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检索条件"丛书名=Lecture Notes in Networks and Systems"

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Exploring the Extremes of Two-Layer Convolutional Autoencoders Architecture in Image Inpainting 6th

Exploring the Extremes of Two-Layer Convolutional Autoencode...

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6th Conference on Computing systems and Applications

作者： Azri, Youcef Amamra, Abdenour Amara, Yacine Ourghi, Fethi Ecole Mil Polytech Bordj El Bahri BP 17 Algiers Algeria

ISBN: (纸本)9783031718472;9783031718489

Inpainting, the process of filling in missing or damaged regions of an image, has witnessed significant progress with the advent of deep learning, particularly neural networks. This investigation delves into the outermost capabilities of two-layer Convolutional Autoencoders (2L-CAEs) architectures applied to image inpainting. The study scrutinizes the boundaries of performance and feasibility within this specific neural network configuration. The overarching objective is to provide an intricate understanding of the potential and limitations inherent in pushing a 2L-CAE architecture to its extremes, particularly when tasked with the intricate process of mending missing or impaired regions within an image.

关键词： Inpainting Neural networks (NNs) Network Structure Design Inpainting Performance Convolutional Neural networks (CNNs) Convolutional Autoencoders (CAEs) Computational Considerations

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Effects of Strong Attribute Cooccurrence Influence Distance on Simple Linear Iterative Clustering Superpixel 9th

Effects of Strong Attribute Cooccurrence Influence Distance ...

引用

9th International Congress on Information and Communication Technology (ICICT)

作者： Moodley, Sadhasivan G. Olugbara, Oludayo O. Adeliyi, Timothy T. Durban Univ Technol MICTSETA 4IR Ctr Excellence Durban South Africa Univ Pretoria Dept Informat Pretoria South Africa

ISBN: (纸本)9789819750344;9789819750351

Digital image segmentation is a well-researched hard problem of highlighting each object in an image with a different shade. It increasingly relies on a superpixel approach to circumvent the computational sumptuousness inherent in a high number of image pixels. Superpixel clusters similar pixels in primitive features such as colors into semantically related groups of fewer pixels to accelerate computation. However, most of the existing superpixel methods rely heavily on Euclidean distance to measure color similarity between two image pixels. Unfortunately, Euclidean distance assumes that data samples are distributed about the mean in a spherical fashion and may not compactly measure color similarity to comply with human perception of object similarity. This study was aimed at experimentally comparing Euclidean distance with strong attribute concurrence influence distance (SAID) in simple linear iterative clustering (SLIC) superpixel segmentation algorithm to determine the effects of distance measures on the performance of SLIC. In addition, the density-based spatial clustering of applications with noise (DBSCAN) was used as a control superpixel algorithm to comprehend the efficiency, ability to adhere to image boundaries, and effects of distance measures on SLIC performance. Experimental results showed that DBSCAN achieved better results in terms of under-segmentation error for regular compactness in complex, multiple, and overlapping images. However, SLIC with SAID distance achieved better results in terms of achievable segmentation accuracy, and superpixel compactness for complex images. Moreover, SLIC superpixel algorithm achieved better results with Euclidean distance for normal images with less complex backgrounds when compared with SLIC with SAID distance and DBSCAN superpixel algorithm.

关键词： Distance metric Image segmentation Perceptual similarity Segmentation performance Superpixel clustering Superpixel compactness

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Enhancing Image Annotation Through Attention Mining: A Grounded SAM Approach 21st

Enhancing Image Annotation Through Attention Mining: A Groun...

引用

21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Corton-Gonzalez, Jaime Mora-Sanchez, Angel Rodriguez-Jimenez, Silvia Sigma Cognit Ave Manoteras 6 Madrid 28050 Spain

ISBN: (纸本)9783031809453;9783031809460

In the domain of image annotation, the involvement of human annotators presents a series of intricate challenges tied to the complexities of visual perception. The manual labeling process demands an understanding of context and visual intricacies, all susceptible to human subjectivity. Furthermore, the presence of visual distractions compromises annotation quality, not only impeding annotation precision but also escalating time expenditures as annotators navigate through visual noise to discern pertinent details. Traditional image annotation pipelines underscore these challenges in favor of automatic or semiautomatic annotation, emphasizing the critical necessity for innovative approaches in annotation tasks where the human annotator role is fundamental. Within this context, the Grounded SAM model, emerges as a potent tool for text-prompt-based panoptic segmentation. This paper proposes a novel annotation pipeline employing Grounded SAM and LaMa cleaner models to augment the indispensable role of human annotators by enhancing annotation efficiency through natural language-based attention mining for visual distractions elimination and preannotation techniques. The effectiveness of distraction elimination is demonstrated through an annotation task involving human annotators, with half of the images processed through our pipeline and the remaining unmodified. With our current approach and with the current data analyzed, image annotation times of 70% of the annotators were reduced by 15.88%, while global annotation time was reduced by a 6.93%.

关键词： image annotation visual distractions attention mining computer vision

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Tunisian Arabic Understanding: Resources Analysis and Evaluation 3rd

Tunisian Arabic Understanding: Resources Analysis and Evalua...

