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Alirezaie, Marjan
Publications (10 of 25) Show all publications
Köckemann, U., Alirezaie, M., Renoux, J., Tsiftes, N., Ahmed, M. U., Morberg, D., . . . Loutfi, A. (2020). Open-Source Data Collection and Data Sets for Activity Recognition in Smart Homes. Sensors, 20(3), Article ID E879.
Open this publication in new window or tab >>Open-Source Data Collection and Data Sets for Activity Recognition in Smart Homes
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2020 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 20, no 3, article id E879Article in journal (Refereed) Published
Abstract [en]

As research in smart homes and activity recognition is increasing, it is of ever increasing importance to have benchmarks systems and data upon which researchers can compare methods. While synthetic data can be useful for certain method developments, real data sets that are open and shared are equally as important. This paper presents the E-care@home system, its installation in a real home setting, and a series of data sets that were collected using the E-care@home system. Our first contribution, the E-care@home system, is a collection of software modules for data collection, labeling, and various reasoning tasks such as activity recognition, person counting, and configuration planning. It supports a heterogeneous set of sensors that can be extended easily and connects collected sensor data to higher-level Artificial Intelligence (AI) reasoning modules. Our second contribution is a series of open data sets which can be used to recognize activities of daily living. In addition to these data sets, we describe the technical infrastructure that we have developed to collect the data and the physical environment. Each data set is annotated with ground-truth information, making it relevant for researchers interested in benchmarking different algorithms for activity recognition.

Place, publisher, year, edition, pages
MDPI, 2020
Keywords
Data collection software, prototype installation, smart home data sets
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-79928 (URN)10.3390/s20030879 (DOI)000517786200303 ()32041376 (PubMedID)2-s2.0-85079189175 (Scopus ID)
Funder
Knowledge Foundation
Available from: 2020-02-20 Created: 2020-02-20 Last updated: 2020-04-14Bibliographically approved
Santini, M., Jönsson, A., Strandqvist, W., Cederblad, G., Nyström, M., Alirezaie, M., . . . Kristoffersson, A. (2019). Designing an Extensible Domain-Specific Web Corpus for “Layfication”: A Case Study in eCare at Home. In: Maya Dimitrova (Bulgarian Academy of Sciences, Bulgaria) and Hiroaki Wagatsuma (Kyushu Institute of Technology, Japan) (Ed.), Cyber-Physical Systems for Social Applications: (pp. 98-155). Hershey, PA, USA: IGI Global
Open this publication in new window or tab >>Designing an Extensible Domain-Specific Web Corpus for “Layfication”: A Case Study in eCare at Home
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2019 (English)In: Cyber-Physical Systems for Social Applications / [ed] Maya Dimitrova (Bulgarian Academy of Sciences, Bulgaria) and Hiroaki Wagatsuma (Kyushu Institute of Technology, Japan), Hershey, PA, USA: IGI Global, 2019, p. 98-155Chapter in book (Refereed)
Abstract [en]

In the era of data-driven science, corpus-based language technology is an essential part of cyber physical systems. In this chapter, the authors describe the design and the development of an extensible domain-specific web corpus to be used in a distributed social application for the care of the elderly at home. The domain of interest is the medical field of chronic diseases. The corpus is conceived as a flexible and extensible textual resource, where additional documents and additional languages will be appended over time. The main purpose of the corpus is to be used for building and training language technology applications for the “layfication” of the specialized medical jargon. “Layfication” refers to the automatic identification of more intuitive linguistic expressions that can help laypeople (e.g., patients, family caregivers, and home care aides) understand medical terms, which often appear opaque. Exploratory experiments are presented and discussed.

Place, publisher, year, edition, pages
Hershey, PA, USA: IGI Global, 2019
National Category
Language Technology (Computational Linguistics)
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-73146 (URN)10.4018/978-1-5225-7879-6.ch006 (DOI)9781522593454 (ISBN)9781522578802 (ISBN)
Projects
E-care@home
Funder
Knowledge Foundation, 20140217
Available from: 2019-03-14 Created: 2019-03-14 Last updated: 2019-03-18Bibliographically approved
Lagriffoul, F. & Alirezaie, M. (2019). Perceiving and acting out of the box. In: Angelo Cangelosi, Antonio Lieto (Ed.), Proceedings of the 7th International Workshop on Artificial Intelligence and Cognition: . Paper presented at 7th International Workshop on Artificial Intelligence and Cognition, Manchester, UK, September 10-11, 2019. CEUR-WS, 2483
Open this publication in new window or tab >>Perceiving and acting out of the box
2019 (English)In: Proceedings of the 7th International Workshop on Artificial Intelligence and Cognition / [ed] Angelo Cangelosi, Antonio Lieto, CEUR-WS , 2019, Vol. 2483Conference paper, Published paper (Refereed)
Abstract [en]

