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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Pande, Sagar Dhanraj Singh, Gurpreet Zekrifa, Djabeur Mohamed Seifeddine Kodgire, Shilpa Prashant Patel, Sunil A. Le, Viet-Thanh School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Faculty of Computational Science GNA University Phagwara India Centre for Quantum Computation and Communication Technology Sydney Australia Maharashtra Institute of Technology Maharashtra Aurangabad India Computer Engineering Department L.D. College of Engineering Ahmedabad India Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam

ISBN: (纸本)9781032669663

A threshold key-sharing technology-based publicly verifiable key-sharing technology is presented to explore blockchain users’ private keys’ security. After receiving key fragments, participating nodes validate them. When splitting the key, the central splitting node does evil. In the critical recovery stage, the key fragments of the nodes participating in key splicing are publicly verified to prevent them from doing evil. In the critical distribution stage, add IDs to participating nodes to track malicious nodes and update node status in real-time, and a dynamic threshold mechanism is designed. To maintain private key fragment integrity, the owner of the key fragment and the controller node redistribute it to the new participating nodes once the vital fragment node goes offline. Experimental results reveal that this scheme’s private key recovery rate is 80% and has threshold properties, traceability, unforgeability, and recoverability. © 2024 The Author(s).

关键词： Blockchain

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Brain-inspired artificial intelligence research: A review

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science China(Technological sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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Quantum-Accelerated Hyperparameter Tuning for Dynamic NLP Models

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Procedia computer science 2024年 235卷 506-519页

作者： Ravikumar S Arockia Raj Y R. Babu Vijay K R. Ramani Associate Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India 600062 Assistant Professor Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Assistant Professor Department of Computational Intelligence Faculty of College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamilnadu India 603203 Assistant Professor Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai Tamilnadu India 602117 Associate Professor Department of Computer Science and Engineering P.S.R. Engineering College Sivakasi Tamilnadu India 626140

In the rapidly evolving field of natural language processing (NLP), performance optimization of large-scale NLP models is crucial. Through the application of Quantum-Accelerated Hyperparameter Tuning (QAHT), this abstract introduces a novel approach to addressing this issue. Our proposed framework leverages quantum computing capabilities to dynamically optimize NLP model hyperparameters in real-time, catering to the ever-changing character of textual data streams. Traditional hyperparameter optimisation methods usually rely on laborious grid searches or random exploration, which may not be suitable for dynamic NLP jobs. Contrarily, QAHT uses Quantum Neural Network (QNN) architectures that have been specially designed for hyperparameter optimisation. These QNNs improve performance and efficacy by dynamically modifying and improving model configurations. This abstract discusses the key elements of the QAHT architecture, including real-time model deployment, adaptive learning, and continuous data stream processing. In addition to speeding up the hyperparameter optimisation process, QAHT makes sure that NLP models are still flexible and responsive to shifts in the sentiment of the data and its distribution. This method has applications beyond NLP since it provides a foundation for effectively optimising machine learning models in complex, real-time situations. As quantum computing develops, QAHT represents a promising future in machine learning, where quantum-enhanced capabilities satisfy the needs of contemporary data-driven applications.

关键词： Hyperparameter optimization Quantum computing Natural language processing (NLP) Quantum Neural Networks (QNN) Real-time processing Adaptive learning

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Dimensionality and dynamics for next-generation artificial neural networks

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Patterns 2025年

作者： Wang, Ge Fan, Feng-Lei Department of Biomedical Engineering Department of Electrical Computer and Systems Engineering Department of Computer Science Center for Computational Innovations Biomedical Imaging Center Center for Biotechnology and Interdisciplinary Studies Rensselaer Polytechnic Institute TroyNY United States Department of Data Science City University of Hong Kong Kowloon Hong Kong

The recent awarding of the Nobel Prize in Physics to Geoffrey E. Hinton and John J. Hopfield highlights their profound impact on artificial neural networks. In this perspective, we explore how their foundational insights can drive the advancement of next-generation artificial intelligence (AI) models. We propose expanding beyond conventional architectures by introducing dimensionality through intra-layer links and dynamics via feedback loops. Network height and additional dimensions, alongside traditional width and depth, enhance learning capabilities, while entangled loops across scales induce emergent behaviors akin to phase transitions in physics. We discuss how these principles extend beyond transformers, fostering a new paradigm of intelligence inspired by physics-driven models and biological cognition mechanisms. © 2025 The Author(s)

关键词： Neural network models

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Fuzzychain: An Equitable Consensus Mechanism for Blockchain Networks

arXiv

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arXiv 2024年

作者： Ramos-Cruz, Bruno Andreu-Pérez, Javier Quesada, Francisco J. Martínez, Luis Computer Science Department University of Jaen Jaen23071 Spain Centre for Computational Intelligencer School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom

