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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Flade, Benedict Kohaut, Simon Eggert, Julian Dhami, Devendra Singh Kersting, Kristian Honda Research Institute Europe GmbH Offenbach63073 Germany Artificial Intelligence and Machine Learning Lab Department of Computer Science Tu Darmstadt Darmstadt64283 Germany Tu Eindhoven Uncertainty in Artificial Intelligence Group Department of Mathematics and Computer Science MB Eindhoven5600 Netherlands Hessian Ai Centre for Cognitive Science Germany

ISBN: (纸本)9798331505929

The growing complexity of intelligent transportation systems and their applications in public spaces has increased the demand for expressive and versatile knowledge representation. While various mapping efforts have achieved widespread coverage, including detailed annotation of features with semantic labels, it is essential to understand their inherent uncertainties, which are commonly underrepresented by the respective geographic information systems. Hence, it is critical to develop a representation that combines a statistical, probabilistic perspective with the relational nature of geospatial data. Further, such a representation should facilitate an honest view of the data's accuracy and provide an environment for high-level reasoning to obtain novel insights from task-dependent queries. Our work addresses this gap in two ways. First, we present Statistical Relational Maps (StaR Maps) as a representation of uncertain, semantic map data. Second, we demonstrate efficient computation of StaR Maps to scale the approach to wide urban spaces. Through experiments on real-world, crowd-sourced data, we underpin the application and utility of StaR Maps in terms of representing uncertain knowledge and reasoning for complex geospatial information. © 2024 IEEE.

关键词： Knowledge representation

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Discrete Improved Grey Wolf Optimizer for Community Detection

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Journal of Bionic Engineering 2023年第5期20卷 2331-2358页

作者： Mohammad H.Nadimi-Shahraki Ebrahim Moeini Shokooh Taghian Seyedali Mirjalili Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad***Iran Big Data Research Center Najafabad BranchIslamic Azad UniversityNajafabad***Iran Centre for Artificial Intelligence Research and Optimisation Torrens UniversityBrisbane4006Australia Yonsei Frontier Lab Yonsei UniversitySeoulSouth Korea

Detecting communities in real and complex networks is a highly contested topic in network *** many metaheuristic-based algorithms for community detection have been proposed,they still cannot effectively fulfill large-scale and real-world ***,this paper presents a new discrete version of the Improved Grey Wolf Optimizer(I-GWO)algorithm named DI-GWOCD for effectively detecting communities of different *** the proposed DI-GWOCD algorithm,I-GWO is first armed using a local search strategy to discover and improve nodes placed in improper communities and increase its ability to search for a better *** a novel Binary Distance Vector(BDV)is introduced to calculate the wolves’distances and adapt I-GWO for solving the discrete community detection *** performance of the proposed DI-GWOCD was evaluated in terms of modularity,NMI,and the number of detected communities conducted by some well-known real-world network *** experimental results were compared with the state-of-the-art algorithms and statistically analyzed using the Friedman and Wilcoxon *** comparison and the statistical analysis show that the proposed DI-GWOCD can detect the communities with higher quality than other comparative algorithms.

关键词： Community detection Complex network Optimization Metaheuristic algorithms Swarm intelligence algorithms Grey wolf optimizer algorithm

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Facial-sketch Synthesis:A New Challenge

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Machine intelligence research 2022年第4期19卷 257-287页

作者： Deng-Ping Fan Ziling Huang Peng Zheng Hong Liu Xuebin Qin Luc Van Gool Computer Vision Laboratory ETH ZürichZürich 8092Switzerland Information and Communication Engineering University of TokyoTokyo 113-8654Japan Computer Vision Mohamed bin Zayed University of Artificial IntelligenceAbu DhabiUAE Digital Content and Media Sciences Research Division National Institute of InformaticsTokyo 101-8430Japan

