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Literatura académica sobre el tema "Graph-based input representation"

Autor: Grafiati

Publicado: 16 de noviembre de 2024

Última modificación: 19 de julio de 2025

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Artículos de revistas sobre el tema "Graph-based input representation"

Lu, Fangbo, Zhihao Zhang, and Changsheng Shui. "Online trajectory anomaly detection model based on graph neural networks and variational autoencoder." Journal of Physics: Conference Series 2816, no. 1 (2024): 012006. http://dx.doi.org/10.1088/1742-6596/2816/1/012006.

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Abstract To efficiently determine whether an entire trajectory exhibits abnormal behavior, we introduce an online trajectory anomaly detection model known as GeoGNFTOD, which employs graph neural networks for road segment representation, creating a directed graph by mapping trajectories onto the road network. The graph representation is constructed based on the road segments in this directed graph. By utilizing Transformer sequence encoding, the trajectory representation is derived and hierarchical geographic encoding captures the GPS mapping of the original trajectories. Merging these two rep

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Aljanabi, Ehssan, and İlker Türker. "Connectogram-COH: A Coherence-Based Time-Graph Representation for EEG-Based Alzheimer’s Disease Detection." Diagnostics 15, no. 11 (2025): 1441. https://doi.org/10.3390/diagnostics15111441.

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Background: Alzheimer’s disease (AD) is a neurological disorder that affects the brain in the elderly, resulting in memory loss, mental deterioration, and loss of the ability to think and act, while being a cause of death, with its rates increasing dramatically. A popular method to detect AD is electroencephalography (EEG) signal analysis thanks to its ability to reflect neural activity, which helps to identify abnormalities associated with the disorder. Originating from its multivariate nature, EEG signals are generally handled as multidimensional time series, and the related methodology is e

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Yu, Xingtong, Zemin Liu, Yuan Fang, and Xinming Zhang. "Learning to Count Isomorphisms with Graph Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4845–53. http://dx.doi.org/10.1609/aaai.v37i4.25610.

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Subgraph isomorphism counting is an important problem on graphs, as many graph-based tasks exploit recurring subgraph patterns. Classical methods usually boil down to a backtracking framework that needs to navigate a huge search space with prohibitive computational cost. Some recent studies resort to graph neural networks (GNNs) to learn a low-dimensional representation for both the query and input graphs, in order to predict the number of subgraph isomorphisms on the input graph. However, typical GNNs employ a node-centric message passing scheme that receives and aggregates messages on nodes,

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Bauer, Daniel. "Understanding Descriptions of Visual Scenes Using Graph Grammars." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 1656–57. http://dx.doi.org/10.1609/aaai.v27i1.8498.

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Automatic generation of 3D scenes from descriptions has applications in communication, education, and entertainment, but requires deep understanding of the input text. I propose thesis work on language understanding using graph-based meaning representations that can be decomposed into primitive spatial relations. The techniques used for analyzing text and transforming it into a scene representation are based on context-free graph grammars. The thesis develops methods for semantic parsing with graphs, acquisition of graph grammars, and satisfaction of spatial and world-knowledge constraints dur

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Wu, Xinyue, and Huilin Chen. "Augmented Feature Diffusion on Sparsely Sampled Subgraph." Electronics 13, no. 16 (2024): 3249. http://dx.doi.org/10.3390/electronics13163249.

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Link prediction is a fundamental problem in graphs. Currently, SubGraph Representation Learning (SGRL) methods provide state-of-the-art solutions for link prediction by transforming the task into a graph classification problem. However, existing SGRL solutions suffer from high computational costs and lack scalability. In this paper, we propose a novel SGRL framework called Augmented Feature Diffusion on Sparsely Sampled Subgraph (AFD3S). The AFD3S first uses a conditional variational autoencoder to augment the local features of the input graph, effectively improving the expressive ability of d

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Cooray, Thilini, and Ngai-Man Cheung. "Graph-Wise Common Latent Factor Extraction for Unsupervised Graph Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6420–28. http://dx.doi.org/10.1609/aaai.v36i6.20593.

