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

共 9243 条记录，以下是141-150 订阅

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Self-supervised Learning to Improve Froth Images Segmentation 9th

Self-supervised Learning to Improve Froth Images Segmentatio...

引用

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

作者： Rumiantceva, Mariia Kriukov, Andrei Prokopov, Egor Efimova, Valeria ITMO Univ Kronverksy Prospekt 49 St Petersburg Russia

ISBN: (纸本)9789819754403;9789819754410

Froth flotation analysis is a critical task in mineral processing and metallurgical applications. Using self-monitoring approaches as MoCo, SparK, Jigsaw Puzzle, and Barlow Twins helps us avoid dependence on extensive labeled datasets. Our experimental setup evaluates segmentation performance metrics, comparison with baseline models, and visualization of segmentation results. The study highlights the unique contributions of each self-supervised method, showcasing their impact on class imbalance handling, robustness to variations, and computational efficiency. The best of proposed methods outperform the baseline on labeled dataset by 3% IoU metric. The findings offer valuable insights into the strengths and limitations of employing self-supervised learning in froth image analysis, paving the way for further advancements in the field.

关键词： Froth Froth size distribution Image segmentation Self-supervised learning

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Analysis of the Audiovisual Narrative of the Film "Fallen Angels" by Director Wong Kar-Wai 9th

Analysis of the Audiovisual Narrative of the Film "Fallen An...

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

作者： Salvatierra, Luis Alonso Vasquez Arellano, Joel Isaac Milla Gayoso, Norma Ines Caldas Turriate-Guzman, Adriana Margarita Univ Privada Norte Lima Peru

ISBN: (纸本)9789819750344;9789819750351

This research answers the question: How are the components of the audiovisual narrative of the film Fallen Angels by director Wong Kar-wai (2022) presented? In this way, the objective is to analyze the components of the audiovisual narrative of the film Fallen Angels by director Wong Kar-wai (2022). The methodology used is qualitative with a phenomenological model, which means that it is interpretative and based on researched documentation that allows a good conclusion.

关键词： Audiovisual narrative Editing Color Fallen Angels Wong Kar-Wai

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Enhancing Power Forecasting Through Contextual Awareness with C-Means and K-Means Approaches 21st

Enhancing Power Forecasting Through Contextual Awareness wit...

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21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Teixeira, Brigida Pinto, Tiago Vale, Zita Polytech Porto GECAD Res Grp Intelligent Engn & Comp Adv Innova LASI Intelligent Syst Assoc Lab R Dr Antonio Bernardino Almeida 431 P-4249015 Porto Portugal Univ Tras Os Montes & Alto Douro P-5000801 Vila Real Portugal INESC TEC P-5000801 Vila Real Portugal

ISBN: (纸本)9783031820724;9783031820731

This study delves into enhancing energy consumption forecasting in smart buildings by analyzing the implications of different contextual detection strategies, employing both K-Means and Fuzzy C-Means clustering techniques. Utilizing a comprehensive dataset from various sensors, the research segments power usage data to uncover distinct consumption patterns, extracted from user's behavior. A suite of seven machine learning forecasting algorithms undergoes evaluation, guided by two distinct methodologies: crafting specialized models for individual clusters and embedding contextual data as features within an integrated model. To perform the selection of the most suitable algorithm for each approach and cluster, an automated machine learning process is utilized, streamlining the optimization and comparison process. Moreover, the study explores the influence of soft clustering in providing nuanced, probabilistic cluster memberships and their impact on forecasting accuracy. A baseline scenario, devoid of contextual considerations, serves as a benchmark for comparison, allowing for a clear evaluation of the benefits introduced by integrating context into predictive models. The research aims to identify the optimal contextual detection strategy, considering the unique advantages of soft and hard clustering, to significantly improve energy consumption forecasts.

关键词： Contextual Awareness Fuzzy C-Means (FCM) K-Means Power Consumption Forecasting

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Estimating the Registration Error of Brain MRI Data Based on Regression U-Net 20th

Estimating the Registration Error of Brain MRI Data Based on...

