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19th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2024

作者： Vonitsanos, Gerasimos Kanavos, Andreas Bardis, Georgios Mylonas, Phivos University of Patras Computer Engineering and Informatics Department Patras Greece Ionian University Department of Informatics Corfu Greece University of West Attica Department of Informatics and Computer Engineering Athens Greece

ISBN: (纸本)9798331504502

Sentiment analysis, a critical branch of Natural Language Processing (NLP), is pivotal for uncovering the emotional undertones within textual data, thereby revealing public sentiments on diverse topics. This study conducts a comparative analysis of two prominent sentiment analysis tools - VADER, a lexicon and rule-based approach, and RoBERTa, a transformer-based deep learning model. It focuses on their efficacy in analyzing tweets related to climate change, a topic of global significance that elicits a wide range of public opinions. The nuanced and dynamic nature of social media language poses unique challenges, particularly in contexts such as climate change discussions. We assess how effectively each model discerns positive, neutral, and negative sentiments across different categories of climate change-related tweets, delineated into Pro, Neutral, News, and Anti stances. Our findings indicate that RoBERTa outperforms VADER in capturing contextual nuances and providing detailed sentiment classifications. This detailed capability allows RoBERTa to better reflect the complex public opinions on climate change, offering invaluable insights to policymakers, researchers, and environmental advocates. This study not only aids in better understanding and engaging with public discourse on social media but also highlights the potential of advanced NLP tools in shaping environmental communication and policy formulation. © 2024 IEEE.

关键词： Tweets

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Joint Controller Placement and TDMA Scheduling in Software Defined Wireless Multihop Networks

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2807-2821页

作者： Papageorgiou, Yiannis Karaliopoulos, Merkouris Choumas, Kostas Koutsopoulos, Iordanis Athens University of Economics and Business Department of Informatics Athens10434 Greece Department of Electrical & Computer Engineering University of Thessaly Volos Greece

We study TDMA-scheduled Software Defined Wireless Multihop Networks (SDWMNs), whereby the data traffic and SDN control messages share the same network links and TDMA resources. Since the topology of WMNs dynamically changes, maintaining a responsive SDN plane is essential for meeting data traffic rate requirements. Placing more SDN controllers reduces communication delays at the SDN layer and increases its responsiveness. However, it demands more TDMA resources and reduces the available ones for data traffic. We analyze this trade-off between data traffic performance and SDN layer responsiveness by delving into two distinct resource allocation mechanisms in the WMN, the SDN controller placement and TDMA scheduling. We capture their interaction into an optimization problem formulation, which aims at maximizing the SDN-responsiveness subject to data traffic rate requirements, topology conditions, and the available TDMA resources. We propose a novel heuristic for the hard-to-solve problem that leverages the network state information gathered at the SDN layer. We find that our heuristic can increase the SDN-responsiveness by 44% when varying the rate reserved for rate-elastic data traffic within 40% of what is nominally requested. The heuristic is modular in accommodating different controller placement algorithms and robust to different alternative for the SDN software implementation. © 2004-2012 IEEE.

关键词： Resource allocation

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Explainable Emotion Recognition in Social Networks with Transformers 24

Explainable Emotion Recognition in Social Networks with Tran...

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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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Late Breaking Results: Energy-Efficient Printed Machine Learning Classifiers with Sequential SVMs

Late Breaking Results: Energy-Efficient Printed Machine Lear...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Besias, Spyridon Sertaridis, Ilias Afentaki, Florentia Balaskas, Konstantinos Zervakis, Georgios University of Patras Department of Computer Engineering & Informatics Patras Greece

ISBN: (纸本)9783982674100

Printed Electronics (PE) provide a mechanically flexible and cost-effective solution for machine learning (ML) circuits, compared to silicon-based technologies. However, due to large feature sizes, printed classifiers are limited by high power, area, and energy overheads, which restricts the realization of battery-powered systems. In this work, we design sequential printed bespoke Support Vector Machine (SVM) circuits that adhere to the power constraints of existing printed batteries while minimizing energy consumption, thereby boosting battery life. Our results show 6.5x energy savings while maintaining higher accuracy compared to the state of the art. © 2025 EDAA.

关键词： Support vector machines

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Decentralized Federated Deep Reinforcement Learning Framework for Energy-Efficient Base Station Switching Control 11

Decentralized Federated Deep Reinforcement Learning Framewor...

