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arXiv 2021年

作者： Li, Xuejun Ding, Ran Liu, Xiao Xu, Jia Yang, Yun Grundy, John School of Computer Science and Technology Anhui University Hefei China School of Information Technology Deakin University Geelong Australia School of Software and Electrical Engineering Swinburne University of Technology Melbourne Australia Faculty of Information Technology Monash University Melbourne Australia

Current cloud-based smart systems suffer from weaknesses such as high response latency, limited network bandwidth and the restricted computing power of smart end devices which seriously affect the system’s QoS (Quality of Service). Recently, given its advantages of low latency, high bandwidth and location awareness, edge computing has become a promising solution for smart systems. However, the development of edge computing based smart systems is a very challenging job for software developers who do not have the skills for the creation of edge computing environments. The management of edge computing resources and computing tasks is also very challenging. Workflow technology has been widely used in smart systems to automate task and resource management, but there does not yet exist a real-world deployable edge computing based workflow execution engine. To fill this gap, we present EdgeWorkflowReal, an edge computing based workflow execution engine for smart systems. EdgeWorkflowReal supports: 1) automatic creation of a real edge computing environment according to user settings;2) visualized modelling of edge workflow applications;and 3) automatic deployment, monitoring and performance evaluation of edge workflow applications in a smart system. © 2021, CC BY-NC-ND.

关键词： Edge computing

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Multi-Resolution Localization of Individual Logs in Wooden Piles Utilizing YOLO with Tiling on Client/Server Architectures 33

Multi-Resolution Localization of Individual Logs in Wooden P...

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33rd European Modeling and Simulation Symposium, EMSS 2021

作者： Praschl, Christoph Auersperg-Castell, Philipp Forster-Heinlein, Brigitte Zwettler, Gerald Adam Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg4232 Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg4232 Austria Bluedynamics Auersperg-Castell KG Kritzing 31 Freinberg4785 Austria Faculty of Computer Science and Mathematics University of Passau Innstraße 43 Passau94032 Germany

In industrial domains with time and cost intensive manual or semi-automated inspection the demand for automation is high. Utilizing state of the art deep learning models for localization in vision-based domains such as wood log analysis, the precision can be increased while reducing the demand for manual inspection. In this paper a YOLO network is trained on wood log images to allow for detection of single wood piles in images with hundreds and thousands instances. Due to the high variability in scale and large amount of wood logs within the images, common YOLO architectures are not applicable. Thus, tiling is necessitated to implicitly form a multi-resolution image pyramid. Due to lack in training data, besides common data augmentation modelling of different seasonal and weather conditions is applied. The wood log detection process can be run on a client/server architecture to allow for both, preview and refined results. Evaluation on real-world data sets shows an log detection accuracy of 82,9% utilizing a tiny YOLO model and 94,1% with a fully connected YOLO model, respectively. © 2021 The Authors.

关键词： Deep learning

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Kurdish Handwritten Character Recognition using Deep Learning Techniques

arXiv

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arXiv 2022年

作者： Ahmed, Rebin M. Rashid, Tarik A. Fattah, Polla Alsadoon, Abeer Bacanin, Nebojsa Mirjalili, Seyedali Vimal, S. Chhabra, Amit IT Department Faculty of Applied Science Tishk International University Erbil Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Software and Informatics Engineering Salahaddin University-Erbil- Erbil Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Sydney Australia Singidunum University Belgrade Serbia Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of Department of Artificial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India

Handwriting recognition is one of the active and challenging areas of research in the field of image processing and pattern recognition. It has many applications that include: a reading aid for visual impairment, automated reading and processing for bank checks, making any handwritten document searchable, and converting them into structural text form, etc. Moreover, high accuracy rates have been recorded by handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. Yet there is no such system available for offline Kurdish handwriting recognition. In this paper, an attempt is made to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. Kurdish (Sorani) contains 34 characters and mainly employs an Arabic\Persian based script with modified alphabets. In this work, a Deep Convolutional Neural Network model is employed that has shown exemplary performance in handwriting recognition systems. Then, a comprehensive dataset was created for handwritten Kurdish characters, which contains more than 40 thousand images. The created dataset has been used for training the Deep Convolutional Neural Network model for classification and recognition tasks. In the proposed system, the experimental results show an acceptable recognition level. The testing results reported a 96% accuracy rate, and training accuracy reported a 97% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and is comparable to the similar model of other languages' handwriting recognition systems. © 2022, CC BY.