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Unsupervised graph-level representation learning plays a crucial role in a variety of tasks such as molecular property prediction and community analysis, especially when data annotation is expensive. Currently, most of the best-performing graph embedding methods are based on Infomax principle. The performance of these methods highly depends on the selection of negative samples and hurt the performance, if the samples were not carefully selected. Inter-graph similarity-based methods also suffer if the selected set of graphs for similarity matching is low in quality. To address this, we focus on

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Gildea, Daniel, Giorgio Satta, and Xiaochang Peng. "Ordered Tree Decomposition for HRG Rule Extraction." Computational Linguistics 45, no. 2 (2019): 339–79. http://dx.doi.org/10.1162/coli_a_00350.

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We present algorithms for extracting Hyperedge Replacement Grammar (HRG) rules from a graph along with a vertex order. Our algorithms are based on finding a tree decomposition of smallest width, relative to the vertex order, and then extracting one rule for each node in this structure. The assumption of a fixed order for the vertices of the input graph makes it possible to solve the problem in polynomial time, in contrast to the fact that the problem of finding optimal tree decompositions for a graph is NP-hard. We also present polynomial-time algorithms for parsing based on our HRGs, where th

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Miao, Fengyu, Xiuzhuang Zhou, Shungen Xiao, and Shiliang Zhang. "A Graph Similarity Algorithm Based on Graph Partitioning and Attention Mechanism." Electronics 13, no. 19 (2024): 3794. http://dx.doi.org/10.3390/electronics13193794.

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In recent years, graph similarity algorithms have been extensively developed based on neural networks. However, with an increase in the node count in graphs, these models either suffer from a reduced representation ability or face a significant increase in the computational cost. To address this issue, a graph similarity algorithm based on graph partitioning and attention mechanisms was proposed in this study. Our method first divided each input graph into the subgraphs to directly extract the local structural features. The residual graph convolution and multihead self-attention mechanisms wer

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Zhang, Dong, Suzhong Wei, Shoushan Li, Hanqian Wu, Qiaoming Zhu, and Guodong Zhou. "Multi-modal Graph Fusion for Named Entity Recognition with Targeted Visual Guidance." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (2021): 14347–55. http://dx.doi.org/10.1609/aaai.v35i16.17687.

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Multi-modal named entity recognition (MNER) aims to discover named entities in free text and classify them into pre-defined types with images. However, dominant MNER models do not fully exploit fine-grained semantic correspondences between semantic units of different modalities, which have the potential to refine multi-modal representation learning. To deal with this issue, we propose a unified multi-modal graph fusion (UMGF) approach for MNER. Specifically, we first represent the input sentence and image using a unified multi-modal graph, which captures various semantic relationships between

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Coşkun, Kemal Çağlar, Muhammad Hassan, and Rolf Drechsler. "Equivalence Checking of System-Level and SPICE-Level Models of Linear Circuits." Chips 1, no. 1 (2022): 54–71. http://dx.doi.org/10.3390/chips1010006.

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Due to the increasing complexity of analog circuits and their integration into System-on-Chips (SoC), the analog design and verification industry would greatly benefit from an expansion of system-level methodologies using SystemC AMS. These can provide a speed increase of over 100,000× in comparison to SPICE-level simulations and allow interoperability with digital tools at the system-level. However, a key barrier to the expansion of system-level tools for analog circuits is the lack of confidence in system-level models implemented in SystemC AMS. Functional equivalence of single Laplace Trans

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Tesis sobre el tema "Graph-based input representation"

Agarwal, Navneet. "Autοmated depressiοn level estimatiοn : a study οn discοurse structure, input representatiοn and clinical reliability". Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMC215.

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Compte tenu de l'impact sévère et généralisé de la dépression, des initiatives de recherche significatives ont été entreprises pour définir des systèmes d'évaluation automatisée de la dépression. La recherche présentée dans cette thèse tourne autour des questions suivantes qui restent relativement inexplorées malgré leur pertinence dans le domaine de l'évaluation automatisée de la dépression : (1) le rôle de la structure du discours dans l'analyse de la santé mentale, (2) la pertinence de la représentation de l'entrée pour les capacités prédictives des modèles de réseaux neuronaux, et (3) l'im

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Capítulos de libros sobre el tema "Graph-based input representation"

Jagan, Balaji, Ranjani Parthasarathi, and Geetha T. V. "Graph-Based Abstractive Summarization." In Innovations, Developments, and Applications of Semantic Web and Information Systems. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5042-6.ch009.

