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The impact of Airbnb on the economic performance of independent hotels: an empirical investigation of the moderating effects

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CURRENT ISSUES IN TOURISM 2022年第21期25卷 3534-3564页

作者： Destefanis, Alessandro Neirotti, Paolo Paolucci, Emilio Raguseo, Elisabetta Politecn Torino SmartData PoliTO Ctr Big Data & Data Sci Turin Italy Politecn Torino EIC Entrepreneurship & Innovat Ctr Turin Italy Politecn Torino FULL Future Urban Legacy Lab Ctr Turin Italy

The evidence on the effect that sharing economy accommodation platforms have on the performance of hotels is not univocal, and a general picture about the circumstances under which hotels may suffer the least from this disruption is still missing. This paper contributes to bridge this gap by examining the role that contingent factors can play in reducing the negative impact of Airbnb on the profitability growth of independent hotels. We examine whether the attractiveness of the city zone where hotels are located and their online reputation moderate the effect that the usage of Airbnb listings has on the profitability growth of independent hotels. Using a panel dataset of a sample of 725 independent hotels located in six Italian cities with high tourism attractiveness, and by triangulating ISTAT, AIDA, AirDNA, TripAdvisor and Trustyou datasets, we found that the negative effect of Airbnb on the profitability growth of hotels is reduced when the hotels are located in attractive city zones. However, the online reputation of hotels does not have any significant moderating effect on the relationship investigated. We discuss how these results contribute to understand competitive dynamics in the hotel industry through a lens based on the disruptive innovation theory.

关键词： Airbnb hotel tourism sharing economy profitability growth

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Discontinuous neural networks and discontinuity learning

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2023年 419卷

作者： Della Santa, Francesco Pieraccini, Sandra Politecn Torino Dept Math Sci Turin Italy SmartData PoliTO Ctr Big Data & Machine Learning T Turin Italy INdAM GNCS Res Grp Pisa Italy

In the framework of discontinuous function approximation and discontinuity interface detection, we consider an approach involving Neural Networks. In particular, we define a novel typology of Neural Network layers endowed with new learnable parameters and discontinuities in the space of the activations. These layers allow to create a new kind of Neural Networks, whose main property is to be discontinuous, able not only to approximate discontinuous functions but also to learn and detect the discontinuity interfaces. A sound theoretical analysis concerning the properties of the new discontinuous layers is performed, and some tests on discontinuous functions are proposed, in order to assess the potential of such instruments. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Discontinuous functions Neural networks Deep learning Automatic detection of discontinuity interface

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An innovative artificial intelligence-based method to compress complex models into explainable, model-agnostic and reduced decision support systems with application to healthcare (NEAR)

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2024年 151卷 102841-102841页

作者： Kassem, Karim Sperti, Michela Cavallo, Andrea Vergani, Andrea Mario Fassino, Davide Moz, Monica Liscio, Alessandro Banali, Riccardo Dahlweid, Michael Benetti, Luciano Bruno, Francesco Gallone, Guglielmo De Filippo, Ovidio Iannaccone, Mario D'Ascenzo, Fabrizio De Ferrari, Gaetano Maria Morbiducci, Umberto Della Valle, Emanuele Deriu, Marco Agostino Politecn Torino Dept Mech & Aerosp Engn PolitoBIO Med Lab Turin Italy Politecn Torino SmartData PoliTO Ctr Big Data Technol Turin Italy Politecn Milan Dept Elect Informat & Bioengn DEIB Via Ponzio 34-5 I-20133 Milan Italy Politecn Milan Dept Math Via Bonardi 9 I-20133 Milan Italy Human Technopole Hlth Data Sci Ctr Viale R Levi-Montalcini 1 I-20157 Milan Italy Politecn Torino Dept Math Sci Turin Italy Dedalus Res Lab Milan Italy Citta Salute & Sci Div Cardiol Cardiovasc & Thorac Dept Turin Italy Univ Turin Dept Med Sci Cardiol Turin Italy SG Bosco Hosp Dept Cardiol Turin Italy

