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

共 1578 条记录，以下是941-950 订阅

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ENACT - A Framework for Adaptive Scheduling and Deployments of data Intensive Workloads on Energy Efficient Edge to Cloud Continuum

ENACT - A Framework for Adaptive Scheduling and Deployments ...

引用

ieee international conference on Engineering, Technology and Innovation

作者： Alexandros Nizamis Juergen Neises Diego Ospina Usman Wajid Clara Isabel Valero López Panagiotis Trakadas Konstantinos Votis Oscar Lazaro Septimiu Nechifor Carlos Palau Information Technologies Institute Centre for Research and Technology Hellas (CERTH) Thessaloniki Greece Fujitsu Technology Solutions Warsaw Poland Innovalia Association Bilbao Spain Information Catalyst Valencia Spain Universitat Politécnica de Valencia Valencia Spain Four Dot Infinit Athens Greece Siemens S.R.L. Bucharest Romania

ISBN: (数字)9798350362435

ISBN: (纸本)9798350362442

In the last couple of decades, enterprises have relished and leveraged the capacity, performance, scalability, and quality of cloud computing services. However, a few years ago, the edge computing concept enabled data processing and application execution near compute and data resources, aiming to reduce latency and promote higher security and sovereignty regarding data transfers. The simultaneous use of these two service models by enterprises has lately resulted in the concept of edge-to-cloud continuum, which combines edge and cloud technologies and promotes standards and algorithms for resource orchestration, data management, and the deployment of solutions across the connected edge and cloud resources. Taking a step further in this direction, this paper introduces a framework to realise the cognitive computing Continuum (CCC). The framework leverages AI techniques to address the needs for optimal (edge and cloud) resource management and dynamic scaling, elasticity, and portability of hyper-distributed data-intensive applications. The proposed ENACT framework enables the automated management of distributed (edge and cloud) resources and the development of hyper-distributed applications that can take advantage of distributed deployment and execution opportunities to optimize their behaviors in terms of execution time, resource utilisation and energy efficiency.

关键词： Cloud computing Computational modeling Elasticity Dynamic scheduling Graph neural networks Energy efficiency data models Real-time systems Resource management cognitive systems

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Certifiably Robust Encoding Schemes

Certifiably Robust Encoding Schemes

引用

Quantum computing and Engineering (QCE), ieee international conference on

作者： Aman Saxena Tom Wollschläger Nicola Franco Jeanette Miriam Lorenz Stephan Günnemann School of Computation Information & Technology Technical Univ. of Munich Germany Fraunhofer Institute for Cognitive Systems IKS Munich Germany

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Quantum machine learning uses principles from quantum mechanics to process data, offering potential advances in speed and performance. However, previous work has shown that these models are susceptible to attacks that manipulate input data or exploit noise in quantum circuits. Following this, various studies have explored the robustness of these models. These works focus on the robustness certification of manipulations of the quantum states. We extend this line of research by investigating the robustness against perturbations in the classical data for a general class of data encoding schemes. We show that for such schemes, the addition of suitable noise channels is equivalent to evaluating the mean value of the noiseless classifier at the smoothed data, akin to Randomized Smoothing from classical machine learning. Using our general framework, we show that suitable additions of phase-damping noise channels improve empirical and provable robustness for the considered class of encoding schemes.

关键词： Smoothing methods Noise Stacking Machine learning Robustness Encoding Mathematical models data models Integrated circuit modeling Certification

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Research on Test data Expansion Method Based on UAV Typical Index Evaluation

Research on Test Data Expansion Method Based on UAV Typical ...

引用

High Performance and Smart computing (HPSC), ieee international conference on

作者： Huapin Geng Hongtao Ma Jiahui Tong Ning Wang Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-mechanical Engineering Institute Beijing China Beijing Xinghang Electro-mechanical Equipment Beijing China Beijing Electro-mechanical Engineering Institute Beijing China

ISBN: (数字)9798350389463

ISBN: (纸本)9798350389470

In order to scientifically and effectively evaluate the performance indexes of aircraft, the common Bayes multi-source data fusion method can comprehensively utilize a variety of information types under the condition that the decision risk is as small as possible, but there are risks such as insufficient data utilization and small sample data being “submerged” by large sample data in the fusion. This paper proposes a test data expansion method based on the combination of probability features and BP neural network model, and is verified by typical application to ensure that the sample size of multi-source data is comparable. Supplementary data not only retains the probability features of the original data, but also the sample generation has physical connotation.

关键词： Atmospheric modeling Computational modeling Neural networks data integration data models Performance analysis Bayes methods

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Minimizing data Transmission Delay in Vehicular Networks through Network Coding-based Broadcasting

Minimizing Data Transmission Delay in Vehicular Networks thr...

