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Cooney, M., Ong, L., Pashami, S., Järpe, E. & Ashfaq, A. (2019). Avoiding Improper Treatment of Dementia Patients by Care Robots. In: : . Paper presented at The Dark Side of Human-Robot Interaction: Ethical Considerations and Community Guidelines for the Field of HRI. HRI Workshop, Daegu, South Korea, March 11, 2019.
Open this publication in new window or tab >>Avoiding Improper Treatment of Dementia Patients by Care Robots
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2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The phrase “most cruel and revolting crimes” has been used to describe some poor historical treatment of vulnerable impaired persons by precisely those who should have had the responsibility of protecting and helping them. We believe we might be poised to see history repeat itself, as increasingly humanlike aware robots become capable of engaging in behavior which we would consider immoral in a human–either unknowingly or deliberately. In the current paper we focus in particular on exploring some potential dangers affecting persons with dementia (PWD), which could arise from insufficient software or external factors, and describe a proposed solution involving rich causal models and accountability measures: Specifically, the Consequences of Needs-driven Dementia-compromised Behaviour model (C-NDB) could be adapted to be used with conversation topic detection, causal networks and multi-criteria decision making, alongside reports, audits, and deterrents. Our aim is that the considerations raised could help inform the design of care robots intended to support well-being in PWD.

Keywords
care robot, therapy robot, dementia, ethics
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:hh:diva-39448 (URN)
Conference
The Dark Side of Human-Robot Interaction: Ethical Considerations and Community Guidelines for the Field of HRI. HRI Workshop, Daegu, South Korea, March 11, 2019
Funder
Knowledge Foundation, 20140220
Note

Funder: EU REMIND project (H2020-MSCA-RISE No 734355)

Available from: 2019-05-22 Created: 2019-05-22 Last updated: 2019-10-09
Pirasteh, P., Nowaczyk, S., Pashami, S., Löwenadler, M., Thunberg, K., Ydreskog, H. & Berck, P. (2019). Interactive feature extraction for diagnostic trouble codes in predictive maintenance: A case study from automotive domain. In: Proceedings of the Workshop on Interactive Data Mining: . Paper presented at WSDM 2019: The 12th ACM International Conference on Web Search and Data Mining, Melbourne, VIC, Australia, 11-15 February, 2019. New York, NY: Association for Computing Machinery (ACM), Article ID 4.
Open this publication in new window or tab >>Interactive feature extraction for diagnostic trouble codes in predictive maintenance: A case study from automotive domain
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2019 (English)In: Proceedings of the Workshop on Interactive Data Mining, New York, NY: Association for Computing Machinery (ACM), 2019, article id 4Conference paper, Published paper (Refereed)
Abstract [en]

Predicting future maintenance needs of equipment can be addressed in a variety of ways. Methods based on machine learning approaches provide an interesting platform for mining large data sets to find patterns that might correlate with a given fault. In this paper, we approach predictive maintenance as a classification problem and use Random Forest to separate data readouts within a particular time window into those corresponding to faulty and non-faulty component categories. We utilize diagnostic trouble codes (DTCs) as an example of event-based data, and propose four categories of features that can be derived from DTCs as a predictive maintenance framework. We test the approach using large-scale data from a fleet of heavy duty trucks, and show that DTCs can be used within our framework as indicators of imminent failures in different components.

Place, publisher, year, edition, pages
New York, NY: Association for Computing Machinery (ACM), 2019
Keywords
Predictive maintenance, failure detection, diagnostic trouble codes, feature extraction
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-40184 (URN)10.1145/3304079.3310288 (DOI)978-1-4503-6296-2 (ISBN)
Conference
WSDM 2019: The 12th ACM International Conference on Web Search and Data Mining, Melbourne, VIC, Australia, 11-15 February, 2019
Available from: 2019-07-07 Created: 2019-07-07 Last updated: 2019-08-02Bibliographically approved
Pashami, S., Holst, A., Bae, J. & Nowaczyk, S. (2018). Causal discovery using clusters from observational data. In: : . Paper presented at FAIM'18 Workshop on CausalML, Stockholm, Sweden, July 15, 2018.
Open this publication in new window or tab >>Causal discovery using clusters from observational data
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

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.

National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:hh:diva-39216 (URN)
Conference
FAIM'18 Workshop on CausalML, Stockholm, Sweden, July 15, 2018
Available from: 2019-04-09 Created: 2019-04-09 Last updated: 2019-04-11Bibliographically approved
Vaiciukynas, E., Uličný, M., Pashami, S. & Nowaczyk, S. (2018). Learning Low-Dimensional Representation of Bivariate Histogram Data. IEEE transactions on intelligent transportation systems (Print), 19(11), 3723-3735
Open this publication in new window or tab >>Learning Low-Dimensional Representation of Bivariate Histogram Data
2018 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 19, no 11, p. 3723-3735Article in journal (Refereed) Published
Abstract [en]