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11th International Symposium on Telecommunication, IST 2024

作者： Movahedkor, Narges Shahbazian, Reza Department of Computer Engineering Tehran Iran Department of Informatics Rende Italy

ISBN: (纸本)9798350356250

G cellular networks have achieved significant improvements in capacity and performance, while they have incurred a substantial increase in energy consumption at the base station (BS) level. To address this escalating energy consumption in dense 5G networks, this paper proposes a decentralized federated learning (DFL)-enhanced Deep Reinforcement Learning (DRL) framework for intelligent BS switching. Our approach leverages the distributed nature of 5G networks to collaboratively learn optimal BS on/off policies without centralized control. By combining DFL with DRL, we enable efficient knowledge sharing among BSs while maintaining privacy and reducing communication overhead. The cost function designed in this paper aims to balance energy savings as well as Quality of Service (QoS) requirements. Moreover, to enhance exploration and accelerate convergence, we incorporate an exploration network into the DRL agent and adopt a novel approach of model training. Performed simulations demonstrate the effectiveness of our proposed framework in achieving significant energy reduction of over 23% and total cost of 19%, while maintaining satisfactory QoS performance compared to the existing methods. © 2024 IEEE.

关键词： Deep reinforcement learning

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DiverseNet: Decision Diversified Semi-supervised Semantic Segmentation Networks for Remote Sensing Imagery

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 14258-14271页

作者： Ma, Wanli Karakus, Oktay Rosin, Paul L. Cardiff University School of Computer Science and Informatics Cardiff United Kingdom University of Cambridge Department of Engineering Cambridge United Kingdom

Semi-supervised learning (SSL) aims to help reduce the cost of the manual labelling process by leveraging a substantial pool of unlabelled data alongside a limited set of labelled data during the training phase. Since pixel-level manual labelling in large-scale remote sensing imagery is expensive and time consuming, semi-supervised learning has become a widely used solution to deal with this. However, the majority of existing SSL frameworks, especially various teacher-student frameworks, are too bulky to run efficiently on a GPU with limited memory. There is still a lack of lightweight SSL frameworks and efficient perturbation methods to promote the diversity of training samples and enhance the precision of pseudo labels during training. In order to fill this gap, we proposed a simple, lightweight, and efficient SSL architecture named DiverseHead, which promotes the utilisation of multiple decision heads instead of multiple whole networks. Another limitation of most existing SSL frameworks is the insufficient diversity of pseudo labels, as they rely on the same network architecture and fail to explore different structures for generating pseudo labels. To solve this issue, we propose DiverseModel to explore and analyse different networks in parallel for SSL to increase the diversity of pseudo labels. The two proposed methods, namely DiverseHead and DiverseModel, both achieve competitive semantic segmentation performance in four widely used remote sensing imagery datasets compared to state-of-the-art semi-supervised learning methods. Meanwhile, the proposed lightweight DiverseHead architecture can be easily applied to various state-of-the-art SSL methods while further improving their performance. The code is available at Here. © 2008-2012 IEEE.

关键词： Semantic Segmentation

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Predicting Student Academic Performance Using an Optimised Regression Machine Learning Model 9

Predicting Student Academic Performance Using an Optimised R...

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9th IEEE International Conference on Adaptive Science and Technology, ICAST 2024

作者： Martey, Ezekiel Mensah Abdullah, Abubakar Appiah, Obed Department of Computer Science and Engineering Tarkwa Ghana Department of Computer Science and Informatics Sunyani Ghana

ISBN: (纸本)9798350385403

Machine learning has emerged as one transformative tool in predicting student academic performance. This study evaluates machine learning models regression in predicting student academic performance. It can be found optimised Support Vector Regression (SVR) model accurately predicts student academic performance using a benchmark dataset from Kaggle. The approach incorporated Recursive Feature Elimination (RFE) for optimal feature selection while key parameters of the SVR model are tuned through grid search optimisation to enhance model performance. The optimised SVR model achieves exceptional predictive accuracy with an R-squared value of 0.97, Mean Absolute Error of 2.98, Mean Squared Error of 13.28, Root Mean Squared Error of 3.64, Median Absolute Error of 2.65, and Mean Absolute Percentage Error of 0.23. Comparative analysis demonstrates the superior performance of optimised SVR model against Decision Tree, Random Forest, K-Nearest Neighbors, Bayesian Ridge, and Gradient Boosting models. Experimental results demonstrate the validity of robustness and reliability of our approach, offering valuable insights for educational institutions to develop targeted interventions and support systems. © 2024 IEEE.

