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IEEE International Conference on Services computing (SCC)

作者： Xiaohui Zhao Sira Yongchareon Namwook Cho Jun Shen Saif Dewan School of Information Technology and Systems University of Canberra Australia Department of Industrial & Information Systems Engineering Seoul National University of Science and Technology South Korea Department of IT and Software Engineering Auckland University of Technology New Zealand School of Computing and Information Technology University of Wollongong Australia Australian National University Australia

Sensing technologies provide system applications with the awareness of environmental conditions, customer behaviours, object movements, etc. Further, with such capability, system applications can be smart to intelligently adapt their responses to the changing conditions. With regard to business operations, these system applications ensure that business processes can run more intelligently and adaptively. These features will undoubtedly improve customer experience, enhance the reliability of service delivery and lower the operational cost for a more competitive and sustainable business. To enable context awareness to business process management, this paper proposes a conceptual method of depicting the context of a business process and the related mechanism of perceiving the contextual dynamics. A running example demonstrates the applicability of the proposed method and the improvements to process performance are evaluated using process simulations.

关键词： Conferences Service computing

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Formal verification of usage control models: A case study of UseCON using TLA+

arXiv

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arXiv 2018年

作者： Gouglidis, Antonios Grompanopoulos, Christos Mavridou, Anastasia School of Computing and Communications Lancaster University Lancaster United Kingdom Department of Mechanical Engineering University of Western Macedonia Kozani Greece Institute for Software Integrated Systems Vanderbilt University NashvilleTN United States

Usage control models provide an integration of access control, digital rights, and trust management. To achieve this integration, usage control models support additional concepts such as attribute mutability and continuity of decision. However, these concepts may introduce an additional level of complexity to the underlying model, rendering its definition a cumbersome and prone to errors process. Applying a formal verification technique allows for a rigorous analysis of the interactions amongst the components, and thus for formal guarantees in respect of the correctness of a model. In this paper, we elaborate on a case study, where we express the high-level functional model of the UseCON usage control model in the TLA+ formal specification language, and verify its correctness for ≤ 12 uses in both of its supporting authorisation models. Copyright © 2018, The Authors. All rights reserved.

关键词： Formal verification

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Exploring representativeness and informativeness for active learning

arXiv

引用

arXiv 2019年

作者： Du, Bo Wang, Zengmao Zhang, Lefei Zhang, Liangpei Liu, Wei Shen, Jialie Tao, Dacheng State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong Collaborative Innovation Center of Geospatial Technology State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China IBM T. J. Watson Research Center Yorktown HeightsNY10598 United States School of Information Systems Singapore Management University Singapore QCIS University of Technology SydneyNSW2007 Australia

How can we find a general way to choose the most suitable samples for training a classifier? Even with very limited prior information? Active learning, which can be regarded as an iterative optimization procedure, plays a key role to construct a refined training set to improve the classification performance in a variety of applications, such as text analysis, image recognition, social network modeling, etc. Although combining representativeness and informativeness of samples has been proven promising for active sampling, state-of-the-art methods perform well under certain data structures. Then can we find a way to fuse the two active sampling criteria without any assumption on data? This paper proposes a general active learning framework that effectively fuses the two criteria. Inspired by a two-sample discrepancy problem, triple measures are elaborately designed to guarantee that the query samples not only possess the representativeness of the unlabeled data but also reveal the diversity of the labeled data. Any appropriate similarity measure can be employed to construct the triple measures. Meanwhile, an uncertain measure is leveraged to generate the informativeness criterion, which can be carried out in different ways. Rooted in this framework, a practical active learning algorithm is proposed, which exploits a radial basis function together with the estimated probabilities to construct the triple measures and a modified Best-versus-Second-Best strategy to construct the uncertain measure, respectively. Experimental results on benchmark datasets demonstrate that our algorithm consistently achieves superior performance over the state-of-the-art active learning algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Classification (of information)

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Siamese-ResNet: Implementing Loop Closure Detection based on Siamese Network

Siamese-ResNet: Implementing Loop Closure Detection based on...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Qiu, Kai Ai, Yunfeng Tian, Bin Wang, Bin Cao, Dongpu School of Aritificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China School of Software Engineering University of Science and Technology of China HeFei230026 China Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada

ISBN: (纸本)9781538644522

Deep learning has made significant breakthroughs in the tasks of image classification, detection, segmentation, etc. However, the application of deep learning in robotics is still scarce. SLAM is a fundamental problem in robotics and loop closure detection is an important part of SLAM. This paper attempts to use supervised learning methods to solve the loop closure detection problem in vision SLAM. We proposed Siamese-ResNet network, which combines Siamese network with ResNet to detect loop closure. To show the effectiveness of Siamese-ResNet, we evaluate Siamese-ResNet and FabMap2.0 on several open published datasets, like TUM SLAM dataset and FabMap SLAM dataset. Compared with FabMap2.0, Siamese-ResNet shows higher accuracy, better robustness and shorter time-consuming. © 2018 IEEE.

