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检索条件"任意字段=4th International Workshop on Graph Grammars and Their Application to Computer Science, 1990"

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6th Asia-Pacific Web and Web-Age Information Management international Joint Conference on Web and Big Data, APWeb-WAIM 2022: 5th international workshop on Knowledge graph Management and applications, KGMA 2022, 4th international workshop on Semi-structured Big Data Management and applications, SemiBDMA 2022, and 3rd international workshop on Deep Learning in Large-scale Unstructured Data Analytics, DeepLUDA 2022

6th Asia-Pacific Web and Web-Age Information Management Inte...

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6th Asia-Pacific Web and Web-Age Information Management international Joint Conference on Web and Big Data, APWeb-WAIM 2022: 5th international workshop on Knowledge graph Management and applications, KGMA 2022, 4th international workshop on Semi-structured Big Data Management and applications, SemiBDMA 2022, and 3rd international workshop on Deep Learning in Large-scale Unstructured Data Analytics, DeepLUDA 2022

ISBN: (纸本)9789819913534

the proceedings contain 24 papers. the special focus in this conference is on Asia-Pacific Web and Web-Age Information Management international Joint Conference on Web and Big Data. the topics include: Analysis of Public Opinion Evolution in Public Health Emergencies Based on Multi-fusion Model;Failure Simulation Study of Double-Layer Exhaust Manifold for Turbocharged Engine Under LCF & HCF;lung Electrical Impedance Tomography Based on Improved Generative Adversarial Network;snake Robot Motion Planning Based on Improved Depth Deterministic Policy Gradient;Global Path Planning for Multi-objective UAV-Assisted Sensor Data Collection: A DRL Approach;Infrared Image Object Detection of Vehicle and Person Based on Improved YOLOv5;slow Port Scanning Attack Detection Algorithm Based on Dynamic Time Window Mechanism;AU-GAN: Attention U-Net Based on a Built-In Attention for Multi-domain Image-to-Image Translation;multi-robot Online Complete Coverage Based on Collaboration;effectively Filtering Images for Better Multi-modal Knowledge graph;A Long Short-Term Urban Air Quality Prediction Model Based on Spatiotemporal Merged GLU and GCN;colour Coherence Entropy for Ceramic Fragment Analysis;a Fast Method of Legal Decision Recommendation System;an Encryption Key Sharing Method for Contact Tracing application;a Non-contact Interaction Method for 3D Display Devices;accelerating Distributed Knowledge graph System Based on Non-Volatile Memory;towards a Unified Storage Scheme for Dual Data Models of Knowledge graphs;cross-platform Product Matching Based on Knowledge graph;A GCN-Based Sequential Recommendation Model for Heterogeneous Information Networks;SP-LAN: A Stock Prediction Model Based on LSTM-Attention Network;research on Composite Index Construction Method Based on Master-Slave Blockchain Structure;multi-source Heterogeneous Blockchain Data Quality Assessment Model.

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4th international workshop on Algorithmic Bias in Search and Recommendation, BIAS 2023, held as part of the 45th European Conference on Information Retrieval, ECIR 2023

4th International Workshop on Algorithmic Bias in Search and...

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4th international workshop on Algorithmic Bias in Search and Recommendation, BIAS 2023, held as part of the 45th European Conference on Information Retrieval, ECIR 2023

ISBN: (纸本)9783031372483

the proceedings contain 14 papers. the special focus in this conference is on Algorithmic Bias in Search and Recommendation. the topics include: Understanding Search Behavior Bias in Wikipedia;Do You MIND? Reflections on the MIND Dataset for Research on Diversity in News Recommendations;detecting and Measuring Social Bias of Arabic Generative Models in the Context of Search and Recommendation;what Are We Missing in Algorithmic Fairness? Discussing Open Challenges for Fairness Analysis in User Profiling with graph Neural Networks;measuring Bias in Multimodal Models: Multimodal Composite Association Score;evaluating Fairness Metrics;utilizing Implicit Feedback for User Mainstreaminess Evaluation and Bias Detection in Recommender Systems;preserving Utility in Fair Top-k Ranking with Intersectional Bias;mitigating Position Bias in Hotels Recommender Systems;improving Recommender System Diversity with Variational Autoencoders;addressing Biases in the Texts Using an End-to-End Pipeline Approach;bootless application of Greedy Re-ranking Algorithms in Fair Neural Team Formation.

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Research on Job Shop Scheduling Problem Based on Multi-Agent Reinforcement Learning 4

Research on Job Shop Scheduling Problem Based on Multi-Agent...

