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A Stepwise Path Selection Scheme Based on Multiple QoS Parameters Evaluation in SDN 17th

A Stepwise Path Selection Scheme Based on Multiple QoS Param...

17th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Liu, Lin Zhou, Jian-Tao Xing, Hai-Feng Guo, Xiao-Yong College of Computer Science Ecological Big Data Engineering Research Center of the Ministry of Education Cloud Computing and Service Software Engineering Laboratory of Inner Mongolia Autonomous Region National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Social Computing and Data Processing Key Laboratory of Inner Mongolia Autonomous Region Big Data Analysis Technology Engineering Research Center of Inner Mongolia Autonomous Region Inner Mongolia University Inner Mongolia Hohhot China College of Computer Science and Technology Inner Mongolia Normal University Inner Mongolia Hohhot China College of Computer Information and Management Inner Mongolia University of Finance and Economics Inner Mongolia Hohhot China

ISBN: (纸本)9783030926342

Nowadays, the best-effort service can not guarantee the quality of service (QoS) for all kinds of services. QoS routing is an important method to guarantee QoS requirements. It involves path selection for flows based on the current network status and the performance criteria of the service requirements. However, it is difficult for proposed solutions to obtain all the available paths owing to not fully considering all the QoS parameters of paths. In this paper, we propose SWQoS, a novel, universal, and stepwise QoS guarantee scheme based on multiple QoS parameter evaluation for selecting the available paths including preferred paths, satisfied paths and reluctant paths in SDN. The experiments show that SWQoS can select all the available paths that meet the performance criteria of the service requirements and have better QoS parameter performance compared with other path selection methods. © 2021, ICST institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Quality of service

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Low-Light Enhancement Effect on Classification and Detection: An Empirical Study

arXiv

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arXiv 2024年

作者： Wu, Xu Lai, Zhihui Jie, Zhou Gao, Can Hou, Xianxu Zhang, Ya-Nan Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China

Low-light images are commonly encountered in real-world scenarios, and numerous low-light image enhancement (LLIE) methods have been proposed to improve the visibility of these images. The primary goal of LLIE is to generate clearer images that are more visually pleasing to humans. However, the impact of LLIE methods in high-level vision tasks, such as image classification and object detection, which rely on high-quality image datasets, is not well explored. To explore the impact, we comprehensively evaluate LLIE methods on these high-level vision tasks by utilizing an empirical investigation comprising image classification and object detection experiments. The evaluation reveals a dichotomy: While Low-Light Image Enhancement (LLIE) methods enhance human visual interpretation, their effect on computer vision tasks is inconsistent and can sometimes be harmful. Our findings suggest a disconnect between image enhancement for human visual perception and for machine analysis, indicating a need for LLIE methods tailored to support high-level vision tasks effectively. This insight is crucial for the development of LLIE techniques that align with the needs of both human and machine vision. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine vision

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A Machine Learning Based Risk Identification Method for Illegal Pig Slaughtering

A Machine Learning Based Risk Identification Method for Ille...

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2023 International Conference on Intelligent Communication and Networking, ICN 2023

作者： Miao, Jiajun Shen, Yujun Zhang, Yan Liu, Lingjie Tian, Ye Zhao, Yu Zhou, Chunyan Harbin Engineering University College of Computer Science and Technology Harbin China Hangzhou Hikvision Digital Technology Co. Ltd Hangzhou China China Industrial Control Systems Cyber Emergency Response Team Beijing China Environment and Food and Drug Crime Investigation Detachment of Hangzhou Public Security Bureau Hangzhou China Xidian University Hangzhou Institute of Technology Harbin China Key Laboratory of Peace-Building Big Data of Zhejiang Province Hangzhou China

ISBN: (纸本)9798350357677

With the impact of African swine fever, the survival rate of domestic pigs has sharply decreased, and pork prices continue to rise. Many unscrupulous elements obtain illegal profits by avoiding inspection and quarantine, processing sick and dead pigs, and injecting water, etc. Unquarantined meat products pose serious food safety risks. In this context, we propose a machine learning based risk identification method for illegal slaughter of live pigs. Firstly, we use the RetinaNet algorithm to detect live pig transport vehicles and use a dual residual network to Super-Resolution the vehicle area in the detected image to improve facial clarity. Subsequently, the ArcFace algorithm is used to match the facial information with the ex-convicts, and finally, the Location of vehicles passing through, time, frequency and facial analysis results are combined for calculation and scoring and provide the risk warning for illegal slaughter of live pigs. © 2023 IEEE.

