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

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Effective Nudging in Digital Environments 8th

Effective Nudging in Digital Environments

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8th International Conference on computer-Human Interaction Research and Applications-CHIRA-Annual

作者： Storhaug, Synne Fagernes, Siri Murano, Pietro Kristiania Univ Coll PB 1190 Sentrum N-0107 Oslo Norway Oslo Metropolitan Univ Postboks 4St Olavs Plass N-0130 Oslo Norway

ISBN: (纸本)9783031838446;9783031838453

This research investigates the user experience of receiving digital nudges. The nudges were designed as being either gain-framed or loss-framed, informing users of the benefits of exercise and were sent as push notifications in a fitness app. Qualitative data from semi-structured interviews were analysed using a thematic analysis. The overall results suggest that nudges via push notifications are well-accepted by users, particularly if trustworthy, short, easy to read and well-timed. Differences in opinion between gain-framed or loss-framed nudges were not strong. However, those who did express a dislike for loss-framed nudges tended to do so strongly. This paper is novel and useful to designers of digital nudges as our results can inform designers of best practices. To the best of our knowledge, there are a lack of studies that actually carry out the design, implementation and evaluation of digital nudging interventions (by its true forms), with the aim of understanding users' perception and experience of digital nudging. Further, as far as we have been able to ascertain, a systematic qualitative study of the user experience of digital nudges using push notifications has not been done until now.

关键词： Digital nudges Warning-type nudges Evaluation Qualitative interviews Thematic analysis

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Detection and Mitigation of IoT Based DDoS Attack Using Extended MUD Enabled Device Profiling Techniques 4th

Detection and Mitigation of IoT Based DDoS Attack Using Exte...

引用

4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Thiruppathi, K. Jaidhar, C. D. Soc Elect Transact & Secur Chennai Tamil Nadu India Natl Inst Technol Karnataka Informat Technol Dept Surathkal India

ISBN: (纸本)9783031837821;9783031837838

In today's landscape, the burgeoning Internet of Things (IoT) infrastructure underscores the imperative for implementing toptier security measures to safeguard the IoT realm. This domain has permeated various sectors, including automotive, smart cities, healthcare, industries, and the power sector. The rising ubiquity of IoT devices has drawn the attention of malicious actors, presenting a significant risk of exploitation in insecure, constrained environments. Among the foremost threats in the IoT domain is the Distributed Denial of Service (DDoS) attack, capable of swiftly devastating entire IoT infrastructures. To address this issue, this work proposes a detection and mitigation model aimed at thwarting DDoS attacks in IoT environments. A hybrid feature selection technique is proposed to identify the most effective features for detecting attacks, and Convolutional Neural Network (CNN) is used to identify the suspicious IoT nodes. Further utilized the extended Manufacturer Usage Description (MUD) enabled device profiling techniques to compare the malicious node profiles with existing benign MUD profiles to find the malicious nodes. Linux IPtables is enabled to efficiently filter DDoS attacks. The proposed work is for detecting IoT DDoS attacks, alongside a self-reliant mitigation strategy to effectively filter these attacks. This strategy aims to minimize the impact of blocking legitimate network traffic from IoT devices. The effectiveness of the proposed hybrid feature selection was evaluated by using the CICIoT2023 dataset.

关键词： Deep Learning Distributed Denial of Service Attack Internet of Things Manufacturer Usage Description

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Exploring Efficacy of Cryptographic Hash Functions SHA-256 and BLAKE2s Against HashBoost 4th

Exploring Efficacy of Cryptographic Hash Functions SHA-256 a...

