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IEEE Conference on Wireless Communications and networking

作者： Kajia Jiao Xuanheng Li Miao Pan Fan Jiang Ming Li School of Information and Communication Engineering Dalian University of Technology Dalian China Department of Electrical and Computer Engineering University of Houston Houston TX USA Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts and Telecommunications Xian Shaanxi China

ISBN: (数字)9781728131061

ISBN: (纸本)9781728131078

The fast development on emerging services makes our telecommunications networks witness two key problems. One is the flexibility to fulfill the diverse service requests and the other is the shortage on spectrum. network slicing and spectrum sharing have been regarded as two prominent solutions, which, however, are barely jointly studied. When taking the shared spectrum into account, its unique feature of heterogeneity and uncertainty will bring new challenges for the slicing. In this paper, we propose a service-oriented spectrum-aware RAN-slicing trading (SSRT) scheme with a comprehensive consideration on both aspects. For the SSRT scheme, we jointly slice three kinds of resources, namely, time, spectrum (including both licensed one and shared one), and network facilities, according to the diverse traffic requests, which are classified into delay-tolerant (DT) ones and delay-sensitive (DS) ones, as well as the willing payments from different service providers (SPs). To achieve both inter-slice and intra-slice isolation, we construct a three-dimensional (3D) conflict graph and formulate the SSRT scheme into a mixed-integer nonlinear programming (MINLP) problem with a cross-layer spectrum-aware resource allocation and a hybrid transmission mode (including both single-hop and multi-hop). Since finding all the maximum independent sets (MIS) for the 3D conflict graph is an NP-hard problem, we further develop an iterative heuristic algorithm for the MIS determination.

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Multiple Spatial Information Weighted Fuzzy Clustering for Image Segmentation

Multiple Spatial Information Weighted Fuzzy Clustering for I...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xiangdao Liu Jin Zhou Hui Jiang C. L. Philip Chen Tong Zhang Lin Wang Shiyuan Han Yuehui Chen Shandong Provincial Key Laboratory of Network based Intelligent Computing University of Jinan Jinan China Development and Test Center Chinabond Fintech Information Technology Co. Ltd. Beijing China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

For image segmentation, fuzzy clustering methods with single spatial information cannot ensure robustness to the image corrupted by different noises. In this paper, to figure out this problem, we propose a multiple spatial information weighted fuzzy clustering method, in which the original pixel intensity and its two spatial information, the mean and median of neighbors within a local window, are combined with different weights to obtain precise segmentation results of noise images. And the entropy-regularized method is employed to optimize the weight of each term to handle the images with different noise. What's more, the kernelization of the proposed method is presented to relief the impact of outliers. It is worth noting that our methods can be further extended by combining with other spatial information. Experiments on synthetic images and natural images show the superiority and efficiency of the proposed methods.

关键词： Image segmentation Clustering methods Scalability Conferences Robustness Entropy Cybernetics

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Revisiting Recent and Current Anomaly Detection based on Machine Learning in Ad-Hoc networks 5

Revisiting Recent and Current Anomaly Detection based on Mac...

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5th Annual International Conference on network and Information Systems for Computers, ICNISC 2019

作者： Wang, Zhixiao Chen, Mingyu Yan, Wenyao Wang, Wendong Gao, Ang Nie, Gaoyang Wang, Feng Yang, Shaobo Xi'An University of Technology Xi'an710048 China Goettingen University Goettingen37077 Germany Xi'An Jiaotong University Xi'an710049 China Shaanxi Provincial Key Laboratory of Network Computing and Security Technology Xi'an710048 China College of xi'An Innovation Yan'An University Xi'an710100 China Yan'An University Yan'an716000 China National Satellite Meteorological Center China Meteorological Administration Beijing100081 China

Ad-Hoc network which is one kind of self-organized networks is much more vulnerable than the infrastructural network with the properties of highly changeable linkage, dynamic structure, and wireless connections so that the tradition intrusion detection system (IDS) should be improved to adapt in such network with limited computing resources and open channels. To ensure the security in Ad-Hoc network, the efficient anomaly detection methods should be probed. Over the past years, many studies have implemented anomaly detection methods (intrusion detection techniques) based on machine-learning methods in this field. This article analyzes the existing security problem in Ad-Hoc network, presents the basic theory of intrusion detection for Ad-Hoc network, and reviews the current and recent anomaly detection methods used machine learning techniques in the intrusion detection system. © Published under licence by IOP Publishing Ltd.

关键词： Intrusion detection

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Beatgan: Anomalous rhythm detection using adversarially generated time series 28

Beatgan: Anomalous rhythm detection using adversarially gene...