Background and objective: In everyday clinical practice, medical decision is currently based on clinical guidelines which are often static and rigid, and do not account for population variability, while individualized, patientoriented decision and/or treatment are the paradigm change necessary to enter into the era of precision medicine. Most of the limitations of a guideline-based system could be overcome through the adoption of Clinical Decision Support Systems (CDSSs) based on Artificial Intelligence (AI) algorithms. However, the black -box nature of AI algorithms has hampered a large adoption of AI-based CDSSs in clinical practice. In this study, an innovative AI-based method to compress AI-based prediction models into explainable, model-agnostic, and reduced decision support systems (NEAR) with application to healthcare is presented and validated. Methods: NEAR is based on the Shapley Additive Explanations framework and can be applied to complex input models to obtain the contributions of each input feature to the output. Technically, the simplified NEAR models approximate contributions from input features using a custom library and merge them to determine the final output. Finally, NEAR estimates the confidence error associated with the single input feature contributing to the final score, making the result more interpretable. Here, NEAR is evaluated on a clinical real-world use case, the mortality prediction in patients who experienced Acute Coronary Syndrome (ACS), applying three different Machine Learning/Deep Learning models as implementation examples. Results: NEAR, when applied to the ACS use case, exhibits performances like the ones of the AI-based model from which it is derived, as in the case of the Adaptive Boosting classifier, whose Area Under the Curve is not statistically different from the NEAR one, even the model's simplification. Moreover, NEAR comes with intrinsic explainability and modularity, as it can be tested on the developed web applicat

关键词： Clinical decision support systems Explainability Artificial intelligence Precision medicine Risk prediction

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Indoor Point-to-Point Navigation with Deep Reinforcement Learning and Ultra-Wideband 13

Indoor Point-to-Point Navigation with Deep Reinforcement Lea...

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13th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Sutera, Enrico Mazzia, Vittorio Salvetti, Francesco Fantin, Giovanni Chiaberge, Marcello Politecn Torino Dept Elect & Telecommun I-10124 Turin Italy Politecn Torino Interdept Ctr Serv Robot PIC4SeR Turin Italy SmartData PoliTo Big Data & Data Sci Lab Turin Italy

ISBN: (纸本)9789897584848

Indoor autonomous navigation requires a precise and accurate localization system able to guide robots through cluttered, unstructured and dynamic environments. Ultra-wideband (UWB) technology, as an indoor positioning system, offers precise localization and tracking, but moving obstacles and non-line-of-sight occurrences can generate noisy and unreliable signals. That, combined with sensors noise, unmodeled dynamics and environment changes can result in a failure of the guidance algorithm of the robot. We demonstrate how a power-efficient and low computational cost point-to-point local planner, learnt with deep reinforcement learning (RL), combined with UWB localization technology can constitute a robust and resilient to noise short-range guidance system complete solution. We trained the RL agent on a simulated environment that encapsulates the robot dynamics and task constraints and then, we tested the learnt point-to-point navigation policies in a real setting with more than two-hundred experimental evaluations using UWB localization. Our results show that the computational efficient end-to-end policy learnt in plain simulation, that directly maps low-range sensors signals to robot controls, deployed in combination with ultra-wideband noisy localization in a real environment, can provide a robust, scalable and at-the-edge low-cost navigation system solution.

关键词： Indoor Autonomous Navigation Autonomous Agents Deep Reinforcement Learning Ultra-Wideband

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Real-Time Apple Detection System Using Embedded Systems With Hardware Accelerators: An Edge AI Application

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IEEE ACCESS 2020年 8卷 9102-9114页

作者： Mazzia, Vittorio Khaliq, Aleem Salvetti, Francesco Chiaberge, Marcello Politecn Torino Dept Elect & Telecommun I-10129 Turin Italy Politecn Torino Interdept Ctr Serv Robot PIC4SeR I-10129 Turin Italy SmartData PoliTo Big Data & Data Sci Lab I-10129 Turin Italy

