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A Method for Facilitating Inventive Design Based on Semantic Similarity and Case-Based Reasoning

Procedia Engineering 2015年 131卷 194-203页

作者： Wei Yan Cecilia Zanni-Merk François Rousselot Denis Cavallucci School of Information Science and Engineering Shandong Normal University Jinan 250014 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan 250014 China LGECO / INSA Strasbourg 24 Boulevard de la Victoire 67084 Strasbourg Cedex France LSIIT / FDBT Team (UMR CNRS 7005) – Pôle API BP 10413 67412 Illkirch Cedex France

While TRIZ is increasingly developing both in research and education, new users always encounter difficulties in their first attempts to practice it. In such situation, Altshuller's original matrix often appears as an “easy-to-begin- with” tool. However, while not being representative of what TRIZ really is, it continues to seduce new users, teachers and trainers. Several approaches to automate the use of the contradiction matrix have been proposed in literature, and researches on the automatic match between specific parameters of the artefact being considered and Altshuller's generalized parameters remains a topic of interest. In this paper, we propose a way to automate the proposal of equivalencies between a specific parameter and a generic parameter from the matrix. This method calculates the semantic distance between short texts, and uses it to fill the semantic gap between the specific parameters of the artefact and the generalized ones. Case-based reasoning is also used to improve the whole accuracy of the method.

关键词： Ontology Semantic distance Information content WordNet Case-based reasoning

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Ontology-based Knowledge Modeling for Using Physical Effects

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Procedia Engineering 2015年 131卷 601-615页

作者： Wei Yan Cecilia Zanni-Merk François Rousselot Denis Cavallucci Pierre Collet School of Information Science and Engineering Shandong Normal University Jinan 250014 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan 250014 China LGECO / INSA Strasbourg 24 Boulevard de la Victoire 67084 Strasbourg Cedex France ICube / BFO Team (UMR CNRS 7005) – Pôle API BP 10413 67412 Illkirch Cedex France

Su-Field analysis, as one of the inventive problem solving tools, can be used to analyse and improve the efficacy of the technical system. Generally, the process of using Su-Field model to solve a specific inventive problem includes: building a Problem Model, mapping to a Generic Problem Model, finding a Generic Solution Model based on the corresponding inventive standard, and finally establishing and instantiating a Solution Model. As one of the most important phases of Su-Field analysis, the last step is normally implemented manually with the help of physical effects, which link generic technical functions with specific applications and systems. The physical effects compatible with the context of the specific problem should be chosen to assist the users to instantiate the Solution Model. However, the physical effects and the specific problems are built at different levels of abstraction, and it is difficult for the users to choose, that is, given a certain function, too many physical effects are chosen while with the detailed context of the problem, no physical effect is returned. This paper firstly proposes a new way of representing physical effects using the change of two states, that is, the couple of two states before and after applying physical effects. Then, the knowledge about using physical effects is formalized in OWL (Ontology Web Language) - an ontology language for semantic web, and the constraint knowledge, such as the condition to use each kind of physical effect, is formalized in SWRL (Semantic Web Rule Language) - a rule language. Finally, the reasoning process of using physical effects is performed with the support of JESS (Java Expert System Shell) rule engine.

关键词： Physical Effects Ontology OWL (Ontology Web Language) SWRL (Semantic Web Rule Language) JESS (Java Expert System Shell)

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Research on discrete fourier transform representation for image quality assessment

Research on discrete fourier transform representation for im...

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3rd International Conference on Applied Materials and Electronics Engineering, AMEE 2014

作者： Zhao, Shi Lin Ji, Xiu Hua School of Computer Science and Technology Shandong University of Finance and Economics Jinan Shandong Prov China Shandong Provincial Key Laboratory of Digital Media Technology Jinan Shandong Prov China

ISBN: (纸本)9783038350583

Recently the Structural Similarity (SSIM) is proposed, and attracts a lot of attentions for its good performance and simple calculation. By deeply studying the SSIM, we find it fails to measure the badly blurred images. Based on this, we develop an improved objective quality assessment method which is based on Discrete Fourier Transform representation (called as MDFT). Experiment results show the proposed method is more consistent with HVS than SSIM especially for blurred images and fading images.

