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arXiv 2022年

作者： Ramezani, Majid Feizi-Derakhshi, Mohammad-Reza Balafar, Mohammad-Ali Computerized Intelligence Systems Laboratory Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

How people think, feel, and behave primarily is a representation of their personality characteristics. By being conscious of the personality characteristics of individuals whom we are dealing with or deciding to deal with, one can competently ameliorate the relationship, regardless of its type. With the rise of Internet-based communication infrastructures (social networks, forums, etc.), a considerable amount of human communications take place there. The most prominent tool in such communications is the language in written and spoken form that adroitly encodes all those essential personality characteristics of individuals. Text-based Automatic Personality Prediction (APP) is the automated forecasting of the personality of individuals based on the generated/exchanged text contents. This paper presents a novel knowledge graph-enabled approach to text-based APP that relies on the Big Five personality traits. To this end, given a text, a knowledge graph which is a set of interlinked descriptions of concepts was built by matching the input text's concepts with DBpedia knowledge base entries. Then, due to achieving a more powerful representation, the graph was enriched with the DBpedia ontology, NRC Emotion Intensity Lexicon, and MRC psycholinguistic database information. Afterwards, the knowledge graph which is now a knowledgeable alternative for the input text was embedded to yield an embedding matrix. Finally, to perform personality predictions the resulting embedding matrix was fed to four suggested deep learning models independently, which are based on convolutional neural network (CNN), simple recurrent neural network (RNN), long short term memory (LSTM), and bidirectional long short term memory (BiLSTM). The results indicated considerable improvements in prediction accuracies in all of the suggested classifiers. Copyright © 2022, The Authors. All rights reserved.

关键词： Knowledge graph

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Text-Based Automatic Personality Prediction Using KGrAt-Net: A Knowledge Graph Attention Network Classifier

arXiv

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arXiv 2022年

Nowadays, a tremendous amount of human communications occur on Internet-based communication infrastructures, like social networks, email, forums, organizational communication platforms, etc. Indeed, the automatic prediction or assessment of individuals’ personalities through their written or exchanged text would be advantageous to ameliorate their relationships. To this end, this paper aims to propose KGrAt-Net, which is a Knowledge Graph Attention Network text classifier. For the first time, it applies the knowledge graph attention network to perform Automatic Personality Prediction (APP), according to the Big Five personality traits. After performing some preprocessing activities, it first tries to acquire a knowing-full representation of the knowledge behind the concepts in the input text by building its equivalent knowledge graph. A knowledge graph collects interlinked descriptions of concepts, entities, and relationships in a machine-readable form. Practically, it provides a machine-readable cognitive understanding of concepts and semantic relationships among them. Then, applying the attention mechanism, it attempts to pay attention to the most relevant parts of the graph to predict the personality traits of the input text. We used 2,467 essays from the Essays Dataset. The results demonstrated that KGrAt-Net considerably improved personality prediction accuracies (up to 70.26% on average). Furthermore, KGrAt-Net also uses knowledge graph embedding to enrich the classification, which makes it even more accurate (on average, 72.41%) in APP. Copyright © 2022, The Authors. All rights reserved.

关键词： Knowledge graph

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Method for improving the accuracy of mobile robot odometry based on machine learning 2

Method for improving the accuracy of mobile robot odometry b...

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2nd International Conference on Algorithms, High Performance Computing and Artificial Intelligence, AHPCAI 2022

作者： Shao, Bingbing Zheng, Tianjiang Li, Qi Chen, Yikun Ningbo University Faculty of Electrical Engineering and Computer Science Zhejiang Ningbo315211 China Key Laboratory of Robotics and Intelligent Equipment Technology of Zhejiang Province Zhejiang Ningbo315201 China

ISBN: (纸本)9798350334784

Omnidirectional mobile robots have the characteristic of moving in any direction with three degrees of freedom in plane XYZ. The positioning accuracy of a mobile robot is not only affected by its manufacturing errors but also may be affected by vibration, load, and other factors, resulting in errors in the process of moving. To reduce the influence of the above factors, this paper presents an error compensation method based on Gaussian process regression (GPR) for omnidirectional mobile robots. The method requires fewer learning parameters and training points, which enables more computational efficiency in the learning process. To verify the effectiveness of the method, a simulation experiment is carried out. The simulation results show that the accuracy of the odometry in the three degrees of freedom of the plane is improved by 89.70%, 88.09%, and 96.32% respectively. © 2022 IEEE.