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ieee/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social computing (CPSCom) Green computing and Communications (GreenCom)

作者： Jiali Hu Long Qu Qinglin Song Ran Zhu Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Faraday Laser Technology co. LTD Wenzhou China

ISBN: (数字)9798350351637

ISBN: (纸本)9798350351644

Advances in wireless communication technology have facilitated the development of vehicular networks. Intelligent vehicles are able to realize a variety of applications and services to enhance the driving experience through Vehicle-to-Everything (V2X) communication. However, meeting the requirement of low latency in dynamic and complex vehicular environments poses a challenge. This paper presents a network coding framework based on a vehicular network data transmission model, aiming to reduce transmission latency and data dropout rate by encoding and broadcasting packets at Roadside Units (RSUs). Due to the mobility of vehicles, interference becomes more complex. To mitigate interference between data transmissions, we employ dynamic power control. However, the optimization problem involved is a mixed integer problem. To reduce the complexity, this paper introduces a Column Generation algorithm to solve the model. The algorithm decomposes the problem into a pricing problem responsible for packet scheduling and a master problem responsible for minimizing the transmission delay to find the optimal coding opportunities and broadcasting strategies for vehicle packets. Experimental results demonstrate that compared to scenarios without network coding, the proposed algorithm reduces transmission latency by 5.694% and decreases data dropout rate by 7.143%.

关键词： Scheduling algorithms Power control Interference Network coding Broadcasting Delays data communication Vehicle dynamics Vehicle-to-everything Optimization

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LGDCloudSim: A Resource Management Simulation System for Large-Scale Geographically Distributed Cloud data Center Scenarios

LGDCloudSim: A Resource Management Simulation System for Lar...

引用

ieee international conference on Cloud computing, CLOUD

作者： Jiawen Liu Yuehao Xu Binbin Feng Zhijun Ding Department of Computer Science and Technology Tongji University Shanghai China The Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (数字)9798350368536

ISBN: (纸本)9798350368543

Current IaaS providers have deployed data centers worldwide, with resources continually increasing. Meanwhile, there is a rising trend in the concurrency of user requests and the diversity of user request types. To achieve better resource allocation, various complex scheduling architectures have been proposed. However, due to the challenges associated with real-world experiments, simulation systems are needed to build exper-imental environments for related research. As existing systems do not perform well enough, we construct LGDCloudSim. It is designed with full consideration of the characteristics of the large-scale geographically distributed cloud data center scenarios. To support large-scale simulations, we propose state management optimization and operation process optimization methods. Exper-iments show that LGDCloudSim can simulate up to 5 X 10 8 hosts and 10 7 request concurrency. It also supports diverse scheduling architectures and different request types.

关键词： Concurrent computing Cloud computing data centers Processor scheduling Optimization methods Distributed databases Computer architecture

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Decoding the Brain Neuro-Inspired Deep Learning Architectures of cognitive computing

Decoding the Brain Neuro-Inspired Deep Learning Architecture...

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Knowledge Engineering and Communication Systems (ICKECS), international conference on

作者： J. Latha S. Karthick Murugan S. Jayachandran G. Maheshwari G Naveen S. Navaneethan Department of Computer Science and Engineering R. M. K. Engineering College Tiruvallur Tamil Nadu India Department of Computer Science and Engineering R. M. D. Engineering College Tiruvallur Tamil Nadu India Department of Computer Science and Application SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science RMK College of Engineering and Technology Tiruvallur Tamil Nadu India Department of Electronics and Communication Engineering Madha Institute of Engineering and Technology Chennai Tami Nadu India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

In response to the growing need for advanced artificial intelligence (AI) systems, the study thoroughly examines neuro-inspired deep learning architectures within the domain of cognitive computing. The need for such innovation originates from the constraints of traditional systems, characterized by their inefficiency in duplicating complex cognitive activities. Traditional techniques often need help with complex tasks and more biological neural network flexibility. The proposed system uses neuroscience principles to solve these limitations and create advanced models such as spiking neural networks and hierarchical attention processes, increasing machine learning capabilities. The results of extensive testing and analysis show that incorporating neuro-inspired designs considerably improves the system’s performance across several dimensions. The results show increased efficiency, flexibility, and an expanded understanding of cognitive processes, indicating an evolution in AI development. As the results prove, adopting neuro-inspired deep learning architectures is essential for progressing toward artificial general intelligence and realizing the full potential of cognitive computing.

