Resource Type

Journal Article 907

Year

2024 5

2023 106

2022 90

2021 82

2020 73

2019 70

2018 72

2017 60

2016 38

2015 32

2014 20

2013 16

2012 10

2011 15

2010 25

2009 17

2008 16

2007 21

2006 16

2005 23

open ︾

Keywords

Machine learning 42

Deep learning 34

Artificial intelligence 14

Reinforcement learning 14

disruptive technology 11

COVID-19 9

reliability 9

Additive manufacturing 8

Safety 6

Sustainability 6

disruptive technologies 6

robustness 5

strategic emerging industries 5

3D printing 4

Active learning 4

Efficacy 4

Gut microbiota 4

extension set 4

Blockchain 3

open ︾

Search scope:

排序： Display mode:

Latent discriminative representation learning for speaker recognition Research Articles

Duolin Huang, Qirong Mao, Zhongchen Ma, Zhishen Zheng, Sidheswar Routryar, Elias-Nii-Noi Ocquaye,2211708034@stmail.ujs.edu.cn,mao_qr@ujs.edu.cn,zhongchen_ma@ujs.edu.cn,1209103822@qq.com,sidheswar69@gmail.com,eocquaye@ujs.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5,   Pages 615-766 doi: 10.1631/FITEE.1900690

Abstract: Extracting discriminative speaker-specific representations from speech signals and transforming them into fixed length vectors are key steps in speaker identification and verification systems. In this study, we propose a method for . We mean that the learned representations in this study are not only discriminative but also relevant. Specifically, we introduce an additional speaker embedded lookup table to explore the relevance between different utterances from the same speaker. Moreover, a reconstruction constraint intended to learn a is introduced to make representation discriminative. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods based on the Apollo dataset used in the Fearless Steps Challenge in INTERSPEECH2019 and the TIMIT dataset.

Keywords： 说话人识别；潜在可区分性表征学习；说话人嵌入查找表；线性映射矩阵    

HTML PDF Collect

Study on Extension Engineering

Yang Chunyan, Cai Wen

Strategic Study of CAE 2000, Volume 2, Issue 12,   Pages 90-96

Abstract:

Extension engineering is the application technology of extension theory and extension method. It has been applied to some engineering fields such as detection, control, management, information and computer, etc. . In this paper, its basic ideas, tools, methods and applications will be introduced.

Keywords： contradictory problem     extension set     extensibility     extension engineering    

HTML PDF Collect

Exploring financially constrained small- and medium-sized enterprises based on a multi-relation translational graph attention network Research Article

Qianqiao LIANG, Hua WEI, Yaxi WU, Feng WEI, Deng ZHAO, Jianshan HE, Xiaolin ZHENG, Guofang MA, Bing HAN

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 3,   Pages 388-402 doi: 10.1631/FITEE.2200151

Abstract: (FNE), which explores financially constrained small- and medium-sized enterprises (SMEs), has become increasingly important in industry for financial institutions to facilitate SMEs' development. In this paper, we first perform an insightful exploratory analysis to exploit the transfer phenomenon of financing needs among SMEs, which motivates us to fully exploit the multi-relation enterprise social network for boosting the effectiveness of FNE. The main challenge lies in modeling two kinds of heterogeneity, i.e., and SMEs' , under different relation types simultaneously. To address these challenges, we propose a graph neural network named Multi-relation tRanslatIonal GrapH aTtention network (M-RIGHT), which not only models the of financing needs along different relation types based on a novel entity–relation composition operator but also enables heterogeneous SMEs' representations based on a translation mechanism on relational hyperplanes to distinguish SMEs' heterogeneous behaviors under different relation types. Extensive experiments on two large-scale real-world datasets demonstrate M-RIGHT's superiority over the state-of-the-art methods in the FNE task.

Keywords： Financing needs exploration     Graph representation learning     Transfer heterogeneity     Behavior heterogeneity    

HTML PDF Collect

Engineering Disease Resistance in Crop Plants: Callosic Papillae as Potential Targets

Geoffrey B. Fincher

Engineering 2020, Volume 6, Issue 5,   Pages 505-508 doi: 10.1016/j.eng.2020.03.012

HTML PDF Collect

A Fault Location Approach for the Testable Realization of Logic Functions

Pan Zhongliang

Strategic Study of CAE 2002, Volume 4, Issue 1,   Pages 69-74

Abstract:

An approach of design for testability(DFT) for logic functions is presented in the paper, which employs AND gates and XOR gates tree to realize the generalized Reed-Muller expression of arbitrary logic functions. The major features of the approach are： 1) The circuits adopting the DPT techniques in the paper are totally fault locatable. 2) The circuits have universal test sets for fault detection, the cardinality of the test sets is (n + 5), where n is equal to the number of input variables in the logic function. A fault location method for the circuits is presented, which can identify all fault equivalence classes in the AND gates, and the faults in XOR gate tree in the circuits.

