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检索条件"丛书名=Communications in Computer and Information Science"

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WPG-CAM: A Novel Weighted Feature Fusion CAM Method Based on information Entropy Using Pooling and Gaussian Upsampling 5th

WPG-CAM: A Novel Weighted Feature Fusion CAM Method Based on...

引用

5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Zhang, Feifei Xiang, Xiaohong Zhang, Fuyuan Zhao, Jun Chongqing Univ Posts & Telecommun Chongqing 400065 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

Exploring the internal visualization techniques of deep neural networks, Class Activation Mapping (CAM) stands out as one of the most prominent directions in current interpretability methods. However, existing CAM methods introduced useless gradients to non-target regions, resulting in noises in saliency maps and limitations in generating fine-grained activation maps. To obtain finer-grained class activation maps and achieve more convincing visual explanations of neural networks, we propose WPG-CAM based on the Group-CAM method in this paper. In this approach, we first filter out noise information from the saliency maps and gradient matrices of the target layer by introducing pooling operations and addressing the low-resolution issue of image features through Gaussian upsampling. Secondly, we calculate the contribution ratio of information from different scales of activation maps using image information entropy. Lastly, we obtain refined activation maps by weighted average activation maps at different scales. Through implementation and analysis, our proposed WPG-CAM method effectively eliminates noise from saliency maps, outperforming other CAM methods in comprehensive metrics, thus providing more precise and higher granularity class activation images.

关键词： CAM Pooling information entropy Gaussian upsampling Multi-scale feature fusion

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Advancing Systematic Literature Reviews Methodology Through Topic Modeling 4th

Advancing Systematic Literature Reviews Methodology Through ...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Mekaoui, Salma Chaker, Ilham Zarghili, Arsalane Nikolov, Nikola S. Univ Limerick Dept Comp Sci & Informat Syst Limerick V94 T9PX Ireland Univ Sidi Mohamed Ben Abdellah Fac Sci & Technol Lab Intelligent Syst & Applicat Fes Morocco

ISBN: (纸本)9783031821493;9783031821509

The benefits of conducting a systematic review (SR) within a research project are well recognised. Nonetheless, nowadays an SR demands significant time and effort as it typically requires manually sifting through a large corpus of papers. In this work, we propose an application that simplifies the SR process for researchers by performing topic modeling. Our approach involves more than just conducting topic modeling;it entails selecting the optimal method tailored to our system's needs. We explore two widely recognized methods in topic modeling, namely Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF), alongside two prominent libraries, Scikit-learn and Gensim, which provide implementations for these methods. The evaluation of these methods and libraries is done with the help of the cosine similarity metric. Our findings indicate that, for our research context, LDA implemented in Scikit-learn stands out as the most effective method for topic modeling. Additionally, our study suggests that employing 10 topics, along with stemming and lemmatization, yields better results. Our findings culminate in the development of an application to facilitate SRs for researchers.

关键词： Topic modeling LDA NMF Systematic review

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Influence of Process Noise Biases to Satellite Attitude Filters' Estimates 2nd

Influence of Process Noise Biases to Satellite Attitude Filt...

引用

2nd International Conference on information Technologies and Their Applications

作者： Hajiyev, Chingiz Cilden-Guler, Demet Istanbul Tech Univ Fac Aeronaut & Astronaut Dept Aeronaut Engn TR-34469 Istanbul Turkiye

ISBN: (纸本)9783031734199;9783031734205

In this study, an attitude filtering technique is proposed that adapts the bias type process noise uncertainties. To begin with, the Extended Kalman Filter (EKF) and Singular Value Decomposition (SVD) methods are combined to estimate a satellite's attitude. Influence of the process noise bias type system changes to the innovation of EKF is investigated. It is proved that the bias type process noise change may be converted to the mean square of innovation of EKF and such type of changes can be compensated using the covariance scaling techniques. For the aim of estimating a satellite's attitude, simulations are compared utilizing the adaptive and non-adaptive versions of the unconventional attitude filter in the presence of process noise bias. Three different forms of attitude estimation methods were investigated in the condition of process noise bias, and the findings results were compared. The simulation results show that, in the investigated cases the multiple fading factors based adaptive SVD-aided EKF can adapt to the changing environment better than the nonadaptive algorithms.

