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arXiv 2024年

作者： Liu, Jian Xiao, Ming Wen, Jinbo Kang, Jiawen Zhang, Ruichen Zhang, Tao Niyato, Dusit Zhang, Weiting Liu, Ying The School of Automation Guangdong University of Technology Guangzhou510006 China The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China The College of Computing and Data Science Nanyang Technological University Singapore The School of Software Engineering Beijing Jiaotong University Beijing100044 China School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

Mobile Artificial Intelligence-Generated Content (AIGC) networks enable massive users to obtain customized content generation services. However, users still need to download a large number of AIGC outputs from mobile AIGC service providers, which strains communication resources and increases the risk of transmission failures. Fortunately, Semantic Communication (SemCom) can improve transmission efficiency and reliability through semantic information processing. Moreover, recent advances in Generative Artificial Intelligence (GAI) further enhanced the effectiveness of SemCom through its powerful generative capabilities. However, how to strike a balance between high-quality content generation and the size of semantic information transmitted is a major challenge. In this paper, we propose a Generative Diffusion Model (GDM)based multi-modal SemCom (GM-SemCom) framework. The framework improves the accuracy of information reconstruction by integrating GDMs and multi-modal semantic information and also adopts a controllable extraction module for efficient and controllable problems of unstable data recovery and slow decoding speed in GAI-enabled SemCom. Then, we introduce a novel metric called Age of Semantic Information (AoSI) based on the concept of Age of Information (AoI) to quantify the freshness of semantic information. To address the resource trading problem within the framework, we propose a Stackelberg game model, which integrates the AoSI with psychological factors to provide a comprehensive measure of user utility. Furthermore, we propose a GDM-based algorithm to solve the game under incomplete information. Compared with the traditional deep reinforcement learning algorithms, numerical results demonstrate that the proposed algorithm converges faster and is closer to the Stackelberg equilibrium. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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An Overview of Blockchain-Based Application in Internet of Things (IoT)

An Overview of Blockchain-Based Application in Internet of T...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Reginald C. Molokwu Bonaventure C. Molokwu Victor C. Molokwu Canadian Institute for Cybersecurity University of New Brunswick Department of Computer Science and Software Engineering Concordia University School of Energy Geoscience Infrastructure and Society Hariot-Watt University Edinburgh

The Internet of Things (IoT) is a very crucial aspect of Computing, and it fosters the interconnection of physical nodes on the Internet via enabling them to interact and share data. However, the potentials for security and privacy breaches increase as the number of connected devices/nodes (in the network) rises. Blockchain technology, with its capacity to provide secure and tamper-proof data storage, possess the potentials to mitigate the aforementioned vulnerabilities in the IoT. Hence, this paper gives a detailed review of the present state of blockchain-based applications in the IoT as well as the prospective benefits of employing blockchain technology with the aim/goal of securing IoT devices and its infrastructure. Also mentioned in this paper are the obstacles, research gaps, etc., currently impeding the implementation of some blockchain-based technologies; and the potential solutions toward surmounting these challenges.

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Quality Evaluation in Guavas using Deep Learning Architectures: An Experimental Review 4

Quality Evaluation in Guavas using Deep Learning Architectur...

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4th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2022

作者： Choudhury, Tanupriya Mahdi, Hussain Falih Agarwal, Archit Chakraborty, Arnav Arunachalaeshwaran, V R Sarkar, Tanmay Tomar, Ravi Uttarakhand Dehradun2480007 India University of Diyala Department of Computer and Software Engineering Baqubah Iraq School of Computing National University of Singapore Kent Ridge119007 Singapore School of Computer Science Lambton College ONL4Z3E9 Canada Malda Polytechnic Department of Food Processing West Bengal Malda732102 India

ISBN: (纸本)9781665468350

Determining the quality of fruit can be very challenging without the proper understanding. There are many factors that can result in determining the quality and therefore the actual taste of any given fruit. In this paper we attempt to determine the quality of the guava fruit on what are considered the three most important aspects of flavour by experts, those being, Colour, Shape and texture. All three of these aspects will be determined on a scale of 1-5. In order to produce these results, we are using an exceptional deep learning architecture and a primary dataset curated by a panel of experts who photographed the fruits in six-hour intervals for many days. We have compared the results over multiple deep learning architectures to find the most accurate results. This paper shows the importance of deep learning as a potential future tool for assessing the quality of fruits. © 2022 IEEE.

