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CrowdLearn: A Crowd-AI Hybrid System for Deep Learning-based Damage Assessment Applications 39

CrowdLearn: A Crowd-AI Hybrid System for Deep Learning-based...

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39th IEEE international conference on distributed computing Systems (ICDCS)

作者： Zhang, Daniel (Yue) Zhang, Yang Li, Qi Plummer, Thomas Wang, Dong Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

ISBN: (纸本)9781728125190

Artificial Intelligence (AI) has been widely adopted in many important application domains such as speech recognition, computer vision, autonomous driving, and AI for social good. In this paper, we focus on the AI-based damage assessment applications where deep neural network approaches are used to automatically identify damage severity of impacted areas from imagery reports in the aftermath of a disaster (e.g., earthquake, hurricane, landslides). While AI algorithms often significantly reduce the detection time and labor cost in such applications, their performance sometimes falls short of the desired accuracy and is considered to be less reliable than domain experts. To exacerbate the problem, the black-box nature of the AI algorithms also makes it difficult to troubleshoot the system when their performance is unsatisfactory. The emergence of crowdsourcing platforms (e.g., Amazon Mechanic Turk, Waze) brings about the opportunity to incorporate human intelligence into AI algorithms. However, the crowdsourcing platform is also black-box in terms of the uncertain response delay and crowd worker quality. In this work, we propose the CrowdLearn, a crowd-AI hybrid system that leverages the crowdsourcing platform to troubleshoot, tune, and eventually improve the black-box AI algorithms by welding crowd intelligence with machine intelligence. The system is specifically designed for deep learning-based damage assessment (DDA) applications where the crowd tend to be more accurate but less responsive than machines. Our evaluation results on a real-world case study on Amazon Mechanic Turk demonstrate that CrowdLearn can provide timely and more accurate assessments to natural disaster events than the state-of-the-art AI-only and human-AI integrated systems.

关键词： Artificial intelligence Crowdsourcing Reliability Task analysis Delays Social networking (online) Training data

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Demonstration of Federated Learning in a Resource-Constrained Networked Environment 5

Demonstration of Federated Learning in a Resource-Constraine...

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5th IEEE international conference on Smart computing (SMARTCOMP)

作者： Conway-Jones, Dave Tuor, Tiffany Wang, Shiqiang Leung, Kin K. IBM Corp IBM Res Hursley Pk Winchester Hants England Imperial Coll London London England IBM TJ Watson Res Ctr Yorktown Hts NY USA

ISBN: (纸本)9781728116891

Many modern applications in the area of smart computing are based on machine learning techniques. To train machine learning models, a large amount of data is usually required, which is often not readily available at a central location. Federated learning enables the training of machine learning models from distributed datasets at client devices without transmitting the data to a central place, which has benefits including preserving the privacy of user data and reducing communication bandwidth. In this demonstration, we show a federated learning system deployed in an emulated wide-area communications network with dynamic, heterogeneous, and intermittent resource availability, where the network is emulated using a CORE/EMANE emulator. In our system, the environment is decentralized and each client can ask for assistance by other clients. The availability of clients is intermittent so only those clients that are available can provide assistance. A graphical interface illustrates the network connections and the user can adjust these connections through the interface. A user interface displays the training progress and each client's contribution to training.

关键词： distributed machine learning federated learning model training networking

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Differentiated Service Mechanism According to Vehicle Environment in Vehicular Edge Network 15

Differentiated Service Mechanism According to Vehicle Enviro...

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15th IEEE international Wireless Communications and Mobile computing conference (IEEE IWCMC)

作者： Tan, Zuoyan Rui, LanLan Li, Wenjing Qiu, Xuesong Guo, Shaoyong Yu, Xiuzhi Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China China Elect Standardizat Inst Beijing Peoples R China

ISBN: (纸本)9781538677476

With the rapid development of communication technologies such as 5G, vehicular information and applications are exploding. Mobile edge computing (MEC) as a new technology can transfer the information more quickly and accurately. Providing differentiated services for the information can affect the performance of the applications. In this study, based on 802.11p EDCA protocol, we propose a new differentiated service scheme called DD-EDCA (Differentiating Density Enhanced distributed Channel Access). Firstly, we use MEC Server to estimate the road density. Secondly, different solutions have been designed according to different vehicle density environment requirements. For example, the displacement trend is considered at a low density, and the multi-hop broadcast information is pre-processed at a high density. And the schemes for dynamically adjusting EDCA parameters are designed. Simulation results show that our method reduces latency and packet loss rate, and improves the throughput.

关键词： vehicular edge network transmission information differentiated services QoS

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Software Defined networking (SDN) based Secure Integrated Framework against distributed Denial of Service (DDoS) Attack in Cloud Environment 4

Software Defined Networking (SDN) based Secure Integrated Fr...