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Customization of information from web documents is an immense job that involves mainly the shortening of original texts. Extractive methods use surface level and statistical features for the selection of important sentences. In contrast, abstractive methods need a formal semantic representation, where the selection of important components and the rephrasing of the selected components are carried out using the semantic features associated with the words as well as the context. In this paper, we propose a semi-supervised bootstrapping approach for the identification of important components for abstractive summarization. The input to the proposed approach is a fully connected semantic graph of a document, where the semantic graphs are constructed for sentences, which are then connected by synonym concepts and co-referring entities to form a complete semantic graph. The direction of the traversal of nodes is determined by a modified spreading activation algorithm, where the importance of the nodes and edges are decided, based on the node and its connected edges under consideration.

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Kumar, P. Krishna, and Harish G. Ramaswamy. "Graph Classification with GNNs: Optimisation, Representation & Inductive Bias." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240726.

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Theoretical studies on the representation power of GNNs have been centered around understanding the equivalence of GNNs, using WL-Tests for detecting graph isomorphism. In this paper, we argue that such equivalence ignores the accompanying optimization issues and does not provide a holistic view of the GNN learning process. We illustrate these gaps between representation and optimization with examples and experiments. We also explore the existence of an implicit inductive bias (e.g. fully connected networks prefer to learn low frequency functions in their input space) in GNNs, in the context of graph classification tasks. We further prove theoretically that the message-passing layers in the graph, have a tendency to search for either discriminative subgraphs, or a collection of discriminative nodes dispersed across the graph, depending on the different global pooling layers used. We empirically verify this bias through experiments over real-world and synthetic datasets. Finally, we show how our work can help in incorporating domain knowledge via attention based architectures, and can evince their capability to discriminate coherent subgraphs.

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Murakami Masaki. "On Congruence Property of Scope Equivalence for Concurrent Programs with Higher-Order Communication." In Concurrent Systems Engineering Series. IOS Press, 2009. https://doi.org/10.3233/978-1-60750-065-0-49.

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Representation of scopes of names is important for analysis and verification of concurrent systems. However, it is difficult to represent the scopes of channel names precisely with models based on process algebra.We introduced a model of concurrent systems with higher-order communication based on graph rewriting in our previous work. A bipartite directed acyclic graph represents a concurrent system that consists of a number of processes and messages in that model. The model can represent the scopes of local names precisely. We defined an equivalence relation such that two systems are equivalent not only in their behavior, but also in extrusion of scopes of names. This paper shows that our equivalence relation is a congruence relation w.r.t. &tau;-prefix, new-name, replication and composition, even when higher-order communication is allowed. We also show our equivalence relation is not congruent w.r.t. input-prefix though it is congruent w.r.t. input-prefix in the first-order case.

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Meng, Yunlei, and Rui Dai. "Knowledge Graph-Powered Question Answering System with Random Forest-Assisted Diagnosis for Elderly Healthcare." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. https://doi.org/10.3233/faia241417.

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As a knowledge representation tool, knowledge graph (KG) has been widely used. In this study, a question answering (Q&A) system for geriatric diseases based on knowledge graph was constructed to help the elderly obtain medical information. Initially, a total of 6,376 disease data items were collected and analyzed in order to identify the characteristics of these diseases. Then, the KG is constructed by Neo4j graph database. The establishment of Q&A system starts from semantic recognition. The Aho-Corasick (AC) automaton is utilized to filter user input questions. The Cypher language is employed for querying graph databases, and the obtained results are then imported into predefined templates for output. The accuracy of our system for different categories of questions is 87% and 94%, respectively. Finally, the random forest model is introduced to solve the problem of disease diagnosis. The feature variables were vectorized using TF-IDF model and the target variables were vectorized using one-hot model. In general, we introduce a novel Knowledge graph-driven Q&A system. Provide a new tool for health management of the elderly population. And the construction of Q&A system will promote the development of smart medicine and solves the health confusion of the elderly.

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Toropov, Andrey A., Alla P. Toropova, Emilio Benfenati, et al. "QSPR/QSAR Analyses by Means of the CORAL Software." In Pharmaceutical Sciences. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1762-7.ch036.

