发布求助
文献互助 智能选刊 最新文献

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)最新文献

筛选
英文 中文
AdaEmb-Encoder: Adaptive Embedding Spatial Encoder-Based Deduplication for Backing Up Classifier Training Data AdaEmb-Encoder:自适应嵌入基于空间编码器的重复数据删除备份分类器训练数据
2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391523
Yaobin Qin, D. Lilja
{"title":"AdaEmb-Encoder: Adaptive Embedding Spatial Encoder-Based Deduplication for Backing Up Classifier Training Data","authors":"Yaobin Qin, D. Lilja","doi":"10.1109/IPCCC50635.2020.9391523","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391523","url":null,"abstract":"The advent of the AI era has made it increasingly important to have an efficient backup system to protect training data from loss. Furthermore, a backup of the training data makes it possible to update or retrain the learned model as more data are collected. However, a huge backup overhead will result if a complete copy of all daily collected training data is always made to backup storage, especially because the data typically contain highly redundant information that makes no contribution to model learning. Deduplication is a common technique in modern backup systems to reduce data redundancy. However, existing deduplication methods are invalid for training data. Hence, this paper proposes a novel deduplication strategy for the training data used for learning in a deep neural network classifier. Experimental results showed that the proposed deduplication strategy achieved 93% backup storage space reduction with only 1.3% loss of classification accuracy.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123772764","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
Spatio-Temporal Data Mining for Aviation Delay Prediction 航空延误预测的时空数据挖掘
2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391561
Kai Zhang, Yushan Jiang, Dahai Liu, H. Song
{"title":"Spatio-Temporal Data Mining for Aviation Delay Prediction","authors":"Kai Zhang, Yushan Jiang, Dahai Liu, H. Song","doi":"10.1109/IPCCC50635.2020.9391561","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391561","url":null,"abstract":"To accommodate the unprecedented increase of commercial airlines over the next ten years, the Next Generation Air Transportation System (NextGen) has been implemented in the USA that records large-scale Air Traffic Management (ATM) data to make air travel safer, more efficient, and more economical. A key role of collaborative decision making for air traffic scheduling and airspace resource management is the accurate prediction of flight delay. There has been a lot of attempts to apply data-driven methods such as machine learning to forecast flight delay situation using air traffic data of departures and arrivals. However, most of them omit en-route spatial information of airlines and temporal correlation between serial flights which results in inaccuracy prediction. In this paper, we present a novel aviation delay prediction system based on stacked Long Short-Term Memory (LSTM) networks for commercial flights. The system learns from historical trajectories from automatic dependent surveillance-broadcast (ADS-B) messages and uses the correlative geolocations to collect indispensable features such as climatic elements, air traffic, airspace, and human factors data along posterior routes. These features are integrated and then are fed into our proposed regression model. The latent spatio-temporal patterns of data are abstracted and learned in the LSTM architecture. Compared with previous schemes, our approach is demonstrated to be more robust and accurate for large hub airports.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125381132","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: 8
A Unite and Conquer Based Ensemble learning Method for User Behavior Modeling 基于联合征服的用户行为建模集成学习方法
2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391528
Abdoulaye Diop, N. Emad, Thierry Winter
{"title":"A Unite and Conquer Based Ensemble learning Method for User Behavior Modeling","authors":"Abdoulaye Diop, N. Emad, Thierry Winter","doi":"10.1109/IPCCC50635.2020.9391528","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391528","url":null,"abstract":"IT companies use tools to analyze user and entity behavior to protect their information assets from insider threats. Although supervised machine learning methods seem to be the ideal solution for solving this problem, situations in which new employee activity data is labeled and balanced, are not so common. Besides, the data can have different origins, structures, and can be substantial. Therefore, it’s difficult for a specific detection model to deal with and identify insiders in all cases effectively. To provide a solution to this problem, we are faced with methodological, algorithmic, and technological challenges. In this article, we try to meet these challenges by proposing a new approach based on ensemble learning methods to improve their performances from the point of view of accuracy and computation efficiency. With the detection of behavioral anomalies as a case study, we show the interest of this approach for its improvement of the prediction results and its efficacy on a high-performance computing system.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"58 27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129710022","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
