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检索条件"任意字段=23rd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2022"

共 102 条记录，以下是51-60 订阅

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Federated learning With Dynamic Staleness Correction for Privacy Protection in Vehicular Networks 23

Federated Learning With Dynamic Staleness Correction for Pri...

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Liu, Jiajia Wu, Hao Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety Beijing China School of Electronics and Information Engineering Beijing Jiaotong University Beijing China Beijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System Beijing China

ISBN: (纸本)9781665494571

Edge intelligence combines mobile edge computing(MEC) with artificial intelligence(AI), which has great potential for improving the security and efficiency of data-driven intelligent transportation systems (ITS) and emerging Internet Of Vehicles (IOV) services. However, when big data and AI empower ITS, the information security issue in vehicular networks needs to be paid more attention. In order to protect the privacy and security of vehicular training data, we propose a privacy protection federated learning scheme with staleness asynchronous update to overcome the differences caused by vehicle heterogeneity. Moreover, unlike the traditional weighted average on the server side only by the number of samples, our solution introduces dynamic time weights according to the calculation and communication capabilities of different vehicles, to make full use of the previously trained local model. The proposed scheme is able to reduce traffic load of the network and improve learning performance while enhancing security and privacy. Experiment results demonstrate that in terms of communication cost and model accuracy, the performance of the proposed asynchronous federated learning is better than the benchmark algorithm, and it also achieves good performance under NIID data. © 2021 IEEE.

关键词： Privacy data privacy Costs Smart cities Training data Telecommunication traffic Collaborative work

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Cherry detection algorithm based on improved YOLOv5s network 23

Cherry detection algorithm based on improved YOLOv5s network

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作者： Gai, Rongli Li, Mengke Chen, Na School of Information Engineering Dalian University Dalian China

ISBN: (纸本)9781665494571

In order to realize the automatic fruit picking by robot in orchard environment, cherry target detection is carried out by studying the algorithm based on deep learning, so as to identify cherry and carry out picking operation. However, most of the existing methods are aimed at relatively sparse fruits and cannot solve the detection problem of small and dense fruits. In this paper, a YOLOv5s-cherry model based on YOLOv5s was proposed for cherry detection. By adjusting the CSP structure of BackBone and Neck, deepening the depth and width of the network, improving the ability of network feature extraction, feature fusion and learning ability of network feature extraction, so as to improve the detection ability of small and dense cherry fruit. The model is trained on the self-made cherry maturity classification data set. The trained YOLOv5s cherry network is used to extract the feature and location information of cherry images with different maturity, so as to realize the classification and detection of cherry. We tested the performance of the model, compared YOLOv5s-cherry with YOLOv5s and YOLOv4, and used F1 value as the evaluation value. The test results show that the F1 of YOLOv5s cherry is 0.08 higher than that of YOLOv4 algorithm and 0.03 higher than that of YOLOv5s algorithm. The improved YOLOv5s cherry model provides better detection performance for small and dense cherry detection, which is conducive to intelligent picking. © 2021 IEEE.

关键词： Training Solid modeling Smart cities Object detection Feature extraction data models Robustness

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Domain Adversarial Training for Aspect-Based Sentiment Analysis 23rd

Domain Adversarial Training for Aspect-Based Sentiment Analy...

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23rd international conference on Web Information Systems engineering (WISE)

作者： Knoester, Joris Frasincar, Flavius Trusca, Maria Mihaela Erasmus Univ Burgemeester Oudlaan 50 NL-3062 PA Rotterdam Netherlands Bucharest Univ Econ Studies Bucharest 010374 Romania

ISBN: (纸本)9783031208904;9783031208911

The continuously expanding digital possibilities, increasing number of social media platforms, and growing interest of companies in online marketing increase the importance of Aspect-Based Sentiment Analysis (ABSA). ABSA focuses on predicting the sentiment of an aspect in a text. In an ideal scenario, we would have labeled data for every existing domain, but acquiring annotated training data is costly. Transfer learning resolves this issue by building models that can be employed in different domains. The proposed work extends the state-of-the-art LCR-Rot-hop++ model for ABSA with the methodology of Domain Adversarial Training (DAT) in order to create a deep learning adaptable cross-domain structure, called the DAT-LCR-Rot-hop++. The major advantage of the DAT-LCR-Rot-hop++ is the fact that it does not require any labeled target domain data. The results are obtained for six different domain combinations with testing accuracies ranging from 37% up until 77%, showing both the limitations and benefits of this approach. Once DAT is able to find the similarities between domains, it produces good results, but if the domains are too distant, it is not capable of generating domain-invariant features.

关键词： Aspect-Based Sentiment Classification Transfer learning Adversarial Training

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Digital Twin Mobility Profiling: A Spatio-Temporal Graph learning Approach 23

Digital Twin Mobility Profiling: A Spatio-Temporal Graph Lea...

