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arXiv 2023年

作者： Hedström, Anna Bommer, Philine Wickstrøm, Kristoffer K. Samek, Wojciech Lapuschkin, Sebastian Höhne, Marina M.-C. Department of Electrical Engineering and Computer Science Technical University of Berlin Germany BIFOLD Berlin Institute for the Foundations of Learning and Data Germany Department of Physics and Technology UiT Arctic University of Norway Norway Department of Artificial Intelligence Fraunhofer Heinrich-Hertz-Institute Germany Department of Computer Science University of Potsdam Germany Germany

One of the unsolved challenges in the field of Explainable AI (XAI) is determining how to most reliably estimate the quality of an explanation method in the absence of ground truth explanation labels. Resolving this issue is of utmost importance as the evaluation outcomes generated by competing evaluation methods (or "quality estimators"), which aim at measuring the same property of an explanation method, frequently present conflicting rankings. Such disagreements can be challenging for practitioners to interpret, thereby complicating their ability to select the best-performing explanation method. We address this problem through a meta-evaluation of different quality estimators in XAI, which we define as "the process of evaluating the evaluation method". Our novel framework, MetaQuantus, analyses two complementary performance characteristics of a quality estimator: its resilience to noise and reactivity to randomness, thus circumventing the need for ground truth labels. We demonstrate the effectiveness of our framework through a series of experiments, targeting various open questions in XAI such as the selection and hyperparameter optimisation of quality estimators. Our work is released under an open-source license1 to serve as a development tool for XAI- and Machine Learning (ML) practitioners to verify and benchmark newly constructed quality estimators in a given explainability context. With this work, we provide the community with clear and theoretically-grounded guidance for identifying reliable evaluation methods, thus facilitating reproducibility in the field. © 2023, CC BY-SA.

关键词： Benchmarking

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Unleashing The Power Of 6g Assisted Wireless Communication Network To Define A New Routing Strategy To Preserve Communication Energy 10

Unleashing The Power Of 6g Assisted Wireless Communication N...

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10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Avinash, S. Sujatha, S. Poornima, D. Vedhashree, S.K. Saravanan, V. Malathi, P. St. Josephs College of Engineering Department of MBA Tamil Nadu Chennai India Sri Sairam College of Engineering Department of EEE Anekal Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India M.Kumarasamy College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Nano Electronics Materials and Sensors Tamilnadu Chennai India SIMATS Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India

ISBN: (纸本)9798350353778

The incorporation of cutting-edge technology into intelligent power grid management becomes a top priority as the globe keeps searching for effective and environmentally friendly energy solutions. The arrival of 6G wireless networks is expected to completely transform the methods used to track, manage, and enhance energy grids. An effort to provide high data speeds has already been made with the deployment of commercial fifth-generation (5G) wireless communications networks. The advent of IoT applications and the proliferation of smart devices will put a heavy strain on 5G wireless networks since they need extremely dependable and lowlatency connection. So, the massive continuous data traffic boom is probably going to be too much for 5G networks to handle. Research into enhancing current wireless networks in preparation for the 6G cellular systems, the next generation of mobile communication, has been spurred by this. This paper introduces the Neural 6G Wireless Routing Protocol (N6GWRP), a new routing protocol that supports intelligent communication. To evaluate the effectiveness of the suggested approach, it is cross-validated with the Multiple-Access Routing Protocol (MARP), a traditional routing model. Users should not experience any disruptions when mobile power and data transmission systems interact. Another major obstacle is connecting the vast array of devices and people with varying degrees of processing power in a way that is both sustainable and environmentally conscious. This study delves into the smart solution to the aforesaid difficulties and examines how 6G wireless networks might improve smart energy grid resilience and dependability through seamless interaction with other sophisticated technologies like blockchain. A more environmentally friendly and smart vision regarding electrical grid administration is possible thanks to the extensive study that strives to illuminate the revolutionary significance of 6G wireless technologies. © 2024 IEEE.

关键词： 5G mobile communication systems

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Fruit Type Classification using Stacked Bi-directional Long Short Term Memory

Fruit Type Classification using Stacked Bi-directional Long ...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Subba Reddy Borra Venkata Ramana Kaneti Vontela Tejaswini S Anitha Vinaykumar R IT Department Malla Reddy Engineering College for Women Hyderabad India Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India Department of Computer Science and Engineering (Data Science) CMR Technical Campus Hyderabad India Department of Computer Science and Engineering CMR Engineering College Hyderabad India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

In recent years, Machine Learning (ML) and Deep Learning (DL) applications have become increasingly important in agriculture, particularly in the classification of fruits based on image data. Fruit recognition and categorization have attained a lot of attention from researchers. Because of the diversity and heterogeneity of fruits, this is a difficult challenge. To solve this, a Stacked Bi-LSTM image classification approach has been proposed to enhance the accuracy of fruit image classification. The process includes feature extraction by a Convolutional Neural Network (CNN), followed by feature selection using a Recurrent Neural Network (RNN). Following that, the proposed Stacked Bi-LSTM model is utilized to classify fruit images, resulting in enhanced accuracy. As indicated by evaluation parameters like as recall, precision, and F-measure, the acquired results outperform previous algorithms. The analyzed outcomes show that the Stacked Bi-LSTM model accomplishes precision of 85.4%, recall of 88.3%, and F1-score of 83.9%, demonstrating greater performance when compared to existing approaches.