This paper discusses potential limitations in learning in au-tonomous robotic systems that integrate several specialized subsystemsworking at different levels of abstraction. If the designers have antici-pated what the system may have to learn, then adding new knowledgeboils down to adding new entries in a database and/or tuning parametersof some subsystem(s). But if this new knowledge does not fit in prede-fined structures, the system can simply not acquire it, hence it cannot“think out of the box” designed by its creators. We show why learningout of the box may be difficult in integrated systems, hint at some exist-ing potential approaches, and finally suggest that a better approach maycome by looking at constructivist epistemology, with focus on Piaget’sschemas theory.

Place, publisher, year, edition, pages
CEUR-WS, 2019
Keywords
Artificial Intelligence, Learning, Cognitive Architecture
National Category
Computer Sciences
Research subject
Computer and Systems Science
Identifiers
urn:nbn:se:oru:diva-79697 (URN)
Conference
7th International Workshop on Artificial Intelligence and Cognition, Manchester, UK, September 10-11, 2019
Available from: 2020-02-03 Created: 2020-02-03 Last updated: 2020-02-14Bibliographically approved
Alirezaie, M., Längkvist, M., Sioutis, M. & Loutfi, A. (2019). Semantic Referee: A Neural-Symbolic Framework for Enhancing Geospatial Semantic Segmentation. Semantic Web, 10(5), 863-880
Open this publication in new window or tab >>Semantic Referee: A Neural-Symbolic Framework for Enhancing Geospatial Semantic Segmentation
2019 (English)In: Semantic Web, ISSN 1570-0844, E-ISSN 2210-4968, Vol. 10, no 5, p. 863-880Article in journal (Refereed) Published
Abstract [en]

Understanding why machine learning algorithms may fail is usually the task of the human expert that uses domain knowledge and contextual information to discover systematic shortcomings in either the data or the algorithm. In this paper, we propose a semantic referee, which is able to extract qualitative features of the errors emerging from deep machine learning frameworks and suggest corrections. The semantic referee relies on ontological reasoning about spatial knowledge in order to characterize errors in terms of their spatial relations with the environment. Using semantics, the reasoner interacts with the learning algorithm as a supervisor. In this paper, the proposed method of the interaction between a neural network classifier and a semantic referee shows how to improve the performance of semantic segmentation for satellite imagery data.

Place, publisher, year, edition, pages
IOS Press, 2019
Keywords
Deep Neural Network, Semantic Referee, Ontological and Spatial Reasoning, Semantic Segmentation, OntoCity, Geo Data
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-77266 (URN)10.3233/SW-190362 (DOI)000488082100003 ()
Projects
Semantic Robot
Funder
Swedish Research Council
Note

Funding Agency:

Swedish Knowledge Foundation under the research profile on Semantic Robots  20140033

Available from: 2019-10-14 Created: 2019-10-14 Last updated: 2019-10-25Bibliographically approved
Alirezaie, M., Längkvist, M., Sioutis, M. & Loutfi, A. (2018). A Symbolic Approach for Explaining Errors in Image Classification Tasks. In: : . Paper presented at 27th International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, Sweden, July 13-19, 2018.
Open this publication in new window or tab >>A Symbolic Approach for Explaining Errors in Image Classification Tasks
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Machine learning algorithms, despite their increasing success in handling object recognition tasks, still seldom perform without error. Often the process of understanding why the algorithm has failed is the task of the human who, using domain knowledge and contextual information, can discover systematic shortcomings in either the data or the algorithm. This paper presents an approach where the process of reasoning about errors emerging from a machine learning framework is automated using symbolic techniques. By utilizing spatial and geometrical reasoning between objects in a scene, the system is able to describe misclassified regions in relation to its context. The system is demonstrated in the remote sensing domain where objects and entities are detected in satellite images.

