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VOR Adaptation on a Humanoid iCub Robot using a Spiking Cerebellar Model

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arXiv 2020年

作者： Naveros, Francisco Luque, Niceto R. Ros, Eduardo Arleo, Angelo Department of Computer Architecture and Technology University of Granada Granada18014 Spain Sorbonne Université INSERM Institut de la Vision 17 rue Moreau ParisF-75012

We embed a spiking cerebellar model within an adaptive real-time control loop able to operate a real robotic body (iCub) when performing different Vestibulo-Ocular Reflex (VOR) tasks. The spiking neural network computation, including event- and time-driven neural dynamics, neural activity and spike-timing dependent plasticity (STDP) mechanisms, leads to a non-deterministic computation time caused by the neural activity volleys encountered during cerebellar simulation. This non-deterministic computation time motivates the integration of a real-time supervisor module able to ensure a well-orchestrated neural computation time and robot operation. Actually, our neurorobotic experimental set-up (VOR) benefits from the biological sensory motor delay between the cerebellum and the body to buffer the computational overloads as well as providing flexibility in adjusting the neural computation time and real-time operation. The real-time supervisor module provides for incremental countermeasures that dynamically slow down or speed-up the cerebellar simulation by either halting the simulation or disabling certain neural computation features (i.e STDP mechanisms, spike propagation, neural updates) to cope with the real-time constraints imposed by the real robot operation. This neurorobotic experimental set-up is applied to different horizontal and vertical VOR adaptive tasks that are widely used by the neuroscientific community to address cerebellar functioning. We aim to elucidate the manner in which the combination of the cerebellar neural substrate and the distributed plasticity shapes the cerebellar neural activity to mediate motor adaptation. This work underlies the need for a two-stage learning process to facilitate VOR acquisition. For Rev Copyright © 2020, The Authors. All rights reserved.

关键词： Neural networks

On the Comparison of Classic and Deep Keypoint Detector and Descriptor Methods

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arXiv 2020年

作者： Bojanić, David Bartol, Kristijan Pribanić, Tomislav Petković, Tomislav Donoso, Yago Diez Mas, Joaquim Salvi University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia Yamagata University Faculty of Science Yamagata Japan University of Girona Department of Computer Architecture and Technology Girona Spain

The purpose of this study is to give a performance comparison between several classic hand-crafted and deep keypoint detector and descriptor methods. In particular, we consider the following classical algorithms: SIFT, SURF, ORB, FAST, BRISK, MSER, HARRIS, KAZE, AKAZE, AGAST, GFTT, FREAK, BRIEF and RootSIFT, where a subset of all combinations is paired into detector-descriptor pipelines. Additionally, we analyze the performance of two recent and perspective deep detector-descriptor models, LF-Net and SuperPoint. Our benchmark relies on the HPSequences dataset that provides real and diverse images under various geometric and illumination changes. We analyze the performance on three evaluation tasks: keypoint verification, image matching and keypoint retrieval. The results show that certain classic and deep approaches are still comparable, with some classic detector-descriptor combinations overperforming pretrained deep models. In terms of the execution times of tested implementations, SuperPoint model is the fastest, followed by ORB. The source code is published on https://***/kristijanbartol/keypoint-algorithms-benchmark. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

Proactive user engagement via friendly survey and data-driven methodologies

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Proactive user engagement via friendly survey and data-drive...

International Conference on Data Engineering Workshops

作者： Paolo Bethaz Riccardo Callà Tania Cerquitelli Arianna Montorsi Claudia De Giorgi Department of Control and Computer Engineering Politecnico di Torino Turin Italy Department of Applied Science and Technology Politecnico di Torino Turin Italy Department of Architecture and Design Politecnico di Torino Turin Italy

ISBN: (数字)9781728142661

ISBN: (纸本)9781728142678

In this paper, we present CAROL (unConventionAl suRvey methOdoLogy) to collect users' opinions, to analyze them effectively, and to graphically provide interesting summaries in near-real-time. CAROL has been designed to collect citizens' opinions, needs, and a perception that can be very useful to redesign cities and drive the innovation process. To this aim, particular importance is given to the architecture implemented to collect the data, which involves sending voice messages instead of text, allowing users greater freedom of expression. Moreover, to make the survey more friendly, an initial analysis is done in real-time thanks to an intuitive dashboard, which shows the first results in the form of graphs and charts, updated after each message. Finally, a more in-depth analysis can be carried out at the end of the survey, thanks to text-mining techniques adapted to the context. As use case, the proposed methodology has been tested in a public event organized by Politecnico di Torino and addressed to female students, with the aim to collect their ideas about new services/opportunities that can be offered to support women in STEM.

