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Blockchain technology in the smart city: a bibliometric review

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Quality and Quantity 2022年第5期56卷 2875-2906页

作者： Rejeb, Abderahman Rejeb, Karim Simske, Steven J. Keogh, John G. Doctoral School of Regional Sciences and Business Administration Széchenyi István University Gyor 9026 Hungary Higher Institute of Computer Science El Manar 2 Rue Abou Raïhan El Bayrouni Ariana 2080 Tunisia Systems Engineering Department Colorado State University Fort Collins CO United States Henley Business School University of Reading Greenlands Henley-on-Thames RG9 3AU United Kingdom

Blockchain can function as a foundational technology with numerous applications in smart cities. The objective of this paper is twofold. First, it provides a detailed overview of the extant literature on blockchain applications in smart cities;second, it reveals the trends and suggests future research directions for scholars who wish to contribute to this rapidly growing field. We conducted a bibliometric review using a keyword co-occurrence network and article co-citation analysis. The analysis includes the assessment of 148 articles published between 2016 and 2020 in 76 academic journals. The review results demonstrate that the number of articles devoted to the study of blockchain applications and smart cities has increased exponentially in recent years. More importantly, the research identifies some of the most influential studies in this area. The paper discusses trends and highlights the challenges related to the deployment of blockchain in smart cities. To the authors’ best knowledge, this represents the first study to review the literature from leading journals on blockchain applications in smart cities using bibliometric techniques. © 2021, The Author(s).

关键词： Bibliometric review Blockchain technology Co-citation analysis Smart city

Spectrum Allocation Exploiting Different Core Selection Patterns for Graceful Degradation of Inter-Core Crosstalk in Space-Division Multiplexing Elastic Optical Networks

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Spectrum Allocation Exploiting Different Core Selection Patt...

Photonics in Switching and Computing, PSC 2021

作者： Seki, Yuya Tanigawa, Yosuke Hirota, Yusuke Tode, Hideki Department of Computer Science and Intelligent Systems Osaka Prefecture University 1-1 Gakuen-cho Naka-ku Sakai-shi Osaka599-8531 Japan National Institute of Information and Communications Technology 4-2-1 Nukui-Kitamachi Koganei Tokyo184-8795 Japan

We propose a spectrum allocation for graceful degradation of intercore crosstalk in space-division multiplexed elastic optical networks, which utilizes precalculated thresholds based on coupled power theory with multi...

ISBN: (纸本)9781557528209

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Using Euler's Elastica Regularization to Reconstruct 3d Surface from Point Clouds

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SSRN

SSRN 2023年

作者： Song, Jintao Pan, Huizhu Lu, Wenqi Ding, Jieyu Wei, Weibo Zhu, Haotian Liu, Wanquan Duan, Jinmng Pan, Zhenkuan College of Computer Science and Technology Qingdao University Qingdao266071 China School of Electrical Engineering Mathematical Science and Computing Curtin University PerthWA6102 Australia Tissue Image Analytics Centre Department of Computer Science University of Warwick CoventryCV4 7AL United Kingdom School of Arts and Sciences University of Pennsylvania Pennsylvania19104 United States School of Intelligent Systems Engineering Sun Yat-Sen university Shenzhen510275 China School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom

The Euler’s elastica energy regularizer, initially employed in mathematical and physical systems, has recently garnered much attention in image processing and computer vision tasks. While implicit surface reconstruction methods generally demonstrate superior robustness and ffexibility, the traditional methods rely on initialization and can easily become trapped in local minima. The Euler’s elastica term, on the other hand, has been found to share the advantages of both the TV regularization term and the curvature regularization term. In this paper, we aim to address the problems of missing details and complex computation in implicit 3D reconstruction by efffciently using the Euler’s elastica term. We propose a surface reconstruction model with the Euler’s elastica energy regularizer as well as a new dual method to simplify the model’s solution. Experimental results show that the proposed model can maintain the robustness and accuracy of existing variational surface reconstruction models while improving efffciency and model usability. © 2023, The Authors. All rights reserved.