关键词： Risk assessment

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Revisiting Explicit Recommendation with Dc-Gcn: Divide-and-Conquer Graph Convolution Network

SSRN

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SSRN 2024年

作者： Peng, Furong Liao, Fujin Lu, Xuan Zheng, Jianxing Li, Ru Institute of Big Data Science and Industry Shanxi University 92 Wucheng Road Taiyuan030006 China College of Physics and Electronic Engineering Shanxi University 92 Wucheng Road Taiyuan030006 China School of Computer and Information Technology Shanxi University 92 Wucheng Road Taiyuan030006 China

In recent years, Graph Convolutional Networks (GCNs) have primarily been applied to implicit feedback recommendation, with limited exploration in explicit scenarios. Large Language Models(LLMs) for implicit recommendation also have been extensively explored, but it's ill-suited for explicit recommendations due to small scale dataset cannot fully reflect LLM's ability. Although explicit recommendations can yield promising results, the conflict between the sparsity of data and the data starvation of deep learning hinders its development. Unlike implicit scenarios, explicit recommendation provides less evidence for predictions and requires distinguishing weights of edges (ratings) in the user-item *** exploit high-order relations by GCN in explicit scenarios, we propose dividing the explicit rating graph into sub-graphs, each containing only one type of rating. We then employ GCN to capture user and item representations within each sub-graph, allowing the model to focus on rating-related user-item relations, and aggregate the representations of all subgraphs by MLP for the final recommendation. This approach, named Divide-and-Conquer Graph Convolution Network (DC-GCN), simplifies each model's mission and highlights the strengths of individual modules. Considering that creating GCNs for each sub-graph may result in over-fitting and faces more serious data sparsity, we propose to share node embeddings for all GCNs to reduce the number of parameters, and create rating-aware embedding for each sub-graph to model rating-related relations. Moreover, to alleviate over-smoothing, we utilize random column mask to randomly select columns of node features to update in GCN layers. This technique can prevent node representations from becoming homogeneous in deep GCN networks. DC-GCN is evaluated on four public datasets and achieves the SOTA experimentally. Furthermore, DC-GCN is analyzed in cold-start and popularity bias scenarios, exhibiting competitive performance in various

关键词： Graph neural networks

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Multi-scale Contrastive Learning for Gastroenteroscopy Classification

Multi-scale Contrastive Learning for Gastroenteroscopy Class...

引用

Annual IEEE Symposium on computer-Based Medical Systems

作者： Dan Li Xuechen Li Zhibin Peng Wenting Chen Linlin Shen Guangyao Wu Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology ShenZhen University Shenzhen China City University of Hong Kong Hong Kong SAR China Shenzhen Institute of Artificial Intelligence & Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University General Hospital

In gastroenteroscopy image analysis, numerous CADs demonstrate that deep learning aids doctors' diagnosis. The shapes and sizes of the lesions are varied. And in the clinic, the dataset appears to be data imbalanced. However, existing methods directly classify by texture and ignore lesions with various shapes and sizes. To address the issue above, we propose a deep neural network, which consists of multi-scale feature extraction, contrastive feature learning and a multi-scale feature fusion module. We train the contrastive feature learning module and multi-scale feature fusion module simultaneously to alleviate the issue of data distribution differences. Thus, the proposed network can better identify various categories. Extensive experiments on the Hyper Kvasir dataset show that the proposed Hybrid-M2CL outperforms the benchmark proposed by the dataset with 5.0% Macro Precision, 3.3% Macro Recall, 3.4% Macro F1-score, 3.3% Micro Precision, 3.6% MCC. In addition, it outperforms the SOTA by 1.1% Macro F1-score, 2.6% MCC, and 2.0% B-ACC.