This paper aims to conduct a comprehensive study on facial-sketch synthesis(FSS).However,due to the high cost of obtaining hand-drawn sketch datasets,there is a lack of a complete benchmark for assessing the development of FSS algorithms over the last *** first introduce a high-quality dataset for FSS,named FS2K,which consists of 2104 image-sketch pairs spanning three types of sketch styles,image backgrounds,lighting conditions,skin colors,and facial ***2K differs from previous FSS datasets in difficulty,diversity,and scalability and should thus facilitate the progress of FSS ***,we present the largest-scale FSS investigation by reviewing 89 classic methods,including 25 handcrafted feature-based facial-sketch synthesis approaches,29 general translation methods,and 35 image-to-sketch *** addition,we elaborate comprehensive experiments on the existing 19 cutting-edge ***,we present a simple baseline for FSS,named *** only two straightforward components,i.e.,facialaware masking and style-vector expansion,our FSGAN surpasses the performance of all previous state-of-the-art models on the proposed FS2K dataset by a large ***,we conclude with lessons learned over the past years and point out several unsolved *** code is available at https://***/DengPingFan/FSGAN.

关键词： Facial sketch synthesis(FSS) facial sketch dataset benchmark attribute style transfer

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Classification on Compressed DNA Sequences: Correlation-Based Feature Extraction Using Self-Supervised Learning

Classification on Compressed DNA Sequences: Correlation-Base...

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IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)

作者： Sheena K. S. Madhu S. Nair Department of Computer Science Artificial Intelligence & Computer Vision Lab Cochin University of Science and Technology Kochi Kerala India

ISBN: (数字)9798350383409

ISBN: (纸本)9798350383416

Next-generation sequencing is producing data at an exponential rate, which creates immense computation hurdles for reliably and efficiently storing, transmitting, and analysing the data. Compression plays a vital role in managing the challenges associated with the growing volume of genomic data. Direct analysis over the compressed representations of DNA sequences can facilitate scalability, enabling the analysis of larger datasets within reasonable computational resources. The proposed scheme develops a methodology to extract meaningful features from compressed DNA sequences that maintain a high correlation with features from the original sequences. We employed deep learning techniques to maximize feature correlation between original and compressed sequence representations. This approach ensures that the features derived from compressed sequences are as informative and representative as those from the original sequences. We evaluated the proposed method with various state-of-the-art approaches for genomic sequence analysis using a dataset that includes genome sequences of different viruses. According to the results, the features extracted from compressed sequences are discriminative for the classification task; thus, downstream analysis of genomic sequences is possible without decompression.

关键词： Sequential analysis Correlation Sequences computer viruses Scalability Genomics Self-supervised learning Feature extraction Reliability Bioinformatics

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Dual Encoder-Decoder U-Net Architecture for Polyp Segmentation in Colonoscopy Images with Shuffle Attention and Conditional Random Fields

Dual Encoder-Decoder U-Net Architecture for Polyp Segmentati...

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IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Lijin P. Santhosh Kumar G. Madhu S. Nair Department of Computer Science Artificial Intelligence & Computer Vision Lab Cochin University of Science and Technology Kochi Kerala India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

Colonoscopy plays a pivotal role in detecting and diagnosing colorectal diseases, with polyp segmentation being a critical step for accurate diagnosis. In this study, we propose a novel approach for polyp segmentation in colonoscopy images, leveraging the Shuffle attention mechanism within the proposed architecture. Our method is rigorously evaluated across three diverse colonoscopy datasets, demonstrating promising results with an mean dice score of 0.93. Furthermore, to enhance segmentation accuracy, we employ a Conditional Random Field (CRF) post-processing method to refine the segmentation results. Through extensive experimentation and analysis, we showcase the effectiveness of our approach in achieving highly accurate polyp segmentation, thereby contributing to improved diagnostic outcomes in colorectal healthcare. Our method holds significant potential for enhancing computer-aided detection systems in clinical practice, facilitating early detection and treatment of colorectal abnormalities.

关键词： Measurement Image segmentation Accuracy Computational modeling Colonoscopy computer architecture Medical services

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Feature Evaluation of EMG Signals for Hand Gesture Recognition Based on Mutual Information, Fuzzy Entropy and RES Index

Feature Evaluation of EMG Signals for Hand Gesture Recogniti...

