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Graph Neural networks in Computer Vision - architectures, Datasets and Common Approaches

Graph Neural Networks in Computer Vision - Architectures, Da...

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ieee International conference on Fuzzy Systems (FUZZ-ieee) / ieee World Congress on Computational Intelligence (ieee WCCI) / International Joint conference on Neural networks (IJCNN) / ieee Congress on Evolutionary Computation (ieee CEC)

作者： Krzywda, Maciej Lukasikt, Szymon Gandomi, Amir H. AGH Univ Sci & Technol Fac Phys & Appl Comp Sci Al Mickiewicza 30 PL-30059 Krakow Poland Univ Technol Sydney Fac Engn & IT 5 Broadway Ultimo NSW 2007 Australia Polish Acad Sci Syst Res Inst Ul Newelska 6 PL-01447 Warsaw Poland

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Graph Neural networks (GNNs) are a family of graph networks inspired by mechanisms existing between nodes on a graph. In recent years there has been an increased interest in GNN and their derivatives, i.e., Graph Attention networks (GAT), Graph Convolutional networks (GCN), and Graph Recurrent networks (GRN). An increase in their usability in computer vision is also observed. The number of GNN applications in this field continues to expand;it includes video analysis and understanding, action and behavior recognition, computational photography, image and video synthesis from zero or few shots, and many more. This contribution aims to collect papers published about GNN-based approaches towards computer vision. They are described and summarized from three perspectives. Firstly, we investigate the architectures of Graph Neural networks and their derivatives used in this area to provide accurate and explainable recommendations for the ensuing investigations. As for the other aspect, we also present datasets used in these works. Finally, using graph analysis, we also examine relations between GNN-based studies in computer vision and potential sources of inspiration identified outside of this field.

关键词： Graph Neural networks (GNNs) Graph Convolutional networks (GCNs) Computer Vision

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Performance of SNMP Protocol in Sensor network Measurement and IoT Web-based HMI Features 8

Performance of SNMP Protocol in Sensor Network Measurement a...

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8th International conference on Instrumentation, Control, and Automation, ICA 2023

作者： Nurmalia, Enung Bashar, Labid Bin Joelianto, Endra Institut Teknologi Bandung Instrumentation and Control Master Program Bandung Indonesia Institut Teknologi Bandung Instrumentation and Control Research Group Bandung Indonesia

ISBN: (纸本)9798350301274

The Simple network Management Protocol (SNMP) is a networking protocol used for the management and monitoring of all connected devices in internet standard protocol networks. Recently, the utilization and capability of SNMP has been enhanced to be able to handle data exchange across a network from the control level to the human machine interface level. Some controllers, including micro-controllers, have upgraded their features and coverage to include SNMP in their communications protocol. The utilization of SNMP is also supported by open Platform Communications (OPC) Server, which plays an important role as data exchange server for various controllers or systems with different gateways and drivers. This paper presents the performance assessment of SNMP implementation in a network, from controller to OPC server, comparing hardwired and wireless automation architectures. The results show that in both cases the round-Trip times (RTT) of SNMP were acceptable according to network criteria, with an average of less than 100 ms. Hence, SNMP is suitable as an industrial automation protocol. © 2023 ieee.

关键词： Electronic data interchange

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Microservice Scheduling for Satellite-Terrestrial Hybrid network with Edge Computing

Microservice Scheduling for Satellite-Terrestrial Hybrid Net...

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ieee/CIC International conference on Communications in China (ICCC)

作者： Huang, Yanzhe Zhang, Xing Beijing Univ Posts & Telecommun Wireless Signal Proc & Networks Lab Beijing Peoples R China

ISBN: (数字)9781665459778

ISBN: (纸本)9781665459778

Satellite-Terrestrial Hybrid network (STHN) has become a focus for future communication architecture to accommodate more services and applications. STHN combined with Edge Computing (EC) could provide lower delay, faster transfer speed, and better information transmission. Meanwhile, microservice-based architecture greatly increases system flexibility, which fits the STHN. In this paper, to verify the efficiency of STHN with EC system, we develop a platform based on an open source EC architecture called KubeEdge. The platform not only helps terrestrial to deploy and manage microservice on satellites but also increases the flexibility of satellite service provision. Besides, in this platform, we design a microservice scheduling algorithm called Optimal Microservice Scheduling with Adaptive Link Changes (OMS-ALC) to minimize communication delay. The experimental results show that OMS-ALC outperforms other solutions in end-to-end delay performance.