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4th international conference on Communication and Electronics Systems, ICCES 2019

作者： Gupta, B.B. Chaturvedi, Chhavi National Institute of Technology Kurukshetra India

ISBN: (纸本)9781728112619

Cloud computing is nowadays developed as a scalable and cost-effective technique. It facilitates many features in the computing environment like resource pooling, multi-tenancy, virtualization etc. In order to avoid resource maintenance, computing has been done on the service provider's infrastructure. Data security is the main concern in cloud computing because it is stored in data centers provided by service providers. DDoS attacks are rapidly growing in the cloud environment and have become a serious threat to cloud consumers. SDN is used to identify and prevent DDoS attacks in the cloud and it simplifies network complexity. In this paper, we have proposed a method that detects and mitigates DDoS attacks in cloud computing using the features of SDN with high accuracy and low computational cost. The proposed system is able to block the illegitimate users and allow only legitimate users to access the cloud services. The results are calculated by simulation experiments. © 2019 IEEE.

关键词： Software defined networking

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Heterogeneous Statistical QoS-Driven Power Allocation for Collaborative D2D Caching Over Edge-computing Networks 39

Heterogeneous Statistical QoS-Driven Power Allocation for Co...

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39th IEEE international conference on distributed computing Systems (ICDCS)

作者： Zhang, Xi Wang, Jingqing Texas A&M Univ Dept Elect & Comp Engn Networking & Informat Syst Lab College Stn TX 77843 USA

ISBN: (纸本)9781728125190

With the exponentially increasing demand for wireless multimedia services over 5G mobile wireless networks, the statistical quality-of-service (QoS) provisioning has been proven to be able to effectively guarantee the multimedia data transmissions over highly time-varying wireless channels. On the other hand, many research efforts have been focused on various 5G-promising candidate techniques, such as device-to-device (D2D) caching based communications to offload cellar traffics and address the data explosion problem for the next generation wireless networks. Furthermore, in order to enhance the reliability of video delivery without causing too much interference to other mobile users, researchers have applied the coordinated joint transmission techniques for collaborative D2D caching scheme, which enables mobile users to share popular multimedia files within a D2D communication group instead of downloading from remote backhaul networks. Towards this end, one of the key issues lies in the power allocation problems subject to the heterogeneous statistical delay-bounded QoS requirements for the collaborative D2D caching over edge-computing networks. To overcome the aforementioned challenges, in this paper we propose the collaborative D2D caching model and D2D communication schemes over edge-computing networks. Under the heterogeneous statistical delay-bounded QoS requirements, we formulate and solve the effective-capacity optimization problem for our proposed collaborative D2D caching schemes over edge-computing networks. Then, we develop the collaborative D2D-cache matching algorithms by using bipartite graph technique for selecting D2D-caching users to maximize the effective capacity. Also conducted is a set of simulations which evaluate the system performances and show that our proposed collaborative D2D caching schemes outperform the other existing schemes under heterogeneous statistical delay-bounded QoS constraints.

关键词： Heterogeneous quality-of-service (QoS) collaborative D2D caching matching algorithm edge-computing networks

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Optimized Service Function Path Selection for IoT Devices Using Virtual Network Function Performance Data 33

Optimized Service Function Path Selection for IoT Devices Us...

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33rd international conference on Information networking (ICOIN)

作者： Abeysiriwardhana, W. A. Shanaka P. Wijekoon, Janaka Nishi, Hiroaki Keio Univ Fac Sci & Technol Sch Sci Open & Environm Syst Kohoku Ku Yagami Campus3-14-1 Hiyoshi Yokohama Kanagawa 2238522 Japan Sri Lanka Inst Informat Technol Fac Comp SLIIT Malabe CampusNew Kandy Rd Malabe Sri Lanka Keio Univ Fac Sci & Technol Sch Syst Design Kohoku Ku Yagami Campus3-14-1 Hiyoshi Yokohama Kanagawa 2238522 Japan

ISBN: (纸本)9781538683507

Software defined networking (SDN) and network function virtualization (NFV) are proposed as software based applications to cater to smart services requirements of smart communities. The services are linked together to support different sets of clients using service function chaining (SFC). Service functions (SFs) in an SFC must be distributed among available computing resources by creating a service function path (SFP) allowing resource management and optimal SF execution. This paper proposes a novel SFP allocation algorithm considering the computation capabilities of hardware resources while minimizing the completion time of SFCs. The proposed algorithm shows 10% performance increment compared to recently developed algorithms such as nearly optimal service function path and optimal service function selection algorithms.

关键词： Internet of Things Network Function Virtualization Software Defined networking Service Function Chaining Virtual Network Function

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Design and Implementation of a Platform for Managing Time-Sensitive IoT Applications

Design and Implementation of a Platform for Managing Time-Se...

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international conference on Collaborative computing: networking, Applications and Worksharing (CollaborateCom)

作者： Harindu Korala Ali Yavari Dimitrios Georgakopoulos Prem Prakash Jayaraman Swinburne University of Technology Melbourne Australia

ISBN: (数字)9781728141466

ISBN: (纸本)9781728185422

This paper proposes a novel platform, called TIDA, for meeting Time-Sensitive IoT (TS-IoT) application requirements. TIDA accomplishes this by performing resource provisioning, scheduling, execution and monitoring of distributed TS-IoT applications across all available IoT devices, close by computers and cloud virtual machines that constitute the IoT infrastructure. TIDA simplifies TS-IoT application management by providing an easy-to-use interface that allows TS-IoT application developers to administer and automate how, where, when, and for how long to execute each distributed TS-IoT application, so it meets its time frame(s). The paper describes the design and implementation of the TIDA platform, which is based on Microsoft Orleans actor software framework.

