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Computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Xin Chen Yuanjie Zheng Yi Niu Chengiiang Li School of Information Science and Engineering Shandong Normal University Jinan China Key Lab of Intelligent Computing and Information Security Universities of Shandong Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Institute of Biomedical Sciences Jinan China Shandong University of Science and Technology Jinan China

ISBN: (数字)9781728194813

ISBN: (纸本)9781728194820

Epilepsy is a brain disorder caused by abnormal discharges of neurons in brain. It is one of the most commonly studied disorders in neurology. The research of epilepsy electroencephalogram (EEG) has become a hot research topic. We find that in epilepsy EEG detection task, many previous methods focused on directly collecting the data of each channel, but these methods seldom analyse relationships between signals. Therefore, we propose the Epilepsy EEG Graph Convolutional network EGCN, which makes full use of correlations between channels to deeply mine data information. We specifically design 5-layer graph convolutional network structure for classification of healthy and epileptic patients. The method is applied to public data set (Boon and CHB-MIT) to establish a reasonable classification model. And we compare it with some advanced algorithms. The experimental results show that the E-GCN method is superior to many existing methods in classification accuracy. In brief, the E-GCN method can be effectively used in classification and detection for epilepsy. This provides new ideas for colleagues, who study epilepsy EEG. In addition, this also provides richer experience for diagnosis of epilepsy.

关键词： Epilepsy Electroencephalography Convolution Brain modeling Feature extraction Correlation Task analysis

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ReGO: Reference-Guided Outpainting for Scenery Image

arXiv

引用

arXiv 2021年

作者： Wang, Yaxiong Wei, Yunchao Qian, Xueming Zhu, Li Yang, Yi The School of Software Engineering Xi’an Jiaotong University Xi'An710049 China Institute of Information Science Beijing Jiaotong University Beijing100000 China The Key Laboratory for Intelligent Networks and Network Security Ministry of Education Xi’an Jiaotong University Xi'An710049 China The SMILES Laboratory Xi’an Jiaotong University Xi'An710049 China Zhibian Technology Co. Ltd. Taizhou317000 China The School of Software Xi’an Jiaotong University Xi'An710049 China School of Computer Science and Technology Zhejiang University Hangzhou310000 China

We aim to tackle the challenging yet practical scenery image outpainting task in this work. Recently, generative adversarial learning has significantly advanced the image outpainting by producing semantic consistent content for the given image. However, the existing methods always suffer from the blurry texture and the artifacts of the generative part, making the overall outpainting results lack authenticity. To overcome the weakness, this work investigates a principle way to synthesize texture-rich results by borrowing pixels from its neighbors (i.e. , reference images), named Reference-Guided Outpainting (ReGO). Particularly, the ReGO designs an Adaptive Content Selection (ACS) module to transfer the pixel of reference images for texture compensating of the target one. To prevent the style of the generated part from being affected by the reference images, a style ranking loss is further proposed to augment the ReGO to synthesize style-consistent results. Extensive experiments on two sceneary benchmarks, NS6K [3], NS8K [9] and SUN Attribute [25], well demonstrate the effectiveness of our ReGO. Our code is available at https://***/wangyxxjtu/ReGOPytorch. © 2021, CC BY.

关键词： Textures

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Weakly supervised learning for textbook question answering

TechRxiv

引用

TechRxiv 2021年

作者： Ma, Jie Chai, Qi Huang, Jingyue Liu, Jun You, Yang Zheng, Qinghua National Engineering Lab for Big Data Analytics School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China The High Performance Computing for Artificial intelligence Lab Department of Computer Science National University of Singapore 117417 Singapore

