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

共 1561 条记录，以下是81-90 订阅

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AUTONODE: A Neuro-Graphic Self-Learnable Engine for cognitive GUI Automation 29

AUTONODE: A Neuro-Graphic Self-Learnable Engine for Cognitiv...

引用

29th international conference on Automation and computing (ICAC)

作者： Datta, Arkajit Verma, Tushar Chawla, Rajat Mukunda, N. S. Bhola, Ishaan SuperAGI Res Palo Alto CA 94306 USA

ISBN: (纸本)9798350360882;9798350360899

In recent advancements within the domain of Large Language Models (LLMs), there has been a notable emergence of agents capable of addressing Robotic Process Automation (RPA) challenges through enhanced cognitive capabilities and sophisticated reasoning. This development heralds a new era of scalability and human-like adaptability in goal attainment. In this context, we introduce AUTONODE (Autonomous User-interface Transformation through Online Neuro-graphic Operations and Deep Exploration). AUTONODE employs advanced neuro-graphical techniques to facilitate autonomous navigation and task execution on web interfaces, thereby obviating the necessity for predefined scripts or manual intervention. Our engine empowers agents to comprehend and implement complex workflows, adapting to dynamic web environments with unparalleled efficiency. Our methodology synergizes cognitive functionalities with robotic automation, endowing AUTONODE with the ability to learn from experience. We have integrated an exploratory module, DoRA (Discovery and mapping Operation for graph Retrieval Agent), which is instrumental in constructing a knowledge graph that the engine utilizes to optimize its actions and achieve objectives with minimal supervision. The versatility and efficacy of AUTONODE are demonstrated through a series of experiments, highlighting its proficiency in managing a diverse array of web-based tasks, ranging from data extraction to transaction processing. The implementation of our paper can be accessed at :https://***/TransformerOptimus/AutoNode

关键词： Self-Operating Computer Generative AI LLMs Transformers Vision-Transformers Graphs Reinforcement Learning

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Study on the Performance Optimization and Application of Big Model in Big data Processing 6

Study on the Performance Optimization and Application of Big...

引用

6th international conference on data-Driven Optimization of complex Systems (DOCS)

作者： Wen, Zebin Wang, Ping Zhang, Jiuyang Xiong, Ping Guangdong Univ Sci & Technol Dongguan Guangdong Peoples R China

ISBN: (纸本)9798350377859;9798350377842

With the surge in data volume, big data processing faces unprecedented challenges, among which large models have become a hot research topic due to their powerful data processing capabilities. This paper delves into the performance bottlenecks of large models in big data processing and proposes a series of performance optimization strategies. Through a review of existing data processing technologies and large model architectures, combined with optimization theory and practice, this study introduces a comprehensive optimization mechanism that includes resource scheduling, model computational efficiency, and storage and IO. Experiments were conducted in a cloud computing environment to validate these strategies. The results indicate that the optimization strategies significantly enhanced performance when processing different scales of data, improved load balancing and resource utilization, and increased system stability. This research enriches the theoretical study of big data processing and provides effective optimization avenues for the practical application of large models in fields such as data mining and parallel computing. It offers guidance for feature engineering and data preprocessing and paves the way for future research directions.

关键词： Big data Processing data Mining Parallel computing Feature Engineering data Preprocessing

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A Method of database Construction Based on AIS data 3

A Method of Database Construction Based on AIS Data

引用

3rd international conference on Cloud computing, Big data Application and Software Engineering, CBASE 2024

作者： Cai, Zhichun Hangzhou Institute of Applied Acoustics Hangzhou China

ISBN: (纸本)9798331506582

Aiming at the problems of explosive growth of AIS data, complex storage management and slow retrieval efficiency, this paper studies the database construction method of massive AIS data based on Elasticsearch, and expounds ths new idea of synchronizing AIS from relational database to Elasticsearch. In the large amount of data, according to the time dimension and geographical dimension of different query methods, the relational database and the Elasticsearch database retrieval speed comparsion, the research results show that the Elasticsearch database in the massive AIS data application scene has more advantage, the retrieval efficiency is faster. © 2024 ieee.

关键词： AIS data Elasticsearch Relational database Synchronization

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Layered Mixing Miner: A Process Mining Algorithm for complex Programming Debugging data 7

Layered Mixing Miner: A Process Mining Algorithm for Complex...