Keywords： logic functions     Reed-Muller expressions     design for testability     single stuck at fault     faults location    

HTML PDF Collect

Quantum security analysis of a lattice-basedoblivious transfer protocol Article

Mo-meng LIU, Juliane KRÄMER, Yu-pu HU, Johannes BUCHMANN

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 9,   Pages 1348-1369 doi: 10.1631/FITEE.1700039

Abstract: Because of the concise functionality of oblivious transfer (OT)protocols, they have been widely used as building blocks in securemultiparty computation and high-level protocols. The security of OTprotocols built upon classical number theoretic problems, such asthe discrete logarithm and factoring, however, is threatened as aresult of the huge progress in quantum computing. Therefore, post-quantumcryptography is needed for protocols based on classical problems,and several proposals for post-quantum OT protocols exist. However,most post-quantum cryptosystems present their security proof onlyin the context of classical adversaries, not in the quantum setting.In this paper, we close this gap and prove the security of the lattice-basedOT protocol proposed by Peikert . (CRYPTO, 2008), which is universally composably secure under theassumption of learning with errors hardness, in the quantum setting.We apply three general quantum security analysis frameworks. First,we apply the quantum lifting theorem proposed by Unruh (EUROCRYPT,2010) to prove that the security of the lattice-based OT protocolcan be lifted into the quantum world. Then, we apply two more securityanalysis frameworks specified for post-quantum cryptographic primitives,i.e., simple hybrid arguments (CRYPTO, 2011) and game-preserving reduction(PQCrypto, 2014).

Keywords： Oblivious transfer     Post-quantum     Lattice-based     Learning with errors     Universally composable    

HTML PDF Collect

Representation learning via a semi-supervised stacked distance autoencoder for image classification Research Articles

Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang,jliang@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7,   Pages 963-1118 doi: 10.1631/FITEE.1900116

Abstract: is an important application of deep learning. In a typical classification task, the classification accuracy is strongly related to the features that are extracted via deep learning methods. An is a special type of , often used for dimensionality reduction and feature extraction. The proposed method is based on the traditional , incorporating the “distance” information between samples from different categories. The model is called a semi-supervised distance . Each layer is first pre-trained in an unsupervised manner. In the subsequent supervised training, the optimized parameters are set as the initial values. To obtain more suitable features, we use a stacked model to replace the basic structure with a single hidden layer. A series of experiments are carried out to test the performance of different models on several datasets, including the MNIST dataset, street view house numbers (SVHN) dataset, German traffic sign recognition benchmark (GTSRB), and CIFAR-10 dataset. The proposed semi-supervised distance method is compared with the traditional , sparse , and supervised . Experimental results verify the effectiveness of the proposed model.

Keywords： 自动编码器；图像分类；半监督学习；神经网络    

HTML PDF Collect

The provable security formal analysis of 802.11i authentication scheme

Song Yubo,Hu Aiqun,Yao Bingxin

Strategic Study of CAE 2010, Volume 12, Issue 1,   Pages 67-73

Abstract:

802.11i standard is proposed by IEEE 802.11 Standard Group to improve the security of the WLAN. In 802.11i, 802.1x standard is used for the authentication and access controll. How to analyze the security of the new protocol to prove its validity is the most interesting problem we are concerned. In order to solve this problem, an expanded Bellare-Rogaway model is established to give a provable security formal analysis on this protocol. By utilizing the expanded Bellare-Rogaway model, a flaw has been found in 802.1X authentication protocols and the corresponding man-in-the-middle attack is given here.

Keywords： 802.11i     Bellare-Rogaway model     provable security     formal analysis    

HTML PDF Collect

Visual interpretability for deep learning: a survey Review

Quan-shi ZHANG, Song-chun ZHU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1,   Pages 27-39 doi: 10.1631/FITEE.1700808

Abstract: This paper reviews recent studies in understanding neural-network representations and learning neural networks with interpretable/disentangled middle-layer representations. Although deep neural networks have exhibited superior performance in various tasks, interpretability is always Achilles’ heel of deep neural networks. At present, deep neural networks obtain high discrimination power at the cost of a low interpretability of their black-box representations. We believe that high model interpretability may help people break several bottlenecks of deep learning, e.g., learning from a few annotations, learning via human–computer communications at the semantic level, and semantically debugging network representations. We focus on convolutional neural networks (CNNs), and revisit the visualization of CNN representations, methods of diagnosing representations of pre-trained CNNs, approaches for disentangling pre-trained CNN representations, learning of CNNs with disentangled representations, and middle-to-end learning based on model interpretability. Finally, we discuss prospective trends in explainable artificial intelligence.

