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IEEE International Conference on Automated software engineering (ASE)

作者： Jia Xu Xiao Liu Xuejun Li Lei Zhang Yun Yang School of Computer Science and Technology Anhui University Hefei China School of Information Technology Deakin University Geelong Australia Antwork Robotics Co. Ltm. Hangzhou China School of Software and Electrical Engineering University of Technology Melbourne Australia

ISBN: (数字)9781450367684

ISBN: (纸本)9781728172811

As most smart systems such as smart logistic and smart manufacturing are delay sensitive, the current mainstream cloud computing based system architecture is facing the critical issue of high latency over the Internet. Meanwhile, as huge amount of data is generated by smart devices with limited battery and computing power, the increasing demand for energy-efficient machine learning and secure data communication at the network edge has become a hurdle to the success of smart systems. To address these challenges with using smart UAV (Unmanned Aerial Vehicle) delivery system as an example, we propose EXPRESS, a novel energy-efficient and secure framework based on mobile edge computing and blockchain technologies. We focus on computation and data (resource) management which are two of the most prominent components in this framework. The effectiveness of the EXPRESS framework is demonstrated through the implementation of a real-world UAV delivery system. As an open-source framework, EXPRESS can help researchers implement their own prototypes and test their computation and data management strategies in different smart systems. The demo video can be found at https://***/r3U1iU8tSmk.

关键词： Systems architecture Blockchain Unmanned aerial vehicles Energy efficiency Edge computing software engineering Smart manufacturing

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SHREC'19 track: Extended 2D scene sketch-based 3D scene retrieval 12

SHREC'19 track: Extended 2D scene sketch-based 3D scene retr...

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12th Eurographics Workshop on 3D Object Retrieval, 3DOR 2019, in conjunction with the 40th Annual Conference of the European Association for computer Graphics, EG 2019

作者： Yuan, Juefei Abdul-Rashid, Hameed Li, Bo Lu, Yijuan Schreck, Tobias Bui, Ngoc-Minh Do, Trong-Le Nguyen, Khac-Tuan Nguyen, Thanh-An Nguyen, Vinh-Tiep Tran, Minh-Triet Wang, Tianyang School of Computing Sciences and Computer Engineering University of Southern Mississippi United States Department of Computer Science Texas State University United States Institute of Computer Graphics and Knowledge Visualization Graz University of Technology Austria Faculty of Information Technology Vietnam National University Ho Chi Minh City Viet Nam Software Engineering Lab Vietnam National University Ho Chi Minh City Viet Nam University of Information Technology Vietnam National University Ho Chi Minh City Viet Nam Department of Computer Science and Information Technology Austin Peay State University United States

ISBN: (纸本)9783038680772

Sketch-based 3D scene retrieval is to retrieve 3D scene models given a user's hand-drawn 2D scene sketch. It is a brand new but also very challenging research topic in the field of 3D object retrieval due to the semantic gap in their representations: 3D scene models or views differ from non-realistic 2D scene sketches. To boost this interesting research, we organized a 2D Scene Sketch-Based 3D Scene Retrieval track in SHREC'18, resulting a SceneSBR18 benchmark which contains 10 scene classes. In order to make it more comprehensive, we have extended the number of the scene categories from the initial 10 classes in the SceneSBR2018 benchmark to 30 classes, resulting in a new and more challenging benchmark SceneSBR2019 which has 750 2D scene sketches and 3,000 3D scene models. Therefore, the objective of this track is to further evaluate the performance and scalability of different 2D scene sketch-based 3D scene model retrieval algorithms using this extended and more comprehensive new benchmark. In this track, two groups from USA and Vietnam have successfully submitted 4 runs. Based on 7 commonly used retrieval metrics, we evaluate their retrieval performance. We have also conducted a comprehensive analysis and discussion of these methods and proposed several future research directions to deal with this challenging research topic. Deep learning techniques have been proved their great potentials again in dealing with this challenging retrieval task, in terms of both retrieval accuracy and scalability to a larger dataset. We hope this publicly available benchmark, together with its evaluation results and source code, will further enrich and promote 2D scene sketch-based 3D scene retrieval research area and its corresponding applications. © 2019 The Author(s)

