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arXiv 2024年

作者： Haefner, Ralf M. Beck, Jeff Savin, Cristina Salmasi, Mehrdad Pitkow, Xaq Department of Brain and Cognitive Sciences University of Rochester RochesterNY United States Department of Neurobiology Duke University DurhamNC United States Departments of Neural Science and Data Science New York University New YorkNY United States Gatsby Computational Neuroscience Unit Max Planck UCL Centre for Computational Psychiatry and Ageing Research University College London United Kingdom Neuroscience Institute Department of Machine Learning Carnegie Mellon University PittsburghPA United States Department of Neuroscience Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Department of Electrical and Computer Engineering Department of Computer Science Rice University HoustonTX United States

This perspective piece is the result of a Generative Adversarial Collaboration (GAC) tackling the question 'How does neural activity represent probability distributions?'. We have addressed three major obstacles to progress on answering this question: first, we provide a unified language for defining competing hypotheses. Second, we explain the fundamentals of three prominent proposals for probabilistic computations - Probabilistic Population Codes (PPCs), Distributed Distributional Codes (DDCs), and Neural Sampling Codes (NSCs) - and describe similarities and differences in that common language. Third, we review key empirical data previously taken as evidence for at least one of these proposal, and describe how it may or may not be explainable by alternative proposals. Finally, we describe some key challenges in resolving the debate, and propose potential directions to address them through a combination of theory and *** Codes 92 Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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The Design and Empirical Research of One Main Stem with Others Branches Monetary System Based on Blockchain Technology 20

The Design and Empirical Research of One Main Stem with Othe...

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20th IEEE International Conference on Communication Technology, ICCT 2020

作者： Feng, Liu Shiying, Lu Jiayin, Qi Zhibing, Li East China Normal University School of Computer Science and Technology Shanghai China Changsha University of Science and Technology Changsha China Shanghai University of International Business and Economics Institute of Artificial Intelligence and Change Management Shanghai China Shanghai International Studies University School of Data Science and Engineering East China Normal University Shanghai China

ISBN: (纸本)9781728181417

The European debt crisis impacted the international monetary status of the euro as well as triggered a large number of discussions and opinions on the design of the monetary mechanism of the eurozone in academic circles. This study adopts DEA method and takes the euro area as an example to evaluate the two different monetary systems from the perspective of technical efficiency, pure technical efficiency and scale efficiency. Through empirical analysis, it can be found that when the one main stem with many other branches monetary system is suffering from crisis, the system is more stable. In contrast, the current scale of the euro monetary system is inefficient which cause a lot of waste of resource. This article gives a new currency system based on blockchain, which can maintain the existing euro-structured currency system, and is compatible with the euro zone sovereign countries to issue their own sovereign electronic currency, and the technology is more efficient. This article has reference significance for trying to solve or improve the European debt crisis. © 2020 IEEE.

关键词： Blockchain

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Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19

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Informatics in Medicine Unlocked 2022年 28卷 100840页

作者： Hasan, Md. Imran Rahman, Md Habibur Islam, M. Babul Islam, Md Zahidul Hossain, Md Arju Moni, Mohammad Ali Department of Computer Science and Engineering Islamic University Kushtia 7003 Bangladesh Department of Electrical and Electronic Engineering University of Rajshahi 6205 Bangladesh Department of Information and Communication Technology Islamic University Kushtia 7003 Bangladesh Department of Biotechnology and Genetic Engineering Mawlana Bhashani Science and Technology University Tangail 1092 Bangladesh Department of Computer Science and Engineering Pabna University of Science and Technology Pabna 6600 Bangladesh Artificial Intelligence & Digital Health Data Science School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St Lucia QLD 4072 Australia

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection results in the development of a highly contagious respiratory ailment known as new coronavirus disease (COVID-19). Despite the fact that the prevalence of COVID-19 continues to rise, it is still unclear how people become infected with SARS-CoV-2 and how patients with COVID-19 become so unwell. Detecting biomarkers for COVID-19 using peripheral blood mononuclear cells (PBMCs) may aid in drug development and treatment. This research aimed to find blood cell transcripts that represent levels of gene expression associated with COVID-19 progression. Through the development of a bioinformatics pipeline, two RNA-Seq transcriptomic datasets and one microarray dataset were studied and discovered 102 significant differentially expressed genes (DEGs) that were shared by three datasets derived from PBMCs. To identify the roles of these DEGs, we discovered disease-gene association networks and signaling pathways, as well as we performed gene ontology (GO) studies and identified hub protein. Identified significant gene ontology and molecular pathways improved our understanding of the pathophysiology of COVID-19, and our identified blood-based hub proteins TPX2, DLGAP5, NCAPG, CCNB1, KIF11, HJURP, AURKB, BUB1B, TTK, and TOP2A could be used for the development of therapeutic intervention. In COVID-19 subjects, we discovered effective putative connections between pathological processes in the transcripts blood cells, suggesting that blood cells could be used to diagnose and monitor the disease's initiation and progression as well as developing drug therapeutics. © 2022 The Authors

