检索结果-内蒙古大学图书馆

Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8862卷 91-103页

作者： Li, Yu-Kun Zhang, Min-Ling School of Computer Science and Engineering Southeast University China Key Laboratory of Computer Network and Information Integration Southeast University China

Binary relevance (BR) is regarded as the most intuitive solution to learn from multi-label data, which decomposes the multi-label learning task into a number of independent binary learning tasks (one per class label). To amend its potential weakness of ignoring label correlations, many correlation-enabling extensions to BR have been proposed based on two major strategies, i.e. assuming random correlations with chaining structure or taking full-order correlations with stacking structure. However, in both strategies label correlations are only exploited in an uncontrolled manner, which may be problematic when error-prone and uncorrelated class labels arise. In this paper, to fulfill controlled label correlations exploitation, a novel enhancement to BR is proposed based on a two-stage filtering procedure. In the first stage, error-prone class labels are pruned from the label space based on holdout validation. In the second stage, closely-related class labels are identified based on supervised feature selection by viewing unpruned labels as features. Extensive experiments across fourteen multi-label data sets confirm the superiority of controlled label correlations exploitation, especially when large number class labels exist in the label space. © Springer International Publishing Switzerland 2014.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Weakly supervised temporal action localization with proxy metric modeling

引用

Frontiers of computer Science 2023年第2期17卷 63-72页

作者： Hongsheng XU Zihan CHEN Yu ZHANG Xin GENG Siya MI Zhihong YANG NARI Group Corporation(State Grid Electric Power Research Institute) Nanjing 211106China School of Computer Science and Engineering and the Key Lab of Computer Network and Information Integration(Ministry of Education)Southeast UniversityNanjing 211189China School of Cyber Science and Engineering Southeast UniversityNanjing 211189China Purple Mountain Laboratories Nanjing 211111China

Temporal localization is crucial for action video *** the manual annotations are expensive and time-consuming in videos,temporal localization with weak video-level labels is challenging but *** this paper,we propose a weakly-supervised temporal action localization approach in untrimmed *** settle this issue,we train the model based on the proxies of each action *** proxies are used to measure the distances between action segments and different original action *** use a proxy-based metric to cluster the same actions together and separate actions from *** with state-of-the-art methods,our method achieved competitive results on the THUMOS14 and ActivityNet1.2 datasets.

关键词： temporal action localization weakly supervised videos proxy metric

来源：评论

学校读者我要写书评

暂无评论

Packet-level adaptive sampling on multi-fluctuation scale traffic

Packet-level adaptive sampling on multi-fluctuation scale tr...

引用

International Conference on Communications, Circuits and Systems (ICCCAS)

作者： Li-Bo Xu Guo-Xin Wu Jian-Fei Li Key Laboratory of Computer Network and Information Integration South-East University Nanjing China

ISBN: (纸本)0780390156

In high-speed Internet links, an effective and efficient packet sampling technology in traffic measurement becomes more and more indispensable for its benefits of reducing data quantity and saving various resources. We address the issue of how to keep or improve sampling accuracy during burst or fluctuation periods. In the paper, an adaptive packet-level sampling method on different traffic fluctuation and burst scales is proposed. The method can dynamically adjust each packet sampling probability dependent on the magnitude of traffic fluctuation. The adjustment granularity is fine because it reaches a single packet. By contrast to the static random sampling method in experiments using real traffic traces, we show that the proposed adaptive sampling has a great capability to trace traffic fluctuations and improve sampling accuracy through capturing more fluctuation or burst details.

关键词： Sampling methods Telecommunication traffic Fluctuations Communication system traffic control Probes High-speed networks Laboratories computer networks Electronic mail IP networks

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Residual Coordinate Attention-Based network for Hat and Mask Wearing Detection in Kitchen Environments

An Adaptive Residual Coordinate Attention-Based Network for ...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Ying Wu, Xiaoming Liu, Xiangzhi Chen, Hao Qi, Bei Dong, Huomin Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