National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-68000 (URN)
Conference
27th International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, Sweden, July 13-19, 2018
Note

IJCAI Workshop on Learning and Reasoning: Principles & Applications to Everyday Spatial and Temporal Knowledge

Available from: 2018-07-18 Created: 2018-07-18 Last updated: 2018-07-26Bibliographically approved
Santini, M., Strandqvist, W., Nyström, M., Alirezaie, M. & Jönsson, A. (2018). Can We Quantify Domainhood?: Exploring Measures to Assess Domain-Specificity in Web Corpora. In: Elloumi, M.; Granitzer, M.; Hameurlain, A.; Seifert, C.; Stein, B.; Tjoa, AM.; Wagner, R. (Ed.), Database and Expert Systems Applications: EXA 2018 International Workshops. Paper presented at 29th International Conference on Database and Expert Systems Applications (DEXA), Regensburg, Germany, September 3-6, 2018 (pp. 207-217). Springer Berlin/Heidelberg
Open this publication in new window or tab >>Can We Quantify Domainhood?: Exploring Measures to Assess Domain-Specificity in Web Corpora
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2018 (English)In: Database and Expert Systems Applications: EXA 2018 International Workshops / [ed] Elloumi, M.; Granitzer, M.; Hameurlain, A.; Seifert, C.; Stein, B.; Tjoa, AM.; Wagner, R., Springer Berlin/Heidelberg, 2018, p. 207-217Conference paper, Published paper (Refereed)
Abstract [en]

Web corpora are a cornerstone of modern Language Technology. Corpora built from the web are convenient because their creation is fast and inexpensive. Several studies have been carried out to assess the representativeness of general-purpose web corpora by comparing them to traditional corpora. Less attention has been paid to assess the representativeness of specialized or domain-specific web corpora. In this paper, we focus on the assessment of domain representativeness of web corpora and we claim that it is possible to assess the degree of domainspecificity, or domainhood, of web corpora. We present a case study where we explore the effectiveness of different measures - namely the Mann-Withney-Wilcoxon Test, Kendall correlation coefficient, Kullback-Leibler divergence, log-likelihood and burstiness - to gauge domainhood. Our findings indicate that burstiness is the most suitable measure to single out domain-specific words from a specialized corpus and to allow for the quantification of domainhood.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2018
Series
Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937 ; 903
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-73233 (URN)10.1007/978-3-319-99133-7_17 (DOI)000460552400017 ()2-s2.0-85052001976 (Scopus ID)978-3-319-99133-7 (ISBN)978-3-319-99132-0 (ISBN)
Conference
29th International Conference on Database and Expert Systems Applications (DEXA), Regensburg, Germany, September 3-6, 2018
Available from: 2019-03-19 Created: 2019-03-19 Last updated: 2019-03-19Bibliographically approved
Köckemann, U., Alirezaie, M., Karlsson, L. & Loutfi, A. (2018). Integrating Ontologies for Context-based Constraint-based Planning. In: MRC 2018: Modelling and Reasoning in Context. Paper presented at Tenth InternationalWorkshop Modelling and Reasoning in Context (MRC), Stockholm, Sweden, July 13, 2018 (pp. 22-29).
Open this publication in new window or tab >>Integrating Ontologies for Context-based Constraint-based Planning
2018 (English)In: MRC 2018: Modelling and Reasoning in Context, 2018, p. 22-29Conference paper, Published paper (Refereed)
Abstract [en]

We describe an approach for integrating ontologies with a constraint-based planner to compile configuration planning domains based on the current context. We consider two alternative approaches: The first one integrates SPARQL queries directly with the planner while the second one generates SPARQL queries dynamically from provided triples. The first approach offers the full freedom of the SPARQL query language, while the second offers a more dynamic way for the planner to influence queries based on what is currently relevant for the planner. We evaluate the approach based on how much redundancy is removed by “outsourcing” knowledge into the ontology compared to modeling it directly into the domain of the planner.

National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-67849 (URN)
Conference
Tenth InternationalWorkshop Modelling and Reasoning in Context (MRC), Stockholm, Sweden, July 13, 2018
Available from: 2018-07-10 Created: 2018-07-10 Last updated: 2018-08-08Bibliographically approved
Alirezaie, M., Hammar, K. & Blomqvist, E. (2018). SmartEnv as a network of ontology patterns. Semantic Web, 9(6), 903-918
Open this publication in new window or tab >>SmartEnv as a network of ontology patterns
2018 (English)In: Semantic Web, ISSN 1570-0844, E-ISSN 2210-4968, Vol. 9, no 6, p. 903-918Article in journal (Refereed) Published
Abstract [en]

In this article we outline the details of an ontology, called SmartEnv, proposed as a representational model to assist the development process of smart (i.e., sensorized) environments. The SmartEnv ontology is described in terms of its modules representing different aspects including physical and conceptual aspects of a smart environment. We propose the use of the Ontology Design Pattern (ODP) paradigm in order to modularize our proposed solution, while at the same time avoiding strong dependencies between the modules in order to manage the representational complexity of the ontology. The ODP paradigm and related methodologies enable incremental construction of ontologies by first creating and then linking small modules. Most modules (patterns) of the SmartEnv ontology are inspired by, and aligned with, the Semantic Sensor Network (SSN) ontology, however with extra interlinks to provide further precision and cover more representational aspects.

