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Enhancing Fruit and Vegetable Image Classification with Attention Mechanisms in Convolutional Neural Networks 18th

Enhancing Fruit and Vegetable Image Classification with Atte...

引用

18th International Conference on Neural Networks (ISNN)

作者： Akorede, Faidat Adekemi Leung, Man-Fai Che, Hangjun Anglia Ruskin Univ Fac Sci & Engn Sch Comp & Informat Sci Cambridge England Southwest Univ Coll Elect & Informat Engn Chongqing Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing Peoples R China South Cent Minzu Univ State Ethn Affairs Commiss Key Lab Cyber Phys Fus Intelligent Comp Wuhan Peoples R China

ISBN: (纸本)9789819743988;9789819743995

Classifying fruits and vegetables is a challenging task for traditional machine learning models, particularly convolutional neural networks (CNNs), which struggle to differentiate between similar-looking items. This differentiation is crucial in agriculture for efficient sorting and quality control. This study aimed to enhance the performance of classification models using a dataset of 3,115 images across 36 fruit and vegetable classes from Kaggle. The research explored four architectures: CNN, MobileNet, DenseNet, and Xception. Fine-tuning each model for the dataset, the CNN achieved a baseline accuracy of 96%. Further exploration with additional layers in MobileNet, DenseNet, and Xception yielded accuracies of 91%, 89%, and 89%, respectively. To address these limitations, squeeze-excitation blocks were integrated, significantly improving accuracies: MobileNet SE and Xception SE reached 97%, and DenseNet SE achieved 98%. This study demonstrates the efficacy of advanced machine learning in fruit and vegetable classification and encourages further innovation for practical agricultural applications.

关键词： Convolutional Neural Networks Squeeze-Excitation Blocks Image Recognition Deep Learning Architectures

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Novelty Detection in Human-Machine Interaction Through a Multimodal Approach 26th

Novelty Detection in Human-Machine Interaction Through a Mul...

引用

26th Iberoamerican Congress on Pattern Recognition (CIARP)

作者： Salas-Caceres, Jose Lorenzo-Navarro, Javier Freire-Obregon, David Castrillon-Santana, Modesto Univ Las Palmas Gran Canaria Inst Univ SIANI Las Palmas Gran Canaria Spain

ISBN: (纸本)9783031490170;9783031490187

As the interest in robots continues to grow across various domains, including healthcare, construction and education, it becomes crucial to prioritize improving user experience and fostering seamless interaction. These human-machine interactions (HMI) are often impersonal. Our proposal, built upon previous work in the field, aims to use biometric data of individuals to detect whether a person has been encountered before. Since many models depend on a threshold set, an optimization method using a genetic algorithm was proposed. The novelty detection is made through a multimodal approach using both voice and facial images from the individuals, although the unimodal approaches of just each single cue were also tested. To assess the effectiveness of the proposed system, we conducted comprehensive experiments on three diverse datasets, namely VoxCeleb, Mobio and AveRobot, each possessing distinct characteristics and complexities. By examining the impact of data quality on model performance, we gained valuable insights into the effectiveness of the proposed solution. Our approach outperformed several conventional novelty detection methods, yielding superior and therefore promising results.

关键词： Novelty Detection Human-Machine Interaction Biometrics

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Trusting Fair Data: Leveraging Quality in Fairness-Driven Data Removal Techniques 26th

Trusting Fair Data: Leveraging Quality in Fairness-Driven Da...

引用

26th International Conference on Data Warehousing and Knowledge Discovery (DaWaK)

作者： Duong, Manh Khoi Conrad, Stefan Heinrich Heine Univ Univ Str 1 D-40225 Dusseldorf Germany

ISBN: (纸本)9783031683220;9783031683237

In this paper, we explore bias mitigation techniques that use undersampling to create a fair representation of sociodemographic groups in training sets. These techniques aim to make machine learning models trained on these datasets fair. However, such approaches may exclude relevant data, reducing trustworthiness and potentially harming models' performances. To address this, we propose two criteria in addition to fairness: (1) group coverage and (2) minimal data loss. Group coverage ensures that no group gets entirely removed, while minimal data loss aims to retain as many data points as possible. By proposing a multi-objective optimization approach, we find Pareto-optimal solutions that balance these objectives. This allows users to make informed decisions about the trade-off between fairness and data quality. Our method is distributed as a Python package via PyPI under the name FairDo (https://***/mkduong-ai/fairdo).

关键词： Fairness Bias mitigation Data quality Coverage AI Act

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Optimisation of Battery Energy Storage Systems Capacity for Purpose to Reduce Energy Cost 23rd

Optimisation of Battery Energy Storage Systems Capacity for ...

