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24th IEEE/ACIS International Conference on computer and Information Science, ICIS 2024

作者： Perikos, Isidoros Pylarinou, Chara University of Patras Computer Technology Institute and Press 'Diophantus' Computer Engineering and Informatics Department Patras Greece University of Patras Computer Engineering and Informatics Department Patras Greece

ISBN: (纸本)9798350376647

Emotion recognition in text has become an essential research area within artificial intelligence and natural language processing due to its applications in sentiment analysis, human-computer interaction, and social media analysis. This paper introduces an approach for emotion recognition in social networks using transformer models enhanced with explainability techniques. By leveraging the advanced capabilities of transformers to analyze and classify emotions in written content, our finetuned models achieves high accuracy in identifying emotions according to the Ekman emotional model. The integration of LIME and SHAP provides transparency and interpretability to our model, making it more trustworthy for practical applications. Our findings indicate that models like XLNET report an exceptional performance in emotion recognition tasks. The explainability techniques illustrate the transformer model decision-making processes and the way that they utilize words and their continual information in sentences. © 2024 IEEE.

关键词： Distribution transformers

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Online Vehicle Routing with Pickups and Deliveries Under Time-Dependent Travel-Time Constraints 24

Online Vehicle Routing with Pickups and Deliveries Under Tim...

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24th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems, ATMOS 2024

作者： Kontogiannis, Spyros Paraskevopoulos, Andreas Zaroliagis, Christos Computer Engineering and Informatics Department University of Patras Greece Computer Technology Institute and Press "Diophantus" Patras Greece Computer Technology Institute and Press "Diophantus" Greece

ISBN: (纸本)9783959773508

The Vehicle Routing Problem with pickups, deliveries and spatiotemporal service constraints (VRPPDSTC) is a quite challenging algorithmic problem that can be dealt with in either an offline or an online fashion. In this work, we focus on a generalization, called VRPPDSTCtd, in which the travel-time metric is time-dependent: the traversal-time per road segment (represented as a directed arc) is determined by some function of the departure-time from its tail towards its head. Time-dependence makes things much more complicated, even for the simpler problem of computing earliest-arrival-time paths which is a crucial subroutine to be solved (numerous times) by VRPPDSTCtd schedulers. We propose two online schedulers of requests to workers, one which is a time-dependent variant of the classical Plain-Insertion heuristic, and an extension of it trying to digest some sort of forecasts for future demands for service. We enrich these two online schedulers with two additional heuristics, one targeting for distance-balanced assignments of work loads to the workers and another that makes local-search-improvements to the produced solutions. We conduct a careful experimental evaluation of the proposed algorithms on a real-world instance, with or without these heuristics, and compare their quality with human-curated assignments provided by professional experts (human operators at actual pickup-and-delivery control centers), and also with feasible solutions constructed from a relaxed MILP formulation of VRPPDSTCtd, which is also introduced in this paper. Our findings are quite encouraging, demonstrating that the proposed algorithms produce solutions which (i) are significant improvements over the human-curated assignments, and (ii) have overall quality pretty close to that of the (extremely time-consuming) solutions provided by an exact solver for the MILP formulation. © Spyros Kontogiannis, Andreas Paraskevopoulos, and Christos Zaroliagis.

关键词： Travel time

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Transaction transmission model for blockchain channels based on non-cooperative games

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Science China(Information Sciences) 2023年第1期66卷 97-114页

作者： Peiyun ZHANG Chenxi LI Mengchu ZHOU Wenjun HUANG Abdullah ABUSORRAH Omaimah O.BAMASAG Engineering Research Center of Digital Forensics of Ministry of Education School of Computer and SoftwareNanjing University of Information Science and Technology School of Computer and Information Anhui Normal University Department of Electrical and Computer Engineering New Jersey Institute of Technology Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering and K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King Abdulaziz University

In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds(also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game *** balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: Silent Whispers and Speedy Murmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay,and effectively decreases transmission overhead in comparison with its two competitive peers.