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In this chapter, the methodology of building up quantitative structure—property/activity relationships (QSPRs/QSARs)—by means of the CORAL software is described. The Monte Carlo method is the basis of this approach. Simplified Molecular Input-Line Entry System (SMILES) is used as the representation of the molecular structure. The conversion of SMILES into the molecular graph is available for QSPR/QSAR analysis using the CORAL software. The model for an endpoint is a mathematical function of the correlation weights for various features of the molecular structure. Hybrid models that are based on features extracted from both SMILES and a graph also can be built up by the CORAL software. The conceptually new ideas collected and revealed through the CORAL software are: (1) any QSPR/QSAR model is a random event; and (2) optimal descriptor can be a translator of eclectic information into an endpoint prediction.

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Toropov, Andrey A., Alla P. Toropova, Emilio Benfenati, et al. "QSPR/QSAR Analyses by Means of the CORAL Software." In Quantitative Structure-Activity Relationships in Drug Design, Predictive Toxicology, and Risk Assessment. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8136-1.ch015.

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Zhang, Taolin, Dongyang Li, Qizhou Chen, et al. "R4: Reinforced Retriever-Reorder-Responder for Retrieval-Augmented Large Language Models." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240755.

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Retrieval-augmented large language models (LLMs) leverage relevant content retrieved by information retrieval systems to generate correct responses, aiming to alleviate the hallucination problem. However, existing retriever-responder methods typically append relevant documents to the prompt of LLMs to perform text generation tasks without considering the interaction of fine-grained structural semantics between the retrieved documents and the LLMs. This issue is particularly important for accurate response generation as LLMs tend to “lose in the middle” when dealing with input prompts augmented with lengthy documents. In this work, we propose a new pipeline named “Reinforced Retriever-Reorder-Responder” (R4) to learn document orderings for retrieval-augmented LLMs, thereby further enhancing their generation abilities while the large numbers of parameters of LLMs remain frozen. The reordering learning process is divided into two steps according to the quality of the generated responses: document order adjustment and document representation enhancement. Specifically, document order adjustment aims to organize retrieved document orderings into beginning, middle, and end positions based on graph attention learning, which maximizes the reinforced reward of response quality. Document representation enhancement further refines the representations of retrieved documents for responses of poor quality via document-level gradient adversarial learning. Extensive experiments demonstrate that our proposed pipeline achieves better factual question-answering performance on knowledge-intensive tasks compared to strong baselines across various public datasets. The source codes and trained models will be released upon paper acceptance.

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Tran, Phuc, and Marina Tropmann-Frick. "Global Contextualized Representations: Enhancing Machine Reading Comprehension with Graph Neural Networks." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2025. https://doi.org/10.3233/faia241571.

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This paper introduces Global Contextualized Representations (GCoRe) – an extension for existing transformer-based language models. GCoRe addresses limitations in capturing global context and long-range dependencies by utilizing Graph Neural Networks for graph inference on a context graph constructed from the input text. Global contextualized features, derived from the context graph, are added to the token representations from the base language model. Experiment results show that GCoRe improves the performance of the baseline model (DeBERTa v3) by 0.57% on the HotpotQA dataset and by 0.15% on the SQuAD v2 dataset. In addition, GCoRe is able to answer questions that require logical reasoning and multi-hop inference, while the baseline model fails to provide correct answers.

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Yang, Zixuan, Xiao Wang, Yanhua Yu, et al. "Hop-based Heterogeneous Graph Transformer." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240759.

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The Graph Transformer (GT) has shown significant ability in processing graph-structured data, addressing limitations in graph neural networks, such as over-smoothing and over-squashing. However, the implementation of GT in real-world heterogeneous graphs (HGs) with complex topology continues to present numerous challenges. Firstly, a challenge arises in designing a tokenizer that is compatible with heterogeneity. Secondly, the complexity of the transformer hampers the acquisition of high-order neighbor information in HGs. In this paper, we propose a novel Hop-based Heterogeneous Graph Transformer (H2Gormer) framework, paving a promising path for HGs to benefit from the capabilities of Transformers. We propose a Heterogeneous Hop-based Token Generation module to obtain high-order information in a flexible way. Specifically, to enrich the fine-grained heterogeneous semantics of each token, we propose a tailored multi-relational encoder to encode the hop-based neighbors. In this way, the resulting token embeddings are input to the Hop-based Transformer to obtain node representations, which are then combined with position embeddings to obtain the final encoding. Extensive experiments on four datasets are conducted to demonstrate the effectiveness of H2Gormer.