关键词： Convolutional neural networks

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Adaptive Fuzzy-PID Controller for Quad-Rotor MAV with Mass Changes 10th

Adaptive Fuzzy-PID Controller for Quad-Rotor MAV with Mass C...

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10th National Technical Seminar on Underwater System Technology, NUSYS 2018

作者： Qian, Goh Ming Pebrianti, Dwi Bayuaji, Luhur Samad, Rosdiyana Mustafa, Mahfuzah Syafrullah, Mohammad Faculty of Electrical & Electronics Engineering Universiti Malaysia Pahang Campus Pekan PekanPahang26600 Malaysia Faculty of Computer Science and Software Engineering Universiti Malaysia Pahang Pekan Malaysia Magister of Computer Science University Budi Luhur Jakarta Indonesia

ISBN: (纸本)9789811337079

Micro Aerial Vehicle (MAV) has become famous to be used in agricultural application such as for spraying operation, for watering plantation or spraying the pesticide, 2-D flow visualization image to measure the droplet distribution and so on. Due to the need to sustain food for all human population, there is need for the development of effective spraying to increase the productivity. In crop spraying, the payload changes against time is the big challenge on the development of MAV. This is because the payload change problem could affect the altitude which is the position along z-axis of the MAV. In this research, a quad-rotor MAV is used as the platform. Then, an adaptive Fuzzy-PID controller for the altitude control by considering payload change is presented. The performance of altitude control by using adaptive Fuzzy-PID controller and PID controller are validated in this research study through simulation. The adaptive Fuzzy-PID controller is successfully designed for the changing of payload. The result shows the performance of adaptive Fuzzy-PID controller is better than PID controller on quad-rotor MAV control considering payload changes. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Altitude control

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Ransomware: A framework for security challenges in internet of things 2

Ransomware: A framework for security challenges in internet ...

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2nd International Conference on computer and Information sciences, ICCIS 2020

作者： Saeed, Soobia Jhanjhi, N.Z. Naqvi, Mehmood Humayun, Mamoona Ahmed, Shakeel Universiti Teknologi Malaysia Dept. of Software Engineering Malaysia Taylor's University Selangor Malaysia Faculty of Engineering Technology Mohawk College Canada Jouf University Dept. of Information Systems Al-Jouf Saudi Arabia King Faisal University Dept. of Computer Science Al-Hofuf Saudi Arabia

ISBN: (数字)9781728154671

ISBN: (纸本)9781728154671

With the increasing volume of smartphones, computers, and sensors in the Internet of Things (IoT) model, enhancing security and preventing ransom attacks have become a major concern. Traditional security mechanisms are no longer applicable due to the involvement of devices with limited resources, which require more computing power and resources. Ransomware is comparatively a new and cruel malware in cyberspace with higher rates of attacks around the world. Ransomware could encrypt entire data to make users unable to access their files and important information. In some cases, the system has been hostage completely by the hackers, and the user may receive a demand for ransom money using different resources o access of his/her own data/system. One of the problems associated with the Internet of Things is how to keep your smartphones secure and keep your data safe as most of the antivirus solutions are not useful in this case. This research concludes the impact of ransomware on the IoT, malware processes, and work on detecting and monitoring smartphone infections. The paper also discusses ransomware awareness to end-user with strategy to defeat it. © 2020 IEEE.