关键词： Blockchain channel transaction transmission channel balance channel state channel transmission probability non-cooperative game

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Extending Online 4D Situational Awareness in Connected and Automated Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年第8期9卷 1-19页

作者： Piperigkos, Nikos Anagnostopoulos, Christos Lalos, Aris S. Berberidis, Kostas Computer Engineering and Informatics Department University of Patras Greece Industrial Systems Institute ATHENA Research Center Patras Greece

Empowering each vehicle with four dimensional (4D) situational awareness, i.e., accurate knowledge of neighboring vehicles' 3D locations over time in a cooperative manner (instead of focusing only on self-localization), is fundamental for improving autonomous driving performance in diverse traffic conditions. For this task, identification, localization and tracking of nearby road users is critical for enhancing safety, motion planning and energy consumption of automated vehicles. Advanced perception sensors as well as communication abilities, enable the close collaboration of moving vehicles and other road users, and significantly increase the positioning accuracy via multi-modal sensor fusion. The challenge here is to actually match the extracted measurements from perception sensors with the correct vehicle ID, through data association. In this paper, two novel and distributed Cooperative Localization or Awareness algorithms are formulated, based on linear least-squares minimization and the celebrated Kalman Filter. They both aim to improve ego vehicle's 4D situational awareness, so as to be fully location aware of its surrounding and not just its own position. For that purpose, ego vehicle forms a star like topology with its neighbors, and fuses four types of multi-modal inter-vehicular measurements (position, distance, azimuth and inclination angle) via the linear Graph Laplacian operator and geometry capturing differential coordinates. Moreover, a data association strategy has been integrated to the algorithms as part of the identification process, which is shown to be much more beneficial than traditional Hungarian algorithm. An extensive experimental study has been conducted in CARLA, SUMO and Artery simulators, highlighting the benefits of the proposed methods in a variety of experimental scenarios, and verifying increased situational awareness ability. The proposed distributed approaches offer high positioning accuracy, outperforming other state-of-the-art c

关键词： Laplace equation

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Model-to-Circuit Cross-Approximation for Printed Machine Learning Classifiers

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2023年第11期42卷 3532-3544页

作者： Armeniakos, Giorgos Zervakis, Georgios Soudris, Dimitrios Tahoori, Mehdi B. Henkel, Jorg Karlsruhe Institute of Technology Karlsruhe76131 Germany National Technical University of Athens School of Electrical and Computer Engineering Athens15780 Greece University of Patras Computer Engineering and Informatics Department Patras26504 Greece Karlsruhe Institute of Technology Department of Computer Science Karlsruhe76131 Germany

Printed electronics (PEs) promises on-demand fabrication, low nonrecurring engineering costs, and subcent fabrication costs. It also allows for high customization that would be infeasible in silicon, and bespoke architectures prevail to improve the efficiency of emerging PE machine learning (ML) applications. Nevertheless, large feature sizes in PE prohibit the realization of complex ML models in PE, even with bespoke architectures. In this work, we present an automated, cross-layer approximation framework tailored to bespoke architectures that enable complex ML models, such as multilayer perceptrons (MLPs) and support vector machines (SVMs), in PE. Our framework adopts cooperatively a hardware-driven coefficient approximation of the ML model at algorithmic level, a netlist pruning at logic level, and a voltage overscaling at the circuit level. Extensive experimental evaluation on 12 MLPs and 12 SVMs and more than 6000 approximate and exact designs demonstrates that our model-to-circuit cross-approximation delivers power and area optimal designs that, compared to the state-of-the-art exact designs, feature on average 51% and 66% area and power reduction, respectively, for less than 5% accuracy loss. Finally, we demonstrate that our framework enables 80% of the examined classifiers to be battery-powered with almost identical accuracy with the exact designs, paving thus the way toward smart complex printed applications. © 1982-2012 IEEE.

关键词： Timing circuits

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Sensitive Content Detection in Social Networks Using Deep Learning Models and Explainability Techniques 9

Sensitive Content Detection in Social Networks Using Deep Le...

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9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024

作者： Perikos, Isidoros University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press 'Diophantus' Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350394191

In the dynamic landscape of online social networks, recognizing sensitive content is essential for safeguarding user privacy, fostering inclusivity, and enhancing diversity awareness. Building on prior research, this study explores new dimensions and methodologies for detecting sensitive content. We examine the temporal evolution of sensitive content, revealing how patterns shift over time, and address cross-linguistic challenges, emphasizing cultural and contextual nuances in detection. We employ advanced machine learning techniques, including deep learning models and BERT that improve the accuracy and robustness of the detection procedure. In the experimental study, BERT transformer reported the best performance in detecting sensitive content in text. Additionally, we incorporate explainability techniques such as LIME and SHAP to provide deeper insights into the model's decision-making processes, ensuring predictions are interpretable and reliable. Our work enhances the theoretical framework of sensitive content detection in social networks and provide methods that are accurate and scalable and can facilitate the creation of user-centric interaction that prioritize privacy and user experience. © 2024 IEEE.

