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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Chang, Shun-Cheng Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan Division of Gastroenterology and Hepatology Department of Internal Medicine National Taiwan University Hospital Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350371499

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages. © 2024 IEEE.

关键词： Distribution transformers

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ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Chang, Shun-Cheng Yu, Hsin-Pei Hsieh, Yi-Hsien Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan National Taiwan University Hospital Division of Gastroenterology and Hepatology Department of Internal Medicine Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350351552

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag. © 2024 IEEE.

关键词： Convolutional neural networks

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Model for Anomaly Detection in Reducing Water Losses in the Distribution Network of Peru using Machine Learning Algorithms

Model for Anomaly Detection in Reducing Water Losses in the ...

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Software engineering and Computer Science (CSECS), International Conference on

作者： Mayra Rivera Pricila Lucero José Luis Castillo-Sequera Lenis Wong Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Peru Department of Computer Science Group: Health Computing and Intelligent System Universidad de Alcalá Alcalá de Henares Spain

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Water leakage in distribution networks is a significant challenge, especially in regions with limited infrastructure like Huancayo, Peru, where losses account for 32.82% of the distributed volume. This study introduces a machine learning-based approach to detect leaks using four algorithms: Autoencoder LSTM, Isolation Forest, One-Class SVM, and K-Nearest Neighbors (KNN). The methodology involved preprocessing historical consumption data (2018–2024) into 12-month temporal sequences per client and evaluating the models based on F1 Score, Precision, and Mean Absolute Error (MAE). Among the algorithms, the Autoencoder LSTM demonstrated superior performance with the highest precision (0.89) and the lowest MAE (0.00402). Its robustness in high-variability contexts enables early and reliable leak detection, providing a cost-effective solution for optimizing water management in resource-constrained environments.

关键词： Support vector machines Machine learning algorithms Autoencoders Distribution networks Forestry Nearest neighbor methods Robustness Leak detection Long short term memory Water resources

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Experimental Evaluation of a Checklist-Based Inspection Technique to Verify the Compliance of Software Systems with the Brazilian General Data Protection Law

arXiv

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arXiv 2023年

作者： Cerqueira, Diego André de Mello, Rafael Maiani Travassos, Guilherme Horta Systems Engineering and Computer Science Graduate Program Federal University of Rio de Janeiro Brazil Computing Institute Federal University of Rio de Janeiro Brazil

Background/Context: Recent laws to ensure the security and protection of personal data establish new software requirements. Consequently, new technologies are needed to guarantee software quality under the perception of privacy and protection of personal data. Therefore, we created a checklist-based inspection technique (LGPDCheck) to support the identification of defects in software artifacts based on the principles established by the Brazilian General Data Protection Law (LGPD). Objective/Aim: To evaluate the effectiveness and efficiency of LGPDCheck for verifying privacy and data protection (PDP) in software artifacts compared to ad-hoc techniques. Method: To assess LGPDCheck and ad-hoc techniques experimentally through a quasi-experiment (two factors, five treatments). The data will be collected from IoT-based health software systems built by software engineering students from the Federal University of Rio de Janeiro. The data analyses will compare results from ad-hoc and LGPDCheck inspections, the participant's effectiveness and efficiency in each trial, defects' variance and standard deviation, and time spent with the reviews. The data will be screened for outliers, and normality and homoscedasticity will be verified using the Shapiro-Wilk and Levene tests. Nonparametric or parametric tests, such as the Wilcoxon or Student's t-tests, will be applied as appropriate. © 2023, CC BY-NC-ND.

关键词： Data privacy

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Critical Factors of Immersive Learning in Higher Education: A Systematic Literature Review 10

Critical Factors of Immersive Learning in Higher Education: ...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Sunardi Meyliana Ramadhan, Arief Rosmansyah, Yusep Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia School of Information System Bina Nusantara University Information System Departement Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798350339543

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education. © 2023 IEEE.

关键词： E-learning

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Getting Situated: Comparative Analysis of Language Models With Experimental Categorization Tasks 44

Getting Situated: Comparative Analysis of Language Models Wi...

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44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022

作者： Edinger, Andy Goldstone, Robert L. Program in Cognitive Science & Luddy School of Informatics Computing and Engineering Indiana University 1101 E. 10th Street BloomingtonIN47405 United States Program in Cognitive Science Department of Psychological and Brain Sciences Indiana University 1101 E. 10th Street BloomingtonIN47405 United States

Common critiques of natural language processing (NLP) methods cite their lack of multimodal sensory information, claiming an inability to learn situated, action-oriented relations through language alone. Barsalou's (1983) theory of ad hoc categories, which are formed from to achieve goals in real-world scenarios, correspond theoretically to those types of relations with which language models ought to have great difficulty. Recent NLP models have developed dynamic approaches to word representations, where the same word can have different encodings depending on the context in which it appears. Testing these models using categorization tasks with human response data demonstrates that situated properties may be partially captured through semantic analysis. We discuss possible ways in which different notions of situatedness may be distinguished for future development and testing of NLP models. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： Semantics

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RPGPref: A Planning Heuristic that Uses Playstyle Preferences to Model Player Action and Choice 18

RPGPref: A Planning Heuristic that Uses Playstyle Preference...

