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2023 international conference on Cloud computing, Performance computing, and Deep Learning, CCPCDL 2023

作者： Li, Zhuofan Wang, Jingping Zhang, Yingjun Huang, Hua Yin, Hui Xu, Hongli Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China Information Technology Center Beijing Jiaotong University Beijing100044 China Key Laboratory of Beijing for Railway Engineering Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781510666474

Fuzzy cognitive maps (FCMs) are an efficient soft computing tool commonly used for modeling and analyzing complex systems, and have significant advantages in knowledge representation and fast reasoning. At present, many researchers have devoted themselves to solving the learning problem of FCMs. However, most of the existing learning algorithms are prone to local convergence and are not suitable for large-scale FCMs learning problems. Moreover, the learning problem of FCMs is often a complex non-convex optimization problem, which is easy to cause the final learning result of the algorithm to be only a local optimal solution. To address these limitations, this paper proposes a novel algorithm for learning FCMs by combining multimodal optimization and modified covariance matrix adaptation evolution strategy (CMA-ES), termed as MCMA-ES. First, the learning problem of FCMs is modeled as a multimodal optimization problem (MMOP), and then a multimodal optimization algorithm is proposed by combining the detect multimodal clustering (DMC) and CMA-ES, and the cubic chaos factor is combined to enhance the capability of local search. Finally the proposed algorithm is combined with the decomposition strategy to learn FCMs. Experiments are carried out on 5 benchmark datasets, and MCMA-ES is also applied to 15 groups of large-scale gene regulatory system reconstruction problems. The experimental results show that MCMA-ES has high learning generalization and accuracy. © 2023 SPIE.

关键词： Fuzzy cognitive Maps

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Offload Shaping for Wearable cognitive Assistance 7

Offload Shaping for Wearable Cognitive Assistance

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7th IEEE international conference on Edge computing and Communications (IEEE EDGE) / IEEE World Congress on Services (SERVICES)

作者： Iyengar, Roger Dong, Qifei Chanh Nguyen Pillai, Padmanabhan Satyanarayanan, Mahadev Carnegie Mellon Univ Pittsburgh PA 15213 USA Intel Labs Pittsburgh PA USA

ISBN: (纸本)9798350304831

Edge computing has much lower elasticity than cloud computing because cloudlets have much smaller physical and electrical footprints than a data center. This hurts the scalability of applications that involve low-latency edge offload. We show how this problem can be addressed by leveraging the growing sophistication and compute capability of recent wearable devices. We investigate four Wearable cognitive Assistance applications on three wearable devices, and show that the technique of offload shaping can significantly reduce network utilization and cloudlet load without compromising accuracy or performance.

关键词： Computer Vision Machine Learning Offloading Wearable computing Mobile computing Edge computing IoT Cloudlet Augmented Reality 5G Wi-Fi

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Edge-Cloud Collaboration Architecture for Efficient Web-Based cognitive Services

Edge-Cloud Collaboration Architecture for Efficient Web-Base...

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IEEE international conference on Big data and Smart computing (BigComp)

作者： Wang, Zhaoyan Ko, In-Young Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea

ISBN: (纸本)9781665475785

Many web services are often latency-sensitive, have high network capabilities, and have computing resource limitations, as demonstrated by deep neural network (DNN) model-based web applications. In this work, a DNN model-based web application providing visual cognitive services is implemented and explored to address latency-sensitive problems, taking into account the broad practicability and importance of web object recognition tasks in intelligent applications and modern systems. We propose a collaborative architecture of end-user, edge server, and cloud server, employing a binary offloading strategy to reduce the upload rate of images while ensuring good detection performance, thereby reducing the response time. Detection scenario-oriented data augmentation is carried out on the dataset to improve detection accuracy. Finally, we compare the performance of the proposed approach with traditional object recognition services running entirely on the cloud. The experimental results show that the detection scenario-oriented strategy significantly improves the detection accuracy of fine-trained YOLOv5 models on the PASCAL VOC 2012 dataset. Compared with the traditional cloud-based architecture, the proposed edge-cloud collaboration architecture can detect more objects and reduce response time efficiently.