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

作者： Nascimento, Leandro Francois, Quentin Duplat, Bertrand Haliyo, Sinan Bloch, Isabelle Sorbonne Univ CNRS LIP6 Paris France Robeaute Paris France Sorbonne Univ CNRS ISIR Paris France

ISBN: (纸本)9783031739965;9783031739972

Some neurosurgery procedures require precise information on the region of interest, and a quantitative control of the overall uncertainty. Such procedures often rely on image registration, which is an essential step in many of these workflows. However, the problem of registration error estimation (REE) remains a challenge, due to its lack of ground-truth. In this work, we establish different criteria to evaluate REE methods and we propose a regression U-Net, a supervised convolutional neural network approach, that is able to compute the REE for the case of deformable brain MRI mono-modal registration. The model is trained and tested separately on four modalities. The best result is for T1 images, with a root mean square deviation (RMSD) on the test set of 0.17mm for images with 1mm(3) isotropic voxels. We also tested the model generalization and transfer learning capabilities on a separate MRI data set with two modalities. For the T1 images, the direct inference has a RMSD of 0.62mm and the transfer learning method leads to a RMSD of 0.19 mm, also for volumes of 1mm(3) voxel size. These results demonstrate the feasibility of our approach and the possible use of a U-Net based model for REE in brain MRI registration. The proposed methods enable a better quantitative control of procedure uncertainty in neurosurgeries and open the way to closed loop robotic control in these procedures.

关键词： Image registration Registration error Uncertainty estimation Brain MRI Convolutional neural networks U-Net

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Fostering Career Decision-Making Through Career Guidance Platform: The Design of the NEFELE OERs Shared Platform 9th

Fostering Career Decision-Making Through Career Guidance Pla...

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

作者： Parola, Anna Diano, Federico Sica, Luigia Simona Univ Naples Federico II Dept Humanities I-80133 Naples Italy

ISBN: (纸本)9789819754403;9789819754410

The paper aims to present the NEFELE OERs Shared Platform, designed to foster adolescent career decision-making. Nowadays, making career choices is a crucial development task for adolescents and young people, and technological assets can be a valuable support for career guidance interventions. The web application is conceived as a "lending platform" for adolescents and all the people involved in supporting roles: it is thoughtfully designed to provide information about educational paths, useful career guidance websites and materials, and games to stimulate personal resources to deal with career uncertainty. The platform was created under the Erasmus+ KA2 NEFELE ("Neet prevention in Education systems through positive Future vision Enhancing Learning and teacher Education") funded by the European Community.

关键词： Web application Career guidance OERs

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Management Model for the Reduction of Particulate Matter Emission Peaks Supported by Sensor networks and Energy Resources of Electric Vehicles 9th

Management Model for the Reduction of Particulate Matter Emi...

引用

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

作者： Arias, L. A. Rico, F. Gonzalez, R. Gomez, R. ECCI Colombia Univ Univ Distrital Francisco Jose Caldas Bogota Colombia

ISBN: (纸本)9789819750344;9789819750351

This article proposes a management model that facilitates the reduction of environmental pollution rates using a network of monitoring stations with low-cost sensors. This sensor network will be supported by electric vehicles (EV) equipped with portable electrostatic precipitators (PE). Methodologically, the design of the management model and its operation have been carried out based on the unified modeling language (UML), and an objective function has been proposed to determine the optimal way to use the EV resources available in the city area that requires intervention.

关键词： Management system Electric vehicles Electric precipitators Unified modeling language Low-cost sensors

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Unveiling Patterns of Hate Speech in the Portuguese Sphere: A Social Network Analysis Approach 20th

Unveiling Patterns of Hate Speech in the Portuguese Sphere: ...

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

作者： Pontes, Catarina Fonseca, Antonio Moro, Sergio Batista, Fernando Ribeiro, Ricardo Marques, Catarina Carvalho, Paula Silva, Claudia Guerra, Rita Inst Univ Lisboa ISCTE IUL ISTAR Lisbon Portugal Inst Univ Lisboa ISCTE IUL Lisbon Portugal INESC ID Lisbon Portugal Univ Jordan Amman Jordan Univ Lisboa & Ctr Psychol Res & Social Intervent ISCTE Inst Lisbon Portugal Univ Lisboa & Business Res Unit BRU ISCTE ISCTE Inst Lisbon Portugal ITI LARSyS & IST Lisbon Portugal

ISBN: (纸本)9783031739965;9783031739972

This study investigates the proliferation of hate speech on Twitter (now known as X) within the Portuguese-speaking community, utilizing Social Network Analysis (SNA) to examine tweets from 2021 to 2022 flagged for hate speech content. Our analysis, grounded in a dataset annotated for direct and indirect hate speech, offensive speech, and counter-speech, aims to unravel the linguistic patterns and the specific targets of online hate. Employing network science methodologies, we dissect the structural dynamics of these interactions, focusing on the influence of central nodes and the formation of communities that facilitate the spread of hate speech. Key findings highlight a prominent volume of hate speech directed towards minority and vulnerable groups, with significant discourse around LGBT rights, racism, xenophobia, and discrimination against the Roma. The study underscores the critical role of online communities and influencers in the dissemination of hate speech and suggests a pressing need for effective monitoring and intervention strategies. Our research contributes to understanding the complexities of online hate speech and offers insights for developing tools to counteract its spread, aiming to foster a safer and more inclusive online discourse.

