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DCIGAN: A Distributed Class-Incremental Learning Method Based on Generative Adversarial networks

DCIGAN: A Distributed Class-Incremental Learning Method Base...

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ieee International conference on Big Data and Cloud Computing (BdCloud)

作者： Hongtao Guan Yijie Wang Xingkong Ma Yongmou Li Science and Technology on Parallel and Distributed Laboratory College of Computer National University of Defense Technology Changsha Hunan P.R.China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Class incremental learning has received wide attention due to its better adaptability to the changing characteristics of online learning. However, data cannot be shared between organizations in the data isolated islands scenario. Existing solutions cannot adapt to incremental classes without aggregating data. In this paper, we propose a distributed class-incremental learning framework, called DCIGAN. It uses GAN generators to store the information of past data and continuously update GAN parameters with new data. In particular, we proposed CIGAN to ensure that the distribution of generated pseudo data is as close as possible to the real data on a single node, which guarantees the accuracy of class-incremental learning. Furthermore, we propose GF, a generators fusion method to integrate local generators of multi-nodes into a new global generator. To evaluate the performance of DCIGAN, we conduct experiments on six datasets under various parameter settings on both two and multi nodes distributed scenarios. Extensive experiments confirm that DCIGAN outperform the general baselines and achieve classification accuracy closes to the method of data aggregating.

关键词： Generators Generative adversarial networks Distributed databases Diseases Servers Hospitals Training

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Optimal Design of Wireless-Powered Hierarchical Fog-Cloud Computing networks

Optimal Design of Wireless-Powered Hierarchical Fog-Cloud Co...

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ieee Global Communications conference

作者： Jingxian Liu Ke Xiong Derrick Wing Kwan Ng Pingyi Fan Zhangdui Zhong School of Computer and Information Technology Beijing Jiaotong University Beijing China State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China School of Electrical Engineering and Telecommunications University of New South Wales NSW Australia Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

This paper investigates the optimal design of wireless-powered hierarchical fog-cloud computing networks, where energy-constrained users first harvest energy from a hybrid access point (HAP) and then offload their computation tasks to fog/cloud servers via the HAP or compute the tasks locally by using the harvested energy. An optimization problem is formulated to maximize the minimal energy balance among multiple users by jointly optimizing offloading decisions, communication and computation resource allocations in the system, where computational capacity, processing delay, energy harvesting (EH) and energy consumption constraints are considered. To efficiently solve such a mixed-integer combinatorial non-convex problem, a penalized successive convex approximation (P-SCA)-based algorithm is designed, which is able to converge to a suboptimal solution with the polynomial time computational complexity. Numerical results show that compared to communication and computation resource allocation, the offloading decision is the dominant factor on affecting the system performance. It is also found that local computing is a better choice for users with relatively poor channel gains while fog/cloud computing is a better choice for users with relatively good channel gains. Specifically, cloud computing is preferred if the cloud computational capacity is strong enough and the wired-link data rate is high enough;Otherwise, fog computing is preferred. Besides, more users are served, less max-min energy balance can be obtained.

关键词： Delays Cloud computing Servers Task analysis Edge computing Optimization Energy consumption

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Exploiting CSI-MIMO for Accurate and Efficient Device Identification

Exploiting CSI-MIMO for Accurate and Efficient Device Identi...

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ieee Global Communications conference

作者： Laxima Niure Kandel Zhuosheng Zhang Shucheng Yu Department of Electrical and Computer Engineering Stevens Institute of Technology

