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SSRN 2022年

作者： Es-Sabry, Mohammed El Akkad, Nabil Merras, Mostafa Satori, Khalid LISAC Computer Science Signals Automation and Cognitivism Laboratory Faculty of Sciences Dhar-Mahraz Sidi Mohamed Ben Abdellah University B.P 1796 Atlas Fez Morocco LESA Laboratory of Engineering Systems and Applications ENSA Sidi Mohamed Ben Abdellah University Fez Morocco Department of Computer Science High School of Technology Moulay Ismail University Meknes Morocco

In this paper, we introduce an improved 32-bit color image encryption method using Four 1D chaotic maps that are Logistic map, Chebyshev map, Tent map, and Sine map. these maps were used to fill the four matrices of our encryption system with unique integers that are between 0 and 255. Each matrix has a size of arranged in a square. The four channels of a 32-bit color image are represented by these matrices (red, green, blue, and alpha). The upper-left and lower-right matrices are used to encrypt respectively the red and the alpha channels. The upper-right and lower-left matrices are used to encrypt respectively the green and the blue channels. For the confusion section, we apply the Arnold Cat Map transformation to shuffle all the pixels and change their position. Several safety criteria were used to evaluate the performance of our algorithm against common attacks and have shown excellent results. © 2022, The Authors. All rights reserved.

关键词： Cryptography

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Structural transition in interdependent networks with regular interconnections

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Physical Review E 2019年第1期99卷 012311-012311页

作者： Xiangrong Wang Robert E. Kooij Yamir Moreno Piet Van Mieghem Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands iTrust Centre for Research in Cyber Security Singapore University of Technology and Design Singapore Institute for Biocomputation and Physics of Complex Systems (BIFI) University of Zaragoza Zaragoza 50009 Spain Department of Theoretical Physics University of Zaragoza Zaragoza 50009 Spain ISI Foundation Turin Italy

Networks are often made up of several layers that exhibit diverse degrees of interdependencies. An interdependent network consists of a set of graphs G that are interconnected through a weighted interconnection matrix B, where the weight of each intergraph link is a non-negative real number p. Various dynamical processes, such as synchronization, cascading failures in power grids, and diffusion processes, are described by the Laplacian matrix Q characterizing the whole system. For the case in which the multilayer graph is a multiplex, where the number of nodes in each layer is the same and the interconnection matrix B=pI, I being the identity matrix, it has been shown that there exists a structural transition at some critical coupling p*. This transition is such that dynamical processes are separated into two regimes: if p>p*, the network acts as a whole; whereas when psystems that deviate from the topological constraints imposed by multiplex networks.

关键词： Collective behavior in networks Dynamics of networks Network phase transitions Topological phase transition

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On the Capacity of Vehicular Communications

On the Capacity of Vehicular Communications

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National Conference with International Participation (TELECOM)

作者： Paschalis C. Sofotasios Seong Ki Yoo Nidhi Simmons Simon L. Cotton Mikko Valkama Department of Electrical Engineering and Computer Science Center for Cyber Physical Systems Khalifa University Abu Dhabi UAE Department of Electrical Engineering Tampere University Tampere Finland Centre for Future Transport and Cities Faculty of Engineering Environment and Computing Coventry University UK Institute of Electronics Communications and Information Technology Queen’s University Belfast Belfast UK

The κ-μ / inverse gamma and η-μ / inverse gamma distributions constitute two of the most distinct composite fading models. The present work offers novel results on the achievable ergodic capacity over these composite multipath/shadowing conditions. The derived expressions are corroborated with respective simulation results and are shown to be useful in practical wireless scenarios in the context of device to device communications, such as off-body, and vehicle-to-vehicle communications.

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Evaluation of Adaptive Bitrate Selection Algorithms for MPEG DASH

Evaluation of Adaptive Bitrate Selection Algorithms for MPEG...

