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6 Papers

SANO: Score-based Anomaly Localization for Dermatology

Authors: Alvaro Gonzalez-Jimenez 1 , Simone Lionetti 2 , Ludovic Amruthalingam 1 , Philippe Gottfrois 1 , Marc Pouly 2 and Alexander Navarini 3

Affiliations: 1 University of Basel, Basel, Switzerland , 2 Lucerne University of Applied Sciences and Arts, Rotkreuz, Switzerland , 3 University Hospital of Basel, Basel, Switzerland

Digital Object Identifier: 10.5220/0011539200003523

Proceedings of SDAIH 2022, ISBN: 978-989-758-629-3

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Supervised learning for dermatology requires a large volume of annotated images, but collecting clinical data is costly and it is virtually impossible to cover all situations. Unsupervised anomaly localization circumvents this problem by learning the distribution of healthy skin. However, algorithms which use a generative model and localize pathologic regions based on a reconstruction error are not robust to domain shift, which is a problem due to the low level of standardization expected in many dermatologic applications. Our method, SANO, uses score-based diffusion models to produce a log-likelihood gradient map that highlights potentially abnormal areas. A segmentation mask can then be calculated based on deviations from typical values observed during training. We train SANO on a public non-clinical dataset of healthy hand images without ornaments and evaluate it on the task of detecting jewelry within images from the same dataset. We demonstrate that SANO outperforms competing approaches from the literature without introducing the additional complexity of solving a Stochastic Differential Equation (SDE) at inference time”.

Tourism Forecast with Weather, Event, and Cross-industry Data

Topics: Machine Learning; Industrial Applications of AI; Data Science; Deep Learning

Authors: Simone Lionetti 1 , Daniel Pfäffli 1 , Marc Pouly 1 , Tim vor der Brück 1 and Philipp Wegelin 2

Affiliations: 1 School of Computer Science and Information Technology, Lucerne University of Applied Sciences and Arts, Suurstoffi 1, 6343 Rotkreuz, Switzerland , 2 School of Business, Lucerne University of Applied Sciences and Arts, Zentralstr. 9, 6002 Lucerne, Switzerland

Digital Object Identifier: 10.5220/0010323010971104

Proceedings of ICAART 2021, ISBN: 978-989-758-484-8

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The ability to make accurate forecasts on the number of customers is a pre-requisite for efficient planning and use of resources in various industries. It also contributes to global challenges of society such as food waste. Tourism is a domain particularly focussed on short-term forecasting for which the existing literature suggests that calendar and weather data are the most important sources for accurate prediction. We collected and make available a dataset with visitor counts over ten years from four different businesses representative for the tourism sector in Switzerland, along with nearly a thousand features comprising weather, calendar, event and lag information. Evaluation of a plethora of machine learning models revealed that even very advanced deep learning models as well as industry benchmarks show performance at most on a par with simple (piecewise) linear models. Notwithstanding the fact that weather and event features are relevant, contrary to expectations, they proved insufficient for high-quality forecasting. Moreover, and again in contradiction to the existing literature, performance could not be improved by including cross-industry data.

A Game-centric Approach to Teaching Artificial Intelligence

Topics: Active Learning; Game-Based and Simulation-Based Learning; Gamification for Learning

Authors: Marc Pouly , Thomas Koller and Ruedi Arnold

Affiliation: School of Information Technology, Lucerne University of Applied Sciences and Arts and Switzerland

Digital Object Identifier: 10.5220/0007745203980404

Proceedings of CSEDU 2019, ISBN: 978-989-758-367-4

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Man vs. machine competitions have always been attracting much public attention and the famous defeats of human champions in chess, Jeopardy!, Go or poker undoubtedly mark important milestones in the history of artificial intelligence. In this article we reflect on our experiences with a game-centric approach to teaching artificial intelligence that follows the historical development of algorithms by popping the hood of these champion bots. Moreover, we made available a server infrastructure for playing card games in perfect information and imperfect information playing mode, where students can evaluate their implementations of increasingly sophisticated game-playing algorithms in weekly online competitions, i.e. from rule-based systems to exhaustive and heuristic search in game trees to deep learning enhanced Monte Carlo methods and reinforcement learning completely freed of human domain knowledge. The evaluation of this particular course setting revealed enthusiastic feedback not only from students but also from the university authority. What started as an experiment became part of the standard computer science curriculum after just one implementation.
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Active Learning