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作者： Chen, Xin Hou, Mingliang Tang, Tao Kaur, Achhardeep Xia, Feng School of Software Dalian University of Technology Dalian116620 China School of Engineering It and Physical Sciences Federation University Australia BallaratVIC3353 Australia

ISBN: (纸本)9781665494571

With the arrival of the big data era, mobility profiling has become a viable method of utilizing enormous amounts of mobility data to create an intelligent transportation system. Mobility profiling can extract potential patterns in urban traffic from mobility data and is critical for a variety of traffic-related applications. However, due to the high level of complexity and the huge amount of data, mobility profiling faces huge challenges. Digital Twin (DT) technology paves the way for cost-effective and performance-optimised management by digitally creating a virtual representation of the network to simulate its behaviour. In order to capture the complex spatio-temporal features in traffic scenario, we construct alignment diagrams to assist in completing the spatio-temporal correlation representation and design dilated alignment convolution network (DACN) to learn the fine-grained correlations, i.e., spatio-temporal interactions. We propose a digital twin mobility profiling (DTMP) framework to learn node profiles on a mobility network DT model. Extensive experiments have been conducted upon three real-world datasets. Experimental results demonstrate the effectiveness of DTMP. © 2021 IEEE.

关键词： Correlation Convolution Smart cities Predictive models Logic gates Digital twins data mining

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Adaptive Network Formation and Trajectory Optimization for Multi-UAV-Assisted Wireless data Offloading 23

Adaptive Network Formation and Trajectory Optimization for M...

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作者： Wang, Meng Long, Yusi Gong, Shimin Xu, Jing School of Intelligent Systems Engineering Sun Yat-sen University China School of Electronic Information and Communications Huazhong University of Science and Technology China

ISBN: (纸本)9781665494571

In this paper, we employ multiple UAVs coordinated by a cellular base station (BS) to help the IoT users to offload their sensing data to the BS. To minimize the overall energy consumption and transmission delay, we allow different UAVs to optimize their trajectories and cooperate in data transmission via multi-hop relaying. This implies complicated spatial and temporal coupling between the UAVs' trajectories optimization and network formation. To solve this problem, we propose a two-step algorithm to iterate between adaptive network formation and trajectory optimization. Firstly, in order to balance the UAVs' energy consumption and the queue size, we adopt a heuristic algorithm to update adaptive network formation. Then, once we update the network formation, the UAVs' trajectories are further optimized by using the multi-agent deep reinforcement learning algorithm. Our simulation results show that the joint trajectory optimization and network formation can better exploit the cooperation among multiple UAVs in data offloading, compared with the conventional non-cooperative cases. © 2021 IEEE.

关键词： Wireless communication Wireless sensor networks Energy consumption Adaptive systems Smart cities Heuristic algorithms Simulation

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data-Driven Prognosis Applied to Complex Vacuum Pumping Systems

Data-Driven Prognosis Applied to Complex Vacuum Pumping Syst...

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23rd international conference on Industrial, engineering and Other Applications of Applied intelligent Systems

作者： Martin, Florent Meger, Nicolas Gahehet, Sylvie Becourt, Nicolas Univ Savoie Polytech Savoie LISTIC Domaine Univ BP80439 F-74944 Annecy Le Vieux France Alcatel Vacuum technol F-74009 Annecy Le Vieux France

ISBN: (纸本)9783642130212

This paper presents a method to address system prognosis It also details a successful application to complex vacuum pumping systems The proposed approach relies on an automated data-driven learning process as opposed to hand-built models that at e based on human expertise Mote precisely using historical vibratory data we first model the behavior of a system by extracting a given type of episode rules, namely First. Local Maximum episode lutes (FLM-rules) A subset, of the extracted FLM-rules is then selected in older to further predict pumping system failures in a datastream context;The results that, we got for production data are very encouraging as we predict failures with a good tune scale precision We are now deploying our solution for a customer of the semi-conductor market

关键词： data mining episode rules predictive maintenance prognosis vibratory data

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Semantic Segmentation of Remote Sensing Image Based on Two-time Augmentation and Atrous Convolution 23

Semantic Segmentation of Remote Sensing Image Based on Two-t...

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作者： Yang, Ruiqi Zhang, Yue Cheng, Haiyan Zhao, Yili Dai, Qinling Chen, Nan College of Big Data and Intelligent Engineering South Forestry University Yunnan Kunming China South Forestry University Faculty of Forestry Yunnan Kunming China Art and Design College South Forestry University Yunnan Kunming China

ISBN: (纸本)9781665494571

Semantic segmentation is a hot topic in remote sensing image research. With the development of deep learning, semantic segmentation based on full convolutional neural network has greatly improved the segmentation accuracy. In order to solve the problem of large information content and uneven samples of RS images, there is still a lot of room for improvement in classification accuracy. In this paper, the main model used is renet-101 network as the main framework, adding atrous convolution to the network to obtain more semantic information. The model is evaluated on the Postdam dataset using a combination of secondary augmented image and ResNET-101 with Atrous Convolution. The results show that: The proposed method achieved 76.43% MIOU on the Postdam dataset, which is 14.82% higher than that of the method without secondary preprocessing and atrous convolution. © 2021 IEEE.