Textbook Question Answering (TQA) is the task of answering diagram and non-diagram questions given large multimodal contexts consisting of abundant text and diagrams. Deep text understandings and effective learning of diagram semantics are important for this task due to its specificity. In this paper, we propose a Weakly Supervised learning method for TQA (WSTQ), which regards the incompletely accurate results of essential intermediate procedures for this task as supervision to develop Text Matching (TM) and Relation Detection (RD) tasks and then employs the tasks to motivate itself to learn strong text comprehension and excellent diagram semantics respectively. Specifically, we apply the result of text retrieval to build positive as well as negative text pairs. In order to learn deep text understandings, we first pre-train the text understanding module of WSTQ on TM and then fine-tune it on TQA. We build positive as well as negative relation pairs by checking whether there is any overlap between the items/regions detected from diagrams using object detection. The RD task forces our method to learn the relationships between regions, which are crucial to express the diagram semantics. We train WSTQ on RD and TQA simultaneously, i.e., multitask learning, to obtain effective diagram semantics and then improve the TQA performance. Extensive experiments are carried out on CK12-QA and AI2D to verify the effectiveness of WSTQ. Experimental results show that our method achieves significant accuracy improvements of 5.02% and 4.12% on test splits of the above datasets respectively than the current state-of-the-art baseline. We have released our code on https://***/dr-majie/WSTQ. © 2021, CC BY.

关键词： Semantics

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Golden grain: Building a secure and decentralized model marketplace for MLaaS

arXiv

引用

arXiv 2020年

作者： Weng, Jiasi Weng, Jian Cai, Chengjun Huang, Hongwei Wang, Cong The College of Information Science and Technology College of Cyber security National Joint Engineering Research Center of Network Security Detection and Protection Technology Guangdong Key Laboratory of Data Security and Privacy Preserving Jinan University Guangzhou510632 China The Department of Computer Science City University of Hong Kong Hong Kong Hong Kong The City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China

ML-as-a-service (MLaaS) becomes increasingly popular and revolutionizes the lives of people. A natural requirement for MLaaS is, however, to provide highly accurate prediction services. To achieve this, current MLaaS systems integrate and combine multiple well-trained models in their services. Yet, in reality, there is no easy way for MLaaS providers, especially for startups, to collect sufficiently well-trained models from individual developers, due to the lack of incentives. In this paper, we aim to fill this gap by building up a model marketplace, called as Golden Grain, to facilitate model sharing, which enforces the fair model-money swapping process between individual developers and MLaaS providers. Specifically, we deploy the swapping process on the blockchain, and further introduce a blockchain-empowered model benchmarking process for transparently determining the model prices according to their authentic performances, so as to motivate the faithful contributions of well-trained models. Especially, to ease the blockchain overhead for model benchmarking, our marketplace carefully offloads the heavy computation and designs a secure off-chain on-chain interaction protocol based on a trusted execution environment (TEE), for ensuring both the integrity and authenticity of benchmarking. We implement a prototype of our Golden Grain on the Ethereum blockchain, and conduct extensive experiments using standard benchmark datasets to demonstrate the practically affordable performance of our design. © 2020, CC BY.

关键词： Blockchain

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TPTVer： A Trusted Third Party Based Trusted Verifier for Multi-Layered Outsourced Big Data System in Cloud Environment

引用

China Communications 2018年第2期15卷 122-137页

作者： Jing Zhan Xudong Fan Lei Cai Yaqi Gao Junxi Zhuang College of Computer Science Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Trusted Computing Beijing China National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing China

Cloud computing is very useful for big data owner who doesn't want to manage IT infrastructure and big data technique details. However, it is hard for big data owner to trust multi-layer outsourced big data system in cloud environment and to verify which outsourced service leads to the problem. Similarly, the cloud service provider cannot simply trust the data computation applications. At last,the verification data itself may also leak the sensitive information from the cloud service provider and data owner. We propose a new three-level definition of the verification, threat model, corresponding trusted policies based on different roles for outsourced big data system in cloud. We also provide two policy enforcement methods for building trusted data computation environment by measuring both the Map Reduce application and its behaviors based on trusted computing and aspect-oriented programming. To prevent sensitive information leakage from verification process,we provide a privacy-preserved verification method. Finally, we implement the TPTVer, a Trusted third Party based Trusted Verifier as a proof of concept system. Our evaluation and analysis show that TPTVer can provide trusted verification for multi-layered outsourced big data system in the cloud with low overhead.