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28th International Joint Conference on Artificial Intelligence, IJCAI 2019

作者： Zhou, Bin Liu, Shenghua Hooi, Bryan Cheng, Xueqi Ye, Jing Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science National University of Singapore Singapore Department of Anesthesiology Nanfang Hospital Southern Medical University China CAS Key Laboratory of Network Data Science and Technology University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9780999241141

Given a large-scale rhythmic time series containing mostly normal data segments (or 'beats'), can we learn how to detect anomalous beats in an effective yet efficient way? For example, how can we detect anomalous beats from electrocardiogram (ECG) readings? Existing approaches either require excessively high amounts of labeled and balanced data for classification, or rely on less regularized reconstructions, resulting in lower accuracy in anomaly detection. Therefore, we propose BeatGAN, an unsupervised anomaly detection algorithm for time series data. BeatGAN outputs explainable results to pinpoint the anomalous time ticks of an input beat, by comparing them to adversarially generated beats. Its robustness is guaranteed by its regularization of reconstruction error using an adversarial generation approach, as well as data augmentation using time series warping. Experiments show that BeatGAN accurately and efficiently detects anomalous beats in ECG time series, and routes doctors' attention to anomalous time ticks, achieving accuracy of nearly 0.95 AUC, and very fast inference (2.6 ms per beat). In addition, we show that BeatGAN accurately detects unusual motions from multivariate motion-capture time series data, illustrating its generality. © 2019 International Joint Conferences on Artificial Intelligence. All rights reserved.

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Joint object contour points and semantics for instance segmentation

arXiv

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arXiv 2020年

作者： Zhang, Wenchao Fu, Chong Zhu, Mai Cao, Lin Tie, Ming Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China Science and Technology on Space Physics Laboratory Beijing100076 China School of Computer Science University of Auckland New Zealand

The attributes of object contours has great significance for instance segmentation task. However, most of the current popular deep neural networks do not pay much attention to the object edge information. Inspired by the human annotation process when making instance segmentation datasets, in this paper, we propose Mask Point R-CNN aiming at promoting the neural network’s attention to the object boundary. Specifically, we innovatively extend the original human keypoint detection task to the contour point detection of any object. Based on this analogy, we present an contour point detection auxiliary task to Mask R-CNN, which can boost the gradient flow between different tasks by effectively using feature fusion strategies and multi-task joint training. As a consequence, the model will be more sensitive to the edges of the object and can capture more geometric features. Quantitatively, the experimental results show that our approach outperforms vanilla Mask R-CNN by 3.8% on Cityscapes dataset and 0.8% on COCO dataset. Copyright © 2020, The Authors. All rights reserved.

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An improved method of cache prefetching for small files in ceph system 5th

An improved method of cache prefetching for small files in c...

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5th Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2018

作者： Fan, Ya Wang, Yong Ye, Miao Lu, Xiaoxia Huan, YiMing School of Computer Science and Information Security Guilin University of Electronic Technology GuilinGuangxi541004 China School of Information and Communication Guilin University of Electronic Technology GuilinGuangxi541004 China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology GuilinGuangxi541004 China

ISBN: (纸本)9783030037659

To improve the inefficiency of file access for massive small files in the distributed file system, prefetching the small files into the cache is the most conventional method to be adopted. As the number of cached files is proportional to time, there will be many redundant small files which occupy the cache space and haven’t been read for a long time, the hit ratio will be decreased in this situation. To solve this defect, we proposed an improved LRU-W algorithm based on the file read times and the time interval of file read and designed a L2 cache optimization mechanism which can search file in the linked list with higher priority firstly and remove the files with lighter weight factor from the linked list with lower priority dynamically. The experiments and its result show that when prefetching massive small files, the proposed method in this paper can increase the hit ratio of cached files and improve the overall performance of Ceph file system. © Springer Nature Switzerland AG 2019.

关键词： File organization

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Optimization Space Exploration of Hardware Design for CRYSTALS-KYBER

Optimization Space Exploration of Hardware Design for CRYSTA...

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Asian Test Symposium (ATS)

作者： Yixuan Zhao Zhiteng Chao Jing Ye Wen Wang Yuan Cao Shuai Chen Xiaowei Li Huawei Li State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Yale University US Hohai University China Rock Solid Security Lab FiberHome Co. Ltd. China

ISBN: (数字)9781728174679

ISBN: (纸本)9781728174686

Public key cryptography is important in the global communication digital infrastructure. However, the emergence of quantum computer and Shor algorithm has greatly threatened the security of public key cryptography. The CRYSTALS-KYBER, as a lattice-based KEM algorithm, passed three rounds of a global solicitation for post-quantum cryptography algorithms held by the National Institute of Standards and technology (NIST). This paper explores the implementation and optimization space of hardware design according to CRYSTALS-KYBER algorithm. We analyze its software code and try different strategies to optimize the hardware implementation, and conduct comparative analysis in terms of area and speed. The experimental results show that the performance can be greatly improved by moderately optimizing the loops. In comparison with optimal results of the work [12], our optimizations improve the performance by up to 74.6% for encapsulation algorithm and 54.4% for decapsulation algorithm.