关键词： Deep learning Image segmentation Convolution Semantics Feature extraction Indexes Convolutional neural networks

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Challenges of Teaching Programming in StackOverflow Era 23rd

Challenges of Teaching Programming in StackOverflow Era

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23rd international conference on Interactive Collaborative learning (ICL) / 49th IGIP international conference on engineering Pedagogy - Educating Engineers for Future Industrial Revolutions

作者： Poial, Jaanus Tallinn Univ Technol Sch Informat Technol Raja 4C EE-12616 Tallinn Estonia

ISBN: (纸本)9783030681982;9783030681975

Teaching computer programming has changed a lot during the last 15 years. Instead of visiting lectures, reading books, documentation and manuals, solving individual programming tasks, etc., students prefer to "find" solutions to all problems in Google, StackOverflow and similar forums, You-Tube videos and other helpful tools. It could be enjoyed, if there were no negative side effects: programs are created without proper understanding using "copy and paste" from different sources and brute force hacks to comply them to the tests. Often these internet resources are incomplete, contain errors and do not fit to solve the given problem. This article covers some techniques used by the author in the following areas: 1. Choice of assignments - full scale projects vs. small (but meaningful and complex enough) exercises. 2. Using version control to track students' progress. 3. Using learning environments, e.g. Moodle. 4. Role of automated tests. 5. Cheating issues. 6. Providing enough feedback to massive amount of homework. 7. Using "flipped classroom" and "work in pairs" approaches. Daily teaching in a rapidly changing environment necessitates constant updating of teaching methods using peer experience, continuous student feedback, professional publications and common sense. Author has tried "flipped classroom" and "work in pairs" approaches combined with automated assessment and version control for a medium level programming course ("Algorithms and data Structures"). The results were impressive - dropout decreased from 54% to 17% in two years.

关键词： Teaching best practices Collaborative learning Flipped classrooms

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intelligent Live Video Streaming for Object Detection 23

Intelligent Live Video Streaming for Object Detection

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作者： Chen, Mingkang Sun, Jingtao Aida, Kento Figueiredo, Renato J. Ku, Yun-Jung Subratie, Kensworth The Graduate University for Advanced Studies Sokendai Department of Informatics Tokyo Japan National Institute of Informatics Tokyo Japan University of Florida Electrical and Computer Engineering Florida United States

ISBN: (纸本)9781665494571

These days, sensors and cameras are being deployed on an increasingly large scale. Furthermore, the rapid development of machine learning models for computer vision now presents novel opportunities for the use of artificial intelligence (AI) and Internet of Things (IoT) combinations in various application scenarios. However, challenges remain in supporting low-latency video streaming from distributed mobile IoT devices under dynamic network environments, and overcoming video data quality degradation that results from weather 'noise', which reduces the accuracy of AI-based data analyses such as object detection. In this paper, we propose a live video stream processing system for supporting intelligent services that integrates the following features. First, to cope with dynamic networks and achieve low latency, our approach employs a peer-to-peer (P2P)-based virtual network at the edge and a multi-tiered architecture composed of IoT cameras, edge, and cloud servers. Second, we construct a flexible messaging system for video analysis built upon SINETStream, which is a messaging system that adopts a topic-based pub/sub model. Third, we implement a framework that can remove weather-related (rain, snow, and fog) noise by applying weather classification and adaptive noise removal models that improve the accuracy of video analysis from data collected outdoors. The latency, throughput, and image quality benchmark experiments conducted to validate the feasibility of our proposed system showed that the process resulted in image quality improvements of approximately 30% (on average). © 2021 IEEE.

关键词： Image quality Adaptation models Image edge detection Object detection Streaming media Throughput Internet of Things

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EEAI: An End-edge Architecture for Accelerating Deep Neural Network Inference 23

EEAI: An End-edge Architecture for Accelerating Deep Neural ...

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作者： Liu, Guozhi Dai, Fei Huang, Bi Qiang, Zhenping Li, LeCheng Wang, Shuai School of Big Data and Intelligent Engineering Southwest Forestry University Kunming China

ISBN: (纸本)9781665494571

Deep Neural Networks (DNNs), as a key technology for Artificial Intelligence (AI) applications in the 5G era, have been widely used in the field of mobile intelligence. However, it is challenging to run computation-intensive DNN-based applications on mobile devices due to the limited resources. What's worse, traditional cloud-assisted approach requires significant amounts of data to be uploaded to the cloud through wireless network, leading to poor real-time performance and low quality of user experience. To address these challenges, in this paper, we propose EEAI, a framework that end-edge collaborative DNN inference. Firstly, we analyze the characteristics of DNN-based applications. Then, we introduce edge computing, and design EEAI, a light-weight scheduler to automatically partition DNN computation between mobile devices and edge servers at the granularity of DNN layers. Finally, we verify the effectiveness of the EEAI under different network environments (4G, 5G, and Wi-Fi). Experiments show that the proposed method is effective and has good performance. © 2021 IEEE.

关键词： Deep learning Performance evaluation Energy consumption 5G mobile communication Neural networks Collaboration Computer architecture

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