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Service-Oriented Computing - ICSOC 2012 Workshops 1

丛书名： Lecture Notes in computer Science

1000年

作者： Aditya Ghose Huibiao Zhu Qi Yu Alex Delis Quang Z. Sheng Olivier Perrin Jianmin Wang Yan Wang

ISBN: (数字)9783642378041

ISBN: (纸本)9783642378034

This book constitutes the thoroughly refereed proceedings of the 2012 ICSOC Workshops consisting of 6 scientific satellite events, organized in 3 main tracks including workshop track (ASC, DISA. PAASC, SCEB, SeMaPS and WESOA 2012), PhD symposium track, demonstration track; held in conjunction with the 10th International Conference on Service-Oriented Computing (ICSOC), in Shanghai, China, November 2012. The 53 revised papers presents a wide range of topics that fall into the general area of service computing such as business process management, distributed systems, computer networks, wireless and mobile computing, grid computing, networking, service science, management science, and software engineering.

关键词： software Engineering Information Systems Applications (incl. Internet) IT in Business computer Appl. in Administrative Data Processing Management of Computing and Information Systems computer Imaging, Vision, Pattern Recognition and Graphics

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Joint SVBRDF recovery and synthesis from a single image using an unsupervised generative adversarial network

Joint SVBRDF recovery and synthesis from a single image usin...

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31st Eurographics Symposium on Rendering, EGSR 2020 - Digital Library Only Track

作者： Zhao, Yezi Wang, Beibei Xu, Yanning Zeng, Zheng Wang, Lu Holzschuch, Nicolas School of Software Shandong University China School of Computer Science and Engineering Nanjing University of Science and Technology China Univ. Grenoble Alpes Inria CNRS Grenoble INP LJK France

ISBN: (纸本)9783038681175

We want to recreate spatially-varying bi-directional reflectance distribution functions (SVBRDFs) from a single image. Producing these SVBRDFs from single images will allow designers to incorporate many new materials in their virtual scenes, increasing their realism. A single image contains incomplete information about the SVBRDF, making reconstruction difficult. Existing algorithms can produce high-quality SVBRDFs with single or few input photographs using supervised deep learning. The learning step relies on a huge dataset with both input photographs and the ground truth SVBRDF maps. This is a weakness as ground truth maps are not easy to acquire. For practical use, it is also important to produce large SVBRDF maps. Existing algorithms rely on a separate texture synthesis step to generate these large maps, which leads to the loss of consistency between generated SVBRDF maps. In this paper, we address both issues simultaneously. We present an unsupervised generative adversarial neural network that addresses both SVBRDF capture from a single image and synthesis at the same time. From a low-resolution input image, we generate a large resolution SVBRDF, much larger than the input images. We train a generative adversarial network (GAN) to get SVBRDF maps, which have both a large spatial extent and detailed texels. We employ a two-stream generator that divides the training of maps into two groups (normal and roughness as one, diffuse and specular as the other) to better optimize those four maps. In the end, our method is able to generate high-quality large scale SVBRDF maps from a single input photograph with repetitive structures and provides higher quality rendering results with more details compared to the previous works. Each input for our method requires individual training, which costs about 3 hours. © 2020 The Author(s) Eurographics Proceedings © 2020 The Eurographics Association.

关键词： Generative adversarial networks

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Design and verification of fault-tolerant components

Design and verification of fault-tolerant components

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作者： Zhang, Miaomiao Liu, Zhiming Morisset, Charles Ravn, Anders P. School of Software Engineering Tongji University China International Institute of Software Technology United Nations University Macau United States Department of Computer Science Aalborg University Denmark UNU-IIST P.O. Box 3058 Macau SAR China

We present a systematic approach to design and verification of fault-tolerant components with real-time properties as found in embedded systems. A state machine model of the correct component is augmented with internal transitions that represent hypothesized faults. Also, constraints on the occurrence or timing of faults are included in this model. This model of a faulty component is then extended with fault detection and recovery mechanisms, again in the form of state machines. Desired properties of the component are model checked for each of the successive models. The models can be made relatively detailed such that they can serve directly as blueprints for engineering, and yet be amenable to exhaustive verification. The approach is illustrated with a design of a triple modular fault-tolerant system that is a real case we received from our collaborators in the aerospace field. We use UPPAAL to model and check this design. Model checking uses concrete parameters, so we extend the result with parametric analysis using abstractions of the automata in a rigorous verification. © 2009 Springer Berlin Heidelberg.

