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Post-quantum blind signcryption scheme from lattice Research Articles

Huifang Yu, Lu Bai,yuhuifang@xupt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 6, Pages 891-901 doi: 10.1631/FITEE.2000099

Abstract: (BSC) can guarantee the blindness and untrackability of signcrypted messages, and moreover, it provides simultaneous unforgeability and confidentiality. Most traditional BSC schemes are based on the number theory. However, with the rapid development of quantum computing, traditional BSC systems are faced with severe security threats. As promising candidate cryptosystems with the ability to resist attacks from quantum computing, s have attracted increasing attention in academic fields. In this paper, a post-quantum scheme from lattice (PQ-LBSCS) is devised by applying BSC to s. PQ-LBSCS inherits the advantages of the and technique. PQ-LBSCS is provably secure under the hard assumptions of the learning with error problem and small integer solution problem in the standard model. Simulations are carried out using the Matlab tool to analyze the computational efficiency, and the simulation results show that PQ-LBSCS is more efficient than previous schemes. PQ-LBSCS has extensive application prospects in e-commerce, mobile communication, and smart cards.

Keywords：格密码系统；盲签密；抗量子计算；带错误学习问题；最短向量问题

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Research on Model of the Shortest Time Path and Transport Cost in Multimodal Transportation

Wei Zhong,Shen Jinsheng,Huang Ailing,Zhang Zhiwen,Shi Dinghuan

Strategic Study of CAE 2006, Volume 8, Issue 8, Pages 61-64

Abstract:

With the rapid development of economy, unimodal transport cannot meet the demand of customers, in respects of agile manufacturing, speed-to-market, logistics supply chain management, and multimodal transportation provides a good solution. Taking into consideration the transport time between node and node, mode-to-mode time and possible transport delay at node in multimodal transportation networks, the shortest time model of path is presented, and then the transport cost of the path in multimodal transportation network is calculated, providing the theory foundation for the relevant research of multimodal transportation.

Keywords： multimodal transportation the shortest time path transport cost

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Generalization and application in time series forecasting of the least square support vector machine method

Xiang Xiaodong

Strategic Study of CAE 2008, Volume 10, Issue 11, Pages 89-92

Abstract:

According to the theory that the present data contains more future information than historical data in time-series,the paper extends the prediction method of least square support vector machine and obtains a more general prediction model of least square support vector machine,and develops algorithm of the extended prediction model.Prediction examples of two time-series show that the extended model is more effective.Therefore it improves the value of the prediction method of least square support vector machine.

Keywords： least square support vector machine generalization time series forecasting

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Vector quantization: a review Regular Papers

Ze-bin WU, Jun-qing YU

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 4, Pages 507-524 doi: 10.1631/FITEE.1700833

Abstract:

Vector quantization (VQ) is a very effective way to save bandwidth and storage for speech coding and image coding. Traditional vector quantization methods can be divided into mainly seven types, tree-structured VQ, direct sum VQ, Cartesian product VQ, lattice VQ, classified VQ, feedback VQ, and fuzzy VQ, according to their codebook generation procedures. Over the past decade, quantization-based approximate nearest neighbor (ANN) search has been developing very fast and many methods have emerged for searching images with binary codes in the memory for large-scale datasets. Their most impressive characteristics are the use of multiple codebooks. This leads to the appearance of two kinds of codebook: the linear combination codebook and the joint codebook. This may be a trend for the future. However, these methods are just finding a balance among speed, accuracy, and memory consumption for ANN search, and sometimes one of these three suffers. So, finding a vector quantization method that can strike a balance between speed and accuracy and consume moderately sized memory, is still a problem requiring study.

Keywords： Approximate nearest neighbor search Image coding Vector quantization

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应用完备集合固有时间尺度分解和混合差分进化和粒子群算法优化的最小二乘支持向量机对柴油机进行故障诊断 Article

俊红张,昱刘

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 2, Pages 272-286 doi: 10.1631/FITEE.1500337

Abstract: 针对固有时间尺度分解算法的模态混叠问题和最小二乘支持向量机的参数优化问题，本文提出了一种新的基于完备集合固有时间尺度分解和混合差分进化和粒子群算法优化最小二乘支持向量机的柴油机故障诊断方法。该方法主要包括以下几个步骤：首先，为解决固有时间尺度分解算法的模态混叠问题，提出了一种完备集合固有时间尺度分解算法。最后，提出了混合差分进化和粒子群算法对最小二乘支持向量机的参数进行优化的方法，并通过将故障特征输入训练好的最小二乘支持向量机模型实现故障诊断。仿真和实验结果表明提出的故障诊断方法可以克服固有时间尺度分解的模态混叠问题，而且能够准确的识别柴油机故障。

Keywords：柴油机；故障诊断；完备集合固有时间尺度分解；最小二乘支持向量机；混合差分进化和粒子群优化算法

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Side-channel attacks and learning-vector quantization Article