Discussion on Application of the Internet of Things in Modern Agricultural Experimental Base 物联网在现代农业实验基地的应用探讨
2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391565
Lihua Jiang, Jiawei Yan, N. Xie
{"title":"Discussion on Application of the Internet of Things in Modern Agricultural Experimental Base","authors":"Lihua Jiang, Jiawei Yan, N. Xie","doi":"10.1109/IPCCC50635.2020.9391565","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391565","url":null,"abstract":"Modern information technologies, such as Internet of Things technology, image recognition technology and artificial intelligence technology, are used to conduct comprehensive intelligent perception and monitoring on the experimental bases distributed all over the country, and an intelligent experimental base management platform is built. All kinds of data acquisition devices are networked through intelligent network to realize automatic data acquisition, advanced computing, collaborative work, data mining and Intelligent and visual management of the experimental base. The construction of intelligent experimental base makes the management of the experimental base more scientific and standardized, avoids repeated labor, improves the efficiency of scientific research and expands the scope of scientific research.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129266852","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
Improved Window Segmentation for Deep Learning Based Inertial Odometry 基于深度学习的惯性里程计改进窗口分割
2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391535
Siyu Chen, Yu Zhu, Xiaoguang Niu, Zhiyong Hu
{"title":"Improved Window Segmentation for Deep Learning Based Inertial Odometry","authors":"Siyu Chen, Yu Zhu, Xiaoguang Niu, Zhiyong Hu","doi":"10.1109/IPCCC50635.2020.9391535","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391535","url":null,"abstract":"The variety of sensors embedded in smartphones makes it possible to develop indoor navigation and localization systems on mobile terminals. However, these cheap sensors are plagued by bias and noise, leading to unbounded system drifts. Inspired by Expectation-Maximization algorithm, this paper proposes to combine zero-velocity detection with gated recurrent unit (GRU) neural networks, make full use of pedestrian motion characteristics, and naturally and accurately split the raw measurements into multiple weakly correlated windows step by step. The GRU is used to exploit dynamic context and predict the polar vector of each window. Several experiments were conducted to test the performance of proposed model, and IONet, a deep learning based inertial odometry model using fixed-size sliding window, was taken as a reference. The results show that the proposed model is able to generate smooth trajectories with high precision. Compared with IONet, the performance of proposed model in turning is better.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115778013","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
A sensor attack detection method based on fusion interval and historical measurement in CPS 基于 CPS 中融合间隔和历史测量的传感器攻击检测方法
2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391541
Xiaobo Cai, Ke Han, Yan Li, Xuefei Li, Jiajin Zhang, Yue Zhang
{"title":"A sensor attack detection method based on fusion interval and historical measurement in CPS","authors":"Xiaobo Cai, Ke Han, Yan Li, Xuefei Li, Jiajin Zhang, Yue Zhang","doi":"10.1109/IPCCC50635.2020.9391541","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391541","url":null,"abstract":"Cyber-physical systems (CPS) is a next-generation intelligent system that realizes close integration of computing, communication and physical elements based on environmental perception. The interaction between information technology and the physical world makes CPS vulnerable to various malicious attacks and damage Its security. The paper designs a sensor attack detection method based on fusion interval and historical measurement. The method first builds different fault models for different sensors, and uses system dynamics equations to integrate historical measurements into the attack detection method. Different aspects of sensor measurement are analyzed. In addition, the use of historical measurement and fusion interval solves the problem of whether there is a failure when the measurements of two sensors intersect. The core idea of this method is to use the paired inconsistent relationship between sensors to detect and identify attacks .","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129373126","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
Paradox of AlphaZero: Strategic vs. Optimal Plays AlphaZero的悖论:策略性vs.最优玩法
2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391562
Ze-Li Dou, Liran Ma, Khiem Nguyen, Kien X. Nguyen
{"title":"Paradox of AlphaZero: Strategic vs. Optimal Plays","authors":"Ze-Li Dou, Liran Ma, Khiem Nguyen, Kien X. Nguyen","doi":"10.1109/IPCCC50635.2020.9391562","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391562","url":null,"abstract":"This article analyzes AlphaZero-type algorithms quantitatively from the viewpoint of local and global optimal sequences of play on a 7×7 board. Through targeted evaluation of the AI agent, the authors reveal the strategic, that is, winrate-dominated, nature of such algorithms, and expose thereby certain inherent obstacles against optimal play. Possible remedies are then explored, leading to techniques that may help further quantitative analysis of those algorithms and for the search for optimal solutions, on 7×7 as well as larger boards.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131062348","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
