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Learning label-specific features for decomposition-based multi-class classification

Frontiers of computer Science 2023年第6期17卷 101-110页

作者： Bin-Bin JIA Jun-Ying LIU Jun-Yi HANG Min-Ling ZHANG College of Electrical and Information Engineering Lanzhou University of TechnologyLanzhou 730050China School of Computer Science and Engineering Southeast UniversityNanjing 210096China Key Laboratory of Computer Network and Information Integration(Ministry of Education) Southeast UniversityNanjing 210096China

Multi-class classification can be solved by decomposing it into a set of binary classification problems according to some encoding rules,e.g.,one-vs-one,one-vs-rest,error-correcting output *** works solve these binary classification problems in the original feature space,while it might be suboptimal as different binary classification problems correspond to different positive and negative *** this paper,we propose to learn label-specific features for each decomposed binary classification problem to consider the specific characteristics containing in its positive and negative ***,to generate the label-specific features,clustering analysis is respectively conducted on the positive and negative examples in each decomposed binary data set to discover their inherent information and then label-specific features for one example are obtained by measuring the similarity between it and all cluster *** clearly validate the effectiveness of learning label-specific features for decomposition-based multi-class classification.

关键词： machine learning multi-class classification error-correcting output codes label-specific features

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A Next Generation Library of AI-Based Data-Driven Services for the Built Environment 14

A Next Generation Library of AI-Based Data-Driven Services f...

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14th International Conference on Information, Intelligence, systems and Applications, IISA 2023

作者： Sarmas, Elissaios Stamatopoulos, Stathis Kapsalis, Panagiotis Touloumis, Konstantinos Marinakis, Vangelis School of Electrical and Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Athens Greece

ISBN: (纸本)9798350318067

Nowadays, buildings account for almost 40% of the overall energy consumption. Thus, it is essential to involve a broad range of stakeholders throughout the building lifecycle, with the development of innovative, cross-cutting energy services, which exploit the ever-increasing volume of available data. In this paper we present a novel library of AI-based data-driven services for the built environment with the aim of addressing decarbonization challenges and of facilitating datadriven decision making. The issue of handling energy-related data in an efficient way is tackled, incorporating them in user-friendly, micro-services oriented technological components. The architecture of this AI-base library is thoroughly presented covering learning, optimization and simulation tasks. showcase our approach to energy resource management. © 2023 IEEE.

关键词： Intelligent systems

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An Integrated ML-Ops Framework for Automating AI-based Photovoltaic Forecasting

An Integrated ML-Ops Framework for Automating AI-based Photo...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Chorozoglou, Symeon Sarmas, Elissaios Marinakis, Vangelis National Technical University of Athens Decision Support Systems Laboratory School of Electrical and Computer Engineering Athens Greece

ISBN: (纸本)9798350324457

Energy digitization holds significant importance for various energy applications, encompassing aspects like production, consumption, and distribution within power grids. The digital transformation of energy plays a pivotal role in enhancing the integration of Artificial Intelligence (AI) into energy management systems, leveraging extensive datasets. The development of AI systems and the utilization of Machine Learning (ML) techniques empower users with precise predictions related to renewable energy production, thereby expediting the shift towards clean energy. Nevertheless, the effective use of data demands a high level of expertise, thereby excluding energy stakeholders from the benefits modern technologies offer. In this paper, we introduce an AI forecasting system designed to bridge the knowledge gap in data processing methods and ML models for energy stakeholders. This system focuses on delivering a user-friendly interface for photovoltaic (PV) production forecasting by automating the entire ML operations pipeline. Consequently, users can obtain data-driven model results without the need to manually code all the requisite steps for model training and fine-tuning. To demonstrate the system's capabilities, we provide an experimental application using real PV data from a Portuguese aggregator. © 2023 IEEE.

关键词： Artificial Intelligence Forecasting Machine Learning Photovoltaics Renewable Energy Sources

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Contract-Based Incentive Mechanisms for UAV Networks in Covert Communications