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International Symposium on Electronics in Marine (ELMAR)

作者： Jelena Vlaović Snježana Rimac-Drlje Filip Vranješ Robert Pečkai Kovač Faculty of Electrical Engineering Computer Science and Information Technology Kneza Trpimira 2b Osijek Croatia Institute RT-RK Osijek LLC for Information Technology Cara Hadrijana 10b Osijek Croatia RT-RK Institute for Computer Based Systems Narodnog Fronta 23a Novi Sad Serbia

Dynamic Adaptive Streaming over Hypertext Transfer protocol- HTTP (DASH) is a video streaming technique used for providing the end user with the best probable quality of claimed video sequence. HTTP based standard called MPEG DASH defines a way of notifying the user about different transport streams (TS) with different quality levels and gives information about the most suitable transmission path. MPEG DASH is used for encoding every video sequence into continuous segments of distinct alternatives: different resolutions, bit-rates, and quality levels. Based on present network conditions, an adaptive bitrate selection (ABR) algorithm at the client side selects the bitrate for each segment. In this paper, we evaluated three published ABR algorithms in a streaming client prototype based on the MPEG DASH standard. In the evaluation procedure, all algorithms were tested using three different video sequences from available MPEG DASH dataset. We determined the spatial and temporal information of all tested video sequences and their influence on PSNR (Peak Signal to Noise Ratio) values. Furthermore, video quality levels were analyzed in five testing scenarios in terms of different network conditions. Obtained results show that ABR algorithms that consider users experience and segment size are less sensitive to changing network conditions. The spatial activity has a high influence on the video quality and can be useful as a parameter in DASH dataset preparation in order to improve adaptation algorithms.

关键词： Bit rate Streaming media Bandwidth Video sequences Transform coding Switches Classification algorithms

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Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans

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Journal of Healthcare engineering 2022年第1期2022卷 3861161页

作者： Alzu'Bi, Dalia Abdullah, Malak Hmeidi, Ismail Alazab, Rami Gharaibeh, Maha El-Heis, Mwaffaq Almotairi, Khaled H. Forestiero, Agostino Hussein, Ahmad Mohdaziz Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid2210 Jordan Department of General Surgery and Urology University of Science and Technology Irbid22110 Jordan Department of Diagnostic and Interventional Radiology Faculty of Medicine Jordan University of Science and Technology Irbid2210 Jordan Computer Engineering Department Computer and Information Systems College Umm Al-Qura University Makkah21955 Saudi Arabia Institute for High Performance Computing and Networking CNR CS Rende Italy Deanship of E-Learning and Distance Education Umm Al-Qura University Makkah21955 Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Faculty of Information Technology Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures that reduce effects, and overcoming the tumor. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of deep learning (DL) can save diagnosis time, improve test accuracy, reduce costs, and reduce the radiologist's workload. In this paper, we present detection models for diagnosing the presence of KTs in computed tomography (CT) scans. Toward detecting and classifying KT, we proposed 2D-CNN models;three models are concerning KT detection such as a 2D convolutional neural network with six layers (CNN-6), a ResNet50 with 50 layers, and a VGG16 with 16 layers. The last model is for KT classification as a 2D convolutional neural network with four layers (CNN-4). In addition, a novel dataset from the King Abdullah University Hospital (KAUH) has been collected that consists of 8,400 images of 120 adult patients who have performed CT scans for suspected kidney masses. The dataset was divided into 80% for the training set and 20% for the testing set. The accuracy results for the detection models of 2D CNN-6 and ResNet50 reached 97%, 96%, and 60%, respectively. At the same time, the accuracy results for the classification model of the 2D CNN-4 reached 92%. Our novel models achieved promising results;they enhance the diagnosis of patient conditions with high accuracy, reducing radiologist's workload and providing them with a tool that can automatically assess the condition of the kidneys, reducing the risk of misdiagnosis. Furthermore, increasing the quality of healthcare service and early detection can change the disease's track and preserve the patient's life. © 2022 Dalia Alzu'bi et al.