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Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI 41

Position: Bayesian Deep Learning is Needed in the Age of Lar...

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41st International Conference on Machine Learning, ICML 2024

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Monte Carlo Tree Search Algorithms for Strategic Planning in Humanoid Robotics

Monte Carlo Tree Search Algorithms for Strategic Planning in...

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Cognitive Robotics and Intelligent Systems (ICC - ROBINS), International Conference on

作者： Praveen Gundla Annapurna Gummadi Ravindra Changala VeeraAnkalu Vuyyuru A Hemantha Kumar B KiranBala NRI Institute of Technology Guntur Andhra Pradesh Department of CSE (Data Science) CVR College of Engineering Hyderabad India Department of Information Technology Guru Nanak Institutions Technical Campus Hyderabad India Department of Computer Science and Engineering KoneruLakshmaiah Education Foundation Vaddeswaram A.P India MED Annamacharya Institute of Technology and Sciences Rajampet AP. Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Trichy

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

With the increasing complexity and versatility of humanoid robots, there is a growing imperative for efficient and adaptable planning methodologies. This evolution marks a crucial step towards enhancing the overall capabilities and practicality of humanoid robotic systems. This research delves into the application of Monte Carlo Tree Search (MCTS) algorithms for enhancing strategic planning in the context of humanoid robotics. The study explores the integration of MCTS into humanoid robotics to enable effective decision-making processes that account for uncertainties, sensor noise, and real-time adaptability. By simulating future scenarios and evaluating potential actions through iterative sampling, MCTS facilitates the creation of robust and context-aware strategies. The research considers various aspects of strategic planning, including path finding, task allocation, and goal achievement. This investigation contributes to the burgeoning field of robotics by providing a comprehensive analysis of the benefits and challenges associated with implementing MCTS in humanoid robotic systems. The proposed MCTSA model shows better efficiency in Resource Allocation 88%, SuccessRate (Navigation) 85%, Average Planning Time 2.5 seconds and Success Rate (Object Manipulation) 92% which is higher when compared to the existing methods. The findings aim to inform the development of intelligent and autonomous robots capable of making strategic decisions in dynamic and uncertain environments, thereby advancing the capabilities of humanoid robotics in various practical applications.

关键词： Monte Carlo methods Uncertainty Navigation Noise Humanoid robots Strategic planning Robot sensing systems

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Implications of Brewer's Rule in data Warehouse Design 7

Implications of Brewer's Rule in Data Warehouse Design

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Raman, Ramakrishnan Peter, John Benito Jesudasan Gokhale, Atul A Manikandan, J. Ganesh, E.N. Srinivasan, C. Maharashtra Pune India Capital One Services Llc Cyber | Data Science & Engineering RichmondVA United States Symbiosis School of Culinary Arts Maharashtra Pune India Hindusthan College of Engineering and Technology Department of Mechanical Engineering Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350341485

Distributed systems cannot achieve consistency, availability, and partition tolerance, according to Brewer's Rule (CAP theorem). Brewer's Rule affects distributed data warehouses. Brewer's Rule improves data warehouse architecture, according to this study. database research Brewer's Rule reliability, accessibility, and tolerance for partitions. Consistency - the condition that all distributed system nodes access the same data simultaneously - is examined. Distributed transactions, two-phase commit, and consensus methods let data warehouses achieve consistency. A distributed system must be available even if some nodes fail, the study says. Replication, load balancing, and failover provide high availability in data warehouses. Finally, partition tolerance - the need for a distributed system to operate after network partitions - is examined. data sharding, distributed caching, and data replication help data warehousing systems achieve partition tolerance. Brewer's Rule helps improve data warehouse architecture for use cases and business needs. The study also discusses trade-offs companies must make when improving consistency, availability, and partition tolerance. To maintain dependable and performant data warehousing solutions, organizations must balance these trade-offs. Organizations designing and implementing distributed data warehouses must consider Brewer's Rule. Organizations may construct high-performing, dependable data warehouse systems that fulfill their business needs by understanding how consistency, availability, and partition tolerance affect data warehouse architecture. © 2023 IEEE.

关键词： data warehouses

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Improvement of Low-Contrast Objective Detecting Capability for YOLOv5 Based on Receptive Field Enhancement and Redundant Feature Reuse

Improvement of Low-Contrast Objective Detecting Capability f...