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7378-7391页

作者： Tian, Wen Fan, Hao Liu, Guangjie Du, Miao Liu, Yinqiu Niyato, Dusit Han, Zhu Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing University of Information Science and Technology Key Laboratory of Intelligent Support Technology In Complex Environment Ministry of Education Nanjing210044 China Southeat University School of Computer Science and Engineering China Southeast University Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Covert communication is considered a promising technology for hiding transmission processes and activities from malicious eavesdropping. With the development of detection technology, the traditional point-to-point covert communication method cannot effectively guarantee the reliability of covert communication. In order to solve this problem, unmanned aerial vehicle (UAV) are widely adopted in covert communication as common relay equipment. However, the UAV maximizes its benefits by taking advantage of the information asymmetry between itself and the covert communication sender. Therefore, it is necessary to encourage UAV-assisted potential users (UPU) to transmit covert data honestly by designing an information asymmetry-based incentive mechanism. In this paper, we propose a UAV-assisted contract-theoretic (UACT) model to improve the covert communication performance, where UPU honestly transmits covert data and the throughput of UACT can be increased. Specifically, we first divide UPU into finite types and model the data transfer contract between the sender, UPU, and receiver. Then, the contract feasibility of UACT is derived with sufficient and necessary conditions. Finally, we analyze the contract optimization in the continuous case of UPUs. Numerical simulations show that the UACT can incentivize UPUs to transmit covert data through UAV assistance and make covert communication performance close to the information symmetry case. © 1967-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Comparison of Machine Learning Algorithms For Predicting CO2Emissions in the maritime domain 14

Comparison of Machine Learning Algorithms For Predicting CO2...

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14th International Conference on Information, Intelligence, systems and Applications, IISA 2023

作者： Michalakopoulos, Vasilis Ilias, Loukas Kapsalis, Panagiotis Mouzakitis, Spiros Askounis, Dimitris School of Electrical & Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Athens Greece

ISBN: (纸本)9798350318067

The maritime industry is a significant source of global carbon dioxide (CO2) emissions, and the accurate prediction of emissions in this domain is of paramount importance. In this paper, we conduct a comparative analysis of machine learning algorithms for predicting CO2 emissions in the maritime domain. Using a unique dataset, comprising historical maritime data for different vessel voyages, including vessel type, voyage information and environmental factors, we employ decision trees, a number of regression, and artificial neural network algorithms. Performance evaluation is conducted using established metrics such as R2 (%), Mean Absolute Error, Normalized Root Mean Squared Error and Symmetric Mean Biased Error. Our findings reveal that the Extra Trees Regressor and Multi-layer Perceptron regressor algorithms outperform the other methods in terms of prediction accuracy demonstrating the least amount of error. Our research contributes to the existing literature by highlighting the potential of machine learning in predicting maritime CO2 emissions and provides insights for further research, such as exploring alternative algorithms and incorporating real-time data Integration. © 2023 IEEE.

关键词： Carbon dioxide

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Language Models as Knowledge Bases for Visual Word Sense Disambiguation 1

Language Models as Knowledge Bases for Visual Word Sense Dis...

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1st Workshop on Knowledge Base Construction from Pre-Trained Language Models and the 2nd Challenge on Language Models for Knowledge Base Construction, KBC-LM + LM-KBC 2023

作者： Kritharoula, Anastasia Lymperaiou, Maria Stamou, Giorgos Artificial Intelligence and Learning Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Greece

Visual Word Sense Disambiguation (VWSD) is a novel challenging task that lies between linguistic sense disambiguation and fine-grained multimodal retrieval. The recent advancements in the development of visiolinguistic (VL) transformers suggest some off-the-self implementations with encouraging results, which however we argue that can be further improved. To this end, we propose some knowledge-enhancement techniques towards improving the retrieval performance of VL transformers via the usage of Large Language Models (LLMs) as Knowledge Bases. More specifically, knowledge stored in LLMs is retrieved with the help of appropriate prompts in a zero-shot manner, achieving performance advancements. Moreover, we convert VWSD to a purely textual question-answering (QA) problem by considering generated image captions as multiple-choice candidate answers. Zero-shot and few-shot prompting strategies are leveraged to explore the potential of such a transformation, while Chain-of-Thought (CoT) prompting in the zero-shot setting is able to reveal the internal reasoning steps an LLM follows to select the appropriate candidate. In total, our presented approach is the first one to analyze the merits of exploiting knowledge stored in LLMs in different ways to solve WVSD. © 2023 CEUR-WS. All rights reserved.

关键词： Computational linguistics

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Latest Innovations in Intelligent Network-on-Chip Architectures: A Systematic Review 17

Latest Innovations in Intelligent Network-on-Chip Architectu...

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17th IEEE/ACM International Workshop on Network on Chip Architectures, NoCArc 2024

作者： Kakoulli, Elena Neapolis University Pafos Intelligent Systems Laboratory Department of Computer Science Pafos Cyprus Cyprus University of Technology Electrical Engineering and Computer Engineering and Informatics Limassol Cyprus

ISBN: (纸本)9798331506421

This review investigates the latest advancements in intelligent Network-on-Chip (NoC) architectures, focusing on innovations from 2022 to 2024. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into NoC designs has driven significant improvements in performance, scalability, energy efficiency, and security. Key contributions include AI/ML-driven techniques to optimize routing, reduce latency, manage power consumption, and enhancing security against emerging threats. Despite these advancements, challenges such as computational overhead and integration complexities remain. This review consolidates these findings, addresses existing challenges, and proposes future research directions to further leverage AI and ML for developing robust and adaptable NoC architectures. © 2024 IEEE.

