2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)最新文献_第2页

Location-Aided Window based Beam Alignment for mmWave Communications 毫米波通信中基于定位辅助窗口的波束对准

2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660423

I. Orikumhi, Jeongwan Kang, Sunwoo Kim

引用次数: 1

An Object Detection Framework for Span Extraction in Question Answering 问答中跨度提取的目标检测框架

2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660483

Tianyu Zhou, Ping Gong

引用次数: 0

Ancient Chinese Recognition Method Based on Attention Mechanism 基于注意机制的古汉语识别方法

2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660518

Lingjing Wu, Chuang Zhang, Mengqiu Xu, Ming Wu

引用次数: 1

Asynchronous Multi-Nets Detailed Routing in VLSI using Multi-Agent Reinforcement Learning 基于多智能体强化学习的VLSI异步多网络详细路由

2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660569

Xuhua Ju, Konglin Zhu, Yibo Lin, Lin Zhang

{"title":"Asynchronous Multi-Nets Detailed Routing in VLSI using Multi-Agent Reinforcement Learning","authors":"Xuhua Ju, Konglin Zhu, Yibo Lin, Lin Zhang","doi":"10.1109/IC-NIDC54101.2021.9660569","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660569","url":null,"abstract":"Detailed routing is a crucial challenge in modern integrated circuit (IC) design. Due to the continuous increase in design complexity and complicated design rules, avoiding routing conflicts between nets becomes more and more challenging. Conventional routing strategies like the rip-up and re-route scheme may need to spend huge efforts on avoiding conflicts between nets with overlapping routing areas. To resolve this challenge, in this paper, we propose a detailed router based on multi-agent reinforcement learning for handling conflicting nets. First, we approximate nets of detailed routing as agents and regard the pin-connection task as path planning to achieve the asynchronization of routing. Second, we assign each agent a local field of view to reduce feature size and difficulty in training. Finally, in order to eliminate routing congestion, we set an information storage unit for the information communication of each agent. The evaluation results show that the proposed multi-agent reinforcement learning scheme outperforms the baseline learning methods by 11.6%.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122784515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Anomaly Detection in Unstructured Logs Using Attention-based Bi-LSTM Network 基于注意力的Bi-LSTM网络非结构化日志异常检测

2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660476

Dongqing Yu, Xia Hou, Ce Li, Qiujian Lv, Yan Wang, Ning Li

引用次数: 5

Research on Object Detection Algorithm Based on UVA Aerial Image 基于UVA航拍图像的目标检测算法研究

2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660571

Huan Liu, Chengpo Mu, Ruixin Yang, Yang He, Nan Wu

引用次数: 2

Enhancing Class-semantics Features' Locating Performance for Temporal Action Localization 增强类语义特征在时间动作定位中的定位性能

2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660459

Jianming Zhang, Jianqin Yin

{"title":"Enhancing Class-semantics Features' Locating Performance for Temporal Action Localization","authors":"Jianming Zhang, Jianqin Yin","doi":"10.1109/IC-NIDC54101.2021.9660459","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660459","url":null,"abstract":"Temporal action localization is a fundamental video understanding task. Meanwhile, due to the complex video background, the varied duration and amplitude of the actions, it is also a considerable challenge. Currently, offline class-semantics representation is the mainstream input of this task since untrimmed videos occupy a large memory, high-quality untrimmed videos and annotations are difficult to access. Because these representations only focus on the class-semantics information, they are sub-optimal for the temporal action localization tasks. At the same time, the exploration of localization-semantics representation is very few due to the high resource consumption. Therefore, it is necessary to improve the detection capability of class-semantics representation directly. As an exploration, we propose the ForeBack module to enhance class-semantics features’ locating performance by augmenting the distinction modeling between foreground and background clips. This module could also eliminate part of the noise of inference probability sequences. Furthermore, we use phased training to learn and use the ForeBack module more effectively. Finally, we reveal the effectiveness of our approach by conduct experiments on THUMOS-14 and the mAP at tIoU@0.5 is improved from 38.8% (BMN action detection baseline) to 47.1%.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133946747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Economic Development Analysis of the Belt and Road Regions Based on Automatic Interpretation of Remote Sensing Images 基于遥感影像自动解译的“一带一路”区域经济发展分析