关键词： Fruits

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Refining the Unseen: Self-supervised Two-stream Feature Extraction for Image Quality Assessment

Refining the Unseen: Self-supervised Two-stream Feature Extr...

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IEEE International Conference on Data Mining (ICDM)

作者： Yiwei Lou Yanyuan Chen Dexuan Xu Doudou Zhou Yongzhi Cao Hanpin Wang Yu Huang Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of Education School of Computer Science Peking University Beijing China School of Software & Microelectronics Peking University Beijing China Department of Biostatistics Harvard T.H. Chan School of Public Health Boston USA National Engineering Research Center for Software Engineering Peking University Beijing China

The inadequacy of labeled datasets for image quality assessment has led to the development and popularity of self-supervised approaches. However, most existing self-supervised methods primarily focus on content and fidelity features extracted with convolutional neural networks, overlooking the crucial importance of structural features in quality assessment. To address this problem, we present a novel self-supervised two-stream feature extraction and representation approach. In our approach, the first stream leverages a contrastive learning framework to extract image fidelity features, while the second stream emphasizes structural features by incorporating an attention mechanism. This innovative combination results in a comprehensive feature representation for quality assessment. Moreover, our proposed method facilitates transfer learning, allowing the pre-trained two-stream model in the source domain to be seamlessly applied to target domains for quality regression. This compatibility with transfer learning enhances the adaptability and generalization of the model. Extensive experiments are carried out on three synthetic distortion datasets to validate the effectiveness of our approach. The results demonstrate that our work not only competes with state-of-the-art self-supervised methods but also outperforms some supervised approaches.

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Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Language Models

arXiv

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arXiv 2023年

作者： Yao, Yao Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China

With the widespread use of language models (LMs) in NLP tasks, researchers have discovered the potential of Chain-of-thought (CoT) to assist LMs in accomplishing complex reasoning tasks by generating intermediate steps. However, human thought processes are often non-linear, rather than simply sequential chains of thoughts. Therefore, we propose Graph-of-Thought (GoT) reasoning, which models human thought processes not only as a chain but also as a graph. By representing thought units as nodes and connections between them as edges, our approach captures the non-sequential nature of human thinking and allows for a more realistic modeling of thought processes. GoT adopts a two-stage framework with an additional GoT encoder for thought graph representation and fuses the graph representation with the original input representation through a gated fusion mechanism. We evaluate GoT’s performance on a text-only reasoning task (AQUA-RAT) and a multimodal reasoning task (scienceQA). Our model achieves significant improvement over the strong CoT baseline on the AQUA-RAT test set and boosts accuracy from 85.19% to 87.59% using the T5-base model over the state-of-the-art Multimodal-CoT (Zhang et al., 2023) on the scienceQA test set. Our code is publicly available at https://***/Zoeyyao27/Graphof-Thought © 2023, CC BY.

关键词： Rats

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An Efficient Deep Learning Model based Diagnosis System for Lung Cancer Disease

An Efficient Deep Learning Model based Diagnosis System for ...