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3rd International Conference on Language Processing and Knowledge Management-LPKM

作者： Besdouri, Fatma Zahra Zribi, Ines Belguith, Lamia Hadrich Univ Sfax ANLP Res Grp MIRACL Lab Sfax Tunisia Univ Monastir Monastir Tunisia

ISBN: (纸本)9783031850660;9783031850677

Natural Language Understanding (NLU) is considered a core component in implementing Dialogue systems (DS). This paper introduces an NLU module tailored to the Tunisian Dialect (TD) that can further be integrated with Automatic Speech Recognition (ASR), a Dialogue Manager (DM), a Natural Language Generator (NLG), and a Speech Synthesis (SS) module to build a fully working DS. Designed specifically for academic and research domain, our closed-domain TUN-NLU corpus undergoes preprocessing, including scraping, automatic normalization, data augmentation, and annotation. We employed the RASA NLU model (https://***/) to evaluate our corpus. The NLU model encompasses tokenization, featurization, intent detection, and entity recognition. Evaluation using standard metrics, including accuracy and F1 score, yielded promising results: 93% accuracy and 92% F1 score for intent detection, and 94% accuracy and 91% F1 score for entity recognition.

关键词： Tunisian Dialect Natural Language Understanding Dialogue System RASA

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The GILL Platform: A Pan-European, Virtual, Learning, Sustainable and Collaborative Hub Built on the Living Lab Principles 21st

The GILL Platform: A Pan-European, Virtual, Learning, Sustai...

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21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Plaza-Hernandez, Marta Liotopoulos, Fotis K. Tello-Alvarado, Carlos Mesonero-Ronco, Enrique Merchan-Benito, Javier Benito-Manzanas, Diego Rodrigo-Marcos, Mario Montero-Fernandez, Alberto Sanchez-Sanchez, David Rosa-Gonzalez, Alvaro Villoria-Torres, Carolina Prieto-Tejedor, Javier Univ Salamanca BISITE Res Grp Edificio Multiusos I D ICalle Espejo 2 Salamanca 37007 Spain SBOING K Karamanli 124 Thessaloniki 57008 Greece

ISBN: (纸本)9783031809453;9783031809460

The Gendered Innovation Living Labs (GILL) project consortium is developing a new IT platform that will be iteratively developed through a series of co-creation approaches to meet the needs of all gender innovation stakeholders. The platform will provide access to training, case studies, tools and methods, supporting gender-responsive innovation related to European challenges. It will facilitate collaborative work, knowledge sharing and creation between scientists, formal organisations and citizens engaged in open innovation cycles or gender innovation careers. Ultimately, it strives to become a sustainable open innovation resource driving cultural and systemic change through inclusive practices. Once the platform is populated, itwill integrate advanced digital technologies to support and enable GILL to reach an interested community of organisations, projects, and individuals across Europe.

关键词： IT platform Living Labs learning hub collaborative hub cloud

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Decision System in a Building to Coordinate the Energy Forecasting Tasks 21st

Decision System in a Building to Coordinate the Energy Forec...

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21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Ramos, Daniel Faria, Pedro Vale, Zita Polytech Porto LASI Intelligent Syst Associate Lab GECAD Res Grp Intelligent Engn & Comp Adv Innovat Porto Portugal

ISBN: (纸本)9783031820724;9783031820731

Nowadays, energy buildings have a huge impact in society regarding the active role in the management of energy consumption. Hence, building owners are required to avoid energy losses and improve energy efficiency as high as possible. Therefore, it is required to plan an optimization strategy to buy and sell energy in the market ahead of time. To formulate this optimization plan, building owners require the work of specialists responsible for processing, training, forecasting, and evaluation tasks regarding the prediction of energy consumption data from a building for a specific target of time. Therefore, a multiagent-system is needed to allow the cooperation of various agents including the building owner, forecast provider, data structurer and error analysis. Moreover, forecasting algorithms such as artificial neural networks should be taken into consideration in order to process large quantities of energy consumption data during the training and forecasting phases.

关键词： agents artificial neural networks building owner energy building

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Deep Learning-Based Diagnosis of Leukemia in Africa: Case of Acute Lymphoblastic and Acute Myeloid Leukemia in Senegal 9th

Deep Learning-Based Diagnosis of Leukemia in Africa: Case of...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Diagne, Therese Daba Siga Ba, Mandicou Sall, Abibatou Toure, Fadel Fall, Ibrahima Diagne, Khadija Diouf, Assane Sene, Ibrahima Ngom, Charles Abdoulaye Diop, Idy Bah, Alassane Cheikh Anta Diop Univ Dakar UCAD Res Inst Dev IRD Unit Math Modeling & Comp Sci Complex Syst UMMISC Polytech Higher Sch ESP Dakar Senegal Univ Quebec Trois Rivieres Dept Math & Comp Sci Trois Rivieres PQ Canada Dalal Jamm Natl Hosp Ctr Hematol Dept Dakar Senegal

ISBN: (纸本)9789819754403;9789819754410

Early and accurate diagnosis of leukemia in Africa, particularly for children, remains a major public health challenge. In the current paper, we propose an application of deep learning (DL) and computer vision approaches in order to improve the diagnosis of acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). We rely on two models, namely ViT and YOLOv8, that have been trained, evaluated on public open dataset (C-NMC dataset [1]) and designed for identifying acute lymphoblastic leukemia cell from microscopic images of immature blood cells. The results of this experimental evaluation reveal that YOLOv8 gives better performance in the context of limited data and resources. We then selected YOLOv8 pre-trained models and adopted a transfer learning approach on augmented data we collected from the Senegalese context of the Dalal Jamm Hospital. The results obtained present a precision of 96.6%, a recall of 92.9%, and an mAP50 of 98.6%. These encouraging performances demonstrate the ability of YOLOv8 to accurately identify ALL and AML cells.