关键词： edge-cloud collaboration deep neural network web application cognitive service

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A Novel Multimodal data Fusion Framework: Enhancing Prediction and Understanding of Inter-State Cyberattacks

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BIG data AND cognitive computing 2025年第3期9卷 63-63页

作者： Dong, Jiping Hao, Mengmeng Ding, Fangyu Chen, Shuai Wu, Jiajie Zhuo, Jun Jiang, Dong Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China Chinese Acad Sci Lab Cyberspace Geog Minist Publ Secur Peoples Republ China Beijing 100101 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 101408 Peoples R China

Inter-state cyberattacks are increasingly becoming a major hidden threat to national security and global order. However, current prediction models are often constrained by single-source data due to insufficient consideration of complex influencing factors, resulting in limitations in understanding and predicting cyberattacks. To address this issue, we comprehensively consider multiple data sources including cyberattacks, bilateral interactions, armed conflicts, international trade, and national attributes, and propose an interpretable multimodal data fusion framework for predicting cyberattacks among countries. On one hand, we design a dynamic multi-view graph neural network model incorporating temporal interaction attention and multi-view attention, which effectively captures time-varying dynamic features and the importance of node representations from various modalities. Our proposed model exhibits greater performance in comparison to many cutting-edge models, achieving an F1 score of 0.838. On the other hand, our interpretability analysis reveals unique characteristics of national cyberattack behavior. For example, countries with different income levels show varying preferences for data sources, reflecting their different strategic focuses in cyberspace. This unveils the factors and regional differences that affect cyberattack prediction, enhancing the transparency and credibility of the proposed model.

关键词： cyberattack prediction graph neural networks attention mechanism multimodal data fusion international relations

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An Analysis of the Concept of cognitive Enterprise Architecture Foundation of cognitive Enterprise Archtecture 14

An Analysis of the Concept of Cognitive Enterprise Architect...

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14th IEEE international conference on cognitive Infocommunications, CogInfoCom 2023

作者： Molnár, Bálint Budapest Hungary

ISBN: (纸本)9798350325652

There are several approaches to dealing with the interactions between humans and computers in the context of Information Systems and Enterprises. The paper proposes an integrated approach and definition of a research field that combines the available cognitive approaches in IT and architectures for Information Systems and Enterprises. There are established scientific and research disciplines in the said domains such as Enterprise Architecture;Analysis and Design Information Systems;Business Process Management and Modelling;data Management and Administration;Human-computer interaction and cognitive Infocommunication. The rapid development of Artificial Intelligence (Machine learning) and cognitive Informatics and their applications in enterprise environments necessitates defining a research framework that can support the alignment of the elements of this puzzle of components for the purpose of research and practical applications. © 2023 IEEE.

关键词： Information systems

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complex Adaptive Systems and Psychophysiological data - An Exploratory Approach 31

Complex Adaptive Systems and Psychophysiological Data - An E...

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31st Irish conference on Artificial Intelligence and cognitive Science (AICS)

作者： Dair, Zachary Dockray, Samantha O'Reilly, Ruairi Munster Technol Univ Comp Sci Cork Ireland Univ Coll Cork Appl Psychol Cork Ireland

ISBN: (纸本)9798350360219

The diverse nature of emotion models presents challenges in psychophysiological analysis, notably affecting the comparability and reliability of research findings across studies. This exploratory work aims to address the diversity of emotion models by abstracting them into a higher-order perspective while preserving the specificity of the original emotions. This will be accomplished by integrating complex Adaptive Systems utilising their inherent interactivity to foster rich interactions between different emotion models, adaptability to adjust to the nuances of each model, and self-organisation to autonomously form a coherent understanding of diverse emotional data. By analysing interactions within the system, this study aims to identify abstract emotion representations that capture dynamic patterns in physiological data that indicate emotional states. Combining these dynamic patterns with an analysis of system interactions offers a novel perspective in emotion modelling.

关键词： Affective computing complex Adaptive Systems Psychophysiology Emotion Models cognitive Modelling complexity Theory

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Topology-Aware-based Traffic Prediction Mechanism for Elastic cognitive Optical Networks 19

Topology-Aware-based Traffic Prediction Mechanism for Elasti...

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19th IEEE international Wireless Communications and Mobile computing (IEEE IWCMC)

作者： Li, Jianxing Wang, Fu Yao, Haipeng Tian, Feng Yin, Xiaoli Zhang, Qi Beijing Univ Posts & Telecommun Sch Elect Engn Beijing Peoples R China

ISBN: (纸本)9798350333398

Elastic cognitive optical network (ECON) embeds artificial intelligence technology into network management to enable resource self-optimization ability, which has aroused the wide interest of researchers. However, realizing precise traffic prediction (TP) in optical networks has been a challenging problem due to channels' complex variable bandwidth conditions. We provide an ECON architecture and propose a graph-convolutional-network-transformer (GCN-transformer) TP algorithm. The proposed algorithm has been evaluated and compared with the traditional schemes. We build a testbed for the proposed algorithm by OMNET++. The results show a prediction accuracy of 99.74%, which reduces the inaccuracy by 2.03% compared with other typical algorithms.

关键词： Elastic cognitive optical network traffic prediction graph-convolutional-network transformer

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Empowering Generative AI with Knowledge Base 4.0: Towards Linking Analytical, cognitive, and Generative Intelligence

Empowering Generative AI with Knowledge Base 4.0: Towards Li...