引用

4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Yadav, Jyoti Yashwant Shevkar, Ranjana Sanjay Savitribai Phule Pune Univ Dept Comp Sci Pune Maharashtra India

ISBN: (纸本)9783031837821;9783031837838

Cryptographic hash functions are the backbone of data integrity and security, playing a pivotal role in safeguarding digital information. The study begins with a detailed comparison of conventional hash functions, examining key parameters such as block size, word size, output size, number of rounds, logical operations as well as their compression function. The novel HashBoost function, designed to enhance both computational efficiency and security, is introduced. This paper presents a comprehensive analysis of three cryptographic hash functions: SHA-256, BLAKE2s, and HashBoost, with a focus on their performance across varying message sizes. The results indicate that while SHA-256 and BLAKE2s offer robust security features, HashBoost significantly enhances in terms of computational speed, particularly with larger data volumes. HashBoost maintains essential cryptographic properties, including determinism, pre-image resistance, collision resistance, and the avalanche effect, making it a strong candidate for time-sensitive applications like blockchain. Future work includes the development of a dedicated blockchain simulator to test and validate these functions in real-world scenarios, ensuring that HashBoost can meet the demanding requirements of modern cryptographic applications. The findings highlight the importance of balancing security and efficiency in the design of cryptographic hash functions.

关键词： Cryptographic Hash Function HashBoost BLAKE2s SHA-256 Efficiency

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Enhancing Sustainability in the Hospital Supply Chain: An Analysis of Advancements, Challenges, and Future Directions 4th

Enhancing Sustainability in the Hospital Supply Chain: An An...

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

作者： Ruiz-Orjuela, Erika Tatiana Gatica, Gustavo Adarme-Jaimes, Wilson Univ Nacl Colombia Bogota Colombia Univ Andres Bello Santiago Chile

ISBN: (纸本)9783031840777;9783031840784

Hospital supply chain management (HSCM) is under pressure to adopt sustainable practices while balancing economic, social, and environmental factors. This systematic literature review explores the advancements, challenges, and future perspectives of sustainability in HSCM. By drawing on articles published between 2013 and 2023, the review identifies key themes such as the adoption of emerging technologies, the impact of socioeconomic changes, the need for resilience and risk management, and the potential of the circular economy. The analysis reveals a growing emphasis on social sustainability, which considers stakeholder perspectives and ethical considerations in supply chain decisions. Challenges include integrating sustainability into existing practices, balancing competing objectives, and measuring the impact of sustainable initiatives. Future research in HSCM should explore emerging technologies, social sustainability, post-pandemic resilience, and circular economy principles. By addressing these challenges and opportunities, stakeholders can contribute to a more sustainable and resilient healthcare system.

关键词： Hospital supply chain review sustainability trends

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Key Dimensions for Human-Robot Interaction in XR for Rescue Robots 1

引用

15th Indian Conference on Human-computer Interaction Design and Research

作者： Rajashekhar, V. S. Indian Inst Technol Dept Design Kanpur Uttar Pradesh India

ISBN: (数字)9783031808326

ISBN: (纸本)9783031808319;9783031808326

The post-earthquake search operation requires faster response by the rescue team since several lives need to be saved quickly. Designing the Human-Robot Interaction (HRI) for Extended Reality (XR) requires knowledge of virtual and remote environments. While interacting with the virtual and remote environments, the user's perspective needs to be addressed based on the scenario. This work focuses on selecting the six key dimensions for XR-based remote HRI. Then, we arrive at the conditions in the local environment and map the key dimensions for each of them. This will help the rescue team face post-earthquake search operations with the available XR technologies.

关键词： Human Robot Interaction Extended Reality Rigid bodied robots Soft robots

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Photovoltaic Cell Defect Classification Using Attention U-Net Image Segmentation 4th

Photovoltaic Cell Defect Classification Using Attention U-Ne...