关键词： Real-time systems Servers computer architecture Technological innovation Data visualization Smart cities

Research on the surface water quality in the Huaihe River Basin and the gensis based on multivariate statistical analysis

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Scientific Reports 2025年第1期15卷 1-12页

作者： Feng, Shuzhen Zhang, Chaokai Yan, Jiaheng Ren, Ke Peng, Ningbo Jiang, Wei Liu, Shouhua Nanjing Vocational Institute of Railway Technology Nanjing 210031 China Faculty of Architecture and Civil Engineering Huaiyin Institute of Technology Huaian 223003 China HYIT-CREC Institute of Science and Technology for Conservation of Cultural Heritage Huaiyin Institute of Technology Huaian 223003 China Key Scientific Research Base of the State Administration of Cultural Heritage for Integrated Technology and Application of Grotto Cultural Heritage Protection Engineering Department Chengdu 610036 China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian 223003 China

The Huaihe River Basin (HRB) is an important water system in eastern China, and its water quality has received widespread attention. This study explored the latest spatial variation patterns of surface water quality in the HRB to cope with the increasingly severe challenges of water resource management. By integrating multidimensional water quality data from surface water monitoring stations, including dissolved oxygen (DO), chemical oxygen demand (CODMn and COD), biochemical oxygen demand (BOD5), ammonia nitrogen (NH3–N), total phosphorus (TP), and total nitrogen (TN), this study utilized a cluster analysis technique to categorize the water quality data and reveal changes in the geographic variability of water quality. Among the 382 monitoring stations in the HRB, 258 stations had TN content lower than Class V, which was the highest among all monitoring indicators. The entropy weight method used to assess the comprehensive water quality showed that there were 157 and 163 monitoring stations belonging to Class III and IV, respectively, and stations with poor water quality were distributed downstream in the river network and estuary area. Correlation and cluster analyses indicated that agricultural and organic matter pollution were the two main factors affecting water quality in the HRB, particularly in the downstream area, and the high loading of nutrient salts such as TP and NH3–N reflected the significant influence of agricultural activities. In addition, the study examined the potential driving role of factors such as topography, geomorphology, and human activities on water quality changes and visualized the relationship between water quality class and cluster categories through spatial distribution maps and Sankey diagrams to clarify the regional patterns of water quality problems.

关键词： Agricultural pollution Cluster analysis Huaihe River Basin Spatial change rules Surface water quality

Generative adversarial networks (GANs) in networking: A comprehensive survey & Evaluation

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arXiv 2021年

作者： Navidan, Hojjat Moshiri, Parisa Fard Nabati, Mohammad Shahbazian, Reza Ghorashi, Seyed Ali Shah-Mansouri, Vahid Windridge, David School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran14395-515 Iran Cognitive Telecommunication Research Group Department of Telecommunications Faculty of Electrical Engineering Shahid Beheshti University Tehran19839-69411 Iran Department of Electrical Engineering Faculty of Technology and Engineering Standard Research Institute Alborz31745-139 Iran School of Architecture Computing and Engineering University of East London LondonE16 2RD United Kingdom Department of Computer Science School of Science and Technology Middlesex University LondonNW4 4BT United Kingdom

Despite the recency of their conception, Generative Adversarial Networks (GANs) constitute an extensively-researched machine learning sub-field for the creation of synthetic data through deep generative modeling. GANs have consequently been applied in a number of domains, most notably computer vision, in which they are typically used to generate or transform synthetic images. Given their relative ease of use, it is therefore natural that researchers in the field of networking (which has seen extensive application of deep learning methods) should take an interest in GAN-based approaches. The need for a comprehensive survey of such activity is therefore urgent. In this paper, we demonstrate how this branch of machine learning can benefit multiple aspects of computer and communication networks, including mobile networks, network analysis, internet of things, physical layer, and cybersecurity. In doing so, we shall provide a novel evaluation framework for comparing the performance of different models in non-image applications, applying this to a number of reference network datasets. © 2021, CC BY-NC-ND.

关键词： Generative adversarial networks

Satire Detection in Turkish News Articles: A Machine Learning Approach 5th

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Satire Detection in Turkish News Articles: A Machine Learnin...

5th International Conference on Big Data Innovations and Applications, Innovate-Data 2019

作者： Toçoğlu, Mansur Alp Onan, Aytuğ Faculty of Technology Department of Software Engineering Manisa Celal Bayar University Manisa45400 Turkey Faculty of Engineering and Architecture Department of Computer Engineering İzmir Katip Çelebi University İzmir35620 Turkey

ISBN: (纸本)9783030273545

With the advances in information and communication technologies, an immense amount of information has been shared on social media and microblogging platforms. Much of the online content contains elements of figurative language, such as, irony, sarcasm and satire. The automatic identification of figurative language can be viewed as a challenging task in natural language processing, where linguistic entities, such as, metaphor, analogy, ambiguity, irony, sarcasm, satire, and so on, have been utilized to express more complex meanings. The predictive performance of sentiment classification schemes may degrade if figurative language within the text has not been properly addressed. Satirical text is a way of figurative communication, where ideas/opinions regarding a people, event or issue is expressed in a humorous way to criticize that entity. Satirical news can be deceptive and harmful. In this paper, we present a machine learning based approach to satire detection in Turkish news articles. In the presented scheme, we utilized three kinds of features to model lexical information, namely, unigrams, bigrams and tri-grams. In addition, term-frequency, term-presence and TF-IDF based schemes have been taken into consideration. In the classification phase, Naïve Bayes, support vector machines, logistic regression and C4.5 algorithms have been examined. © 2019, Springer Nature Switzerland AG.