With an increasing amount of data in intelligent transportation systems, methods are needed to automatically extract general representations that accurately predict not only known tasks but also similar tasks that can emerge in the future. Creation of low-dimensional representations can be unsupervised or can exploit various labels in multi-task learning (when goal tasks are known) or transfer learning (when they are not) settings. Finding a general, low-dimensional representation suitable for multiple tasks is an important step toward knowledge discovery in aware intelligent transportation systems. This paper evaluates several approaches mapping high-dimensional sensor data from Volvo trucks into a low-dimensional representation that is useful for prediction. Original data are bivariate histograms, with two types--turbocharger and engine--considered. Low-dimensional representations were evaluated in a supervised fashion by mean equal error rate (EER) using a random forest classifier on a set of 27 1-vs-Rest detection tasks. Results from unsupervised learning experiments indicate that using an autoencoder to create an intermediate representation, followed by $t$-distributed stochastic neighbor embedding, is the most effective way to create low-dimensional representation of the original bivariate histogram. Individually, $t$-distributed stochastic neighbor embedding offered best results for 2-D or 3-D and classical autoencoder for 6-D or 10-D representations. Using multi-task learning, combining unsupervised and supervised objectives on all 27 available tasks, resulted in 10-D representations with a significantly lower EER compared to the original 400-D data. In transfer learning setting, with topmost diverse tasks used for representation learning, 10-D representations achieved EER comparable to the original representation.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2018
Keywords
Task analysis, Histograms, Engines, Intelligent transportation systems, Maintenance engineering, Machine learning, Feature extraction
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:hh:diva-38252 (URN)10.1109/TITS.2018.2865103 (DOI)2-s2.0-85053294183 (Scopus ID)
Available from: 2018-11-04 Created: 2018-11-04 Last updated: 2018-11-20Bibliographically approved
Bouguelia, M.-R., Karlsson, A., Pashami, S., Nowaczyk, S. & Holst, A. (2018). Mode tracking using multiple data streams. Information Fusion, 43, 33-46
Open this publication in new window or tab >>Mode tracking using multiple data streams
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2018 (English)In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 43, p. 33-46Article in journal (Refereed) Published
Abstract [en]

Most existing work in information fusion focuses on combining information with well-defined meaning towards a concrete, pre-specified goal. In contradistinction, we instead aim for autonomous discovery of high-level knowledge from ubiquitous data streams. This paper introduces a method for recognition and tracking of hidden conceptual modes, which are essential to fully understand the operation of complex environments. We consider a scenario of analyzing usage of a fleet of city buses, where the objective is to automatically discover and track modes such as highway route, heavy traffic, or aggressive driver, based on available on-board signals. The method we propose is based on aggregating the data over time, since the high-level modes are only apparent in the longer perspective. We search through different features and subsets of the data, and identify those that lead to good clusterings, interpreting those clusters as initial, rough models of the prospective modes. We utilize Bayesian tracking in order to continuously improve the parameters of those models, based on the new data, while at the same time following how the modes evolve over time. Experiments with artificial data of varying degrees of complexity, as well as on real-world datasets, prove the effectiveness of the proposed method in accurately discovering the modes and in identifying which one best explains the current observations from multiple data streams. © 2017 Elsevier B.V. All rights reserved.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2018
Keywords
Mode tracking, Clustering, Data streams, Time series, Knowledge discovery
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-35729 (URN)10.1016/j.inffus.2017.11.011 (DOI)2-s2.0-85037072003 (Scopus ID)
Projects
BIDAF
Available from: 2017-12-01 Created: 2017-12-01 Last updated: 2019-04-12Bibliographically approved
Cooney, M., Pashami, S., Pinheiro Sant'Anna, A., Fan, Y. & Nowaczyk, S. (2018). Pitfalls of Affective Computing: How can the automatic visual communication of emotions lead to harm, and what can be done to mitigate such risks?. In: WWW '18 Companion Proceedings of the The Web Conference 2018: . Paper presented at The Web Conference 2018 (WWW '18), Lyon, France, April 23-27, 2018 (pp. 1563-1566). New York, NY: ACM Publications
Open this publication in new window or tab >>Pitfalls of Affective Computing: How can the automatic visual communication of emotions lead to harm, and what can be done to mitigate such risks?
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2018 (English)In: WWW '18 Companion Proceedings of the The Web Conference 2018, New York, NY: ACM Publications, 2018, p. 1563-1566Conference paper, Published paper (Refereed)
Abstract [en]

What would happen in a world where people could "see'' others' hidden emotions directly through some visualizing technology Would lies become uncommon and would we understand each other better Or to the contrary, would such forced honesty make it impossible for a society to exist The science fiction television show Black Mirror has exposed a number of darker scenarios in which such futuristic technologies, by blurring the lines of what is private and what is not, could also catalyze suffering. Thus, the current paper first turns an eye towards identifying some potential pitfalls in emotion visualization which could lead to psychological or physical harm, miscommunication, and disempowerment. Then, some countermeasures are proposed and discussed--including some level of control over what is visualized and provision of suitably rich emotional information comprising intentions--toward facilitating a future in which emotion visualization could contribute toward people's well-being. The scenarios presented here are not limited to web technologies, since one typically thinks about emotion recognition primarily in the context of direct contact. However, as interfaces develop beyond today's keyboard and monitor, more information becomes available also at a distance--for example, speech-to-text software could evolve to annotate any dictated text with a speaker's emotional state.