Ehsan SAEEDI, Yinan KONG, Md. Selim HOSSAIN

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 4, Pages 511-518 doi: 10.1631/FITEE.1500460

Abstract: The security of cryptographic systems is a major concern for cryptosystem designers, even though cryptography algorithms have been improved. Side-channel attacks, by taking advantage of physical vulnerabilities of cryptosystems, aim to gain secret information. Several approaches have been proposed to analyze side-channel information, among which machine learning is known as a promising method. Machine learning in terms of neural networks learns the signature (power consumption and electromagnetic emission) of an instruction, and then recognizes it automatically. In this paper, a novel experimental investigation was conducted on field-programmable gate array (FPGA) implementation of elliptic curve cryptography (ECC), to explore the efficiency of side-channel information characterization based on a learning vector quantization (LVQ) neural network. The main characteristics of LVQ as a multi-class classifier are that it has the ability to learn complex non-linear input-output relationships, use sequential training procedures, and adapt to the data. Experimental results show the performance of multi-class classification based on LVQ as a powerful and promising approach of side-channel data characterization.

Keywords： Side-channel attacks Elliptic curve cryptography Multi-class classification Learning vector quantization

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A robust intelligent audio watermarking scheme using support vector machine Article

Mohammad MOSLEH,Hadi LATIFPOUR,Mohammad KHEYRANDISH,Mahdi MOSLEH,Najmeh HOSSEINPOUR

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 12, Pages 1320-1330 doi: 10.1631/FITEE.1500297

Abstract: Rapid growth in information technology and computer networks has resulted in the universal use of data transmission in the digital domain. However, the major challenge faced by digital data owners is protection of data against unauthorized cop-ying and distribution. Digital watermark technology is starting to be considered a credible protection method to mitigate the potential challenges that undermine the efficiency of the system. Digital audio watermarking should retain the quality of the host signal in a way that remains inaudible to the human hearing system. It should be sufficiently robust to be resistant against potential attacks. One of the major deficiencies of conventional audio watermarking techniques is the use of non-intelligent decoders in which some sets of specific rules are used for watermark extraction. This paper presents a new robust intelligent audio water-marking scheme using a synergistic combination of singular value decomposition (SVD) and support vector machine (SVM). The methodology involves embedding a watermark data by modulating the singular values in the SVD transform domain. In the extraction process, an intelligent detector using SVM is suggested for extracting the watermark data. By learning the destructive effects of noise, the detector in question can effectively retrieve the watermark. Diverse experiments under various conditions have been carried out to verify the performance of the proposed scheme. Experimental results showed better imperceptibility, higher robustness, lower payload, and higher operational efficiency, for the proposed method than for conventional techniques.

Keywords： Audio watermarking Copyright protection Singular value decomposition (SVD) Machine learning Support vector machine (SVM)

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Google Takes a Big Step Toward Quantum Computing

Chris Palmer

Engineering 2020, Volume 6, Issue 4, Pages 381-383 doi: 10.1016/j.eng.2020.02.003

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Coalition formation based on a task-oriented collaborative ability vector Article

Hao FANG,Shao-lei LU,Jie CHEN,Wen-jie CHEN

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 1, Pages 139-148 doi: 10.1631/FITEE.1601608

Abstract: Coalition formation is an important coordination problem in multi-agent systems, and a proper description of collaborative abilities for agents is the basic and key precondition in handling this problem. In this paper, a model of task-oriented collaborative abilities is established, where five task-oriented abilities are extracted to form a collaborative ability vector. A task demand vector is also described. In addition, a method of coalition formation with stochastic mechanism is proposed to reduce excessive competitions. An artificial intelligent algorithm is proposed to compensate for the difference between the expected and actual task requirements, which could improve the cognitive capabilities of agents for human commands. Simulations show the effectiveness of the proposed model and the distributed artificial intelligent algorithm.

Keywords： Collaborative vector Task allocation Multi-agent system Coalition formation Artificial intelligence

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An evaluation method for link importance based on the characteristic of network communication

Jiang Yu,Hu Aiqun,He Ming

Strategic Study of CAE 2009, Volume 11, Issue 9, Pages 64-67

Abstract:

A method for finding the most vital edge based on the characteristic of network communication is proposed. The link importance is determined by its using frequency in all-pairs shortest paths and the most vital edge results in the highest frequency. Without the commonly used edge-deletion and edge-contraction methods, the proposed algorithm directly reflects the edge's contribution to the network communication and the relative importance of the two edges in the network can be evaluated. The algorithm analyses and the experimental results show that this algorithm overcomes the currently existent problems and provides a more reasonable principle for ranking edges which is consistent with our intuitive judgments.