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Omerovic, Aida, Amela Karahasanovic, and Ketil Stølen. "Uncertainty Handling in Weighted Dependency Trees." In Dependability and Computer Engineering. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-60960-747-0.ch016.

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Weighted dependency trees (WDTs) are used in a multitude of approaches to system analysis, such as fault tree analysis or event tree analysis. In fact, any acyclic graph can be transformed to a WDT. Important decisions are often based on WDT analysis. Common for all WDT-based approaches is the inherent uncertainty due to lack or inaccuracy of the input data. In order to indicate credibility of such WDT analysis, uncertainty handling is essential. There is however, to our knowledge, no comprehensive evaluation of the uncertainty handling approaches in the context of the WDTs. This chapter aims to rectify this. We concentrate on approaches applicable for epistemic uncertainty related to empirical input. The existing and the potentially useful approaches are identified through a systematic literature review. The approaches are then outlined and evaluated at a high-level, before a restricted set undergoes a more detailed evaluation based on a set of pre-defined evaluation criteria. We argue that the epistemic uncertainty is better suited for possibilistic uncertainty representations than the probabilistic ones. The results indicate that precision, expressiveness, predictive accuracy, scalability on real-life systems, and comprehensibility are among the properties which differentiate the approaches. The selection of a preferred approach should depend on the degree of need for certain properties relative to others, given the context. The right trade off is particularly important when the input is based on both expert judgments and measurements. The chapter may serve as a roadmap for examining the uncertainty handling approaches, or as a resource for identifying the adequate one.

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Actas de conferencias sobre el tema "Graph-based input representation"

Morris, Matthew, David J. Tena Cucala, Bernardo Cuenca Grau, and Ian Horrocks. "Relational Graph Convolutional Networks Do Not Learn Sound Rules." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/84.

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Graph neural networks (GNNs) are frequently used to predict missing facts in knowledge graphs (KGs). Motivated by the lack of explainability for the outputs of these models, recent work has aimed to explain their predictions using Datalog, a widely used logic-based formalism. However, such work has been restricted to certain subclasses of GNNs. In this paper, we consider one of the most popular GNN architectures for KGs, R-GCN, and we provide two methods to extract rules that explain its predictions and are sound, in the sense that each fact derived by the rules is also predicted by the GNN, f

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Guo, Zhichun, Kehan Guo, Bozhao Nan, et al. "Graph-based Molecular Representation Learning." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/744.

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Molecular representation learning (MRL) is a key step to build the connection between machine learning and chemical science. In particular, it encodes molecules as numerical vectors preserving the molecular structures and features, on top of which the downstream tasks (e.g., property prediction) can be performed. Recently, MRL has achieved considerable progress, especially in methods based on deep molecular graph learning. In this survey, we systematically review these graph-based molecular representation techniques, especially the methods incorporating chemical domain knowledge. Specifically,

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Jin, Ming, Yizhen Zheng, Yuan-Fang Li, Chen Gong, Chuan Zhou, and Shirui Pan. "Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/204.

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Graph representation learning plays a vital role in processing graph-structured data. However, prior arts on graph representation learning heavily rely on labeling information. To overcome this problem, inspired by the recent success of graph contrastive learning and Siamese networks in visual representation learning, we propose a novel self-supervised approach in this paper to learn node representations by enhancing Siamese self-distillation with multi-scale contrastive learning. Specifically, we first generate two augmented views from the input graph based on local and global perspectives. T

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Jin, Di, Luzhi Wang, Yizhen Zheng, et al. "CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/292.

