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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

The Quantum Internet (Technical Version)

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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

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

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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

Mural sketch generation via style-aware convolutional neural network 2018

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

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

A Survey on Hand-based Behavioral Activity Recognition

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A Survey on Hand-based Behavioral Activity Recognition

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

Linear adjusting programming in factor space

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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

ROBY: Evaluating the robustness of a deep model by its decision boundaries

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arXiv 2020年

作者： Chen, Jinyin Wang, Zhen Zheng, Haibin Xiao, Jun Ming, Zhaoyan Institute of Cyberspace Security College of Information Engineering Zhejiang University of Technology Hangzhou310023 China College of Software Technology Zhejiang University Hangzhou310007 China College of Information Engineering Zhejiang University of Technology Hangzhou310007 China College of Computer Science and Technology Zhejiang University Hangzhou310007 China Institute of Computing Innovation Zhejiang Univeristy Hangzhou310023 China

With the successful application of deep learning models in many real-world tasks, the model robustness becomes more and more critical. Often, we evaluate the robustness of the deep models by attacking them with purposely generated adversarial samples, which is computationally costly and dependent on the specific attackers and the model types. This work proposes a generic evaluation metric ROBY, a novel attack-independent robustness measure based on the model’s decision boundaries. Independent of adversarial samples, ROBY uses the inter-class and intra-class statistics to capture the features of the model’s decision boundaries. We experimented on ten state-of-the-art deep models and showed that ROBY matches the robustness gold standard of attack success rate (ASR) by a strong first-order generic attacker. With only 1% of time cost. To the best of our knowledge, ROBY is the first lightweight attack-independent robustness evaluation metric that can be applied to a wide range of deep models. The code of ROBY is open-sourced at https://***/baaaad/ROBY-Evaluating-the-Robustness-of-a-Deep-Model-by-its-Decision-Boundaries. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

BuildSenSys: Reusing building sensing data for traffic prediction with cross-domain learning

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arXiv 2020年

作者： Fan, Xiaochen Xiang, Chaocan Chen, Chao Yang, Panlong Gong, Liangyi Song, Xudong Nanda, Priyadarsi He, Xiangjian School of Electrical and Data Engineering Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education China College of Computer Science Chongqing University Chongqing400044 China School of Computer Science and Technology University of Science and Technology of China Hefei Anhui230026 China School of Software BNRist Tsinghua University Beijing100084 China

With the rapid development of smart cities, smart buildings are generating a massive amount of building sensing data by the equipped sensors. Indeed, building sensing data provides a promising way to enrich a series of data-demanding and cost-expensive urban mobile applications. In this paper, as a preliminary exploration, we study how to reuse building sensing data to predict traffic volume on nearby roads. Compared with existing studies, reusing building sensing data has considerable merits of cost-efficiency and high-reliability. Nevertheless, it is non-trivial to achieve accurate prediction on such cross-domain data with two major challenges. First, relationships between building sensing data and traffic data are not unknown as prior, and the spatio-temporal complexities impose more difficulties to uncover the underlying reasons behind the above relationships. Second, it is even more daunting to accurately predict traffic volume with dynamic building-traffic correlations, which are cross-domain, non-linear, and time-varying. To address the above challenges, we design and implement BuildSenSys, a first-of-its-kind system for nearby traffic volume prediction by reusing building sensing data. Our work consists of two parts, i.e., Correlation Analysis and Cross-domain Learning. First, we conduct a comprehensive building-traffic analysis based on multi-source datasets, disclosing how and why building sensing data is correlated with nearby traffic volume. Second, we propose a novel recurrent neural network for traffic volume prediction based on cross-domain learning with two attention mechanisms. Specifically, a cross-domain attention mechanism captures the building-traffic correlations and adaptively extracts the most relevant building sensing data at each predicting step. Then, a temporal attention mechanism is employed to model the temporal dependencies of data across historical time intervals. The extensive experimental studies demonstrate that BuildSenSys outperf

关键词： Internet of things

Some characterizations of coherent rings in terms of strongly FP-injective modules

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Communications in Algebra 2020年第7期48卷 2857-2871页

作者： Chen, Mingzhao Kim, Hwankoo Wang, Fanggui Zhang, Xiaolei College of Mathematics and Information Science Leshan Normal University Leshan China Division of Computer & Information Engineering Hoseo University Asan South Korea College of Mathematics and Software Science Sichuan Normal University Chengdu China Department of Basic Courses Chengdu Aeronautic Polytechnic Chengdu China

An R-module M is called strongly FP-injective if (Formula presented.) for any finitely presented R-module P and any i > 0. Denoted by (Formula presented.) the class of all strongly FP-injective R-modules and by (Formula presented.) the left orthogonal class with respect to (Formula presented.) Comparing with some classical results of Noetherian rings, we show that a ring is coherent if and only if (Formula presented.) is closed under pure submodules;if and only if any finitely generated module in (Formula presented.) is finitely presented;if and only if R is (Formula presented.) -coherent and (Formula presented.) is closed under direct sums;if and only if (Formula presented.) for any nonnegative integer n, any finitely presented left R-module X, any R-T-bimodule M and any injective right T-module E. In addition, we show that a module is injective if and only if it is a pure (Formula presented.) -periodic module, where (Formula presented.) denotes the class of all injective modules. © 2020, © 2020 Taylor & Francis Group, LLC.

关键词： (Strongly) FP-injective module coherent ring pure submodule von Neumann regular ring

Quantum circuit architecture search for variational quantum algorithms

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arXiv 2020年

作者： Du, Yuxuan Huang, Tao You, Shan Hsieh, Min-Hsiu Tao, Dacheng Ubtech Sydney Ai Centre School of Computer Science Faculty of Engineering The University of Sydney NSW2008 Australia Jd Explore Academy Beijing101111 China SenseTime Beijing100080 China Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney NSW2007 Australia Hon Hai Quantum Computing Research Center Taipei114 Taiwan

Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices. However, both empirical and theoretical results exhibit that the deployed ansatz heavily affects the performance of VQAs such that an ansatz with a larger number of quantum gates enables a stronger expressivity, while the accumulated noise may render a poor trainability. To maximally improve the robustness and trainability of VQAs, here we devise a resource and runtime efficient scheme termed quantum architecture search (QAS). In particular, given a learning task, QAS automatically seeks a near-optimal ansatz (i.e., circuit architecture) to balance benefits and side-effects brought by adding more noisy quantum gates to achieve a good performance. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks. In the problems studied, numerical and experimental results show that QAS can not only alleviate the influence of quantum noise and barren plateaus, but also outperforms VQAs with pre-selected ansatze. © 2020, CC BY-NC-ND.

关键词： Timing circuits