关键词： big data security outsourced ser-vice security MapReduce behavior trustedverification trusted third party

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Semantically Secure and Verifiable Multi-keyword Search in Cloud computing 1

引用

2nd International Conference on Machine Learning for Cyber security, ML4CS 2019

作者： Zhang, Lili Wang, Wenjie Zhang, Yuqing National Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China Henan International Joint Laboratory of Cyberspace Security Applications Information Engineering College Henan University of Science and Technology Luoyang471023 China National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China

ISBN: (数字)9783030306199

ISBN: (纸本)9783030306182

In cloud computing model, the data are usually encrypted before outsourced to the cloud server, which protects the data privacy, but also leaves keyword searches over ciphertext data a challenging problem. A keyword search scheme over encrypted data should achieve both index privacy and query privacy;moreover, verification of search results is desirable because the incorrectf results can be returned owing to system defects or the cloud server’s motivation to save computation recourses. Many multi-keyword search schemes have been proposed;however, few of these schemes are verifiable and adaptively index-hiding and adaptively query-hiding. In this paper, a semantically secure multi-keyword search scheme is constructed, which is adaptively index-hiding and adaptively query-hiding, also supports the correctness verification of search results. We provide a detailed performance comparison and give a thorough security proof by a sequence of games. The combined results demonstrate that our scheme is secure and practical. © 2019, Springer Nature Switzerland AG.

关键词： Cloud computing

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A novel deep learning approach for lung cancer recognition based on 1-D deep convolutional neural network 2019

A novel deep learning approach for lung cancer recognition b...

引用

4th International Conference on Mathematics and Artificial Intelligence, ICMAI 2019

作者： Mubashir, Muhammad Ahmed, Md Rishad Ahmad, Maqsood Siddiqui, Sarah Ali Ahmad, Mudaser Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China Khwaja Fareed University of Engineering and Information Technology Pakistan Macquarie University Sydney Australia. The Islamia University of Bahawalpur Pakistan

ISBN: (纸本)9781450362580

Pulse diagnosis has been a requisite facet in traditional Chinese medicine as well as in western medicine, yet the prognosis of lung cancer hinged on wrist pulse analysis entails the quotidian approaches. In spite of diagnosing the lung cancer, in traditional methods, the identification stratagem is divaricated into assorted steps: analysis of signals procured, synthetic extraction and selection of features, and subsequently the classification. However, the vague and mundane feature selection and signal analysis steers to the inadequate classification accuracy due to intrinsic deficiencies. In this study, we have proposed a novel deep convolutional neural network (DCNN) based approach to discern the lung cancer against the acquired wrist pulse signals. In order to ensnare the features, vanquishing the overfitting, a 1- dimensional 15-layers DCNN model is devised hinged on 1-D convolutional, batch normalization, and pooling layers. Considering the instinctive feature extraction, from the experimental data comprised of 45,969 samples of 16 lung cancer and 20 healthy individuals, assorted units are heaped in the lodged DCNN. The experimental comparison with the stateof- art deep neural networks (DNNs) and conventional methods evinced that our lodged approach conquer the deficiencies of conventional signal processing and manual feature selection approaches. Finally, the results, with the validation precision of 97.67%, outperform the recent existing approach for lung cancer recognition. © 2019 Association for computing Machinery.

关键词： Feature extraction

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ACWB: Access-characteristic write buffer management scheme for flash memory 21

ACWB: Access-characteristic write buffer management scheme f...