关键词： Educational Data Mining Grid Search Optimization Machine Learning Recursive Feature Elimination Student Performance Prediction

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Towards LLM-Generated Affective Phrase Sets for Text Entry Evaluation 24

Towards LLM-Generated Affective Phrase Sets for Text Entry E...

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23rd International Conference on Mobile and Ubiquitous Multimedia, MUM 2024

作者： Komninos, Andreas Simou, Ioulia Fotopoulos, Angelos Michanetzi, Eleftheria Lito Xenos, Michalis Computer Engineering & Informatics Department University of Patras Rio Greece

ISBN: (纸本)9798400712838

We present a novel method to generate affective phrase sets using Large Language Models (LLMs), for use in text entry study transcription tasks. These phrases could be used to adjust participant emotions, and enable performance assessment under various affective conditions. We found that the affective phrases impacted participants' valence, but not arousal or text entry performance. Though partially succesful, our work provides a foundation for exploring the relationship between emotion and text entry performance in lab studies. © 2024 Copyright held by the owner/author(s).

关键词： Text Entry

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CBIR framework for Diagnosing COVID 19 in Chest X-Ray Images using Deep learning by integrating Photometric Data Augmentation 9

CBIR framework for Diagnosing COVID 19 in Chest X-Ray Images...

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9th IEEE International Conference on Adaptive Science and Technology, ICAST 2024

作者： Martey, Ezekiel Mensah Appiah, Obed Sitti, Mensah Nusetor, Foster Delali Department of Computer Science and Engineering Tarkwa Ghana Department of Computer Science and Informatics Sunyani Ghana

ISBN: (纸本)9798350385403

computer-Aided Diagnostic (CAD) systems enhance clinical decision-making, providing more accurate and streamlined processes. CAD systems based on Content-Based Image Retrieval (CBIR) serve as visual tools that strengthen the reliability of clinical decisions. While deep learning-based CAD systems have shown significant progress in extracting insights from X-rays and medical images, they often struggle due to their reliance on large datasets and high computational demands. These challenges are particularly acute in the medical domain, where datasets are frequently limited and imbalanced. This paper presents a framework for diagnosing COVID-19 from X-ray images, combining deep learning with photometric data augmentation. The proposed approach focuses on two core tasks: image retrieval and classification, which depend on robust feature representation. Using Euclidean distance measures, we utilise a modified EfficientNet-B3 model for feature extraction and examine the effects of transfer learning and fine-tuning on retrieval and diagnostic accuracy. Evaluating binary and multiclass datasets reveals that the proposed method performs strongly in classification tasks, with notable effectiveness in binary and multiclass scenarios. Compared to state-of-the-art techniques, our approach demonstrates superior accuracy for binary classification of X-ray images, outperforming most existing models. © 2024 IEEE.

关键词： COVID-19

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Personalized User Experience and Engagement in Interactive Mobile Augmented Reality Software Through Fuzzy Weights 19th

Personalized User Experience and Engagement in Interactive M...

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19th International Conference on Web Information Systems and Technologies, WEBIST 2023

作者： Papakostas, Christos Troussas, Christos Krouska, Akrivi Mylonas, Phivos Sgouropoulou, Cleo Department of Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (纸本)9783031896200

The integration of digital content into the real world through mobile augmented reality (AR) systems presents captivating prospects, yet user engagement and experience within such environments are largely contingent upon individual computer skills, which can vary significantly among users, thereby influencing their proficiency in utilizing this technology. This paper aims to bridge this gap by examining the impact of users’ computer skills on interactions within mobile AR settings. To this end, a novel fuzzy logic-based model is introduced to evaluate and refine users’ computer skills within the realm of mobile AR systems. By modeling users’ computer aptitude, the system is capable of delivering tailored assistive prompts and feedback. These prompts are strategically aligned with established fuzzy weights, thereby augmenting users’ interactions within the mobile AR sphere. The proposed methodology is seamlessly integrated into a personalized mobile AR platform designed for spatial ability training. Evaluation findings reveal promising outcomes, underscoring the substantial potential of fuzzy logic modeling to elevate user experiences and propel advancements in mobile augmented reality technology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Teaching

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