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7th international conference on Algorithms, computing and Artificial Intelligence, ACAI 2024

作者： Chen, Ruiqi Kong, Jiangli Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798331529314

In the context of increasingly complex application scenarios and unstructured debugging data in software engineering education, analyzing students' programming and debugging processes presents significant challenges. This study introduces the Layered Mixed Miner (LMM), a novel process mining algorithm designed to address these challenges by generating inter-pretable models from complex and unstructured student programming and debugging data. The algorithm integrates various process mining techniques by constructing an initial process tree, calculating filtering thresholds, and employing diverse methods to construct process models based on different filtering results. Consistency tests demonstrate that LMM achieves higher accuracy and precision compared to established benchmarks, providing a reliable and insightful representation of debugging behaviors. Moreover, expert feedback further confirms the algorithm's superior interpretability and usability over baseline methods. © 2024 ieee.

关键词： Miners

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Effective cognitive State Monitoring Using FBNN Architecture 7

Effective Cognitive State Monitoring Using FBNN Architecture

引用

7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Sivasaravana Babu, S. Dinesh Kumar, T.R. Saravana Kumar, G. Stanly Ebineser Paul, J. Surriya, R. Rakshan, M. Vel Tech High Tech Dr Rangarajan Dr Sakunthala Engineering College Department of Electronics and Communication Engineering Chennai India Karpagam College of Engineering Department of Electronics and Telecommunication Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350372816

The integrated Electroencephalogram (EEG) signal processing and deep learning for robust cognitive state classification is the idea behind this paper. The EEG data that are received are first preprocessed. To preserve the accuracy of the data, noise artifacts from the raw EEG signals are removed during the preprocessing stage. The decomposed signals are subjected to statistically feature extraction in order to extract pertinent properties and produce an accessible summary of the data. By exposing the frequency domain data, spectral estimating improves the analysis even more and makes it possible to identify certain patterns linked to different cognitive states. Using deep learning methods for cognitive state classification is the last stage of the recommended methodology. Convolutional Neural Network (CNNs) are used because they are able to identify spatial connections in the data, while, Feed Forward Back Propagation Neural Networks (FFBNNs) have an architecture that is flexible enough to understand intricate temporal interactions. Mat lab software is utilized in the implementation of this paper. The comparison diagram between the algorithms as find for accuracy, sensitivity and specificity. The accuracy level of the obtained CNN and FFBNN at signal 120 is 91.7% and 92.8% individually. © 2024 ieee.

关键词： Convolutional neural networks

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FlexAuth: A Decentralized Authorization System with Flexible Delegation 22

FlexAuth: A Decentralized Authorization System with Flexible...

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ieee 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigdataSE conference / CSE conference / EUC conference / ISCI conference

作者： Fei, Ziyu Li, Ying Wei, Jiuqi Fu, Yufan Peng, Botao Li, Xiaodong Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350381993;9798350382006

The dispersion of resource authorization across various authorization systems raises the complexity and inconsistency of authorization management. Therefore, the pursuit of a unified resource authorization management system is necessary. Most widely used authorization systems are based on centralized services with a single delegation pattern. These authorization systems can be easily compromised, leading to permissions tampering, and are unsuitable for complex usage scenarios. We propose a decentralized authorization system that provides flexible delegation called FlexAuth. We first define a decentralized data storage layer based on blockchain, using smart contracts to implement data writing and resolving, preventing data from being tampered with. On top of this, we implement active and passive delegation patterns of authorization services. We allow users to delegate permissions actively. Also, FlexAuth enables them to respond to authorization requests passively by making flexible and expressive access control policies based on relationships and attributes, using the proposed Hybrid Access Control Model (HACM). Furthermore, all delegations in FlexAuth are transitive. Finally, through analysis and experiments, we validate the usability and efficiency of FlexAuth. To our knowledge, FlexAuth is the first decentralized authorization system with transitive delegation in active and passive patterns, achieving flexible delegation.

关键词： Access control Decentralized Authorization Delegation Blockchain data security

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Proceedings - 2024 ieee international conference on Edge computing and Communications, EDGE 2024

Proceedings - 2024 IEEE International Conference on Edge Com...

引用

8th ieee international conference on Edge computing and Communications, EDGE 2024

ISBN: (纸本)9798350368499

The proceedings contain 20 papers. The topics discussed include: split DNN inference for exploiting near-edge accelerators;DONNA: distributed optimized neural network allocation on CIM-based heterogeneous accelerators;reliable network performance for edge networks with QoS-aware adaptive routing;the impact of GPU on containerized computer vision applications on edge nodes;cross network layer cognitive service orchestration in edge computing systems;a case for deploying dynamic neural network on edge-cloud continuum environment;data sharing-aware online algorithms for task allocation in edge computing systems;and SlimNet: a lightweight attentive network for speech-music-noise classification and voice activity detection.

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Modeling and Optimization of Industrial Engineering complex System Based on Quantum computing and Big data Analysis

Modeling and Optimization of Industrial Engineering Complex ...

