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23rd International Conference on New Interfaces for Musical Expression, NIME 2023

作者： Díaz-Durán, Joaquín R. Vidal, Laia Turmo Tajadura-Jiménez, Ana DEI Interactive Systems Group Department of Computer Science and Engineering Universidad Carlos III de Madrid Madrid Spain

Joakinator is a wearable interactive interface that allows users to activate different media materials, such as sound, music, and video, through body gestures. The device, designed in the context of music and performing arts, integrates surface electromyogram, force sensors, and machine learning algorithms with tailored-made software for sonifying muscle-tone and force. This allows the body to reflect expressively the content of the media and the architecture of the device. Recently, we have started to investigate the potential of Joakinator to alter body perception in the context of BODYinTRANSIT, a European Research Council Project focused on the transformations of body perception through the use of interactive sound/haptics technology. At NIME-2023, we will showcase Joakinator and invite visitors to experience the device firsthand. Visitors will have the opportunity to try on the device, observe others using it, and reflect on its capabilities to transform body movement and perception through the sonification of muscle-tone and force. Overall, Joakinator is a technology that pushes the boundaries of bodycomputer interaction and opens new possibilities for human-computer interaction and expression. © 2023, International Conference on New Interfaces for Musical Expression. All rights reserved.

关键词： Machine Perception

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Conditioned fully convolutional denoising autoencoder for multi-target NILM

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Neural Computing and Applications 2025年第17期37卷 10491-10505页

作者： García, Diego Pérez, Daniel Papapetrou, Panagiotis Díaz, Ignacio Cuadrado, Abel A. Enguita, José M. Domínguez, Manuel Department of Electrical Engineering University of Oviedo Principado de Asturias Gijón33209 Spain SUPPRESS Research Group University of León León24007 Spain Department of Computer and Systems Science Stockholm University Kista SE-164 07 Stockholm Sweden

Energy management requires reliable tools to support decisions aimed at optimising consumption. Advances in data-driven models provide techniques like Non-Intrusive Load Monitoring (NILM), which estimates the energy demand of appliances from total consumption. Common single-target NILM approaches perform energy disaggregation by using separate learned models for each device. However, the use of single-target systems in real scenarios is computationally expensive and can obscure the interpretation of the resulting feedback. This study assesses a conditioned deep neural network built upon a Fully Convolutional Denoising AutoEncoder (FCNdAE) as multi-target NILM model. The network performs multiple disaggregations using a conditioning input that allows the specification of the target appliance. Experiments compare this approach with several single-target and multi-target models using public residential data from households and non-residential data from a hospital facility. Results show that the multi-target FCNdAE model enhances the disaggregation accuracy compared to previous models, particularly in non-residential data, and improves computational efficiency by reducing the number of trainable weights below 2 million and inference time below 0.25 s for several sequence lengths. Furthermore, the conditioning input helps the user to interpret the model and gain insight into its internal behaviour when predicting the energy demand of different appliances. © The Author(s) 2024.

关键词： Convolutional neural networks

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A Graph-based Approach to Human Activity Recognition

A Graph-based Approach to Human Activity Recognition

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IEEE International Conference on Cognitive Machine Intelligence (CogMI)

作者： Thomas Peroutka Ilir Murturi Praveen Kumar Donta Schahram Dustdar Distributed Systems Group TU Wien Vienna Austria Department of Computer and Systems Sciences Stockholm University Stockholm Sweden

ISBN: (数字)9798350386721

ISBN: (纸本)9798350386738

Advanced wearable sensor devices have enabled the recording of vast amounts of movement data from individuals regarding their physical activities. This data offers valuable insights that enhance our understanding of how physical activities contribute to improved physical health and overall quality of life. Consequently, there is a growing need for efficient methods to extract significant insights from these rapidly expanding real-time datasets. This paper presents a methodology to efficiently extract substantial insights from these expanding datasets, focusing on professional sports but applicable to various human activities. By utilizing data from Inertial Measurement Units (IMU) and Global Navigation Satellite systems (GNSS) receivers, athletic performance can be analyzed using directed graphs to encode knowledge of complex movements. Our approach is demonstrated on biathlon data and detects specific points of interest and complex movement sequences, facilitating the comparison and analysis of human physical performance.

关键词： Performance evaluation Global navigation satellite system Measurement units Tracking Receivers Fatigue Real-time systems Recording Wearable sensors Sports

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The Inception of Electronic Digital Computing

The Inception of Electronic Digital Computing

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Automatics and Informatics (ICAI), International Conference

作者： Vladimir S. Getov Distributed and Intelligent Systems Research Group School of Computer Science and Engineering University of Westminster London United Kingdom

ISBN: (数字)9798350353907

ISBN: (纸本)9798350353914

The goal of this paper is to review the first projects for building automatic computing machines during the first half of the 20th century. The presented timeline shows that 85 years ago – in October 1939 – the first breadboard proof-of-concept prototype of an electronic digital computer became operational. The credit for this revolutionary invention goes to John Vincent Atanasoff at Iowa State College. Using fresh insights and some nearly forgotten facts, we also review the origins of the IEEE computer Society and the role of organized professional activities, as well as the customer demand for spreading new ideas and design solutions for the fast-growing computer industry. The paper recognizes and explains the contribution of John Vincent Atanasoff to the invention and early development of electronic digital computing and the design of the modern computers that changed the world.