关键词： Mobile robots

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An AOP-Based Security Verification Environment for KECCAK Hash Algorithm

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computers, Materials & Continua 2022年第11期73卷 4051-4066页

作者： Hassen Mestiri Imen Barraj Mohsen Machhout Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj 11942Saudi Arabia Higher Institute of Applied Sciences and Technology of Sousse University of SousseTunisia Electronics and Micro-Electronics Laboratory Faculty of Sciences of MonastirUniversity of MonastirTunisia Electrical Engineering Department Systems Integration&Emerging Energies(SI2E)National Engineers School of SfaxUniversity of SfaxTunisia Higher Institute of Computer Science and Multimedia of Gabes University of GabesTunisia

Robustness of the electronic cryptographic devices against fault injection attacks is a great concern to ensure *** to significant resource constraints,these devices are limited in their *** increasing complexity of cryptographic devices necessitates the development of a fast simulation environment capable of performing security tests against fault injection *** is a good choice for Electronic System Level(ESL)modeling since it enables models to run at a faster *** enable fault injection and detection inside a SystemC cryptographic model,however,the model’s source code must be *** altering the source code,Aspect-Oriented Programming(AOP)may be used to evaluate the robustness of cryptographic *** might replace conventional cryptanalysis methods in the real *** the ESL,we discuss a unique technique for simulating security fault attacks on cryptographic *** current study presents a fault injection/detection environment for assessing the KECCAK SystemC model’s resistance against fault injection *** approach of injecting faults into KECCAK SystemC model is accomplished via the use of weaving faults in AspectC++based on AOP programming *** confirm our technique by applying it to two scenarios using a SystemC KECCAK hash algorithm case study:The first concerns discuss the effect of the AOP on fault detection capabilities,while the second concerns discuss the effect of the AOP on simulation time and executable file *** simulation results demonstrate that this technique is fully capable of evaluating the fault injection resistance of a KECCAK *** demonstrate that AOP has a negligible effect on simulation time and executable file size.

关键词： Aspect-Oriented Programming SystemC AspectC++ cryptographic circuits KECCAK fault detection fault attacks

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Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data

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Intelligent Medicine 2024年第3期4卷 170-176页

作者： Bijan Roudini Boshra Khajehpiri Hamid Abrishami Moghaddam Mohamad Forouzanfar Department of Systems Engineering School of Advanced TechnologyUniversity of QuebecMontrealCanada Machine Vision and Medical Image Processing(MVMIP)Laboratory Faculty of Electrical and Computer EngineeringK.N.Toosi University of TechnologyTehranIran Research Center of the Montreal University Institute of Geriatrics MontrealCanada

Background Precise estimation of current and future comorbidities of patients with cardiovascular disease is an important factor in prioritizing continuous physiological monitoring and new *** learning(ML)models have shown satisfactory performance in short-term mortality prediction in patients with heart disease,whereas their utility in long-term predictions is *** study aimed to investigate the performance of tree-based ML models on long-term mortality prediction and effect of two recently introduced biomarkers on long-term *** This study used publicly available data from the Collaboration Center of Health Information Appli-cation at the Ministry of Health and Welfare,Taiwan,*** collected data were from patients admitted to the cardiac care unit for acute myocardial infarction(AMI)between November 2003 and September *** collected and analyzed mortality data up to December *** records were used to gather demo-graphic and clinical data,including age,gender,body mass index,percutaneous coronary intervention status,and comorbidities such as hypertension,dyslipidemia,ST-segment elevation myocardial infarction,and non-ST-segment elevation myocardial *** the data,collected from 139 patients with AMI,from medical and demographic records as well as two recently introduced biomarkers,brachial pre-ejection period(bPEP)and brachial ejection time(bET),we investigated the performance of advanced ensemble tree-based ML algorithms(random forest,AdaBoost,and XGBoost)to predict all-cause mortality within 14 years.A nested cross-validation was performed to evaluate and compare the performance of our developed models precisely with that of the conventional logistic regression(LR)as the baseline *** The developed ML models achieved significantly better performance compared to the baseline LR(C-Statistic,0.80 for random forest,0.79 for AdaBoost,and 0.78 for XGBoost,vs.0.77 for LR)(PRF<0.001,PAdaBoost<0.001,a

关键词： Electrocardiogram Predication Mortality prediction Machine learning

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Classification of skin lesions using morpho-granulometry features derived from color clusters

Classification of skin lesions using morpho-granulometry fea...

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International Conference on System Theory, Control, and Computing (ICSTCC)

作者： Gigi Tăbăcaru Simona Moldovanu Marian Barbu Department of Automatic Control and Electrical Engineering Faculty of Automation Computers Electrical Engineering and Electronics "Dunarea de Jos" University of Galati Galati Romania Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics The Modelling & Simulation Laboratory "Dunarea de Jos" University of Galati Galati Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

The skin lesion can be thought of as a biological system, so the morpho-granulometry of significant color clusters found in skin lesions is one of the elements that reproduce in a natural way the structure of the lesion, this novelty is highlighted in this study. Important features of skin lesions can be modulated by fusing neural networks (NN) and machine learning (ML). By choosing the nevus and melanoma classes, the primary goal was accomplished, and three databases were used to test the methodology. The characteristics based on morpho-granulometry allowed for the identification of microstructure within the images, which can be very helpful in characterizing the biological system. Based on random forest (RF) and extreme gradient boosting (XGboost) classifiers, this work aimed to improve the classification performance of important feature selection. The selected features from three free image databases with three NNs were classified. In a binary classification of nevus vs. melanoma, the results showed that the pattern recognition neural network (PRNN), according to the PH2 database, provided an accuracy of 0.923 and an F1-score of 0.876. The classification is interpretable if it is not validated. In our study, the best results were verified with a logistic regression (LR) classifier.