关键词： Convolutional neural networks

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G-image Segmentation: Similarity-preserving fuzzy C-means with spatial information constraint in wavelet space

arXiv

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arXiv 2020年

作者： Wang, Cong Pedrycz, Witold Li, ZhiWu Zhou, MengChu Ge, Shuzhi Sam School of Electro-Mechanical Engineering Xidian University Xi’an710071 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada School of Electro-Mechanical Engineering Xidian University Xi’an710071 China Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia School of Electro-Mechanical Engineering Xidian University Xi’an710071 China Institute of Systems Engineering Macau University of Science and Technology China Institute of Systems Engineering Macau University of Science and Technology 999078 China Department of Electrical and Computer Engineering National University of Singapore Singapore117576 Singapore Institute of Future Qingdao University Qingdao266071 China

G-images refer to image data defined on irregular graph domains. This work elaborates a similarity-preserving Fuzzy C-Means (FCM) algorithm for G-image segmentation and aims to develop techniques and tools for segmenting G-images. To preserve the membership similarity between an arbitrary image pixel and its neighbors, a Kullback-Leibler divergence term on membership partition is introduced as a part of FCM. As a result, similarity-preserving FCM is developed by considering spatial information of image pixels for its robustness enhancement. Due to superior characteristics of a wavelet space, the proposed FCM is performed in this space rather than Euclidean one used in conventional FCM to secure its high robustness. Experiments on synthetic and real-world G-images demonstrate that it indeed achieves higher robustness and performance than the state-of-the-art FCM algorithms. Moreover, it requires less computation than most of *** Codes 62H30 Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

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Evaluation of the Montenegrin Academic Digital Innovation Hub

Evaluation of the Montenegrin Academic Digital Innovation Hu...

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International Scientific-Professional Conference on Information Technology (IT)

作者： Luka Laković Peter A. Kara Ivana Ognjanović Laszlo Bokor Ramo Šendelj Christoph Reich Milovan Roganović Emmanouil Zoulias Jevto Eraković Tanja Radusinović Nada Rakočević Aniko Simon Faculty for Information Systems and Technologies University of Donja Gorica 81000 Podgorica Montenegro Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics 1111 Budapest Hungary Department of Computer Science School of Computer Science and Mathematics Wireless Multimedia and Networking Research Group Faculty of Science Engineering and Computing Kingston University London KT1 2EE UK Humanistic Studies University of Donja Gorica 81000 Podgorica Montenegro Institute of Data Science Cloud Computing and IT Security Furtwangen University 78120 Furtwangen Germany Clinic for Neurology Clinical Centre of Montenegro 81000 Podgorica Montenegro Faculty of Nursing School of Health Sciences National and Kapodistrian University of Athens 11527 Goudi Greece Chamber of Economy of Montenegro 81000 Podgorica Montenegro Sigma Technology 1093 Budapest Hungary

In the era of global digitalization, digital innovation hubs have become essential, as they function as transforming forces. In this paper, we provide an analysis of the Montenegrin Academic Digital Innovation Hub by applying multi-criteria qualitative evaluation over several criteria: process flow of fully digitized operations of the Hub (with aspects of the innovation life-cycle, ethics, and security), resource management, strategic goals, and feasibility. The paper also proposes a model of comprehensive resource management via the perspective of smart specialization priorities of Montenegro.

关键词： Technological innovation Ethics Resource management Security Information technology

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An efficient internet traffic classification system using deep learning for IoT

arXiv

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arXiv 2021年

作者： Umair, Muhammad Basit Iqbal, Zeshan Bilal, Muhammad Almohamad, Tarik Adnan Nebhen, Jamel Mehmood, Raja Majid Department of Computer Science University of Engineering and Technology Taxila47050 Pakistan Division of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies Yongin-si Korea Republic of Electrical-Electronics Engineering Department Faculty of Engineering Karabük University Karabük78050 Turkey Department of Electronics and Communication Engineering A'Sharqiyah University Ibra400 Oman Prince Sattam bin Abdulaziz University College of Computer Engineering and Sciences P.O. Box 151 Alkharj 11942 Saudi Arabia Information and Communication Technology Department School of Electrical and Computer Engineering Xiamen University Malaysia Sepang43900 Malaysia

Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper feature selection. In this research, an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic. To examine the performance of the proposed technique, Moore-dataset is used for training the classifier. The proposed scheme takes the preprocessed data and extracts the flow features using a deep neural network (DNN). In particular, the maximum entropy classifier is used to classify the internet traffic. The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification, i.e., 99.23%. Furthermore, the proposed algorithm achieved the highest accuracy compared to the support vector machine (SVM) based classification technique and k-nearest neighbours (KNNs) based classification *** Codes 62M45, 68T07 © 2021, CC BY.

关键词： Internet of things

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Deep Reinforcement Learning for Robotics Motion Planning Combining Graph Neural Networks and Multi-Agent systems

SSRN

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SSRN 2024年

作者： Rjoub, Gaith Bentahar, Jamal Elmekki, Hanae Pedrycz, Witold Drawel, Nagat Kassaymeh, Sofian Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Montreal Canada Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul Turkey

In the dynamic realm of robotics, efficiently navigating complex and ever-changing environments remains a complex challenge, particularly in motion planning. Addressing this, we introduce a new approach to robotic motion planning that leverages a well-designed integration of Proximal Policy Optimization (PPO), Graph Neural Networks (GNNs), and Multi-Agent systems (MASs) within a Centralized Training with Decentralized Execution (CTDE) framework. This method uniquely addresses the challenges in dynamic environments by enabling continuous information sharing among robots at each step, rather than merely at the conclusion of their paths as done in other benchmarks. This innovation not only facilitates real-time decision-making in complex scenarios but also demonstrates superior performance and robustness across various environments, including urban, rural, and industrial settings. Our rigorous comparative analysis establishes this methodology as a significant advancement in the field, markedly enhancing efficiency and adaptability in robotic motion planning. © 2024, The Authors. All rights reserved.

关键词： Multi agent systems

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Assistive Technical Communication of Montenegrin eServices: a Case Study

Assistive Technical Communication of Montenegrin eServices: ...

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International Scientific-Professional Conference on Information Technology (IT)

作者： Aniko Simon Luka Laković Pavle Kovačević Peter A. Kara Ivana Ognjanović Ramo Šendelj Christoph Reich Milovan Roganović John Mantas Laszlo Bokor Sigma Technology 1093 Budapest Hungary Faculty for Information Systems and Technologies University of Donja Gorica 81000 Podgorica Montenegro Budapest University of Technology and Economics 1111 Budapest Hungary Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics 1111 Budapest Hungary Department of Computer Science School of Computer Science and Mathematics Faculty of Science Engineering and Computing Wireless Multimedia and Networking Research Group Kingston University London KT1 2EE UK Humanistic Studies University of Donja Gorica 81000 Podgorica Montenegro Institute of Data Science Cloud Computing and IT Security Furtwangen University 78120 Furtwangen Germany Clinic for Neurology Clinical Centre of Montenegro 81000 Podgorica Montenegro Faculty of Nursing School of Health Sciences National and Kapodistrian University of Athens 11527 Goudi Greece

With the advancement of information technology in the recent decades, digital and online services have become essential components of everyday life in Europe. Montenegro is no different in this regard. However, as digital literacy in the country is still in a developing phase in comparison with most countries of the EU, a higher portion of its citizens may rely on assistive technical communication. In this paper, we provide a case study on the assistive technical communication of Montenegrin eServices. The work addresses the sectors of online banking, healthcare services, higher education, national administrative services, and utilities. Our findings highlight the diversity in terms of implemented assistive technical communication, and distinguish usage contexts on the level of adequacy. Yet as the functionalities of Montenegrin eServices are expected to gradually expand in the upcoming years, assistive technical communication must aim to handle the higher extents of service complexity.

关键词： Online banking Education Europe Medical services Complexity theory Information technology

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