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A new verifier-based anonymous password-authenticated key exchange protocol

Journal of Information Hiding and Multimedia Signal Processing 2018年第6期9卷 1595-1602页

作者： Chen, Chien-Ming Wang, Guangjie Fang, Weicheng Wu, Tsu-Yang School of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate School Shenzhen China College of Computer Science and Engineering Shandong University of Technology Shandong China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou China

In the information age, privacy have aroused wide concern. User identity can be tracked in a public network if it is not stored or transmitted in a secure way. To enhance the security, many anonymous password authenticated key exchange protocols have been proposed to anonymize user’s identity from the server. However, few of them focus on the stolen verifier attacks which assumes a powerful adversary who is accessible to the server’s database or even secret keys. In this paper, we propose a new verifier-based anonymous password-authenticated key exchange protocols. It employs an existed algebraic MAC as verifier to resist such attacks. We also show that the protocol is secure and efficient through analysis. © 2018 Ubiquitous International. All rights reserved.

关键词： Authentication

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A semi-automatic segmentation for tooth on cone beam CT volume following the anatomic guidance

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Journal of Information Hiding and Multimedia Signal Processing 2017年第4期8卷 774-783页

作者： Zou, Zheng Luo, San-Ding Pan, Jeng-Shyang Liu, Shi-Jian Liao, Sheng-Hui School of Information Science and Engineering Central South University No.932 Lushannan Road ChangshaHunan410005 China School of Information Science and Engineering Fujian University of Technology No.3 Xueyuan Road University Town Minhou FuzhouFujian350118 China The Key Laboratory of Big Data Mining and Applications of Fujian Province Fujian University of Technology No.3 Xueyuan Road University Town Minhou FuzhouFujian350118 China

Cone beam computed tomography(CBCT) technique is popular in three dimension imaging of the jaw bones and teeth due to its high resolution and relatively lower radiation exposure compared with multi-slice computed tomography(MSCT). But segmentation of the tooth from CBCT is challenging due to reasons such as the lower bone signal-to-noise and the topology variances. In order to accurately segment the teeth, we propose a novel volume-based interactive segmentation framework. First, valley-emphasized image is generated from original images to enhance voxels in high relief to assist the separation of tooth region from the alveolar bone with the similar intensity. Meanwhile the information about root canal is abstracted to guide the segmentation. In order to obtain the root canal curve, an energy minimization combined with the directional characterise is introduced in our method. Incorporated with the constraint offered by the root canal curve, a edge-aware harmonic field is designed to avoid leakage and shrinkage problems in the segmentation process. The experimental results demonstrated that our method is better than two other most cited methods in accuracy and efficiency. © 2017, Ubiquitous International. All rights reserved.

关键词： Bone

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A method of fundamental matrix estimation based on nsga-ii and improved quasi-affine transform

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第2期6卷 313-327页

作者： Fan, Yi-Kai Liu, Shi-Jian Kong, Ling-Ping Pan, Jeng-Shyang College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China VSB-Technical University of Ostrava Czech Republic Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Information Management Chaoyang University of Technology Taiwan

The fundamental matrix is the mainstream solution to computer vision problems such as 3D reconstruction, real-time location and map building. Accuracy and efficiency are two main measurement indexes in fundamental matrix estimation. When the accuracy is not enough, it often needs to be corrected through back-end optimization and other costly ways, and low efficiency will affect the real-time performance of the system. In order to solve this problem, this paper proposes a new estimation method of fundamental matrix based on improved quasi affine transformation. Specifically, based on the QUATRE algorithm, this method first proposes a population cooperation method based on a specific "gene-chromosome" pattern. Secondly, combining the advantages of NSGA-II in solving multi-objective problems, the Pareto dominance relationship of the population chromosome was firstly calculated according to the objective function of mean polarity distance and internal points in the way of NSGA-II, and the crowding degree was also calculated. The operations of population initialization, mutation and crossover in the discrete solution space represented by the homogeneous coordinate system are redefined. The selection operation is then performed according to the elite policy of NSGA-II. In addition, a confidence-based method to determine the number of iterations is proposed to accelerate the algorithm. Experimental results show that the proposed method can effectively eliminate noise and mismatching, and is superior to the current mainstream methods in accuracy and efficiency, and can effectively solve the problem of fundamental matrix estimation. © 2021 Global Research Online. All rights reserved.

