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2nd ACM Conference on Information technology for Social Good, GoodIT 2022

作者： Mettouris, Christos Vanezi, Evangelia Zampas, George Argyrou, Stylianos Achilleos, Achilleas Constantinides, Avgoustinos Papadopoulos, George Angelos Department of Computer Science University of Cyprus Nicosia2109 Cyprus Department of Electrical and Computer Engineering and Informatics Frederick University 7 Yianni Frederickou Street Nicosia1036 Cyprus IBSCY Ltd Office 11 Harmony House 4 Theklas Lisioti street LimassolCY-3030 Cyprus

ISBN: (纸本)9781450392846

Although the adoption and use of cloud computing in large, medium and small enterprises is increasing every year, statistics show that medium and especially small enterprises have a much slower adoption than large enterprises. This suggests that various types of issues may affect Small to Medium Enterprises (SMEs) in making the choice to transit towards the public cloud. Based on experts and literature, SMEs' adoption of cloud is impacted by lack of resources, concerns on security and data control and uncontrolled increasing cloud costs due to the lack of cloud experience. A platform is not yet available that would assist non-technical SME managers in specifying non-technical business requirements in order to receive assistance in the form of recommendations of cloud resources, cloud applications and deployment plans. The CloudRecoMan platform aims to enable this through the development of a web-based cloud recommender and deployment platform. © 2022 Owner/Author.

关键词： Cloud computing

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Depth and Pitch Control of Autonomous Underwater Glider Using Model Predictive Control with ARX Model

Depth and Pitch Control of Autonomous Underwater Glider Usin...

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2023 International Conference on Electrical engineering and informatics, ICEEI 2023

作者： Jeremy, Jason Hidayat, Egi Muhammad Idris Siregar, Simon Trilaksono, Bambang Riyanto Habibullah, Natsir School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Telkom University Faculty of Applied Science Department Computer Engineering Bandung Indonesia

ISBN: (纸本)9798350381290

Autonomous Underwater Gliders (AUGs) are extensively developed vehicles capable of prolonged exploration and observation in complex marine environments. Control of the AUG is challenging due to its slow response system and its constraints. In this research, a linear model representation of AutoRegressive eXogenous input (ARX) will be constructed using input and output data from the AUG system, built Model Predictive Control (MPC), analyzed the performance, comparing with traditional Proportional-Integral-Derivative (PID). The objective is to enhance setpoint tracking accuracy and minimize energy to extend exploration time while faced with constraints. Simulation results reveal MPC exhibits potential setpoint tracking, low overshoot as low as 0.6% up to 0.3m/s maximum depth rate, has relatively low input changes indicating good efficiency compared to PID. MPC approach effectively addresses slow response systems, managing momentum, and handling actuator constraints commonly encountered in AUG. © 2023 IEEE.

关键词： Model predictive control

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Sentiment Analysis of Israeli-Palestinian Conflict on Indonesian Tweets Using Machine Learning 4

Sentiment Analysis of Israeli-Palestinian Conflict on Indone...

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4th International Conference on Electrical engineering and computer Science, ICECOS 2024

作者： Rahardi, Majid Arsya, Muhammad Rezzha Riamrizal Aminuddin, Afrig Abdulloh, Ferian Fauzi Miah, Mohammad Badrul Alam Pratikno, Heri Universitas Amikom Yogyakarta Faculty of Computer Science Sleman Indonesia Mawlana Bhashani Science and Technology University Faculty of Engineering Tangail Bangladesh Universitas Dinamika Faculty of Technology and Informatics Surabaya Indonesia

ISBN: (纸本)9798350368253

The social media of X has become a focal point for discussions on sensitive issues, including the Israeli-Palestinian conflict. This research analyzes sentiment toward tweets expressing support for Palestine on X, comparing the Support Vector Machine (SVM) and Naïve Bayes methods. Given the global attention to the conflict, sentiment analysis on this topic is crucial. The results reveal that the SVM model outperforms Naïve Bayes, achieving accuracy, precision, recall, and F1-score of 85%, 87%, 85%, and 85% for Naïve Bayes, and 91%, 92%, 90%, and 90% for SVM, respectively. It demonstrates the SVM model's capability to distinguish between positive and negative sentiments. Comparison with previous research indicates that this study surpasses the sentiment analysis results of previous SVM models, while Naïve Bayes excels in specific research. With superior performance, this research provides a new contribution to recognizing and classifying sentiments on a particular dataset related to the Israeli-Palestinian conflict on X social media. © 2024 IEEE.