关键词： Deep learning Computer vision Cancer blood cells Lymphoblasts Myeloblasts ViT YOLOv8 Data augmentation Transfer learning

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Blockchain-Based Secured Ration Distribution System 3rd

Blockchain-Based Secured Ration Distribution System

引用

3rd International Conference on Network Security and Blockchain Technology

作者： Malasane, Ayush Parida, Milan Kumar Kotkar, Harshal Vaijapurkar, V. B. SCTRs Pune Inst Comp Technol Pune Maharashtra India

ISBN: (纸本)9789819780501;9789819780518

This survey paper explores the integration of blockchain technology into Ration Distribution systems (RDS) and its potential to address long-standing challenges. It begins by explaining blockchain technology and its various types. The paper then meticulously examines the persistent issues in traditional ration distribution systems, emphasizing the advantages and limitations of adopting blockchain solutions. Real- world examples, such as India's blockchain-based PDS and the World Food Program's "Building Blocks" initiative, demonstrate how blockchain can enhance transparency, traceability, security, and operational efficiency in ration distribution. The paper highlights the benefits of integrating blockchain, including improved transparency, traceability, security, and operational efficiency. It also discusses architectural aspects and practical applications of smart contracts within blockchain integration. The importance of permissioned blockchains and userfriendly interfaces is emphasized. Challenges in adopting blockchain technology, such as scalability, privacy, and adoption barriers, are identified. The authors suggest future research directions, including incorporating IoT and AI, pursuing interoperability, sustainability initiatives, and designing user-centric interfaces. This comprehensive analysis serves as a roadmap for creating more equitable, secure, and efficient ration distribution systems worldwide.

关键词： Ration distribution system (RDS) Public distribution system (PDS) World food programme (WFP)

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Chatbots in Paradigm Shifting for Improving Gig Workers' Labor Conditions 9th

Chatbots in Paradigm Shifting for Improving Gig Workers' Lab...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Arruda, Monique de Souza Catholic Univ Portugal Porto Fac Law Rua Diogo Botelho 1327 P-4169005 Porto Portugal

ISBN: (纸本)9789819750344;9789819750351

In the field of gig work and digital platforms, this article aims to investigate the improvement of workers' working conditions through the strategic implementation of chatbots. Using the resources of artificial intelligence, the study explores how chatbots could work in favor of platform workers, in the opposite direction to the current application of algorithmic management and its tools, such as recommendation algorithms, used for the unilateral purpose of potential increased profit by platforms. After problematizing the gaps that are harmful to platform workers when interacting with them in their daily work routine, an empirical typology of chatbots adjustable to the presupposed needs is suggested. These AI-driven chatbots, functioning as virtual advisors, not only tailor recommendations based on individual skills and preferences but also serve as immediate channels for answering queries. The expected results highlight a potential transformative impact on worker satisfaction, task efficiency and general well-being, portraying the integration of chatbot technology as a promising path to promoting a more humane and supportive work environment.

关键词： Conversational interface to gig workers Digital labor platforms Labor chatbot

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Liver Cancer Classification Approach Using Yolov8 20th

Liver Cancer Classification Approach Using Yolov8

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20th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based systems

作者： Abdulsahib, Fatimah I. Al-Khateeb, Belal Koczy, Laszlo T. Nagy, Szilvia Szechenyi Istvan Univ Egyet ter 1 H-9026 Gyor Hungary Univ Anbar Anbar Iraq Al Ayen Iraqi Univ Engn Tech Coll Thi Qar Iraq

ISBN: (纸本)9783031739965;9783031739972

Liver cancer is a common and often fatal disorder that is becoming more commonplace worldwide. An accurate and timely diagnosis is necessary for both effective treatment and patient survival. In machine learning techniques, particularly deep learning, obtaining a large and diverse dataset is still a challenge for deep neural network training, particularly in the medical industry. This paper presents a classification of circulating tumor cells based on the YOLOv8 algorithm. Tumor cell identification and classification can be achieved by utilizing the algorithm's multi-layer high-level stacking, weight sharing, local connection, and pooling characteristics. The goal is to design a liver cancer classification system that makes it easier and increases the efficiency of doctors in analyzing the results of liver cancer. The models show the absolute the accuracy is 100%, 100%, 98%, 96% to Yolov8n, Yolov8s, Yolov8m, and Yolov8l respectively.

关键词： Liver cancer classification Deep Learning YOLO Yolov8 Liver CT

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