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Due to the inherent broadcast nature of the wireless medium, Wireless local Area networks (WLANs) are targets of a variety of malicious attacks, for example, MAC identity spoofing, rogue AP attack, and network freeloading. These attacks invite security and privacy threats and hinder the worry-free deployment of WLAN networks. To thwart these attacks, existing research has proposed to use hardware-specific imperfections as a unique unforgeable fingerprint for the APs and/or clients. Unfortunately, existing solutions are limited to static and stable environments or use customized hardware preventing their wide-scale adoption. To overcome the limitations, in this work, we propose to use the distribution of relative phase differences between MIMO-radio transmitter oscillators as a distinguishing trait or fingerprint. More specifically, we show that the nonidealities of the multiple RF chains on a single MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) transmitter can be extracted and utilized as a reliable device fingerprint. Each transmitter RF chain has a random initial phase offset, and their difference relative to one another is stable over time, differs uniquely for each transmitter device and cannot be altered by the adversary without significant effort and cost. Our functional prototype measures these unknown phase differences using PHY-layer Channel State Information (CSI) of the in-band channel obtained from off-the-shelf hardware. Our design eliminates expensive custom-built hardware, is invariant to environmental variations and supports device mobility making it practical and deployable in real indoor settings. Experimental evaluation using 17 Intel Network Interface Cards (NICs) resulted in 97% and 92% device identification accuracy for static and mobile device states respectively. Such promising results with identical model and manufacturer devices wherein underlying manufacturing variations are typically low showcase th

关键词： Hardware Radio frequency Transmitters Time-frequency analysis Receivers OFDM Wireless communication

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Investigation on the performance of the iee802.11n based wireless networks for multimedia services 2

Investigation on the performance of the iee802.11n based wir...

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2nd International conference for Engineering, Technology and Sciences of Al-Kitab, ICETS 2018

作者： Farej, Ziyad Khalaf Jasim, Mustafa Mohammad Computer Eng. Dept. Technical Eng. College Northern Technical University Mosul Iraq

ISBN: (纸本)9781538635780

The high data rate of the ieee 802.11n standard is due to its Physical layer Multiple Input Multiple Output (MIMO) system and MAC layer enhancement features. This rate enables the standard to support multimedia with Quality of Service (QoS) application through its Enhanced Distributed Channel Access (EDCA) mechanism. This paper focuses on evaluating the QoS for the ieee 802.11n Wireless local Area Network (WLAN). Three different number of node scenarios with one access point at different spatial streams (1, 2, 3 and 4) are considered in the process of investigating the network performance. The Riverbed Modeler 17.5 is used to model and simulate the proposed scenarios. Simulation results show an effective improvement in the performance when QoS is applied and with respect to the no QoS case, the 18 node number scenario (at 4×4 MIMO) shows a maximum improvement of 91.6% and 34.8% for throughput and delay respectively. © 2018 ieee.

关键词： Quality of service

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Human Motion Classification with Micro-Doppler Radar and Bayesian-Optimized Convolutional Neural networks

Human Motion Classification with Micro-Doppler Radar and Bay...

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2018 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP 2018

作者： Le, Hoang Thanh Phung, Son Lam Bouzerdoum, Abdesselam Tivive, Fok Hing Chi Computer and Telecommunications Engineering University of Wollongong School of Electrical NSW Australia College of Science and Engineering Hamad Bin Khalifa University Doha Qatar

ISBN: (纸本)9781538646588

In recent years, Doppler radar has emerged as an alternative sensing modality for human gait classification since it measures not only the target speed, but also the local dynamics of the moving body parts, thereby creating a unique spectral signature. This paper presents a learning-based method for classifying human motions from micro-Doppler signals. Inspired by the applications of deep learning, the proposed method extracts features from the time-frequency representation of the radar signal using a cascaded of convolutional network layers. To design a optimal network architecture, the Bayesian optimization with Gaussian process priors is employed. Experimental results on real data are presented, which show a significant improvement compared to three existing approaches. © 2018 ieee.

关键词： Feature extraction Doppler radar Time-frequency analysis Two dimensional displays Training Legged locomotion"

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A new feature set for masking-based monaural speech separation 52

A new feature set for masking-based monaural speech separati...