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Graph similarity learning refers to calculating the similarity score between two graphs, which is required in many realistic applications, such as visual tracking, graph classification, and collaborative filtering. As most of the existing graph neural networks yield effective graph representations of a single graph, little effort has been made for jointly learning two graph representations and calculating their similarity score. In addition, existing unsupervised graph similarity learning methods are mainly clustering-based, which ignores the valuable information embodied in graph pairs. To th

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Guan, Sheng, Hanchao Ma, and Yinghui Wu. "RoboGNN: Robustifying Node Classification under Link Perturbation." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/420.

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Graph neural networks (GNNs) have emerged as powerful approaches for graph representation learning and node classification. Nevertheless, they can be vulnerable (sensitive) to link perturbations due to structural noise or adversarial attacks. This paper introduces RoboGNN, a novel framework that simultaneously robustifies an input classifier to a counterpart with certifiable robustness, and suggests desired graph representation with auxiliary links to ensure the robustness guarantee. (1) We introduce (p,θ)-robustness, which characterizes the robustness guarantee of a GNN-based classifier if it

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Ahmetaj, Shqiponja, Robert David, Magdalena Ortiz, Axel Polleres, Bojken Shehu, and Mantas Šimkus. "Reasoning about Explanations for Non-validation in SHACL." In 18th International Conference on Principles of Knowledge Representation and Reasoning {KR-2021}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/kr.2021/2.

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The Shapes Constraint Language (SHACL) is a recently standardized language for describing and validating constraints over RDF graphs. The SHACL specification describes the so-called validation reports, which are meant to explain to the users the outcome of validating an RDF graph against a collection of constraints. Specifically, explaining the reasons why the input graph does not satisfy the constraints is challenging. In fact, the current SHACL standard leaves it open on how such explanations can be provided to the users. In this paper, inspired by works on logic-based abduction and database

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Li, Zuchao, Xingyi Guo, Letian Peng, Lefei Zhang, and Hai Zhao. "iRe2f: Rethinking Effective Refinement in Language Structure Prediction via Efficient Iterative Retrospecting and Reasoning." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/570.

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Refinement plays a critical role in language structure prediction, a process that deals with complex situations such as structural edge interdependencies. Since language structure prediction usually modeled as graph parsing, typical refinement methods involve taking an initial parsing graph as input and refining it using language input and other relevant information. Intuitively, a refinement component, i.e., refiner, should be lightweight and efficient, as it is only responsible for correcting faults in the initial graph. However, current refiners add a significant burden to the parsing proce

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Fan, Zhihao, Zhongyu Wei, Siyuan Wang, et al. "TCIC: Theme Concepts Learning Cross Language and Vision for Image Captioning." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/91.

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Existing research for image captioning usually represents an image using a scene graph with low-level facts (objects and relations) and fails to capture the high-level semantics. In this paper, we propose a Theme Concepts extended Image Captioning (TCIC) framework that incorporates theme concepts to represent high-level cross-modality semantics. In practice, we model theme concepts as memory vectors and propose Transformer with Theme Nodes (TTN) to incorporate those vectors for image captioning. Considering that theme concepts can be learned from both images and captions, we propose two settin

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Sun, Tien-Lung, Chuan-Jun Su, Richard J. Mayer, and Richard A. Wysk. "Shape Similarity Assessment of Mechanical Parts Based on Solid Models." In ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0234.

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Abstract Previous research toward part similarity assessment has been based on two kinds of symbolic part representation schemes, i.e., GT coding schemes and form feature based descriptions. The performance of these approaches is limited because the symbolic part representation is information incomplete and ambiguous. A solid model, on the other hand, is an unambiguous and information complete computer representation for 3D objects. In this work, the shape similarity between two polyhedra is formalized based on isomorphic subgraphs of graph representations extracted from their boundary models.

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Miller, Michael G., James L. Mathieson, Joshua D. Summers, and Gregory M. Mocko. "Representation: Structural Complexity of Assemblies to Create Neural Network Based Assembly Time Estimation Models." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71337.

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Assembly time estimation is traditionally a time intensive manual process requiring detailed geometric and process information to be available to a human designer. As a result of these factors, assembly time estimation is rarely applied during early design iterations. This paper explores the possibility that the assembly time estimation process can be automated while reducing the level of design detail required. The approach presented here trains artificial neural networks (ANNs) to estimate the assembly times of vehicle sub-assemblies at various stages using properties of the connectivity gra

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