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19th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2018

作者： Laddha, Abhishek Mukherjee, Arjun Indian Institute of Technology Delhi New Delhi110016 India Department of Computer Science University of Houston HoustonTX United States

ISBN: (纸本)9783031238031

Opinion phrase extraction is one of the key tasks in fine-grained sentiment analysis. While opinion expressions could be generic subjective expressions, aspect specific opinion expressions contain both the aspect as well as the opinion expression within the original sentence context. In this work, we formulate the task as an instance of token-level sequence labeling. When multiple aspects are present in a sentence, detection of opinion phrase boundary becomes difficult and label of each word depend not only upon the surrounding words but also with the concerned aspect. We propose a neural network architecture with bidirectional LSTM (Bi-LSTM) and a novel attention mechanism. Bi-LSTM layer learns the various sequential pattern among the words without requiring any hand-crafted features. The attention mechanism captures the importance of context words on a particular aspect opinion expression when multiple aspects are present in a sentence via location and content based memory. A Conditional Random Field (CRF) model is incorporated in the final layer to explicitly model the dependencies among the output labels. Experimental results on Hotel dataset from *** showed that our approach outperformed several state-of-the-art baselines. © 2023, Springer Nature Switzerland AG.

关键词： Sentiment analysis

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lecture notes in computer science (including subseries lecture notes in Artificial Intelligence and lecture notes in Bioinformatics): Preface

Lecture Notes in Computer Science (including subseries Lectu...

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lecture notes in computer science (including subseries lecture notes in Artificial Intelligence and lecture notes in Bioinformatics) 2014年 8441 LNCS卷 V-VI页

作者： Nguyen, Phong Q. Oswald, Elisabeth Ecole Normale Supérieure Départment d'Informatique 45 rue d'Ulm 75230 Paris Cedex 05 France University of Bristol Department of Computer Science Merchant Venturers Building Woodland Road Bristol BS8 1UB United Kingdom

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Spectral Transforms for Caustic Denoising: A Comparative Analysis for Monte Carlo Rendering 41st

Spectral Transforms for Caustic Denoising: A Comparative An...

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41st computer Graphics International Conference, CGI 2024

作者： Napoli, Kevin Bugeja, Keith Spina, Sandro Magro, Mark CGVG Department of Computer Science FICT University of Malta Msida Malta

ISBN: (纸本)9783031818059

This paper investigates the effectiveness of spectral transforms in denoising caustics in physically based Monte Carlo rendering, where preserving high-frequency details such as contours within caustics is crucial. We approach the challenge posed by noise and the complexity of caustics in rendering by integrating various search techniques, image similarity metrics, thresholding functions and spectral transforms to optimise thresholding coefficients. Our comparative analysis shows that spectral methods can outperform denoisers like ReBLUR and ReLAX in certain scenarios by preserving details within caustic patterns more effectively. However, AI-based denoisers generally deliver better overall noise reduction, albeit at the cost of losing some image details. While spectral transforms currently require offline processing and reference images to fine-tune denoising coefficients, initial results show promising potential for applying these coefficients to different scenes with similar characteristics, enhancing applicability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Intelligent systems

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3rd International Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

3rd International Workshop on Applications of Medical Artifi...

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3rd International Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031820069