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4th international Conference on Artificial Intelligence and computer Engineering, ICAICE 2023

作者： Hu, Zhikun Chen, Yimin Li, Hao Deng, Xuegang Yunnan University School of Information Science and Engineering Yunnan Kunming650504 China Yunnan Development and Reform Commission Yunnan Kunming650051 China

ISBN: (纸本)9798400708831

the job shop scheduling problem is an abstraction of the job processing process in the manufacturing workshop, which has important economic value in real life. At present, most of the research on job shop scheduling problems based on reinforcement learning is based on a single agent architecture, which makes the number of interactions required in the solving process directly related to the size of the problem. there are difficulties in training the model on large-scale instances. Propose an end-to-end method based on multi-agent reinforcement learning, treating each machine as an agent and making processing decisions for multiple machines in one interaction, while training problem scale independent strategies. Using disjunctive graphs as a representation of states in problem modeling to model the execution order relationships between operations. In network design, a graph neural network based on induced point attention is further proposed for feature extraction to overcome the problems of high computational complexity and attention degradation. the experiments on the Taillard dataset show that the performance of our model is superior to multiple traditional methods and a recent single agent reinforcement learning method. © 2023 ACM.

关键词： Reinforcement learning

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CompeGe: Computing Company Competitor Pairs by Knowledge Based Inference Combined with Empirical Validation 4

CompeGe: Computing Company Competitor Pairs by Knowledge Bas...

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4th international workshop on the Resurgence of Datalog in Academia and Industry, Datalog-2.0 2022

作者： Gottlob, Georg Guo, Jinsong Jami, Aditya Kröll, Markus Reissfelder, Stéphane Schweizer, Lukas Aichinger, Eric Sferrazza, Stefano Department of Computer Science University of Oxford OxfordOX1 3QD United Kingdom Meltwater San FranciscoCA United States

this paper is an interim report about an industrial application that uses Datalog combined with empirical methods to compute competitor information from a knowledge graph. the Owler knowledge graph is one of the world's largest companies information systems (CIS). It contains data about 16+ million companies crowd-sourced from over 1 million experts. In particular, for most companies, it contains a set of competitors. Such competitor information is very useful for many B2B applications such as lead generation. However, competitor relations in crowd-sourced CIS are naturally incomplete. this paper presents CompeGen, a method that applies Vadalog (a particular variant of Datalog) rules to infer new competitor pairs from existing ones in the Owler CIS. Since using the Vadalog inference program alone is insufficient, CompeGen combines its inference process with a "learning"process to acquire some required logical facts and further validates the inference results via an empirical validation process. CompeGen was tested using the companies belonging to the "Internet Software"sector in Owler. It has discovered 23,180 new competitors of which over 80% were correct. We are improving the system and will report further results in the full paper. © 2022 Copyright for this paper by its authors.

关键词： Knowledge graph

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Find-replace as a service of graph-based DSL tool development framework 9

Find-replace as a service of graph-based DSL tool developmen...

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STAF 2021 workshop: 9th international workshop on Bidirectional Transformations, Joint workshop on Foundations and Practice of Visual Modeling and Data for Model-Driven Engineering, international workshop on MDE for Smart IoT Systems, 4th international workshop on (Meta) Modeling for Healthcare Systems, and 20th international workshop on OCL and Textual Modeling, STAF-WS 2021

作者： Kalnina, Elina Kosarevskis, Dmitrijs Sostaks, Agris Institute of Mathematics and Computer Science University of Latvia Riga Latvia

the tool support of modelling languages including graphical domain-specific languages is not at the level tool users have got used to in other areas. Various features typical in other tools are missing in modelling tools, for example, find, find-replace and others. We discuss principles of concrete syntax-based find-replace for graph-based languages. We show how to add find-replace as a service of graph-based DSL tool development framework. the approach makes find-replace available to any language built with the framework. © 2021 Copyright (c) 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0)

关键词： Modeling languages

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5th Asia-Pacific Web and Web-Age Information Management international Joint Conference on Web and Big Data, APWeb-WAIM 2021, 4th international workshop on Knowledge graph Management and applications, KGMA 2021, 3rd international workshop on Semi-structured Big Data Management and application, SemiBDMA 2021, and 2nd international workshop on Deep Learning in Large-scale Unstructured Data Analytics, DeepLUDA 2021

5th Asia-Pacific Web and Web-Age Information Management Inte...