Place, publisher, year, edition, pages
New York, NY: ACM Publications, 2018
Keywords
Affective computing, emotion visualization, Black Mirror, privacy, ethics, intention recognition
National Category
Robotics
Identifiers
urn:nbn:se:hh:diva-37664 (URN)10.1145/3184558.3191611 (DOI)
Conference
The Web Conference 2018 (WWW '18), Lyon, France, April 23-27, 2018
Projects
CAISR 2010/0271
Funder
Knowledge Foundation, CAISR 2010/0271
Note

Funding: Swedish Knowledge Foundation (CAISR 2010/0271 and Sidus AIR no. 20140220)

Available from: 2018-07-25 Created: 2018-07-25 Last updated: 2019-04-12Bibliographically approved
Bouguelia, M.-R., Pashami, S. & Nowaczyk, S. (2017). Multi-Task Representation Learning. In: Niklas Lavesson (Ed.), 30th Annual Workshop ofthe Swedish Artificial Intelligence Society SAIS 2017: May 15–16, 2017, Karlskrona, Sweden. Paper presented at 30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2017, May 15–16, 2017, Karlskrona, Sweden (pp. 53-59). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Multi-Task Representation Learning
2017 (English)In: 30th Annual Workshop ofthe Swedish Artificial Intelligence Society SAIS 2017: May 15–16, 2017, Karlskrona, Sweden / [ed] Niklas Lavesson, Linköping: Linköping University Electronic Press, 2017, p. 53-59Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2017
Series
Linköping Electronic Conference Proceedings, ISSN 1650-3686, E-ISSN 1650-3740 ; 137
Keywords
Representation Learning, Multi-Task Learning, Machine Learning, Supervised Learning, Feature Learning
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-36755 (URN)978-91-7685-496-9 (ISBN)
Conference
30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2017, May 15–16, 2017, Karlskrona, Sweden
Available from: 2018-05-14 Created: 2018-05-14 Last updated: 2019-04-12Bibliographically approved
Helldin, T., Riveiro, M., Pashami, S., Falkman, G., Byttner, S. & Slawomir, N. (2016). Supporting Analytical Reasoning: A Study from the Automotive Industry. In: Sakae Yamamoto (Ed.), Human Interface and the Management of Information: Applications and Services: 18th International Conference, HCI International 2016: Toronto, Canada, July 17-22, 2016. Proceedings, Part II. Paper presented at 18th International Conference, HCI International 2016, Toronto, Canada, July 17-22, 2016 (pp. 20-31). Cham: Springer, 9735
Open this publication in new window or tab >>Supporting Analytical Reasoning: A Study from the Automotive Industry
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2016 (English)In: Human Interface and the Management of Information: Applications and Services: 18th International Conference, HCI International 2016: Toronto, Canada, July 17-22, 2016. Proceedings, Part II / [ed] Sakae Yamamoto, Cham: Springer, 2016, Vol. 9735, p. 20-31Conference paper, Published paper (Refereed)
Abstract [en]

In the era of big data, it is imperative to assist the human analyst in the endeavor to find solutions to ill-defined problems, i.e. to “detect the expected and discover the unexpected” (Yi et al., 2008). To their aid, a plethora of analysis support systems is available to the analysts. However, these support systems often lack visual and interactive features, leaving the analysts with no opportunity to guide, influence and even understand the automatic reasoning performed and the data used. Yet, to be able to appropriately support the analysts in their sense-making process, we must look at this process more closely. In this paper, we present the results from interviews performed together with data analysts from the automotive industry where we have investigated how they handle the data, analyze it and make decisions based on the data, outlining directions for the development of analytical support systems within the area. © Springer International Publishing Switzerland 2016.

Place, publisher, year, edition, pages
Cham: Springer, 2016
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9735
Keywords
Analytical reasoning, Sense-making, Visual analytics, Truck data analysis, Big data
National Category
Computer Systems Signal Processing
Identifiers
urn:nbn:se:hh:diva-32048 (URN)10.1007/978-3-319-40397-7_3 (DOI)000389467600003 ()2-s2.0-84978877445 (Scopus ID)978-3-319-40396-0 (ISBN)978-3-319-40397-7 (ISBN)
Conference
18th International Conference, HCI International 2016, Toronto, Canada, July 17-22, 2016
Projects
BIDAF
Funder
Knowledge Foundation, BIDAF 2014/32
Available from: 2016-09-19 Created: 2016-09-19 Last updated: 2019-04-12Bibliographically approved
Projects
Data-Driven Predictive Maintenance for Trucks [2016-03451_Vinnova]; Halmstad University
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org//0000-0003-3272-4145

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