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12th International Conference on Smart Grid, icSmartGrid 2024

作者： Oyucu, Saadin Sagiroglu, Seref Aksoz, Ahmet Bicer, Emre Faculty of Engineering Department of Computer Engineering Adiyaman Turkey Gazi University Artificial Intelligence and Big Data Analytics Security R&d Center Ankara Turkey Sivas Cumhuriyet University Mobilers Team Sivas Turkey Sivas University of Science and Technology Faculty of Engineering and Natural Sciences Battery Research Laboratory Sivas Turkey

ISBN: (纸本)9798350361612

Smart grids are faced with a range of challenges, such as the development of communication infrastructure, cybersecurity threats, data privacy, and the protection of user information, due to their complex structure. Another key challenge faced by smart grids is the stability issues arising from variable energy sources and consumption patterns. In these complex grid systems where energy demand and supply need to be balanced instantly, stability predictions play a significant role in foreseeing potential disruptions and optimizing energy flow. Therefore, within the scope of this study, a hybrid structure utilizing Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks is employed for stability classification to predict grid stability. This hybrid model combines the ability of RNN to recognize relationships between consecutive data points with LSTM's capability to preserve long-term dependencies. The results obtained indicate that the model exhibited stable performance with accuracy rates of 98.06% and 98.02% at 50 and 100 epochs, respectively. The findings of this study contribute valuable insights to research on the management and stability of smart grids, enabling energy systems to be operated more reliably and efficiently. © 2024 IEEE.

关键词： Smart power grids

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DASCDC: Dual Attention-based Sparse Common Deep Clustering Method 9

DASCDC: Dual Attention-based Sparse Common Deep Clustering M...

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9th IEEE International Conference on Behavioural and Social Computing, BESC 2022

作者： Dong, Yao Shi, Jin Deng, Yahan Dong, Yongfeng Wang, Ziqiu School of Artificial Intelligence Hebei University of Technology Tianjin300401 China Hebei Province Key Laboratory of Big Data Computing Tianjin300401 China Data Driven Industrial Intelligent Hebei Engineering Research Center Tianjin300401 China

ISBN: (数字)9798350398144

ISBN: (纸本)9798350398144

Graph convolutional network (GCN) has been successfully applied in deep clustering on the high dimensional dataset, which is usually processed by extracting the feature and structure information. However, it lacks an effective fusion mechanism of common information between the feature and structure information and does not perform well on the sparse dataset. Therefore, we propose a dual attention-based sparse common deep clustering method(DASCDC). Firstly, we advance a sparse feature representation method with an adversarial loss function to solve the data sparsity problem and improve the discriminator's performance. Then, an information interaction operator is designed to deeply mine the common information and enhance the information fusion capability. Finally, considering the different contribution degrees of various neighbour nodes and information, we promote a dual self-attention mechanism from the node and model levels. Experiment results indicate that the proposed DASCDC algorithm overperforms other state-of-art deep clustering algorithms on non-graph and graph datasets with high dimension and sparsity. © 2022 IEEE.

关键词： Cluster analysis

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Terahertz metamaterial narrowband absorber based on metal split ring

Terahertz metamaterial narrowband absorber based on metal sp...

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2023 Applied Optics and Photonics China: Optical Sensing, Imaging, and Display technology and Applications;and Biomedical Optics, AOPC 2023

作者： Ji, Xiaodi Ge, Hongyi Jiang, Yuying Wu, Xuyang Jia, Zhiyuan Sun, Zhenyu Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education Henan China Henan Provincial Key Laboratory of Grain Photoelectric Detection and Control Henan China College of Information Science and Engineering Henan University of Technology Henan Zhengzhou450001 China School of Artificial Intelligence and Big Data Henan University of Technology Henan Zhengzhou450001 China

ISBN: (纸本)9781510672321

Multi-band terahertz metamaterial absorbers offer new perspectives to achieve perfect absorption and multipoint information matching, which enable an ever-growing number of applications. In this study, a dual-band terahertz metamaterial absorber based on the metal split ring is designed. The absorber has perfect absorption peaks at 1.15 THz and 2.47 THz, and the absorption rate is more than 99%. The absorber produces a harp peak with a bandwidth of 0.008 at 2.47 THz, which has an extremely high quality factor of 308. The distribution of the electric field and surface current at two resonance points is analyzed using the finite element integration method. Through full wave simulation calculation, the maximum sensitivity of the analyte refractive index of the absorber is 400 GHz/RIU, and the maximum sensitivity of the thickness is 35 GHz/μm. The results show that the absorber can achieve highly sensitive detection of trace substances. © 2023 SPIE. All rights reserved.