引用

23rd International Conference on computer Information Systems and Industrial Management (CISIM)

作者： Parczyk, Pawel Burduk, Robert Wroclaw Univ Sci & Technol Dept Syst & Comp Networks Wybrzeze Wyspianskiego 27 PL-50370 Wroclaw Poland

ISBN: (纸本)9783031711145;9783031711152

High energy prices are a pressing problem for both developed and developing societies. They deepen social inequalities by causing energy poverty. Therefore, in this article, we propose a method for optimizing the capacity of Battery Energy Storage Systems, which operate in residential billed by a time-of-use tariff. It can reduce the cost of electricity and counteract energy poverty. We propose a control algorithm that minimizes energy costs and an optimization algorithm based on an objective function reflecting profitability. Simulations carried out on half a year of data showed the possibility of achieving savings of 23% for a tariff divided into two zones (on-peak and off-peak) and 21% for a three-zone tariff. An appropriately selected energy storage facility would pay off in 4.5 years and 5 years for subsequent scenarios, compared to 4.7 years and 5.1 years for the reference selection.

关键词： Energy usage management Energy Cost Optimisation Battery Energy Storage Systems Battery Optimisation

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Distributed Backdoor Attacks in Federated Learning Generated by DynamicTriggers 1

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14th IFIP WG 11.2 International Conference on Information Security Theory and Practice (WISTP)

作者： Wang, Jian Shen, Hong Liu, Xuehua Zhou, Hua Li, Yuli Macao Polytech Univ Fac Appl Sci Macau Peoples R China Cent Queensland Univ Sch Engn & Technol Rockhampton Qld Australia Guangzhou Inst Software Sch Software Technol Guangzhou Peoples R China

ISBN: (数字)9783031603914

ISBN: (纸本)9783031603907;9783031603914

The emergence of federated learning has alleviated the dual challenges of data silos and data privacy and security in machine learning. However, this distributed learning approach makes it more susceptible to backdoor attacks, where malicious participants can conduct adversarial attacks by injecting backdoor triggers into their local training datasets, aiming to manipulate model predictions, for example, make the classifier recognize poisoned samples (injected with specific triggers) as specific images. In order to effectively detect backdoor attacks and protect federated learning systems, we need to know how backdoor attacks are generated and developed. Currently, most backdoor attacks to federated learning use centralized attacks with static triggers, which are easily detectable by current defense methods. In this work, we propose a distributed backdoor attack method that fully leverages the distributed nature of federated learning. It starts by generating unique and independent global dynamic triggers for infected benign samples and then decomposes the global trigger into multiple sub-triggers, embedding them into the training sets of multiple participants. During the training phase, data poisoning is introduced. Through extensive experiments, we demonstrate that this attack method exhibits higher persistence and stealthiness, achieving a significantly higher success rate than standard centralized backdoor attacks. Compared to classical distributed backdoor attack (DBA) methods, it shows noticeable improvements in attack performance.

关键词： Federated learning data poisoning security backdoor Attack

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Design Principles for Machine Learning Based Clinical Decision Support Systems: A Design science Study 19th

Design Principles for Machine Learning Based Clinical Decisi...

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19th International Conference on Design science Research in Information Systems and Technology (DESRIST) - Design science Research for a Resilient Future

作者： Sjostrom, Jonas Dryselius, Petra Nygren, Jens Nair, Monika Soliman, Amira Lundgren, Lina E. Halmstad Univ Halmstad Sweden Cambio AB Stockholm Sweden Boras Univ Boras Sweden

ISBN: (纸本)9783031611742;9783031611759

Employing a design science research approach building on four modes of inquiry, this study presents a Clinical Decision Support System for predicting heart failure readmissions, combining machine learning, inpatient care process analysis, and user experience design. It introduces three key design principles: contextual integration, actionable insights, and adaptive explanation levels, to support the design of decision support in clinical settings. The research, while focused on a specific healthcare context, offers a model for integrating technical precision and user-centric design in inpatient care processes, suggesting broader applications and future research directions in diverse healthcare environments.