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52nd Asilomar conference on Signals, Systems and computers, ACSSC 2018

作者： Pirhosseinloo, Shadi Brumberg, Jonathan S. Electrical Engineering Computer Science University of Kansas LawrenceKS United States Speech-Language-Hearing University of Kansas LawrenceKS United States

ISBN: (纸本)9781538692189

We propose a new feature based on a gammatone filter bank for improving monaural speech separation using neural networks. This new feature encodes not only the local information of cochleagram, and spectrotemporal context, similar to previous approaches, but also captures time-frequency dynamics in the spectrotemporal context using an image processing technique. Speech separation was achieved by computing optimal time-frequency masks using two types of neural networks (DNN and LSTM) to determine the interactions between feature and training model properties. The performance of our feature was evaluated in a variety of simulated environments having different non-stationary noises and reverberation times and quantified using three objective measures. Experimental results show that the proposed monaural feature set improves the objective speech intelligibility, speech quality and signal-to-noise ratio compared to prior feature sets in noisy and reverberant environments with particular benefit in speech intelligibility. © 2018 ieee.

关键词： Speech intelligibility

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How local Information Improves Rendezvous in Cognitive Radio networks 15

How Local Information Improves Rendezvous in Cognitive Radio...

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15th ieee Annual International conference on Sensing, Communication, and Networking (SECON)

作者： Fu, Yongqin Wang, Yuexuan Gu, Zhaoquan Zheng, Xiaolin Wei, Tianhao Cao, Zhen Cui, Heming Lau, Francis C. M. Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Zhejiang Peoples R China Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Guangzhou Univ Cyberspace Inst Adv Technol Guangzhou 510006 Guangdong Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China

ISBN: (纸本)9781538642818

Cognitive Radio Network (CRN) is a promising technique for solving the wireless spectrum scarcity problem. Rendezvous is the fundamental process of CRNs. We aim at designing faster rendezvous algorithms for CRNs. We find that local information such as user's identifier (ID) and the label of an available channel is very useful for designing faster rendezvous algorithms. First, we propose the Sequence-Rotating Rendezvous (SRR) algorithm. The SRR algorithm can guarantee rendezvous for any two users i and j in (2P(2) + 2P) timeslots, where P is the least prime not less than the total number of channels in the network. Second, we utilize the user's ID to design an ID-based Rendezvous (IDR) algorithm. The IDR algorithm can guarantee rendezvous for any two users i and j in (l + 1)(P-i + 2)(P-j + 2) timeslots, where l is the length of ID and P-i and P-j are the smallest primes which are not less than the numbers of available channels of users i and j respectively. Third, we propose a Channel-Label-based Rendezvous (CLR) algorithm which can guarantee rendezvous for any two users in ((P-i+2)(P-j+2)+PN)([log(2)N] + 1) timeslots, where N is the total number of channels in the network and P-N is the least prime which is not less than N. The theoretical Maximum Time To Rendezvous (MTTRs) of the three algorithms we propose are less than those of the state-of-the-art algorithms in the corresponding categories respectively in certain scenarios. All of our algorithms can be used in multi-user scenarios. We conduct a number of experiments to compare our algorithms with state-of-the-art rendezvous algorithms in different scenarios, the results of which confirm our theoretical analysis.

关键词： Cognitive Radio Network local Information Rendezvous Algorithms Channel Label identifier

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A Structure-Based De-Anonymization Attack on Graph Data Using Weighted Neighbor Match

A Structure-Based De-Anonymization Attack on Graph Data Usin...