Deep Learning for IoT 物联网的深度学习
2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391558
Tao Lin
{"title":"Deep Learning for IoT","authors":"Tao Lin","doi":"10.1109/IPCCC50635.2020.9391558","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391558","url":null,"abstract":"Deep learning and other machine learning approaches are deployed to many systems related to Internet of Things or IoT. However, it faces challenges that adversaries can take loopholes to hack these systems through tampering history data.This paper first presents overall points of adversarial machine learning. Then, we illustrate traditional methods, such as Petri Net cannot solve this new question efficiently. After that, this paper uses the example from triage(filter) analysis from IoT cyber security operations center. Filter analysis plays a significant role in IoT cyber operations. The overwhelming data flood is obviously above the cyber analyst’s analytical reasoning. To help IoT data analysis more efficient, we propose a retrieval method based on deep learning (recurrent neural network). Besides, this paper presents a research on data retrieval solution to avoid hacking by adversaries in the fields of adversary machine leaning. It further directs the new approaches in terms of how to implementing this framework in IoT settings based on adversarial deep learning.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114805712","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: 2
A Dynamic Task Assignment Framework based on Prediction and Adaptive Batching 基于预测和自适应批处理的动态任务分配框架
2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391525
Lijun Sun, Xiaojie Yu, Shicong Chen, Yang Yan
{"title":"A Dynamic Task Assignment Framework based on Prediction and Adaptive Batching","authors":"Lijun Sun, Xiaojie Yu, Shicong Chen, Yang Yan","doi":"10.1109/IPCCC50635.2020.9391525","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391525","url":null,"abstract":"With the continuous popularization of smart devices, a new computing paradigm–Spatial Crowdsourcing(SC) came into being. As an important part of SC, task assignment has received more and more attention. However, in the real scenario, the emergence of tasks is random and dynamic, which pose a huge challenge to task assignment. In order to solve this challenge, we propose a Dynamic Task Assignment Framework based on Prediction and Adaptive Batching (DTAF-PAB), which utilizes the Gated Recurrent Unit (GRU) in deep learning to predict the number of tasks entering a specific area, and propose an adaptive batching algorithm based on Deep Q Network (DQN) to dynamically adjust the size of batches, thereby improving the overall benefit of assignment. We use datasets from the real world to evaluate the competitiveness of DTAF-PAB and the experimental results show that the proposed framework is superior to other existing technologies in terms of both predictive performance and crowdsourcing platform benefit.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129345320","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
NRV: Leveraging Secure Multi-Party Computation for Lightweight BGP Security Enhancement NRV:利用安全多方计算增强BGP轻量级安全性
2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391537
Guoqiang Zhang, Mingwei Xu, Jiang Li
{"title":"NRV: Leveraging Secure Multi-Party Computation for Lightweight BGP Security Enhancement","authors":"Guoqiang Zhang, Mingwei Xu, Jiang Li","doi":"10.1109/IPCCC50635.2020.9391537","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391537","url":null,"abstract":"The Border Gateway Protocol (BGP) is the de facto standard interdomain routing protocol. A major problem affecting the operation of BGP is its failure to provide security guarantees. Despite some high-profile security extensions proposed, none of them has been largely deployed by Autonomous Systems (AS) in the global Internet. Previous studies show that three main factors hinder the adoption of BGP security solutions: limited benefits in partial deployment, computational overheads, and the trouble of coordinating among tens of thousands of independent ASes. In this paper, we present Neighbor Routes Validator (NRV), a lightweight prototype system of BGP security enhancement. Instead of depending on a single centralized authority, NRV focuses on neighboring ASes’ self-driven collaborations that significantly reduce the scale of coordination. It aims to address real-world security issues of BGP and uses the privacy-preserving capability of Secure Multi-Party Computation (SMPC) to dispel ASes’ privacy concerns. Security analyses and simulations demonstrate the feasibility of NRV, and we also argue that network operators have incentives to deploy it after weighing the pros and cons.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130031436","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
下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信