关键词： Scalability

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Kannada-Mnist Classification Using Skip CNN

Kannada-Mnist Classification Using Skip CNN

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International Conference on Wavelet Active Media Technology and information Processing (ICWAMTIP)

作者： ELVIS S. GATI BENJAMIN D. NIMO ELISHA K. ASIAMAH School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781728142425

ISBN: (纸本)9781728142432

In this paper, we tried out a new pipeline on the new handwritten digits dataset termed Kannada-MNIST for the Kannada script as well as a real-world handwritten dataset termed Dig-MNIST. The Kannada-MNIST was created to potentially serve as a direct drop-in replacement for the original MNIST dataset whiles Dig-MNIST serves as an out-ofdomain test *** achieved a state-of-the-art result in challenging the baseline. Our model achieved 97. 53% accuracy on KannadaMNIST against 96. 8% and 85. 02% accuracy against 76. 1% on the Dig-MNIST dataset.

关键词： Training Handwriting recognition Machine learning Benchmark testing Network architecture Neurons

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The Quantum Internet (Technical Version)

arXiv

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arXiv 2025年

作者： Rohde, Peter P. Huang, Zixin Ouyang, Yingkai Huang, He-Liang Su, Zu-En Devitt, Simon Ramakrishnan, Rohit Mantri, Atul Tan, Si-Hui Liu, Nana Harrison, Scott Radhakrishnan, Chandrashekar Brennen, Gavin K. Baragiola, Ben Q. Dowling, Jonathan P. Byrnes, Tim Munro, William J. Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW Australia Hearne Institute for Theoretical Physics Louisiana State University Baton Rouge United States School of Mathematical and Physical Sciences Macquarie University NSW2109 Australia School of Mathematical and Physical Sciences University of Sheffield SheffieldS3 7RH United Kingdom Henan Key Laboratory of Quantum Information and Cryptography Henan Zhengzhou450000 China Hefei National Laboratory for Physical Sciences at Microscale Department of Modern Physics University of Science & Technology of China Hefei China CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information & Quantum Physics University of Science & Technology of China Hefei China CAS-Alibaba Quantum Computing Laboratory Shanghai China InstituteQ Aalto University Espoo02150 Finland Indian Institute of Science Bangalore India Singapore University of Technology & Design Singapore Centre for Quantum Technologies National University of Singapore Singapore Horizon Quantum Ireland 24 Fitzwilliam Place Dublin 2 D02 T296 Ireland Institute of Natural Sciences Shanghai Jiao Tong University Shanghai200240 China School of Mathematical Sciences Shanghai Jiao Tong University Shanghai200240 China Ministry of Education Key Laboratory in Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai200240 China Shanghai Artificial Intelligence Laboratory Shanghai China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai200240 China Leibniz Institute for Research & Information in Education Frankfurt am Main Germany Department of Computer Science and Engineering NYU Shanghai 567 West Yangsi Road Shanghai200124 China Centre of Excellence in Engineered Quantum Systems Macquarie University NSW Australia Centre for Quantum Computation and Communication Technology School of Science RMIT University VIC

The desire to share and unite remote digital assets motivated the development of the classical internet, the enabler of the entire 21st century economy and our modern way of life. As we enter the quantum era, it is to be expected there will be a similar demand for networking quantum assets, motivating a global quantum internet for bringing together the world's quantum resources, leveraging off their exponential trajectory in capability. We present models for quantum networking, how they might be applied in the future, and the implications they will have. Socially, economically, politically and geostrategically, the upcoming era of quantum supremacy will be as significant for the 21st century as the transistor was for the 20th. The inherently different scaling in the computational power of quantum computers fundamentally changes the dynamics of how they will operate in the future. Given their high expected initial cost, a client/server model for outsourcing computation will be essential for enabling the accessibility and proliferation of this technology, and ensuring its economic viability. We therefore anticipate the emergence of cloud quantum computing, a model for outsourcing quantum computations to the network. We argue that economic efficiency will mandate that all future quantum computers be united into a single global virtual quantum computer, offering exponentially more power to all network participants than if they were to keep their resources to themselves. This model for the allocation of computational resources is uniquely quantum, with no classical analogue, completely altering the economic landscape for the future of computation. Given the sensitivity of much of the data to which future quantum computers are going to foreseeably be applied, protocols for encrypted quantum computation will be essential - the outsourcing of computations that neither an eavesdropper nor even the server performing the computation can spy upon. This will enable new models fo