关键词： Metamaterials

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Effective and Imperceptible Adversarial Textual Attack Via Multi-objectivization

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ACM Transactions on Evolutionary Learning and Optimization 2024年第3期4卷 1-23页

作者： Liu, Shengcai Lu, Ning Hong, Wenjing Qian, Chao Tang, Ke Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen College of Engineering South Tower 1088 Xueyuan Avenue Guangdong Shenzhen518055 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong999077 Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen 10F Zhizhen Building Canghai Campus 3688 Nanhai Avenue Guangdong Shenzhen China National Key Laboratory for Novel Software Technology School of Artificial Intelligence Nanjing University Nanjing International College Area Xianlin Campus 163 Xianlin Avenue Jiangsu Nanjing China

The field of adversarial textual attack has significantly grown over the past few years, where the commonly considered objective is to craft adversarial examples (AEs) that can successfully fool the target model. However, the imperceptibility of attacks, which is also essential for practical attackers, is often left out by previous studies. In consequence, the crafted AEs tend to have obvious structural and semantic differences from the original human-written text, making them easily perceptible. In this work, we advocate leveraging multi-objectivization to address such an issue. Specifically, we reformulate the problem of crafting AEs as a multi-objective optimization problem, where the attack imperceptibility is considered as an auxiliary objective. Then, we propose a simple yet effective evolutionary algorithm, dubbed HydraText, to solve this problem. HydraText can be effectively applied to both score-based and decision-based attack settings. Exhaustive experiments involving 44,237 instances demonstrate that HydraText consistently achieves competitive attack success rates and better attack imperceptibility than the recently proposed attack approaches. A human evaluation study also shows that the AEs crafted by HydraText are more indistinguishable from human-written text. Finally, these AEs exhibit good transferability and can bring notable robustness improvement to the target model by adversarial training. © 2024 Copyright held by the owner/author(s).

关键词： Multiobjective optimization

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ReFrame: A Resource-Friendly Cloud-Assisted On-Device Deep Learning Framework for Vision Services

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IEEE Transactions on Services Computing 2025年第3期18卷 1711-1723页

作者： Xie, Jianhang Ding, Chuntao Guan, Qingji Zhou, Ao Li, Yidong Beijing Jiaotong University Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education School of Computer and Information Technology Beijing China Beijing Normal University School of Artificial Intelligence Beijing China Beijing Jiaotong University Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

Cloud-assisted Internet of Things (IoT) device deployment of deep neural networks (DNNs) promotes On-device deep learning to provide users with ubiquitous high-quality services by solving the contradiction between insufficient IoT device resources and intensive demand for high-performance DNN resources. However, most existing methods optimize DNNs by considering one or two terms of transmission, computation, and storage resources, but do not consider all three terms at the same time in cloud-assisted IoT device deployment and updating DNNs. To this end, we propose a non-learnable module-based ResNet and a cloud-assisted on-device deep learning framework, ReFrame, based on the consideration of three indicators: model transmission parameters, computation resources, and storage resources. In the proposed method, we first specify that some parameters in DNNs are non-learnable and randomly initialized, so that, these parameters can be saved and reproduced with a few random seeds. By doing so, the cloud only transmits random seeds and learnable parameters to reduce the number of parameter transmissions. Secondly, we reduce the computation resource consumption of the model by introducing computation-friendly operators, such as pooling, to replace vanilla convolutions. Finally, since random seeds are used to save non-learnable model parameters, on IoT devices we only need to store random seeds and learnable parameters to reproduce the well-trained model. Compared with saving the complete model, our method greatly reduces IoT device storage resource consumption. Experimental results on image classification, object detection, and semantic segmentation tasks demonstrate the effectiveness of the proposed method. Specifically, on the CIFAR-10, our proposed method reduces approximately 89% of FLOPs and 90% of transmitted data in the prototype system compared to ResNet-18. © 2008-2012 IEEE.

