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Discriminative Graph Convolutional networks for Semi-supervised Node Classification

Discriminative Graph Convolutional Networks for Semi-supervi...

引用

International conference on Tools for Artificial Intelligence (ICTAI)

作者： Guoguo Ai Hui Yan Yuxin Chen School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Graph Convolutional networks (GCNs) gain remarkable success in graph-based semi-supervised node classification task. Despite their success, most GCNs still exist several challenges. For this task, it is necessary to draw nodes in the same class close and push ones from different classes apart. However, GCNs smooth the node's representation by aggregating information within node neighborhoods, despite whether the connected nodes are from the same class or not. the smooth property overlooks the intra-class similarity and inter-class diversity, which leads to GCNs failing especially on low homophilic or heterophilic graphs where most nodes have neighbors from different classes. In this paper, we propose the Discriminative Graph Convolution networks (DGCN), an extension of GCN model with discriminant modules: intra-class smoothness and inter-class sharpness. the modules effectively strengthen the intra-class similarity and the inter-class differences by introducing available label information into the convolution process. Extensive experiments on six benchmark datasets demonstrate the effectiveness of DGCN in semi-supervised node classification. Code available at https://***/AIG22/DGCN.

关键词： Convolutional codes Convolution Benchmark testing Convolutional neural networks Task analysis Artificial intelligence

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Time Series Augmentation with Time-Scale Modifications and Piecewise Aggregate Approximation for Human Action Recognition

Time Series Augmentation with Time-Scale Modifications and P...

引用

International conference on Tools for Artificial Intelligence (ICTAI)

作者： Mariusz Oszust Dawid Warchoł Department of Computer and Control Engineering Rzeszów University of Technology Rzeszów Poland

In this paper, a method for time series augmentation, aiming at the improvement of human action recognition accuracy of a deep learning classifier, is proposed. the approach performs time-scale modifications of the input time series and transforms them into compact sequences of time segments using Piecewise Aggregate Approximation (PAA) to facilitate the training of a neural network. the approach is compared against related methods on six representative datasets using Bidirectional Long Short-Term Memory (BiLSTM) classifier. It is shown that the resulting artificial time series lead to a better performance of the deep learning model than augmented data samples generated by popular approaches. the source code of the method is available at https://***/***.

关键词： Deep learning Training Aggregates Source coding Time series analysis Neural networks Memory management

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A Lipreading Model Based on Fine-Grained Global Synergy of Lip Movement

A Lipreading Model Based on Fine-Grained Global Synergy of L...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Baosheng Sun Dongliang Xie Dawei Luo Xiaojie Yin School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China

Lipreading is a type of speech recognition based on visual information. It is instructive to design a lipreading model according to the lip movement law. Algorithms in the field of computer vision cannot fully satisfy the characteristics of lipreading, and direct use does not necessarily improve the performance of lipreading. In this paper, we propose that lipreading has fine-grained global synergy by comparing other computer vision tasks and analyzing lip muscle motion patterns. To address this feature, we propose a tailored model and name it Fine-Grained Global Synergy Lipreading (FGSLip). Our model aims to make features synergistic to improve lipreading performance. We introduce global features to represent the overall characteristics of the lip, and local features to learn coarse-grained and fine-grained correlations between features. then, diffusion and fusion methods are used to make the local features and global features synergistic. Based on the above, several different feature extraction structures are constructed to demonstrate the fine-grained global synergy of lipreading. To verify the effectiveness of the proposed model, extensive experiments are conducted on the laboratory record dataset ICSLR and the public dataset CMLR, and the experimental results show that the proposed method can effectively improve the accuracy of lipreading.

关键词： computer vision Analytical models Visualization Correlation Lips Computational modeling Speech recognition

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Incorporating the Graph Representation of Video and Text into Video Captioning

Incorporating the Graph Representation of Video and Text int...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Min Lu Yuan Li The Key Laboratory of Smart Airport Theory and System CAAC School of Computer Science and Technology Civil Aviation University of China Tianjin China School of Computer Science and Technology Civil Aviation University of China Tianjin China

Video captioning is to translate the video content into the textual descriptions. In the encoding phase, the existing approaches encode the irrelevant background and uncorrelated visual objects into visual features. that leads to semantic aberration between the visual features and the expected textual caption. In the decoding phase, the word-by-word prediction infers the next word only from the previously generated caption. that local text context is insufficient for word prediction. To tackle the above two issues, the representations of video and text stem from the convolution on two graphs. the convolution on the video graph distills the visual features by filtering the irrelevant background and uncorrelated salient objects. the key issue is to figure out the similar videos according to the video semantic feature. the word graph is constructed to help incorporate global neighborhood among words into word representation. that word global neigh-borhood serves as the global text context and compensates the local text context. Results on two benchmark datasets show the advantage of the proposed method. Experimental analysis is also conducted to verify the effectiveness of the proposed modules.