关键词： Clinical Decision Support System CDSS Design Principles Actionable Insights Inpatient care process

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Ultrasound Image Reconstruction with Denoising Diffusion Restoration Models 1

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3rd Workshop on Deep Generative Models for Medical Image Computing and computer Assisted Intervention (DGM4MICCAI) at the 26th International Conference on Medical Image Computing and computer Assisted Intervention (MICCAI)

作者： Zhang, Yuxin Huneau, Clement Idier, Jerome Mateus, Diana Nantes Univ Ecole Cent Nantes LS2N CNRSUMR 6004 F-44000 Nantes France

ISBN: (数字)9783031537677

ISBN: (纸本)9783031537660;9783031537677

Ultrasound image reconstruction can be approximately cast as a linear inverse problem that has traditionally been solved with penalized optimization using the l(1) or l(2) norm, or wavelet-based terms. However, such regularization functions often struggle to balance the sparsity and the smoothness. A promising alternative is using learned priors to make the prior knowledge closer to reality. In this paper, we rely on learned priors under the framework of Denoising Diffusion Restoration Models (DDRM), initially conceived for restoration tasks with natural images. We propose and test two adaptions of DDRM to ultrasound inverse problem models, DRUS and WDRUS. Our experiments on synthetic and PICMUS data show that from a single plane wave our method can achieve image quality comparable to or better than DAS and state-of-the-art methods. The code is available at https://***/Yuxin-Zhang-Jasmine/DRUS-v1/.

关键词： Ultrasound imaging Inverse Problems Diffusion models

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Enhancing Remote Collaboration Through Drone-Driven Agent and Mixed Reality 16th

Enhancing Remote Collaboration Through Drone-Driven Agent an...

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16th International Conference on Virtual, Augmented and Mixed Reality (VAMR)

作者： Xu, Shihui Wu, Like Liao, Wenjie Fujimuni, Shigeru Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka Japan

ISBN: (纸本)9783031610431;9783031610448

Mixed Reality (MR) and Augmented Reality (AR) technologies have been used to improve remote collaboration. However, existing MR- or AR-based remote collaboration systems lack of a fully independent view sharing between the local user and remote user. This research propose a novel approach to enhance the remote collaboration using a drone and MR technology. By augmenting a virtual 3D avatar on the drone in the local environment, we propose the drone-driven agent to embody the remote user. And the view sharing between local and remote user is achieved by sending a real-time video stream of the local environment captured with the drone. There are three novelties including 1) fully independent view sharing, 2) augmenting virtual character on the drone to embody remote user, and 3) 3D AR sketching o facilitate communication between local and remote users. We implemented a proof-of-concept prototype to illustrate our design using a see-through type head-mounted display and a small-size drone. In addition, we provide discussion and implication for the future work to design drone-based remote collaboration systems.

关键词： Remote collaboration Mixed reality Augmented reality Drone Embodiment using character

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Image Matting Based on Deep Equilibrium Models 33rd

Image Matting Based on Deep Equilibrium Models

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33rd International Conference on Artificial Neural Networks and Machine Learning (ICANN)

作者： Liu, Xinshuang Zhao, Yue Univ Calif San Diego La Jolla CA 92093 USA Univ Penn Philadelphia PA 19104 USA

ISBN: (纸本)9783031723346;9783031723353

Recent works have shown that additional improvement stages can further enhance the performance of image matting models. Inspired by this, we propose deep equilibrium matting (DEQ-Matt), which improves the feature maps for infinite times to achieve optimal performance by using the deep equilibrium (DEQ) models. We further tailor a loss function to train the DEQ models on the image matting task. Besides, we propose to use saliency maps to guide the image matting models, because they can be automatically and reliably predicted. In experiments, our method outperforms state-of-the-art methods and is superior in both semantic estimation and detail processing. Furthermore, we observe an increasing trend in the model's performance as the number of feature improvement steps approaches infinity, which supports the motivation of this paper. The code is available at https://***/XinshuangL/DEQ-Matt.

关键词： Image Matting Deep Equilibrium Models

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Hitching a Ride to a Lasso: Massively Parallel On-The-Fly LTL Model Checking 30th

Hitching a Ride to a Lasso: Massively Parallel On-The-Fly LT...

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30th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS) Held as Part of the 27th European Joint Conferences on Theory and Practice of Software (ETAPS)

作者： Osama, Muhammad Wijs, Anton Leiden Univ Leiden Inst Adv Comp Sci LIACS Leiden Netherlands Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9783031572487;9783031572494

The need for massively parallel algorithms, suitable to exploit the computational power of hardware such as graphics processing units, is ever increasing. In this paper, we propose a new algorithm for the on-the-fly verification of Linear-Time Temporal Logic (LTL) formulae [45] that is aimed at running on such devices. We prove its correctness and termination guarantee, and experimentally compare a GPU implementation with state-of-the-art LTL model checkers. Our new GPU LTLchecking algorithm is up to 150x faster on proving the correctness of a system than LTSmin running on a 32-core high-end CPU, and is more economic in using the available memory.

关键词： Temporal logic LTL model checking automata-based verification finite-state machines GPU

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