关键词： Adaptive scheduling Satellites Scheduling algorithms conferences Microservice architectures Computer architecture Information processing

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open Source Compiling for V1Model RMT Switch: Making Data Center networking Innovation Accessible 15

Open Source Compiling for V1Model RMT Switch: Making Data Ce...

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ieee/ACM 15th International conference on Utility and Cloud Computing (UCC) / 9th International conference on Big Data Computing, Applications and Technologies (BDCAT)

作者： Das Robin, Debobroto Khan, Javed I. Kent State Univ Kent OH 44242 USA

ISBN: (纸本)9781665460873

Very few of the innovations in deep networking have seen data center scale implementation. Because, the Data Center network's extreme scale performance requires hardware implementation, which is only accessible to a few. However, the emergence of reconfigurable match-action table (RMT) paradigm-based switches have finally opened up the development life cycle of data plane devices. The P4 language is the dominant language choice for programming these devices. Now, network operators can implement the desired feature over white box RMT switches. The process involves an innovator writing new algorithms in the P4 language and getting them compiled for the target hardware. However, there is still a roadblock. After designing an algorithm, the P4 program's compilation technology is not fully open-source. Thus, it is very difficult for an average researcher to get deep insight into the performance of his/her innovation when executed at the silicon level. There is no open-source compiler backend available for this purpose. Proprietary compiler backends provided by different hardware vendors are available for this purpose. However, they are closed-source and do not provide access to the internal mapping mechanisms. Which inhibits experimenting with new mapping algorithms and innovative instruction sets for reconfigurable matchaction table architecture. This paper describes our work toward an open-source compiler backend for compiling P416 targeted for the V1Model architecture-based programmable switches.

关键词： Compiler Forwarding ASIC In network Computation P4 V1Model Programmable Data Plane Programmable Switch SDN

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LSTM-based MPC Method for Stochastic Energy Management of Microgrid 3

LSTM-based MPC Method for Stochastic Energy Management of Mi...

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3rd International conference on New Energy and Power Engineering, ICNEPE 2023

作者： An, Na Liu, Tingxiang Chen, Ke Huang, Haojiang Yang, Libin Li, Zhengxi Gao, Jin Zhu, Jianquan Clean Energy Development Research Institute of State Grid Qinghai Electric Power Company Economic and Technological Research Institute of State Grid Qinghai Electric Power Company Xining China South China University of Technology Guangzhou China

ISBN: (纸本)9798350393224

The intermittency and randomness of renewable energy sources (RESs) bring great challenge to the stochastic energy management (SEM) problem of microgrids (MGs). To solve this problem, we embed long short-term memory (LSTM) networks into model predictive control (MPC). The performance of the MPC method relies on the efficiency of real-time prediction and rolling optimization. Therefore, during the real-time prediction stage, a prediction network is constructed to improve the accuracy of prediction for random variables. Furthermore, during the rolling optimization stage, since commercial solvers achieve better performance when solving nonlinear programming (NLP) problems compared to mixed-integer nonlinear programming (MINLP) problems, a policy network is constructed to convert the MINLP into an NLP problem, reducing the complexity of the problem. In this paper, a LSTM-based MPC method is proposed for SEM of MG. Compared to traditional MPC methods, the proposed method demonstrates superior computational efficiency in addressing the SEM problem. Finally, case studies on the given test system fully verifies the accuracy and effectiveness of the proposed method. © 2023 ieee.

关键词： long short-term memory network microgrid MINL model predictive control stochastic energy management

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Optimizing IoT network Intrusion Detection: A Deep Learning Approach 7

Optimizing IoT Network Intrusion Detection: A Deep Learning ...