关键词： Outsourcing

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Identity-based public key cryptographic primitive with delegated equality test against insider attack in cloud computing

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International Journal of Network Security 2020年第5期22卷 743-751页

作者： Alornyo, Seth Mensah, Acheampong Edward Abbam, Abraham Opanfo School of Information and Software Engineering University of Electronic Science and Technology of China 4 E 2nd Sec. 1st Ring Rd Jianshe Road Chenghua Chengdu Sichuan China Computer Science Department Koforidua Technical University Koforidua Ghana

The notion of attacks perpetuated by an insider (cloud server) is paramount in this era of cloud computing and data analytics. When a cloud server is delegated with certain responsibilities, it is possible for a cloud server to peddle with users' encrypted data for profit gains. The cloud server takes advantage of it's authorization to launch what we referred to as insider attack. We put forward a new improved scheme on identity-based public key cryptographic primitive which integrate delegated equality test to resist insider attack in cloud computing. Our scheme resist the insider attack perpetuated by the cloud server (insider). We refer to our new scheme as identity-based public key cryptographic primitive with delegated equality test against insider attack in cloud computing (IB-PKC-DETIA). We construct our scheme using a witness based cryptographic primitive with an added pairing operation. Our scheme achieves weak indistinquishable identity chosen ciphertext (W-IND-ID-CCA) security using the random oracle model. © Femto Technique Co., Ltd.

关键词： Cryptography

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PGD-UNet: A Position-Guided Deformable Network for Simultaneous Segmentation of Organs and Tumors

arXiv

引用

arXiv 2020年

作者： Li, Ziqiang Zhu, Yaping Pan, Hong Qin, A.K. School of Automation Southeast University No.2 Sipailou Nanjing210096 China School of Information and Communication Engineering Communication University of China No.1 Dingfuzhuang East Street Beijing100024 China Department of Computer Science and Software Engineering Swinburne University of Technology John St HawthornVIC3122 Australia

Precise segmentation of organs and tumors plays a crucial role in clinical applications. It is a challenging task due to the irregular shapes and various sizes of organs and tumors as well as the significant class imbalance between the anatomy of interest (AOI) and the background region. In addition, in most situation tumors and normal organs often overlap in medical images, but current approaches fail to delineate both tumors and organs accurately. To tackle such challenges, we propose a position-guided deformable UNet, namely PGD-UNet, which exploits the spatial deformation capabilities of deformable convolution to deal with the geometric transformation of both organs and tumors. Position information is explicitly encoded into the network to enhance the capabilities of deformation. Meanwhile, we introduce a new pooling module to preserve position information lost in conventional max-pooling operation. Besides, due to unclear boundaries between different structures as well as the subjectivity of annotations, labels are not necessarily accurate for medical image segmentation tasks. It may cause the overfitting of the trained network due to label noise. To address this issue, we formulate a novel loss function to suppress the influence of potential label noise on the training process. Our method was evaluated on two challenging segmentation tasks and achieved very promising segmentation accuracy in both tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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Forewords

Proceedings of the Confluence 2020 - 10th International Conf...

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Proceedings of the Confluence 2020 - 10th International Conference on Cloud Computing, Data science and engineering 2020年 xviii页

作者： Debnath, Narayan C. School of Computing and Information Technology Eastern International University Viet Nam Department of Software Engineering Eastern International University Viet Nam International Society for Computers and Their Applications United States Computer Science Department Winona State University United States

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Mural sketch generation via style-aware convolutional neural network 2018

Mural sketch generation via style-aware convolutional neural...