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18th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2022

作者： Lang, Eric W. Young, R. Michael School of Computing University of Utah 201 Presidents’ Circle Salt Lake CityUT84112 United States Entertainment Arts and Engineering Program University of Utah 201 Presidents’ Circle Salt Lake CityUT84112 United States

ISBN: (纸本)1577358775

Recent work extending planning algorithms that reason about action and change has been successful at supporting game design, player modeling, and story generation. Incorporating agent preferences over actions and propositions into a planning process allows for a more accurate prediction of what a human might do when solving a problem like playing through a game level. This paper presents the preference-based planning heuristic RPGPREF which uses relaxed plan graphs (RPGs) and preference sets to guide a planner toward a preference-conforming path to its goal. A human subjects evaluation confirms that RPGPREF successfully guides the planning process toward solution plans that recognizably match and differentiate player playstyles. Copyright © 2022, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Artificial intelligence

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HeadSpace: Incorporating Action Failure and Character Beliefs into Narrative Planning 18

HeadSpace: Incorporating Action Failure and Character Belief...

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18th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2022

作者： Sanghrajka, Rushit Young, R. Michael Thorne, Brandon School of Computing University of Utah Salt Lake CityUT United States Entertainment Arts and Engineering Program University of Utah Salt Lake CityUT United States Spectra Logic Salt Lake CityUT United States

ISBN: (纸本)1577358775

Previous work on story planning has lacked a knowledge representation for characters that attempt actions that fail because of the characters’ misconceptions about the world state. This work describes HEADSPACE, a state-space heuristic search planning system that generates stories that track and manipulate characters’ beliefs about the story world. The planner produces story plans with actions that are attempted but fail. We show an example story plan that contains failed-action content that cannot be generated by typical planning-based approaches to story creation, and we provide an analytical evaluation that characterizes our planner’s increased expressive range relative to other narrative planners addressing character belief and/or failed action execution. Copyright © 2022, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Knowledge representation

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Low Complexity Broadband Array Processing in Dynamic Scenarios with Jamming

Low Complexity Broadband Array Processing in Dynamic Scenari...

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IEEE Conference on Vehicular Technology (VTC)

作者： Daniel V. De Lima João Paulo J. Da Costa Ricardo K. Miranda Antonio Arlis S. Da Silva Giovanni A. Santos José Alfredo R. Vargas Auzuir R. De Alexandria Postgraduate Program in Telecommunications Engineering (PPGET) IFCE Fortaleza Brazil Hamm-Lippstadt University of Applied Sciences (HSHL) Lippstadt Germany Graduate School for Applied Research in North Rhine-Westphalia (PKNRW) Bochum Germany Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Graduate Program in Computing (PPGC) UFRGS Porto Alegre Brazil Postgraduate Program in Mechatronics Engineering (PPMEC) UnB Brasilia Brazil

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

Vehicle-to-Everything (V2X) networks require low-latency communications utilizing a broad spectrum while operating under jamming. In this sense, low complexity antenna array-based broadband jamming mitigation schemes are crucial in order to allow low latency communication and low-cost hardware. In this paper we propose low-complexity algorithms for signal recovery in broadband processing scenario applied to V2X. Numerical simulation of a jamming scenario demonstrate the proposed algorithm achieving the same performance in terms of signal-to-interference plus noise ratio (SINR) as the state-of-the-art while taking signiflcantly less time to compute.

关键词： Heuristic algorithms Prevention and mitigation Interference Numerical simulation Broadband communication Broadband antennas Jamming

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Goal-oriented inference of environment from redundant observations

arXiv

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arXiv 2023年

作者： Takahashi, Kazuki Fukai, Tomoki Sakai, Yutaka Takekawa, Takashi Informatics Program Graduate School of Engineering Kogakuin University of Technology and Engineering Japan Neural Coding and Brain Computing Unit Okinawa Institute of Science and Technology Japan Brain Science Institute Tamagawa University Japan

The agent learns to organize decision behavior to achieve a behavioral goal, such as reward maximization, and reinforcement learning is often used for this optimization. Learning an optimal behavioral strategy is difficult under the uncertainty that events necessary for learning are only partially observable, called as Partially Observable Markov Decision Process (POMDP). However, the real-world environment also gives many events irrelevant to reward delivery and an optimal behavioral strategy. The conventional methods in POMDP, which attempt to infer transition rules among the entire observations, including irrelevant states, are ineffective in such an environment. Supposing Redundantly Observable Markov Decision Process (ROMDP), here we propose a method for goal-oriented reinforcement learning to efficiently learn state transition rules among reward-related"core states" from redundant observations. Starting with a small number of initial core states, our model gradually adds new core states to the transition diagram until it achieves an optimal behavioral strategy consistent with the Bellman equation. We demonstrate that the resultant inference model outperforms the conventional method for POMDP. We emphasize that our model only containing the core states has high explainability. Furthermore, the proposed method suits online learning as it suppresses memory consumption and improves learning speed. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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