关键词： Smartphones

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On the utility of power spectral techniques with feature selection techniques for effective mental task classification in non-invasive BCI

arXiv

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arXiv 2021年

作者： Gupta, Akshansh Agrawal, Ramesh Kumar Kirar, Jyoti Singh Andreu-Perez, Javier Ding, Wei-Ping Lin, Chin-Teng Prasad, Mukesh School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India School of Computer Science and Electronic Engineering University of Essex United Kingdom Faculty of Medicine Imperial College London United Kingdom School of Computer and Technology Nantong University Nantong China Centre for Artifical Intelligence School of Software Feit University of Technology Sydney Australia

In this paper classification of mental task-root Brain-computer Interfaces (BCI) is being investigated, as those are a dominant area of investigations in BCI and are of utmost interest as these systems can be augmented life of people having severe disabilities. The BCI model's performance is primarily dependent on the size of the feature vector, which is obtained through multiple channels. In the case of mental task classification, the availability of training samples to features are minimal. Very often, feature selection is used to increase the ratio for the mental task classification by getting rid of irrelevant and superfluous features. This paper proposes an approach to select relevant and non-redundant spectral features for the mental task classification. This can be done by using four very known multivariate feature selection methods viz, Bhattacharya's Distance, Ratio of Scatter Matrices, Linear Regression and Minimum Redundancy & Maximum Relevance. This work also deals with a comparative analysis of multivariate and univariate feature selection for mental task classification. After applying the above-stated method, the findings demonstrate substantial improvements in the performance of the learning model for mental task classification. Moreover, the efficacy of the proposed approach is endorsed by carrying out a robust ranking algorithm and Friedman's statistical test for finding the best combinations and comparing different combinations of power spectral density and feature selection methods. © 2021, CC BY-NC-ND.

关键词： Feature extraction

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A New Approach to Secure and Manage Load Balancing of the Distributed Database Using SQL Firewall 1

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2nd International Conference on Advanced Data and Information engineering, DaEng 2015

作者： Fairuzullah, A. Noraziah, Ahmad Mohd, Wan Maseri Wan Herawan, Tutut Faculty of Computer Systems & Software Engineering University Malaysia Pahang Gambang KuantanPahang26300 Malaysia Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

ISBN: (数字)9789811317996

ISBN: (纸本)9789811317972

This paper is the current approach database security of organization need layers of security around their data, with each layer protecting a specific area. The current trend of security attack is targeting any valuable data stored in a database such as financial data and student mark, including data theft, data modification or replay and data disruption. To understand how the system works, the system setting and the weaknesses of Enterprise Distributed Database. This paper is about the design and development to secure database access, a framework based on packet and content filtering on IP address, port and SQL statement. And simulate the framework and design database security framework. We address how to build the outcome of this is to reduce the risk of data manipulation and reduce the impact of internal threat and malicious insiders. The result shows that the proposed method succeeded in meeting those aspects of information security. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Security of data

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Optimize Performance Load Balancing Techniques Using Binary Vote Assignment Grid Quorum (BVAGQ): A Systematic Review 1

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2nd International Conference on Advanced Data and Information engineering, DaEng 2015

作者： Fairuzullah, A. Noraziah, Ahmad Arshah, Ruzaini Abdullah Herawan, Tutut Faculty of Computer Systems & Software Engineering University Malaysia Pahang Gambang KuantanPahang26300 Malaysia Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

ISBN: (数字)9789811317996

ISBN: (纸本)9789811317972

This paper present load balancing technique in a heterogeneous environment allows the usage for geographically widely distributed and multi-owner resources to solve large-level application, usage of load balancing algorithms was important to keep maintaining the balance of workload between emerged infrastructures like grid. This replication generally referred as mechanism to improve availability and performance in distributed databases especially handling fragmented database replication becomes demanding issue. Intended in this paper we address various kinds of load balancing algorithms for the heterogeneous network like grid, especially Binary Vote Assignment Grid Quorum (BVAGQ) and to identify various metric and gaps between them. Many load balancing algorithms are already implemented which work against various issues like heterogeneity, scalability, etc. Different load balancing algorithms for the grid environment work on various metrics such as make span, time, average resource utilization rate, communication overhead, reliability, stability, and fault tolerance. However the aim is to find improved query response time and overall throughput as compared to other scheme. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Fault tolerance