Keywords： communication networks link importance shortest paths serial links

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An artificial intelligence based method for evaluating power grid node importance using network embedding and support vector regression Research Papers

Hui-fang WANG, Chen-yu ZHANG, Dong-yang LIN, Ben-teng HE

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 6, Pages 816-828 doi: 10.1631/FITEE.1800146

Abstract:

The identification of important nodes in a power grid has considerable benefits for safety. Power networks vary in many aspects, such as scale and structure. An index system can hardly cover all the information in various situations. Therefore, the efficiency of traditional methods using an index system is case-dependent and not universal. To solve this problem, an artificial intelligence based method is proposed for evaluating power grid node importance. First, using a network embedding approach, a feature extraction method is designed for power grid nodes, considering their structural and electrical information. Then, for a specific power network, steady-state and node fault transient simulations under various operation modes are performed to establish the sample set. The sample set can reflect the relationship between the node features and the corresponding importance. Finally, a support vector regression model is trained based on the optimized sample set for the later online use of importance evaluation. A case study demonstrates that the proposed method can effectively evaluate node importance for a power grid based on the information learned from the samples. Compared with traditional methods using an index system, the proposed method can avoid some possible bias. In addition, a particular sample set for each specific power network can be established under this artificial intelligence based framework, meeting the demand of universality.

Keywords： Power grid Artificial intelligence Node importance Text-associated DeepWalk Network embedding Support vector regression

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Anefficient parallel and distributed solution to nonconvex penalized linear SVMs Personal View

Lei GUAN, Tao SUN, Lin-bo QIAO, Zhi-hui YANG, Dong-sheng LI, Ke-shi GE, Xi-cheng LU

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 4, Pages 587-603 doi: 10.1631/FITEE.1800566

Abstract: Support vector machines (SVMs) have been recognized as a powerful tool to perform linear classification. When combined with the sparsity-inducing nonconvex penalty, SVMs can perform classification and variable selection simultaneously. However, the nonconvex penalized SVMs in general cannot be solved globally and efficiently due to their nondifferentiability, nonconvexity, and nonsmoothness. Existing solutions to the nonconvex penalized SVMs typically solve this problem in a serial fashion, which are unable to fully use the parallel computing power of modern multi-core machines. On the other hand, the fact that many real-world data are stored in a distributed manner urgently calls for a parallel and distributed solution to the nonconvex penalized SVMs. To circumvent this challenge, we propose an efficient alternating direction method of multipliers (ADMM) based algorithm that solves the nonconvex penalized SVMs in a parallel and distributed way. We design many useful techniques to decrease the computation and synchronization cost of the proposed parallel algorithm. The time complexity analysis demonstrates the low time complexity of the proposed parallel algorithm. Moreover, the convergence of the parallel algorithm is guaranteed. Experimental evaluations on four LIBSVM benchmark datasets demonstrate the efficiency of the proposed parallel algorithm.

Keywords： Linear classification Support vector machine (SVM) Nonconvex penalty Alternating direction method of multipliers (ADMM) Parallel algorithm

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Major Considerations in Development of City Gas in China

Li Youjia

Strategic Study of CAE 2002, Volume 4, Issue 11, Pages 26-31

Abstract:

As a result of the advent of natural gas in China, the city gas faces a good situation of great development at the beginning of the 21 st century. Over 400 cities are involved in the overall planning of natural gas supply, but many careful considerations should be made at present. This article makes the analysis and discussion of the major problems existed, in the hope of attracting universal attention to decrease the risks existed in the construction of projects.

Keywords： city gas regularity of development problems existed suggestions

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Management problems in development process of remanufacturing industry

Xu Binshi

Strategic Study of CAE 2012, Volume 14, Issue 12, Pages 10-14

Abstract:

Remanufacturing in China is still in its early stage and faces pressures from society, policy, technology and management. Considering the actual states of remanufacturing in China, this paper researched several key management issues which were cared by various aspects from angle of view of remanufacturing players. Based on need analysis on trend of development of remanufacturing in China, six key management problems were mainly researched, and they were: risk management of remanufacturing players, remanufacturing production mana gement, remanufacturing quality management, authentication mode of remanufacturing in China, subsidy policy of remanufacturing industry, and performance assessment of remanufacturing. The characteristics of six issues were analyzed and the corresponding countermeasures were put forward.

Keywords： remanufacturing industry development management problems

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Five important issues in the present field of knowledge discovery

Yang Bingru

Strategic Study of CAE 2009, Volume 11, Issue 4, Pages 76-83

Abstract:

In this paper, the author summarized and proposed five important issues in the present field of knowledge discovery and data mining, which comprise two important core issues, two conjectures, challenge issues in mainstream development, important issues of relevant fields in appliance research and technique standard in knowledge discovery (data mining).Some achievements and special analysis was also presented. Those five issues correlate closely, and these researches will push the development of knowledge discovery (data mining) to a higher stage, and bring great influence to the field inside and outside.

Keywords： important core issues in KD conjectures in KD challenge issues in KD protein secondary structure prediction technique standard in KD