In order to ensure food safety, it is required for personnel to wear hats and masks during food handling processes. To accurately detect the wearing status of kitchen staff, the ARP-YOLO model is proposed. Firstly, images are obtained from multiple kitchens and angles to construct a dataset reflecting the wearing status of hats and masks. To simulate more complex kitchen environments, Gaussian noise is added to the data and lighting conditions are adjusted for data augmentation. Lighting conditions in the kitchen can affect detection, causing the same target to exhibit different shapes and features under different lighting conditions, leading to missed detections. To address the above issues, we propose ARCA (Adaptive-Residual-Coordinate-Attention), which uses residual connections to strengthen attention to important features while preserving original features, and employs adaptive convolution reduction to reduce module parameters. To improve target localization accuracy, P2 detection layers are added in the Neck to obtain more accurate target position information. The ARP-YOLO model demonstrates significant improvements over the baseline model, with a 13.6% increase in Recall, allowing for more effective target capture and reduced missed detection risk. Additionally, mAP@0.5 has increased by 10%, enhancing target localization accuracy. The F1-Score has also increased by 7.4%, better balancing the relationship between Precision and Recall. To validate the effectiveness of the model, comparative experiments with other models are conducted, showing that ARP-YOLO model's Recall and Average Precision(AP) are higher than those of other models. © 2024 IEEE.

关键词： Lighting

来源：评论

学校读者我要写书评

暂无评论

Weighted Adversarial Transfer network-Based Fault Diagnosis Method for Gearboxes 11

Weighted Adversarial Transfer Network-Based Fault Diagnosis ...

引用

11th Joint International information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Wang, Dan Meng, Qinglong Hao, Huijuan Cheng, Guanghe Tang, Yongwei Bai, Jinqiang Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350333664

In this paper, we address the issue of gearbox fault diagnosis under changing working conditions and the limitations of migration learning algorithms that are only applicable to scenarios with identical labels in both the source and target domains. Our proposed solution is a weighted adversarial migration network model that is based on weighted adversarial migration. To transfer knowledge from the source domain to the target domain, we use transfer learning. Additionally, to tackle the problem of new fault classes in the target domain, we utilize a deep residual network to extract similar data features between different working conditions. This allows us to train a labelled classifier to identify known fault classes and some new target samples in both the source and target domains. We generate weights using auxiliary classifiers and discriminators to assess the importance of matching feature distributions between the known and unknown classes of samples. The experimental results demonstrate that our model can successfully migrate labelled datasets to unlabelled similar datasets in open set scenarios. It also has excellent fault diagnosis capabilities and high accuracy for gearboxes with variable operating conditions. © 2023 IEEE.

关键词： adversarial training deep transfer learning fault diagnosis weighting strategy

来源：评论

学校读者我要写书评

暂无评论

Finding the most reliable maximum flow in transport network 2nd

Finding the most reliable maximum flow in transport network

引用

2nd International Cognitive Cities Conference, IC3 2019

作者： Wang, Jie Cai, Wei Zhou, Sihai Liu, Yundi Liao, Weicheng Zhang, Baili School of Computer Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information Integration of Ministry of Education Nanjing China Research Center for Judicial Big Data Supreme Count of China Nanjing211189 China

ISBN: (纸本)9789811561122

This paper intends to solve the most reliable maximum flow problem (MRMF) on transport network. A subgraph path division algorithm (SPDA) is proposed to get the most reliable maximum flow distribution, which avoid the negative impact of the number of simple paths and its bottleneck capacity. SPDA divides the sub-graph space of a transport network into a set of disjoint closed intervals, which satisfies the maximum flow constraints. Among the lower bounds of all the intervals, the one with discovered probability has proven to be the most reliable maximum. Finally, experimental results reveal the effectiveness and efficiency of the proposed algorithm. © Springer Nature Singapore Pte Ltd 2020.

关键词： Flow graphs

来源：评论

学校读者我要写书评

暂无评论

Job2Vec: A Self-Supervised Contrastive Learning Based HPC Job Power Consumption Prediction Framework 29

Job2Vec: A Self-Supervised Contrastive Learning Based HPC Jo...

