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On Bidirectional Deterministic Finite Automata 28th

On Bidirectional Deterministic Finite Automata

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28th International Conference on Implementation and Application of Automata (CIAA)

作者： Dieck, Simon Verwer, Sicco Delft Univ Technol Delft Netherlands

ISBN: (纸本)9783031711114;9783031711121

Bidirectional deterministic finite automata (biDFA) are a recent innovation with many potential applications. In this paper, we present novel theoretical results for bidirectional automata. We show that there exist regular languages, where minimal biDFA models are exponentially smaller than minimal DFA models. We show this for a language that has a structure common to software logs. This makes biDFA especially interesting when inferring models from such data. However, we also prove that the problem of biDFA minimization is NP-hard. As our key contribution, we provide a Myhill-Nerode style congruence-based characterization for the languages they can recognize. Since most algorithms for learning DFAs are based on such a congruence, this characterization is an important building block for obtaining learning algorithms.

关键词： DFA Regular languages Context-free languages Linear languages

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Towards Adaptive Multi-modal Augmentative and Alternative Communication for Children with CP 19th

Towards Adaptive Multi-modal Augmentative and Alternative Co...

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19th International Conference on computers Helping People with Special Needs (ICCHP)

作者： Zisman, Andrea Katz, Dmitri Bennasar, Mohamed Alrimawi, Faeq Price, Blaine Johnston, Anthony Open Univ Milton Keynes Bucks England Univ Limerik Limerick Ireland

ISBN: (纸本)9783031628481;9783031628498

Effective communication can pose significant challenges for nonverbal children with Cerebral Palsy (CP). Augmentative and Alternative Communication (AAC) systems help many but can fail to meet the needs of some users. This research proposes a hybrid adaptive approach, utilizing sensors and machine learning (ML) algorithms to create a personalized mobile communication system for those whose abilities are ill-suited to existing approaches. The system aims to tailor to individual abilities, reducing the need for users to adapt to system requirements. Online surveys gathered data on gestures, actions, and sounds used by non-verbal CP children, informing a classification system and functional requirements. The participants reported 28 communication messages with diverse means of expression. Representative examples and their classification highlight the intricacies of non-verbal communication. The proposed architecture emphasizes real-time classification, multiple sensors, and a feedback loop for continuous improvement, enhancing communication for non-verbal children with CP.

关键词： Cerebral Palsy Augmentative Alternative Communication (AAC) Adaptive Technology Machine Learning User-Centered Design Multi-modal

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Semantics-Preserved Distortion for Personal Privacy Protection in Information Management 33rd

Semantics-Preserved Distortion for Personal Privacy Protecti...

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33rd International Conference on Artificial Neural Networks and Machine Learning (ICANN)

作者： Li, Jiajia Yang, Lu Peng, Letian Zhang, Shitou Wang, Ping Li, Zuchao Zhao, Hai Wuhan Univ Sch Informat Management Wuhan Peoples R China NAAC Key Lab Archival Intelligent Dev & Serv Wuhan Peoples R China Wuhan Univ Sch Comp Sci Wuhan Peoples R China Univ Calif San Diego Comp Sci & Engn San Diego CA 92103 USA Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9783031723438;9783031723445

In recent years, machine learning-particularly deep learning-has significantly impacted the field of information management. While several strategies have been proposed to restrict models from learning and memorizing sensitive information from raw texts, this paper suggests a more linguistically-grounded approach to distort texts while maintaining semantic integrity. To this end, we leverage Neighboring Distribution Divergence, a novel metric to assess the preservation of semantic meaning during distortion. Building on this metric, we present two distinct frameworks for semantic-preserving distortion: a generative approach and a substitutive approach. Our evaluations across various tasks, including named entity recognition, constituency parsing, and machine reading comprehension, affirm the plausibility and efficacy of our distortion technique in personal privacy protection. We also test our method against attribute attacks in three privacy-focused assignments within the NLP domain, and the findings underscore the simplicity and efficacy of our data-based improvement approach over structural improvement approaches. Moreover, we explore privacy protection in a specific medical information management scenario, showing our method effectively limits sensitive data memorization, underscoring its practicality.

关键词： Semantic-Preserved Distortion Personal Privacy Protection Neighboring Distribution Divergence

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Interactive Evolutionary Computation Improving Voice Impressions with Keeping Speaker Personality for Real-Time Speech 23rd

Interactive Evolutionary Computation Improving Voice Impress...