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ieee International conference on Data Science in Cyberspace (DSC)

作者： Junbin Fang Aiping Li Qianyue Jiang Shudong Li Weihong Han College of Computer National University of Defense Technology Changsha China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Cyberspace Institute of Advanced Technology Institute of Electronic and Information Engineering of UESTC in Guangdong Guangzhou China

Nowadays, many large-scale graph datasets, such as social networks data, are often published for scientific research, information sharing and data mining. Privacy protection issues in publishing datasets have also received widespread attention. Usually, in order to preserve the individual privacy, the datasets will be anonymized before released. Many researchers have proposed various of De-anonymization algorithms to re-identify nodes in the anonymized dataset which is an effective way to evaluate the anonymity of the published datasets. There are many problems when the de-anonymization algorithms apply to the anonymized datasets. The method might require high-quality seeds, or had a low noise tolerance, so that the de-anonymization process can only achieve poor results. In this paper, we propose a structure-based weighted neighborhood matching algorithm, named SWNM, which have a comprehensive consideration of local and global features when calculating the nodes similarity between anonymized nodes and auxiliary nodes. SWNM also using a dynamic similarity matrix and weighted neighborhood matching algorithm to ensure a nice noise tolerance and de-anonymization accuracy. Finally, experiments on real graph datasets show that the SWNM has better performance than baseline method.

关键词： Social network services Data models Heuristic algorithms Privacy Data privacy Cyberspace Publishing

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CALC2.0: Combining Appearance, Semantic and Geometric Information for Robust and Efficient Visual Loop Closure

CALC2.0: Combining Appearance, Semantic and Geometric Inform...

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2019 ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Nathaniel Merrill Guoquan Huang Dept. of Computer and Information Sciences and Dept. of Mechanical Engineering University of Delaware Newark DE

Traditional attempts for loop closure detection typically use hand-crafted features, relying on geometric and visual information only, whereas more modern approaches tend to use semantic, appearance or geometric features extracted from deep convolutional neural networks (CNNs). While these approaches are successful in many applications, they do not utilize all of the information that a monocular image provides, and many of them, particularly the deep-learning based methods, require user-chosen thresholding to actually close loops - which may impact generality in practical applications. In this work, we address these issues by extracting all three modes of information from a custom deep CNN trained specifically for the task of place recognition. Our network is built upon a combination of a semantic segmentator, Variational Autoencoder (VAE) and triplet embedding network. The network is trained to construct a global feature space to describe both the visual appearance and semantic layout of an image. Then local keypoints are extracted from maximally-activated regions of low-level convolutional feature maps, and keypoint descriptors are extracted from these feature maps in a novel way that incorporates ideas from successful hand-crafted features. These keypoints are matched globally for loop closure candidates, and then used as a final geometric check to refute false positives. As a result, the proposed loop closure detection system requires no touchy thresholding, and is highly robust to false positives - achieving better precision-recall curves than the state-of-the-art NetVLAD, and with real-time speeds.

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Fast and Incremental Loop Closure Detection Using Proximity Graphs

Fast and Incremental Loop Closure Detection Using Proximity ...

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2019 ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Shan An Guangfu Che Fangru Zhou Xianglong Liu Xin Ma Yu Chen AR/VR department *** Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Control Science and Engineering Shandong University Jinan China

Visual loop closure detection, which can be considered as an image retrieval task, is an important problem in SLAM (Simultaneous localization and Mapping) systems. The frequently used bag-of-words (BoW) models can achieve high precision and moderate recall. However, the requirement for lower time costs and fewer memory costs for mobile robot applications is not well satisfied. In this paper, we propose a novel loop closure detection framework titled FILD' (Fast and Incremental Loop closure Detection), which focuses on an on-line and incremental graph vocabulary construction for fast loop closure detection. The global and local features of frames are extracted using the Convolutional Neural networks (CNN) and SURF on the GPU, which guarantee extremely fast extraction speeds. The graph vocabulary construction is based on one type of proximity graph, named Hierarchical Navigable Small World (HNSW) graphs, which is modified to adapt to this specific application. In addition, this process is coupled with a novel strategy for real-time geometrical verification, which only keeps binary hash codes and significantly saves on memory usage. Extensive experiments on several publicly available datasets show that the proposed approach can achieve fairly good recall at 100% precision compared to other state-of-the-art methods. The source code can be downloaded at https://***/FILD for further studies.

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