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5th Asia-Pacific Web and Web-Age Information Management international Joint Conference on Web and Big Data, APWeb-WAIM 2021, 4th international workshop on Knowledge graph Management and applications, KGMA 2021, 3rd international workshop on Semi-structured Big Data Management and application, SemiBDMA 2021, and 2nd international workshop on Deep Learning in Large-scale Unstructured Data Analytics, DeepLUDA 2021

ISBN: (纸本)9789811681424

the proceedings contain 11 papers. the special focus in this conference is on Web and Big Data. the topics include: Survival Effect of Internet Macroscopic Topology Evolution;the Method for Image Noise Detection Based on the Amount of Knowledge Associated with Intuitionistic Fuzzy Sets;link Prediction of Heterogeneous Information Networks Based on Frequent Subgraph Evolution;memory Attentive Cognitive Diagnosis for Student Performance Prediction;A Study on the Privacy threat Analysis of PHI-Code;preface;should I Stay or Should I Go: Predicting Changes in Cluster Membership;logistics Policy Evaluation Model Based on Text Mining;MRRVOS: Modular Refinement Referring Video Object Segmentation.

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TSICNet: Importance of Connectome Information for Epilepsy Classification 4th

TSICNet: Importance of Connectome Information for Epilepsy...

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4th international workshop on Human Brain and Artificial Intelligence, HBAI 2024

作者： Du, Zonghan Lai, Zhongyuan School of Computer Science Sun Yat-sen University Guangzhou510275 China DeepVerse Inc. Shanghai200135 China

ISBN: (纸本)9789819640003

Epilepsy is a chronic brain disease characterized by recurrent seizures. these episodes are usually triggered by abnormal firing of neurons in the brain and appear as brief, recurring episodes. From the point of view of mitigation and treatment, a major problem is timely and accurate classification of epileptic seizure episodes, which is important for rapid intervention and possible prevention of future episodes. In recent years, with the rapid development of artificial intelligence technology, its application in the field of epilepsy diagnosis is increasingly extensive. In particular, electroencephalography (EEG), as a non-invasive neurophysiological examination method, plays an important role in the diagnosis of epilepsy. In particular, it allows the learning of connectomic information from the relative positions of EEG electrodes on the scalp. In this paper, a deep learning model, Temporal-Spatio-Importance Correlation Network (TSICNet), is proposed to classify the onset of epileptic seizures. Our model design combines a variety of neural network components and training methods, such as graph, spatio-temporal and separable convolutional networks, as well as sliding windows data extraction techniques and ATCNet’s time-sensitive attention mechanism, to realize effective recognition and extraction of epilepsy data features. In particular, TSICNet makes heavy use of learned connectomic information to enhance classification accuracy. Experimental results show excellent performance on the CHB-MIT Scalp EEG dataset and HUH neonatal epilpsy dataset, and has good results in accuracy and True (TPR) and False Positive Rates (FPR). Moreover, ablation analysis shows that each part of the model contributes significantly, thus confirming the validity our design principles. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： graph neural networks

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4th international workshop on Predictive Intelligence in Medicine, PRIME 2021, held in conjunction with 24th international Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2021

4th International Workshop on Predictive Intelligence in Med...

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4th international workshop on Predictive Intelligence in Medicine, PRIME 2021, held in conjunction with 24th international Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2021

ISBN: (纸本)9783030876012

the proceedings contain 25 papers. the special focus in this conference is on Predictive Intelligence in Medicine. the topics include: Self Supervised Contrastive Learning on Multiple Breast Modalities Boosts Classification Performance;self-guided Multi-attention Network for Periventricular Leukomalacia Recognition;opportunistic Screening of Osteoporosis Using Plain Film Chest X-Ray;multi-task Deep Segmentation and Radiomics for Automatic Prognosis in Head and Neck Cancer;Integrating Multimodal MRIs for Adult ADHD Identification with Heterogeneous graph Attention Convolutional Network;probabilistic Deep Learning with Adversarial Training and Volume Interval Estimation - Better Ways to Perform and Evaluate Predictive Models for White Matter Hyperintensities Evolution;a Multi-scale Capsule Network for Improving Diagnostic Generalizability in Breast Cancer Diagnosis Using Ultrasonography;prediction of Pathological Complete Response to Neoadjuvant Chemotherapy Using Multi-scale Patch Learning with Mammography;the Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification;a Few-Shot Learning graph Multi-trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint;anatomical Structure-Aware Pulmonary Nodule Detection via Parallel Multi-task RoI Head;towards Cancer Patients Classification Using Liquid Biopsy;foreseeing Survival through ‘Fuzzy Intelligence’: A Cognitively-Inspired Incremental Learning Based de novo Model for Breast Cancer Prognosis by Multi-Omics Data Fusion;improving Across Dataset Brain Age Predictions Using Transfer Learning;uncertainty-Based Dynamic graph Neighborhoods for Medical Segmentation;FLAT-Net: Longitudinal Brain graph Evolution Prediction from a Few Training Representative Templates;one Representative-Shot Learning Using a Population-Driven Template with application to Brain Connectivity Classification and Evolution Prediction;Mixing-AdaSIN: Constructing a De-biased Dataset Using