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23rd International Conference on computer Information Systems and Industrial Management (CISIM)

作者： Fukumoto, Makoto Fukushima, Yuta Miyamoto, Taichi Fukuoka Inst Technol Fukuoka 8110295 Japan Fukuoka Inst Technol Grad Sch Engn Fukuoka 8110295 Japan

ISBN: (纸本)9783031711145;9783031711152

Recently, we generally have meetings via the Internet. In this situation, we use background display to improve our impression of other members of the meetings. To improve the users' voice via the Internet, this study proposes an Interactive Evolutionary Computation (IEC) that adjusts the voice filter based on real-time pronunciations while keeping user's personality. The concrete system was constructed by employing a Genetic Algorithm and Koigoe, a software voice filter. The listening experiments were conducted to investigate the efficiencies of the proposed IEC from perspectives of increasing the fitness values and keeping the speaker's personality. The results showed that the proposed IEC has enough possibility to find a good parameter set of the voice filter;however, we need to improve its performance because the obtained best filter did not overcome the impression of the original voice without any filter. Furthermore, the proposed IEC could be considered to keep the user's personality based on the result of the evaluation experiment.

关键词： Interactive Evolutionary Computation Genetic Algorithm Voice Filter Impression

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Q-Learning-Augmented Grant-Free NOMA for URLLC 9th

Q-Learning-Augmented Grant-Free NOMA for URLLC

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9th International Conference on Ubiquitous Networking (UNet)

作者： Oueslati, Ibtissem Habachi, Oussama Cances, Jean Pierre Meghdadi, Vahid Sabir, Essaid Xlim 123 Av Albert Thomas F-87000 Limoges France UCA 49 Bd Francois Mitterrand F-63000 Clermont Ferrand France TELUQ Dept Sci & Technol Montreal PQ H2S 3L4 Canada

ISBN: (纸本)9783031624872;9783031624889

Grant-Free (GF) Non-Orthogonal Multiple Access (GFNOMA) has emerged as a promising technology for 5G networks requiring Ultra-Reliable Low Latency Communications (URLLC). However, the grant-free nature of these transmissions can introduce significant interference, thereby, negatively affecting URLLC system performance. To address this challenge, this paper introduces a novel, distributed GF-NOMA-based Q-learning framework that aims to minimize network latency based on a developed Mean Opinion Score (MOS) of packet age, while also maintaining high transmission success rates. Real-time feedback from the gNodeB (gNB) is employed to assist Machine-Type Devices (MTDs) in making adaptive decisions of joint power control and sub-carrier selection. Simulation results validate the effectiveness of our approach in minimizing delay and optimizing overall system performance.

关键词： NOMA URLLC Q-Learning Grant-Free Access Low Latency

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Explainable Hybrid Semi-parametric Model for Prediction of Power Generated by Wind Turbines 24th

Explainable Hybrid Semi-parametric Model for Prediction of P...

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24th International Conference on Computational science (ICCS)

作者： Gijon, Alfonso Eiraudo, Simone Manjavacas, Antonio Bottaccioli, Lorenzo Lanzini, Andrea Molina-Solana, Miguel Gomez-Romero, Juan Univ Granada Dept Comp Sci & AI Granada Spain Imperial Coll London Dept Comp London England Politecn Torino Energy Ctr Lab Turin Italy

ISBN: (纸本)9783031637742;9783031637759

The ever-growing sector of wind energy underscores the importance of optimizing turbine operations and ensuring their maintenance with early fault detection mechanisms. Existing empirical and physics-based models provide approximate predictions of the generated power as a function of the wind speed, but face limitations in capturing the non-linear and complex relationships between input variables and output power. Data-driven methods present new avenues for enhancing wind turbine modeling using large datasets, thereby improving accuracy and efficiency. In this study, we use a hybrid semi-parametric model to leverage the strengths of two distinct approaches in a dataset with four turbines of a wind farm. Our model comprises a physics-inspired submodel, which offers a reliable approximation of the power, combined with a non-parametric submodel to predict the residual component. This non-parametric submodel is fed with a broader set of variables, aiming to capture phenomena not addressed by the physics-based part. For explainability purposes, the influence of input features on the output of the residual submodel is analyzed using SHAP values. The proposed hybrid model finally yields a 35-40% accuracy improvement in the prediction of power generation with respect to the physics-based model. At the same time, the explainability analysis, along with the physics grounding from the parametric submodel, ensure deep understanding of the analyzed problem. In the end, this investigation paves the way for assessing the impact, and thus the potential optimization, of several unmodeled independent variables on the power generated by wind turbines.

关键词： Hybrid semi-parametric models Explainable AI Wind Turbines

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Theorizing the Lean Startup Approach: Towards Well-Grounded Design Principles 19th

Theorizing the Lean Startup Approach: Towards Well-Grounded ...

