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International Workshop on Modeling in software engineering (MISE)

作者： Önder Babur Matthew Stephan Dept. of Mathematics & Computer Science Eindhoven University of Technology Eindhoven The Netherlands Dept. of Computer Science & Software Engineering Miami University Oxford Ohio USA

Widespread and mature practice of model-driven engineering is leading to a growing number of modeling artifacts and challenges in their management. Model clone detection (MCD) is an important approach for managing and maintaining modeling artifacts. While its counterpart in traditional source code development, code clone detection, is enjoying popularity and more than two decades of development, MCD is still in its infancy in terms of research and tooling. We aim to develop a portal for model clone detection, MoCoP, as a central hub to mitigate adoption barriers and foster MCD research. In this short paper, we present our vision for MoCoP and its features and goals. We discuss MoCoP's key components that we plan on realizing in the short term including public tooling, curated data sets, and a body of MCD knowledge. Our longer term goals include a dedicated service-oriented infrastructure, contests, and forums. We believe MoCoP will strengthen MCD research, tooling, and the community, which in turn will lead to better quality, maintenance, and scalability for model-driven engineering practices.

关键词： Unified modeling language Cloning Tools Analytical models Portals Charge coupled devices software

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Pre-trained image processing transformer

arXiv

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arXiv 2020年

作者： Chen, Hanting Wang, Yunhe Guo, Tianyu Xu, Chang Deng, Yiping Liu, Zhenhua Ma, Siwei Xu, Chunjing Xu, Chao Gao, Wen Dept. of Machine Intelligence Peking University China Noah's Ark Lab Huawei Technologies School of Computer Science Faculty of Engineering The University of Sydney Australia Central Software Institution Huawei Technologies Institute of Digital Media School of Electronic Engineering and Computer Science Peking University China Peng Cheng Laboratory

As the computing power of modern hardware is increasing strongly, pre-trained deep learning models (e.g., BERT, GPT-3) learned on large-scale datasets have shown their effectiveness over conventional methods. The big progress is mainly contributed to the representation ability of transformer and its variant architectures. In this paper, we study the low-level computer vision task (e.g., denoising, super-resolution and deraining) and develop a new pre-trained model, namely, image processing transformer (IPT). To maximally excavate the capability of transformer, we present to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs. The IPT model is trained on these images with multi-heads and multi-tails. In addition, the contrastive learning is introduced for well adapting to different image processing tasks. The pre-trained model can therefore efficiently employed on desired task after fine-tuning. With only one pre-trained model, IPT outperforms the current state-of-the-art methods on various low-level benchmarks. Code is available at https://***/huawei-noah/Pretrained-IPT and https://***/mindspore/mindspore/tree/master/model_zoo/research/cv/IPT. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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An IoT and blockchain-based multi-sensory in-home quality of life framework for cancer patients 15

An IoT and blockchain-based multi-sensory in-home quality of...

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15th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2019

作者： Rahman, Md. Abdur Rashid, Mamunur Barnes, Stuart Shamim Hossain, M. Hassanain, Elham Guizani, Mohsen Cyber Security and Forensic Computing Department University of Prince Mugrin Madinah Saudi Arabia Research Centre King's College London United Kingdom Department of Software Engineering King Saud University Riyadh11543 Saudi Arabia Dept. of Electrical and Computer Engineering University of Idaho MoscowID83844-1023 United States Dept. of Computer Science Umm Al-Qura University Makkah Saudi Arabia

ISBN: (纸本)9781538677476

Once a subject is diagnosed with cancer, a patient goes through a series of diagnosis and tests, referred to as after cancer treatment. Due to the nature of the treatment and side effects on regular lifestyles, maintaining quality of life in the home environment is a challenging task. Sometimes within a home environment, a cancer patient's situation changes abruptly, as the functionality of certain organs deteriorate, which affects their quality of life. In this paper, we propose a Blockchain and off-chain based framework which will allow multiple medical and ambient intelligent IoT sensors to capture quality of life information from one's home environment and securely share it with one's community of interest. Using our proposed framework, both transactional records and multimedia big data - consisting of a user's physiological as well as mental states - can be shared with an oncologist or palliative care unit for real-time decision support. We have also developed Blockchain-based data analytics, which will allow a clinician to visualize the immutable history of the patient's data available from an in-home secure monitoring system for a better understanding of a patient's current or historical states. We further designed a generic oncologist smart contract and digital wallet for different stakeholders to automate the treatment plan of a particular patient. Finally, we will present our current implementation status, which provides significant encouragement for further development. © 2019 IEEE.

