Autonomous search in a social and ubiquitous Web

Ciortea, Andrei; Mayer, Simon; Bienz, Simon; Gandon, Fabien; Corby, Olivier

doi:10.1007/s00779-020-01415-1

Cited by 4 publications

(1 citation statement)

References 42 publications

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“…The contextual explanation system proposed in this paper uses a Context Discovery component, similar to the crawler component described in [5]. Such a component allows the proposed system to find entities (e.g., sensors) capable of providing contextual information that describes CI on the CPS.…”

Section: Context Modelingmentioning

confidence: 99%

Poster: Towards Explaining the Effects of Contextual Influences on Cyber-Physical Systems

Jha

Mayer

García

2021

Proceedings of the 11th International Conference on the Internet of Things

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The increasing complexity of Cyber-Physical Systems (CPS) increases the difficulty for users to understand their behavior. Using existing Explainable Artificial Intelligence (XAI) methods, CPS can explain their behavior to the users. However, the input-output correlations used in XAI methods are not capable of explaining certain anomalies on CPS behavior caused by contextual influences (CIs) since they do not consider the context of the CPS. Some well-known techniques used for understanding such CIs on CPS are test chambers and the analysis of logged CPS data. However, test chambers are typically only available to the manufacturer of a CPS, thus not useful for understanding CIs on the shop floors. Data analysis methods focus on data correlations, which are insufficient to explain causal relationships without using expert (human) knowledge. Hence, we propose a context-aware log-based explanation system to explain the causal relationship between CIs and the behavior of a CPS. The proposed solution employs semantic technologies to access the context of the CPS. It demonstrates the causal relationship between the CPS and CIs through counterfactual explanation and abductive reasoning methods. The contextual explanations offered by the proposed system will assist users in visualizing diverse scenarios in order to improve the CPS' behavior accordingly. CCS CONCEPTS• Computing methodologies → Causal reasoning and diagnostics; Machine learning; • Human-centered computing → Text input.

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Section: Context Modelingmentioning

confidence: 99%

Poster: Towards Explaining the Effects of Contextual Influences on Cyber-Physical Systems

Jha

Mayer

García

2021

Proceedings of the 11th International Conference on the Internet of Things

Self Cite

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Indexing in WoT to Locate Indoor Things

et al. 2023

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The Web of Things (WoT) is an enhanced form of the Internet of Things (IoT) that has changed the trend of life nowadays. Due to IoT, life is transformed into smart life, such as smart buildings, smart vehicles, smart agriculture, smart businesses, etc., by connecting a certain number of things to the internet. Many people are now working on ways to locate indoor things to interact and exchange data between smart things and web services and apps, which is called ''WoT,'' or ''Web of Things.'' To interact and exchange the data, researchers need a search engine on WoT. However, locating indoor things in the Web of Things (WoT) remains a challenge due to the lack of a unified indexing system. In this research, we propose a novel approach to index indoor things in the WoT by leveraging machine learning and web technologies. Our approach includes a data preprocessing step, where we extract relevant features from the sensor data, followed by a clustering algorithm to group similar devices. We then use a semantic model to assign meaning to the clusters and develop a search engine to enable efficient searching of indoor things. Our proposed approach improves the accuracy and efficiency of locating indoor things in the WoT, paving the way for new applications in smart homes, healthcare, and industrial automation.

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