关键词： Attitude Estimation Process Noise Extended Kalman Filter Singular Value De-composition Nanosatellite Magnetometer Sun Sensor

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Robotic Depalletizing via Reinforcement Learning of a Pushing Policy 5th

Robotic Depalletizing via Reinforcement Learning of a Pushin...

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5th 5th Olympus - International Conference on Supply Chain

作者： Dimou, Argyris Kiatos, Marios Malassiotis, Sotiris Informat Technol Inst ITI Ctr Res & Technol Hellas CERTH Thessaloniki 57001 Greece Univ Thessaloniki Dept Phys Thessaloniki 54124 Greece Progress Robot Thessaloniki 54645 Greece

ISBN: (纸本)9783031693434;9783031693441

Depalletizing, the process of unloading objects from pallets, is a critical task in warehouse automation. Robotic systems face challenges due to the diverse shapes and sizes of objects, often leading to failed grasps in cluttered environments. Pushing actions offer a promising solution to facilitate object singulation. In this work, we employ reinforcement learning (RL) for obtaining optimal pushing policies in depalletizing. We formulate the singulation task as goal reaching, where the target object is moved towards a goal area free from obstacles. Our approach, Goal-FCN (Goal-Driven Fully Convolutional Network), combines feature extraction with a fully convolutional neural network to predict effective pushing actions based on RGB-D images and scene segmentation. Experiments demonstrate the efficacy of our RL-based approach in achieving high success rates in challenging depalletization scenarios.

关键词： Robotics Machine learning Reinforcement learning

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Self-consistent Deep Approximation of Retinal Traits for Robust and Highly Efficient Vascular Phenotyping of Retinal Colour Fundus Images 1st

Self-consistent Deep Approximation of Retinal Traits for Rob...

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1st MICCAI Meets Africa Workshop

作者： Gago, Lucas Remeseiro, Beatriz Igual, Laura Storkey, Amos Bernabeu, Miguel O. Engelmann, Justin Univ Barcelona Dept Matemat & Informat Gran Via Corts Catalanes 585 Barcelona Spain Univ Oviedo Dept Comp Sci Campus Gijon Gijon Spain Univ Edinburgh Inst Adapt & Neural Computat Sch Informat Edinburgh Midlothian Scotland Univ Edinburgh Usher Inst Populat Hlth Sci & Informat Med Informat Ctr Edinburgh Midlothian Scotland

ISBN: (纸本)9783031791024;9783031791031

Retinal colour fundus images are a fast, low-cost, non-invasive way of imaging the retinal vasculature which could provide information about non-ocular, systemic health. Traditional approaches for retinal vascular phenotyping use handcrafted, multi-step pipelines that are computationally expensive and not robust to common quality issues. Recently, Deep Approximation of Retinal Traits (DART) was proposed which trains a neural network to mimic an existing pipeline in a more efficient and robust way. DART is orders of magnitude faster, more robust and repeatable. However, the original DART was not explicitly trained for repeatability, only provides a single retinal trait, Fractal Dimension (FD), and uses a limited set of augmentations. We propose DARTv2 that increases repeatability with a self-consistency loss, robustness with additional augmentations such as imaging overlays, and utility by adding Vessel Density (VD) as a second retinal trait in addition to FD. DARTv2 shows very high agreement (Pearson 0.9392 for FD and 0.9612 for VD, both p << 0.05) with AutoMorph, the pipeline it is based on. DARTv2 is far more robust than AutoMorph and also more robust than the original DART. Finally, DARTv2 is 200 times faster than AutoMorph and 4 times faster than the original DART, while taking up less storage space. DARTv2 will be made available to researchers upon publication.

