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Global Graph Clustering and Local Graph Exploration for Community Detection in Twitter 7

Global Graph Clustering and Local Graph Exploration for Comm...

7th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Karras, Christos Karras, Aristeidis Giannoukou, Ioanna Giotopoulos, Konstantinos C. Tsolis, Dimitrios Sioutas, Spyros University of Patras Decentralized Systems Computing Group Computer Engineering and Informatics Department Greece University of Patras Department of Management Science and Technology Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

In this paper, the concepts and techniques for global graph clustering are examined, or the process of locating related clusters of vertices within a graph. We introduce the construction of a graph clustering technique based on an eigenvector embedding and a local graph clustering method based on stochastic exploration of the graph. Then, the developed implementations of both methods are presented and assessed in terms of performance. In addition, the difficulties associated with assessing clusterings and benchmarking cluster algorithms are explored where PageRank and EigEmbed algorithms are utilized. The experiments show that the EigEmbed outperformed PageRank across all experiments as it detected more communities with the same number of clusters. Ultimately, we apply both algorithms to a real-world graph representing Twitter network and the followers and tweets therein. © 2022 IEEE.

关键词： Random processes

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Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach

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computers, Materials & Continua 2023年第5期75卷 4485-4499页

作者： Fatma Taher Hamoud Alshammari Lobna Osman Mohamed Elhoseny Abdulaziz Shehab Eman Elayat College of Technological Innovation Zayed UniversityDubaiUAE Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Electronics and Communications Engineering Delta Higher Institute for Engineering&TechnologyMansouraEgypt College of Computing and Informatics University of SharjahSharjahUnited Arab Emirates Department of Information Systems Mansoura UniversityMansoura35516Egypt Department of Teacher Preparation Faculty of Specific EducationMansoura UniversityMansouraEgypt

Cardiac diseases are one of the greatest global health *** to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent *** article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia *** fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection *** ensemble of classifiers is then applied to the fusion’s *** proposed model classifies the arrhythmia dataset from the university of California,Irvine into normal/abnormal classes as well as 16 classes of ***,at the preprocessing steps,for the miss-valued attributes,we used the average value in the linear attributes group by the same class and the most frequent value for nominal ***,in order to ensure the model optimality,we eliminated all attributes which have zero or constant values that might bias the results of utilized *** preprocessing step led to 161 out of 279 attributes(features).Thereafter,a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection *** short,our study comprises three main blocks:(1)sensing data and preprocessing;(2)feature queuing,selection,and extraction;and(3)the predictive *** proposed method improves classification performance in terms of accuracy,F1measure,recall,and precision when compared to state-of-the-art *** achieves 98.5%accuracy for binary class mode and 98.9%accuracy for categorized class mode.

关键词： Cardiac arrhythmia preprocessing missing values classification model fusion

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Multi-talker Verbal Interaction for Humanoid Robots

Multi-talker Verbal Interaction for Humanoid Robots

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Bartlomiej Klin Ryszard Beniak Michal Podpora Arkadiusz Gardecki Joanna Rut Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Department of Computer Science Opole University of Technology Faculty of Production Engineering and Logistics Opole University of Technology

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

Working in multi-talker mode is viable under certain conditions, such as the fusion of audio and video stimuli along with smart adaptive beamforming of received audio signals. In this article, the authors verify part of the researched novel framework, which focuses on adapting to dynamic interlocutor’s location changes in the engagement zone of humanoid robots during the multi-talker conversation. After evaluating the framework, the authors confirm the necessity of a complementary and independent method of increasing the interlocutor’s signal isolation accuracy. It is necessary when video analysis performance plummets. The authors described the leading cause as insufficient performance during dynamic conversations. The video analysis cannot derive a new configuration when the interlocutor’s speech apparatus moves beyond the expected margin and the video frame rate drops.

关键词： Automation Accuracy Array signal processing Humanoid robots Oral communication Human factors

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SparkReact: A Novel and User-friendly Graphical Interface for the Apache Spark MLlib Library 26

SparkReact: A Novel and User-friendly Graphical Interface fo...

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26th Pan-Hellenic Conference on informatics, PCI 2022

作者： Karras, Aristeidis Karras, Christos Bompotas, Agorakis Bouras, Panagiotis Theodorakopoulos, Leonidas Sioutas, Spyros Department of Computer Engineering and Informatics University of Patras Rion Patras Greece Department of Management Science and Technology University of Patras Koukouli Patras Greece

ISBN: (纸本)9781450398541

Visualization is a critical component across every software as it enables users to familiarize themselves with the environment and perform certain tasks with ease. Therefore, straightforward yet interactive and easy-to-understand tools let users' complex demands be satisfied within minutes. The objective of this work is to give an optimized graphical user interface for the Apache Spark MLlib library to apply machine learning algorithms quickly, conveniently, and effectively. We introduce SparkReact, a responsive graphical user interface that allows users to apply clustering, classification, and regression techniques within just a few mouse clicks by implementing and evaluating a certain algorithm and pre-building the code ready for import to Spark. To evaluate the usefulness of our tool we performed crowdsourcing to two categories, computer experts and ordinary users. The results indicate that both populations were satisfied with the tool at a surprising 98 percent. As per the time required to construct and evaluate a machine learning model, it took approximately 4 minutes using SparkReact while with ordinary methods it took almost 4 times longer. Ultimately, future extensions will seek to provide more algorithmic choices. © 2022 ACM.