关键词： Federated learning

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Alkali Atomic Density Detection with Arbitrarily Polarized Light

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IEEE Sensors Journal 2025年第11期25卷 20466-20473页

作者： Liu, Xuejing Wang, Menglu Li, Yuhan Wang, Yangtao Zhang, Xuedian Li, Yang University of Shanghai for Science and Technology Key Laboratory of Optical Technology and Instrument for Medicine Ministry of Education School of Optical-Electrical and Computer Engineering Shanghai200093 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Tongji University Shanghai Institute of Intelligent Science and Technology Shanghai200093 China

The atomic density of an alkali vapor cell is a critical factor in determining the performance of atomic magnetometers. This paper presents a novel method to detect the density of an alkali atomic ensemble under arbitrary polarization states. By establishing a comprehensive model that correlates the absorption spectrum with the incident light’s polarization, our approach enables real-time and flexible measurements across varying temperatures and polarization angles. Experimental validations are conducted by systematically rotating a quarter-wave plate and controlling the vapor cell temperature, thereby collecting transmission spectra at multiple polarizations. The results demonstrate that the proposed method simplifies density measurements while preserving high accuracy, laying a solid foundation for further research on Spin-Exchange Relaxation-Free (SERF) atomic magnetometers. This technique serves as a versatile and effective tool for in-situ monitoring of alkali metal vapor density in various sensor applications. © 2001-2012 IEEE.

关键词： Absorption spectroscopy

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A Comprehensive Approach to Real-Time and Batch Processing for Energy-Efficient IoT Homes: Leveraging Lambda Architecture and Data Lakes 15

A Comprehensive Approach to Real-Time and Batch Processing f...

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15th International Conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Serepas, Filippos Papias, Ioannis Bellos, Nikolaos Marinakis, Vangelis Holistic Ike Athens Greece School of Electrical & Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Athens Greece

ISBN: (纸本)9798350368833

The incorporation of IoT technology into energy-efficient home systems has resulted in a surge in data volume, prompting the need for sophisticated storage and processing solutions. This paper proposes a system that integrates the Lambda Architecture with data lakes to address real-time and batch processing needs in the context of energy-efficient homes. By leveraging technologies such as TimescaleDB for short-term storage and Apache Hudi for long-term storage, coupled with Kafka for data streaming, the system ensures efficient data management and analysis. Real-time insights are provided through GraphQL-powered visualizations, while batch processing facilitates advanced analytics and machine learning model training. The proposed system addresses the dual demands of end-users seeking real-time insights and data scientists requiring extensive datasets for analysis. © 2024 IEEE.

关键词： Smart homes

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Combining Automatic Annotation with Human Validation for the Semantic Enrichment of Cultural Heritage Metadata

Combining Automatic Annotation with Human Validation for the...

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2024 Computational Humanities Research Conference, CHR 2024

作者： Kaldeli, Eirini Chortaras, Alexandros Lyberatos, Vassilis Liartis, Jason Kantarelis, Spyridon Stamou, Giorgos AI and Learning Systems Lab School of Electrical and Computer Engineering National Technical University of Athens Greece

The addition of controlled terms from linked open datasets and vocabularies to metadata can increase the discoverability and accessibility of digital collections. However, the task of semantic enrichment requires a lot of effort and resources that cultural heritage organizations often lack. State-of-the-art AI technologies can be employed to analyse textual metadata and match it with external semantic resources. Depending on the data characteristics and the objective of the enrichment, different approaches may need to be combined to achieve high-quality results. What is more, human inspection and validation of the automatic annotations should be an integral part of the overall enrichment methodology. In the current paper, we present a methodology and supporting digital platform, which combines a suite of automatic annotation tools with human validation for the enrichment of cultural heritage metadata within the European data space for cultural heritage. The methodology and platform have been applied and evaluated on a set of datasets on crafts heritage, leading to the publication of more than 133K enriched records to the Europeana platform. A statistical analysis of the achieved results is performed, which allows us to draw some interesting insights as to the appropriateness of annotation approaches in different contexts. The process also led to the creation of an openly available annotated dataset, which can be useful for the in-domain adaptation of ML-based enrichment tools. © 2024 Copyright for this paper by its authors.

关键词： Semantics

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