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A hybrid approach using human posture and contour for gait recognition under body occlusion 7

A hybrid approach using human posture and contour for gait r...

引用

7th International Conference on Information science and Control engineering, ICISCE 2020

作者： Ma, Yue Wei, Chenghao Long, Hao Key Laboratory of Impression Evidence Examination and Identification Technology Ministry of Public Security China Shenzhen University Big Data Institute College of Computer Science and Software Engineering China

ISBN: (纸本)9781728164069

This paper presents a hybrid method using a Gaitset network for gait recognition. We firstly use Alphpose model to obtain key points of human posture to improve the extraction of human contour under body occlusion. The human contour map is obtained by pose2Seg model with the key points of human posture. Both human posture and body contour are used as input of the network. By using the hybrid method, it can effectively improve the accuracy and robustness of gait recognition with the upper body information under occlusion. In scenes of human walking under body occlusion, our experimental results show that the accuracy of the proposed method in self-built database can reach 84.6% accuracy. © 2020 IEEE.

关键词： Gait analysis

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ConsistencyDet: A Robust Object Detector with a Denoising Paradigm of Consistency Model

arXiv

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arXiv 2024年

作者： Jiang, Lifan Wang, Zhihui Wang, Changmiao Li, Ming Leng, Jiaxu Wu, Xindong the College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266510 China Shenzhen Research Institute of Big Data Shenzhen518172 China Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua321004 China the Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing400065 China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing210094 China the Key Laboratory of Knowledge Engineering with Big Data the Ministry of Education of China China School of Computer Science and Information Technology Hefei University of Technology Hefei230009 China

Object detection, a quintessential task in the realm of perceptual computing, can be tackled using a generative methodology. In the present study, we introduce a novel framework designed to articulate object detection as a denoising diffusion process, which operates on the perturbed bounding boxes of annotated entities. This framework, termed ConsistencyDet, leverages an innovative denoising concept known as the Consistency Model. The hallmark of this model is its self-consistency feature, which empowers the model to map distorted information from any temporal stage back to its pristine state, thereby realizing a "one-step denoising" mechanism. Such an attribute markedly elevates the operational efficiency of the model, setting it apart from the conventional Diffusion Model. Throughout the training phase, ConsistencyDet initiates the diffusion sequence with noise-infused boxes derived from the ground-truth annotations and conditions the model to perform the denoising task. Subsequently, in the inference stage, the model employs a denoising sampling strategy that commences with bounding boxes randomly sampled from a normal distribution. Through iterative refinement, the model transforms an assortment of arbitrarily generated boxes into definitive detections. Comprehensive evaluations employing standard benchmarks, such as MS-COCO and LVIS, corroborate that ConsistencyDet surpasses other leading-edge detectors in performance metrics. Our code is available at https://***/Tankowa/ConsistencyDet. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Adaptive Student Inference Network for Efficient Single Image Super-Resolution

Adaptive Student Inference Network for Efficient Single Imag...

引用

IEEE International Conference on data Mining (ICDM)

作者： Kang Miao Zhao Zhang Jiahuan Ren Mingbo Zhao Haijun Zhang Richang Hong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Kunming China School of Information Science and Technology Donghua University Shanghai China Department of Computer Science Harbin Institute of Technology (Shenzhen) Shenzhen China

