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Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including data scientists, domain experts, end-users, and more. It aids in deciphering the intricate internal mechanisms of ``black box'' Machine Learning (ML), rendering the reasons behind their decisions more understandable. However, current research in XAI primarily focuses on two aspects; ways to facilitate user trust, or to debug and refine the ML model. The majority of it falls short of recognising the diverse types of explanations needed in broader contexts, as different users and varied application areas necessitate solutions tailored to their specific needs. One such domain is Predictive Maintenance (PdM), an exploding area of research under the Industry 4.0 \& 5.0 umbrella. This position paper highlights the gap between existing XAI methodologies and the specific requirements for explanations within industrial applications, particularly the Predictive Maintenance field. Despite explainability's crucial role, this subject remains a relatively under-explored area, making this paper a pioneering attempt to bring relevant challenges to the research community's attention. We provide an overview of predictive maintenance tasks and accentuate the need and varying purposes for corresponding explanations. We then list and describe XAI techniques commonly employed in the literature, discussing their suitability for PdM tasks. Finally, to make the ideas and claims more concrete, we demonstrate XAI applied in four specific industrial use cases: commercial vehicles, metro trains, steel plants, and wind farms, spotlighting areas requiring further research., 51 pages, 9 figures

Machine Activity Recognition (MAR) can be used to monitor manufacturing processes and find bottlenecks and potential for improvement in production. Several interesting results on MAR techniques have been produced in the last decade, but mostly on construction equipment. Forklift trucks, which are ubiquitous and highly important industrial machines, have been missing from the MAR research. This paper presents a data-driven method for forklift activity recognition that uses Controller Area Network (CAN) signals and semi-supervised learning (SSL). The SSL enables the utilization of large quantities of unlabeled operation data to build better classifiers; after a two-step post-processing, the recognition results achieve balanced accuracy of 88% for driving activities and 95% for load-handling activities on a hold-out data set. In terms of the Matthews correlation coefficient for five activity classes, the final score is 0.82, which is equal to the recognition results of two non-domain experts who use videos of the activities. A particular success is that context can be used to capture the transport of small weight loads that are not detected by the forklift’s built-in weight sensor. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

The data shared over the Internet tends to originate from ubiquitous and autonomous sources such as mobile phones, fitness trackers, and IoT devices. Centralized and federated machine learning solutions represent the predominant way of providing smart services for users. However, moving data to central location for analysis causes not only many privacy concerns, but also communication overhead. Therefore, in certain situations machine learning models need to be trained in a collaborative and decentralized manner, similar to the way the data is originally generated without requiring any central authority for data or model aggregation. This paper presents a decentralized and adaptive k-means algorithm that clusters data from multiple sources organized in peer-to-peer networks. Our algorithm allows peers to reach an approximation of the global model without sharing any raw data. Most importantly, we address the challenge of decentralized clustering with skewed non-IID data and asynchronous computations by integrating HyperLogLog counters with k-means algorithm. Furthermore, our clustering algorithm allows nodes to individually determine the number of clusters that fits their local data. Results using synthetic and real-world datasets show that our algorithm outperforms state-of-the-art decentralized k-means algorithms achieving accuracy gain that is up-to 36%. QC 20200319

Predictive Maintenance (PM) is a proactive maintenance strategy that tries to minimize a system’s downtime by predicting failures before they happen. It uses data from sensors to measure the component’s state of health and make forecasts about its future degradation. However, existing PM methods typically focus on individual measurements. While it is natural to assume that a history of measurements carries more information than a single one. This paper aims at incorporating such information into PM models. In practice, especially in the automotive domain, diagnostic models have low performance, due to a large amount of noise in the data and limited sensing capability. To address this issue, this paper proposes to use a specific type of ensemble learning known as Stacked Ensemble. The idea is to aggregate predictions of multiple models—consisting of Long Short-Term Memory (LSTM) and Convolutional-LSTM—via a meta model, in order to boost performance. Stacked Ensemble model performs well when its base models are as diverse as possible. To this end, each such model is trained using a specific combination of the following three aspects: feature subsets, past dependency horizon, and model architectures. Experimental results demonstrate benefits of the proposed approach on a case study of heavy-duty truck turbochargers. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). HEALTH-VINNOVA

Predictive Maintenance (PM) is a proactive maintenance strategy that tries to minimize a system&rsquo, s downtime by predicting failures before they happen. It uses data from sensors to measure the component&rsquo, s state of health and make forecasts about its future degradation. However, existing PM methods typically focus on individual measurements. While it is natural to assume that a history of measurements carries more information than a single one. This paper aims at incorporating such information into PM models. In practice, especially in the automotive domain, diagnostic models have low performance, due to a large amount of noise in the data and limited sensing capability. To address this issue, this paper proposes to use a specific type of ensemble learning known as Stacked Ensemble. The idea is to aggregate predictions of multiple models&mdash, consisting of Long Short-Term Memory (LSTM) and Convolutional-LSTM&mdash, via a meta model, in order to boost performance. Stacked Ensemble model performs well when its base models are as diverse as possible. To this end, each such model is trained using a specific combination of the following three aspects: feature subsets, past dependency horizon, and model architectures. Experimental results demonstrate benefits of the proposed approach on a case study of heavy-duty truck turbochargers.