关键词： Digital computers Technological innovation Reviews Buildings Breadboard computer industry History Informatics Arithmetic

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Compact Multitasking Multichromosome Genetic Algorithm for Heuristic Selection in Ontology Matching

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6752-6766页

作者： Xue, Xingsi Lin, Jerry Chun-Wei Su, Tong Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian Fuzhou350118 China Silesian University of Technology Department of Distributed Systems and IT Devices Akademicka Gliwice44-100 Poland Fujian University of Technology School of Computer Science and Mathematics Fujian Fuzhou350118 China

Ontology matching (OM) is critical for knowledge integration and system interoperability on the semantic web, tasked with identifying semantically related entities across different ontologies. Despite its importance, the complexity of terminology semantics and the large number of potential matches present significant challenges. Existing methods often struggle to balance between accurately capturing the multifaceted nature of semantic relationships and computational efficiency. This work introduces a novel approach, a compact multitasking multichromosome genetic algorithm for Heuristic selection (HS) in OM, designed to navigate the nuanced hierarchical structure of ontologies and diverse entity mapping preferences. Our method combines compact genetic algorithms with multichromosome optimization for entity sequencing and assigning HS, alongside an adaptive knowledge transfer mechanism to finely balance exploration and exploitation efforts. Evaluated on the ontology alignment evaluation initiative's benchmark, our algorithm demonstrates superior ability to produce high-quality ontology alignments efficiently, surpassing comparative methods in both effectiveness and efficiency. These findings underscore the potential of advanced genetic algorithms in enhancing OM processes, offering significant contributions to the broader AI field by improving the interoperability and knowledge integration capabilities of semantic web technologies. © 2024 IEEE.

关键词： Genetic algorithms

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Blockchain-Based Privacy-Aware Voting System

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SN computer science 2024年第8期5卷 1079页

作者： Bendjemaa, Isra Cherifi, Katia Ines Benarous, Leila Boudjit, Saadi Department of Computer Science Faculty of Science University of Amar Telidji Laghouat Algeria LIM Laboratory Faculty of Science University of Amar Telidji Laghouat Algeria LITIS Lab Intelligent Transportation Systems Group University of Rouen Normandy Rouen France

Traditional voting methods, conducted at physical ballot boxes, incur numerous expenses related to campaign logistics, equipping voting centers, and compensating workers. However, these methods may not guarantee the honesty, traceability, and integrity of vote results. In contrast, centralized electronic voting streamlines the process, saving resources and time while automating ballot counting for faster and more accurate outcomes. Despite these advantages, centralized systems are vulnerable to forgery and lack transparency due to their reliance on private servers controlled by the voting authority. To address the issue of single points of failure associated with private servers and to enhance transparency, a distributed, secure, and fully connected technology is required. Therefore, we propose a blockchain-based electronic voting system with a web application as its frontend. The implementation of blockchain technology helps prevent forgery and ensures immutability, as votes are openly monitored by all participants. However, the transparency and traceability provided by blockchain—essential for ensuring that no voter casts multiple votes and that no unauthorized votes are injected—contradict the confidentiality and privacy requirements of the voting process. To reconcile the need for transparency with confidentiality, our proposal incorporates additional security measures, including key images, group addresses, and group signatures. This privacy-aware blockchain-based solution guarantees the uniqueness of each vote and prevents multiple voting attempts without compromising voter confidentiality. We evaluated the security of our designed solution using an attack tree to identify potential threats and estimate their likelihood of occurrence. The results shows that the proposed system is 3.2 time more secure compared to the traditional centralized voting system. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain e-Voting Forgery-resilient Privacy Security Transparency

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GPU accelerated blockchain over key-value database transactions

IET Blockchain

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IET Blockchain 2022年第1期2卷 1-12页

作者： Iliakis, Konstantinos Koliogeorgi, Konstantina Litke, Antonios Varvarigou, Theodora Soudris, Dimitrios Microprocessors and Digital Systems Laboratory Department of Electrical and Computer Engineering National Technical University of Athens Zografou Campous 9 Iroon Polytechniou str Athens Greece Distributed Knowledge and Media Systems group Department of Electrical and Computer Engineering National Technical University of Athens Zografou Campous 9 Iroon Polytechniou str Athens Greece

Blockchain is a distributed ledger based on peer-to-peer networks, originally used for crypto-currency systems. Blockchains are being used as an enabling technology for decentralised applications in the areas of Internet-of-Things, finance, supply-chain and others. Consistency, data privacy, performance, and energy efficiency are of paramount importance in such applications. The full nodes of public, permissionless blockchains undertake the task of verifying the transactions generated by the network. Full nodes perform operations such as confirming balances, transactions, and history, i.e. mostly database search queries. Consequently, their throughput is crucial for the performance of blockchain systems. In this work, the benefits of accelerating the blockchain search and insert queries by leveraging GPU platforms are studied. An extensive comparison between the most dominant utilized database is provided, i.e. LevelDB, and MegaKV, a high-performance GPU-accelerated database. Realistic operations that take place in blockchain systems are emulated and evaluated over representative scenarios, showing three orders of magnitude gains in throughput and energy efficiency without compromising the security aspect. The extensive comparison between LevelDB and MegaKV indicates that GPU acceleration is an effective solution for runtime and energy efficiency enhancement of blockchain systems, and the integration of the two technologies is a promising field of research. © 2022 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Blockchain

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Towards Sustainable Energy Communities: Local Optimization of Battery Usage in P2P Energy Trading

Towards Sustainable Energy Communities: Local Optimization o...