Keywords： Artificial intelligence     Deep learning     Interpretable model    

HTML PDF Collect

Theory Model and a Method for Qualitative Assessment of Sustainable Development of Regional Water Resources

Chen Shouyu

Strategic Study of CAE 2001, Volume 3, Issue 2,   Pages 33-38

Abstract:

Based on analysis of the relationship between sustainable development and bearing capacity of regional water resources, this paper presents a fuzzy pattern recognition model and a method for qualitative assessment of sustainable development of regional water resources, which are applied to assess the sustainable development of water resources of Hanzhong Basin. The case study proves that the model and method are reliable and that results are reasonable and practicable. They can also be applied to the assessment of the sustainable development of social economies.

Keywords： water resources     sustainable development     assessment     pattern recognition     fuzzy    

HTML PDF Collect

Learning embeddings of a heterogeneous behavior network for potential behavior prediction Article

Yue-yang WANG, Wei-hao JIANG, Shi-liang PU, Yue-ting ZHUANG

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 3,   Pages 422-435 doi: 10.1631/FITEE.1800493

Abstract: Potential behavior prediction involves understanding the latent human behavior of specific groups, andcan assist organizations in making strategic decisions. Progress in information technology has made it possible to acquire more and more data about human behavior. In this paper, we examine behavior data obtained in realworld scenarios as an information network composed of two types of objects (humans and actions) associated with various attributes and three types of relationships (human-human, human-action, and action-action), which we call the heterogeneous behavior network (HBN). To exploit the abundance and heterogeneity of the HBN, we propose a novel network embedding method, human-action-attribute-aware heterogeneous network embedding (a4HNE), which jointly considers structural proximity, attribute resemblance, and heterogeneity fusion. Experiments on two real-world datasets show that this approach outperforms other similar methods on various heterogeneous information network mining tasks for potential behavior prediction.

Keywords： Network embedding     Representation learning     Human behavior     Social networks     Heterogeneous information network     Attribute    

HTML PDF Collect

State-of-the-Art Review of High-Throughput Statistical Spatial-Mapping Characterization Technology and Its Applications Review

Haizhou Wang, Lei Zhao, Yunhai Jia, Dongling Li, Lixia Yang, Yuhua Lu, Guang Feng, Weihao Wan

Engineering 2020, Volume 6, Issue 6,   Pages 621-636 doi: 10.1016/j.eng.2020.05.005

Abstract:

Macroscopic materials are heterogeneous, multi-elementary, and complex. No material is homogeneous or isotropic at a certain small scale. Parts of the material that differ from one another can be termed ‘‘natural chips.” At different spots on the material, the composition, structure, and properties vary slightly, and the combination of these slight differences establishes the overall material performance. This article presents a state-of-the-art review of research and applications of high-throughput statistical spatialmapping characterization technology based on the intrinsic heterogeneity within materials. Highthroughput statistical spatial-mapping uses a series of rapid characterization techniques for analysis from the macroscopic to the microscopic scale. Datasets of composition, structure, and properties at each location are obtained rapidly for practical sample sizes. Accurate positional coordinate information and references to a point-to-point correspondence are used to set up a database that contains spatialmapping lattices. Based on material research and development design requirements, dataset spatialmapping within required target intervals is selected from the database. Statistical analysis can be used to select a suitable design that better meets the targeted requirements. After repeated verification, genetic units that reflect the material properties are determined. By optimizing process parameters, the assembly of these genetic unit(s) is verified at the mesoscale, and quantitative correlations are established between the microscale, mesoscale, macroscale, practical sample, across-the-scale span composition, structure, and properties. The high-throughput statistical spatial-mapping characterization technology has been applied to numerous material systems, such as steels, superalloys, galvanization, and ferrosilicon alloys. This approach has guided the composition and the process optimization of various materials.

Keywords： Material heterogeneity     High-throughput characterization     Statistical spatial-mapping     Original-position statistical-distribution analysis    

HTML PDF Collect

Reliability and Life Management on Aeroengine Used in Army

Xu Kejun,Jiang Longping

Strategic Study of CAE 2003, Volume 5, Issue 1,   Pages 82-88

Abstract:

Based on the reliability and life management on aeroengines used in army in western countries, the basic elements of aeroengine reliability and life management are expatiated, and the differences and errors about reliability in China are discussed. The reason that the reliability and life management lag behind is pointed out, for example, outmoded management concept, inadequate management system, weak groundwoork, imperfect standard Finally, the paper points out the proper way to construct and improve the reliability and life managtment in China.

