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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yuchen Liu Hu Liang Ran Qiu Shengrong Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

In many underwater application scenarios, recognition tasks need to be executed promptly on computationally limited platforms. However, models designed for this field often exhibit spatial locality, and existing works lack the ability to capture crucial details in images. Therefore, a lightweight and detail-aware vision network (LDVNet) for resource-constrained environments is proposed to overcome the limitations of these approaches. Firstly, in order to enhance the accuracy of target image recognition, we introduce transformer modules to acquire global information, thus addressing the issue of spatial locality inherent in traditional convolutional neural networks (CNNs). Secondly, to maintain the network’s lightweight nature, we integrate the transformer module with convolutional operations, thereby mitigating the substantial parameter and floating point operations (FLOPs) overhead. Thirdly, for the efficient extraction of crucial fine-grained details from feature maps, we have devised a channel and spatial attention module (C&SA). This module aids in recognizing intricate and fine-grained visual tasks and enhances image understanding. It is seamlessly integrated into LDVNet with nearly negligible parameter overhead. The experimental results demonstrate that LDVNet outperforms other lightweight networks and hybrid networks in different recognition tasks, while being suitable for resource-constrained environments.

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Multilingual Knowledge Graph Completion via Multimodal Decouple and Relation-based Ensemble Inference

Multilingual Knowledge Graph Completion via Multimodal Decou...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Xiaojing Liu Xuesong Jiang Fengge Yi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

The objective of Multimodal Knowledge Graph Completion (MKGC) is to forecast absent entities within a knowledge graph by leveraging additional textual and visual modalities. Existing studies commonly utilize a singular relationship embedding to depict all modalities within an entity pair, thus connecting several relationships derived from diverse modalities. However, this coupling may introduce interference from conflicting information between modalities, as the relationships between modalities for a given entity pair can be contradictory. Moreover, existing Ensemble Inference methods fail to dynamically adjust modal weights based on their differences and importance, despite the varying contributions of different modalities. In this paper, we propose the Multimodal Decouple and Relation-based Ensemble inference (MDRE) model. For each modality, we construct corresponding relationship embeddings and build separate triple representations to avoid interferences among modalities. During the training phase, we employ confidence-constrained training with temperature scaling to alleviate conflicting information in textual and visual modalities. For inference, we utilize the Relation-based Ensemble Inference method to adjust modal weights at the relationship level, thus achieving improved prediction results. Experimental results on two datasets demonstrate that MDRE outperforms existing single-modal and multimodal knowledge graph completion methods in terms of performance.

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GAF-GAN: Gated Attention Feature Fusion Image Inpainting Network Based on Generative Adversarial Network

GAF-GAN: Gated Attention Feature Fusion Image Inpainting Net...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Ran Qiu Hu Liang Yuchen Liu Shengrong Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Image inpainting, which aims to reconstruct reasonably clear and realistic images from known pixel information, is one of the core problems in computer vision. However, due to the complexity and variability of the underwater environment, the inability to extract valid pixel points and insufficient correlation between feature information in existing image inpainting techniques lead to blurring in the generated images. Therefore, a novel gated attention feature fusion image inpainting network based on generative adversarial networks (GAF-GAN) is proposed. The accuracy of feature similarity matching depends heavily on the validity of the information contained in the features. On the one hand, gating values are dynamically generated by gated convolution to reduce the interference of invalid information. On the other hand, semantic information at distant locations in an image is accurately acquired by the attention mechanism. For these reasons, we designed an improved gated attention mechanism. Gated attention mechanism make the network focus on effective information such as high-frequency texture and color fidelity of restored images. In addition, the dense feature fusion module is added to expand the overall receptive field of the network to fully learn the image features. Experimental results show that the proposed method can effectively repair defective images with complex texture structures and improve the reality and integrity of image details and structures.