关键词： Fault tolerance

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Hiding Information in MPEG Sequences by Using of B-Frames

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Wuhan University Journal of Natural Sciences 2012年第3期17卷 238-242页

作者： ZHENG Peng WANG Weihua ZHAO Bo LIU Minzhong School of Computer Wuhan University Wuhan 430072 Hubei China The Key Laboratory of Aerospace Information Security and Trust Computing Ministry of Education Wtthan 430072 Hubei China School of Software Anyang Institute of Technology Anyang 455000 Henan China

In this paper,in order to increase the amount of hiding information,we choose MPEG sequences as carriers and present a practical method focus on the steganographic method in MPEG compressed video ***,there are many B-frames in each *** utilize the B-frames to hide information so that we can increase the amount of hiding information and decrease the tamper of the *** and extracting are performed in compressed video sequences without requiring original *** experimental results show that the proposed method has the characteristics of less tampering on the visual effect and larger hiding capacity without increasing the volume of the carrier file.

关键词： steganography MPEG B-flames motion vector

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EEG-based epileptic seizure pattern decoding using vision transformer 15

EEG-based epileptic seizure pattern decoding using vision tr...

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15th International Conference on Innovations in Information technology, IIT 2023

作者： Hireche, Abdelhadi Damseh, Rafat Sirpal, Parikshat Belkacem, Abdelkader Nasreddine United Arab Emirates University College of Information Technology Department of Computer and Network Engineering United Arab Emirates United Arab Emirates University College of Information Technology Department of Computer Science and Software Engineering United Arab Emirates University of Oklahoma School of Electrical and Computer Engineering Gallogly College of Engineering United States

ISBN: (纸本)9798350382396

Epilepsy is a prevalent neurological disorder and has been studied through the analysis of Electroencephalogram (EEG) signals. However, the identification and classification of epileptic seizure patterns remains challenging due to the non-stationary nature of EEG signals and the presence of artifacts. In this paper, we investigate the applicability of a transformer-based deep learning model to classify seizure patterns observed in epileptic patients. We employed the self-Attention mechanism inherent in transformers to capture complex temporal relationships in the EEG recordings. By prepossessing the EEG signals into suitable input sequences and adapting the transformer architecture, we achieved 78.11% in distinguishing between different epileptic seizure patterns. Our findings indicate that the transformer model, with its ability to manage long-range dependencies, offers a robust approach to EEG-based seizure pattern classification. This work is important for building advanced automated diagnostic tools for epilepsy and related neurological disorders. © 2023 IEEE.

关键词： Electroencephalography

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UA-HGAT: Uncertainty-aware Heterogeneous Graph Attention Network for Short Text Classification 15

UA-HGAT: Uncertainty-aware Heterogeneous Graph Attention Net...

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022

作者： Kong, DeYan Ji, ZhenYan Sang, Yanjuan Dong, Wei Yang, Yanyan Beijing Jiaotong University School of Software Engineering Beijing China Blueintelligence Technology Co.Ltd. R&d Department Wuhan China Institute of Software Chinese Academy of Sciences State Key Laboratory of Computer Science Beijing100190 China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

During the last decade, deep learning has played a significant role in improving text classification which is widely applied in question answering systems, comment extraction, sentiment analysis, etc. Considering that few labeled training data are available, semi-supervised short text classification needs to be studied urgently. Existing works focus on long texts and cannot achieve satisfactory performance on short texts due to the sparsity, non-standardization, and insufficient labeled data. In this paper, we propose a novel uncertainty-aware heterogeneous graph attention network for semi-supervised short text classification (UA-HGAT), which makes full use of limited labeled data and large amounts of unlabeled data via exploiting an uncertainty-aware mechanism. In the model, entity description from Wikipedia is introduced to empower short texts, and word-level information is acquired by Word2Vec. The entity description and word-level information are combined with topic information to address the issue of short text sparseness. In particular, we introduce uncertainty-aware mechanism and negative sample learning to relieve the erroneous high confidence predictions from poorly calibrated models, so that it can offer enough pseudo-label samples for the model training. Extensive experiments show that our model significantly outperforms the SOTA methods on AGNews and Snippets datasets. © 2022 IEEE.

关键词： Sentiment analysis

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Helmet Pose Measurement Method Based on Feature Learning 6

Helmet Pose Measurement Method Based on Feature Learning

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6th International Conference on Information Communication and Signal Processing, ICICSP 2023

作者： Fu, Gaobo Zhao, Xinbo Zou, Xiaochun Yan, Linjie Zhang, Baoshang School of Software Northwestern Polytechnical University Xi'an China Science and Technology on Electro-optic Control Laboratory Luoyang China School of Computer Science Northwestern Polytechnical University Xi'an China School of Electronic Information Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350339994