关键词： Biomarker signatures Blood common gene COVID-19 Differentially expressed genes Gene ontology PBMCs cell

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Enhancing IoT Network Security through AIDriven Intrusion Detection with Hybrid AutoencoderGAN Fusion 23

Enhancing IoT Network Security through AIDriven Intrusion De...

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Proceedings of the 5th International Conference on Information Management & Machine intelligence

作者： Rupali A. Mahajan Sukhvinder Singh Dari Ritika Dhabliya Gabriela Michael Anil Kumar Nidhi Ranjan Computer Associate Professor COMP VIIT Pune Maharashtra India Computer Associate Professor Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) Pun India Computer Director Yashika Journal Publications Pvt. Limited Wardha Maharashtra India India Computer Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) Pune India India Department of Artificial Intelligence & Data Science Professor Poornima Institute Of Engineering &Technology Jaipur Rajasthan India India Computer Associate Professor Vasantdada Patil Pratishthan?s College of engineering and Visual Arts Mumbai Maharashtra India India

ISBN: (纸本)9798400709418

The rapid growth of Internet of Things (IoT)IoT devices has changed how we connect and interact with our environment. This advancement has also complicated network security. As IoT networks grow, data integrity and confidentiality become more important. This study integrates a hybrid autoencoder and Generative Adversarial Network (GAN) model to use AI to solve security problems. The UNSW-NB15 dataset, a benchmark for network IDSs, is used to rigorously evaluate the proposed model. An autoencoder and GAN combination offers promising IDS for IoT networks. The autoencoder component excels at capturing concise and illustrative data representations, enabling it to spot network anomalies. The GAN component improves the model's ability to generate synthetic data samples that closely resemble network traffic patterns, making it easier to identify complex and novel attacks. The UNSW-NB15 dataset is known for its extensive network traffic data, making it ideal for testing the proposed artificial intelligence-based IDS. The proposed hybrid model achieves 99.13% accuracy. The result outperforms traditional DL methods. The proposed model's increased precision shows its ability to detect and address network anomaly risks. The proposed system is evaluated using precision, recall, F1-Score, AUC-ROC, MCC, and Cohen's Kappa. The thorough evaluation shows that the Proposed hybrid autoencoder-GAN model provides a complete and efficient IoT network security solution in all dimensions. In conclusion, artificial intelligence-based intrusion detection and a hybrid model that combines autoencoder and generative adversarial network techniques improve IoT network security. The model's UNSW-NB15 dataset accuracy shows its ability to protect IoT ecosystems from many threats. This study helps improve the reliability and robustness of networks, boosting confidence in the growing world of interconnected devices.

关键词： Deep Learning

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Methodology review for profile scanning and validation using 3D reconstruction by context capture and comparevidia

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AIP Conference Proceedings 2022年第1期2460卷

作者： Vasanth CH Kumar Raghavendra V. Rao Joshan Athanesious Ann Medona 1)Department of Artificial Intelligence and Data science Saveetha Engineering College Tamilnadu INDIA 2Department of Mechanical Engineering SRM institute of science and Technology Tamilnadu INDIA

The aim is to develop an alternative means to Industrial Metrology, by integrating 3D Scanning and 3D Reconstruction. Traditional mean of modern metrology includes Manual Measurement, Measurement by CMM and Laser Bases Scanning. These means of “measurement” are often too expensive as is in the case of CMMs or too prone to human error. Thus, this resulted in the need for development of a new method for metrology for mass manufactured components. This need lead to the integration of 3D Scanning by means of a simple webcam and 3D Reconstruction of the object using Context capture. The camera is rotated, by means of a DC Motor, around the object that is fixed in its position. At regular intervals of rotation, takes pictures and stores them. A curved guideway is provided for the webcam mount so that pictures can be taken from different angles of orientation. The pictures are then fed into Context Capture, where they are stitched and result in a reconstructed model. For profile validation we use Compare Vidia, where we compare the results of the two scans, one of master or reference piece and one of the test or samples. After setting angular thresholds and tolerances, the two geometry profiles are compared and depending on the tolerances, the result is a “match” or “not-a-match”.