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International Conference on computer Science, Electronics and Industrial Engineering, CSEI 2020

作者： Barona-Lopez, Lorena Isabel Valdivieso-Caraguay, Angel Leonardo Benalcazar, Marco E. Aguas, Xavier Zea, Jonathan A. Artificial Intelligence and Computer Vision Research Lab Escuela Politécnica Nacional Quito Ecuador

ISBN: (纸本)9789813345645

In the field of hand gesture recognition based on electromyography, the selection of features is still a matter of research because the accuracy of feature evaluation techniques depends on the distribution underling a feature space. Currently, there are a lot of kinds of features, so a selection method is required in order to find an optimal feature subset. The selection of features can be done based on either classifier methods or statistical measurement indexes. On one hand, the use of classifier methods takes considerable time, consequently, they are computationally expensive. On the other hand, the use of a statistical index allows to select features based on properties, and they are less expensive in terms of time and computational costs. In this context, this study analyzed different statistical indexes to discover optimal feature subsets and predicts which one may have the best performance in terms of classification and recognition accuracy. For this purpose, four indexes were analyzed: the sum of RES (separation and compactness index), the sum of Fuzzy Entropies, and both of these indexes divided by the mutual information of feature subset. These index values were calculated for 10 different feature subsets obtained from nineteen time-domain features which have been investigated by many researchers for extracting useful information from an electromyography signal. The feature subsets were implemented in four classifiers (artificial Neural Network, k-nearest Neighbors Algorithm, Support Vector Machine, and decision trees) to find the best statistical index which tries to quantify the suitableness of the feature space. Each tested index in this work was analyzed in terms of classification and recognition accuracy for each machine learning algorithm. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Lossless DNA Sequence Compression: Exploring Spatial and Sequential Redundancies of DNA Sequence with 2-D CNN Autoencoder

Lossless DNA Sequence Compression: Exploring Spatial and Seq...

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IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Sheena K. S. Madhu S. Nair Department of Computer Science Artificial Intelligence & Computer Vision Lab Cochin University of Science and Technology Kochi Kerala India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

The volume of genomic data being generated is growing due to the technological breakthroughs in genome sequencing and the economic affordability of sequencing. Consequently, the need for compression techniques that are specifically designed for genomic data is rising. Deep learning provides robust methods for reducing the data size and unlocking the full potential of genomic information. The proposed method minimizes storage requirements by applying a 2-D convolutional neural network autoencoder, which learns spatial and sequential redundancies in the sequence to compress the data losslessly. In contrast to conventional compression methods, which handle data as a one-dimensional sequence, the proposed approach uses the spatial structure of the data to produce more compact representations. We evaluated the proposed method with various compression approaches, including state-of-the-art DNA sequence compression algorithms, on two homo sapiens genomes. Experimental results indicate that the proposed method can compress the genomic data effectively, with an improvement of 31.3% compression over the best existing method.

关键词： Deep learning Sequential analysis Biological system modeling Redundancy Genomics DNA Data models

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A Practical Predefined-Time Stability Criterion and Its Application to Uncertain Nonlinear Systems

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Journal of Systems Science & Complexity 2024年第6期37卷 2556-2578页

作者： XU Hao YU Dengxiu SUI Shuai XU Bowen CHEN C.L.Philip Unmanned System Research Institute Northwestern Polytechnical UniversityXi'an 710072China College of Science Liaoning University of TechnologyJinzhou 121001China School of Artificial Intelligence Optics and ElectronicsNorthwestern Polytechnical UniversityXi'an 710072China School of Computer Science and Engineering South China University of Technology and Pazhou LabGuangzhou 510641China

In this paper,the singularity-free predefined-time fuzzy adaptive tracking control problem is studied for non-strict feedback(NSF)nonlinear systems considering mismatched external *** innovative practical predefined-time stability(PPTS)criterion is proposed to provide the theoretical basis for subsequent control *** to the existing predefined-time stability(PTS)criterion,this criterion has a broader application range and can solve the control design issues of nonlinear systems with system *** logic systems(FLSs)are employed to identify unknown nonlinear *** on the backstepping control technology,a singularity-free predefined-time control(PTC)design method is proposed,in which the hyperbolic tangent function is utilized to avoid the singular problem,and the nature of fuzzy basis function is adopted to resolve the algebraic loop *** PPTS of the closed-loop NSF nonlinear system is proven with the Lyapunov theory and the proposed PPTS ***,the efficiency of the presented PTC design method is verified by several sets of simulations on a single link manipulator system.