In modern aviation systems, the helmet sight (HMS) assists the pilot to cooperate with the warplane to quickly complete the target tracking and aiming, and the helmet pose measurement is the core technology of the helmet sight. Since the helmet pose measurement technology involved in aviation safety, this technology is kept confidential at home and abroad, and there is a lack of open source helmet data set and high-precision helmet posture measurement algorithm. For this purpose, this paper produced the first helmet dataset, Helment 8K, and proposed the CSPDarkNet-Ht helmet pose measurement algorithm based on feature learning. The target is to form the helmet dataset image on the helmet model according to the unique feature points of the helmet model design. Helment 8K data set is made by building a helmet model with 3dmax and then rendering 8282 helmet images at different angles with targets. The CSPDarkNet-Ht algorithm divides the yaw angle and pitch angle of the helmet pose angle into two sub-tasks for classification and regression. Firstly, The algorithm uses CSPDarkNet to extract the low, medium and high semantic information of the target features for the backbone network. Then, different semantic feature layers are classified from different angles and regression of the highest semantic feature layer is performed to obtain the predicted pose angles. Finally, the loss function was optimized to include the cross-entropy loss of the five coarse and fine categories as the total loss. Moreover, the average absolute error of the angular prediction on the Helment 8K dataset is 0.5, and the prediction accuracy is 91.09% within 1 degree error, which is consistent with the accurate measurement of the helmet pose. © 2023 IEEE.

关键词： helmet pose measurement hybrid coarse-fine classification target

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Research on key technology of remote experimental platform for open network laboratory

Research on key technology of remote experimental platform f...

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2011 IEEE International Symposium on IT in Medicine and Education, ITME 2011

作者： Wen, Wu He, Dehua Zhuang, Yufei Chen, Yaohua School of Computer Science and Educational Software Guangzhou University Guangzhou China Dept. of Electronics and Information Engineering Huazhong University of Science and Technology Wuhan China School of Mechanical and Electric Engineering Guangzhou University Guangzhou China

ISBN: (纸本)9781612847023

In order to satisfy the development of contemporary higher education better, after analyzing different experimental methods, this paper proposes a new idea to combine real laboratory equipments and virtual simulation system for building the remote experimental platform. Therefore students are no longer limited in the visible laboratory. As a result, the experimental resources can be made used of completely, the manual management can be reduced, the application of laboratory equipments can be improved efficiently. © 2011 IEEE.

关键词： Laboratories

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Quantum Searchable Encryption for Cloud Data Based on Delegating Quantum Computing 9th

Quantum Searchable Encryption for Cloud Data Based on Delega...

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9th EAI International Conference on Cloud Computing, CloudComp 2019 and the 4th EAI International Conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2019

作者： Xu, Yinsong Liu, Wenjie Chen, Junxiu Tong, Lian School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing210044 China School of Information Engineering Jiangsu Maritime Institute Nanjing211100 China

ISBN: (纸本)9783030485122

Based on delegating quantum computing (DQC), a DQC model that adapts to multi-qubit and composite quantum circuits is given firstly. In this model, the single client with limited quantum ability can give her encrypted data to a powerful but untrusted quantum data server and let the data server computes over the encrypted data without decryption, where the computation is a quantum circuit composed of multiple quantum gates. Then, the client generates the decryption key to decrypt the computing result according to the circuit of computation. However, this model cannot meet the situation of multi-client accessing or computing encrypted cloud data in the cloud environment. To solve this problem, we let the client outsource key generation to a trusted key server, which composes the quantum cloud center with the data server. The clients only perform X and Z operation according to the encryption or decryption key. Then, combined with Grover algorithm, a quantum searchable encryption scheme for cloud data based on delegating quantum computing is proposed in this paper. The data server mainly uses Grover algorithm to perform search computation on the encrypted data. Moreover, a concrete example of our scheme is discussed next, where the data server searches for 2 target items from 8 items of the encrypted data. Finally, security of our proposed scheme is analysed, which can protect the security of the data. © 2020, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Trusted computing

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An integrated algorithm based on artificial bee colony and particle swarm optimization

An integrated algorithm based on artificial bee colony and p...

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International Conference on Natural Computation

作者： Shi, Xiaohu Li, Yanwen Li, Haijun Guan, Renchu Wang, Liupu Liang, Yanchun College of Computer Science and Technology Jilin University Jilin Changchun 130012 China State Key Lab. for Novel Software Technology Nanjing University Jiangsu Nanjing 210093 China College of Computer Northeast Normal University Changchun 130117 China School of Computer Science Yantai University Shandong Yantai 264005 China

ISBN: (纸本)9781424459612

Based on particle swarm optimization (PSO) and artificial bee colony (ABC), a novel hybrid swarm intelligent algorithm is developed in this paper. Two information exchanging processes are introduced to share valuable information mutually between particle swarm and bee colony. Numerical results show that the proposed method is effective and performs better than both PSO and ABC. © 2010 IEEE.

关键词： Swarm intelligence

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