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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Tanmay Badageri Bechir Hamdaoui Rami Langar School of Electrical Engineering and Computer Science Oregon State University Corvallis Oregon USA Software and IT Engineering Department Ecole de Technologie Superieure (ETS) Montreal Canada CNRS LIGM Univ. Gustave Eiffel Marne-la-Vallee France

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

software-Defined Networking (SDN) provides the flexibility to dynamically manage network paths, facilitating efficient traffic flow and mutipath routing, thereby improving network resiliency to congestion. This study investigates three distinct SDN-enabled routing strategies: shortest path routing, Equal-Cost Multi-Path (ECMP) routing, and bandwidth-based multipath routing. The comparative result analysis across these three routing strategies revealed that bandwidth-based routing consistently outperforms Shortest Path and ECMP routing across key performance metrics. The study found that Bandwidth-based routing maintains lower delay and jitter, indicating its superior ability to manage network traffic efficiently even as data flow increases. Furthermore, it demonstrates a more modest increase in packet loss, underscoring its effective congestion management. These findings suggest that Bandwidth-based routing provides a more reliable and efficient network performance, particularly in high-traffic conditions, making it a preferable solution for SDN implementations seeking to optimize data flow and network stability.

关键词： Wireless communication Packet loss Telecommunication traffic Routing Reliability engineering Stability analysis software defined networking

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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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MambaIR: A Simple Baseline for Image Restoration with State-Space Model

arXiv

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arXiv 2024年

作者： Guo, Hang Li, Jinmin Dai, Tao Ouyang, Zhihao Ren, Xudong Xia, Shu-Tao Tsinghua Shenzhen International Graduate School Tsinghua University China College of Computer Science and Software Engineering Shenzhen University China ByteDance Inc. China Aitist.ai Peng Cheng Laboratory China

Recent years have seen significant advancements in image restoration, largely attributed to the development of modern deep neural networks, such as CNNs and Transformers. However, existing restoration backbones often face the dilemma between global receptive fields and efficient computation, hindering their application in practice. Recently, the Selective Structured State Space Model, especially the improved version Mamba, has shown great potential for long-range dependency modeling with linear complexity, which offers a way to resolve the above dilemma. However, the standard Mamba still faces certain challenges in low-level vision such as local pixel forgetting and channel redundancy. In this work, we introduce a simple but effective baseline, named MambaIR, which introduces both local enhancement and channel attention to improve the vanilla Mamba. In this way, our MambaIR takes advantage of the local pixel similarity and reduces the channel redundancy. Extensive experiments demonstrate the superiority of our method, for example, MambaIR outperforms SwinIR by up to 0.45dB on image SR, using similar computational cost but with a global receptive field. Code is available at https://***/csguoh/MambaIR. © 2024, CC BY-SA.

关键词： Pixels

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Leveraging Predicate and Triplet Learning for Scene Graph Generation

Leveraging Predicate and Triplet Learning for Scene Graph Ge...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Jiankai Li Yunhong Wang Xiefan Guo Ruijie Yang Weixin Li 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

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

关键词： Visualization Head Triples (Data structure) Semantics Genomics Tail Pattern recognition

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Bi-CLKT: Bi-Graph Contrastive Learning based Knowledge Tracing

arXiv

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arXiv 2022年

作者： Song, Xiangyu Li, Jianxin Lei, Qi Zhao, Wei Chen, Yunliang Mian, Ajmal School of IT Faculty of Science Engineering and Built Environment Deakin University GeelongVIC3220 Australia School of Information Engineering Chang'an University Xi'An China School of Computer Science and Technology Xidian University Xi'An China School of Computer Science China University of Geosciences Wuhan China Department of Computer Science and Software Engineering The University of Western Australia Perth Australia