Blockchain technology has become a trusted method for establishing secure and transparent transactions through a distributed, encrypted network. The operation of blockchain is governed by consensus algorithms, among which Proof of Stake (PoS) is popular yet has its drawbacks, notably the potential for centralising power in nodes with larger stakes or higher rewards. Fuzzychain, our proposed solution, introduces the use of fuzzy sets to define stake semantics, promoting decentralised and distributed processing control. This system selects validators based on their degree of membership to the stake fuzzy sets rather than just the size of their stakes. As a pioneer proposal in applying fuzzy sets to blockchain, Fuzzychain aims to rectify PoS’s limitations. Our results indicate that Fuzzychain not only matches PoS in functionality but also ensures a fairer distribution of stakes among validators, leading to more inclusive validator selection and a better-distributed network. © 2024, CC BY-NC-ND.

关键词： Blockchain

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Underwater estimation of audio signal prediction using fruit fly algorithm and hybrid wavelet neural network

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Journal of Reliable Intelligent Environments 2022年第2期8卷 211-221页

作者： Sagayam, K. Martin Ghosh, Ahona Bhushan, Bharat Andrew, J. Cengiz, Korhan Elngar, Ahmed A. Department of Electronics and Communication Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computational Science Brainware University Kolkata India School of Engineering and Technology Sharda University Greater Noida India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Electrical and Electronics Engineering Traka University Edirne Turkey Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef City62511 Egypt

Underwater acoustic signal is one of its most vital features in oceanography for predicting the local variants, helps to process the signal, and also assists to discover non-stationary signals. wavelet neural network (WNN) architecture acquires a huge benefit from the artificial neural network (ANN) and time-frequency localizing features of wavelet analysis (WA). However, the system could train using the ANN model, it has tendency of falling into local minima and generating convergence. A wavelet-aided neural network is integrated with a meta-heuristic optimizer named fruit fly algorithm (FFA) that aids our proposed system in which reduces the computational complexity and improves performance measures. The main objective of the proposed work is of modelling an optimized wavelet neural network for efficient measurement of the mean absolute error and the root mean square error. The simulation of the sound signals has been performed with the use of optimized neural networks that has predicted the unknown signal. The proposed model is assessed by comparing the outcomes with the complements of it, i.e. the traditional neural networks. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Forecasting

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Fast multiplication of random dense matrices with sparse matrices

Fast multiplication of random dense matrices with sparse mat...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Tianyu Liang Riley Murray Aydın Buluç James Demmel Electrical Engineering and Computer Science Department UC Berkeley Computational Research Division Lawrence Berkeley National Lab International Computer Science Institute Sandia National Laboratories

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

This work focuses on accelerating the multiplication of a dense random matrix with a (fixed) sparse matrix, which is frequently used in sketching algorithms. We develop a novel scheme that takes advantage of blocking and recomputation (on-the-fly random number generation) to accelerate this operation. The techniques we propose decrease memory movement, thereby increasing the algorithm’s parallel scalability in shared memory architectures. On the Intel Frontera architecture, our algorithm can achieve 2x speedups over libraries such as Eigen and Intel MKL on some examples. In addition, with 32 threads, we can obtain a parallel efficiency of up to 45%.We also present a theoretical analysis for the memory movement lower bound of our algorithm, showing that under mild assumptions, it’s possible to beat the data movement lower bound of general matrix-matrix multiply (GEMM) by a factor of M, where M is the cache size. Finally, we incorporate our sketching method into a randomized algorithm for overdetermined least squares with sparse data matrices. Our results are competitive with SuiteSparse for highly overdetermined problems; in some cases, we obtain a speedup of 10x over SuiteSparse.

关键词： Distributed processing Scalability Instruction sets Memory management Memory architecture Libraries Sparse matrices

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DFD-Net:lung cancer detection from denoised CT scan image using deep learning

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Frontiers of computer science 2021年第2期15卷 119-131页

作者： Worku J.SORI Jiang FENG Arero W.GODANA Shaohui LIU Demissie J.GELMECHA School of Electrical Engineering and Computing Department of Computer Science and EngineeringAdama Science and Technology UniversityAdama1888Ethiopia Faculty of Natural Computational Science Department of Mathematics Bule Hora UniversityBule HoraHagere Maryam144Ethiopia School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China Peng Cheng Laboratory Shenzhen518052China School of Electrical Engineering and Computing Department of Electronics and Communications EngineeringAdama Science and Technology UniversityAdama1888Ethiopia