关键词： Discrete Fourier transforms

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Construction of extraction model for ontology concept of four diagnostics information

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Journal of Computational Information Systems 2015年第7期11卷 2367-2376页

作者： Yuan, Feng Chen, Shouqiang Liu, Hong Xu, Liang School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Shandong Normal University Jinan China School of Information Engineering Shandong Management University Jinan China Center of Hear of the Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine Jinan China Shandong University of Traditional Chinese Medicine Jinan China

This paper is aimed at extraction of ontology concept from four diagnostics information. Due to the diversity and complexity of the four diagnostics information, there are still some difficulties when practicing ontology learning methods for TCM medical cases, so a new concept extraction method combining composite seed extraction and extended concept extraction is proposed for four diagnostics information corpus of TCM. Composite seed extraction refers to a method in which the mutual information is used to determine the appropriateness of word formation between seed and collocations by using seeds as headword to generate concepts. Extended concept extraction refers to a method in which the membership in the domain is used set the fitness function so as to extract extended concept with the assistance of artificial bee colony (ABC) algorithm. The candidate set of concepts is built by combining composite seed concept and extended concept. Through a confirmatory experiment on four diagnostics information corpus of TCM medical cases, this method is cautiously confirmed effective for concept extraction. Hence, this method has a certain reference value for ontology construction of four diagnostics information. ©, 2015, Binary Information Press. All right reserved.

关键词： Extraction

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A KVM Virtual Machine Memory Forensics Method Based on VMCS

A KVM Virtual Machine Memory Forensics Method Based on VMCS

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International Conference on Computational Intelligence and Security

作者： Shuhui Zhang Lianhai Wang Xiaohui Han Shandong Computer Science Center Shandong Provincial Key Laboratory of Computer Networks Jinan China School of Computer Science and/Technology Shandong University Jinan China

ISBN: (纸本)9781479974351

As the use of virtual machine environments increases, virtual machines forensics is becoming more and more important and emergent. Current forensics solutions to virtualized environments mainly focus on static data analysis, which cannot provide a complete picture of events. In this paper, a novel method used for KVM (Kernel-based Virtual Machine) virtual machine memory forensics has been proposed. By analyzing the memory image of a host machine, active virtual machines can be detected, and a complete picture of the virtual machine's states can be also obtained, such as running processes, loaded modules, network connections, registry, system logs, user accounts, services, hook analysis info and so on. The proposed method has been proved to be more effective in machines with current mainstream CPUs and Fedora version 16-19 for both 32-bit and 64-bit.

关键词： Virtual machining Forensics Virtualization Operating systems Program processors Security Linux

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New impossible differential cryptanalysis on improved LBlock

New impossible differential cryptanalysis on improved LBlock

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5th International Symposium on IT in Medicine and Education, ITME 2013

作者： Liu, Xuan Liu, Feng Meng, Shuai School of Information Science and Engineering Shandong Normal University Jinan 250014 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan 250014 China

ISBN: (纸本)9789400776173

LBlock is a 64-bit lightweight block cipher which can be implemented in both hardware environments and software platforms. It was proposed by Wu Wenling and Zhang Lei at ACNS2011. We studied the security of LBlock found that the permutation layer can getting better on security in the internet of things. In order to assure it can achieve enough security, we gave an improvement on permutation layer of LBlock. By analyzing the property of the diffusion transformation, it has shown that a new kind of 11-round impossible differential was presented. To the best of our knowledge, this is the first paper that proposes this way analysis on improved LBlock. © Springer science+Business Media Dordrecht 2014.

关键词： Cryptography

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Semi-supervised image classification learning based on random feature subspace 6

Semi-supervised image classification learning based on rando...