The proceedings contain 24 papers. The special focus in this conference is on Applications of Medical Artificial Intelligence. The topics include: SP-NAS: Surgical Phase Recognition-Based Navigation Adjustment System for Distal Gastrectomy;transforming Multimodal Models into Action Models for Radiotherapy;enhanced Interpretability in Histopathological Images via Combined Tissue and Cell-Level Graph Analysis;targeted Visual Prompting for Medical Visual Question Answering;deep Learning for Resolving 3D Microstructural Changes in the Fibrotic Liver;predicting Falls Through Muscle Weakness from a Single Whole Body Image: A Multimodal Contrastive Learning Framework;Optimizing ICU Readmission Prediction: A Comparative Evaluation of AI Tools;source Matters: Source Dataset Impact on Model Robustness in Medical Imaging;evaluating Perceived Workload, Usability and Usefulness of Artificial Intelligence Systems in Low-Resource Settings: Semi-automated Classification and Detection of Community Acquired Pneumonia;incremental Augmentation Strategies for Personalised Continual Learning in Digital Pathology Contexts;assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears;automated Feedback System for Surgical Skill Improvement in Endoscopic Sinus Surgery;quantifying Knee Cartilage Shape and Lesion: From Image to Metrics;RadImageGAN – A Multi-modal Dataset-Scale Generative AI for Medical Imaging;Ensemble-KAN: Leveraging Kolmogorov Arnold Networks to Discriminate Individuals with Psychiatric Disorders from Controls;SCIsegV2: A Universal Tool for Segmentation of Intramedullary Lesions in Spinal Cord Injury;EHRmonize: A Framework for Medical Concept Abstraction from Electronic Health Records using Large Language Models;evaluating the Impact of Pulse Oximetry Bias in Machine Learning Under Counterfactual Thinking;normative Modeling with Focal Loss and Adversarial Autoencoders for Alzheimer’s Disease Diagnosis and Biomarker Identification;one-Shot Medical

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Re-designing the Interaction of Day-to-Day Applications to Support Sustained Attention Level 25th

Re-designing the Interaction of Day-to-Day Applications to S...

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25th International Conference on Human-computer Interaction, HCII 2023

作者： Hacioglu, Naile Leva, Maria Chiara Lee, Hyowon School of Computing Dublin City University Glasnevin Dublin 9 Ireland School of Food Science and Environmental Health Technological University Dublin Grangegorman Dublin Ireland Insight Centre for Data Analytics Dublin City University Glasnevin Dublin 9 Ireland

ISBN: (纸本)9783031480379

Digital distractions in decreasing people’s attentional abilities have become a subject of increasing concern and scrutiny in recent years. Based on the existing literature regarding the negative impact of technology on attention, this paper examines various solutions, encompassing mostly reactive strategies. It also questions the prevailing design practices prioritising learnability and efficiency and proposes a shift towards designing interactions that minimise distractions and promote sustained attention while emphasising the negative consequences of distraction-prone interactions on users’ attentional resources. As a proactive solution, we present a novel interaction strategy called "Attention mode," which selectively reduces distractions when using apps. We developed three prototypes that consistently incorporate this strategy, each used in a different application on a different device: a news website on a desktop, an e-book reading app on a tablet, and a video-watching app on a smartphone. We conducted usability testing with 13 participants to evaluate this design feature and address the implications of these in the interaction design knowledge and practice today. By advocating for considering users’ attentional abilities in design, this work contributes to the field of Human-computer Interaction (HCI) and calls for a balance between cognitive well-being and traditional usability criteria. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Human computer interaction

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lecture notes in computer science (including subseries lecture notes in Artificial Intelligence and lecture notes in Bioinformatics): Preface

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lecture notes in computer science (including subseries lecture notes in Artificial Intelligence and lecture notes in Bioinformatics) 2014年 8771 LNCS卷 VI页

作者： Pereira, Fernando Magno Quintão Federal University of Minas Gerais Av. Antônio Carlos Belo Horizonte Minas Gerais Brazil

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CHSR: Cross-view Learning from Heterogeneous Graph for Session-Based Recommendation 28th

CHSR: Cross-view Learning from Heterogeneous Graph for Ses...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Wang, Junchen Duan, Lei Ma, Runze Zhang, Yidan Luo, Zhaohang School of Computer Science Sichuan University Chengdu China Nuclear Power Institute of China Chengdu China