The availability of pulmonary nodules in CT scan image of lung does not completely specify *** noise in an image and morphology of nodules,like shape and size has an implicit and complex association with cancer,and thus,a careful analysis should be mandatory on every suspected nodules and the combination of information of every *** this paper,we introduce a“denoising first”two-path convolutional neural network(DFD-Net)to address this *** introduced model is composed of denoising and detection part in an end to end ***,a residual learning denoising model(DR-Net)is employed to remove noise during the preprocessing ***,a two-path convolutional neural network which takes the denoised image by DR-Net as an input to detect lung cancer is *** two paths focus on the joint integration of local and global *** this end,each path employs different receptive field size which aids to model local and global *** further polish our model performance,in different way from the conventional feature concatenation approaches which directly concatenate two sets of features from different CNN layers,we introduce discriminant correlation analysis to concatenate more representative ***,we also propose a retraining technique that allows us to overcome difficulties associated to the image labels *** found that this type of model easily first reduce noise in an image,balances the receptive field size effect,affords more representative features,and easily adaptable to the inconsistency among nodule shape and *** intensive experimental results achieved competitive results.

关键词： medical image discriminant correlation analysis features fusion image detection denoising

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VILMA: A ZERO-SHOT BENCHMARK FOR LINGUISTIC AND TEMPORAL GROUNDING IN VIDEO-LANGUAGE MODELS 12

VILMA: A ZERO-SHOT BENCHMARK FOR LINGUISTIC AND TEMPORAL GRO...

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12th International Conference on Learning Representations, ICLR 2024

作者： Kesen, Ilker Pedrotti, Andrea Dogan, Mustafa Cafagna, Michele Acikgoz, Emre Can Parcalabescu, Letitia Calixto, Iacer Frank, Anette Gatt, Albert Erdem, Aykut Erdem, Erkut Koç University KUIS AI Center Turkey Koç University Department of Computer Engineering Turkey University of Pisa Department of Computer Science Italy Institute of Information Science and Technologies Italian National Council of Research Italy Hacettepe University Department of Computer Engineering Turkey Aselsan Research Turkey University of Malta Institute of Linguistics and Language Technology Malta Heidelberg University Department of Computational Linguistics Germany Amsterdam UMC University of Amsterdam Department of Medical Informatics Netherlands Amsterdam Public Health Methodology & Mental Health Amsterdam Netherlands Utrecht University Department of Information and Computing Sciences Netherlands

With the ever-increasing popularity of pretrained Video-Language Models (VidLMs), there is a pressing need to develop robust evaluation methodologies that delve deeper into their visio-linguistic capabilities. To address this challenge, we present VILMA), a task-agnostic benchmark that places the assessment of fine-grained capabilities of these models on a firm footing. Task-based evaluations, while valuable, fail to capture the complexities and specific temporal aspects of moving images that VidLMs need to process. Through carefully curated counterfactuals, VILMA offers a controlled evaluation suite that sheds light on the true potential of these models, as well as their performance gaps compared to human-level understanding. VILMA also includes proficiency tests, which assess basic capabilities deemed essential to solving the main counterfactual tests. We show that current VidLMs' grounding abilities are no better than those of vision-language models which use static images. This is especially striking once the performance on proficiency tests is factored in. Our benchmark serves as a catalyst for future research on VidLMs, helping to highlight areas that still need to be explored. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： computational linguistics

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Weather Prediction from Imbalanced Data Stream using 1D-Convolutional Neural Network 10

Weather Prediction from Imbalanced Data Stream using 1D-Conv...

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10th International Conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET-SIP-22 2022

作者： Alex, Suja A. Ghosh, Uttam Mohammad, Nazeeruddin St. Xavier's Catholic College of Engineering Department of Information Technology India Meharry School of Applied Computational Sciences Department of Computer Science and Data Science NashvilleTN United States College of Computer Science Engineering Prince Mohammad Bin Fahd University Saudi Arabia

ISBN: (纸本)9781665467414

Data stream classification is a complex task in the real world due to its varying characteristics. The most common challenges are concept drift and class imbalance. Concept drift shifts in the underlying function generating the data. The biggest obstacle in achieving an effective classifier is due to class imbalance. In general, batch and ensemble solutions are mainly used to train the classifier with chunks of imbalanced data streams. In the paper, we proposed a framework called Self Organizing Auto-Encoder based 1D-CNN for Weather data forecasting. The objective of this framework is to classify imbalanced non-stationary data streams. A proposed model's concept drift handling strength is assessed using Population Stability Index (PSI). The Weather dataset is used for the evaluation of the proposed model. The results shows improvement in prediction accuracy and the classifier is stable with good PSI. © 2022 IEEE.

关键词： Neural networks Stability criteria Sociology Weather forecasting Information processing Market research Indexes

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