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International Conference on Computing, Mathematics and engineering Technologies (iCoMET)

作者： Gul Zaman Khan Ibrar Ali Shah Farhatullah Muhammad Ikram Ullah Inam Ullah Muhammad Ihtesham Hazrat Junaid Spogmay Yousafzai Fouzia Sardar Department of Computer software Engineering UET Mardan Pakistan School of Automation China University of Geosciences Wuhan China School of software Dalian Jiaotong University Dalian China Department of Computer science Abdul Wali Khan University Maradan Pakistan Department of Computer Science and Information Technology University of Malakand Dir Lower Pakistan Department of zoology University of Malakand Dir. Lower Pakistan

Lung cancer illness seriously impacts people's health. Medical history-based detection of lung cancers has been utilized but it has unsatisfactory results. Artificial intelligence algorithms are more precise and efficient in classifying lung cancer patients and healthy persons. Additionally, the medical history-based diagnosis of lung cancer disease is costly and time consuming. The life of lung cancer disease is very short after detection. Artificial intelligence-based diagnosis systems can detect the lung cancer disease early and efficiently. However, previous research work as several limitations, for example, some techniques computation time is very high but their accuracy is good while some techniques have less computation time but accuracy is not good. The proposed work suggests a deep convolutional neural network-based diagnosis system for lung cancer disease early and accurate detection. We made use of publically available dataset downloaded from Kaggle online repository and applied deep convolutional neural network for accurate lung cancer detection. Furthermore, we have applied some preprocessing and features selection techniques such as max, min, standard deviation and variance threshold. The proposed CNN model achieved 99.2% validation accuracy, 99.8% training accuracy, 99% precision, and 99% recall in minimum computation time of 6 sec which is acceptable.

关键词： Training Deep learning Computational modeling Lung cancer Lung Feature extraction Mathematical models

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Mutual Graph Learning Network and Diffusion Probabilistic Model-based Medical Image Segmentation

Mutual Graph Learning Network and Diffusion Probabilistic Mo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Huaqiang Su Haijun Lei Guoliang Chen Xin Chen Baiying Lei College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Radiology Guangzhou First People’s Hospital Guangzhou China School of Biomedical Engineering & Shenzhen University Medical School Shenzhen University Shenzhen China

Diffusion probabilistic models (DPM) can generate semantically valuable pixel-level representations and are widely used in medical image segmentation tasks. However, DPM faces challenges when dealing with medical image segmentation problems due to the irregular structure of medical images and the similarity between lesions and their surrounding environments. Therefore, this paper proposes a dual-branch Diff-UNet architecture to solve the medical image segmentation problem. Specifically, this architecture introduces the Transformer internal network on top of the standard UNet architecture based on DPM and realizes the interaction of UNet and Transformer branch features through bidirectional connection units to capture local features and remote dependencies better. In addition, through the feature fusion module (FFM), the global context information extracted by DPM is combined with the local detail features captured by the segmentation network. Simultaneously, this paper introduces a mutual graph learning (MGL) network to decompose the image into two task-specific feature maps, which are used to roughly locate the object position and capture the fine details of the object boundary. Finally, the cross attention (CA) module combines the edge information of the diffusion model with the features of the segmentation network to enhance the network’s ability to perceive images. Experiments demonstrate the effectiveness of our Diff-UNet on challenging datasets, including self-collected databases and LUNA16.

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Stellar Spectral Classification Based on Capsule Network

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Chinese Astronomy and Astrophysics 2021年第3期45卷 352-363页

作者： Li-ting, D.U. Li-hua, H.O.N.G. Jin-tao, Y.A.N.G. Ting-ting, X.U. Jing-min, ZHANG Lin-pin, A.I. Wei-hong, Z.H.O.U. School of Mathematics and Computer Science Yunnan Minzu University Kunming 650500 School of Software Engineering Xiamen Institute of Software Technology Xiamen 361000 Center for Astrophysics Guangzhou University Guangzhou 510006 Key Laboratory of the Structure and Evolution of Celestial Objects Chinese Academy of Sciences Kunming 650011