The result is a network of 8 ontology patterns together forming a generic representation for a smart environment. The patterns have been submitted to the ODP portal and are available on-line at stable URIs.

Place, publisher, year, edition, pages
IOS Press, 2018
Keywords
Smart Environments, SmartEnv Ontology, Ontology Design Pattern, Semantic Sensor Network
National Category
Computer Sciences
Research subject
Computer and Systems Science
Identifiers
urn:nbn:se:oru:diva-67889 (URN)10.3233/SW-180303 (DOI)000444433900008 ()2-s2.0-85050478389 (Scopus ID)
Projects
E-care@home
Note

Funding Agency:

project E-care@home - Swedish Knowledge Foundation 2015-2019

Available from: 2018-07-14 Created: 2018-07-14 Last updated: 2018-10-01Bibliographically approved
Alirezaie, M., Renoux, J., Köckemann, U., Kristoffersson, A., Karlsson, L., Blomqvist, E., . . . Loutfi, A. (2017). An Ontology-based Context-aware System for Smart Homes: E-care@home. Sensors, 17(7), Article ID 1586.
Open this publication in new window or tab >>An Ontology-based Context-aware System for Smart Homes: E-care@home
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2017 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 7, article id 1586Article in journal, Editorial material (Refereed) Published
Abstract [en]

Smart home environments have a significant potential to provide for long-term monitoring of users with special needs in order to promote the possibility to age at home. Such environments are typically equipped with a number of heterogeneous sensors that monitor both health and environmental parameters. This paper presents a framework called E-care@home, consisting of an IoT infrastructure, which provides information with an unambiguous, shared meaning across IoT devices, end-users, relatives, health and care professionals and organizations. We focus on integrating measurements gathered from heterogeneous sources by using ontologies in order to enable semantic interpretation of events and context awareness. Activities are deduced using an incremental answer set solver for stream reasoning. The paper demonstrates the proposed framework using an instantiation of a smart environment that is able to perform context recognition based on the activities and the events occurring in the home.

Place, publisher, year, edition, pages
Basel: MDPI AG, 2017
Keywords
ambient assisted living; context awareness; Internet of Things; ontologies; activity recognition; smart homes
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-58600 (URN)10.3390/s17071586 (DOI)000407517600125 ()2-s2.0-85021911117 (Scopus ID)
Projects
E-Care@Home
Funder
Knowledge Foundation
Available from: 2017-07-07 Created: 2017-07-07 Last updated: 2020-01-28Bibliographically approved
Alirezaie, M., Kiselev, A., Längkvist, M., Klügl, F. & Loutfi, A. (2017). An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring. Sensors, 17(11), Article ID 2545.
Open this publication in new window or tab >>An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring
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2017 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 11, article id 2545Article in journal, Editorial material (Refereed) Published
Abstract [en]

This paper presents a framework in which satellite images are classified and augmented with additional semantic information to enable queries about what can be found on the map at a particular location, but also about paths that can be taken. This is achieved by a reasoning framework based on qualitative spatial reasoning that is able to find answers to high level queries that may vary on the current situation. This framework called SemCityMap, provides the full pipeline from enriching the raw image data with rudimentary labels to the integration of a knowledge representation and reasoning methods to user interfaces for high level querying. To illustrate the utility of SemCityMap in a disaster scenario, we use an urban environment—central Stockholm—in combination with a flood simulation. We show that the system provides useful answers to high-level queries also with respect to the current flood status. Examples of such queries concern path planning for vehicles or retrieval of safe regions such as “find all regions close to schools and far from the flooded area”. The particular advantage of our approach lies in the fact that ontological information and reasoning is explicitly integrated so that queries can be formulated in a natural way using concepts on appropriate level of abstraction, including additional constraints.

Place, publisher, year, edition, pages
M D P I AG, 2017
Keywords
satellite imagery data; natural hazards; ontology; reasoning; path finding
National Category
Computer Systems
Research subject
Computer and Systems Science
Identifiers
urn:nbn:se:oru:diva-62134 (URN)10.3390/s17112545 (DOI)000416790500107 ()29113073 (PubMedID)2-s2.0-85033372857 (Scopus ID)
Projects
Semantic Robot
Available from: 2017-11-05 Created: 2017-11-05 Last updated: 2018-01-03Bibliographically approved
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