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Specification-Driven Predictive Business Process Monitoring

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arXiv 2019年

作者： Santoso, Ario Felderer, Michael Faculty of Computer Science Free University of Bozen-Bolzano Italy Department of Software Engineering Blekinge Institute of Technology Sweden

Predictive analysis in business process monitoring aims at forecasting the future information of a running business process. The prediction is typically made based on the model extracted from historical process execution logs (event logs). In practice, different business domains might require different kinds of predictions. Hence, it is important to have a means for properly specifying the desired prediction tasks, and a mechanism to deal with these various prediction tasks. Although there have been many studies in this area, they mostly focus on a specific prediction task. This work introduces a language for specifying the desired prediction tasks, and this language allows us to express various kinds of prediction tasks. This work also presents a mechanism for automatically creating the corresponding prediction model based on the given specification. Differently from previous studies, instead of focusing on a particular prediction task, we present an approach to deal with various prediction tasks based on the given specification of the desired prediction tasks. We also provide an implementation of the approach which is used to conduct experiments using real-life event logs. Copyright © 2019, The Authors. All rights reserved.

关键词： Specifications

1st International Workshop on Essence in Education & Training (WEE&T 2020)

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1st International Workshop on Essence in Education & Trainin...

Conference on software engineering Education & Training (CSEE&T)

作者： Luis Fernando Castro Kendra M. L. Cooper Anh Nguyen Duc Jöran Pieper Michael Striewe Faculty of Engineering University of Quindío Quindío Colombia Independent Scholar Kelowna Canada USN School of Business University of South Eastern Norway Bø Norway Faculty of Electrical Engineering and Computer Science IACS University of Applied Sciences Stralsund Germany paluno – The Ruhr Institute for Software Technology University of Duisburg-Essen Essen Germany

ISBN: (纸本)9781728168074

The workshop connects to the central theme “Educating for the Future” of CSEE&T 2020 and thus explores opportunities to improve software engineering education and training by using Essence. Essence, an OMG Standard, delivers essential, universal elements found in all software engineering (SE) endeavours and a language to describe and extend these elements and their use in concrete practices to tailor SE methods to teams' needs. Its Kernel separates the stable What and Why from the more adaptable How. That way, it provides an essential thinking framework facilitating the adoption and customization of practices and methods. As such, it supports multiple ways of educating SE students as well as training practitioners for the future. The first deployments of Essence in industry and academic courses around the globe show promising results. But since it is an entirely new way to think about SE practices, methods, and their building blocks—it requires some effort and thought to dive into the standard. This workshop facilitates the adoption by sharing experiences, best practices, and lessons learned.

关键词： Conferences software engineering Training Kernel Games

Human-Robot Interaction and Self-Driving Cars Safety Integration of Dispositif Networks *

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Human-Robot Interaction and Self-Driving Cars Safety Integra...

IEEE International Conference on Intelligence and Safety for Robotics (ISR)

作者： Amir A. Mokhtarzadeh Zhao Jian Yangqing Computer and Software Engineering faculty Huaiyin Institute of Technology Huai'an Jiangsu China

In recent years, despite many researches and progress in artificial intelligence, we have witnessed many accidents involving self-driving cars. For self-driving cars to have potential for positive impact on road safety, a different Human-Robot Interaction (HRI) model is required that provides a learning algorithm mechanism to recognize other vehicles, not just as a moving object, but as a vehicle intelligently controlled by a human driver. Then, self-driving cars may successfully deliver on their promise to save thousands of lives annually. Current algorithms used in the development of self-driving cars are mainly invested in the deep learning of which neural networks need to be trained on representative datasets that include examples of all possible driving, weather, and situational conditions. Until recently, HRI was researched in light of human perception of self-driving cars and improved collision avoidance, and to predict other driver's intentions based on monitoring their movement. However, with recent accidents involving self-driving cars, more than at any other time, there is a need for an advanced HRI model to improve safety and human trust for autonomous vehicles. A human driver's way of thinking leads them to make certain decisions which may not be logical or familiar to current robot algorithms. For humans, factors shaping the way of seeing and behaving are not static; rather, they are varying in different societies, cultures, and countries, and are also subject to continuous changes. Such factors are explained by researchers as inter-related networks of dispositifs. In this paper, we present, that if self-driving cars are able to integrate such dispositifs networks within their HRI model, by creating two algorithms; a) local-signature; and, (b) individual-signature for regional and on-road; then, it would be more likely and globally possible to predict what humans will do on the road, thereby correctly determining how to behave appropriately around them

关键词： Autonomous automobiles Service robots Safety Automobiles Accidents

Leveraged Matrix Completion with Noise

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arXiv 2020年

作者： Huang, Xinjian Liu, Weiwei Du, Bo Tao, Dacheng The School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China The School of Computer Science Wuhan University Wuhan China The School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China The School of Computer Science The Faculty of Engineering The University of Sydney 6 Cleveland St DarlingtonNSW2008 Australia

Completing low-rank matrices from subsampled measurements has received much attention in the past decade. Existing works indicate that O(nr log2(n)) datums are required to theoretically secure the completion of an n × n noisy matrix of rank r with high probability, under some quite restrictive assumptions: (1) the underlying matrix must be incoherent;(2) observations follow the uniform distribution. The restrictiveness is partially due to ignoring the roles of the leverage score and the oracle information of each element. In this paper, we employ the leverage scores to characterize the importance of each element and significantly relax assumptions to: (1) not any other structure assumptions are imposed on the underlying low-rank matrix;(2) elements being observed are appropriately dependent on their importance via the leverage score. Under these assumptions, instead of uniform sampling, we devise an ununiform/biased sampling procedure that can reveal the "importance" of each observed element. Our proofs are supported by a novel approach that phrases sufficient optimality conditions based on the Golfing Scheme, which would be of independent interest to the wider areas. Theoretical findings show that we can provably recover an unknown n × n matrix of rank r from just about O(nr log2(n)) entries, even when the observed entries are corrupted with a small amount of noisy information. The empirical results align precisely with our theories. Copyright © 2020, The Authors. All rights reserved.