关键词： Accuracy Recurrent neural networks Image color analysis Melanoma Biological systems Artificial neural networks Feature extraction Skin Lesions Classification tree analysis

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A Queuing Model for Software-Defined Networking in Cloud and Multi-Controller Environments

A Queuing Model for Software-Defined Networking in Cloud and...

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International Conference on Pattern Analysis and Intelligent Systems (PAIS)

作者： Islem Teboub Amar Aissani Fatiha Merazka Department of Artificial Intelligence and Data Sciences Computer Engineering Faculty RIIMA Laboratory USTHB University Algiers Algeria Department of Telecommunications Electrical Engineering Faculty LISIC Lab. USTHB University Algiers Algeria

ISBN: (数字)9798350350265

ISBN: (纸本)9798350350272

Enterprises are increasingly adopting Software-Defined Networking (SDN) for advanced infrastructure management, cost reduction, and adaptation to technological challenges. Cloud-based SDN controllers play a pivotal role in ensuring flexibility, security, and scalability. The transition from a single-controller to a multi-controller paradigm in cloud networks not only improves security and availability but also reinforces the overall resilience of SDN. Our scientific study aims to propose a novel approach for deploying SDN networks and to introduce a queueing model for this proposed concept. The work focuses on modeling SDN networks using Markovian stochastic processes, taking into consideration the availability criterion through the multiplicity of SDN controllers at the control layer, specifically utilizing queuing theory. The proposed model brings improvement and optimization compared to existing ones. The evaluation of Quality of Service (QoS), based on response time measurements, has demonstrated the efficiency of the model. Additionally, observing the model through the conducted experiments has led us to conclude that scalability is ensured concerning the multiplicity of controllers. Our model has successfully addressed the multi-criteria constraint and ensured an optimal combination that incorporates cloud concepts, QoS, and multi-controllers.

关键词： Scalability Stochastic processes Quality of service Time measurement Security Software defined networking Pattern analysis

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Navigating Data Governance in the Telecom Industry

Navigating Data Governance in the Telecom Industry

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Advances in Enterprise Architecture (NCAEA), National Conference on

作者： Fatemeh Rahimi Farhoud Jafari Kaleibar Fatemehzahra Feizi Amirhossein Hossein Nia Houman Kashfi Artificial Intelligence Laboratory Irancell Labs Tehran Iran Faculty of Electrical & Computer Engineering Tabriz University Tabriz Iran

Data governance plays a crucial role in the telecommunications industry, where effective management of data assets is vital for operational efficiency, customer satisfaction, and regulatory compliance. This paper presents a comprehensive roadmap for data governance in the telecom sector, addressing the challenges and encompassing all pillars of data governance. The roadmap outlines step-by-step guidelines for establishing a data governance program, assessing current state and defining governance policies, implementing data stewardship and quality management, ensuring privacy and regulatory compliance, enhancing data security and risk management, managing data lifecycle, enabling data integration and interoperability, and overcoming industry-specific challenges. Finally, a case study demonstrates the successful implementation of data governance principles in the network domain, following a roadmap approach, resulting in significant improvements and positive outcomes.

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Learning to control the complex light propagation through few-mode fiber without a reference wave

Learning to control the complex light propagation through fe...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Stefan Rothe David Krause Qian Zhang Dennis Pohle Nektarios Koukourakis Jürgen W. Czarske Faculty of Electrical and Computer Engineering Laboratory of Measurement and Sensor System Technique Technische Universität Dresden Dresden Germany

We show reference-less transmission matrix measurements using a neural network operating with up to 23 modes in real-time. We use synthetic training data enabling optical predistortion through an SLM for light control... 详细信息

ISBN: (纸本)9781957171258

关键词： Few mode fibers Light propagation Modal crosstalk Neural networks Spatial light modulators Transmission measurement

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Digital Disruption in Telecommunication: Shifting from Telco to Tech Co

Digital Disruption in Telecommunication: Shifting from Telco...

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Advances in Enterprise Architecture (NCAEA), National Conference on

作者： Fatemehzahra Feizi Farhoud Jafari Kaleibar Fatemeh Rahimi Houman Kashfi Amirhossein Hossein Nia Artificial Intelligence Laboratory Irancell Labs Tehran Iran Faculty of Electrical & Computer Engineering Tabriz University Tabriz Iran

The telecommunications industry (telcos) is undergoing a significant transformation driven by digital technologies and changing customer expectations. Telcos are compelled to embrace digital transformation to remain competitive and meet evolving customer demands. This paper presents a comprehensive roadmap for telcos to transition from telco to tech co (technological company), recognizing the strategic and holistic approach required for successful transformation. The roadmap encompasses key aspects such as technological infrastructure, customer-centricity, agile business models, ecosystem partnerships, and organizational culture. By following this roadmap, telcos can unlock new revenue streams, deliver innovative digital services, and maintain competitiveness in the dynamic digital landscape. Finally, this paper conducted a case study on the practical implementation of the digital transformation roadmap by MTN Irancell, providing real-world insights into its effectiveness and outcomes.

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