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International Joint Conference on Neural networks (IJCNN)

作者： Qingyu Xu Jiguo Yu Anming Dong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

YOLOv5s is a classic deep learning target detection framework with balanced speed and performance in recognition, which is widely used in industrial defect inspections. YOLOv5s relies on the residual structure in the C3 module for feature extraction, but due to its simple structure and the small, fixed convolution kernel, it is difficult to accurately and completely extract target features when applied to the detection of defects with inconspicuous features and irregular shapes, which results in a decrease in recognition accuracy. To solve this problem, this paper proposes an improved YOLO architecture based on receptive field enhancement and redundant feature reuse. Firstly, the method designs a redundant feature reuse module (RFRM) to replace the residual structure in the C3 module for feature extraction. This module can deepen the network with a smaller cost and enhance the network’s ability to extract features. Furthermore, the feature reuse process allows the network to deepen while retaining detailed information, enabling the network to classify and localize more accurately. Secondly, a simplified convolutional block attention module (SCBAM) is designed and embedded into the neck of the detection network. This module directs the detection network to focus more on inconspicuous defect features and reduce interference from background noise in the feature map, thereby improving the accuracy of defect classification and localization. Finally, a receptive field enhancement module (RFEM) is designed and inserted between the neck and head of the detection network. This module adds only a small computational cost to expand the receptive field of the network, enabling it to further extract and comb irregular features from the neck in a comprehensive manner, making the features reaching the detection head more complete. Applying the above improvements to defect detection, the experimental results show that the improved YOLO architecture based on receptive field enhancem

关键词： YOLO Head Accuracy Target recognition Shape computer architecture Feature extraction

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A 3D Memristive Cubic Map with Dual Discrete Memristors: Design, Implementation, and Application in Image Encryption

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Gao, Suo Iu, Herbert Ho-Ching Erkan, Ugur Simsek, Cemaleddin Toktas, Abdurrahim Cao, Yinghong Wu, Rui Mou, Jun Li, Qi Wang, Chunpeng Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering Perth6009 Australia Faculty of Engineering Electrical and Electronics Engineering Respectively Karamanoglu Mehmetbey University Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system's potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme for image encryption with the *** results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks. © 1991-2012 IEEE.

关键词： Bifurcation (mathematics)

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Weisfeiler and Leman go machine learning: the story so far

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 15865-15923页

作者： Christopher Morris Yaron Lipman Haggai Maron Bastian Rieck Nils M. Kriege Martin Grohe Matthias Fey Karsten Borgwardt Department of Computer Science RWTH Aachen University Aachen Germany Meta AI Research Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel NVIDIA Research Tel Aviv Israel AIDOS Lab Institute of AI for Health Helmholtz Zentrum München and Technical University of Munich Munich Germany Faculty of Computer Science and Research Network Data Science University of Vienna Vienna Austria Kumo.AI Mountain View CA Machine Learning & Computational Biology Lab Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland and Swiss Institute of Bioinformatics Lausanne Switzerland

In recent years, algorithms and neural architectures based on the Weisfeiler-Leman algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a powerful tool for machine learning with graphs and relational data. Here, we give a comprehensive overview of the algorithm's use in a machine-learning setting, focusing on the supervised regime. We discuss the theoretical background, show how to use it for supervised graph and node representation learning, discuss recent extensions, and outline the algorithm's connection to (permutation-)equivariant neural architectures. Moreover, we give an overview of current applications and future directions to stimulate further research.

关键词： machine learning for graphs graph neural networks Weisfeiler-Leman algorithm expressivity equivariance

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Hierarchical Federated Learning-Based Method for privacy-preserving in the Healthcare Environment

Hierarchical Federated Learning-Based Method for privacy-pre...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Manoj Kumar D P Ananda Babu. J Jaladi NagaSireesha Bhattu Venkatesh Ayesha Siddiqua Department of Computer Science and Engineering Kalpataru Institute of Technology Tiptur India Department of Information Science and Engineering Malnad College of Engineering Hassan India Malla Reddy Engineering College for Women Autonomous India Department of Computer Science and Engineering (Data Science) CMR Technical Campus Hyderabad India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

The COVID-19 pandemic has led to increase the demand for medical equipment with internet of things (IoMT) capabilities forcing doctors to diagnose and treat patients remotely. This has increased awareness of health, leading to a surge in purchasing of IoMT-capable medical equipment. However, medical data is sensitive and valuable on the dark web, making it vulnerable to cybercriminals. Due to limited storage and processing capabilities of network devices, system administrators struggle to enhance security measures. This work aims to investigate rapidly spreading attacks before they compromise wellness systems. Hierarchical federated learning is (HFL) proposed using a model built on Dew-cloud, which offers improved data privacy due to the increased availability of IoMT critical applications. Dew servers with distributed architecture and a cloud computing-backed backend implement hierarchical long-term memory (HLSTM) method. The implemented HFL-HLSTM method’s performance is evaluated using NSL-KDD dataset. Implemented method achieved better performance than the existing intrusion detection methods including MiTed, LS2DNN, and MetaCID and the metrics are 98.62% of f-measure, 99.53% of accuracy, 99.21% of precision, and 99.10% of recall.

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