关键词： Retinal image analysis Deep learning Robustness

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Detection of Potato Leaf Diseases Using PSOK Image Segmentation and CNN Model 2nd

Detection of Potato Leaf Diseases Using PSOK Image Segmentat...

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2nd International Conference on Agriculture-Centric Computation

作者： Barui, Partho Bag, Soumen Das, Ayan Indian Inst Technol ISM Dhanbad Dept Comp Sci & Engn Dhanbad 826004 Bihar India

ISBN: (纸本)9783031744396;9783031744402

Potatoes are one of the most cultivated and consumed crops worldwide. Disease control in potato plants is crucial for efficient production of potatoes. In this paper, we propose a method to detect and classify diseases from the images of potato leaves. For this, we propose a deep neural architecture to classify the plant diseases from leaf images. We have used the PSOK image segmentation method to segment the leaf images into meaningful features. Data augmentation techniques like flipping, rotating, skewing, and zooming are used to increase the dataset. We compare the performance of our proposed model with pretrained model. We show that our proposed architecture combined with XGBoost trained on the augmented dataset achieves an accuracy of 97.81%, outperforming the existing methods.

关键词： Image segmentation

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Optimal Tuning of the Nanosatellite Attitude Controller Using TRIAD-Aided Kalman Filter and Particle Swarm Optimization 2nd

Optimal Tuning of the Nanosatellite Attitude Controller Usin...

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2nd International Conference on information Technologies and Their Applications

作者： Erturk, Mehmet Fatih Hajiyev, Chingiz Turkish Aerosp Ind TR-06980 Ankara Turkiye Istanbul Tech Univ TR-34467 Istanbul Turkiye

ISBN: (纸本)9783031734168;9783031734175

Satellite attitude control for nanosatellites is important research topic since the number of launched nanosatellites increasing year by year. Magnetic actuators are one of the most popular attitude control systems for them. A magnetic torquer is basic wrapped wire which current passes through itself. Interaction between generated magnetic dipole and magnetic field of Earth creates torque. It is also possible to use this magnetic torque to obtain three axis attitude control. PD controllers are basic control methods, that also can be applied to attitude control problem of a nanosatellite by magnetorquers. Accuracy of the controller can be affected from the gains. In this study, particle swarm optimization method is used to present a basic way to tune gains of PD controller for an accuracy increment. On the other hand, attitude knowledge of a satellite can be obtained from different attitude sensors. Since there is noise on these sensors, it is not possible to obtain exact knowledge of attitude. TRIAD (three axis attitude determination) method is used in this work with Earth-Sun sensors and magnetometer. Also, to decrease the effect of noise on the determination results, a basic Kalman filter is used. Simulation results show that, the proposed optimization strategy to optimize PD gains for nanosatellite attitude control with noisy measurements works well.

关键词： Attitude Control Gain Optimization Kalman Filter Particle Swarm Optimization

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Determining Factors for Digital Start-Up Business Development in the Socio-Economics Challenges 4th

Determining Factors for Digital Start-Up Business Developmen...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Rachmawatie, Dessy Erwin Nisa, Hoerun Univ Muhammadiyah Master Econ Yogyakarta Indonesia Univ Islam Indonesia Master Informat Yogyakarta Indonesia

ISBN: (纸本)9783031840777;9783031840784

In this article, we empirically assess the potential and constraints of digital start-up business development, a case study in Yogyakarta Province, Indonesia. Based on the primary and secondary data analysis results, it was found that digital start-up businesses did not affect regional economic growth and employment. Digital companies have the potential to be developed. The human resource factor is a determinant of the success of a digital start-up business. Factors of age, gender, education, and demographic bonus positively affect regional economic growth. The high number of adults contributes to hedonic buying motivation and increased consumer tendency index (ITK) as a potential market segmentation for digital start-up businesses. The obstacles to developing a digital start-up business are internet speed, cybersecurity, and limited capital for investing in a digital start-up business.