关键词： Cloud platforms

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CAAVM-TransUNet: Integrating Context Anchor Attention with Transformer U-Net for Single Image Dehazing

CAAVM-TransUNet: Integrating Context Anchor Attention with T...

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International Conference on Computer and Communications (ICCC)

作者： Hongwei Zeng Lijun Fu Jin Li Xu Li Xiangdong He Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China Laboratory of Big Data and Artificial Intelligence Technolgy Shandong University Jinan China Institute of Future Artificial Intelligence Technology and Innovative Applications Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Image dehazing has always been a challenging visual task. Most of the existing image dehazing algorithms are constructed based on convolutional neural networks, which perform very well in capturing local features and detail information, but fall short of the ability to capture remote contextual information. In this paper, we introduce a novel method called CAAVM-TransUNet. The method integrates Transformer into the traditional U -shaped architecture. Unlike earlier Transformer-based U-Net, CAA VM-TransUNet introduces the State Space Sequence Model (SSM) to enhance the ability of capturing remote dependencies, and adds Context Anchor Attention (CAA) to filter redundant features in the hopping connectivity layer prior to the encoder and decoder, so that the model can better focus on the key feature information and reduce the interference of irrelevant information, thus improving the effect of image dehazing. The results show that the dehazing effect of CAAVM-TransUNet on RESIDE dataset is better than the existing methods, especially the PSNR in the SOTS-outdoor dataset reaches 36.13 dB, which provides a new idea to solve the complex image dehazing problem.

关键词： Visualization PSNR Interference Drives Transformers Information filters Decoding Environmental monitoring Convolutional neural networks Context modeling

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Talk With Human-like Agents: Empathetic Dialogue Through Perceptible Acoustic Reception and Reaction

arXiv

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arXiv 2024年

作者： Yan, Haoqiu Zhu, Yongxin Zheng, Kai Liu, Bing Cao, Haoyu Jiang, Deqiang Xu, Linli School of Computer Science and Technology University of Science and Technology China School of Data Science University of Science and Technology China State Key Laboratory of Cognitive Intelligence China Tencent Youtu Lab China

Large Language Model (LLM)-enhanced agents become increasingly prevalent in Human-AI communication, offering vast potential from entertainment to professional domains. However, current multi-modal dialogue systems overlook the acoustic information present in speech, which is crucial for understanding human communication nuances. This oversight can lead to misinterpretations of speakers' intentions, resulting in inconsistent or even contradictory responses within dialogues. To bridge this gap, in this paper, we propose PerceptiveAgent, an empathetic multi-modal dialogue system designed to discern deeper or more subtle meanings beyond the literal interpretations of words through the integration of speech modality perception. Employing LLMs as a cognitive core, PerceptiveAgent perceives acoustic information from input speech and generates empathetic responses based on speaking styles described in natural language. Experimental results indicate that PerceptiveAgent excels in contextual understanding by accurately discerning the speakers' true intentions in scenarios where the linguistic meaning is either contrary to or inconsistent with the speaker's true feelings, producing more nuanced and expressive spoken dialogues. Code is publicly available at: https://***/Haoqiu-Yan/PerceptiveAgent. Copyright © 2024, The Authors. All rights reserved.

关键词： Speech communication

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CoDTS: Enhancing Sparsely Supervised Collaborative Perception with a Dual Teacher-Student Framework 39

CoDTS: Enhancing Sparsely Supervised Collaborative Perceptio...