关键词： Visualization Convolution Filtering Semantics Benchmark testing Feature extraction Encoding

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MFSE: A Meta-Fusion Model for Polypharmacy Side-Effect Prediction with Graph Neural networks

MFSE: A Meta-Fusion Model for Polypharmacy Side-Effect Predi...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Aggelos Ragkousis Olga Flogera Vasileios Megalooikonomou Department of Electrical and Computer Engineering University of Patras Patras Greece Department of Computer Science and Informatics University of Patras Patras Greece

Despite being a very popular approach for treating complex diseases, polypharmacy can lead to increased risk of adverse side effects, many of which are observed after the drugs have been released in the market. Luckily, the significant increase in data availability of observed adverse side-effects has paved the way for machine learning approaches to assist in their prediction. In this work, we first present a novel framework for multi-relational link prediction with graph neural networks. Given a multi-relational graph, we create relation-specific vector representations for each node of the graph. With this approach, we create drug vector representations that are side-effect specific, by integrating external molecular and protein-target information with the drug information that is generated directly from the drug-drug interaction prediction graph. With our new meta-fusion approach, each information type is produced from a distinct G NN - based encoder architecture and then the integration is performed according to the side-effect type being predicted. While state-of-the-art models report maximum AUROC scores of 0.91, our technique reaches a score of 0.95. Also, we show that our fusion approach provides valuable external knowledge particularly to drug nodes in the prediction graph that have a smaller node degree.

关键词： Drugs Proteins Machine learning Artificial neural networks Predictive models Graph neural networks Diseases

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Optimal Scheduling of Distributed Energy Resources Aggregators in Energy and Flexibility Markets

Optimal Scheduling of Distributed Energy Resources Aggregato...

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Australasian Universities Power Engineering conference, AUPEC

作者： Afshin Najafi-Ghalelou Mohsen Khorasany M. Imran Azim Reza Razzaghi Department of Electrical and Computer Systems Engineering Monash University Clayton VIC Australia

ISBN: (数字)9798350377941

ISBN: (纸本)9798350377958

this paper investigates the stochastic scheduling of a Distributed Energy Resources Aggregator participating in wholesale day-ahead energy and flexibility markets. the proposed framework focuses on maximizing the aggregator’s profit while representing entities such as PV-based prosumers, electric vehicles, and large-scale battery within an unbalanced distribution network. It incorporates uncertainties related to PV generation, market prices, and flexibility deployment requests. In addition to profit maximization, the framework manages the distribution network’s voltage and line congestion while meeting the basic energy demand of the players. the aggregator leverages the flexibility of these players to participate in the flexibility electricity market, enabling it to adjust its total power production as requested by the flexibility market operator. the study evaluates two cases: Case 1, where the aggregator participates only in the energy market, and Case 2, where the aggregator joins both energy and flexibility markets. the results show that participating in both markets significantly increases profit, while maintaining compliance with the distribution network’s voltage and thermal limits and meeting participants’ demand requirements.

关键词： Power engineering Uncertainty Stochastic processes Optimal scheduling Distribution networks Voltage Production Electricity supply industry Batteries Distributed power generation

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Robust Decoupling Control Based on Linear Extended State Observers for Grid-Forming Inverter

Robust Decoupling Control Based on Linear Extended State Obs...

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Australasian Universities Power Engineering conference, AUPEC

作者： Yue Qu Hui Li Feifei Bai Junwei Lu Fuwen Yang School of Electrical Engineering and Computer Science the University of Queensland Brisbane Australia College of Automation Engineering Shanghai University of Electric Power Shanghai China School of Engineering and Built Environment Griffith University Brisbane Australia

ISBN: (数字)9798350377941

ISBN: (纸本)9798350377958

Grid-forming inverters (GFMIs) based on virtual synchronous generators (VSGs) have emerged as a promising solution for providing inertia and enhancing renewable system stability. However, the inherent power coupling caused by the low reactance-to-resistance ratio in low-voltage networks negatively impacts the control performance of VSGs. Commonly used feedforward decoupling methods rely heavily on system parameters. Although these methods can effectively reduce power coupling under nominal systems, their decoupling capability is significantly limited when dynamic variations occur in the system. therefore, a robust decoupling control strategy based on linear extended state observers (LESOs) is proposed in this paper. the proposed method achieves faster and more efficient decoupling and demonstrates strong robustness in the non-nominal system. Finally, the effectiveness and decoupling ability of the proposed strategy are verified by simulation results.