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HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN

arXiv

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arXiv 2023年

作者： Islam, Md Shofiqul Hasan, Khondokar Fida Sultana, Sunjida Uddin, Shahadat Lio', Pietro Quinn, Julian M.W. Moni, Mohammad Ali Faculty of Computing Universiti Malaysia Pahang Gambang Pahang Kuantan26300 Malaysia Lebuhraya Tun Razak Gambang Pahang Kuantan26300 Malaysia 2 George Street Brisbane4000 Australia Department of Computer Science and Engineering Islamic University Kushtia7600 Bangladesh School of Project Management Faculty of Engineering The University of Sydney Sydney Australia Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Bone Research Group The Garvan Institute of Medical Research DarlinghurstNSW Australia Artificial Intelligence & Data Science School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St LuciaQLD4072 Australia

Deep learning-based models have achieved significant success in detecting cardiac arrhythmia by analyzing ECG signals to categorize patient heartbeats. To improve the performance of such models, we have developed a novel hybrid hierarchical attention-based bidirectional recurrent neural network with dilated CNN (HARDC) method for arrhythmia classification. This solves problems that arise when traditional dilated convolutional neural network (CNN) models disregard the correlation between contexts and gradient dispersion. The proposed HARDC fully exploits the dilated CNN and bidirectional recurrent neural network unit (BiGRU-BiLSTM) architecture to generate fusion features. As a result of incorporating both local and global feature information and an attention mechanism, the model's performance for prediction is improved. By combining the fusion features with a dilated CNN and a hierarchical attention mechanism, the trained HARDC model showed significantly improved classification results and interpretability of feature extraction on the PhysioNet 2017 challenge dataset. Sequential Z-Score normalization, filtering, denoising, and segmentation are used to prepare the raw data for analysis. CGAN (Conditional Generative Adversarial Network) is then used to generate synthetic signals from the processed data. The experimental results demonstrate that the proposed HARDC model significantly outperforms other existing models, achieving an accuracy of 99.60%, F1 score of 98.21%, a precision of 97.66%, and recall of 99.60% using MIT-BIH generated ECG. In addition, this approach substantially reduces run time when using dilated CNN compared to normal convolution. Overall, this hybrid model demonstrates an innovative and cost-effective strategy for ECG signal compression and high-performance ECG recognition. Our results indicate that an automated and highly computed method to classify multiple types of arrhythmia signals holds considerable promise. © 2023, CC BY.

关键词： Electrocardiograms

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Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity

arXiv

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arXiv 2022年

作者： Li, Jiangmeng Qiang, Wenwen Zheng, Changwen Su, Bing Razzak, Farid Wen, Ji-Rong Xiong, Hui The University of Chinese Academy of Sciences Beijing China The Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China The New York University Columbia University New York United States The Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China Guangzhou China Department of Computer Science & Engineering The Hong Kong University of Science and Technology Hong Kong

While self-supervised learning techniques are often used to mine hidden knowledge from unlabeled data via modeling multiple views, it is unclear how to perform effective representation learning in a complex and inconsistent context. To this end, we propose a new multi-view self-supervised learning method, namely consistency and complementarity network (CoCoNet), to comprehensively learn global inter-view consistent and local cross-view complementarity-preserving representations from multiple views. To capture crucial common knowledge which is implicitly shared among views, CoCoNet employs a global consistency module that aligns the probabilistic distribution of views by utilizing an efficient discrepancy metric based on the generalized sliced Wasserstein distance. To incorporate cross-view complementary information, CoCoNet proposes a heuristic complementarity-aware contrastive learning approach, which extracts a complementarity-factor jointing cross-view discriminative knowledge and uses it as the contrast to guide the learning of view-specific encoders. Theoretically, the superiority of CoCoNet is verified by our information-theoretical-based analyses. Empirically, our thorough experimental results show that CoCoNet outperforms the state-of-the-art self-supervised methods by a significant margin, for instance, CoCoNet beats the best benchmark method by an average margin of 1.1% on ImageNet. Copyright © 2022, The Authors. All rights reserved.

关键词： Supervised learning

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Multimodal Priors Guided Segmentation of Liver Lesions in MRI Using Mutual Information Based Graph Co-Attention Networks 23rd

Multimodal Priors Guided Segmentation of Liver Lesions in MR...