关键词： Digital Start-up business Hedonic Buying Motivation Demographic Bonus Cyber Security

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Identifying Shopping Intent in Product QA for Proactive Recommendations 1st

Identifying Shopping Intent in Product QA for Proactive Reco...

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1st International Workshop on Graph-Based Approaches in information Retrieval

作者： Fetahu, Besnik Cohen, Nachshon Haramaty, Elad Lewin-Eytan, Liane Rokhlenko, Oleg Malmasi, Shervin Amazon Com Inc Seattle WA 98109 USA

ISBN: (纸本)9783031713811;9783031713828

Voice assistants have become ubiquitous in smart devices allowing users to instantly access information via voice questions. While extensive research has been conducted in question answering for voice search, little attention has been paid on how to enable proactive recommendations from a voice assistant to its users. This is a highly challenging problem that often leads to user friction, mainly due to recommendations provided to the users at the wrong time. We focus on the domain of e-commerce, namely in identifying Shopping Product Questions (SPQs), where the user asking a product-related question may have an underlying shopping need. Identifying a user's shopping need allows voice assistants to enhance shopping experience by determining when to provide recommendations, such as product or deal recommendations, or proactive shopping actions recommendation. Identifying SPQs is a challenging problem and cannot be done from question text alone, and thus requires to infer latent user behavior patterns inferred from user's past shopping history. We propose features that capture the user's latent shopping behavior from their purchase history, and combine them using a novel Mixture-of-Experts (MoE) model. Our evaluation shows that the proposed approach is able to identify SPQs with a high score of F1 = 0.91. Furthermore, based on an online evaluation with real voice assistant users, we identify SPQs in real-time and recommend shopping actions to users to add the queried product into their shopping list. We demonstrate that we are able to accurately identify SPQs, as indicated by the significantly higher rate of added products to users' shopping lists when being prompted after SPQs vs random PQs.

关键词： Intent Detection Shopping Intent Detection on Voice Queries Customer Behavior Understanding

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Explainable Few-Shot Learning for Multiple Sclerosis Detection in Low-Data Regime 1st

Explainable Few-Shot Learning for Multiple Sclerosis Detecti...

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1st MICCAI Meets Africa Workshop

作者： Ben Dhifallah, Montassar Kanzari, Dalel Naija, Salma Ben Amor, Sana Zrig, Ahmed Maatouk, Mezri Abdelaali, Mabrouk Saad, Jamel Achour, Asma Chortane, Sofiane Gaied Hadhri, Maher Dahmoul, Ahmed Ben Ali, Azza Selim, Sahar Nebli, Ahmed Univ Sousse Higher Inst Appl Sci & Technol Sousse Tunisia Operat Res Decis & Proc Control Lab LARODEC 41 Liberty St Tunis 2000 Tunisia Sahloul Hosp Dept Neurol Sousse Tunisia Univ Sousse Fac Med Sousse Sousse Tunisia A Fattouma Bourguiba Hosp Dept Radiol Monastir Tunisia Fattouma Bourguiba Hosp Dept Neurosurg Monastir Tunisia Univ Monastir Res Unity Intervent Radiol LR18SP08 Monastir Tunisia Nile Univ Sch Informat Technol & Comp Sci Giza Egypt

ISBN: (纸本)9783031791024;9783031791031

Diagnosing multiple sclerosis (MS) accurately is highly challenging due to symptom overlap with other demyelinating diseases. Here, we present DemyeliNeXt, an explainable few-shot learning framework designed to classify MS and other demyelinating diseases from MRI scans. This framework employs a prototypical network with a 3D DenseNet-121 backbone and uses Deep SHAP for feature importance visualization. We train our DemyeliNeXt on a dataset from African populations and we test it for different datasets including MICCAI MSSEG2 public dataset. Our findings demonstrate robust performance across diverse datasets highlighting the model's potential to enhance diagnosis accuracy and generalizability in various clinical settings.

关键词： Few-Shot Learning Explainable AI Multiple Sclerosis 3D MRI Deep Learning

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