关键词： Robotics

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Enhancing sample-based scheduler with collaborate-state in big data cluster 29

Enhancing sample-based scheduler with collaborate-state in b...

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29th International Conference on software engineering and Knowledge engineering, SEKE 2017

作者： Hao, Chunliang Chen, Celia Shen, Jie Li, Mingshu Boehm, Barry Institute of Software Chinese Academy of Science Beijing China Center for Systems and Software Engineering University of Southern California United States Department of Computing Imperial College London United Kingdom University of Chinese Academy of Science China

ISBN: (纸本)1891706411

Sample-based scheduler design has become an emerging research topic for its high scalability and simple scheduling process in today's big data cluster. One major limitation of such design is its lack of global cluster knowledge, which leads to sub-optimal decisions. Some cutting edge schedulers solve this issue by deploying an extra centralized component in the cluster to capture the real-Time cluster state and inform all schedulers. However, such solution is with high cost and low scalability. As an alternative, we introduce the Collaborated-Cluster State(CCS) technique in this paper. CCS is a low cost solution that merely harms the scalability of sample-based design, while achieving similar performance gain as ECC. Experiments with Google and Yahoo production trace both show that CCS under most scenarios can keep up with ECC's performance while reducing 87.7% (in Google trace) and 73.9% (in Yahoo trace) of communications.

关键词： Scalability

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Managing IoT-Based Smart Healthcare systems Traffic with software Defined Networks

Managing IoT-Based Smart Healthcare Systems Traffic with Sof...

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International Symposium on Networks, Computers and Communications (ISNCC)

作者： Farag Sallabi Faisal Naeem Mamoun Awad Khaled Shuaib Department of Computer and Network Engineering United Arab Emirates University Al Ain UAE Department of Computing and Technology Abasyn University Peshawar Pakistan Department of Computer Science and Software Engineering United Arab Emirates University Al Ain UAE Department of Information Systems and Security United Arab Emirates University Al Ain UAE

Internet of things (IoT) aims to connect billions of devices to the Internet. These devices generate an enormous amount of traffic. A Wireless Body Area Network (WBAN) aggregates the data generated from the body sensors deployed on the human body or inside it. Due to resource constraints of the deployed sensors, aggregated data is forwarded to a server at an edge node or a cloud for further processing and decision making. With the wide acceptance of IoT deployments, WBANs can also be integrated with IoT to provide a clear contextual environment for smart healthcare applications' users. software Defined Networks (SDN) is a new networking paradigm providing centralized control and programmability of the networks. These features of SDN open new vistas for WBAN and IoT applications. In this paper, a personal digital assistant (PDA) is configured as an SDN switch, and we emulate the SDN functionality with Mininet to verify that this approach can be used in WBANs with minimum deployment complexity and network overhead, while efficiently manage the connected devices and the connections between them.

关键词： Sensors Wireless communication Body area networks Handheld computers Protocols Wireless sensor networks Medical services

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Quantum invariants and the graph isomorphism problem

引用

Physical Review A 2019年第5期100卷 052317-052317页

作者： P. W. Mills R. P. Rundle J. H. Samson Simon J. Devitt Todd Tilma V. M. Dwyer Mark J. Everitt Quantum Systems Engineering Research Group Department of Physics Loughborough University Leicestershire LE11 3TU United Kingdom The Wolfson School Loughborough University Loughborough Leicestershire LE11 3TU England United Kingdom Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW 2007 Australia Turing Inc. Berkeley California 94701 USA Department of Physics College of Science Tokyo Institute of Technology H-63 2-12-1 Oookayama Meguro-ku Tokyo 152-8550 Japan Quantum Computing Unit Institute of Innovative Research Tokyo Institute of Technology S1-16 4259 Nagatsuta-cho Midori-ku Yokohama 226-8503 Japan

Three graph invariants are introduced which may be measured from a quantum graph state and form examples of a framework under which other graph invariants can be constructed. Each invariant is based on distinguishing a different number of qubits. This is done by applying different measurements to the qubits to be distinguished. The performance of these invariants is evaluated and compared to classical invariants. We verify that the invariants can distinguish all nonisomorphic graphs with nine or fewer nodes. The invariants have also been applied to “classically hard” strongly regular graphs, successfully distinguishing all strongly regular graphs of up to 29 nodes, and preliminarily to weighted graphs. We have found that, although it is possible to prepare states with a polynomial number of operations, the average number of preparations required to distinguish nonisomorphic graph states scales exponentially with the number of nodes. We have so far been unable to find operators which reliably compare graphs and reduce the required number of preparations to feasible levels.