ISBN: (纸本)9783031306716

Session-based recommendation (SBR) aims to predict the next item based on short behavior sequences for anonymous users. Most of the current SBR methods consider the scenario that a session just consists of a series of items. However, the multiple item attributes can also reflect user behaviors and provide information for recommendation. In other words, a session in the real world should consist of items and multiple item attributes, which means that the session is heterogeneous. In this paper, we propose a novel method for the anonymous recommendation with heterogeneous item attributes, named CHSR. Firstly, we construct homogeneous session graph and heterogeneous global graph for heterogeneous sessions to map the relationships among different item attributes. Secondly, homo-view and hetero-view of these two kinds of graph encoders are proposed to capture both intra and inter patterns of heterogeneous sessions. Thirdly, a cross-view fusion strategy with consistency loss is introduced to integrate the heterogeneous attribute information by fusing the representations from the two-type views. Finally, the interest preference of anonymous users is represented from the above steps. Extensive experiments conducted on three large-scale real-world datasets demonstrate the superior performance of CHSR over the state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Behavioral research

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Movie Recommendation Using Web Crawling 2nd

Movie Recommendation Using Web Crawling

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2nd International Conference on Applied Algorithms, ICAA 2025

作者： Raj, Pronit Kumar, Chandrashekhar Shekhar, Harshit Kumar, Amit Paul, Kritibas Jana, Debasish Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India

ISBN: (纸本)9783031845420

In today’s digital world, streaming platforms offer a vast array of movies, making it hard for users to find content matching their preferences. This paper explores integrating real-time data from popular movie websites using advanced HTML scraping techniques and APIs. It also incorporates a recommendation system trained on a static Kaggle dataset, enhancing the relevance and freshness of suggestions. By combining content-based filtering, collaborative filtering, and a hybrid model, we create a system that utilizes both historical and real-time data for more personalized suggestions. Our methodology shows that incorporating dynamic data not only boosts user satisfaction but also aligns recommendations with current viewing trends. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Data reduction

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Complexity-Driven Sampling for Bagging 24th

Complexity-Driven Sampling for Bagging

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24th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2023

作者： Lancho, Carmen C. P. de Souto, Marcilio Lorena, Ana C. Martín de Diego, Isaac Data Science Laboratory Rey Juan Carlos University C/ Tulipán s/n Móstoles28933 Spain Fundamental Computer Science Laboratory University of Orléans Léonard de Vinci B.P. 6759 Orleans Cedex 2F-45067 France Aeronautics Institute of Technology Praça Marechal Eduardo Gomes 50 São José dos Campos São Paulo12228-900 Brazil

ISBN: (纸本)9783031482311

Ensemble learning consists of combining the prediction of different learners to obtain a final output. One key step for their success is the diversity among the learners. In this paper, we propose to reach the diversity in terms of the classification complexity by guiding the sampling of instances in the Bagging algorithm with complexity measures. The proposed Complexity-driven Bagging algorithm complements the classic Bagging algorithm by considering training samples of different complexity to cover the complexity space. Besides, the algorithm admits any complexity measure to guide the sampling. The proposal is tested in 28 real datasets and for a total of 9 complexity measures, providing satisfactory and promising results and revealing that training with samples of different complexity, ranging from easy to hard samples, is the best strategy when sampling based on complexity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Computational complexity

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Melanoma Detection Using Convolutional Neural Network 1

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Meenakshi, N. Manika, S. Hariharan, M. Madhavan, S. Department of Computational Intelligence SRM Institute of Science and Technology Chennai Kattankulathur India Department of Information Technology Hindustan Institute of Technology and Science Chennai India

ISBN: (数字)9789819765812

ISBN: (纸本)9789819765805

Melanoma is a fatal form of skin cancer that needs to be detected as early as possible for life-saving and better survival. The existing method is the manual examination which might lack accuracy or lead to misdiagnosis with respect to the training and experience of the dermatologist, who detects if it is melanoma or other skin disease using dermoscopy, will be very time-consuming while the patient goes through much suffering hence we are pushed to the scope of computer-assisted technique where in deep learning technology has a major role nowadays. This paper will evolve around the hybrid combination of deep learning techniques such as Convolutional neural network, encoder-decoder network, and Recurrent Neural network for finding the best accuracy and sensitivity of the lesion from the datasets taken from the ISIC archive (International Skin Imaging Collaboration). The images are pre-processed, and segmented using pixel-based—ANN and fuzzy c-means clustering, Features are extracted, and finally the results are classified in a relatively faster time to help doctors treat the patient more efficiently. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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