关键词： Graphical user interfaces

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Hyper Elliptic Curve Based Certificateless Signcryption Scheme for Secure IIoT Communications

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computers, Materials & Continua 2022年第5期71卷 2515-2532页

作者： Usman Ali Mohd Yamani Idna Idris Jaroslav Frnda Mohamad Nizam Bin Ayub Roobaea Alroobaea Fahad Almansour Nura Modi Shagari Insaf Ullah Ihsan Ali Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia Department of Computer Science University of SwatSaidu Sharif19130Pakistan Center for Research in Mobile Cloud Computing University of MalayaKuala Lumpur50603Malaysia Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026 ZilinaSlovakia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Sciences and Arts in RassQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science Hamdard Institute of Engineering and TechnologyIslamabad44000Pakistan

Industrial internet of things (IIoT) is the usage of internet of things(IoT) devices and applications for the purpose of sensing, processing andcommunicating real-time events in the industrial system to reduce the unnecessary operational cost and enhance manufacturing and other industrial-relatedprocesses to attain more profits. However, such IoT based smart industriesneed internet connectivity and interoperability which makes them susceptibleto numerous cyber-attacks due to the scarcity of computational resourcesof IoT devices and communication over insecure wireless channels. Therefore, this necessitates the design of an efficient security mechanism for IIoTenvironment. In this paper, we propose a hyperelliptic curve cryptography(HECC) based IIoT Certificateless Signcryption (IIoT-CS) scheme, with theaim of improving security while lowering computational and communicationoverhead in IIoT environment. HECC with 80-bit smaller key and parameterssizes offers similar security as elliptic curve cryptography (ECC) with 160-bitlong key and parameters sizes. We assessed the IIoT-CS scheme security byapplying formal and informal security evaluation techniques. We used Realor Random (RoR) model and the widely used automated validation of internet security protocols and applications (AVISPA) simulation tool for formalsecurity analysis and proved that the IIoT-CS scheme provides resistance tovarious attacks. Our proposed IIoT-CS scheme is relatively less expensivecompared to the current state-of-the-art in terms of computational cost andcommunication overhead. Furthermore, the IIoT-CS scheme is 31.25% and 51.31% more efficient in computational cost and communication overhead,respectively, compared to the most recent protocol.

关键词： IoT security authentication protocols hyperelliptic curve cryptography certificateless public key cryptography

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Deep Reinforcement Learning for Network Security Applications With A Safety Guide

Deep Reinforcement Learning for Network Security Application...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Liu, Zhibo Lu, Xiaozhen Chen, Yuhan Xiao, Yilin Xiao, Liang Bu, Yanling Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Xiamen University Department of Informatics and Communication Engineering Xiamen China

ISBN: (纸本)9798350345384

Most of the typical reinforcement learning algorithms help wireless devices choose the security policy such as the moving strategy and communication policy by exploring all the possible state-action pairs including the risky policies that cause a severe collision or network disaster. In this paper, we design a safe reinforcement learning algorithm for safety-critical applications (e.g., intelligent transportation systems) to guide the learning agent to avoid exploring risky policies. This algorithm uses Q-network (i.e., a convolutional neural network or a deep neural network) to choose the policy and designs a safety guide to modify the chosen policy that results in dangerous status. More specifically, the safety guide includes a risk alarm module that evaluates the immediate warning value corresponding to the risk of each state-action pair and a G-network that estimates the long-term risk value. By adding the long-term risk value and the long-term expected reward output by the Q-network, this algorithm uses a safety dock to modify the chosen policy. This algorithm uses the immediate warning value to formulate a safe buffer and a risky buffer for the G-network updating to ensure fully exploration in the initial learning process. As a case study, we apply the designed algorithm in a cargo transportation system, in which the experimental results verify the effectiveness of our algorithm compared with the benchmark safe deep Q-network. © 2023 IEEE.

关键词： Network security

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An Efficient and Lightweight Commitment Scheme for IoT Data Streams 19

An Efficient and Lightweight Commitment Scheme for IoT Data ...