关键词： Cryptography Optimization Hardware Pipeline processing Encapsulation Computers Space exploration

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Short Text Topic Recognition and Optimization Method for University Online Community 1

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5th International Conference on Artificial Intelligence and security, ICAIS 2019

作者： Wu, Xu Wu, Haitao Xie, Xiaqing Xu, Jin Zhang, Tianle Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China School of Cyberspace Security BUPT Beijing China Beijing University of Posts and Telecommunications Library Beijing100876 China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China

ISBN: (数字)9783030242688

ISBN: (纸本)9783030242671

The university online community mainly records what happens in target areas and groups of people. It has the characteristics of timeliness, regional strong and clear target groups. Compared with Weibo and post-bar, university community’s text topic recognition needs to solve the problems of large text noise, fast text update and short single text content. To this end, this paper proposes a method of building university topic model based on LDA topic model. Through the steps of original text’s noise reduction, LDA (Latent Dirichlet Allocation (LDA), is a topic model commonly used in the field of machine learning and is often used for text categorization.) model recognition and weighted calculation of recognition results, etc., the event themes that characterize the common characteristics for university online community are obtained. Experiments based on real university online community’s data show that the topic model of university popular events established by the topic recognition model of this paper can reflect some popular events in colleges and universities, so as to provide reasonable support for university management. © 2019, Springer Nature Switzerland AG.

关键词： Character recognition

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DPNet: Dynamic Pooling network for Accurate and Efficient Size-Aware Tiny Object Detection

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IEEE Internet of Things Journal 2025年

作者： Gong, Luqi Chen, Haotian Chen, Yikun Yao, Tianliang Li, Chao Zhao, Shuai Han, Guangjie Beijing University of Posts and Telecommunications School of Computer Science Beijing China Zhejiang Lab Research Center for Space Computing System Hangzhou China Southwest Jiaotong University SWJTU-LEEDS Joint School Chengdu China LTD Guangdong Zhiyun City construction Technology Co Zhuhai China Tongji University College of Electronic and Information Engineering Department of Control Science and Engineering Shanghai China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

In unmanned aerial systems, especially in complex environments, accurately detecting tiny objects is crucial. Resizing images is a common strategy to improve detection accuracy, particularly for small objects. However, simply enlarging images significantly increases computational costs and the number of negative samples, severely degrading detection performance and limiting its applicability. This paper proposes a Dynamic Pooling network (DPNet) for tiny object detection to mitigate these issues. DPNet employs a flexible down-sampling strategy by introducing a factor (df) to relax the fixed down-sampling process of the feature map to an adjustable one. Furthermore, we design a lightweight predictor to predict df for each input image, which will be used to decrease the resolution of feature map in backbone. Thus, we achieve input-aware down-sampling. We design an Adaptive Normalization Module (ANM) to make a unified detector well compatible with different dfs. At the same time, we also design a guidance loss to supervise the predictor’s training. DPNet realizes the dynamic allocation of computing resources to trade off detection accuracy and efficiency through this. Experiments on the TinyCOCO and TinyPerson datasets show that our DPNet can save over 35% and 25% GFLOPs, respectively, while maintaining comparable detection *** code will be made publicly available. © 2014 IEEE.

关键词： Object detection

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Energy efficiency evaluation based on QoS parameter specification for cloud systems

Energy efficiency evaluation based on QoS parameter specific...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Saiqin Long Hao Yu Zhetao Li Shujuan Tian Yun Li Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent network School of Computer Science Xiangtan University Xiangtan China School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781728176505

Energy efficiency evaluation (EEE) is a research difficulty in the field of cloud computing. The current research mainly considers the relevant energy efficiency indicators of cloud systems and weights the interrelationship between energy consumption, system performance and QoS requirements. However, it lacks a combination of subjective and objective, qualitative and quantitative evaluation method to accurately evaluate the energy efficiency of cloud systems. We propose a novel EEE method based on the QoS parameter specification for cloud systems (EEE-QoS). Firstly, it reduces the metric values that affect QoS requirements to the same dimension range and then establishes a belief rule base (BRB). The best-worst method is utilized to determine the initial weights of the premise attributes in the BRB model. Then, the BRB model parameters are optimized by the mean-square error, the activation weight is calculated, and the activation rules of the evidence reasoning algorithm are integrated to evaluate the belief of the conclusion. The quantitative and qualitative evaluation of the energy efficiency of cloud systems is realized. The experiments show that the proposed method can accurately and objectively evaluate the energy efficiency of cloud systems.

关键词： Measurement Cloud computing Energy consumption System performance High performance computing Conferences Quality of service

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