2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660561

Xinzhu Qiu, Yunzhe Wang, Jingyi Cao, Guannan Xu, Yanan You, Junlong Ren

{"title":"Economic Development Analysis of the Belt and Road Regions Based on Automatic Interpretation of Remote Sensing Images","authors":"Xinzhu Qiu, Yunzhe Wang, Jingyi Cao, Guannan Xu, Yanan You, Junlong Ren","doi":"10.1109/IC-NIDC54101.2021.9660561","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660561","url":null,"abstract":"The Belt and Road (B&R) initiative is proposed to promote common development among countries along the B&R. In recent years, although the B&R has contributed to the regions along the route, it is always a controversial topic in the international community. A number of scholars have done a set of research works to analyze the effects of the B&R projects based on traditional economic methods. However, the drawbacks of subjectivity and delay reduce the conviction of the analysis results. In this paper, we leverage the objectivity and real-time features of remote sensing (RS) images to analyze the effects of the B&R project. Our research takes Voi town along the Mongolia-Nairobi Railway as the representative city. In addition, in order to prove the causal relationship between the B&R and economic development, we select the Taveta town as the comparison city. The semantic segmentation based on deep learning is applied to the multi-temporal RS images, to retrieve the economic development by automatically recognizing houses. On this basis, the construction and development of both the studied region and the comparison are quantitatively analyzed by meshing analysis and standard deviation elliptic methods. For overcoming the shortages of the conventional algorithms, a novel segmentation network based on the attention mechanism is proposed. The evaluation proves the semantic segmentation results can fully support the follow-up data analysis. In addition, the analysis results show that our work is a convincing initiative to reveal the values of the B&R projects for economic developments in the B&R-related regions.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132725693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Federated Learning Based Chinese Text Classification Model with Parameter Factorization Weighting 基于联邦学习的参数分解加权中文文本分类模型

2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660471

Huan Wang, Zerong Zeng, Ruifang Liu, Sheng Gao

引用次数: 1

Distributed Learning in Trusted Execution Environment: A Case Study of Federated Learning in SGX 可信执行环境下的分布式学习:SGX中联邦学习的案例研究

2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660433

Tianxing Xu, Konglin Zhu, A. Andrzejak, Lin Zhang

{"title":"Distributed Learning in Trusted Execution Environment: A Case Study of Federated Learning in SGX","authors":"Tianxing Xu, Konglin Zhu, A. Andrzejak, Lin Zhang","doi":"10.1109/IC-NIDC54101.2021.9660433","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660433","url":null,"abstract":"Federated Learning (FL) is a distributed machine learning paradigm to solve isolated data island problems under privacy constraints. Recent works reveal that FL still exists security problems in which attackers can infer private data from gradients. In this paper, we propose a distributed FL framework in Trusted Execution Environment (TEE) to protect gradients in the perspective of hardware. We use trusted Software Guard eXtensions (SGX) as an instance to implement the FL, and proposed an SGX-FL framework. Firstly, to break through the limitation of physical memory space in SGX and meanwhile preserve the privacy, we leverage a gradient filtering mechanism to obtain the “important” gradients which preserve the utmost data privacy and put them into SGX. Secondly, to enhance the global adhesion of gradients so that the important gradients can be aggregated at maximum, a grouping method is carried out to put the most appropriate number of members into one group. Finally, to keep the accuracy of the FL model, the secondary gradients of group members and aggregated important gradients are simultaneously uploaded to the server and the computation procedure is validated by the integrity method of SGX. The evaluation results show that the proposed SGX-FL reduces the computation cost by 19 times compared with the existing approaches.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"232 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115266790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1