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23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Mo, Shaocong Cai, Ming Lin, Lanfen Tong, Ruofeng Chen, Qingqing Wang, Fang Hu, Hongjie Iwamoto, Yutaro Han, Xian-Hua Chen, Yen-Wei College of Computer Science and Technology Zhejiang University Hangzhou China Department of Radiology Sir Run Run Shaw Hospital Hangzhou China College of Information Science and Engineering Ritsumeikan University Kusatsu Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China Artificial Intelligence Research Center Yamaguchi University Yamaguchi Japan

ISBN: (纸本)9783030597184

Segmentation of focal liver lesions serves as an essential preprocessing step for initial diagnosis, stage differentiation, and post-treatment efficacy evaluation. Multimodal MRI scans (e.g., T1WI, T2WI) provide complementary information on liver lesions and is widely used for diagnosis. However, some modalities (e.g., T1WI) have high resolution but lack of important visual information (e.g., edge) belonged to other modalities (T2WI), it is significant to enhance tissue lesion quality in T1WI using other modality priors (T2WI) and improve segmentation performance. In this paper, we propose a graph learning based approach with the motivation of extracting modality-specific features efficiently and establishing the regional correspondence effectively between T1WI and T2WI. We first project deep features into a graph domain and employ graph convolution to propagate information across all regions for extraction of modality-specific features. Then we propose a mutual information based graph co-attention module to learn weight coefficients of one bipartite graph, which is constructed by the fully-connection of graphs with different modalities in the graph domain. At last, we get the final refined features for segmentation by re-projection and residual connection. We validate our method on a multimodal MRI liver lesion dataset. Experimental results show that the proposed approach achieves improvement of liver lesion segmentation in T1WI by learning guided features from multimodal priors (T2WI) compared to existing methods. © 2020, Springer Nature Switzerland AG.

关键词： Medical imaging

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Internal search of the evolution matrix in QUasi-Affine TRansformation Evolution (QUATRE) algorithm

Internal search of the evolution matrix in QUasi-Affine TRan...

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作者： Chu, Shu-Chuan Chen, Yuxin Meng, Fanjia Yang, Chen Pan, Jeng-Shyang Meng, Zhenyu College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Institute of Artificial Intelligence Fujian University of Technology Fuzhou China Guanzhuang Central Primary School of Zhangqiu District Jinan China Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou China

Optimization demands exist everywhere in the real world especially in science studies and engineering practices, and it is important that the method to deal with intricacy optimization problems should itself be relative simple. Particle Swarm Optimization (PSO) and Differential Evolution (DE) both are simple evolutionary algorithms (EAs) which are proposed for single-objective optimization and both of them have been proved to be efficient methods for optimizing applications, however, there are still some weakness existing within them. A innovative evolutionary method named QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm which is derived from, also tackles some weaknesses of PSO and DE algorithm, and obtains better performance on commonly used test suites. The key characteristic of QUATRE is that an automatically generated matrix named evolution matrix M is implemented in evolutionary process, which is taken as an alternative of employing the crossover rate CR. Here in this paper, we present a novel QUATRE variant, named IS-QUATRE, which can explore the search area in a better way comparison with the previous method, and relatively good optimization ability can be obtained by our proposed IS-QUATRE algorithm under CEC2013 test suit. And the conducted experimental results validate that our proposed IS-QUATRE is competitive with some other famous PSO and DE variants. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Particle swarm optimization (PSO)

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A parameter adaptive de algorithm on real-parameter optimization

A parameter adaptive de algorithm on real-parameter optimiza...

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作者： Pan, Jeng-Shyang Yang, Cheng Meng, Fanjia Chen, Yuxin Meng, Zhenyu College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Institute of Artificial Intelligence Fujian University of Technology Fuzhou China Guanzhuang Central Primary School of Zhangqiu District Jinan China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China

Differential Evolution (DE) algorithm generates a population of individuals by encoding with a floating point vector, and it is a simple and effective population-based stochastic optimization algorithm for global optimization of continuous space. Because of its excellent performance, DE variants can be applied in a wide range of applications in science and engineering. However, the performance of DE is sensitive to the choice of trial vector generation strategy and the associated control parameters. Therefore, it is necessary to choose appropriate mutation strategy and control parameters when tackling optimization applications. In this paper, an adaptive update mechanism is proposed to update control parameters F and Cr. The experimental results are verified on the CEC 2013 test suite which contains 28 benchmark functions for the evaluation of single objective real parameter optimization. The proposed algorithm is compared with jDE, iwPSO and ccPSO, and experiment results show its good performance. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Global optimization

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