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Research on long and short-term social recommendation based on convolutional and gated recurrent units

Research on long and short-term social recommendation based ...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Zihe Jia Peng Xue Zhiqiang Dai Qian Gao Xiaomeng Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

The development of the Internet has made people more closely related and has put forward higher requirements for recommendation models. Most recommendation models are studied only for the long-term interests of users. In this paper, the interaction time between the user and the item is introduced as auxiliary information in the model construction. Interaction time is used to determine users’ long-term preferences and short-term preferences. In this paper, temporal features are extracted by building a convolutional gated recurrent unit with attention neural network (CNN-GRU-Attention). Firstly, for the problem of accurate feature extraction, CNN are constructed to extract higher-level and more abstract features of themselves and transform high-dimensional data into low-dimensional data; secondly, for the problem of social temporality, GRU are used to not only extract temporal information, but also effectively reduce gradient dispersion, making model convergence and training easier; finally, Graph Attention networks are used to aggregate the social relationship information of users and items respectively, which constitute the final feature representation of users and items respectively. In particular, a modified cosine similarity is used to reduce the error caused by data insensitivity when constructing the social information of the item. In this study, simulation experiments are conducted on two publicly available datasets (Epinions and Ciao), and the experimental results show that the proposed recommended model performs better than other social recommendation models, improving the evaluation metrics of MAE and RMSE by 1.06%-1.33% and 1.19%-1.37%, respectively. The effectiveness of the model innovation is proved.

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GA-Net: Gated Attention Mechanism Based Global Refinement Network for Image Inpainting

GA-Net: Gated Attention Mechanism Based Global Refinement Ne...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Ran Qiu Shengrong Zhao Hu Liang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Underwater images are often affected by problems such as light attenuation, color distortion, noise and scattering, resulting in image defects. A novel image inpainting method is proposed to intelligently predict and fill damaged areas for complete and continuous visualization of the image. First, in order to effectively solve the problem of color distortion caused by light refraction in underwater environments, the improved gated attention mechanism is used. This mechanism improves the local details by learning and weighting the important features of the image. Second, gated convolution automatically determines the degree of restoration for each pixel based on local features of the original image. It eliminates distractions such as low contrast and scattering, retaining more original detailed information. By doing so, image inpainting techniques improve the quality and visualization of underwater images.

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Shuo Zhao Qiliang Gu Jianqiang Zhang Gongpeng Song Qin Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets.

关键词： Deep learning Computational modeling Semantics Neural networks Feature extraction Natural language processing Multilingual Convolutional neural networks Cybernetics Overfitting

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Pre-ictal causal connectivity reveals the epileptic network characteristics for deep brain stimulation

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Neuroscience 2025年 579卷 329-343页

作者： Yifan Wang Tao Yu Yulan Ma Weigang Cui Xueyuan Wang Liankun Ren Yang Li Department of Automation Science and Electrical Engineering Beihang University Beijing 100191 China. Beijing Institute of Functional Neurosurgery Department of Functional Neurosurgery Xuanwu Hospital Capital Medical University Beijing 100053 China. School of Engineering Medicine Beihang University Beijing 100191 China. Department of Neurology Xuanwu Hospital Capital Medical University Beijing 100053 China. Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Department of Automation Science and Electrical Engineering Beihang University Beijing 100191 China. Electronic address: liyang@***.

Deep brain stimulation of anterior nucleus of the thalamus (ANT-DBS) is an effective clinical treatment for drug-resistant focal epilepsy. However, the complex epileptic network characteristics underlying ANT-stimulation effectiveness remain unknown, owing to currently unclear connectivity between ANT and seizure-related cortex in pre-ictal periods. Here, we developed a novel individualized pre-ictal ANT-cortical tripartite connectivity network (PANT-CTCNet), aiming to reveal epileptic network characteristics using intracranial stereo-electroencephalography (sEEG) recordings in five patients with focal epilepsy for 90 trials. Each trial represented a pre-ictal or post-stimulus sEEG duration, which was used to construct the epileptic network. By employing conditional Granger causality, we constructed individualized ANT-cortical connectivity networks and found a common epileptic network centred on ANT closely connected with seizure-related cortex in pre-ictal periods. After ANT stimulation for clinical validation, strengthened pre-ictal connectivity between ANT and epileptogenic zones led to significant decline in the causal intensity of epileptic networks. The PANT-CTCNet can give a quantitative reference for clinical preoperative evaluation of patient suitability for ANT-DBS treatment. These findings regarding epileptic network characteristics provide theoretical basis in the selection of optimal surgical candidates for personalized ANT-DBS.