关键词： Tweets

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A Toolbox for Supporting Research on AI inWater Distribution Networks

arXiv

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arXiv 2024年

作者： Artelt, André Kyriakou, Marios S. Vrachimis, Stelios G. Eliades, Demetrios G. Hammer, Barbara Polycarpou, Marios M. Faculty of Technology Bielefeld University Germany KIOS Research and Innovation Center of Excellence University of Cyprus Cyprus Department of Electrical and Computer Engineering University of Cyprus Cyprus

Drinking water is a vital resource for humanity, and thus,Water Distribution Networks (WDNs) are considered critical infrastructures in modern societies. The operation of WDNs is subject to diverse challenges such as water leakages and contamination, cyber/physical attacks, high energy consumption during pump operation, etc. With model-based methods reaching their limits due to various uncertainty sources, AI methods offer promising solutions to those challenges. In this work, we introduce a Python toolbox for complex scenario modeling & generation such that AI researchers can easily access challenging problems from the drinking water domain. Besides providing a high-level interface for the easy generation of hydraulic and water quality scenario data, it also provides easy access to popular event detection benchmarks and an environment for developing control algorithms. © 2024, CC BY.

关键词： Potable water

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Arabic Named Entity Recognition Using Clustered Word Embedding 19th

Arabic Named Entity Recognition Using Clustered Word Embeddi...

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19th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2018

作者： Sabty, Caroline Elmahdy, Mohamed Abdennadher, Slim Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Faculty of Informatics and Computer Science German International University Cairo Egypt

ISBN: (纸本)9783031237928

Named Entity Recognition in Arabic is a challenging topic because of morphological and lexical richness of Arabic. In this paper, we propose an Arabic NER system that is based on word embedding. Word embedding hold semantic information about the context of the words. We hypothesized that the integration of word embedding features to the conventional lexical and contextual features could improve Arabic NER performance. The Conditional Random Field (CRF) sequence classifier was used. Since most CRF implementations only support categorical features, continuous word embedding vectors are clustered. In this paper, we are mainly investigating the effect of the number of clusters on NER performance. Moreover, the combination of fine and coarse grained clusters has resulted in further recognition improvement. © 2023, Springer Nature Switzerland AG.

关键词： Embeddings

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Adjustable Gating Prompt Transformer for Facial Attribute Recognition with Limited Labeled Data 27th

Adjustable Gating Prompt Transformer for Facial Attribute R...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Ye, Qinxian Chen, Si Wang, Da-Han Jiang, Nanfeng Su, Yanfei Yan, Yan Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China School of Informatics Xiamen University Xiamen361005 China

ISBN: (纸本)9783031781032

Existing supervised facial attribute recognition (FAR) methods that rely on large labeled datasets can pose a challenge in real-world scenarios. In the case of limited labeled data, the current methods that introduce auxiliary tasks with a large number of parameters are not conducive to the embedded applications of FAR. To overcome these challenges, this paper develops an adjustable gating prompt Transformer that can handle the limited labeled FAR task with a small number of training parameters. Specifically, we employ an effective image-guided prompt tuning, where the image-related prompt sequence is first generated by feeding image tokens into an image-guided prompt generation network (IPG-Net). Then, the prompt sequence can learn facial image information and guide the frozen pre-trained Transformer to fine-tune the model. In addition, dynamically adjustable gating is applied to the prompt sequence to adaptively adjust the contribution of the prompts from different encoder layers, which enhances the interaction between the different encoder layers and retains effective feature information during the iterative process. Experimental results on the CelebA and LFWA datasets demonstrate that our method outperforms competitive methods with a very small amount of training parameters when only limited labeled data are used. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Signal encoding

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Introduction of hydrogen technologies to mitigate the impact of thermal consumption in a university campus 3

Introduction of hydrogen technologies to mitigate the impact...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Bracho, Jorge Charalambous, Chrysanthos Efthimiou, Venizelos Lagos, Dimitrios Samotyjak, Tomasz Askeland, Magnus Georghiou, George E. University of Cyprus PV Technology Laboratory Department of Electrical and Computer Engineering Nicosia Cyprus Efficient and Sustainable Energy Solutions PHAETHON Centre of Excellence for Intelligent Nicosia Cyprus Energy Systems Analytics Group Barcelona Spain Instytut Energetyki Gdansk Poland SINTEF Energy Research Energy Systems Trondheim Norway

ISBN: (纸本)9798350375923

It is imperative for energy systems to reduce their environmental footprint in a cost-efficient manner, for which renwable energy sources (RES) and complementary technologies become desirable options to achieve said task. This is particularly the case for systems with different energy sources, as they'd be able to either address one specific type of energy demand and/or allow for the coupling of different energy carriers that would allow for technologies that normally don't interact to start complementing each other. Therefore, this paper proposes various scenarios for the UCY campus in which hydrogen technologies would allow for the electrification of thermal demand while harnessing the surplus production coming from a solar park, which is modelled through the Integrate optimization tool and validated through DidSILENT PowerFactory. The results showcased a rise of up to 58% in self-sufficiency while the annual energy costs and CO2 emissions were reduced by 45% and 48% respectively. © 2024 IEEE.