关键词： Couplings Renewable energy sources Power engineering Simulation Power system stability Synchronous generators Robustness Stability analysis Feedforward systems Grid forming

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Automated construction of an Object-Oriented Bayesian Network (OOBN) Class Hierarchy

Automated construction of an Object-Oriented Bayesian Networ...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Md Samiullah Ann Nicholson David Albrecht Department of Computer Science & Engineering University of Dhaka Dhaka Bangladesh Faculty of Information Technology Monash University Clayton Victoria Australia

Bayesian networks (BNs) are a widely used probabilistic modelling tool for reasoning under uncertainty, though scaling them up for complex real-world problems can be challenging. Object-Oriented Bayesian networks (OOBNs) have been proposed to address this challenge, providing modellers with the ability to define hierarchies of classes and use these classes to construct models with a compositional and hierarchical structure, enabling reuse and supporting maintenance. the object-oriented concept of inheritance supports reuse of existing components, but comes with the challenge of building, and then maintaining, an efficient hierarchy of classes. this paper proposes a supergraph based method that constructs class inheritance hierarchies from a set of OOBN classes; this can be used either to form an initial inheritance hierarchy, or to reform an existing hierarchy into a more efficient one. We also present heuristics to convert a BN to an OOBN class, measures to evaluate a constructed hierarchy and empirical analyses of the proposed approach on synthetic hierarchies and on a real-world OOBN project; results show the algorithm works well in practice.

关键词： Uncertainty Object oriented modeling Heuristic algorithms Buildings Maintenance engineering Probabilistic logic Cognition

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Privacy-preserving Cost-sensitive Federated Learning from Imbalanced Data

Privacy-preserving Cost-sensitive Federated Learning from Im...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Xiaowei Liu Yuanzhi Yao Yuting Ma Nenghai Yu School of Information Science and Technology University of Science and Technology of China Hefei China School of Computer and Information Hefei University of Technology Hefei China

Federated learning allows multiple clients to collab-oratively train a global deep learning model without revealing their local data to a centralized server. However, the existence of clients whose datasets have imbalanced class distribution has a significant impact on model accuracy. Imbalance makes it challenging for a model to distinguish between the majority and minority classes without accessing clients' local data. In this paper, we aim to tackle this problem by privacy-preserving cost-sensitive federated learning. We design the joint cost-sensitive and differentially private model parameter optimization mechanism which maintains the model accuracy while satisfying differential privacy constraints. Moreover, this mechanism does not alter the original data distribution. Experimental results demonstrate the superior performance of our proposed scheme in terms of model accuracy and privacy preservation.

关键词： Deep learning Degradation Privacy Differential privacy Federated learning Learning (artificial intelligence) Data models

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Video Face Recognition Using Neural Aggregation networks with Mutual Relational Learning

Video Face Recognition Using Neural Aggregation Networks wit...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Kangli Zeng Zhongyuan Wang Tao Lu Jianyu Chen NERCMS School of Computer Science Wuhan University School of Computer Science and Engineering Wuhan Institute of Technology

Video face recognition benefits profoundly from deep convolutional neural networks (CNNs), which learn robust feature embeddings. However, due to their fixed geometric structures, CNNs are inherently limited in modeling the significant variations from the angle, pose, occlusion and other factors of face images. In this paper, a neural aggregation network based on mutual relation learning is proposed for video face recognition. First, Intra-frame Relational Learning network (Intra-Net) is introduced, which models the interdependencies between the re-gional components of individual features and develops relevance between fine-grained features. Such processing can determine the region of interest adaptively according to the quality of the input face image to achieve the extraction of valuable information. Secondly, we introduce Inter-frame Relational Learning Network (Inter-Net), which considers the most significant appearance representation in the overall structure of the face image to cor-relate the complementarity of features between frames. Finally, information aggregation is performed by combining Inter-Net and Intra-Net. Joint optimization of the two branches allows our model to effectively exploit the complementary information between them to improve the aggregation capability. We validate the effectiveness of our model for video face recognition, proving its superiority over state-of-the-art methods on two benchmark datasets.

关键词： Visualization Face recognition Surveillance Neural networks Learning (artificial intelligence) Benchmark testing Feature extraction

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