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39th Annual AAAI Conference on Artificial intelligence, AAAI 2025

作者： Han, Yushan Zhang, Hui Zhang, Honglei Wang, Jing Li, Yidong Key Laboratory of Big Data & Artificial Intelligence in Transportation (Beijing Jiaotong University) Ministry of Education School of Computer Science and Technology Beijing Jiaotong University Beijing 100044 China

ISBN: (纸本)157735897X

Current collaborative perception methods often rely on fully annotated datasets, which can be expensive to obtain in practical situations. To reduce annotation costs, some works adopt sparsely supervised learning techniques and generate pseudo labels for the missing instances. However, these methods fail to achieve an optimal confidence threshold that harmonizes the quality and quantity of pseudo labels. To address this issue, we propose an end-to-end Collaborative perception Dual Teacher-Student framework (CoDTS), which employs adaptive complementary learning to produce both high-quality and high-quantity pseudo labels. Specifically, the Main Foreground Mining (MFM) module generates high-quality pseudo labels based on the prediction of the static teacher. Subsequently, the Supplement Foreground Mining (SFM) module ensures a balance between the quality and quantity of pseudo labels by adaptively identifying missing instances based on the prediction of the dynamic teacher. Additionally, the Neighbor Anchor Sampling (NAS) module is incorporated to enhance the representation of pseudo labels. To promote the adaptive complementary learning, we implement a staged training strategy that trains the student and dynamic teacher in a mutually beneficial manner. Extensive experiments demonstrate that the CoDTS effectively ensures an optimal balance of pseudo labels in both quality and quantity, establishing a new state-of-the-art in sparsely supervised collaborative perception. © 2025, Association for the Advancement of Artificial intelligence (***). All rights reserved.

关键词： Supervised learning

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Cross-Modality Comprehensive Feature Aggregation Network for Road Scene Segmentation

Cross-Modality Comprehensive Feature Aggregation Network for...

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International Symposium on Computer Science and Intelligent Controls (ISCSIC)

作者： Yale Yang Zihao Zhang Huifang Hou Wenwen Zhang Chunsheng Zhuang College of Information Science and Engineering Henan University of Technology Zhengzhou China Key Laboratory of Grain Information Processing and Control Ministry of Education Henan Key Laboratory of Grain Photoelectric Detection and Control and school of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou China Henan Academy of Sciences Zhengzhou China

ISBN: (数字)9798350380286

ISBN: (纸本)9798350380293

RGB-thermal semantic segmentation is a potential solution for reliable semantic scene understanding under adverse weather and complex lighting conditions. However, past studies have primarily focused on the design of fusion paradigms. Unfortunately, these methods have not fully considered the modality differences caused by different imaging mechanisms, thereby reducing the performance of pixel-level semantic analysis. In response to this issue, this paper proposes an innovative Cross-Modality Comprehensive Feature Aggregation Network (CFANet). This method first designs a Cross-Modality Feature Rectification Module (CFRM) to align modality differences; then, it constructs a Global-Local Fusion Module (GLF) to sharpen object edges and refine the segmentation map. Results on two urban scene understanding datasets demonstrate that CFANet can effectively aggregate multi-layer deep features and outperform existing advanced deep learning methods in scene understanding performance.

关键词： Deep learning Semantic segmentation Roads Design methodology Semantics Lighting Imaging Reliability Meteorology Intelligent control

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Automatic Recognition of Parallel Sentence Based on Sentences-Interaction CNN and Its Application 7

Automatic Recognition of Parallel Sentence Based on Sentence...

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7th International Conference on Computer and Communication Systems, ICCCS 2022

作者： Liu, Guanghui Fu, Lijun Yu, Bo Cui, Li University of Chinese Academy of Sciences Laboratory of Big Data and Artificial Intelligence Technology Shandong University !!!Shenyang Institute of Computing Technology @@@Chinese Academy of Sciences Beijing China Nangong No.1 Middle School Hebei China

ISBN: (数字)9781665450607

ISBN: (纸本)9781665450607

Automated essay evaluation is to make the computer simulating human evaluate students' essay. The final grade of one essay is highly related to its literacy. The use of rhetorical devices can increase the literacy of one essay, and parallelism is one of the common rhetoric. Therefore, the identification of parallelism can contribute to automated essay evaluation. However, the research of automatic recognition of parallel sentence is so rare so far. In this paper, we design a deep learning method based on sentences interactive-convolutional neural network to recognize parallelism in the light of word co-occurrence, similar syntactic structure and tone of expression. Firstly, one or more interactive matrices whose elements represent the similarities in some ways between every token in two sentences are constructed. Then the convolutional neural network is employed to capture local features. The experimental results show that our feature selection is effective and the recognition effect is comparable with similar studies. © 2022 IEEE.

关键词： Convolutional neural networks

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