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IEEE International Symposium on Innovations in Intelligent systems and Applications (INISTA)

作者： Kareem Amin Stelios Kapetanakis Nikolaos Polatidis Klaus-Dieter Althoff Andreas Dengel Smart Data and Knowledge Services German Research Center for Artificial Intelligence Technische Universität Kaiserslautern Kaiserslautern Germany School of Computing Engineering and Mathematics University of Brighton Brighton United Kingdom Intelligent Information Systems Lab Institute of Computer Science University of Hildesheim Hildesheim Germany

ISBN: (数字)9781728167992

ISBN: (纸本)9781728168005

With widespread modernization, digitization and transformations of most of industries, Artificial Intelligence (AI) has become the key enabler in that modernization journey. AI offers substantial capabilities to solve new problems and optimise existing solutions specialising on specific problems and learning from different domains. AI solutions can be either explainable or black box ones with the latter being urged to improve since they cannot trust. Case-based Reasoning (CBR) is an explainable AI approach where solutions are provided along with relevant explanations in terms of why a solution was selected. However, CBR, like most other explainable approaches, has several limitations in terms of scalability, large data volumes, domain complexity, that reduce its ability to scale any CBR system in industrial applications. In this paper, we provide a heterogeneous CBR framework - DeepKAF where we combine CBR paradigm with Deep Learning architectures to solve complicated Natural Language Processing (NLP) problems (eg. mixed language and grammatically incorrect text).DeepKAF is built based on continuous research in the area of Deep Learning and CBR. DeepKAF has been implemented and used across different domains, test use cases and research models as an ensemble deep learning and CBR Architecture.

关键词： Machine learning Training Neural networks Natural language processing Cognition Buildings Task analysis

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Correction: Privacy preservation in the internet of vehicles using local differential privacy and IOTA ledger

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Cluster Computing 2023年第6期26卷 3379-3379页

作者： Iftikhar, Zainab Anjum, Adeel Khan, Abid Shah, Munam Ali Jeon, Gwanggil Department of Computer Science COMSATS University Islamabad Islamabad Pakistan Institute of Information Technology Quaid i Azam University Islamabad Pakistan College of Science and Engineering University of Derby Derby UK Department of Embedded Systems Engineering Incheon National University Incheon Republic of Korea Energy Excellence & Smart City Lab Incheon National University Incheon Republic of Korea

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Multi-objective Optimal Control of Dynamic Integrated Model of Climate and Economy: Evolution in Action

arXiv

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arXiv 2020年

作者： Heris, Mostapha Kalami Rahnamayan, Shahryar Intelligent Systems Lab. Research and Development Department FaraDars Online Education Organization Tehran Iran Lab Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

One of the widely used models for studying economics of climate change is the Dynamic Integrated model of Climate and Economy (DICE), which has been developed by Professor William Nordhaus, one of the laureates of the 2018 Nobel Memorial Prize in Economic Sciences. Originally a single-objective optimal control problem has been defined on DICE dynamics, which is aimed to maximize the social welfare. In this paper, a bi-objective optimal control problem defined on DICE model, objectives of which are maximizing social welfare and minimizing the temperature deviation of atmosphere. This multi-objective optimal control problem solved using Non-Dominated Sorting Genetic Algorithm II (NSGA-II) also it is compared to previous works on single-objective version of the problem. The resulting Pareto front rediscovers the previous results and generalizes to a wide range of non-dominant solutions to minimize the global temperature deviation while optimizing the economic welfare. The previously used single-objective approach is unable to create such a variety of possibilities, hence, its offered solution is limited in vision and reachable performance. Beside this, resulting Pareto-optimal set reveals the fact that temperature deviation cannot go below a certain lower limit, unless we have significant technology advancement or positive change in global conditions. Copyright © 2020, The Authors. All rights reserved.

关键词： Climate change

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Resistive memory-based zero-shot liquid state machine for multimodal event data learning

arXiv

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arXiv 2023年

作者： Lin, Ning Wang, Shaocong Li, Yi Wang, Bo Shi, Shuhui He, Yangu Zhang, Woyu Yu, Yifei Zhang, Yue Zhang, Xinyuan Wong, Kwunhang Wang, Songqi Chen, Xiaoming Jiang, Hao Zhang, Xumeng Lin, Peng Xu, Xiaoxin Qi, Xiaojuan Wang, Zhongrui Shang, Dashan Liu, Qi Liu, Ming Department of Electrical and Electronic Engineering the University of Hong Kong Hong Kong Key Lab of Fabrication Technologies for Integrated Circuits and Key Laoratory of Microelectronic Devices and Integrated Technology Institute of Microelectronics the Chinese Academy of Sciences Beijing100029 China The School of Microelectronics Southern University of Science and Technology Shenzhen518055 China ACCESS – AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong University of Chinese Academy of Sciences Beijing100049 China Frontier Institute of Chip and System Fudan University Shanghai200433 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