关键词： Surface reconstruction

A principle feature analysis

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arXiv 2021年

作者： Breitenbach, Tim Rasbach, Lauritz Liang, Chunguang Jahnke, Patrick Biozentrum Universität Würzburg Am Hubland Würzburg97074 Germany Department of Computer Science Distributed Systems Programming Technische Universität Darmstadt Hochschulstraße 10 Darmstadt64289 Germany

A key task of data science is to identify relevant features linked to certain output variables that are supposed to be modeled or predicted. To obtain a small but meaningful model, it is important to find stochastically independent variables capturing all the information necessary to model or predict the output variables sufficiently. Therefore, we introduce in this work a framework to detect linear and non-linear dependencies between different features. As we will show, features that are actually functions of other features do not represent further information. Consequently, a model reduction neglecting such features conserves the relevant information, reduces noise and thus improves the quality of the model. Furthermore, a smaller model makes it easier to adopt a model of a given system. In addition, the approach structures dependencies within all the considered features. This provides advantages for classical modeling starting from regression ranging to differential equations and for machine learning. To show the generality and applicability of the presented framework 2154 features of a data center are measured and a model for classification for faulty and non-faulty states of the data center is set up. This number of features is automatically reduced by the framework to 161 features. The prediction accuracy for the reduced model even improves compared to the model trained on the total number of features. A second example is the analysis of a gene expression data set where from 9513 genes 9 genes are extracted from whose expression levels two cell clusters of macrophages can be *** Codes 00A71, 93A30, 65C60, 93B40, 93E25 © 2021, CC BY-NC-SA.

关键词： Differential equations

Zero-Shot Audio Captioning Using Soft and Hard Prompts

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arXiv 2024年

作者： Zhang, Yiming Xu, Xuenan Du, Ruoyi Liu, Haohe Dong, Yuan Tan, Zheng-Hua Wang, Wenwu Ma, Zhanyu The Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Electronic Systems Aalborg University Aalborg9220 Denmark The Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

In traditional audio captioning methods, a model is usually trained in a fully supervised manner using a human-annotated dataset containing audio-text pairs and then evaluated on the test sets from the same dataset. Such methods have two limitations. First, these methods are often data-hungry and require time-consuming and expensive human annotations to obtain audio-text pairs. Second, these models often suffer from performance degradation in cross-domain scenarios, i.e., when the input audio comes from a different domain than the training set, which, however, has received little attention. We propose an effective audio captioning method based on the contrastive language-audio pre-training (CLAP) model to address these issues. Our proposed method requires only textual data for training, enabling the model to generate text from the textual feature in the cross-modal semantic space. In the inference stage, the model generates the descriptive text for the given audio from the audio feature by leveraging the audio-text alignment from CLAP. We devise two strategies to mitigate the discrepancy between text and audio embeddings: a mixed-augmentation-based soft prompt and a retrieval-based acoustic-aware hard prompt. These approaches are designed to enhance the generalization performance of our proposed model, facilitating the model to generate captions more robustly and accurately. Extensive experiments on AudioCaps and Clotho benchmarks show the effectiveness of our proposed method, which outperforms other zero-shot audio captioning approaches for in-domain scenarios and outperforms the compared methods for cross-domain scenarios, underscoring the generalization ability of our method. © 2024, CC BY.

关键词： Semantics

How more data can hurt: Instability and regularization in next-generation reservoir computing

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arXiv 2024年

作者： Zhang, Yuanzhao dos Santos, Edmilson Roque Cornelius, Sean P. Santa Fe Institute 1399 Hyde Park Road Santa FeNM87501 United States Department of Electrical and Computer Engineering Clarkson University PotsdamNY13699 United States Clarkson Center for Complex Systems Science Clarkson University PotsdamNY13699 United States Department of Physics Toronto Metropolitan University TorontoONM5B 2K3 Canada

It has been found recently that more data can, counter-intuitively, hurt the performance of deep neural networks. Here, we show that a more extreme version of the phenomenon occurs in data-driven models of dynamical systems. To elucidate the underlying mechanism, we focus on next-generation reservoir computing (NGRC)—a popular framework for learning dynamics from data. We find that, despite learning a better representation of the flow map with more training data, NGRC can adopt an ill-conditioned "integrator" and lose stability. We link this data-induced instability to the auxiliary dimensions created by the delayed states in NGRC. Based on these findings, we propose simple strategies to mitigate the instability, either by increasing regularization strength in tandem with data size, or by carefully introducing noise during training. Our results highlight the importance of proper regularization in data-driven modeling of dynamical systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Spatio-temporal data