关键词： Adversarial machine learning

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Early Parkinson's Disease Detection and Classification using Machine Learning and Neutrosophic Sets

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Neutrosophic Sets and Systems 2025年 82卷 873-886页

作者： Oudah, Hasan H. Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed Nagm, Ahmad M. Salama, Ahmed S. College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Engineering and Electronics Cairo Higher Institute for Engineering Computer Science and Management New Cairo Egypt

Optimizing therapy and rehabilitation for Parkinson's disease (PD) requires early identification and precise evaluation of the illness's course. However, there is disagreement about the best way to use gait analysis to categorize the severity of motor symptoms and identify early-stage Parkinson's disease. The precision of machine learning (ML) models in identifying early and intermediate stages of Parkinson's disease was assessed in this study. Six ML models are used in this study for the prediction of PD. Different metrics are used to evaluate ML models such as accuracy, precision, recall, f1-score, and AUC score. Then we propose a multi-criteria decision-making methodology (MCDM) to evaluate the ML models and select the best one. Two MCDM methods are used such as CRITIC method to compute the criteria weights and the TOPSIS method to rank the alternatives. These methods are used under the bipolar neutrosophic sets (BNSs) to deal with uncertainty information. The results show the support vector machine is the best ML model for the prediction of PD. © 2025, University of New Mexico. All rights reserved.

关键词： Neurodegenerative diseases

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The Novel DeepBoost Multi Output Regressor for Household Finance Intelligence 5

The Novel DeepBoost Multi Output Regressor for Household Fin...

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5th International Conference on Advanced research in Computing, ICARC 2025

作者： Suwandaratna, S.H.S.V. Priyankara, G.O. Institute of Technology Department of Computer Science and Engineering Informatics No 10 Trelawney PI Colombo00400 Sri Lanka

ISBN: (纸本)9798331530983

This paper forecasts the microeconomic level household expenditures using a novel hybrid deep learning approach. In terms of research significance, household finance control has a major influence on the finance system within the economy. Accurate forecasting of household finances assists in maintaining positive financial behavior among individuals and the economy. The DeepBoost multi-output regressor proposed in this paper is based on the 1D CNN-ANN and the XGBoost. The proposed model in this paper is compared with the R2, MSE, and MAE since it's a regression problem. The experimental results reveal that the proposed DeepBoost multi-output regressor has the best application in forecasting the multiple expenditures of households by outperforming the ANN, 1D CNN-ANN, and Random Forest Regressor models. The proposed DeepBoost multi-output regressor evaluated the housing, food, transportation, healthcare, other necessities, childcare, and tax expenditures that had 0.94, 0.98, 0.83, 0.94, 0.97, 0.97, and 0.99 values for the R2, 9037.71, 2692.12, 9788, 15077.33, 1373.93, 13629.36, and 1904.52 values for the MSE, and 66.07, 34.05, 73.17, 87.05, 26.25, 78.74, and 29.47 MAE values than the ANN, RFR, and 1D CNN-ANN models. © 2025 IEEE.

关键词： Taxation

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(POSTER) A Holistic IoT-Enabled Approach for Indoor Air Quality Control 19

(POSTER) A Holistic IoT-Enabled Approach for Indoor Air Qual...

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

作者： Kalioras, Fotios Filios, Gabriel Karatzas, Stylianos Nikoletseas, Sotiris University of Patras Department of Computer Engineering and Informatics Greece University of Patras Computer Technology Institute and Press Diophantus Department of Computer Engineering and Informatics Greece University of Patras Department of Civil Engineering Greece

ISBN: (纸本)9798350346497

Indoor air quality (IAQ) is an important yet often overlooked aspect of public health, with poor IAQ contributing to a significant number of diverse health problems worldwide. Existing air quality standards have failed to fully address the problem, however emerging technologies in the field of IoT have demonstrated promise in effectively addressing this issue. This concept paper advocates a hybrid hierarchy system for IAQ monitoring that incorporates both offline and online connectivity. The system utilizes sensing elements that can perform a wide range of functions, including autocalibration, pollution event detection, and more, autonomously from the network. By combining a gateway device, the system's capabilities are enhanced, providing increased data granularity, additional calibration and sensing options as well as connection with Building Management Systems (BMS). Furthermore, by connecting the gateway to the cloud, the system can perform more advanced data analysis and machine learning, providing users with insights into not only air quality but also general environmental quality. Key features of the envisioned system include the integration of subjective perception of pollution via crowdsourcing, a TinyML technology at the edge, digital twins, and an optimised redundancy of sensors that can alleviate poor accuracy and drift as well as capture the spatial dispersion of pollution. © 2023 IEEE.

关键词： Crowdsourcing

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