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7th conference on Cloud and Internet of Things, CIoT 2024

作者： Alam, Khorshed Monir, Md Fahad Hassan, Zoheb Habib, Md Tarek Department of Computer Science & Engineering Bangladesh Virginia Tech Department of Electrical and Computer Engineering United States Université Laval Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350356717

network Intrusion Detection System (NIDS) serves as a essential component in data protection by monitoring computer networks for threats that can bypass conventional defenses such as malware and hackers. Deep learning (DL) techniques provide a promising approach for analyzing raw IoT network data to identify subtle patterns indicative of intrusion attempts. This study addresses a crucial research gap by developing a Deep Convolutional Neural network (DCNN) model specifically designed for the efficient detection of stealthy and polymorphic variants while reducing false positives. Utilizing the NF-ToN-IoT dataset, the proposed model achieves outstanding performance metrics on test data, with an accuracy of 0.9923, precision of 0.9925, recall of 0.9979, and F1 score of 0.9952. To comprehensively evaluate the robustness of the model, a multi-dataset validation strategy is employed. The model is retrained and assessed on established benchmark datasets on IoT networks, including NF-UNSW-NB15, NF-UNSW-NB15-v2 and NF-BoTIoT, demonstrating exceptional performance. Furthermore, the significance of the contribution is validated by comparing the proposed model against previously established architectures such as CNN+BiLSTM, DNN, GRU+RNN, and CNN+LSTM using the NF-ToN-IoT dataset. The proposed model consistently outperforms these prior models, highlighting its efficacy and advancements in the field. Additionally, an ablation study is conducted to analyze the individual components of the Deep CNN model, providing insights into their contributions towards detecting malware traffic and offering guidance for optimizing future NIDS models in the cybersecurity domain. Making our work available open-source on https://***/codewithkhurshed/IDSIUB can enhance its accessibility and promote future research opportunities in network Intrusion Detection. © 2024 ieee.

关键词： network intrusion

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Near-Optimal Glimpse Sequences for Improved Hard Attention Neural network Training

Near-Optimal Glimpse Sequences for Improved Hard Attention N...

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作者： Harvey, William Teng, Michael Wood, Frank Univ British Columbia Dept Comp Sci Vancouver BC Canada Univ Oxford Dept Engn Sci Oxford England

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Hard visual attention is a promising approach to reduce the computational burden of modern computer vision methodologies. However, hard attention mechanisms can be difficult and slow to train, which is especially costly for applications like neural architecture search where multiple networks must be trained. We introduce a method to amortise the cost of training by generating an extra supervision signal for a subset of the training data. This supervision is in the form of sequences of 'good' locations to attend to for each image. We find that the best method to generate supervision sequences comes from framing hard attention for image classification as a Bayesian optimal experimental design (BOED) problem. From this perspective, the optimal locations to attend to are those which provide the greatest expected reduction in the entropy of the classification distribution. We introduce methodology from the BOED literature to approximate this optimal behaviour and generate 'near-optimal' supervision sequences. We then present a hard attention network training objective that makes use of these sequences and show that it allows faster training than prior work. We finally demonstrate the utility of faster hard attention training by incorporating supervision sequences in a neural architecture search, resulting in hard attention architectures which can outperform networks with access to the entire image.

关键词： hard attention Bayesian experimental design

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Efficient Uncertainty Quantification for Multilabel Text Classification

Efficient Uncertainty Quantification for Multilabel Text Cla...

