer a mechanism for executing code in a credible, transparent and uncensorable way. These characteristics lend themselves to the implementation of applications that guarantee different actors regarding the credible execution of mechanisms, including insurance and impartial certification. A significant challenge in this domain is enabling smart contracts to reliably access real-world state information. Our research centers on scenarios requiring smart contracts to evaluate complex or ambiguous conditions, such as identifying specific elements within an image. The proposed solution introduces an oracle utilizing machine learning classifiers to aid smart contracts in assessing such intricate predicates. We incorporate SNARK arguments to facilitate efficient and secure evaluation of these classifiers, and introduce a jury-based system to address classifier inaccuracies. We evaluate the proposed architecture with respect to an example application.

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Neural cognitive Diagnosis for Empirical Q-Matrix

Neural Cognitive Diagnosis for Empirical Q-Matrix

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2022 international conference on Artificial Intelligence and Computer Information Technology, AICIT 2022

作者： Fan, Jiaqin Tian, Yi Huang, Likun Wuhan Institute of Technology Wit School of Electrical and Information Engineering Wuhan China

ISBN: (数字)9781665450874

ISBN: (纸本)9781665450874

The growth of educational informatization has been aided by the constant updating and switching of application modes brought about by the continual development of a new generation of information technologies such as artificial intelligence, cloud computing, data mining, and mobile Internet. cognitive diagnosis aims to assess students' knowledge competency and offer a foundation for targeted instruction and teacher feedback. The neural cognitive diagnosis framework for the empirical Q-matrix is proposed in the research. The neural cognitive diagnosis framework for empirical Q-matrix introduces empirical Q-matrix, which to a certain extent reduces the subjectivity of artificial Q-matrix, to construct and select the most appropriate Q-matrix;accurate and interpretable diagnostic results are obtained by combining neural network and learning complex motion interaction function. © 2022 ieee.

关键词： Informatization

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Internet Of Rights(IOR) in Role Based Blockchain 1

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2nd international conference on cognitive Computation and Systems, ICCCS 2023

作者： Shi, Yunling Guan, Jie Xiao, Junfeng Zhang, Huai Yuan, Yu Beijing Tsingzhi Shuyuan Technology Co. Ltd. Beijing China Tsinghua University Beijing China IEEE Beijing China

ISBN: (数字)9789819708857

ISBN: (纸本)9789819708840

A large amount of data has been accumulated. with the development of the Internet industry. Many problems have been exposed with data explosion: 1. The contradiction between data privacy and data collaborations;2. The contradiction between data ownership and the right of data usage;3. The legality of data collection and data usage;4. The relationship between the governance of data and the governance of rules;5. Traceability of evidence chain. To face such a complicated situation, many algorithms were proposed and developed. This article tries to build a model from the perspective of blockchain to make some breakthroughs. The Internet Of Rights(IOR) model uses multi-chain technology to logically break down the consensus mechanism into layers, including storage consensus, permission consensus, role consensus, transaction consensus, etc., thus building new infrastructure, that enables data sources with complex organizational structures and interactions to collaborate smoothly on the premise of protecting data privacy. With blockchain’s nature of decentralization, openness, autonomy, immutability, and controllable anonymity, the Internet Of Rights(IOR) model registers the ownership of data and enables applications to build an ecosystem based on responsibilities and rights. It also provides cross-domain processing with privacy protection, as well as the separation of data governance and rule governance. With the processing capabilities of artificial intelligence and big data technology, as well as the ubiquitous data collection capabilities of the Internet of Things, the Internet Of Rights(IOR) model may provide a new infrastructure concept for realizing swarm intelligence and building a new paradigm of the Internet, i.e. intelligent governance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： data privacy

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