关键词： Support vector machines

Linearization-based quantized stabilization of nonlinear systems under DoS attacks

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arXiv 2020年

作者： Kato, Rui Cetinkaya, Ahmet Ishii, Hideaki Department of Computer Science Tokyo Instutite of Technology Yokohama226-8502 Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo101-8430 Japan

Motivated by recent security issues in cyber-physical systems, this technical note studies the stabilization problem of networked control systems under Denial-of-Service (DoS) attacks. In particular, we consider to stabilize a nonlinear system with limited data rate via linearization. We employ a deterministic DoS attack model constrained in terms of attacks’ frequency and duration, allowing us to cover a large class of potential attacks. To achieve asymptotic stabilization, we propose a resilient dynamic quantizer in the sense that it does not saturate in the presence of packet losses caused by DoS attacks. A sufficient condition for stability is derived by restricting the average DoS frequency and duration. In addition, because of the locality of linearization, we explicitly investigate an estimate of the region of attraction, which can be expected to be reduced depending on the strength of DoS attacks. A simulation example is presented for demonstration of our results. Copyright © 2020, The Authors. All rights reserved.

关键词： Denial-of-service attack

A scholarly backcasting approach to a novel model for smart sustainable cities of the future: strategic problem orientation

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City, Territory and architecture 2019年第1期6卷 1-27页

作者： Bibri, Simon Elias Krogstie, John Department of Computer Science The Norwegian University of Science and Technology Sem Saelands veie 9 Trondheim 7491 Norway Department of Architecture and Planning Alfred Getz vei 3 Sentralbygg 1 5th Floor Trondheim 7491 Norway

Sustainable cities have, since the early 1990s, been the leading global paradigm of urban planning and development thanks to the different models of sustainable urban form proposed as new frameworks for redesigning and restructuring urban places to achieve sustainability. Indeed, huge advances in some areas of sustainability knowledge and a multitude of exemplary practical initiatives have been realized, thereby raising the profile of sustainable cities worldwide. The change is still inspiring and the challenge continues to induce scholars and practitioners to enhance existing, and propose new, models. Especially, sustainable urban forms have been problematic, whether in theory or practice, so is yet knowing to what extent progress has been made towards sustainable cities. They are associated with a number of problems, issues, and challenges and thus much more needs to be done considering the very fragmented, conflicting picture that arises of change on the ground in the face of the expanding urbanization. This involves the question of how they should be monitored, understood, analyzed, planned, and even integrated so as to improve, advance, and maintain their contribution to sustainability. This brings us to the issue of sustainable cities and smart cities being extremely fragmented as landscapes and weakly connected as approaches, despite the proven role and untapped potential of advanced ICT, especially big data technology, for advancing sustainability under what is labeled ‘smart sustainable cities.’ Essentially, there are multiple visions of, and pathways to achieving, such cities, which depends on how they can be conceptualized. This paper details the two parts of strategic problem orientation by answering the guiding questions for Steps 1 and 2 of the futures study being conducted. This study aims to analyze, investigate, and develop a novel model for smart sustainable cities of the future using backcasting as a scholarly approach. It involves a series of pap

关键词： Backcasting Big data science and analytics Compact cities Design principles and strategies Eco-cities Futures study Planning practices Smart cities Smart sustainable cities Sustainable cities Sustainable development

Generation expansion planning in the presence of wind power plants using a genetic algorithm model

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arXiv 2020年

作者： Sahragard, Ali Falaghi, Hamid Farhadi, Mahdi Mosavi, Amir Estebsari, Abouzar Khorasan Regional Electricity Company Birjand Iran Department of Electric and Computer Engineering University of Birjand Iran Department of Computer and Industries Birjand University of Technology Birjand Iran Department of Informatics J. Selye University Komarno94501 Slovakia School of the Built Environment and Architecture London South Bank University London United Kingdom

One of the essential aspects of power system planning is generation expansion planning (GEP). The purpose of GEP is to enhance construction planning and reduce the costs of installing different types of power plants. This paper proposes a method based on Genetic Algorithm (GA) for GEP in the presence of wind power plants. Since it is desired to integrate the maximum possible wind power production in GEP, the constraints for incorporating different levels of wind energy in power generation are investigated comprehensively. This will allow obtaining the maximum reasonable amount of wind penetration in the network. Besides, due to the existence of different wind regimes, the penetration of strong and weak wind on GEP is assessed. The results show that the maximum utilization of wind power generation capacity could increase the exploitation of more robust wind regimes. Considering the growth of the wind farm industry and the cost reduction for building wind power plants, the sensitivity of GEP to the variations of this cost is investigated. The results further indicate that for a 10% reduction in the initial investment cost of wind power plants, the proposed model estimates that the overall cost will be *** Codes 68T01 Copyright © 2020, The Authors. All rights reserved.

关键词： Electric power generation