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

作者： Alzu'bi, Ahmad Saleh, Zaid Jordan Univ Sci & Technol Dept Comp Sci Irbid 22110 Jordan Jordan Univ Sci & Technol Dept Elect Engn Irbid 22110 Jordan

ISBN: (纸本)9783031821523;9783031821530

The need to automate solar cell quality monitoring techniques is growing as solar power utilization gets significant momentum. However, the detection of internal defects in photovoltaic (PV) cells is still quite a challenge, highlighting the demand for a reliable method to reduce the requirement for manual inspections. This paper presents a deep learning model to automatically detect and classify defective PV cells in Electroluminescence images using attention-based U-Net image segmentation. The proposed deep learning architecture uses ResNet50 and attention U-Net networks to formulate a set of discriminating image features, providing an effective procedure of deep transfer learning. The extracted image descriptors are encoded and learned from image masks and annotations that were reprocessed to improve the generalization capability of the photovoltaic cell classifier. The semantic deep learning model with image augmentation and mask processing provides a baseline to detect and classify any possible faults in photovoltaic cells. The experimental results demonstrate the effectiveness of semantic attention U-Net-Resnet50 deep architecture in classifying PV cells, achieving a precision of 95% and an F1-score of 94%. It also achieved an IoU of 90% for PV image segmentation.

关键词： Photovoltaic Cell Deep Learning Attention U-Net Image Semantic Segmentation

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Brain Tumor Classification from MRI Scans Using a Novel CNN Architecture and Optimization Techniques 4th

Brain Tumor Classification from MRI Scans Using a Novel CNN ...

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4th International Conference on Artificial Intelligence and Knowledge Processing

作者： Hussain, Shaik Jaffar Devi, B. Rupa Mahalakshmi, V. Harikala Parveen, S. Z. Athinarayanan, S. Sri Venkateswara Inst Sci & Technol Dept CSE Kadapa 516003 India Annamacharya Inst Technol & Sci Dept CSE Tirupati Andhra Pradesh India Jazan Univ Coll Engn & Comp Sci Dept Comp Sci Jazan Saudi Arabia Annamacharya Univ Dept ECE Rajampet India Annamacharya Inst Technol & Sci UGC AUT ONOMOUS Dept CSE AI & ML Kadapa 516003 AP India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Sch Comp Dept CSE Chennai Tamil Nadu India

ISBN: (纸本)9783031734762;9783031734779

Classification of brain tumours is crucial for computer-aided diagnostics (CAD) in health assessments. In light of the extensive procedures involved, manually identifying brain tumors using magnetic resonance imaging (MRI) is frequently labor-intensive and difficult, with the possibility of errors in detection and classification. Healthcare has significantly benefited from current developments in Deep Learning (DL), which have greatly enhanced the automation of medical image processing and diagnoses. One subclass of DL techniques, Convolutional Neural Networks (CNNs), is particularly good at visual learning and image categorization. To categorize brain tumours into three groups: gliomas, meningiomas, and pituitary tumours, we introduced the CNN method. We assessed the algorithm's performance using a benchmark dataset and contrasted it with pre-trained models already in use, including VGG16, VGG19, ResNet50, MobileNetV2, and InceptionV3. According to the experimental findings, our suggested model had a high classification accuracy of 98.5%, with 99% precision, recall, and f1-score. These outcomes suggest that our approach accurately classifies the most prevalent brain tumours. The algorithm is a valuable tool to help clinicians identify brain tumours quickly and accurately because of its excellent generalization ability and speed of execution.

关键词： Brain Tumor MRI CNN Deep Learning Transfer Learning

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Non-parametric Neighborhood Test-Time Generalization: Application to Medical Image Classification 1st

Non-parametric Neighborhood Test-Time Generalization: Applic...

引用

1st MICCAI Meets Africa Workshop

作者： Ambekar, Sameer Schnabel, Julia A. Lang, Daniel M. Tech Univ Munich Sch Computat Informat & Technol Munich Germany Helmholtz Munich Inst Machine Learning Biomed Imaging Munich Germany Kings Coll London Sch Biomed Engn & Imaging Sci London England