Real-time apple detection in orchards is one of the most effective ways of estimating apple yields, which helps in managing apple supplies more effectively. Traditional detection methods used highly computational machine learning algorithms with intensive hardware set up, which are not suitable for infield real-time apple detection due to their weight and power constraints. In this study, a real-time embedded solution inspired from "Edge AI" is proposed for apple detection with the implementation of YOLOv3-tiny algorithm on various embedded platforms such as Raspberry Pi 3 B+ in combination with Intel Movidius Neural Computing Stick (NCS), Nvidia's Jetson Nano and Jetson AGX Xavier. data set for training were compiled using acquired images during field survey of apple orchard situated in the north region of Italy, and images used for testing were taken from widely used google data set by filtering out the images containing apples in different scenes to ensure the robustness of the algorithm. The proposed study adapts YOLOv3-tiny architecture to detect small objects. It shows the feasibility of deployment of the customized model on cheap and power-efficient embedded hardware without compromising mean average detection accuracy (83.64%) and achieved frame rate up to 30 fps even for the difficult scenarios such as overlapping apples, complex background, less exposure of apple due to leaves and branches. Furthermore, the proposed embedded solution can be deployed on the unmanned ground vehicles to detect, count, and measure the size of the apples in real-time to help the farmers and agronomists in their decision making and management skills.

关键词： Edge AI machine learning real-time embedded systems object detection

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Autonomous Navigation in Vineyards with Deep Learning at the Edge

Autonomous Navigation in Vineyards with Deep Learning at the...

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29th International Conference on Robotics in Alpe-Adria-Danube Region (RAAD)

作者： Aghi, Diego Mazzia, Vittorio Chiaberge, Marcello Politecn Torino DET Turin Italy PIC4SeR Politecn Interdept Ctr Serv Robot Turin Italy SmartData PoliTo Big Data & Data Sci Lab Turin Italy

ISBN: (纸本)9783030489915;9783030489892;9783030489885

With the rapid growth of the world population over the past years, the agriculture industry is asked to respond properly to the exponential augmentation of global demand for food production. In the past few years, autonomous agricultural field machines have been gaining significant attention from farmers and industries in order to reduce costs, human workload, and required resources. Nevertheless, achieving sufficient autonomous navigation capabilities requires the simultaneous cooperation of different processes;localization, mapping, and path planning are just some of the steps that aim at providing to the machine the right set of skills to operate in semi-structured and unstructured environments. In this context, the presented research exploits later advancement in deep learning and edge computing technologies to provide a robust and fully integrable local planner for autonomous navigation along vineyards rows. Moreover, the devised and tested platform necessitates only of low range and low-cost hardware with minimal power and bandwidth requirements. The machine learning algorithm has been trained and tested with acquired images during different field surveys in the north region of Italy. Then, after performing an optimization process, the overall system has been validated with a customized robot platform in the appropriate environment.

关键词： Deep learning

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DeepWay: A Deep Learning waypoint estimator for global path generation

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2021年 184卷 106091-106091页

作者： Mazzia, Vittorio Salvetti, Francesco Aghi, Diego Chiaberge, Marcello Politecn Torino Dept Elect & Telecommun I-10124 Turin Italy Politecn Torino Interdept Ctr Serv Robot PIC4SeR Turin Italy SmartData PoliTo Big Data & Data Sci Lab Turin Italy

Agriculture 3.0 and 4.0 have gradually introduced service robotics and automation into several agricultural processes, mostly improving crops quality and seasonal yield. Row-based crops are the perfect settings to test and deploy smart machines capable of monitoring and manage the harvest. In this context, global path generation is essential either for ground or aerial vehicles, and it is the starting point for every type of mission plan. Nevertheless, little attention has been currently given to this problem by the research community and global path generation automation is still far to be solved. In order to generate a viable path for an autonomous machine, the presented research proposes a feature learning fully convolutional model capable of estimating waypoints given an occupancy grid map. In particular, we apply the proposed data-driven methodology to the specific case of row-based crops with the general objective to generate a global path able to cover the extension of the crop completely. Extensive experimentation with a custom made synthetic dataset and real satellite-derived images of different scenarios have proved the effectiveness of our methodology and demonstrated the feasibility of an endto-end and completely autonomous global path planner.