引用

21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Zhu, Lei Liu, Yan Zhang, Zaimei Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha Hunan410082 China School of Economics and Management Changsha University of Science and Technology Changsha Hunan410076 China

ISBN: (纸本)9781728120584

Compared with traditional hard disk drives (HDD), solid state drives (SDD) have distinct characteristics, such as efficient continuous access and random reading capability. Using of SSD is greatly limited due to the out-of-place update and endurance problem. Many flash-aware buffer schemes have been proposed to reduce write and erase operations. However, a good buffer management scheme should reduce the effects of random writes and suit access patterns. In this paper, we propose an access characteristic write buffer management scheme (ACWB) on flash memory that integrates write buffer and cost regulation based on access characteristic. Experimental results show that ACWB increases the response time by 35%, reduces erase times by 25%, and improves the hit ratio by 27% on the average compared with current flash-aware write buffer schemes. © 2019 IEEE.

关键词： Flash memory

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B2SFinder: Detecting Open-Source Software Reuse in COTS Software

B2SFinder: Detecting Open-Source Software Reuse in COTS Soft...

引用

IEEE International Conference on Automated Software Engineering (ASE)

作者： Zimu Yuan Muyue Feng Feng Li Gu Ban Yang Xiao Shiyang Wang Qian Tang He Su Chendong Yu Jiahuan Xu Aihua Piao Jingling Xuey Wei Huo Institute of Information Engineering Chinese Academy of Sciences Key Laboratory of Network Assessment Technology Chinese Academy of Sciences Beijing Key Laboratory of Network Security and Protection Technology and School of Cyber Security University of Chinese Academy of Sciences University of New South Wales

COTS software products are developed extensively on top of OSS projects, resulting in OSS reuse vulnerabilities. To detect such vulnerabilities, finding OSS reuses in COTS software has become imperative. While scalable to tens of thousands of OSS projects, existing binary-to-source matching approaches are severely imprecise in analyzing COTS software products, since they support only a limited number of code features, compute matching scores only approximately in measuring OSS reuses, and neglect the code structures in OSS projects. We introduce a novel binary-to-source matching approach, called B2SFINDER 1 , to address these limitations. First of all, B2SFINDER can reason about seven kinds of code features that are traceable in both binary and source code. In order to compute matching scores precisely, B2SFINDER employs a weighted feature matching algorithm that combines three matching methods (for dealing with different code features) with two importance-weighting methods (for computing the weight of an instance of a code feature in a given COTS software application based on its specificity and occurrence frequency). Finally, B2SFINDER identifies different types of code reuses based on matching scores and code structures of OSS projects. We have implemented B2SFINDER using an optimized data structure. We have evaluated B2SFINDER using 21991 binaries from 1000 popular COTS software products and 2189 candidate OSS projects. Our experimental results show that B2SFINDER is not only precise but also scalable. Compared with the state ofthe art, B2SFINDER has successfully found up to 2.15× as many reuse cases in 53.85 seconds per binary file on average. We also discuss how B2SFINDER can be leveraged in detecting OSS reuse vulnerabilities in practice.

关键词： Open source software Libraries Software algorithms security Switches Arrays

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Few-shot Object Detection with Camouflage Animals

引用

Journal of Physics: Conference Series 2021年第1期1961卷

作者： Yi Lei Lei Lei Network and Data Security Key Laboratory of Sichuan Province University of Electr onic Science and Technology of China Primary school in Gao sheng Zi yang China

Object detection is a hot issue in the field of computer vision, which is widely used in intelligent video surveillance, medical image analysis, and practice in the fields of military strategy. Previous object detection is based on a lot of training data, and for some special target, it is difficult to find enough annotation data to training model, so few-shot object detection was born. In this paper, a data set of animals with protective colors is constructed(CAD), which contains ten categories of different objects with high quality annotations. We combine the existing attention RPN and Feature Reweighting module to train our net, the experimental results prove that the Camouflage animals in nature can be well identified, and our results obtain 1.9% AP50, 1.2% AP75 improvement compared with before work.

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