The human brain is a complex spiking neural network (SNN), capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains minimal power consumption through event-based signal propagation. However, replicating the human brain in neuromorphic hardware presents both hardware and software challenges. Hardware limitations, such as the slowdown of Moore’s law and Von Neumann bottleneck, hinder the efficiency of digital computers. Additionally, SNNs are characterized by their software training complexities. To this end, we propose a hardware-software co-design on a 40 nm 256 Kb in-memory computing macro that physically integrates a fixed and random liquid state machine (LSM) SNN encoder with trainable artificial neural network (ANN) projections. We showcase the zero-shot LSM-based learning of multimodal events on the N-MNIST and N-TIDIGITS datasets, including visual and audio data association, as well as neural and visual data alignment for brain-machine interfaces. Our co-design achieves classification accuracy comparable to fully optimized software models, resulting in a 152.83 and 393.07-fold reduction in training costs compared to SOTA contrastive language-image pre-training (CLIP) and Prototypical networks, and a 23.34 and 160-fold improvement in energy efficiency compared to cutting-edge digital hardware, respectively. These proof-of-principle prototypes demonstrate zero-shot multimodal events learning capability for emerging efficient and compact neuromorphic hardware. © 2023, CC BY-NC-SA.

关键词： Image enhancement

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Gray Wolf Optimization of Fractional Order Control of 3-Omni Wheels Mobile Robot: Experimental Study

Gray Wolf Optimization of Fractional Order Control of 3-Omni...

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International computer engineering Conference (ICENCO)

作者： Hossam Ammar Mohammed Ibrahim Ahmad Azar Raafat Shalaby School of Engineering and Applied Science Smart Engineering Systems Research Center (SESC) Nile University Giza Egypt Menofia University Menouf Egypt Robotics and Internet-of-Things Lab (RIOTU) Prince Sultan Uni. Riyadh Saudi Arabia

ISBN: (数字)9781728163925

ISBN: (纸本)9781728163932

Committing robotics with artificial intelligence becomes mandatory collaboration with distinct environments. Omnidirectional Wheeled (Omni-WD) mobile robots are one of the robots that interact with humans in various circumstances, where it is important to function effectively and accurately. In this paper, the distinction of a 3WD-Omni model and control using machine vision is demonstrated. The use of fractional order (FO) calculus has been stated to increase the degrees of freedom of the controller over the integer ones. Hybridization of FO control and metaheuristics optimization is reported to enhance the control performance. Particle Swarm Optimizer (PSO) and Gray Wolf Optimizer (GWO) have been used for tuning the classical PID and FO-PID controllers. A comparative study between the optimized classical PID and FOPID has been performed using different paths and optimization methods. The results of the comparative study are mentored using different performance indices. The superior performance of FOPID tuned by GWO has been proven over all other techniques.

关键词： Machine vision Wheels Optimization methods Collaboration Mobile robots Particle swarm optimization Tuning

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Fusion of CNN ensemble for Remote Sensing Scene Classification

Fusion of CNN ensemble for Remote Sensing Scene Classificati...

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International Conference on computer Applications & Information Security (ICCAIS)

作者： Najd Alosaimi Haikel Alhichri Department of Computer Engineering King Saud University Riyadh Saudi Arabia Advanced Lab for Intelligent Systems Research (ALISR) King Saud University Riyadh Saudi Arabia

ISBN: (数字)9781728142135

ISBN: (纸本)9781728142142

Scene classification problem in remote sensing (RS) images has attracted many researchers recently. Different fusion methods have been widely used by the machine learning community to fuse classifiers. In this paper, a decision-level fusion method has been proposed to fuse a set of stat-of-the-art CNN classifiers, namely VGG-16, SqueezeNet, and DenseNet. First, the experiment proves that these classifiers do not make the same classification mistakes, i.e. most of the time at least one of them provides correct classification. Thus these three classifiers are diverse and hence complement each other. To exploit this discovery, a novel decision-level fusion method that combines the classification decisions using voting and confidence fusion techniques has been developed. To show the effectiveness of the proposed fusion method, the results demonstrate how the accuracy of the classification can be enhanced using fusion versus training individual networks. The preliminary results for the UC Merced dataset, the KSA multisensor dataset, Aerial Image Datasets (AID), Optimal31 dataset and Whurs19 dataset have been presented. Preliminary comparison to state-of-the-art methods show the promising capabilities of this solution and encourages to investigate this method further.

关键词： Computational modeling Training Predictive models Feature extraction Remote sensing Testing Load modeling

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SqueezeNet with Attention for Remote Sensing Scene Classification

SqueezeNet with Attention for Remote Sensing Scene Classific...