引用

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhang, Jie Song, Jian Li, Xiang Tian, Xuesen Zhao, Zhigang Wu, Lu Wang, Chunxiao Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

In the context of the rapid development of big data and artificial intelligence, the field of HPC is facing significant challenges in energy consumption. To address this challenge, this paper proposes a power consumption prediction framework for HPC jobs based on self-supervised contrastive learning, named Job2Vec. Firstly, HPC jobs are clustered into different clusters based on job logs and power consumption curve shapes. Next, high-quality features of power consumption data at different granularities within each cluster are captured through self-supervised contrastive learning. Lastly, an incremental update strategy is introduced to handle the dynamic changes in HPC data. The experimental results demonstrate that, on the Jinan Supercomputing dataset comprising 1170 HPC jobs, the Job2Vec framework outperforms the baseline models overall in terms of prediction performance. Additionally, we also investigated the model's performance in single-step and multi-step forecasting, the impact of different indicators on prediction accuracy, and conducted ablation experiments to confirm the indispensability of each module. This is the first time that self-supervised contrastive learning has been introduced into the HPC field, aiming to provide support for energy scheduling in data centers. © 2023 IEEE.

关键词： Contrastive Learning HPC Power Consumption Prediction

来源：评论

学校读者我要写书评

暂无评论

Optimization Strategy for Remote Differential Upgrade of IoT Firmware 11

Optimization Strategy for Remote Differential Upgrade of IoT...

引用

11th Joint International information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Ding, Wenkang Sun, Ruirui Meng, Qinglong Wan, Zhong Cheng, Guanghe Hao, Huijuan Bai, Jinqiang Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350333664

With the continuous increase of IoT devices, the management and optimization of devices become particularly important. The update cycle of device functions is short, and in order to facilitate management and optimize device performance, a fast and convenient upgrade method is needed to solve this problem. Remote upgrade technology is mainly divided into full package upgrade and differential upgrade. Compared to full package upgrade, differential upgrade has obvious advantages in remote upgrade of large files. The main problem with current differential upgrade is the problem of slow generation speed while ensuring a certain size range of differential packages. This article proposes an improved Bsdiff algorithm to solve the problem of slow generation speed of differential packages. Experimental verification shows that the efficiency of remote upgrade has been improved, Improved the speed of differential packet generation and upgrade efficiency while ensuring the size of differential packets. © 2023 IEEE.

关键词： Block Optimization Strategy Bsdiff Difference Algorithm IoT Firmware Libdivsufsort Remote Upgrade

来源：评论

学校读者我要写书评

暂无评论

A secure and high-performance multi-controller architecture for software-defined networking

引用

Frontiers of information Technology & Electronic Engineering 2016年第7期17卷 634-646页

作者： Huan-zhao WANG Peng ZHANG Lei XIONG Xin LIU Cheng-chen HU Department of Computer Science and Technology Xi'an Jiaotong UniversityXi'an 710059China Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory Shijiazhuang 050081China MOE Key Laboratory for Intelligent Networks and Network Security Xi'an Jiaotong UniversityXi'an 710049China

Controllers play a critical role in software-defined networking(SDN).However,existing singlecontroller SDN architectures are vulnerable to single-point failures,where a controller's capacity can be saturated by flooded flow *** addition,due to the complicated interactions between applications and controllers,the flow setup latency is relatively *** address the above security and performance issues of current SDN controllers,we propose distributed rule store(DRS),a new multi-controller architecture for *** DRS,the controller caches the flow rules calculated by applications,and distributes these rules to multiple controller *** controller instance holds only a subset of all rules,and periodically checks the consistency of flow rules with each *** from switches are distributed among multiple controllers,in order to mitigate controller capacity saturation *** the same time,when rules at one controller are maliciously modified,they can be detected and recovered in *** implement DRS based on Floodlight and evaluate it with extensive *** results show that DRS can effectively maintain a consistently distributed rule store,and at the same time can achieve a shorter flow setup time and a higher processing throughput,compared with ONOS and Floodlight.

关键词： Software-defined networking(SDN) Security Multi-controller Distributed rule store

来源：评论

学校读者我要写书评

暂无评论

Large scale temporal RDFS reasoning using MapReduce 26

Large scale temporal RDFS reasoning using MapReduce

引用

26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12

作者： Liu, Chang Qi, Guilin Yu, Yong Shanghai Jiao Tong University China Key Laboratory of Computer Network and Information Integration of State Education Ministry School of Computer Science and Engineering Southeast University China 311 Yifu Building 800 Dongchuan Road Shanghai 200240 China

ISBN: (纸本)9781577355687

In this work, we build a large scale reasoning engine under temporal RDFS semantics using MapReduce. We identify the major challenges of applying MapReduce framework to reason over temporal information, and present our solutions to tackle them. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： MapReduce

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：