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2023 IEEE international conference on Web Services, ICWS 2023

作者： Beheshti, Amin Macquarie University School of Computing Sydney Australia

ISBN: (纸本)9798350304855

Intelligence refers to the ability to acquire and apply knowledge and skills, which comprises three fundamental components, namely knowledge, experience, and creativity. Consequently, there exist three primary Artificial Intelligence (AI) systems, namely Analytical AI, cognitive AI, and Generative AI. Analytical AI is primarily concerned with comprehending the data and transforming it into contextualized data and knowledge. On the other hand, cognitive AI is centered on understanding experience and aims to annotate, enrich, and utilize the knowledge, to facilitate decision-making. Lastly, Generative AI delves into the neural mechanisms involved in creative thinking and problem-solving, with a focus on enhancing the process of acquiring and applying knowledge and skills. This paper presents Knowledge Base 4.0 as the backend data for AI engines, which allows for linking knowledge and experience to enable empowering generative AI. The objective is not only to facilitate generating new content (such as text and images) but also to generate new processes when/if needed. We present the architecture of Knowledge Base 4.0 and the design and development of data services that construct and maintain this robust Knowledge Base. Additionally, we provide use cases in various domains, including health, policing, banking, and education. © 2023 IEEE.

关键词： Crowdsourcing

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HYPERDIMENSIONAL cognitive computing FOR LIGHTWEIGHT CYBERATTACK DETECTION IN INDUSTRIAL INTERNET OF THINGS

HYPERDIMENSIONAL COGNITIVE COMPUTING FOR LIGHTWEIGHT CYBERAT...

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ASME international Design Engineering Technical conferences / 43rd Annual Computers and Information in Engineering conference (IDETC-CIE) / 19th IEEE/ASME international conference on Mechatronic and Embedded Systems and Applications (MESA)

作者： Piran, Fardin Jalil Barkam, Hamza Errahmouni Imani, Mohsen Imani, Farhad Univ Connecticut Dept Mech Engn Storrs CT 06269 USA Univ Calif Irvine Dept Comp Sci Irvine CA USA

ISBN: (纸本)9780791887356

Although the connectivity offered by industrial internet of things (IIoT) enables enhanced operational capabilities, the exposure of systems to significant cybersecurity risks poses critical challenges. Recently, machine learning (ML) algorithms such as feature-based support vector machines and logistic regression, together with end-to-end deep neural networks, have been implemented to detect intrusions, including command injection, denial of service, reconnaissance, and backdoor attacks, by capturing anomalous patterns. However, ML algorithms not only fall short in agile identification of intrusion with few samples, but also fail in adapting to new data or environments. This paper introduces hyperdimensional computing (HDC) as a new cognitive computing paradigm that mimics brain functionality to detect intrusions in IIoT systems. HDC encodes real-time data into a high-dimensional representation, allowing for ultra-efficient learning and analysis with limited samples and a few passes. Additionally, we incorporate the concept of regenerating brain cells into hyperdimensional computing to further improve learning capability and reduce the required memory. Experimental results on the WUSTL-IIOT-2021 dataset show that HDC detects intrusion with the accuracy of 92.6%, which is superior to multi-layer perceptron (40.2%), support vector machine (72.9%), logistic regression (84.2%), and Gaussian process classification (89.1%) while requires only 300 data and 5 iterations for training.

关键词： Industrial internet of things Cyberattack detection cognitive computing

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cognitive Controller for 6G-Enabled Edge Autonomic 14

Cognitive Controller for 6G-Enabled Edge Autonomic

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14th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th international conference on Emerging data and Industry 4.0, EDI40 2023

作者： Rahmani, Rahim Firouzi, Ramin Sadique, Kazi Masum Department of Computer and Systems Sciences Stockholm University Sweden

This article proposes a new Artificial Intelligent (AI) and Machine Learning (ML) based framework for 6G-enabled Intelligent edge computing. The framework will be equipped with multiple cognitive controllers to harmoniously control various aspects in distributed intelligence toward edge nodes collaboration. Autonomic cognitive controller for edge computing is a popular computing paradigm where the distributed metadata processing and edge intelligence are performed at edge node in 5G/6G network for management, connectivity and interoperability. Some of studies focused on edge management improvement such as reduce the response time and bandwidth costs. However, the previous approaches are inadequate to support autonomously management for large-scale deployment for connectivity for dynamic and reliable communication. We propose a cognitive controller for edge autonomy and collaboration application development. Finally, we discuss challenges and open issues toward cognitive controller and distributed edge intelligence. © 2023 Elsevier B.V.. All rights reserved.

关键词： Edge computing

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