Many methods have been proposed over the years for distinguishing causes from effects using observational data only, and new ones are continuously being developed – deducing causal relationships is difficult enough that we do not hope to ever get the perfect one. Instead, we progress by creating powerful heuristics, capable of capturing more and more of the hints that are present in real data. One type of such hints, quite surprisingly rarely explicitly addressed by existing methods, is in-homogeneities in the data. Clusters are a very typical occurrence that should be taken into account, and exploited, in the process of identifying causes and effects. In this paper, we discuss the potential benefits, and explore the hints that clusters in the data can provide for causal discovery. We propose a new method, and show, using both artificial and real data, that accounting for clusters in the data leads to more accurate learning of causal structures.

In order for autonomous robots to be able to support people's well-being in homes and everyday environments, new interactive capabilities will be required, as exemplified by the soft design used for Disney's recent robot character Baymax in popular fiction. Home robots will be required to be easy to interact with and intelligent--adaptive, fun, unobtrusive and involving little effort to power and maintain--and capable of carrying out useful tasks both on an everyday level and during emergencies. The current article adopts an exploratory medium fidelity prototyping approach for testing some new robotic capabilities in regard to recognizing people's activities and intentions and behaving in a way which is transparent to people. Results are discussed with the aim of informing next designs., 28 pages, 11 figures

The majority of existing machine learning algorithms assume that training examples are already represented with sufficiently good features, in practice ones that are designed manually. This traditional way of preprocessing the data is not only tedious and time consuming, but also not sufficient to capture all the different aspects of the available information. With big data phenomenon, this issue is only going to grow, as the data is rarely collected and analyzed with a specific purpose in mind, and more often re-used for solving different problems. Moreover, the expert knowledge about the problem which allows them to come up with good representations does not necessarily generalize to other tasks. Therefore, much focus has been put on designing methods that can automatically learn features or representations of the data instead of learning from handcrafted features. However, a lot of this work used ad hoc methods and the theoretical understanding in this area is lacking.

This thesis addresses the problem of detecting changes in the activity of a distant gas source from the response of an array of metal oxide (MOX) gas sensors deployed in an Open Sampling System (OSS). Changes can occur due to gas source activity such as a sudden alteration in concentration or due to exposure to a different compound. Applications such as gas-leak detection in mines or large-scale pollution monitoring can benefit from reliable change detection algorithms, especially where it is impractical to continuously store or transfer sensor readings, or where reliable calibration is difficult to achieve. Here, it is desirable to detect a change point indicating a significant event, e.g. presence of gas or a sudden change in concentration. The main challenges are turbulent dispersion of gas and the slow response and recovery times of MOX sensors. Due to these challenges, the gas sensor response exhibits fluctuations that interfere with the changes of interest. The contributions of this thesis are centred on developing change detection methods using MOX sensor responses. First, we apply the Generalized Likelihood Ratio algorithm (GLR), a commonly used method that does not make any a priori assumption about change events. Next, we propose TREFEX, a novel change point detection algorithm, which models the response of MOX sensors as a piecewise exponential signal and considers the junctions between consecutive exponentials as change points. We also propose the rTREFEX algorithm as an extension of TREFEX. The core idea behind rTREFEX is an attempt to improve the fitted exponentials of TREFEX by minimizing the number of exponentials even further. GLR, TREFEX and rTREFEX are evaluated for various MOX sensors and gas emission profiles. A sensor selection algorithm is then introduced and the change detection algorithms are evaluated with the selected sensor subsets. A comparison between the three proposed algorithms shows clearly superior performance of rTREFEX both in detection performance and in estimating the change time. Further, rTREFEX is evaluated in real-world experiments where data is gathered by a mobile robot. Finally, a gas dispersion simulation was developed which integrates OpenFOAM flow simulation and a filament-based gas propagation model to simulate gas dispersion for compressible flows with a realistic turbulence model.