关键词： Anterior nucleus of the thalamus Epilepsy Epileptic network characteristics Neuromodulation Stereo-electroencephalography

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Semantic Relation-Based Cross Attention Network for Image-Text Retrieval

Semantic Relation-Based Cross Attention Network for Image-Te...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Huanxiao Zhou Yushui Geng Jing Zhao Xishan Ma Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Image-text retrieval refers to querying the text or image of another modality given an image or text, and its key lies in the ability to accurately measure the similarity between text and image. Most of the existing retrieval methods only utilize the intramodal relations of each modality or the intermodal relations of the two modalities to perform the retrieval task. To address the above problems, we integrate the intra-modal and inter-modal relations and propose a Semantic Relation-based Cross Attention Network(SRCAN). In our proposed method, we first mine the possible intra-modal associations between regions in an image and between words in a text to obtain features with semantic relationships, and then capture the fine-grained associations between segments through the cross-attention mechanism. Finally, we balance the intra-modal and inter-modal relationships to improve the performance of the model. Our proposed method is experimentally validated on two datasets, Flickr30K and MS-COCO, and the experimental results show that our method achieves superior results.

关键词： Image segmentation Semantics Bidirectional control Feature extraction Cognition Feeds Cybernetics

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A BCS-GDE Multi-objective Optimization Algorithm for Combined Cooling, Heating and Power Model with Decision Strategies

arXiv

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arXiv 2022年

作者： Sun, Jiaze Deng, Jiahui Li, Yang Han, Nan School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121 China Xi 'an Key Laboratory of Big Data and Intelligent Computing Xi’an710127 China School of Information Science and Technology Northwest University Xi’an710127 China School of Electrical Engineering Northeast Electric Power University Jilin132012 China

District energy systems can not only reduce energy consumption but also set energy supply dispatching schemes according to demand. In addition to economic cost, energy consumption and pollutant are more worthy of attention when evaluating combined cooling, heating and power (CCHP) models. In this paper, the CCHP model is established with the objective of economic cost, primary energy consumption, and pollutant emissions. The mathematical expression of the CCHP system is proposed, and a multi-objective optimization model with constraints is established. According to different usage requirements, two decision-making strategies are designed, which can adapt to different scenarios. Besides, a generalized differential evolution with the best compromise solution processing mechanism (BCS-GDE) algorithm is proposed to optimize the CCHP model for the first time. The algorithm provides the optimal energy scheduling scheme by optimizing the production capacity of different capacity equipment. The simulation is conducted in three application scenarios: hotels, offices, and residential buildings. The simulation results show that the model established in this paper can reduce economic cost by 72%, primary energy consumption by 73%, and pollutant emission by 88%. Concurrently, the Wilcoxon signed-rank test shows that BCS-GDE is significantly better than OMOPSO, NSGA-II, and SPEA2 with greater than 95% confidence. Copyright © 2022, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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Semantic Information Reasoning and Multi-Step Cross-Modal Interaction Network for Image-Text Retrieval

Semantic Information Reasoning and Multi-Step Cross-Modal In...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xishan Ma Yushui Geng Jing Zhao Huanxiao Zhou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Image-text retrieval is a basic cross-modal task, and the basic idea is to learn image-text matching. The key to this task is to accurately measure the similarity between these two modalities. However, most existing methods tend to measure image-text similarity based on highly correlated region-word pairs, which lacks the ability to capture complex cross-modal semantic associations, thereby resulting in failure to distinguish hard negative examples that are similar in semantic content. Furthermore, existing methods focus more on alignment of different modal features, thus neglecting deep reasoning about the semantic concepts within the modality. In this paper, we propose a new semantic information reasoning and multi-step cross-modal interaction (SRMCI) network to simultaneously explore intra-modal and inter-modal relationships. Specifically, we first propose a semantic information reasoning module to fully explore potential semantic relationships within modalities and focus on fragments with important semantic information. Then, we design a multi-step cross-modal interaction module that utilizes two different inter-modal interactive attentions and an effective gated fusion strategy to capture fragment-level and fragment-instance-level cross-modal semantic associations. In particular, this gated fusion strategy is used to learn better fine-grained feature representations to aid in understanding and establishing semantic associations between different modalities. Experiments on the benchmark datasets MS-COCO and Flickr30K demonstrate the effectiveness of our method.

关键词： Correlation Semantics Neural networks Logic gates Benchmark testing Cognition

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