关键词： Internet of Things Task analysis Engines Tools distributed databases Data models Performance evaluation

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13th international conference on Collaborative computing: networking, Applications and Worksharing, CollaborateCom 2017

13th International Conference on Collaborative Computing: Ne...

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13th international conference on Collaborative computing: networking, Applications and Worksharing, CollaborateCom 2017

ISBN: (纸本)9783030009151

The proceedings contain 65 papers. The special focus in this conference is on Collaborative computing: networking, Applications and Worksharing. The topics include: A Human-Machine Collaborative Detection Model for Identifying Web Attacks;a Reinforcement Learning Based Workflow Application Scheduling Approach in Dynamic Cloud Environment;p-Faster R-CNN Algorithm for Food Detection;PUED: A Social Spammer Detection Method Based on PU Learning and Ensemble Learning;position vs. Attitude: How Topological Factors Influence Our Difference in the Attitudes on Online Interrelationships? A Case Study with Language Use;route-Oriented Participants Recruitment in Collaborative Crowdsensing;management Node Selection Based on Cloud Model in a distributed Network;CNTE: A Node Centrality-Based Network Trust Evaluation Method;minimum Cost Load Migration in distributed Software Defined Networks;collaborative and Green Resource Allocation in 5G HetNet with Renewable Energy;hybrid-Aware Collaborative Multipath Communications for Heterogeneous Vehicular Networks;RETaIL: A Machine Learning-Based Item-Level Localization System in Retail Environment;a Point of Interest Recommendation Approach by Fusing Geographical and Reputation Influence on Location Based Social Networks;an Electricity Power Collection Data Oriented Missing Data Imputation Solution;curve-Registration-Based Feature Extraction for Predictive Maintenance of Industrial Equipment;predicting Next Points of Interests Based on a Markov Model;A Topology-Aware Reliable Broadcast Scheme for Multidimensional VANET Scenarios;MAC-ILoc: Multiple Antennas Cooperation Based Indoor Localization Using Cylindrical Antenna Arrays;easing Traffic Congestion: An Improved Clustering Method for Sharing Bike Station Deployment;sensor Data Classification for the Indication of Lameness in Sheep;On Cost Efficient Dataflow computing Program Deployment in SDN Managed distributed computing Environment.

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Better User Clustering Scheme in distributed NOMA Systems 34th

Better User Clustering Scheme in Distributed NOMA Systems

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34th international conference on Advanced Information networking and Applications, AINA 2020

作者： Mounchili, Salifou Hamouda, Soumaya University of Carthage Tunis Tunisia

ISBN: (纸本)9783030440404

Non-orthogonal multiple access (NOMA) is one of the promising techniques for 5G due to its good spectral efficiency for two-user cluster, with a distinctive channel gain difference. Generally before forming two-user clusters, NOMA divides users into groups (strong and weak channel gain groups). However, when the number of users in one group is larger than the others, the admission of single users into two-user clusters can cause a drop in performance of the NOMA system due to the strong interference. Therefore, these single users deploy the old orthogonal multiple access (OMA). This hybrid technique is known as the conventional NOMA (C-NOMA). In this paper, we examine in detail the admission conditions of additional users in two-user NOMA clusters in order to form three-user NOMA clusters and promote the massive connectivity. Then, we propose a new dynamic clustering scheme called DC-NOMA based on the channel gain difference to form clusters of more than two users. The simulation results show that, our proposed scheme can achieve a slightly better performance in terms of radio capacity and individual cluster throughput, compared to other NOMA schemes such as Conventional-NOMA (C-NOMA) and Random clustering. Specifically, DC-NOMA helps to promote massive connectivity as it allows more users to be inserted into NOMA clusters without degrading system performance. © 2020, Springer Nature Switzerland AG.

关键词： Spectrum efficiency

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Proceedings of PyHPC 2017: 7th Workshop on Python for High-Performance and Scientific computing - Held in conjunction with SC 2017: The international conference for High Performance computing, networking, Storage and Analysis

Proceedings of PyHPC 2017: 7th Workshop on Python for High-P...

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7th Workshop on Python for High-Performance and Scientific computing, PyHPC 2017

ISBN: (纸本)9781450354806

The proceedings contain 8 papers. The topics discussed include: enabling python to execute efficiently in heterogeneous distributed infrastructures with PyCOMPSs;efficient pattern matching in python;real-time financial risk measurement of dynamic complex portfolios with Python and PyOpenCL;Python in the NERSC exascale science applications program for data;real-time thermal medium-based breathing analysis with Python;GPUMap: a transparently GPU-accelerated Python map function;nbodykit: a Python toolkit for cosmology simulations and data analysis on parallel HPC systems;and Python and HPC for high energy physics data analyses.

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