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SusTrainable: Promoting Sustainability as a Fundamental Driver in software Development Training and Education Teacher Training November 1–5, Nijmegen, The Netherlands Revised lecture notes

arXiv

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arXiv 2022年

作者： Koopman, Pieter Lubbers, Mart Fernandes, João Paulo Korečko, Štefan Paquete, Luís Porkoláb, Zoltán Szalay, Richárd Mauša, Goran Oprescu, Ana Koedijk, Lukas van Oostveen, Sander Kok, Stephan Li, Jianhao Zsók, Viktória Grbac, Tihana Galinac Grbac, Neven Valkanov, Vladimir Petrov, Mihail Radboud University Nijmegen Netherlands University of Porto Portugal Department of Computers and Informatics Faculty of Electrical Engineering and Informatics Technical University of Košice Slovakia CISUC Department of Informatics Engineering University of Coimbra Portugal Department of Programming Languages and Compilers Institute of Computer Science Faculty of Informatics Eötvös Lorád University Budapest Hungary University of Rijeka Faculty of Engineering Vukovarska 58 Rijeka51000 Croatia Complex Cyber Infrastructure University of Amsterdam Netherlands KPMG Software Engineering Master University of Amsterdam Netherlands Informatics Institute University of Amsterdam Netherlands Juraj Dobrila Univeristy of Pula Zagrebačka 30 PulaHR-52100 Croatia University Of Plovdiv "Paisii Hilendarski" Faculty of Mathematics and Informatics 24 Bulgaria Blvd. Plovdiv4027 Bulgaria

These are the proceedings of the first teacher training of the Erasmus+ project No. 2020-1-PT01-KA203-078646 -- Sustrainable. The full title of this project is Promoting Sustainability as a Fundamental Driver in software Development Training and Education and the interested reader may know more about it at: https://*** The flagship contribution is the organization of two summer schools on sustainable software production. The first summer school is moved due to the Corona pandemic. It is now scheduled for July 2022 in Rijeka, Croatia. This summer school will consist of lectures and practical work for master and PhD students in the area of computing science and closely related fields. There will be contributions from Plovdiv University, University of Ko\v{s}ice, University of Coimbra, University of Minho, E\"otv\"os Lor\'and University, University of Rijeka, Radboud University Nijmegen, University of Pula, University of Amsterdam and Babe\c{s}-Bolyai University. To prepare and streamline this summer school, the consortium organized a teacher training in Nijmegen, the Netherlands. This was an event of five full days on November 1-5, 2022 organized by Ingrid Berenbroek, Mart Lubbers and Pieter Koopman on the campus of the Radboud University Nijmegen, The Netherlands. Sustainability is one of the themes within the strategy of the Radboud University called `A Significant Impact', see https://***/sustainability/organisation/radboud-sustainable/. Sustainability plays an important role in education, research, and management. The contributions were reviewed and give a good overview of what can be expected from the summer school. Based on these papers and the very positive and constructive atmosphere, we expect a high quality and pleasant summer school. We are looking forward to contributing to a sustainable future. © 2022, CC BY-NC-ND.

关键词： Personnel training

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Opinion spam detection with attention-based neural networks 32

Opinion spam detection with attention-based neural networks

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32nd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2019

作者： Sedighi, Zeinab Ebrahimpour-Komleh, Hossein Bagheri, Ayoub Kosseim, Leila Dept. of Computer Engineering Faculty of Computer and Electrical Engineering University of Kashan Kashan Iran Dept. of Methodology and Statistics Faculty of Social Sciences Utrecht University Utrecht Netherlands Dept. of Computer Science and Software Engineering Concordia University Montreal Canada

ISBN: (纸本)9781577358053

Fake opinion attacks, consisting of the malicious dissemination of fake reviews, can be detrimental to both customers as well as organizations. Several methods have been proposed to automatically detect deceptive reviews;however, these rely on manual feature engineering methods to build classifiers based on labeled data. Deep Neural Networks coupled with an Attention mechanism have recently been shown to improve the performance of many classification tasks, as it enables the model to learn and focus automatically on the most the important features. This paper describes our approach to apply an attention based deep neural network for the detection of truthful versus fake reviews. The evaluation of our model shows that its performance is significantly better than traditional models, and requires no hand feature engineering. © 2019, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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