关键词： Quantum algorithms Graph theory Phase space methods

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The MOOC Offer in Open Education: A Revision of the First IEEE Education Society' MOOC

The MOOC Offer in Open Education: A Revision of the First IE...

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Learning With MOOCS (LWMOOCS)

作者： Edmundo Tovar Rosa Cabedo Ahmad Alzaghoul Sergio Martín Martín Llamas Manuel Caeiro Oscar Martínez Bonastre Rebecca Strachan Manuel Castro Department of Computer Languages and Systems and Software Engineering Universidad Politécnica de Madrid Madrid Spain Department of Electrical Electronical and Control Engineering National Distance Education University Madrid Spain Department of Telematic Engineering University of Vigo Vigo Spain Department of Statistics Miguel Hernández University Elche Spain Department of Computer and Information Sciences Northumbria University Newcastle United Kingdom Department of Electrical and Computer Engineering National Distance Education University Madrid Spain

The following topics are dealt with: educational courses; computer aided instruction; Internet; further education; educational institutions; teaching; distance learning; mobile computing; learning (artificial intellig... 详细信息

ISBN: (纸本)9781538665343

关键词： Open Educational Resources Licenses Industries Computer languages software engineering Europe

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Anomaly Detection Based on Spatio-Temporal and Sparse Features of Network Traffic in VANETs

Anomaly Detection Based on Spatio-Temporal and Sparse Featur...

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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Laisen Nie Huizhi Wang Shimin Gong Zhaolong Ning Mohammad S. Obaidat Kuei-Fang Hsiao School of Electronics and Information Northwestern Polytechnical University Xi'an P.R.China Qingdao Research Institute of Northwestern Polytechnical University Qingdao P.R.China School of Intelligent Systems Engineering Sun Yat-sen University China State Key Laboratory for Novel Software Technology Nanjing University China ECE Department Nazarbayev University Astana Kazakhstan College of Computing and Informatics University of Sharjah UAE The King Abdullah II School of Information Technology The University of Jordan Amman Jordan University of Science and Technology Beijing China Department of Information Management Ming Chuan University Taiwan

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Vehicular Ad-Hoc Networks (VANETs) have received a great attention recently due to their potential and various applications. However, the initial phase of the VANET has many research challenges that need to be addressed, such as the issues of security and privacy protection caused by the openness of wireless communication networks among the city-wide applied regions. Specially, anomaly detection for a VANET has become a challenging problem, due to the changes in the scenario of VANETs comparing with traditional wireless networks. Motivated by this issue, we focus on the problem of anomaly detection in VANETs, and propose an effective anomaly detection approach based on the convolutional neural network in this paper. The proposed approach takes into account the spatio-temporal and sparse features of VANET traffic, and it uses a convolutional neural network architecture and a loss function based on Mahalanobis distance for anomaly detection. Furthermore, a comprehensive assessment is provided to validate the proposed approach, which illustrates the effectiveness of this approach.

关键词： Anomaly detection Feature extraction Vehicular ad hoc networks Kernel Estimation Security Convolutional neural networks

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Artificial Intelligence Powered MOOCs: A Brief Survey

Artificial Intelligence Powered MOOCs: A Brief Survey

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IEEE International Conference on Agents (ICA)

作者： Simon Fauvel Han Yu Chunyan Miao Lizhen Cui Hengjie Song Liang Zhang Xiaoming Li Cyril Leung Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY) Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore School of Software Engineering Shandong University Jinan China School of Software Engineering South China University of Technology Guangzhou China Institute of Network Computing and Information Systems Peking University Beijing China Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada

Massive Open Online Courses (MOOCs) have gained tremendous popularity in the last few years. Thanks to MOOCs, millions of learners from all over the world have taken thousands of high-quality courses for free. Artificial intelligence (AI) has played an important role in making MOOCs what they are today. By exploiting the vast amount of data generated by learners engaging in MOOCs, AI techniques have been proposed to improve our understanding of MOOC participants and enable MOOC practitioners to deliver better courses. These approaches have also greatly improved student experience and learning outcomes through constructing intelligent and personalized learning trajectories. In this paper, we first review the state-of-the-art AI research making an impact on MOOCs education, emphasizing on works which aim to enhance our understanding of student learning behaviours, improve student engagement, and improve learning outcomes. We then offer an overview of important future research to carry out in sub-fields of AI to enable MOOCs to reach their full potential.

关键词： Videos Artificial intelligence Tools Navigation Computational modeling Trajectory Education

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