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作者： Yu, Jialin Cristea, Alexandra, I Harit, Anoushka Sun, Zhongtian Aduragba, Olanrewaju Tahir Shi, Lei Al Moubayed, Noura Univ Durham Dept Comp Sci Durham England

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Despite rapid advances of modern artificial intelligence (AI), there is a growing concern regarding its capacity to be explainable, transparent, and accountable. One crucial step towards such AI systems involves reliable and efficient uncertainty quantification methods. Existing approaches to uncertainty quantification in natural language processing (NLP) take a Bayesian Deep Learning approach. However, the latter is known to not be computationally efficient in testing time, thus hindering its applicability in real-life scenarios. This paper proposes a new focus on the efficiency of uncertainty quantification methods, evaluating them on four multi-label text classification tasks. Our novel methods of representing epistemic and aleatoric uncertainties enable efficient uncertainty quantification (around 13 to 45 times faster than existing approaches, depending on architecture) with posterior analysis in the (approximated) latent- and data space. We conduct extensive experiments and studies on diverse neural network architectures (LSTM, CNN and Transformer) to analyse their power. Our results prove the benefits of explicitly modelling uncertainty in neural networks.

关键词： Bayesian method neural network deep learning natural language processing uncertainty quantification

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EARNEST: Experimental Analysis of RAN Energy with open-Source Software Tools 16

EARNEST: Experimental Analysis of RAN Energy with Open-Sourc...

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16th International conference on COMmunication Systems and networkS, COMSNETS 2024

作者： Gudepu, Venkateswarlu Chirumamilla, Bhargav Tella, Rajashekhar Reddy Bhattacharyya, Abhishek Agarwal, Shubh Malakalapalli, Lavanya Centofanti, Carlo Santos, José Kondepu, Koteswararao Indian Institute of Technology Dharwad Dharwad India University of l'Aquila Disim Department L'Aquila Italy Ghent University - Imec IDLab Department of Information Technology Gent Belgium

ISBN: (纸本)9798350383119

The 5G and Beyond (B5G) networks aim to support diverse use cases - extremely low latency, high data rates, and dense user connectivity. However, meeting these use cases results in an increase in energy consumption due to the use of computing resources in the B5G networks. While several studies focus on 5G Core network (5G CN) energy consumption, it's essential to acknowledge that a substantial 75% of the network's overall energy usage occurs within the Radio Access network (RAN). Hence, it's crucial to focus on RAN energy performance improvements. This paper exploits various open-source software tools that measure and monitor RAN energy, which helps design energy-efficient RAN. Different RAN architectures such as Monolithic, Disaggregated, and Control Plane and User Plane Separation (CUPS) are considered to measure and monitor energy consumption using open-source software tools - S-tui and Scaphandre. We study energy consumption as a function of the number of connected User Equipments (UEs) and the impact of connecting multiple Distributed Units (DUs) on the energy consumption of both the Control Plane (CP) and User Plane (UP) of gNB Central Unit (CU). We also study the energy consumption of various open source 5G CN. Finally, this study examines the influence of various RAN parameters on energy consumption by using a real-time dataset of the monolithic RAN scenario. © 2024 ieee.

关键词： 5G and Beyond (B5G) Energy Efficiency Power consumption Radio Access network (RAN)

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Comparison of Parallel and Serial Execution of Shortest Path Algorithms 3

Comparison of Parallel and Serial Execution of Shortest Path...

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3rd ieee International conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023

作者： Ameen, Ahmed Taha Fouad, Ahmed M. Matta, Emil Mourad Abdelkhalek Elkhishen, Rawan Alalem, Shorouk Abdelfattah Mohamed Safan, Aya Abdel Halim, Islam Tharwat Nile University School of Engineering and Applied Sciences Giza Egypt Giza Egypt

ISBN: (纸本)9798350338263

Shortest Path Algorithms are an important set of algorithms in today's world. It has many applications like Traffic Consultation, Route Finding, and network Design. It is essential for these applications to be fast and efficient as they mostly require real-Time execution. Sequential execution of shortest path algorithms for large graphs with many nodes is time-consuming. On the other hand, parallel execution can make these applications faster. In this paper, three popular shortest path algorithms-Dijkstra, Bellman-Ford, and Floyd Warshall-Are both implemented as serial and parallel programs and tested on various problem sizes. The performance of these algorithms is evaluated by comparing their execution times. To achieve parallelization, the openMP (open multiprocessing) framework is employed. © 2023 ieee.

关键词： Application programming interfaces (API)

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