关键词： Deep learning Unmanned ground vehicles Precision agriculture Global path planning

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Robust ultra-wideband range error mitigation with deep learning at the edge

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 102卷 104278-104278页

作者： Angarano, Simone Mazzia, Vittorio Salvetti, Francesco Fantin, Giovanni Chiaberge, Marcello Politecn Torino Dept Elect & Telecommun I-10124 Turin Italy Politecn Torino PIC4SeR Interdept Ctr Serv Robot Turin Italy SmartData PoliTo Big Data & Data Sci Lab Turin Italy

Ultra-wideband (UWB) is the state-of-the-art and most popular technology for wireless localization. Nevertheless, precise ranging and localization in non-line-of-sight (NLoS) conditions is still an open research topic. Indeed, multipath effects, reflections, refractions, and complexity of the indoor radio environment can easily introduce a positive bias in the ranging measurement, resulting in highly inaccurate and unsatisfactory position estimation. This article proposes an efficient representation learning methodology that exploits the latest advancement in deep learning and graph optimization techniques to achieve effective ranging error mitigation at the edge. Channel Impulse Response (CIR) signals are directly exploited to extract high semantic features to estimate corrections in either NLoS or LoS conditions. Extensive experimentation with different settings and configurations has proved the effectiveness of our methodology and demonstrated the feasibility of a robust and low computational power UWB range error mitigation.

关键词： Deep learning Ultra-wideband Edge AI Indoor positioning

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Efficient-CapsNet: capsule network with self-attention routing

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sciENTIFIC REPORTS 2021年第1期11卷 1-13页

作者： Mazzia, Vittorio Salvetti, Francesco Chiaberge, Marcello Politecn Torino Dept Elect & Telecommun I-10129 Turin Italy Politecn Torino Interdept Ctr Serv Robot PIC4SeR Turin Italy SmartData PoliTo Big Data & Data Sci Lab Turin Italy

Deep convolutional neural networks, assisted by architectural design strategies, make extensive use of data augmentation techniques and layers with a high number of feature maps to embed object transformations. That is highly inefficient and for large datasets implies a massive redundancy of features detectors. Even though capsules networks are still in their infancy, they constitute a promising solution to extend current convolutional networks and endow artificial visual perception with a process to encode more efficiently all feature affine transformations. Indeed, a properly working capsule network should theoretically achieve higher results with a considerably lower number of parameters count due to intrinsic capability to generalize to novel viewpoints. Nevertheless, little attention has been given to this relevant aspect. In this paper, we investigate the efficiency of capsule networks and, pushing their capacity to the limits with an extreme architecture with barely 160 K parameters, we prove that the proposed architecture is still able to achieve state-of-the-art results on three different datasets with only 2% of the original CapsNet parameters. Moreover, we replace dynamic routing with a novel non-iterative, highly parallelizable routing algorithm that can easily cope with a reduced number of capsules. Extensive experimentation with other capsule implementations has proved the effectiveness of our methodology and the capability of capsule networks to efficiently embed visual representations more prone to generalization.

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Domain-Adversarial Training of Self-Attention-Based Networks for Land Cover Classification Using Multi-Temporal Sentinel-2 Satellite Imagery

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REMOTE SENSING 2021年第13期13卷 2564-2564页

作者： Martini, Mauro Mazzia, Vittorio Khaliq, Aleem Chiaberge, Marcello Politecn Torino Dept Elect & Telecommun I-10124 Turin Italy Politecn Torino PIC4SeR Interdept Ctr Serv Robot I-10129 Turin Italy Big Data & Data Sci Lab SmartData PoliTo I-10129 Turin Italy Int Islamic Univ Dept Elect Engn Islamabad 44000 Pakistan

The increasing availability of large-scale remote sensing labeled data has prompted researchers to develop increasingly precise and accurate data-driven models for land cover and crop classification (LC&CC). Moreover, with the introduction of self-attention and introspection mechanisms, deep learning approaches have shown promising results in processing long temporal sequences in the multi-spectral domain with a contained computational request. Nevertheless, most practical applications cannot rely on labeled data, and in the field, surveys are a time-consuming solution that pose strict limitations to the number of collected samples. Moreover, atmospheric conditions and specific geographical region characteristics constitute a relevant domain gap that does not allow direct applicability of a trained model on the available dataset to the area of interest. In this paper, we investigate adversarial training of deep neural networks to bridge the domain discrepancy between distinct geographical zones. In particular, we perform a thorough analysis of domain adaptation applied to challenging multi-spectral, multi-temporal data, accurately highlighting the advantages of adapting state-of-the-art self-attention-based models for LC&CC to different target zones where labeled data are not available. Extensive experimentation demonstrated significant performance and generalization gain in applying domain-adversarial training to source and target regions with marked dissimilarities between the distribution of extracted features.

关键词： domain adaptation Transformers deep learning land cover classification

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