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6th Chinese Conference on Pattern Recognition, CCPR 2014

作者： Li, Liu Huaxiang, Zhang Xiaojun, Hu Feifei, Sun School of Information Science and Engineering Shandong Normal University Jinan250014 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250014 China

ISBN: (纸本)9783662456453

Image classification is a well-known classical problem in multimedia content analysis. In this paper a framework of semi-supervised image classification method is presented based on random feature subspace. Firstly, color spatial distribution entropy is introduced to represent the color spatial information, and texture feature are extracted by using Gabor filter. Then random subspaces of the feature vector are dynamically generated from mixed feature vector as different views. Finally, three classifiers are trained by the classified images and tri-training algorithm is applied to classify sample images. Experimental results strongly demonstrate the effectiveness and robustness of the proposed system. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Image classification

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A digital watermarking algorithm based on CDMA and HVS

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Journal of Information and Computational science 2014年第15期11卷 5593-5602页

作者： Yang, Feng Ji, Chunfeng Yang, Qianli School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China Tianjin Maritime Safety Administration Tianjin China University of California Irvine Irvine CA United States

Digital watermarking technology is an effective means to resolve problems on information security and copyright protection for digital media. The paper introduces CDMA (Code Division Multiple Access) to digital watermarking and proposes a digital watermarking algorithm for grayscale images based on CDMA and HVS (Human Visual System). The algorithm has adopted CDMA spread spectrum technology to orthogonalize the watermark to enhance security and considered human visual system when watermarks are embedded into the original images. Experiments have shown that the watermark can achieve invisibility and the algorithm is effective and robust. Copyright © 2014 Binary Information Press.

关键词： Code division multiple access

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Locally Linear Embedding Algorithm Based on OMP for Incremental Learning

Locally Linear Embedding Algorithm Based on OMP for Incremen...

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International Joint Conference on Neural networks

作者： Yiqin Leng Li Zhang Jiwen Yang School of Computer Science and Technology & Provincial Key Laboratory for Computer Information Processing Technology Soochow University

ISBN: (纸本)9781479914821

Locally Linear Embedding (LLE) is a sort of powerful nonlinear dimensionality reduction algorithms. The basic idea behind the LLE method is that each data point and its neighbors lie on or close to a locally linear patch of the manifold if there is sufficient data. Then the local geometry of these patches is described by using linear coefficients which can reconstruct each data point from its neighbors. However, LLE operates in a batch way and its dimension reduction cannot be generalized to unseen samples. If a test sample arrives, LLE must run repeatedly and the former computational results are discarded. Thus, some incremental methods have been proposed for LLE to solve this problem. In these incremental methods, the neighbor number is globally fixed, which may result in selecting points from another linear space as neighbors. This paper presents LLE based on orthogonal matching pursuit (OMP) and applies it to classification tasks. In the classification tasks, dimensionality reduction on test samples is implemented by applying dimension reduction on training samples. The new LLE method could select a more appropriate neighbors from the selected neighbors. OMP is applied to not only LLE for training samples, but also the incremental learning of LLE for test samples. Compared with other linear incremental methods, experimental results show that the proposed method is promising.

关键词： Joints Conferences Neural networks

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Feature Ensemble Learning based on Sparse Autoencoders for Image Classification

Feature Ensemble Learning based on Sparse Autoencoders for I...

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International Joint Conference on Neural networks

作者： Yaping Lu Li Zhang Bangjun Wang Jiwen Yang School of Computer Science and Technology & Provincial Key Laboratory for Computer Information Processing Technology Soochow University

ISBN: (纸本)9781479914821

Deep networks are well known for their powerful function approximations. To train a deep network efficiently, greedy layer-wise pre-training and fine tuning are required. Typically, pre-training, aiming to initialize a deep network, is implemented via unsupervised feature learning, with multiple feature representations generated. However, in general only the last layer representation is to be employed because of its abstraction and compactness being the best with comparisons to the ones of lower layers. To make full use of the representations of all layers, this paper proposes a feature ensemble learning method based on sparse autoencoders for image classification. Specifically, we train three softmax classifiers by using the representations of different layers, instead of one classifier trained by applying the last layer representation. Of the three softmax classifiers, two are obtained by training stacked auto-encoders with fine tuning, and the other one is obtained by directly using a concatenation of two representations. To improve accuracy and stability of a single softmax classifier, the ensemble of multiple classifiers is considered, and some Naive Bayes combination rules are introduced to integrate the three classifiers. Experimental results on the MNIST and COIL datasets are presented, with comparisons to other classification methods.

关键词： Deep network Feature representation Feature ensemble Autoencoder Softmax Naive Bayes

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