Detecting changes in the response of metal oxide (MOX) gas sensors deployed in an open sampling system is a hard problem. It is relevant for applicationssuch as gas leak detection in coal mines[1],[2] or large scale pollution monitoring [3],[4] where it is unpractical to continuously store or transfer sensor readings and reliable calibration is hard to achieve. Under these circumstances it is desirable to detect points in the signal where a change indicates a significant event, e.g. the presence of gas or a sudden change of concentration. The key idea behind the proposed change detection approach isthat a change in the emission modality of a gas source appears locally as an exponential function in the response of MOX sensors due to their long response and recovery times. The proposed method interprets the sensor responseby fitting piecewise exponential functions with different time constants for the response and recovery phase. The number of exponentials is determined automatically using an approximate method based on the L1-norm. This asymmetric exponential trend filtering problem is formulated as a convex optimization problem, which is particularly advantageous from the computational point of view. The algorithm is evaluated with an experimental setup where a gas source changes in intensity, compound, and mixture ratio, and it is compared against the previously proposed Generalized Likelihood Ratio (GLR) based algorithm [6].

This paper presents a framework of ICT solutions developed in the EU research project DIADEM that supports environmental management with an enhanced capacity to assess population exposure and health risks, to alert relevant groups and to organize efficient response. The emphasis is on advanced solutions which are economically feasible and maximally exploit the existing communication, computing and sensing resources. This approach enables efficient situation assessment in complex environmental management problems by exploiting relevant information obtained from citizens via the standard communication infrastructure as well as heterogeneous data acquired through dedicated sensing systems. This is achieved through a combination of (i) advanced approaches to gas detection and gas distribution modelling, (ii) a novel service-oriented approach supporting seamless integration of human-based and automated reasoning processes in large-scale collaborative sense making processes and (iii) solutions combining Multi-Criteria Decision Analysis, Scenario-Based Reasoning and advanced human-machine interfaces. This paper presents the basic principles of the DIADEM solutions, explains how different techniques are combined to a coherent decision support system and briefly discusses evaluation principles and activities in the DIADEM project., DMCR: the joint environmental protection agency of the province of South Holland and 16 municipalities

In this paper, we present a gas dispersal simulation package which integrates OpenFOAM flow simulation and a filament-based gas propagation model to simulate gas dispersion for compressible flows with a realistic turbulence model. Gas dispersal simulation can be useful for many applications. In this paper, we focus on the evaluation of statistical gas distribution models. Simulated data offer several advantages for this purpose, including the availability of ground truth information, repetition of experiments with the exact same constraints and that intricate issue which come with using real gas sensors can be avoided.Apart from simulation results obtained in a simulated wind tunnel (designed to be equivalent to its real-world counterpart), we present initial results with time-independent and time-dependent statistical modelling approaches applied to simulated and real-world data., Proceedings available (after registration) athttp://www.opensourcecfd.com/conference2010/proceedings/content/home.php

Code Search is one of the most common tasks for developers. The open-source software movement and the rise of social media have made this process easier thanks to the vast public software repositories available to everyone and the Q&A sites where individuals can resolve their doubts. However, in the case of poorly documented code that is difficult to search in a repository, or in the case of private enterprise frameworks that are not publicly available, so there is not a community on Q&A sites to answer questions, searching for code snippets to solve doubts or learn how to use an API becomes very complicated. In order to solve this problem, this thesis studies the use of natural language in code retrieval. In particular, it studies transformer-based models, such as Bidirectional Encoder Representations from Transformers (BERT), which are currently state of the art in natural language processing but present high latency in information retrieval tasks. That is why this project proposes a multi-stage architecture that seeks to maintain the performance of standard BERT-based models while reducing the high latency usually associated with the use of this type of framework. Experiments show that this architecture outperforms previous non- BERT-based models by +0.17 on the Top 1 (or Recall@1) metric and reduces latency with inference times 5% of those of standard BERT models. Kodsökning är en av de vanligaste uppgifterna för utvecklare. Rörelsen för öppen källkod och de sociala medierna har gjort denna process enklare tack vare de stora offentliga programvaruupplagorna som är tillgängliga för alla och de Q&A-webbplatser där enskilda personer kan lösa sina tvivel. När det gäller dåligt dokumenterad kod som är svår att söka i ett arkiv, eller när det gäller ramverk för privata företag som inte är offentligt tillgängliga, så att det inte finns någon gemenskap på Q&AA-webbplatser för att besvara frågor, blir det dock mycket komplicerat att söka efter kodstycken för att lösa tvivel eller lära sig hur man använder ett API. För att lösa detta problem studeras i denna avhandling användningen av naturligt språk för att hitta kod. I synnerhet studeras transformatorbaserade modeller, såsom BERT, som för närvarande är den senaste tekniken inom behandling av naturliga språk men som har hög latenstid vid informationssökning. Därför föreslås i detta projekt en arkitektur i flera steg som syftar till att bibehålla prestandan hos standard BERT-baserade modeller samtidigt som den höga latenstiden som vanligtvis är förknippad med användningen av denna typ av ramverk minskas. Experiment visar att denna arkitektur överträffar tidigare icke-BERT-baserade modeller med +0,17 på Top 1 (eller Recall@1) och minskar latensen, med en inferenstid som är 5% av den för standard BERT-modeller.