Game-Based and Simulation-Based Learning

Pattern Recognition

Computer-Supported Education

Learning/Teaching Methodologies and Assessment

Theory and Methods
e-Learning

On using Support Vector Machines for the Detection and Quantification of Hand Eczema

Topics: Machine Learning; Pattern Recognition; Industrial Applications of AI

Authors: Stefan Schnürle 1 , Marc Pouly 1 , Tim vor der Brück 1 , Alexander Navarini 2 and Thomas Koller 1

Affiliations: 1 Lucerne University of Applied Sciences and Arts, Switzerland , 2 University Hospital Zurich, Switzerland

Digital Object Identifier: 10.5220/0006125000750084

Proceedings of ICAART 2017, ISBN: 978-989-758-220-2

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Hand eczema is one of the most frequent skin diseases affecting up to 14% of the population. Early detection and continuous observation of eczemas allows for efficient treatment and can therefore relieve symptoms. However, purely manual skin control is tedious and often error prone. Thus, an automatic approach that can assist the dermatologist with his work is desirable. Together with our industry partner swiss4ward, we devised an image processing method for hand eczema segmentation based on support vector machines and conducted several experiments with different feature sets. Our implementation is planned to be integrated into a clinical information system for operational use at University Hospital Zurich. Instead of focusing on a high accuracy like most existing state-of-the-art approaches, we selected F1 score as our primary measure. This decision had several implications regarding the design of our segmentation method, since all popular implementations of support vector machines aim for optimizing accuracy. Finally, we evaluated our system and achieved an F1 score of 58.6% for front sides of hands and 43.8% for back sides, which outperforms several state-of-the-art methods that were tested on our gold standard data set as well.
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Artificial Intelligence

Human-Computer Interaction

Neurotechnology, Electronics and Informatics

Biomedical Engineering

Industrial Applications of AI

Pattern Recognition

Biomedical Signal Processing

Knowledge Discovery and Information Retrieval

Physiological Computing Systems

Computational Intelligence

Knowledge-Based Systems

Sensor Networks

Data Manipulation

Machine Learning

Soft Computing

Evolutionary Computing

Methodologies and Methods

Symbolic Systems
Health Engineering and Technology Applications

Neurocomputing

Skyline Computation on Commercial Data

Topics: Industrial Applications of AI; Data Science

Authors: Michael Galli , Stefan Schnürle , Ruedi Arnold and Marc Pouly

Affiliation: Lucerne University of Applied Sciences and Arts, Switzerland

Digital Object Identifier: 10.5220/0005766604650471

Proceedings of ICAART 2016, ISBN: 978-989-758-172-4

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Many different skyline algorithms for preference-based search have been proposed and compared in the literature, but most of these evaluations were based on synthetic data. In this paper, we present a case study of skyline computation on commercial data that we consider representative for many e-commerce platforms. The results of our measurements differ significantly from the results reported on synthetic data.
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Artificial Intelligence

Industrial Applications of AI

Soft Computing

SECURING ACCESS TO EMBEDDED SYSTEMS - An Effective Concept for Devices Lacking Internet Connection

Topics: Accessibility Issues and Technology; Usability and Ergonomics; Authentication and Access Control; Web Security and Privacy

Authors: Bruno Juchli , Peter Sollberger and Roland Portmann

Affiliation: Lucerne University of Applied Sciences & Arts, Switzerland

Digital Object Identifier: 10.5220/0002779401520158

Proceedings of WEBIST 2010, ISBN: 978-989-674-025-2

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Many embedded systems provide a web interface for maintenance tasks such as system configuration, test execution and firmware updating. Access to this interface usually needs to be restricted to authorized employees. This paper shows an efficient and cost-effective concept to secure maintenance interfaces using widespread standards and technology. By storing authorisation information in standard compliant X.509 certificate extensions Transport Layer Security (TLS) and X.509 Public Key Infrastructure (PKI) provide mutual authentication, message integrity as well as confidentiality and enable authorisation of employees. Practical experience of the implementation completes this paper.
Collapse Button Related Ontology Subjects/Areas/Topics (10)
Access Control

Information and Systems Security

Web Information Systems and Technologies

Accessibility Issues and Technology

Internet Technology

Web Interfaces and Applications

Data Engineering

Usability and Ergonomics

Web Security and Privacy
Databases and Data Security

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