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International Conference on computer Applications & Information Security (ICCAIS)

作者： Asmaa S. Alswayed Haikel S. Alhichri Yakoub Bazi Department of Computer Engineering King Saud University Riyadh Saudi Arabia Advanced Lab for Intelligent Systems Research (ALISR) King Saud University Riyadh Saudi Arabia

ISBN: (数字)9781728142135

ISBN: (纸本)9781728142142

Scene classification is an important problem in remote sensing (RS) since it is a prerequisite to other more intelligent analysis operations. Given an RS scene, not all of its parts are important for classification. Thus, using an attention mechanism that directs the classification system to focus on the parts that are important and ignore the irrelevant background should enhance the system's accuracy. In this work we propose a deep CNN architecture based on the pre-trained SqueezeNet CNN. This CNN is composed of nine fire modules (fire 1 to fire 9) each consisting of Squeeze followed by expansion convolution layers. First, we improve the SqueezeNet CNN by introducing several modifications to the architecture. Then we introduce a separate branch to the network that implements an attention mechanism. Each neuron in this activation map of the fire 9 module covers a different receptive field in the original scene. An attention mechanism is applied to these neurons to learn the appropriate weighing scheme for merging the feature vectors corresponding to each neuron. Feature vectors that are assigned a higher weight indicate that the network has given more attention to the receptive field in the scene corresponding to that feature vector. Preliminary results are presenting on five popular scene datasets, namely UC Merced, KSA, AID, Whurs19, and Optimal31 datasets.

关键词： Fires Feature extraction Remote sensing Image analysis Spatial resolution Machine learning Meters

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TGGLinesPlus: A robust topological graph-guided computer vision algorithm for line detection from images

arXiv

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arXiv 2024年

作者： Yang, Liping Driscol, Joshua Gong, Ming Slack, Katie Zhang, Wenbin Wang, Shujie Potts, Catherine G. GeoAIR Lab Department of Geography and Environmental Studies University of New Mexico AlbuquerqueNM87131 United States University of New Mexico AlbuquerqueNM87131 United States Department of Computer Science University of New Mexico AlbuquerqueNM87106 United States Woodwell Climate Research Center FalmouthMA02540 United States Department of Electrical and Computer Engineering University of Dayton DaytonOH45469 United States Knight Foundation School of Computing and Information Sciences Florida International University MiamiFL33199 United States Department of Geography The Pennsylvania State University University ParkPA16802 United States Earth and Environmental Systems Institute The Pennsylvania State University University ParkPA16802 United States D-Wave Quantum Inc. BurnabyBC Canada

Line detection is a classic and essential problem in image processing, computer vision and machine intelligence. Line detection has many important applications, including image vectorization (e.g., document recognition and art design), indoor mapping, and important societal challenges (e.g., sea ice fracture line extraction from satellite imagery). Many line detection algorithms and methods have been developed, but robust and intuitive methods are still lacking. In this paper, we proposed and implemented a topological graph-guided algorithm, named TGGLinesPlus, for line detection. Our experiments on images from a wide range of domains have demonstrated the flexibility of our TGGLinesPlus algorithm. We benchmarked our algorithm with five classic and state-of-the-art line detection methods and evaluated the benchmark results qualitatively and quantitatively, the results demonstrate the robustness of TGGLinesPlus. Copyright © 2024, The Authors. All rights reserved.

关键词： Sea ice

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Mining the rank of universities with Wikipedia

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Science China(Information Sciences) 2019年第10期62卷 200-202页

作者： Zongjian LI Cong LI Xiang LI Adaptive Networks and Control Lab Department of Electronic EngineeringFudan University Research Center of Smart Networks and Systems School of Information Science EngineeringFudan Univeristy

Dear editor,Wikipedia is a free-access and web-based multilingual encyclopedia that voluntaries from all around the world can write and edit. Previous studies on Wikipedia mainly focused on its collaborative systems, i.e., edit patterns [1, 2]. Besides the relation network of editors in Wikipedia, the articles of Wikipedia form a large-scale complex network, the Wikipedia article reference network(WARN). Articles are regarded as nodes, which

关键词： red net No Mining the rank of universities with Wikipedia

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Hyperseed: Unsupervised Learning with Vector Symbolic Architectures

arXiv

引用

arXiv 2021年

作者： Osipov, Evgeny Kahawala, Sachin Haputhanthri, Dilantha Kempitiya, Thimal De Silva, Daswin Alahakoon, Damminda Kleyko, Denis The Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå97187 Sweden La Trobe University Melbourne Australia The Redwood Center for Theoretical Neuroscience The University of California BerkeleyCA94720 United States The Intelligent Systems Lab Research Institutes of Sweden Kista16440 Sweden

Motivated by recent innovations in biologically-inspired neuromorphic hardware, this article presents a novel unsupervised machine learning algorithm named Hyperseed that draws on the principles of Vector Symbolic Architectures (VSA) for fast learning of a topology preserving feature map of unlabelled data. It relies on two major operations of VSA, binding and bundling. The algorithmic part of Hyperseed is expressed within Fourier Holographic Reduced Representations model, which is specifically suited for implementation on spiking neuromorphic hardware. The two primary contributions of the Hyperseed algorithm are, few-shot learning and a learning rule based on single vector operation. These properties are empirically evaluated on synthetic datasets as well as on illustrative benchmark use-cases, IRIS classification, and a language identification task using n-gram statistics. The results of these experiments confirm the capabilities of Hyperseed and its applications in neuromorphic hardware. Copyright © 2021, The Authors. All rights reserved.

关键词： Self organizing maps

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