Continual Learning through Human-Robot Interaction: Human Perceptions of a Continual Learning Robot in Repeated Interactions

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arXiv 2023年

作者： Ayub, Ali De Francesco, Zachary Holthaus, Patrick Nehaniv, Chrystopher L. Dautenhahn, Kerstin Concordia University MontrealQCH3G 1M8 Canada Department of Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada School of Physics Engineering and Computer Science University of Hertfordshire HertfordshireAL10 9AB United Kingdom

For long-term deployment in dynamic real-world environments, assistive robots must continue to learn and adapt to their environments. Researchers have developed various computational models for continual learning (CL) that can allow robots to continually learn from limited training data, and avoid forgetting previous knowledge. While these CL models can mitigate forgetting on static, systematically collected datasets, it is unclear how human users might perceive a robot that continually learns over multiple interactions with them. In this paper, © 2023, CC BY.

关键词： Adversarial machine learning

Wild Visual Navigation: Fast Traversability Learning via Pre-Trained Models and Online Self-Supervision

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arXiv 2024年

作者： Mattamala, Matias Frey, Jonas Libera, Piotr Chebrolu, Nived Martius, Georg Cadena, Cesar Hutter, Marco Fallon, Maurice Dynamic Robot Systems Group University of Oxford 23 Banbury Road Oxford OxfordshireOX2 6NN United Kingdom Robotic Systems Lab ETH Zurich Leonhardstrasse 21 Zurich Zurich8092 Switzerland Autonomous Learning Group Max Planck Institute for Intelligent Systems Max-Planck-Ring 4 Tübingen Baden-Württemberg72076 Germany Computer Science Department University of Tüüingen Maria-von-Linden-Strasse 6 Tübingen Baden-Württemberg72076 Germany

Natural environments such as forests and grasslands are challenging for robotic navigation because of the false perception of rigid obstacles from high grass, twigs, or bushes. In this work, we present Wild Visual Navigation (WVN), an online self-supervised learning system for visual traversability estimation. The system is able to continuously adapt from a short human demonstration in the field, only using onboard sensing and computing. One of the key ideas to achieve this is the use of high-dimensional features from pre-trained self-supervised models, which implicitly encode semantic information that massively simplifies the learning task. Further, the development of an online scheme for supervision generator enables concurrent training and inference of the learned model in the wild. We demonstrate our approach through diverse real-world deployments in forests, parks, and grasslands. Our system is able to bootstrap the traversable terrain segmentation in less than 5 min of in-field training time, enabling the robot to navigate in complex, previously unseen outdoor terrains. © 2024, CC BY.

关键词： Parks

Model Checking Combined Trust and Commitments in Multi-Agent systems

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SSRN

SSRN 2023年

作者： Bentahar, Jamal Baharloo, Narges Drawel, Nagat Pedrycz, Witold CIISE Concordia University 1515 Ste. Catherine St. W. MontrealQCH3G 2W1 Canada Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Istinye University Sariyer Istanbul Turkey Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

Trust and social commitments have been studied with different objectives for communication in Multi-Agent systems (MASs) separately. The purpose of this paper is to present the first logical framework to explore the relationship between two key concepts in autonomous MASs, namely trust and commitments among agents, along with its model checking algorithms. An analysis is performed on this relationship, with a specific emphasis on the perspectives of semantics and model checking. The analysis is carried out within the framework of CTLC logic, an extended logic built on top of the standard branching temporal logic CTL by including modalities for reasoning about commitments and their fulfillment. Moreover, the study employs a trust operator to facilitate trust reasoning. We propose thus TCTLC, a new logic able to express properties about trust and commitments simultaneously. Such rich properties cannot be expressed in other languages. We introduce the semantics using the extended formalism of interpreted systems. We propose some postulates and proofs that show how we can reason about combined trust and commitments in the logic proposed. New BDD-based algorithms for our logic are presented and implemented as additional libraries of MCMAS, the default model checker of MASs. Finally, to evaluate the proposed technique, we report the experimental results using an industrial case study and compare the results with those obtained on relevant benchmarks. © 2023, The Authors. All rights reserved.

关键词： Multi agent systems