The goal of Knowledge Tracing (KT) is to estimate how well students have mastered a concept based on their historical learning of related exercises. The benefit of knowledge tracing is that students' learning plans can be better organised and adjusted, and interventions can be made when necessary. With the recent rise of deep learning, Deep Knowledge Tracing (DKT) has utilised Recurrent Neural Networks (RNNs) to accomplish this task with some success. Other works have attempted to introduce Graph Neural Networks (GNNs) and redefine the task accordingly to achieve significant improvements. However, these efforts suffer from at least one of the following drawbacks: 1) they pay too much attention to details of the nodes rather than to high-level semantic information;2) they struggle to effectively establish spatial associations and complex structures of the nodes;and 3) they represent either concepts or exercises only, without integrating them. Inspired by recent advances in self-supervised learning, we propose a Bi-Graph Contrastive Learning based Knowledge Tracing (Bi-CLKT) to address these limitations. Specifically, we design a two-layer comparative learning scheme based on an "exercise-to-exercise" (E2E) relational subgraph. It involves node-level contrastive learning of subgraphs to obtain discriminative representations of exercises, and graph-level contrastive learning to obtain discriminative representations of concepts. Moreover, we designed a joint contrastive loss to obtain better representations and hence better prediction performance. Also, we explored two different variants, using RNN and memory-augmented neural networks as the prediction layer for comparison to obtain better representations of exercises and concepts respectively. Extensive experiments on four real-world datasets show that the proposed Bi-CLKT and its variants outperform other baseline models. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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Gradient Compression and Correlation Driven Federated Learning for Wireless Traffic Prediction

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IEEE Transactions on Cognitive Communications and Networking 2024年

作者： Zhang, Chuanting Zhang, Haixia Dang, Shuping Shihada, Basem Alouini, Mohamed-Slim Shandong University School of Software Jinan250101 China Shandong University School of Control Science and Engineering Jinan250061 China Shandong University Shandong Key Laboratory of Wireless Communication Technologies Jinan250061 China University of Bristol Department of Electrical and Electronic Engineering BristolBS8 1UB United Kingdom Computer Electrical and Mathematical Science and Engineering Division Thuwal23955-6900 Saudi Arabia

Wireless traffic prediction plays an indispensable role in cellular networks to achieve proactive adaptation for communication systems. Along this line, Federated Learning (FL)-based wireless traffic prediction at the edge attracts enormous attention because of the exemption from raw data transmission and enhanced privacy protection. However FL-based wireless traffic prediction methods still rely on heavy data transmissions between local clients and the server for local model updates. Besides, how to model the spatial dependencies of local clients under the framework of FL remains uncertain. To tackle this, we propose an innovative FL algorithm that employs gradient compression and correlation-driven techniques, effectively minimizing data transmission load while preserving prediction accuracy. Our approach begins with the introduction of gradient sparsification in wireless traffic prediction, allowing for significant data compression during model training. We then implement error feedback and gradient tracking methods to mitigate any performance degradation resulting from this compression. Moreover, we develop three tailored model aggregation strategies anchored in gradient correlation, enabling the capture of spatial dependencies across diverse clients. Experiments have been done with two real-world datasets and the results demonstrate that by capturing the spatio-temporal characteristics and correlation among local clients, the proposed algorithm outperforms the state-of-the-art algorithms and can increase the communication efficiency by up to two orders of magnitude without losing prediction accuracy. © 2015 IEEE.

关键词： Prediction models

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LCM: locally constrained compact point cloud model for masked point modeling 24

LCM: locally constrained compact point cloud model for maske...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yaohua Zha Naiqi Li Yanzi Wang Tao Dai Hang Guo Bin Chen Zhi Wang Zhihao Ouyang Shu-Tao Xia Tsinghua Shenzhen International Graduate School Tsinghua University and Institute of Visual Intelligence Pengcheng Laboratory Tsinghua Shenzhen International Graduate School Tsinghua University College of Computer Science and Software Engineering Shenzhen University Harbin Institute of Technology Shenzhen Bytedance Inc.