Keywords： aeroengine     reliability     life     management    

HTML PDF Collect

New directions for artificial intelligence: human, machine, biological, and quantum intelligence Comment

Li WEIGANG,Liriam Michi ENAMOTO,Denise Leyi LI,Geraldo Pereira ROCHA FILHO

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 6,   Pages 984-990 doi: 10.1631/FITEE.2100227

Abstract:

This comment reviews the “once learning” mechanism (OLM) that was proposed byWeigang (1998), the subsequent success of “one-shot learning” in object categories (Li FF et al., 2003), and “you only look once” (YOLO) in objective detection (Redmon et al., 2016). Upon analyzing the current state of research in artificial intelligence (AI), we propose to divide AI into the following basic theory categories: artificial human intelligence (AHI), artificial machine intelligence (AMI), artificial biological intelligence (ABI), and artificial quantum intelligence (AQI). These can also be considered as the main directions of research and development (R&D) within AI, and distinguished by the following classification standards and methods: (1) human-, machine-, biological-, and quantum-oriented AI R&D; (2) information input processed by dimensionality increase or reduction; (3) the use of one/a few or a large number of samples for knowledge learning.

Keywords： 人工智能；机器学习；一次性学习；一瞥学习；量子计算    

HTML PDF Collect

Personalizing a Service Robot by Learning Human Habits from Behavioral Footprints Article

Kun Li, Max Q.-H. Meng

Engineering 2015, Volume 1, Issue 1,   Pages 79-84 doi: 10.15302/J-ENG-2015024

Abstract:

For a domestic personal robot, personalized services are as important as predesigned tasks, because the robot needs to adjust the home state based on the operator's habits. An operator's habits are composed of cues, behaviors, and rewards. This article introduces behavioral footprints to describe the operator's behaviors in a house, and applies the inverse reinforcement learning technique to extract the operator's habits, represented by a reward function. We implemented the proposed approach with a mobile robot on indoor temperature adjustment, and compared this approach with a baseline method that recorded all the cues and behaviors of the operator. The result shows that the proposed approach allows the robot to reveal the operator's habits accurately and adjust the environment state accordingly.

Keywords： personalized robot     habit learning     behavioral footprints    

HTML PDF Collect

Title Author Date Type Operation

Latent discriminative representation learning for speaker recognition

Duolin Huang, Qirong Mao, Zhongchen Ma, Zhishen Zheng, Sidheswar Routryar, Elias-Nii-Noi Ocquaye,2211708034@stmail.ujs.edu.cn,mao_qr@ujs.edu.cn,zhongchen_ma@ujs.edu.cn,1209103822@qq.com,sidheswar69@gmail.com,eocquaye@ujs.edu.cn

Journal Article

Study on Extension Engineering

Yang Chunyan, Cai Wen

Journal Article

Exploring financially constrained small- and medium-sized enterprises based on a multi-relation translational graph attention network

Qianqiao LIANG, Hua WEI, Yaxi WU, Feng WEI, Deng ZHAO, Jianshan HE, Xiaolin ZHENG, Guofang MA, Bing HAN

Journal Article

Engineering Disease Resistance in Crop Plants: Callosic Papillae as Potential Targets

Geoffrey B. Fincher

Journal Article

A Fault Location Approach for the Testable Realization of Logic Functions

Pan Zhongliang

Journal Article

Quantum security analysis of a lattice-basedoblivious transfer protocol

Mo-meng LIU, Juliane KRÄMER, Yu-pu HU, Johannes BUCHMANN

Journal Article

Representation learning via a semi-supervised stacked distance autoencoder for image classification

Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang,jliang@zju.edu.cn

Journal Article

The provable security formal analysis of 802.11i authentication scheme

Song Yubo,Hu Aiqun,Yao Bingxin

Journal Article

Visual interpretability for deep learning: a survey

Quan-shi ZHANG, Song-chun ZHU

Journal Article

Theory Model and a Method for Qualitative Assessment of Sustainable Development of Regional Water Resources

Chen Shouyu

Journal Article

Learning embeddings of a heterogeneous behavior network for potential behavior prediction

Yue-yang WANG, Wei-hao JIANG, Shi-liang PU, Yue-ting ZHUANG

Journal Article

State-of-the-Art Review of High-Throughput Statistical Spatial-Mapping Characterization Technology and Its Applications

Haizhou Wang, Lei Zhao, Yunhai Jia, Dongling Li, Lixia Yang, Yuhua Lu, Guang Feng, Weihao Wan

Journal Article

Reliability and Life Management on Aeroengine Used in Army

Xu Kejun,Jiang Longping

Journal Article

New directions for artificial intelligence: human, machine, biological, and quantum intelligence

Li WEIGANG,Liriam Michi ENAMOTO,Denise Leyi LI,Geraldo Pereira ROCHA FILHO

Journal Article

Personalizing a Service Robot by Learning Human Habits from Behavioral Footprints

Kun Li, Max Q.-H. Meng

Journal Article