The rapid development of large-scale sky survey project has produced a large amount of stellar spectral data, which make the automatic classification of stellar spectral data a challenging task. In this paper, we have proposed a stellar spectral classification method based on a capsule network. At first, by using the one-dimensional convolutional network and short-time Fourier transform (STFT), the one-dimensional spectra of the F5, G5, and K5 types selected from the LAMOST Data Release 5 (DR5) are converted into the two-dimensional Fourier spectrum images. Then, the two-dimensional Fourier spectrum images are classified automatically by the capsule network. Because the capsule network can preserve the hierarchical pose relationships among the entities in the image, and it does not need any pooling layers, the experimental results show that the capsule network has a better classification performance, for the classifications of the F5, G5, and K5-type stellar spectra, its classification accuracy is superior to other classification methods. © 2021

关键词： methods: data analysis Stars: fundamental parameters techniques: spectral analysis

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Leveraging Predicate and Triplet Learning for Scene Graph Generation

arXiv

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arXiv 2024年

作者： Li, Jiankai Wang, Yunhong Guo, Xiefan Yang, Ruijie Li, Weixin IRIP Lab School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Complex & Critical Software Environment Beihang University Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China

Scene Graph Generation (SGG) aims to identify entities and predict the relationship triplets in visual scenes. Given the prevalence of large visual variations of subject-object pairs even in the same predicate, it can be quite challenging to model and refine predicate representations directly across such pairs, which is however a common strategy adopted by most existing SGG methods. We observe that visual variations within the identical triplet are relatively small and certain relation cues are shared in the same type of triplet, which can potentially facilitate the relation learning in SGG. Moreover, for the long-tail problem widely studied in SGG task, it is also crucial to deal with the limited types and quantity of triplets in tail predicates. Accordingly, in this paper, we propose a Dual-granularity Relation Modeling (DRM) network to leverage fine-grained triplet cues besides the coarse-grained predicate ones. DRM utilizes contexts and semantics of predicate and triplet with Dual-granularity Constraints, generating compact and balanced representations from two perspectives to facilitate relation recognition. Furthermore, a Dual-granularity Knowledge Transfer (DKT) strategy is introduced to transfer variation from head predicates/triplets to tail ones, aiming to enrich the pattern diversity of tail classes to alleviate the long-tail problem. Extensive experiments demonstrate the effectiveness of our method, which establishes new state-of-the-art performance on Visual Genome, Open Image, and GQA datasets. Our code is available at https://***/jkli1998/DRM Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing

DeepRicci: Self-supervised Graph Structure-Feature Co-Refine...

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IEEE International Conference on Data Mining (ICDM)

作者： Li Sun Zhenhao Huang Hua Wu Junda Ye Hao Peng Zhengtao Yu Philip S. Yu School of Control and Computer Engineering North China Electric Power University Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Department of Computer Science University of Illinois at Chicago IL USA

Graph Neural Networks (GNNs) have shown great power for learning and mining on graphs, and Graph Structure Learning (GSL) plays an important role in boosting GNNs with a refined graph. In the literature, most GSL solutions either primarily focus on structure refinement with task-specific supervision (i.e., node classification), or overlook the inherent weakness of GNNs themselves (e.g., over-squashing), resulting in suboptimal performance despite sophisticated designs. In light of these limitations, we propose to study self-supervised graph structure-feature co-refinement for effectively alleviating the issue of over-squashing in typical GNNs. In this paper, we take a fundamentally different perspective of the Ricci curvature in Riemannian geometry, in which we encounter the challenges of modeling, utilizing and computing Ricci curvature. To tackle these challenges, we present a self-supervised Riemannian model, DeepRicci. Specifically, we introduce a latent Riemannian space of heterogeneous curvatures to model various Ricci curvatures, and propose a gyrovector feature mapping to utilize Ricci curvature for typical GNNs. Thereafter, we refine node features by geometric contrastive learning among different geometric views, and simultaneously refine graph structure by backward Ricci flow based on a novel formulation of differentiable Ricci curvature. Finally, extensive experiments on public datasets show the superiority of DeepRicci, and the connection between backward Ricci flow and over-squashing. Codes of our work are given in https://***/RiemanGraph/.

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