ISBN: (纸本)9783031791024;9783031791031

Reliable and stable performance is crucial for the application of computer-aided medical image systems in clinical settings. However, approaches based on deep learning often fail to generalize well under distribution shifts. In medical imaging, such distribution shifts can, for example, be introduced by changes in scanner types or imaging protocols. To counter this, test-time generalization aims to optimize a model that has been trained on single or multiple source domains to an unseen target domain. Common test-time adaptation methods fine-tune model weights utilizing losses with gradient-based optimization, a time-consuming and computationally demanding procedure. In contrast, our approach adopts a non-parametric method that is entirely feed forward and utilizes information from target samples to extract neighborhood information with dynamic voting. By doing so, we avoid fine-tuning or optimization procedures, enabling our method to be more efficient and achieve stable adaptation. We demonstrate the effectiveness of our approach by benchmarking it against different state-of-the-art methods with three backbones on two publicly available medical imaging datasets, consisting of fetal ultrasound and retinal images, and achieve classification accuracy improvements by up to 3.4% and 1.1%, respectively. Moreover, we also demonstrate the utility of our method in practical scenarios, proving efficiency in terms of computational run-time and handling of uncertainty. Our code is publicly available at: https://***/compai-lab/2024-miccai-emerge-ambekar.

关键词： domain adaptation generalization unsupervised learning parameter-free optimization

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A Design of Experiments to Analyze Crop Growth Based on Seed Position in Sowing 4th

A Design of Experiments to Analyze Crop Growth Based on Seed...

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

作者： Torres-Mercado, Alina Eustate-Daza, Karen Moreno-Cruz, Jeffrey Pais Cerna, Gabriel Troncoso-Palacio, Alexander Univ Costa Especializac Gest Integral Calidad Barranquilla Colombia Univ Andres Bello Fac Engn Antonio Varas 880 Providencia Santiago 7500000 Chile

ISBN: (纸本)9783031832093;9783031832109

The methods of sowing and the specific conditions of the seeds used have a significant impact on the complex process of plant growth. The choice of seed location not only reflects the genetic and physiological characteristics of the plant but also helps to make it healthier. This, in turn, leads to improved growth and higher yields in various agricultural environments. In this study, an experimental design was implemented to analyze different methods of planting cacao seeds to determine which method is most effective in minimizing root and hypocotyl malformations. For the seeding process, it was ensured that all samples were from the same source. Two types of substrates and three different levels of seed placement were selected to serve as the basis for cultivation in soil contained in polyethylene bags. The experimental design was randomized with two factors and three levels, resulting in a total of 18 samples. The results were analyzed using the Design of Experiments (DOE) methodology in Minitab (R) software, along with photographic documentation of seed growth after 15 days. The analysis revealed that the seeds sown in the vertical position had fewer malformations. Specifically, 83% of the total samples fell into this category, which is consistent with a normal distribution. It can therefore be concluded that vertical seed positioning is the most effective planting method for promoting healthy growth and minimizing malformations.

关键词： Cocoa crops Design of experiments Planting methods Seed position

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Integrating Machine Learning and Deep Learning Techniques for Enhanced Historical Script Classification 4th

Integrating Machine Learning and Deep Learning Techniques fo...

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

作者： Boudraa, Merouane Bennour, Akram Al-Sarem, Mohammed Shukla, Piyush Kumar Echahid Cheikh Larbi Tebessi Univ LAMIS Lab Tebessa Algeria Taibah Univ Coll Comp Sci & Engn Medina Saudi Arabia Technol Univ Madhya Pradesh Bhopal Madhya Pradesh India

ISBN: (纸本)9783031821554;9783031821561

The accurate identification of script styles in historical manuscripts is crucial for gaining insights into their historical context and significance. This study proposes an integrated system that combines manual and machine learning features to effectively identify script styles within manuscripts, utilizing the ClAMM dataset. The system comprises three primary steps: preprocessing the dataset using denoising and binarization techniques, extracting manual features using the Harris detector, and performing script classification using pretrained CNN models. By merging manual feature engineering with advanced deep learning techniques, our system showcases its ability to accurately recognize script styles competing with state-of-the-art methods with accuracy of 89.2%. These results not only validate the effectiveness of our approach but also contribute significantly to the broader advancement of script classification in historical manuscript analysis.

关键词： Script Classification historical manuscripts feature extraction deep learning transfer-learning

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