关键词： Probability distributions

Blind source separation using Fast-ICA with a novel nonlinear function

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arXiv 2019年

作者： Xu, Pengfei Jia, Yinjie Wang, Zhijian College of Computer and Information Hohai University Nanjing Jiangsu210098 China Faculty of Electronic Information Engineering Huaiyin Institute of Technology Huaian Jiangsu223003 China

Blind source separation(BSS) is a hotspot in signal processing, and independent component analysis (ICA) is a very effective tool for solving the BSS problem. In order to improve the performance of the separation, a new nonlinear function 'sin' was introduced. It can replace the commonly used classical functions ('tanh', 'gauss' and 'pow3') and does not need to select different nonlinear functions according to the Gauss property of signals. The two Matlab simulation results show that the improved Fast-ICA algorithm with the proposed nonlinearity can not only improve the separation accuracy but also speed up the convergence of blind source separation. Copyright © 2019, The Authors. All rights reserved.

关键词： Functions

Blind separation of rotor vibration signals in high-noise environments

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arXiv 2019年

During the operation of the engine rotor, the vibration signal measured by the sensor is the mixed signal of each vibration source, and contains strong noise at the same time. In this paper, a new separation method for mixed vibration signals in strong noise environment(SNR=-5) is proposed. Firstly, the time-delay auto-correlation de-noising method is used to de-noise the mixed signals, and then the common blind separation algorithm (MSNR algorithm is used here) is used to separate the mixed vibration signals, which improves the separation performance. The simulation results verify the validity of the method. The proposed method provides a new idea for health monitoring and fault diagnosis of engine rotor vibration signals. Copyright © 2019, The Authors. All rights reserved.

关键词： Blind source separation

Blind Source Separation of Optical Wireless Communications in Noisy Environments

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arXiv 2019年

Blind source separation is a research hotspot in the field of signal processing because it aims to separate unknown source signals from observed mixtures through an unknown transmission channel. A low computational complexity instantaneous linear mixture signals blind separation algorithm was introduced and improved. There is only one variable parameter named the length of moving average in the algorithm, which has a significant impact on the separation effect. This paper gives some suggestions on the reasonable value through experiments. The algorithm is extended to the separation of visible light communication signals in different noise environments, and has achieved certain results, which further expands the applicability of the algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Mixtures

Biometric authentication integrated with wireless communication malicious activity detection in a cyber physical system-based Fintech banking

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Optik 2023年 272卷

作者： Almuhannad Sulaiman Alorfi Saud Yonbawi Sultan Alahmari Abdullaev Abror Bozorboevich Mahendran Arumugam Pham Quang Huy Department of Information System Faculty of Computing and Information Technology King Abdulaziz University City Rabigh Saudi Arabia Department of Software Engineering College Of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia King Abdul Aziz City for Science and Technology Riyadh Saudi Arabia The Department of Acconting Economic Analysis and Audit Tashkent Institute of Finance Tashkent Uzbekistan Center for Transdisciplinary Research Saveetha Dental College Saveetha institute of Medical and Technical Science Chennai India University of Economics Ho Chi Minh City (UEH) Vietnam

Recently, biometric technology has been extensively embedded in mobile devices to enhance security of mobile devices. With rise of financial technology (FinTech) that employs mobile applications as well as devices as promotional platforms, biometrics has a significant role in strengthening the detection of this privacy application. This manuscript offers the design of salp swarm optimization with auto-encoder based biometric authentication (SSOAE-BMA) model for the recognition of abnormal activities in the Fintech banking applications based on wireless communication. The major aim of the SSOAE-BMA model lies in the proper authentication of persons via biometric matching process. Initially, the presented SSOAE-BMA model makes use of stacked ResNet-50 model for deriving feature vectors. Next, the SSOAE-BMA model utilizes AE for biometric verification and the performance of the AE model is adjusted using the Social Spider Optimization (SSO) Algorithm which in turn enhances the recognition outcomes. To demonstrate the improved performance of SSOAE-BMA model, a series of simulations were carried out. The experimental outcomes signified the enhancements of the SSOAE-BMA model over existing models.

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Retraction notice to “Biometric authentication integrated with wireless communication malicious activity detection in a cyber physical system-based Fintech banking” [Optik 272 (2023) 170294]

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Optik 2023年 288卷

作者： Almuhannad Sulaiman Alorfi Saud Yonbawi Sultan Alahmari Abdullaev Abror Bozorboevich Mahendran Arumugam Pham Quang Huy Department of Information System Faculty of Computing and Information Technology King Abdulaziz University City Rabigh Saudi Arabia Department of Software Engineering College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia King Abdul Aziz City for Science and Technology Riyadh Saudi Arabia The Department of Acconting Economic Analysis and Audit Tashkent Institute of Finance Tashkent Uzbekistan Center for Transdisciplinary Research Saveetha Dental College Saveetha institute of Medical and Technical Science Chennai India University of Economics Ho Chi Minh City (UEH) Viet Nam