关键词： Efficiency

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AutoSTL: Automated Spatio-Temporal Multi-Task Learning

arXiv

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arXiv 2023年

作者： Zhang, Zijian Zhao, Xiangyu Miao, Hao Zhang, Chunxu Zhao, Hongwei Zhang, Junbo College of Computer Science and Technology Jilin University China School of Data Science City University of Hong Kong Hong Kong Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University China Department of Computer Science Aalborg University Denmark JD Intelligent Cities Research China JD iCity JD Technology China Hong Kong Institute for Data Science City University of Hong Kong Hong Kong

Spatio-temporal prediction plays a critical role in smart city construction. Jointly modeling multiple spatio-temporal tasks can further promote an intelligent city life by integrating their inseparable relationship. However, existing studies fail to address this joint learning problem well, which generally solve tasks individually or a fixed task combination. The challenges lie in the tangled relation between different properties, the demand for supporting flexible combinations of tasks and the complex spatio-temporal dependency. To cope with the problems above, we propose an Automated Spatio-Temporal multi-task Learning (AutoSTL) method to handle multiple spatio-temporal tasks jointly. Firstly, we propose a scalable architecture consisting of advanced spatio-temporal operations to exploit the complicated dependency. Shared modules and feature fusion mechanism are incorporated to further capture the intrinsic relationship between tasks. Furthermore, our model automatically allocates the operations and fusion weight. Extensive experiments on benchmark datasets verified that our model achieves state-of-the-art performance. As we can know, AutoSTL is the first automated spatio-temporal multi-task learning method. Copyright © 2023, The Authors. All rights reserved.

关键词： Smart city

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Two Novel Semantics of Top-k Queries Processing in Uncertain database

Two Novel Semantics of Top-k Queries Processing in Uncertain...

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International Conference on Computer and Information Technology (CIT)

作者： Dexi Liu Changxuan Wan N. Xiong Laurence T. Yang Lei Chen School of Information Technology Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China Department of Computer Science Georgia State University Atlanta GA USA Department of Computer Science Saint Francis Xavier University Antigonish NS Canada Department of Computer Science HK University of Science and Technology Hong Kong China

Top-k query is a powerful technique in uncertain databases because of the existence of exponential possible worlds, and it is necessary to combine score and confidence of tuples to derive top k answers. Different semantics, the combination methods of score and confidence, lead to different results. U-kRanks and Global Top-k are two semantics of Top-k queries in uncertain database, which consider every alternative in x-tuple as single one and return the tuple which has the highest probability appearing at top k or a given rank. However, no matter which alternative (tuple) of an x-tuple appears in a possible world, it undoubtedly believes that this x-tuple appears in the same possible world accordingly. Thus, instead of ranking every individual tuple, we define two novel Top-k queries semantics in uncertain database, Uncertain x-kRanks queries (U-x-kRanks) and Global x-Top-k queries (G-x-Top-k), which return k entities according to the score and the confidence of alternatives in x-tuple, respectively. In order to reduce the search space, we present an efficient algorithm to process U-x-kRanks queries and G-x-Top-k queries. Comprehensive experiments on different data sets demonstrate the effectiveness of the proposed solutions.