Recent advances in single image super-resolution (SISR) have achieved remarkable performance through deep learning. However, the high computational cost hinders the deployment of SISR models on edge devices. Instead of proposing new SISR models, a new trend is emerging to improve network efficiency by reducing parameters, FLOPs, and inference time through slight modifications to the original models. However, recent methods usually focus on reducing only one of three metrics, i.e., FLOPs, parameters and inference time, which inevitably increases the other two metrics. In this paper, we propose a novel Adaptive Student Inference Network (ASIN) on popular SISR models, which aims at reducing FLOPs and inference time while maintaining the number of parameters and restoring clearer high-resolution images. Specifically, our ASIN divides a SISR model into three components (head, body and tail) and adopts various strategies for each part. For head and tail parts, to ensure the restored images contain more detailed information, a novel auxiliary Enhanced Teacher Network (ETNet) is designed, which is trained with the ground-truth images to obtain more prior knowledge to guide student network to extract more accurate textures using a new knowledge distillation method. For the body part, owing to the varying difficulties of the reconstructions in different regions, we propose an Adaptive Depth Predicted Module (ADPM) to dynamically shorten average depth of network to reduce the computational cost of overall network. Extensive experiments on two datasets demonstrate the effectiveness and state-of-the-art performance of our ASIN compared to its counterparts.

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A First Principles Study of the Surface Electronic Properties of Mo2c

SSRN

引用

SSRN 2022年

作者： Xu, Hua Cen, Weifu Tian, Zean Zheng, Quan Gao, Tinghong Liang, Yongchao Xie, Quan Institute of Advanced Optoelectronic Materials and Technology School of Big Data and Information Engineering Guizhou University Guiyang550025 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

The electronic structure and optical properties of Mo2C with different surface atomic layer thicknesses (SALT) have been investigated by employing the plane wave ultra-soft pseudopotential method based on the first principles. It is found that the LDA density generalization approximation is most suitable for the optimization of the geometry of Mo2C, the lowest energy and most stable structure are obtained. As the SALT decreases, the hybridization of Mo d and Mo p state electrons in the valence band, forming strongly hybridized orbitals, inhibits photoelectron leap, the band gap increases, and Mo2C transforms from a metal to a semiconductor. Therefore, the dielectric function, refractive index, reflection spectrum, and absorption spectrum can be improved by adjusting the SALT of Mo2C to improve the optical utilization, the results can provide a theoretical basis for improving and optimizing its photoelectronic properties. © 2022, The Authors. All rights reserved.

关键词： Electronic properties

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Exact Fit Attention in Node-Holistic Graph Convolutional Network for Improved EEG-Based Driver Fatigue Detection

arXiv

引用

arXiv 2025年

作者： Xu, Meiyan Chen, Qingqing Chen, Duo Ding, Yi Wang, Jingyuan Gu, Peipei Pan, Yijie Huang, Deshuang Guo, Jiayang Zhang, Xun Department of Computer Science The Institute of Human Factors Engineering Minnan Normal University Zhangzhou363000 China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing210023 China College of Computing and Data Science Nanyang Technological University Singapore School of Digital Media and Design Arts Beijing University of Posts and Telecommunications Beijing100088 China College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450000 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Eastern Institute for Advanced Study Eastern Institute of Technology Ningbo315200 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315200 China National Institute for Data Science in Health and Medicine Xiamen University Xiamen361005 China

EEG-based fatigue monitoring can effectively reduce the incidence of related traffic accidents. In the past decade, with the advancement of deep learning, convolutional neural networks (CNN) have been increasingly used for EEG signal processing. However, due to the data’s non-Euclidean characteristics, existing CNNs may lose important spatial information from EEG, specifically channel correlation. Thus, we propose the node-holistic graph convolutional network (NHGNet), a model that uses graphic convolution to dynamically learn each channel’s features. With exact fit attention optimization, the network captures inter-channel correlations through a trainable adjacency matrix. The interpretability is enhanced by revealing critical areas of brain activity and their interrelations in various mental states. In validations on two public datasets, NHGNet outperforms the SOTAs. Specifically, in the intra-subject, NHGNet improved detection accuracy by at least 2.34% and 3.42%, and in the inter-subjects, it improved by at least 2.09% and 15.06%. Visualization research on the model revealed that the central parietal area plays an important role in detecting fatigue levels, whereas the frontal and temporal lobes are essential for maintaining vigilance. Copyright © 2025, The Authors. All rights reserved.

关键词： Electroencephalography

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