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19th International Conference on Design science Research in Information Systems and Technology (DESRIST) - Design science Research for a Resilient Future

作者： Sielski, Katharina Seckler, Christoph ESCP Business Sch Berlin Germany

ISBN: (纸本)9783031611742;9783031611759

The lean startup approach and its principles are increasingly gaining relevance in entrepreneurial theory and practice. At the same time, key principles such as 'pivoting' and the 'build-measure-learn cycle' remain under-theorized. This hurts the clarity of these concepts, and it hinders more effective use of them in practice. We tackle both issues in this article. First, we draw on action regulation theory to theorize the practice concepts of 'pivoting,' as well as of the 'build-measure-learn cycle.' Subsequently, we build on this theorizing to develop theoretically grounded design principles. This article contributes to literature in two ways. First, theorizing the lean startup principles improves the clarity of the focal concepts and helps to understand why, for whom, and when they work. Second, the developed design principles contribute to the increasing body of design knowledge which provides scientifically grounded guidance for entrepreneurs and entrepreneurship educators.

关键词： Entrepreneurship Lean Startup Design Principles

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Two Heads Are Better Than One: Teaching MLPs with Multiple Graph Neural Networks via Knowledge Distillation 29th

Two Heads Are Better Than One: Teaching MLPs with Multiple G...

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29th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Yang, Bo-Wei Chang, Ming-Yi Lui, Chia-Hsun Shen, Chih-Ya Natl Tsing Hua Univ Dept Comp Sci Hsinchu Taiwan Fu Jen Catholic Univ Dept Sociol Taipei Taiwan

ISBN: (纸本)9789819755615;9789819755622

Knowledge Distillation (KD) has been successfully applied to compress and accelerate Graph Neural Networks (GNNs) in recent years. Recently, KD has been adeptly applied to boost Multi-Layer Perceptrons (MLPs), enabling them to parallel GNNs in performance. This is notable as MLPs depend solely on node features and require extensive knowledge from multiple teachers for enhancement. However, since existing multi-teacher KD methods often rely on a graph's topological data, they are typically limited to GNN-based student models, thus excluding MLPs. In this paper, we introduce an innovative method that amalgamates various GNNs into a super teacher, which is then distilled into an MLP student. Moreover, our experiments suggest the promising number of selected teachers configurations to boost the performance of the student model. Extensive experiments on five benchmark datasets show that our proposed approaches outperform the state-of-the-art methods and achieve even higher accuracy than the teacher models. Also, the inference time of our approach is 30x-60x faster than GNNs and other KD methods.

关键词： Graph neural networks Knowledge distillation Knowledge amalgamation

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Addressing the Privacy and Complexity of Urban Traffic Flow Prediction with Federated Learning and Spatiotemporal Graph Convolutional Networks 33rd

Addressing the Privacy and Complexity of Urban Traffic Flow ...

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33rd International Conference on Artificial Neural Networks and Machine Learning (ICANN)

作者： Zhou, Keyi Liu, Yuan Jiangnan Univ Wuxi Jiangsu Peoples R China

ISBN: (纸本)9783031723469;9783031723476

Traffic flow prediction is significant for metropolitan life today. However, existing systems predominantly adopt a deep learning model, often falling short of adequately safeguarding user privacy. Moreover, these systems tend to overlook how external factors affect traffic flow. To tackle these concerns, we propose a novel architecture based on federated learning and Spatiotemporal GCN. Simultaneously, we employ graph embedding techniques to incorporate external factors into the road network, which helps the model to consider multiple factors affecting traffic flow more fully. Evaluation on the real dataset shows that our framework can achieve high accuracy while preserving privacy.

关键词： Traffic flow prediction Federated learning Graph convolutional network

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The Downgrade of Bilbao, Spain's Leading Smart City 24th

The Downgrade of Bilbao, Spain's Leading Smart City

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24th International Conference on Computational science and Its Applications (ICCSA)

作者： Alvarez, Aimar Mora, Carlota Martis, Massimiliano Mutzu Garau, Chiara Univ Basque Country Bilbao Spain Univ Castilla La Mancha Toledo Spain Univ Cagliari Dept Civil & Environm Engn & Architecture DICAAR Via Marengo 2 I-09123 Cagliari Italy

ISBN: (纸本)9783031653285;9783031653292

This article aims to analyse the main variables responsible for the downgrade in the ranking of Bilbao in Spain as the top smart city, according to the International Institute for Management Development (IMD) index. The factors that will undergo rigorous study are those that contribute to either improvement or exacerbation between 2019 and 2023. The objective is to identify the primary causes for the decline in Bilbao's ranking places. This research may be of interest to cities seeking to enhance the quality of life for its residents by gaining insights from this report. This study examines the primary enhancements and failures that the city has implemented in certain areas. Gaining knowledge on how to prevent these failures and implementing enhancements in future urban areas is significant and remarkable.

关键词： Smart city Urban Sustainability Health and safety Mobility Technology Governance Liveable

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