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One Representative-Shot Learning Using a Population-Driven Template with application to Brain Connectivity Classification and Evolution Prediction 1

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4th international workshop on Predictive Intelligence in Medicine (PRIME)

作者： Guvercin, Umut Gharsallaoui, Mohammed Amine Rekik, Islem Istanbul Tech Univ BASIRA Lab Fac Comp & Informat Istanbul Turkey

ISBN: (数字)9783030876029

ISBN: (纸本)9783030876012;9783030876029

Few-shot learning presents a challenging paradigm for training discriminative models on a few training samples representing the target classes to discriminate. However, classification methods based on deep learning are ill-suited for such learning as they need large amounts of training data -let alone one-shot learning. Recently, graph neural networks (GNNs) have been introduced to the field of network neuroscience, where the brain connectivity is encoded in a graph. However, with scarce neuroimaging datasets particularly for rare diseases and low-resource clinical facilities, such data-devouring architectures might fail in learning the target task. In this paper, we take a very different approach in training GNNs, where we aim to learn with one sample and achieve the best performance -a formidable challenge to tackle. Specifically, we present the first one-shot paradigm where a GNN is trained on a single population-driven template -namely a connectional brain template (CBT). A CBT is a compact representation of a population of brain graphs capturing the unique connectivity patterns shared across individuals. It is analogous to brain image atlases for neuroimaging datasets. Using a one-representative CBT as a training sample, we alleviate the training load of GNN models while boosting their performance across a variety of classification and regression tasks. We demonstrate that our method significantly outperformed benchmark one-shot learning methods with downstream classification and time-dependent brain graph data forecasting tasks while competing with the "train on all" conventional training strategy. Our source code can be found at https://***/ basiralab/one-representative-shot- learning.

关键词： One-shot learning graph neural networks Connectional brain templates Time-dependent graph evolution prediction Brain disorder classification

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4th international workshop on Multimodal Brain Image Analysis, MBAI 2019, and the 7th international workshop on Mathematical Foundations of Computational Anatomy, MFCA 2019, held in conjunction with the 22nd international Conference on Medical Imaging and computer Assisted Intervention, MICCAI 2019

4th International Workshop on Multimodal Brain Image Analysi...

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4th international workshop on Multimodal Brain Image Analysis, MBAI 2019, and the 7th international workshop on Mathematical Foundations of Computational Anatomy, MFCA 2019, held in conjunction with the 22nd international Conference on Medical Imaging and computer Assisted Intervention, MICCAI 2019

ISBN: (纸本)9783030332259

the proceedings contain 23 papers. the special focus in this conference is on Multimodal Brain Image Analysis. the topics include: Biological Knowledge Guided Deep Neural Network for Brain Genotype-Phenotype Association Study;Learning Human Cognition via fMRI Analysis Using 3D CNN and graph Neural Network;CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation;brainPainter: A Software for the Visualisation of Brain Structures, Biomarkers and Associated Pathological Processes;structural Similarity Based Anatomical and Functional Brain Imaging Fusion;multimodal Brain Tumor Segmentation Using Encoder-Decoder with Hierarchical Separable Convolution;prioritizing Amyloid Imaging Biomarkers in Alzheimer’s Disease via Learning to Rank;diffeomorphic Metric Learning and Template Optimization for Registration-Based Predictive Models;3D Mapping of Serial Histology Sections with Anomalies Using a Novel Robust Deformable Registration Algorithm;spatiotemporal Modeling for Image Time Series with Appearance Change: application to Early Brain Development;An Edge Enhanced SRGAN for MRI Super Resolution in Slice-Selection Direction;surface Foliation Based Brain Morphometry Analysis;mixture Probabilistic Principal Geodesic Analysis;a Geodesic Mixed Effects Model in Kendall’s Shape Space;An As-Invariant-As-Possible GL+(3) -Based Statistical Shape Model;exploring Functional Connectivity Biomarker in Autism Using Group-Wise Sparse Representation;classifying Stages of Mild Cognitive Impairment via Augmented graph Embedding;mapping the Spatio-Temporal Functional Coherence in the Resting Brain;Species-Preserved Structural Connections Revealed by Sparse Tensor CCA;identification of Abnormal Cortical 3-Hinge Folding Patterns on Autism Spectral Brains;exploring Brain Hemodynamic Response Patterns via Deep Recurrent Autoencoder;3D Convolutional Long-Short Term Memory Network for Spatiotemporal Modeling of fMRI Data.

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