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19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Katsika, Angeliki Papageorgiou, Konstantinos Fakis, Alexandros Plagianakos, Vassilis Spathoulas, Georgios University of Thessaly Department of Computer Science and Biomedical Informatics Lamia Greece University of the Aegean Department of Information and Communication Systems Engineering Samos Greece Department of Information Security and Communication Technology Gjøvik Norway

ISBN: (纸本)9798350346497

Data integrity is a crucial property for offering Internet of Things (IoT) services. Usually, this is achieved by assuming a high level of trust in the service provider, a practice that induces negative implications. In the present paper, an efficient and lightweight commitment scheme for IoT data streams is presented. The proposed scheme combines KZG polynomial commitments and a blockchain smart contract to enable full data integrity while requiring trust only at the IoT device level. Given that the IoT device operates properly, no tampering can happen at any other point of the IoT workflow, without being noticed, while the protocol offers the option for automated settlement of cases where the operator tampers with users' data. A number of experiments have been conducted to assess the computation/gas fees requirements of the scheme and validate its real-world applicability. © 2023 IEEE.

关键词： Internet of things

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An Intelligent Microprocessor Integrating TinyML in Smart Hotels for Rapid Accident Prevention 7

An Intelligent Microprocessor Integrating TinyML in Smart Ho...

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7th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Zacharia, Angelos Zacharia, Dimitris Karras, Aristeidis Karras, Christos Giannoukou, Ioanna Giotopoulos, Konstantinos C. Sioutas, Spyros University of Patras Decentralized Systems Computing Group Computer Engineering and Informatics Department Greece University of Patras Department of Management Science and Technology Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

In the modern era of Internet of Things (IoT) and Industry 4.0 there is a growing need for intelligent microcontrollers that can collect, sense and analyse data effectively and efficiently. Such devices can be installed in large scale IoT deployments ranging from smart homes to smart cities and smart buildings. The aim of these devices shall be not only data monitoring but at the same time energy saving and overall building management. In the context of this paper, an all-in-one microprocessor is presented, namely ZAC888DP, which can sense data from multivariate sources and perform data analytics on top of the collected data. Moreover, machine learning (ML) models are deployed in the embedded memory of the device and specifically TinyML methods using a tflite file. The aim of the developed ML model is to collect data from four heterogeneous sources (water sensor, light sensor, humidity and temperature) in order to identify and forecast possible lavatory accidents. The experimental results of this work are encouraging as the model managed to achieve 100 percentage accuracy after 256 iterations. Future directions include the integration of the device with a neural network that will be trained on top of the pre-trained model in order to increase the overall precision even further. © 2022 IEEE.

关键词： Internet of things

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Characterization and Degradation of perovskite mini-modules

Characterization and Degradation of perovskite mini-modules

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International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Vasiliki Paraskeva Matthew Norton Maria Hadjipanayi Elias Peraticos George E.Georghiou Aranzazu Aguirre Santhosh Ramesh Anurag Krishna Tom Aernouts Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Nicosia Cyprus Imec Imo-Imomec Thin Film PV Technology Genk Belgium

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

A holistic approach was utilized in this work to characterize perovskite mini-modules. Outdoor testing in combination with advanced optoelectronic techniques (spatially resolved Electroluminescence/Photoluminescence) were applied for the testing of the devices. In this way, investigation of the power output evolution of the devices in the field as well as study of their defects and shunts evolution will be performed. A considerable reduction of the power output was obtained from the devices exposed outdoors from the first days of testing. Moreover, reference modules of the same structure were kept indoors in dark storage and measured regularly with means of IV and spatially-resolved EL/PL for the study of their performance degradation under dark and controlled conditions indoors.

关键词： Degradation Performance evaluation Systematics Nonhomogeneous media Perovskites Spatial resolution Testing

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Colorectal Polyp Localization: From Image Restoration to Real-time Detection with Deep Learning 17

Colorectal Polyp Localization: From Image Restoration to Rea...

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17th IEEE International Symposium on Applied Computational Intelligence and informatics, SACI 2023

作者： Manshadi, Mahsa Dehghan Mousavi, Milad Golzarian, Arian Soltani, Madjid Mosavi, Amir K.N. Toosi University of Technology Department of Mechanical Engineering Tehran Iran University of Waterloo Department of Electrical and Computer Engineering Ontario Canada Obuda University John von Neumann Faculty of Informatics Budapest Hungary University of Public Service Budapest Hungary

ISBN: (纸本)9798350321104

Increasing demands for artificial intelligence tools in the medical sector show the huge interest of physicians. In recent years, one of the most effective assistant systems in this field is the real-time detection of early-stage colorectal polyps during colonoscopy. Since even experienced physicians may miss polyps during colonoscopy, the real-time assistance system is designed to prevent this and hence contributes to diminish the number of missed critical cases. One challenge in this field is the detection of false positives. These systems are prone to mistake artifacts for colorectal polyps. This review provides an overview over current quality assessment and restoration techniques to make a high-quality training dataset for training deep neural network algorithms. Furthermore, four of the latest, fastest, and most accurate methods are introduced and analyzed in the rest of the review. Our main contribution is to provide an analysis of current methods used to detect colorectal polyps. We present a list of available datasets and present a range of challenges colorectal cancer detection systems face. © 2023 IEEE.

关键词： Image reconstruction

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