关键词： Semantics Upper bound Heuristic algorithms Query processing Equations Artificial neural networks

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Structure-aware random fourier kernel for graphs 21

Structure-aware random fourier kernel for graphs

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Jinyuan Fang Qiang Zhang Zaiqiao Meng Shangsong Liang School of Computer Science and Engineering Sun Yat-sen University China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Hangzhou Innovation Center Zhejiang University China and College of Computer Science and Technology Zhejiang University China and AZFT Knowledge Engine Lab China School of Computing Science University of Glasgow United Kingdom and Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates School of Computer Science and Engineering Sun Yat-sen University China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China and Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

ISBN: (纸本)9781713845393

Gaussian Processes (GPs) define distributions over functions and their generalization capabilities depend heavily on the choice of kernels. In this paper, we propose a novel structure-aware random Fourier (SRF) kernel for GPs that brings several benefits when modeling graph-structured data. First, SRF kernel is defined with a spectral distribution based on the Fourier duality given by the Bochner's theorem, transforming the kernel learning problem to a distribution inference problem. Second, SRF kernel admits a random Fourier feature formulation that makes the kernel scalable for optimization. Third, SRF kernel enables to leverage geometric structures by taking subgraphs as inputs. To effectively optimize GPs with SRF kernel, we develop a variational EM algorithm, which alternates between an inference procedure (E-step) and a learning procedure (M-step). Experimental results on five real-world datasets show that our model can achieve state-of-the-art performance in two typical graph learning tasks, i.e., object classification and link prediction.

关键词：

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Fog-assisted multi-user swipt networks: Local computing or offloading

arXiv

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arXiv 2019年

作者： Zheng, Haina Xiong, Ke Fan, Pingyi Zhong, Zhangdui Letaief, Khaled Ben School of Computer and Information Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing100044 China National Laboratory for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Lab of Rail Traffic Control and Safety Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China Clear Water Bay Hong Kong

This paper investigates a fog computing-assisted multi-user simultaneous wireless information and power transfer (SWIPT) network, where multiple sensors with power splitting (PS) receiver architectures receive information and harvest energy from a hybrid access point (HAP), and then process the received data by using local computing mode or fog offloading mode. For such a system, an optimization problem is formulated to minimize the sensors' required energy while guaranteeing their required information transmissions and processing rates by jointly optimizing the multi-user scheduling, the time assignment, the sensors' transmit powers and the PS ratios. Since the problem is a mixed integer programming (MIP) problem and cannot be solved with existing solution methods, we solve it by applying problem decomposition, variable substitutions and theoretical analysis. For a scheduled sensor, the closed-form and semi-closedform solutions to achieve its minimal required energy are derived, and then an efficient multi-user scheduling scheme is presented, which can achieve the suboptimal user scheduling with low computational complexity. Numerical results demonstrate our obtained theoretical results, which show that for each sensor, when it is located close to the HAP or the fog server (FS), the fog offloading mode is the better choice;otherwise, the local computing mode should be selected. The system performances in a frame-by-frame manner are also simulated, which show that using the energy stored in the batteries and that harvested from the signals transmitted by previous scheduled sensors can further decrease the total required energy of the sensors. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy harvesting

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RNaAE: A Novel Approach for Identifying Unseen Railway Anomalies

RNaAE: A Novel Approach for Identifying Unseen Railway Anoma...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Jiayu Zhang Qingji Guan Junbo Liu Yaping Huang Jianyong Guo Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Infrastructure Inspector Center China Academy of Railway Science Corporation Limited Beijing China Tangshan Research Institute of Beijing Jiaotong University Tangshan China Urban Rail Transit National Engineering Research Center for Digital Construction and Evaluation Tianjin China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Detecting abnormal objects in railway track inspection images using vision-based technology is a crucial task for ensuring the safety of railway transportation. Traditional supervised object detection methods fail to achieve satisfactory results due to the diverse categories of abnormal objects and the lack of abnormal samples. Even though the widely used Autoencoder can leverage reconstruction errors to detect anomalies without using abnormal data, they tend to generate a relatively high number of false positives. In this paper, we address the task in an unsupervised manner and propose a novel Random Network-Assisted Autoencoder, called RNaAE, for identifying unseen abnormal objects. Specifically, we first design a learnable network to fit a randomly initialized stochastic network with fixed weights, where the difference between two predictions can then be used to estimate whether the candidate object is anomalous. After combined with a traditional Autoencoder, a Gaussian mixture model is then used to classify the candidate box into normal and abnormal by anomaly scores. Extensive experiments conducted on our collected railway anomaly dataset demonstrate that the proposed RNaAE exceeds previous stateof-the-art methods, achieving 98.23% and 92.02% in terms of AUROC and F1-score.