引用

2024 international conference on Telecommunications and Power Electronics, TELEPE 2024

作者： Gu, Shuheng Liu, Yijiang Gu, Zihan Wang, Xinzi Shi, Jianan Zhu, Sijin Naniing University of Finance and Economics Jiangsu Nanjing210046 China Massey University Auckland1010 New Zealand

ISBN: (纸本)9798350369212

Quantum computing, with its unique parallel computing ability and efficient optimization algorithm, provides new possibilities for the optimization of complex systems. This paper aims to explore the modeling and optimization methods of complex industrial engineering systems based on quantum computing and big data analysis technology. Through the comprehensive application of quantum approximate optimization algorithm, quantum support vector machine, data mining and machine learning technology, this study has realized the accurate modeling and optimization of complex industrial engineering systems. The research results show that compared with the traditional ID3 algorithm, the method adopted in this study shows better real-time performance and smaller relative error in industrial system assessment, which significantly improves the accuracy of assessment. This achievement not only provides more reliable data support for the prediction and risk control of industrial systems, but also verifies the effectiveness of quantum computing and big data analysis in dealing with complex system modeling and optimization problems. To sum up, the method proposed in this paper is expected to bring sustainable development momentum to the field of industrial engineering, and improve the decision-making support ability and risk control level of enterprises. © 2024 ieee.

关键词： data accuracy

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Thinking is Like Processing a Sequence of Spatial and Temporal Words

Thinking is Like Processing a Sequence of Spatial and Tempor...

引用

international Joint conference on Neural Networks (IJCNN)

作者： Gallo, Ignazio Corchs, Silvia Univ Insubria Dept Theoret & Appl Sci DiSTA Varese Italy

ISBN: (纸本)9798350359329;9798350359312

Leveraging state-of-the-art advancements from natural language processing (NLP), this paper pioneers the application of Transformer Neural Networks in decoding electroencephalographic (EEG) data for cognitive analysis. Harnessing the transformative power of Transformers, traditionally utilized in language understanding, this study introduces the NetTraST (Network Transformer Spatio-Temporal) model for EEG classification tasks. NetTraST integrates the paradigmshifting capabilities of Transformer architectures with Conv1D, establishing a dual-branch architecture for extracting and processing spatial EEG features, capturing intricate neural spatial dynamics and temporal relationships within EEG sequences, illuminating nuanced temporal dynamics crucial for cognitive analysis. Transformer architecture enables the assimilation of cutting-edge techniques from the NLP domain and empowers the model to comprehend complex EEG patterns. Rigorous evaluation across diverse datasets-Thinking Out Loud, Kumar's, and Kaneshiro's EEG datasets-demonstrates NetTraST's superiority over existing EEG classification methodologies not based on Transformer networks. Noteworthy advancements in accuracy substantiate the model's efficacy, surpassing previous benchmarks in multiple cognitive tasks. Furthermore, an indepth ablation study underscores the pivotal roles played by the dual branches, shedding light on their contributions to overall performance enhancement. The introduction of NetTraST marks a pivotal stride, not only harnessing the potential of Transformer architectures from NLP but also emphasizing the significance of a dual-branch approach in EEG analysis. This research's used data and implementation code is accessible at https://***/ignaziogallo/gallo-IJCNN2024

关键词： Deep Neural Network Transformer Attention EEG Electroencephalography data.

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Empowering Autonomous Decision-Making Through Quantum Reinforcement Learning and cognitive Neuromorphic Frameworks 4

Empowering Autonomous Decision-Making Through Quantum Reinfo...

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4th ieee international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Balasubramani, R. Biradar, Vidyadevi G. Nmam Institute of Technology Department of Information Science and Engineering Udupi India

ISBN: (纸本)9798350352931

The concept of Agentic AI, which focuses on empowering autonomous systems with human-like decision-making capabilities, has gained substantial attention in recent years. By leveraging advanced reinforcement learning (RL) techniques and cognitive frameworks, Agentic AI aims to enable machines to make complex decisions in dynamic environments with minimal human intervention. This paper introduces Agentic AI, a novel approach that combines Quantum Reinforcement Learning (QRL) and cognitive Neuromorphic Frameworks to empower autonomous decision-making in complex environments. The proposed model leverages quantum computing principles to accelerate learning in reinforcement-based systems, while cognitive neuromorphic frameworks enhance adaptability and real-time decision-making by mimicking human brain functionality. The integration of QRL allows for faster convergence in learning algorithms, improving decision accuracy by 25% and reducing training time by 40%. cognitive neuromorphic architectures offer enhanced scalability and resilience in dynamic environments, resulting in a 35% improvement in system robustness during testing. Simulations in autonomous navigation and robotic control show that Agentic AI achieves 20% more efficient task execution compared to traditional AI models. These values underscore the potential of the proposed system to revolutionize autonomous systems, providing a robust foundation for decision-making across a range of applications. © 2024 ieee.

关键词： Quantum computers

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