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2018 computer Graphics International Conference, CGI 2018

作者： Pan, Gang Sun, Di Zhan, Rui Zhang, Jiawan School of Computer Software Tianjin University China School of Computer Science and Information Engineering Tianjin University of Science and Technology China

ISBN: (纸本)1595930361

Sketch is one of the most important art expression forms for traditional Chinese painting. This paper presents a complete sketch generation framework for ancient mural paintings. First, we propose a deep learning network to perform mural-to-sketch prediction by combining meaningful convolutional features in a holistic manner. A dedicated mural database with fine-grained ground truth is built for network training and testing. Then we design a style-aware image fusion approach by detecting the specific feature region in a mural, from which the artistic style can be maximally preserved. Experimental results have demonstrated its validity in extracting style mural sketch. This work has the potential to provide a computer aided tool for artists and restorers to imitate and restore time-honored paintings. © 2018 ACM.

关键词： Convolution

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A Survey on Hand-based Behavioral Activity Recognition

A Survey on Hand-based Behavioral Activity Recognition

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International Conference on Wavelet Active Media Technology and information Processing (ICWAMTIP)

作者： Happy Nkanta Monday Jian Ping Li Grace Ugochi Nneji Mordecai Folarin Raji Chima C. Ukwuoma Jalaluddin Khan Chukwuebuka J. Ejiyi Amin Ulhaq Saifun Nahar Hassan S. Abubakar Chikwendu A. Ijeoma School of Computer Science and engineering University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu

ISBN: (数字)9781728142425

ISBN: (纸本)9781728142432

Wrist-worn devices such as smart-watches can be a perfect way to collect personalized user data, since these devices are embedded with various sensors such as the accelerometer and gyroscope which can sense user's hand movement, as well as help in identifying the activity performed by the user. Additionally, smart-watches are light and unobtrusive unlike the wristbands used in past studies which require the user to be connected by several wires and bulky sensors. In this study, we examined the different neural network adopted for the recognition of hand-based behavioral activity to recognize six complex hand motion activities such as smoking, drinking, eating, typing giving a talk and writing with a pen in a non-controlled environment, using accelerometer and gyroscope sensor signals acquired from smart-watch. Unlike other machine learning methods which use hand crafted features, CNN can learn features automatically from the data as well as capture local dependency.

关键词： Activity recognition Machine learning Feature extraction Smart phones Accelerometers Performance evaluation

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Linear adjusting programming in factor space

arXiv

引用

arXiv 2020年

作者： He, Jing Kong, Qi-Wei Lui, Ho-Chung Liu, Hai-Tao Ji, Yi-Mu Yao, Hai-Chang Liu, Mo-Zhengfu Chen, Jin-Jun School of Software and Electrical Engineering Swinburne University of Technology Hawthorn Australia Institute of Information Engineering Nanjing University of Finance and Economics Nanjing China Research Center on Fictitious economy & Data Science Chinese Academy of Sciences Beijing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Swinburne University of Technology Melbourne3122 Australia

The definition of factor space and a unified optimization based classification model were developed for linear programming and supervised learning. Intelligent behaviour appeared in a decision process can be treated as a point y, the dynamic state observed and controlled by the agent, moving in a factor space impelled by the goal factor and blocked by the constraint factors. Suppose that the feasible region is cut by a group of hyperplanes, when point y reaches the region’s wall, a hyperplane will block the moving and the agent needs to adjust the moving direction such that the target is pursued as faithful as possible. Since the wall is not able to be represented to a differentiable function, the gradient method cannot be applied to describe the adjusting process. We, therefore, suggest a new model, named linear adjusting programming (LAP) in this paper. LAP is similar as a kind of relaxed linear programming (LP), and the difference between LP and LAP is: the former aims to find out the ultimate optimal point, while the latter just does a direct action in short period. You may ask: Where will a blocker encounter? How can the moving direction be adjusted? Where further blockers may be encountered next, and how should the direction be adjusted again?... If the ultimate best is found, that’s a blessing;if not, that’s fine. We request at least an adjusting should be achieved at the first time. what are the former and latter? possible to be more exact? In place of gradient vector, the projection of goal direction g in a subspace plays a core role in linear adjusting programming. If a hyperplane blocks y going ahead along with the direction d, then we must adjust the new direction d’ as the projection of g in the blocking plane. If there is only one blocker at a time, it is straightforward to calculate the projection, but how to calculate the projection when there are more than one blocker encountered simultaneously? It is an open problem for LP researchers still (M. Has

关键词： Linear programming

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