关键词： Clean energy

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Prediction of compressive strength of high-performance concrete using optimization machine learning approaches with SHAP analysis

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Journal of Building Pathology and Rehabilitation 2024年第2期9卷 1-20页

作者： Islam, Md Mahamodul Das, Pobithra Rahman, Md Mahbubur Naz, Fasiha Kashem, Abul Nishat, Mosaraf Hosan Tabassum, Nujhat Department of Informatics University of Oslo Oslo Norway Department of Civil Engineering Leading University Sylhet Bangladesh Department of Industrial and Systems Engineering Lamar University Beaumont United States Department of Computer Science University of Agriculture Faisalabad Faisalabad Pakistan Department of Civil and Environmental Engineering Shahjalal University of Science & amp Technology Sylhet Bangladesh Department of Civil and Environmental Engineering Islamic University Of Technology -IUT(OIC) Gazipur Bangladesh Department of Electrical and Computer Engineering North South University Dhaka Bangladesh

Forecasting the compressive strength of high-performance concrete (HPC) is crucial for its practical applications. However, conducting experimental tests for this purpose demands significant resources and time. In recent times, data-driven modeling, particularly machine learning, has become prominent in this field. Thus, this study employed machine learning methods, including Gradient Boosting, Random Forest, and CatBoost algorithms, to predict the compressive strength of high-performance concrete (HPC). Furthermore, interpretable algorithms like SHapley Additive exPlanations (SHAP) were utilized in this study to assess the contribution of input variables to predictions. The dataset includes parameters such as cement, blast furnace slag, fly ash, water, coarse aggregate, sand, and age as input variables, with compressive strength as the output variable for models development. The CatBoost model performance was superior to GBM and RF models, with R² values of 0.979 for the training stage and 0.959 for the testing stage. Moreover, it demonstrated minimal mean absolute errors (MAE) of 2.37 MPa and 3 MPa for compressive strength prediction in the training and testing stages, respectively. Furthermore, the SHAP analysis revealed that age, cement, and superplasticizer are the primary influencers in determining compressive strength. The machine learning models presented in this study exhibit promising potential for practical implementation in different engineering applications. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Compressive strength High-performance concrete Machine learning SHAP analysis

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Guaranteed Coverage Prediction Intervals with Gaussian Process Regression

arXiv

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arXiv 2023年

作者： Papadopoulos, Harris The Department of Electrical Engineering Computer Engineering and Informatics Frederick University Nicosia1036 Cyprus

Gaussian Process Regression (GPR) is a popular regression method, which unlike most Machine Learning techniques, provides estimates of uncertainty for its predictions. These uncertainty estimates however, are based on the assumption that the model is well-specified, an assumption that is violated in most practical applications, since the required knowledge is rarely available. As a result, the produced uncertainty estimates can become very misleading;for example the prediction intervals (PIs) produced for the 95% confidence level may cover much less than 95% of the true labels. To address this issue, this paper introduces an extension of GPR based on a Machine Learning framework called, Conformal Prediction (CP). This extension guarantees the production of PIs with the required coverage even when the model is completely misspecified. The proposed approach combines the advantages of GPR with the valid coverage guarantee of CP, while the performed experimental results demonstrate its superiority over existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Prediction models

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Empirical mode decomposition of near-infrared spectroscopy signals for predicting oil content in palm fruits

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Information Processing in Agriculture 2023年第3期10卷 289-300页

作者： Inna Novianty Ringga Gilang Baskoro Muhammad Iqbal Nurulhaq Muhammad Achirul Nanda Computer Engineering Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Informatics Management Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Technology and Plantation Production Management Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Department of Agricultural and Biosystem Engineering Faculty of Agro-Industrial TechnologyUniversitas PadjadjaranJatinangor 45363Indonesia

Oil content estimation in palm fruits is a precious property that significantly impacts oil palm production,starting from the upstream and *** content can be used to monitor the progress of the oil palm fresh fruit bunch(FFB)and be applied to identify product *** on the near-infrared(NIR)signals,this study proposes an empirical mode decomposition(EMD)technique to decompose signals and predict the oil content of palm ***,350 palm fruits with Tenera varieties(Elaeis guineensis ***),at various ages of maturity,were harvested from the Cikabayan Oil Palm Plantation(IPB university,Indonesia).Second,each sample was sent directly to the laboratory for NIR signal measurements and oil content ***,the EMD analysis and arti-ficial neural network(ANN)were employed to correlate the NIR signals and oil ***,a robust EMD-ANN model is generated by optimizing the lowest possible *** on performance evaluation,the proposed technique can predict oil content with a coefficient of determination(R2)of 0.933±0.015 and a root mean squared error(RMSE)of 1.446±*** results demonstrate that the model has a good predictive capacity and has the potential to predict the oil content of palm fruits directly,without neither solvents nor reagents,which makes it environmentally ***,the proposed technique has a promising potential to be applied in the oil palm *** like this will lead to the effective and efficient management of oil palm production.

关键词： Artificial neural network Empirical mode decomposition Oil palm Oil content prediction

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