关键词： Fuzzy adaptive control nonlinear systems practical predefined-time stability predefinedtime control singularity-free control

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A Global Model-Agnostic XAI method for the Automatic Formation of an Abstract Argumentation Framework and its Objective Evaluation 1

A Global Model-Agnostic XAI method for the Automatic Formati...

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1st International Workshop on Argumentation for eXplainable AI, ArgXAI 2022

作者： Vilone, Giulia Longo, Luca The Artificial Intelligence and Cognitive Load research lab The applied Intelligence Research Center School of Computer Science Technological University Dublin Dublin Ireland

Explainable artificial intelligence (XAI) aims to train data-driven, machine learning (ML) models possessing both high predictive accuracy and a high degree of explainability for humans. Comprehending and explaining the inferences of a model can be seen as a defeasible reasoning process which is expected to be non-monotonic meaning that a conclusion, linked to a set of premises, can be withdrawn when new information becomes available. Computational argumentation, a paradigm within artificial intelligence (AI), focuses on modeling defeasible reasoning. This research study explored a new way for the automatic formation of an argument-based representation of the inference process of a data-driven ML model to enhance its explainability by employing principles and techniques from computational argumentation, including weighted attacks within its argumentation process. An experiment was conducted on five datasets to test, in an objective manner, if the explanations of the proposed XAI method are more comprehensible than decision trees, which are considered naturally transparent. Findings demonstrate that usually the argument-based method can represent the logic of the model with fewer rules than a decision tree, but further work is required to achieve the same performances in terms of other characteristics, such as fidelity to the model. © 2022 Copyright for this paper by its authors.

关键词： Decision trees

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3D printed-electrospun PCL/hydroxyapatite/MWCNTs scaffolds for the repair of subchondral bone

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Regenerative Biomaterials 2023年第1期10卷 294-305页

作者： Yanyan Cao Lei Sun Zixian Liu Zhizhong Shen Wendan Jia Peiyi Hou Shengbo Sang College of Information Science and Engineering Hebei North UniversityZhangjiakou 075000China Shanxi Key Laboratory of Micro Nano Sensors&Artificial Intelligence Perception College of Information and ComputerTaiyuan University of TechnologyTaiyuan 030024China Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of TechnologyTaiyuan 030024China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan 030031China

Osteochondral defect caused by trauma or osteoarthritis exhibits a major challenge in clinical treatment with limited symptomatic effects at *** regeneration and remodeling of subchondral bone play a positive effect on cartilage regeneration and further promotes the repair of osteochondral *** use of the strengths of each preparation method,the combination of 3D printing and electrospinning is a promising method for designing and constructing multi-scale scaffolds that mimic the complexity and hierarchical structure of subchondral bone at the microscale and nanoscale,*** this study,the 3D printed-electrospun poly(ɛ-caprolactone)/nano-hydroxyapatites/multi-walled carbon nanotubes(PCL/nHA/MWCNTs)scaffolds were successfully constructed by the combination of electrospinning and layer-by-layer 3D *** resulting dual-scale scaffold consisted of a dense layer of disordered nanospun fibers and a porous microscale 3D scaffold layer to support and promote the ingrowth of subchondral ***,the biomimetic PCL/nHA/MWCNTs scaffolds enhanced cell seeding efficiency and allowed for higher cell-cell interactions that supported the adhesion,proliferation,activity,morphology and subsequently improved the osteogenic differentiation of bone marrow mesenchymal stem cells in ***,this study elucidates that the construction of 3D printed-electrospun PCL/nHA/MWCNTs scaffolds provides an alternative strategy for the regeneration of subchondral bone and lays a foundation for subsequent in vivo studies.

关键词： additive manufacturing 3D printing electrospinning subchondral bone

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