关键词： Blockchain

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Safety and Security Analysis of Control Chains in SCADA Using the NFR Approach

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IFAC-PapersOnLine 2018年第6期51卷 214-219页

作者： Subramanian, Nary Zalewski, Janusz Dept. of Computer Science University of Texas at Tyler TylerTX75799 United States Dept. of Software Engineering Florida Gulf Coast University Fort MyersFL33965 United States

The objective of this research is to shed light on the applicability of the Non-Functional Requirements (NFR) Approach to the concept of control chains for the combined analysis of safety and security of SCADA systems and, more generally, to computer systems used in automation, including functional safety and security standards. The paper discusses the concept of control chains, presents an overview of the NFR Approach, and applies this approach to the safety and security analysis of control chains in a case study of the automatic control of the hydrocarbon tank pressure control system. © 2018

关键词： computer control systems Automation Chains Embedded systems Network security SCADA systems Security of data Security systems ANSI/ISA 62443 Automation systems Cyber physical systems (CPSs) Cyber security IEC 61508 IEC 61511 NFR Approach SCADA

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Network2Vec: Learning Node Representation Based on Space Mapping in Networks

Network2Vec: Learning Node Representation Based on Space Map...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Zhenhua Huang Zhenyu Wang Rui Zhang Yangyang Zhao Xiaohui Xie Sharad Mehrotra School of Software Engineering South China University of Technology Dept. of Computer Science University of California Irvine

ISBN: (纸本)9781728148977

Complex networks represented as node adjacency matrices constrains the application of machine learning and parallel algorithms. To address this limitation, network embedding (i.e., graph representation) has been intensively studied to learn a fixed-length vector for each node in an embedding space, where the node properties in the original graph are preserved. Existing methods mainly focus on learning embedding vectors to preserve nodes proximity, i.e., nodes next to each other in the graph space should also be closed in the embedding space, but do not enforce algebraic statistical properties to be shared between the embedding space and graph space. In this work, we propose a lightweight model, entitled Network2Vec, to learn network embedding on the base of semantic distance mapping between the graph space and embedding space. The model builds a bridge between the two spaces leveraging the property of group homomorphism. Experiments on different learning tasks, including node classification, link prediction, and community visualization, demonstrate the effectiveness and efficiency of the new embedding method, which improves the state-of-the-art model by 19% in node classification and 7% in link prediction tasks at most. In addition, our method is significantly faster, consuming only a fraction of the time used by some famous methods.

关键词： Task analysis Markov processes Matrix decomposition software engineering Bridges Mathematical model Probability

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Dual Convolutional Neural Network for Image Steganalysis

Dual Convolutional Neural Network for Image Steganalysis

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Jaeyoung Kim Sanghoon Kang Hanhoon Park Jong-Il Park Dept. of Electronic Engineering Pukyong National University Busan Republic of Korea Dept. of Computer Software Hanyang University Seoul Republic of Korea

In this paper, we propose a new steganalytic method that uses dual convolutional neural network (CNN) of which each has different inputs. To construct the dual CNN structure, two pairs of the preprocessing filters and the convolutional layers were brought from the conventional CNN-based steganalytic methods and the outputs of the dual CNN were concatenated and fed together into a following affine layer. Given an input image, a stego image is created by embedding some additional data into the input image using one of steganographic methods and a difference image is computed between the input and stego images. Then, the input and difference images are fed into each CNN, respectively. This indicates that the proposed method extracts /learns additional features from the difference image using the additional CNN. Experimental results demonstrated that the proposed dual CNN with additional input can identify whether the S-UNIWARD steganography was applied to the input image with an accuracy of 80.43%, and can improve the accuracy by approximately 5% when compared with the conventional CNN-based steganalytic method.

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An efficient adaptive online neural spikes detection and classification engine based on Bayesian inference 6

An efficient adaptive online neural spikes detection and cla...