关键词： Autoencoders Transportation Stochastic processes Object detection Signal processing Inspection Rail transportation Safety Object recognition Anomaly detection

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Biomass Energy May Mitigate Energy Shortages and Reduce Carbon Emissions in Asia

SSRN

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SSRN 2022年

作者： Li, Yong Wang, Zhaosheng Huang, Mei Gong, He Sun, Hongfu Zhu, Lin National Ecosystem Science Data Center Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China College of Geoscience and Surveying Engineering China University of Mining and Technology Beijing100083 China National Satellite Meteorological Center China Meteorological Administration Beijing China

Biomass energy is a key component of renewable new energy, which is very important for energy security and environmental protection. Meanwhile, Asia is a region with intensive global energy consumption. It is of great significance to clarify its biomass energy potential and its role in energy and environment. So, we used MODIS NPP to evaluate potential biomass energy reserves and their temporal and spatial changes in Asia. The results show that the total potential biomass energy in Asia is 327.68 ± 11.11 EJ, which is approximately equal to the total energy use in all of Asia over the four years. In addition, we used the AHP and cluster analysis to investigate the degree of energy shortage in Asian *** supplying biomass energy for energy use in various countries, the number of countries with severe, moderate and mild energy shortages in Asia will decrease by 8.5%, 12.7% and 6.4% respectively. In addition, if 50% of conventional energy in Asia is replaced by biomass, its total carbon dioxide emissions will be reduced by 33% to 44%. Taken together, this shows that Asia's abundant biomass energy reserves can help alleviate energy shortages in various countries and reduce regional carbon dioxide emissions. © 2022, The Authors. All rights reserved.

关键词： Energy security

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Mvqs: Robust Multi-View Instance-Level Cost-Sensitive Learning Method for Imbalanced data Classification

SSRN

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SSRN 2023年

作者： Hou, Zhaojie Tang, Jingjing Li, Yan Fu, Saiji Tian, Yingjie School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu611130 China Institute of Big Data Southwestern University of Finance and Economics Chengdu611130 China School of Economics and Management Beijing University of Posts and Telecommunications Beijing100876 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation UCAS Beijing100190 China

During multi-view imbalanced learning, data is collected from different sources and class labels are heavily skewed. Multi-view imbalanced learning has been studied extensively, with two main categories: multi-view cost-sensitive learning and multi-view ensemble learning. However, those methods are subject to certain limitations: 1) omission of either consensus or complementary information, 2) complex preprocessing of multi-view ensemble learning and manual assignment of misclassification costs of multi-view cost-sensitive learning, and 3) limited ability to handle noisy data. Therefore, we aim to design a concise and unified framework to grapple with the multi-view representations, imbalanced classes and noisy samples simultaneously. Inspired by the elegant metrics of SVM-style models and asymmetric quadratic type squared error loss function (QTSELF), we propose a robust multi-view instance-level cost-sensitive support vector machine for imbalanced data classification called MVQS. The consensus regularization term and combination weight strategy are employed to fully exploit multi-view information. The QTSELF loss can not only adaptively impose instance-level penalties to samples, but also make MVQS more robust to noisy samples. We solve MVQS with the gradient descent and the alternating direction method of multipliers algorithms. The comprehensive experimental results on various datasets validate that MVQS is more competitive and robust than other benchmark approaches. © 2023, The Authors. All rights reserved.

关键词： Support vector machines

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