关键词： Adaptation models Technological innovation Neuroscience Computational modeling Artificial general intelligence Deep architecture Spiking neural networks

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Knowledge Engineering-based cognitive Science to Enhance the cognitive Ability of the Student

Knowledge Engineering-based Cognitive Science to Enhance the...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： Shaik Afreen Uppala Reshmitha Karnam Akhil Varagani Divya Spurthi Yadagiri Department of CSE&CSBS VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad Department of CSE VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad CSBS: Computer Science and Business Systems

Education plays an essential role in a student’s career. From ancient to the present education involves knowledge gaining, conceptual understanding, ethical values, behavior, ability to solve the problem, and mind-mapping. Due to technological advancements, digital transformation changed the education pathway from imagination to implementation. With the rise in digital smart classes, enhanced board teaching, and hybrid learning students can understand the concepts and visualize. However, Current education focuses more on fetching marks for a student which overloads to memorize and affects the lack to bridge the gap between theoretical conceptualization to practical impairment. Students are undergoing severe pressure, and issues relating to stress, depression, and anxiety, and many times, there is an increase in dropout and suicidal rates. The theme of the proposed framework is based on empirical observation, ethnographic observations, and hypothesis based on proof of study to enhance the quality of education in students’ careers. Dropout ratio and suicidal impact are presented in the research based on available real-time data. The proposed framework highlights the revision of the curriculum for a student enriching and enhancing the concepts of design thinking, psychology, sociology, and cognitive science. cognitive science helps in developing the intelligence Quotient (IQ) and Emotional Quotient (EQ) of a student which helps in solving and designing an optimal solution framework for complex problems in real time. The proposed architecture highlights the application of ontologies to build the relationship among the factors affecting the child and ethnographic studies based on observations.

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Research on the Prediction Application of Multiple Classification datasets Based on Random Forest Model

Research on the Prediction Application of Multiple Classific...

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ieee international conference on Power, Intelligent computing and Systems (ICPICS)

作者： Jinming Zhao Chang-Der Lee Guoya Chen Jingyue Zhang Wuchang University of Technology Wuhan China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

In the field of machine learning, random forest attracts much attention as an integrated learning algorithm because of its excellent generalization ability and processing effect on nonlinear complex data. This study aims to explore the application of random forests to multi-classification data, evaluating the performance and limitations by building a random forest model and conducting empirical analysis. Through a series of experiments, we determined an optimal set of parameters that achieved a test accuracy of 80.21%. Therefore, the random forest model shows significant advantages in dealing with multi-classification problems, and can effectively improve the prediction accuracy and stability. The findings of this study provide strong support for the application of random forest in multi-classification datasets and provide valuable reference and guidance for research and practitioners in related fields.

关键词： Training Accuracy Machine learning algorithms Predictive models data models Stability analysis Robustness Decision trees Random forests Optimization

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Identifying Focus-of-Analysis Regions in MPI-Traces Using Transfer-Efficiency Monitors

Identifying Focus-of-Analysis Regions in MPI-Traces Using Tr...

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ieee international conference on High Performance computing Workshops (HiPCW)

作者： Kingshuk Haldar High Performance Computing Center Stuttgart (HLRS) University of Stuttgart Stuttgart Germany

ISBN: (数字)9798331509118

ISBN: (纸本)9798331509125

Trace visualisation is the only reliable (identify structures [1] ) way to identify the different execution phases (Focus-of-Analysis or FoA) in HPC applications. Identifying them enables inspection of the differently behaving areas separately afterwards. Real-world HPC applications run long, almost entirely consisting of calculation phases (applications’ main work), while the initialisation or I/O durations are comparatively shorter. However, trace files are recorded for a much shorter duration due to time and disk space constraints, resulting in over-representation of the initialisation or domain decomposition steps compared to production runs. Any specific FoA must be identified in the timeline before analysis. Trace size increases with recording duration and number of MPI ranks, often making visualisation impractical. Even after visualising, complex communication patterns are hard to decipher. The appropriate outlook of exascale-ready methodologies is to identify and analyse FoAs in large trace files easily.

关键词： High performance computing conferences data visualization Production Inspection Recording Reliability Monitoring

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iEEG Based Brain-Computer Interfaces for Neural Encoding of Dynamic Natural Vision

iEEG Based Brain-Computer Interfaces for Neural Encoding of ...

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Modeling, Simulation & Intelligent computing (MoSICom), international conference on

作者： Kunal Raghavendra Ganiga Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India

ISBN: (数字)9798331533311

ISBN: (纸本)9798331533328

This research delves into the complex task of interpreting visual stimuli based on intracranial electroencephalogram (iEEG) recordings, leveraging the power of advanced machine learning techniques, specifically Convolutional Neural Networks (CNNs). The study meticulously tackles the challenges encountered in the preprocessing phase of the data and the intricate process required to map the vast iEEG datasets accurately to corresponding visual stimuli. Despite facing these significant hurdles, the research provides profound insights into how brain activity is linked to visual perception. It significantly contributes to the understanding and development of brain-computer interfaces (BCIs) and advances the field of neuroscience. By exploring the potential of CNNs to decode visual information from iEEG data, this study not only pushes the boundaries of current technological capabilities but also opens new avenues for exploring the cognitive processes underlying human vision.

关键词： Visualization Neuroscience Refining Machine learning Brain-computer interfaces Encoding data models Decoding Convolutional neural networks Image reconstruction

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