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2023 IEEE International Conference on Omni-Layer Intelligent systems, COINS 2023

作者： Grob, Christoph Gehring, Florian Bringmann, Oliver FZI Research Center for Information Technology Karlsruhe Germany University of Tübingen Faculty of Science Embedded Systems Group Department of Computer Science Germany

ISBN: (纸本)9798350346473

As renewable energy resources are integrated into the power grid to address the global energy and climate crisis, local energy markets have emerged as a possible solution. In such markets, peers in a smart grid trade locally generated energy. To reduce the stress on the grid caused by the volatility of renewable energy sources, households use electric batteries to maximize their self-consumption of generated energy. In this paper, we propose a peer-To-peer trading platform that integrates batteries into the distributed energy management trading system. We show that local optimization of single households not only benefits the individual households in terms of increased self-consumption and financial gains but also has an overall positive impact on the energy community compared to a simple battery implementation. © 2023 IEEE.

关键词： Smart power grids

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Evaluation of Bfloat16, Posit, and Takum Arithmetics in Sparse Linear Solvers

arXiv

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arXiv 2024年

作者： Hunhold, Laslo Quinlan, James Parallel and Distributed Systems Group University of Cologne Cologne Germany Department of Computer Science University of Southern Maine Portland ME United States

Solving sparse linear systems lies at the core of numerous computational applications. Consequently, understanding the performance of recently proposed alternatives to the established IEEE 754 floating-point numbers, such as bfloat16 and the tapered-precision posit and takum machine number formats, is of significant interest. This paper examines these formats in the context of widely used solvers, namely LU, QR, and GMRES, with incomplete LU preconditioning and mixed precision iterative refinement (MPIR). This contrasts with the prevailing emphasis on designing specialized algorithms tailored to new arithmetic formats. This paper presents an extensive and unprecedented evaluation based on the SuiteSparse Matrix Collection—a dataset of real-world matrices with diverse sizes and condition numbers. A key contribution is the faithful reproduction of SuiteSparse’s UMFPACK multifrontal LU factorization and SPQR multifrontal QR factorization for machine number formats beyond single and double-precision IEEE 754. Tapered-precision posit and takum formats show better accuracy in direct solvers and reduced iteration counts in indirect solvers. Takum arithmetic, in particular, exhibits exceptional stability, even at low precision. © 2024, CC BY.

关键词： Iterative methods

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Hybrid-MELAu: A Hybrid Mixing Engineered Linguistic Features Framework Based on Autoencoder for Social Bot Detection

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Informatica (Slovenia) 2022年第6期46卷 143-158页

作者： Hamida, Zineb Ferhat Refoufi, Allaoua Drif, Ahlem Giordano, Silvia Networks and Distributed Systems Laboratory Department of Computer Science University of Sétif 1 Sétif Algeria Networking Lab SUPSI University of Applied Sciences of Southern Switzerland Lugano Switzerland

Social bots are defined as computer algorithms that generate massive amounts of obnoxious or meaningful information. Most bot detection methods leverage multitudinous characteristics, from network features, temporal dynamics features, activities features, and sentiment features. However, there has been fairly lower work exploring lexicon measurement and linguistic indicators to detect bots. The main purpose of this research is to recognize the social bots through their writing style. Thus, we carried out an exploratory study on the effectiveness of only a set of linguistic features (17 features) exploitable for bot detection, without the need to resort to other types of features. And we develop a novel framework in a hybrid fashion of Mixing Engineered Linguistic features based on Autoencoders (Hybrid-MELAu). The semi-supervised Hybrid-MELAu framework is composed of two essential constituents: the features learner and the predictors. We establish the features learner innovated on two powerful structures: a) the first is a Deep dense Autoencoder fed by the Lexical and the Syntactic content (DALS) that represents the high order lexical and syntactic features in latent space, b) the second one is a Glove-BiLSTM autoencoder, which sculpts the semantic features;subsequently, we generate elite elements from the pre-trained encoder part from each latent space with transfer learning. We consider a sample of 1 Million from Cresci datasets to conduct our linguistic analysis comparison between the writing style of humans and bots. With this dataset, we observe that the bot's textual lexical diversity median is greater than the human one and the syntactic analysis based on speech-tagging shows a creative behavior in human writing style. Finally, we test the model's robustness on several public dataset (celebrity, pronbots-2019, and political bots). The proposed framework achieves a good accuracy of 92.22%. Overall, the results shown in this paper, and the related discussion, ar

关键词： Classification (of information)

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