ISBN: (纸本)9798331314385

The pre-trained point cloud model based on Masked Point Modeling (MPM) has exhibited substantial improvements across various tasks. However, these models heavily rely on the Transformer, leading to quadratic complexity and limited decoder, hindering their practice application. To address this limitation, we first conduct a comprehensive analysis of existing Transformer-based MPM, emphasizing the idea that redundancy reduction is crucial for point cloud analysis. To this end, we propose a Locally constrained Compact point cloud Model (LCM) consisting of a locally constrained compact encoder and a locally constrained Mamba-based decoder. Our encoder replaces self-attention with our local aggregation layers to achieve an elegant balance between performance and efficiency. Considering the varying information density between masked and unmasked patches in the decoder inputs of MPM, we introduce a locally constrained Mamba-based decoder. This decoder ensures linear complexity while maximizing the perception of point cloud geometry information from unmasked patches with higher information density. Extensive experimental results show that our compact model significantly surpasses existing Transformer-based models in both performance and efficiency, especially our LCM-based Point-MAE model, compared to the Transformer-based model, achieved an improvement of 1.84%, 0.67%, and 0.60% in average accuracy on the three variants of ScanObjectNN while reducing parameters by 88% and computation by 73%. Code is available at https://***/zyh16143998882/LCM.

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Intelligent New Energy Vehicle Battery Swap Scheme based on Blockchain Technology 23

Intelligent New Energy Vehicle Battery Swap Scheme based on ...

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Proceedings of the 2023 5th International Conference on Blockchain Technology

作者： Ya Han Jianbo Gao Zhi Guan Zhong Chen Lifeng Ren School of Computer Science Peking University China and Peking University Chongqing Research Institute of Big Data China National Engineering Research Center for Software Engineering Peking University China Peking University Chongqing Research Institute of Big Data China

ISBN: (纸本)9798400708930

With the development of electric vehicle technology, electric vehicles surpass fuel vehicles in terms of power performance and intelligence, and the cruising range has also increased to more than 400km with the increase of battery energy density and power consumption. However, the overall is still facing the problem of energy refueling anxiety, and the fuel vehicle refueling time is 5 minutes, and the current fast charge takes at least 60 minutes. The waiting time for charging during peak hours has also been further lengthened. Increasing the speed of energy replenishment has become an urgent need for new energy vehicle owners. The way electric vehicles are supplied with energy is fundamentally different from that of traditional cars. The battery swap mode naturally separates the vehicle and electricity, providing the possibility for novel business models (vehicle electricity separation, battery sharing). Let the battery return to its "energy carrier" use attribute, realize the sharing of batteries, create conditions for battery financialization, carry out full life cycle value management of batteries, implement battery gradient utilization, and provide a feasible path for future energy storage business. However, in battery swap mode, there are many divisions of responsibilities. For example, if the battery burns the car, should I find the main engine factory or the power exchange company/high-voltage electrical failure after the power exchange, especially when the responsibility is not clear, who will check and repair. This paper proposes a new energy vehicle monitoring platform based on blockchain technology, which can manage the whole process life cycle of new energy batteries through blockchain traceability technology. At the same time, according to the current responsibility division system, the transparent division of responsibilities is realized in combination with blockchain smart contracts.

关键词： Battery swap mode Blockchain New energy Responsibility division

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Design of Multimedia Database Online Integration System Based on Artificial Intelligence

Design of Multimedia Database Online Integration System Base...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Jiaqi Li Gaojie Xiong Xing Wu Computer Sciense and Technology Harbin Institute of Technology Shenzhen Guangdong China School of Information and Software Engineering University of Electronic Science and Technology Chengdu Sichuan China Zhejiang Vocational Academy of Art Hangzhou Zhejiang China

Multimedia database online integration system plays an important role in today’s information age. However, the performance of the integrated system is limited by the problems of low accuracy, insufficient recall rate and long running time of traditional methods in processing multimedia data. This paper designs an online integration system based on artificial intelligence (AI) and verifies its effectiveness and feasibility through experiments so as to overcome these problems. In the study, multimedia datasets suitable for the experimental needs are selected and the AI-based online integration system is compared with traditional methods. Through the analysis of accuracy, recall, F1 value and mean square error, it is found that the online integration system based on AI has obvious advantages in multimedia data integration. The results of this paper demonstrate the feasibility and effectiveness of an online integration system based on AI. It is able to enhance the accuracy and efficiency of multimedia data integration while reducing runtime and resource consumption.

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