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6th International Conference on Systems and Informatics, ICSAI 2019

作者： Li, Nan Sawan, Mohamad Wang, Lei College of Computer Science National University of Defense Technology Changsha China Polystim Neurotech. Lab Polytechnique Montreal Electrical Engineering Dept. Montreal Canada School of Engineering Westlake University Hangzhou310024 China Institute of Advanced Study Westlake Institute for Advanced Study Hangzhou310024 China

ISBN: (纸本)9781728152561

A new method, called Bayesian inference-based template matching (BIBTM) method, is proposed in this article, which is designed to detect and classify neural spikes from real neural signals. Through this spike detection and classification method, the templates do not need be given in advance, and they can be automatically generated. To evaluate the performance of our method, we built signals with diverse signal-to-noise ratios and firing rates, and also researched two spike template generation methods. Based on the experimental results and comparison, BIBTM method has excellent detection performance. The true positive rates (TPR) and false positive rates (FPR) of the spike detection can reach 0.92 and 0.05 respectively, and the average FPR and average TPR of the spike classification can reach 0.05 and 0.6 respectively. From the discussion and analysis, our proposed BIBTM method not only has high detection and classification accuracy, but also has a simple structure and low complexity. © 2019 IEEE.

关键词： Template matching

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Disruptive IoT-Based Healthcare Insurance Business Model

Disruptive IoT-Based Healthcare Insurance Business Model

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IEEE International Conference on Computational Science and engineering, CSE

作者： Maryam Al Thawadi Farag Sallabi Mamoun Awad Khaled Shuaib Dept. of Information Systems and Security UAE University Al Ain UAE Dept. of Computer and Network Engineering UAE University Al Ain UAE Dept. of Computer Science and Software Engineering UAE University Al Ain UAE

Information revolution and technology growth have made a considerable contribution to restraining the cost expansion and empowering the customer. They disrupted most business models in different industries. The customer-centric business model has pervaded the different sectors. Smart healthcare has made an enormous shift in patient life and raised their expectation of healthcare services quality. Healthcare insurance is an essential business in the healthcare sector; patients expect a new business model to meet their needs and enhance their wellness. This paper presents a smart healthcare architecture based on the recent development of information and communications technology. Then develops a disruptive healthcare insurance business model that adapts to this architecture. The paper presents a use case to show in part the application of this business model.

关键词： Insurance Temperature sensors Diseases Solid modeling

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A Machine Learning Approach for Power Gating the FPGA Routing Network

A Machine Learning Approach for Power Gating the FPGA Routin...

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IEEE International Conference on Field-Programmable Technology (FPT)

作者： Zeinab Seifoori Hossein Asadi Mirjana Stojilović Dept. of Computer Engineering Sharif University of Technology Tehran Iran Ecole Polytechnique Federale de Lausanne Lausanne Switzerland

Power gating is a common approach for reducing circuit static power consumption. In FPGAs, resources that dominate static power consumption lie in the routing network. Researchers have proposed several heuristics for clustering multiplexers in routing network into power-gating regions. In this paper, we propose a fundamentally different approach based on K-means clustering, an algorithm commonly used in machine learning. Experimental results on Titan benchmarks and Stratix-IV FPGA architecture show that our proposed clustering algorithms outperform the state of the art. For example, for 32 power-gating regions in FPGA routing switch matrices, we achieve (on average) almost 1.4× higher savings (37.48% vs. 26.94%) in the static power consumption of the FPGA routing resources at lower area overhead than the most efficient heuristic published so far.

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Path Planning for Multiple Mobile Robots in Smart Warehouse

Path Planning for Multiple Mobile Robots in Smart Warehouse

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International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Ali Bolu Ömer Korçak Tekhnelogos Software Co. Ltd. Istanbul Turkey Computer Engineering Marmara University Dept. of Computer Engineering Marmara University Istanbul Turkey

Warehouse operations need high labor force and physical space. Currently, companies with huge warehouses are investing on autonomous robots to save time and energy, and to prevent human-based errors. One of the most important challenges in a smart warehouse with multiple moving robots is path planning because of its dynamics. This paper provides a complete and error-free solution to the path-planning problem, and describes its performance in various warehouse scenarios with different number of robots and different design considerations.

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