关键词： Hate Speech Analysis Social Network Analysis Online Communities Twitter Portuguese Social Media

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FuzzTheREST: An Intelligent Automated Black-Box RESTful API Fuzzer 21st

FuzzTheREST: An Intelligent Automated Black-Box RESTful API ...

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21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Dias, Tiago Maia, Eva Praca, Isabel Porto Sch Engn ISEP Res Grp Intelligent Engn & Comp Adv Innovat & Dev P-4200072 Porto Portugal

ISBN: (纸本)9783031820724;9783031820731

Software's pervasive impact and increasing reliance in the era of digital transformation raise concerns about vulnerabilities, emphasizing the need for software security. Fuzzy testing is a dynamic analysis software testing technique that consists of feeding faulty input data to a System Under Test (SUT) and observing its behavior. Specifically regarding black-box RESTful API testing, recent literature has attempted to automate this technique using heuristics to perform the input search and using the HTTP response status codes for classification. However, most approaches do not keep track of code coverage, which is important to validate the solution. This work introduces a black-box RESTful API fuzzy testing tool that employs Reinforcement Learning (RL) for vulnerability detection. The fuzzer operates via the OpenAPI Specification (OAS) file and a scenarios file, which includes information to communicate with the SUT and the sequences of functionalities to test, respectively. To evaluate its effectiveness, the tool was tested on the Petstore API. The tool found a total of six unique vulnerabilities and achieved 55% code coverage.

关键词： Fuzzy Testing RESTful APIs Vulnerability Detection Code Security Artificial Intelligence

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Discrete and Smoothed Resampling Methods for Interval Numbers 20th

Discrete and Smoothed Resampling Methods for Interval Number...

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

作者： Liubonko, Yelyzaveta Romaniuk, Maciej Warsaw Univ Technol Fac Math & Informat Sci Koszykowa 75 PL-00662 Warsaw Poland PAS Syst Res Inst Newelska 6 PL-01447 Warsaw Poland WIT Acad Newelska 6 PL-01447 Warsaw Poland

ISBN: (纸本)9783031739965;9783031739972

The classical Efron's bootstrap is a widely used tool in statistical inference. However, because of its disadvantages, many other resampling algorithms were proposed in the literature, especially for the real-valued data. In this paper, we consider three resampling methods for the special case of the interval real-valued data. They were inspired by the smoothed bootstrap and special algorithms known for fuzzy numbers. Using numerical simulations and statistical tools, the introduced methods are compared with the Efron's bootstrap. It seems that these new algorithms produce samples that can be considered as "similar, but not exactly the same" as the initial data, which is an important aim in the case of resampling methods.

关键词： Statistical simulations Smoothed bootstrap Non-parametric model Bootstrap Imprecise data

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From Measured pH to Hidden BOD: Quasi Real-Time Estimation of Key Indirect Water Quality Parameters Through Direct Sensor Measurements 1

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

作者： Islam, Masabah Bint E. Faheem, Yasir Ahmad, Arsalan Fraz, Muhammad Moazam Natl Univ Sci & Technol NUST Sect H-12 Islamabad Pakistan Canadian Univ Dubai U Arab Emirates Iowa State Univ Ames IA USA

ISBN: (数字)9789819754410

ISBN: (纸本)9789819754403;9789819754410

Securing water quality is essential for environmental sustainability, public health, and the preservation of biodiversity. However, traditional water monitoring methods often fall short in providing real time, comprehensive insights. Our research introduces an innovative framework that exploits cutting-edge sensor technologies and the Internet of Things (IoT) for the real time and continuous monitoring of a broad array of water quality metrics. Our system adeptly measures direct parameters like pH, temperature, and dissolved oxygen, and innovatively employs machine learning to estimate traditionally unmeasurable parameters such as biochemical oxygen demand (BOD), nitrate-nitrogen, and phosphate concentrations. This estimation can be made possible using machine learning based algorithms on extensive data captured by IoT sensor network. The framework is complemented by sophisticated data visualization tools, providing intuitive, actionable insights. Our integrated approach ushers in a proactive paradigm for water resource management, promising significant advancements in environmental governance, sustainable development, and health policy, thereby establishing a new standard in IoT-enabled environmental monitoring.

关键词： Artificial intelligence Internet of Things Water quality monitoring Early contamination detection

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