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Alirezaie, Marjan
Publications (10 of 20) Show all publications
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
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
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 2210-4968Article in journal (Refereed) Epub ahead of print
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)
Projects
E-care@home
Available from: 2018-07-14 Created: 2018-07-14 Last updated: 2018-08-08Bibliographically 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: 2017-10-24Bibliographically 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
Renoux, J., Alirezaie, M., Karlsson, L., Köckemann, U., Pecora, F. & Loutfi, A. (2017). Context Recognition in Multiple Occupants Situations: Detecting the Number of Agents in a Smart Home Environment with Simple Sensors. In: Knowledge-based techniques for problem solving and reasoning(KnowProS 2017): A workshop at AAAI 2017, February 5, 2017, San Francisco, U.S.A.. Paper presented at Workshop on Knowledge-Based Techniques for Problem Solving and Reasoning (KnowProS’17) (pp. 758-764). Palo Alto: AAAI Press, ws17, Article ID WS-17-12.
Open this publication in new window or tab >>Context Recognition in Multiple Occupants Situations: Detecting the Number of Agents in a Smart Home Environment with Simple Sensors
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2017 (English)In: Knowledge-based techniques for problem solving and reasoning(KnowProS 2017): A workshop at AAAI 2017, February 5, 2017, San Francisco, U.S.A., Palo Alto: AAAI Press, 2017, Vol. ws17, p. 758-764, article id WS-17-12Conference paper, Published paper (Refereed)
Abstract [en]

Context-recognition and activity recognition systems in multi-user environments such as smart homes, usually assume to know the number of occupants in the environment. However, being able to count the number of users in the environment is important in order to accurately recognize the activities of (groups of) agents. For smart environments without cameras, the problem of counting the number of agents is non-trivial. This is in part due to the difficulty of using a single non-vision based sensors to discriminate between one or several persons, and thus information from several sensors must be combined in order to reason about the presence of several agents. In this paper we address the problem of counting the number of agents in a topologically known environment using simple sensors that can indicate anonymous human presence. To do so, we connect an ontology to a probabilistic model (a Hidden Markov Model) in order to estimate the number of agents in each section of the environment. We evaluate our methods on a smart home setup where a number of motion and pressure sensors are distributed in various rooms of the home.

Place, publisher, year, edition, pages
Palo Alto: AAAI Press, 2017
Series
The Workshops of the Thirty-First AAAI Conference on Artificial Intelligence: Technical Reports WS-17-01 - WS-17-15
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-62763 (URN)9781577357865 (ISBN)1577357868 (ISBN)
Conference
Workshop on Knowledge-Based Techniques for Problem Solving and Reasoning (KnowProS’17)
Available from: 2017-11-22 Created: 2017-11-22 Last updated: 2018-01-26Bibliographically approved
Alirezaie, M., Kiselev, A., Klügl, F., Längkvist, M. & Loutfi, A. (2017). Exploiting Context and Semantics for UAV Path-finding in an Urban Setting. In: Emanuele Bastianelli, Mathieu d'Aquin, Daniele Nardi (Ed.), Proceedings of the 1st International Workshop on Application of Semantic Web technologies in Robotics (AnSWeR 2017), Portoroz, Slovenia, May 29th, 2017: . Paper presented at International Workshop on Application of Semantic Web technologies in Robotics co-located with 14th Extended Semantic Web Conference (ESWC), Portoroz, Slovenia, 28th May-1st June, 2017 (pp. 11-20). Technical University Aachen
Open this publication in new window or tab >>Exploiting Context and Semantics for UAV Path-finding in an Urban Setting
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2017 (English)In: Proceedings of the 1st International Workshop on Application of Semantic Web technologies in Robotics (AnSWeR 2017), Portoroz, Slovenia, May 29th, 2017 / [ed] Emanuele Bastianelli, Mathieu d'Aquin, Daniele Nardi, Technical University Aachen , 2017, p. 11-20Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we propose an ontology pattern that represents paths in a geo-representation model to be used in an aerial path planning processes. This pattern provides semantics related to constraints (i.e., ight forbidden zones) in a path planning problem in order to generate collision free paths. Our proposed approach has been applied on an ontology containing geo-regions extracted from satellite imagery data from a large urban city as an illustrative example.

Place, publisher, year, edition, pages
Technical University Aachen, 2017
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; 1935
Keywords
Semantic Web for Robotics, Representation and reasoning for Robotics, Ontology Design Pattern, Path Planning
National Category
Engineering and Technology Computer Sciences
Research subject
Computer and Systems Science
Identifiers
urn:nbn:se:oru:diva-64603 (URN)2-s2.0-85030752502 (Scopus ID)
Conference
International Workshop on Application of Semantic Web technologies in Robotics co-located with 14th Extended Semantic Web Conference (ESWC), Portoroz, Slovenia, 28th May-1st June, 2017
Projects
Semantic Robot
Available from: 2018-01-29 Created: 2018-01-29 Last updated: 2018-09-10Bibliographically approved
Längkvist, M., Kiselev, A., Alirezaie, M. & Loutfi, A. (2016). Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks. Remote Sensing, 8(4), Article ID 329.
Open this publication in new window or tab >>Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks
2016 (English)In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 8, no 4, article id 329Article in journal (Refereed) Published
Abstract [en]

The availability of high-resolution remote sensing (HRRS) data has opened up the possibility for new interesting applications, such as per-pixel classification of individual objects in greater detail. This paper shows how a convolutional neural network (CNN) can be applied to multispectral orthoimagery and a digital surface model (DSM) of a small city for a full, fast and accurate per-pixel classification. The predicted low-level pixel classes are then used to improve the high-level segmentation. Various design choices of the CNN architecture are evaluated and analyzed. The investigated land area is fully manually labeled into five categories (vegetation, ground, roads, buildings and water), and the classification accuracy is compared to other per-pixel classification works on other land areas that have a similar choice of categories. The results of the full classification and segmentation on selected segments of the map show that CNNs are a viable tool for solving both the segmentation and object recognition task for remote sensing data.

Place, publisher, year, edition, pages
Basel: MDPI AG, 2016
Keywords
remote sensing, orthoimagery, convolutional neural network, per-pixel classification, segmentation, region merging
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-50501 (URN)10.3390/rs8040329 (DOI)000375156500062 ()
Funder
Knowledge Foundation, 20140033
Available from: 2016-05-31 Created: 2016-05-31 Last updated: 2018-07-13Bibliographically approved
Längkvist, M., Alirezaie, M., Kiselev, A. & Loutfi, A. (2016). Interactive Learning with Convolutional Neural Networks for Image Labeling. In: International Joint Conference on Artificial Intelligence (IJCAI): . Paper presented at International Joint Conference on Artificial Intelligence (IJCAI), New York, USA, 9-15th July, 2016.
Open this publication in new window or tab >>Interactive Learning with Convolutional Neural Networks for Image Labeling
2016 (English)In: International Joint Conference on Artificial Intelligence (IJCAI), 2016Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Recently, deep learning models, such as Convolutional Neural Networks, have shown to give good performance for various computer vision tasks. A pre-requisite for such models is to have access to lots of labeled data since the most successful ones are trained with supervised learning. The process of labeling data is expensive, time-consuming, tedious, and sometimes subjective, which can result in falsely labeled data, which has a negative effect on both the training and the validation. In this work, we propose a human-in-the-loop intelligent system that allows the agent and the human to collabo- rate to simultaneously solve the problem of labeling data and at the same time perform scene labeling of an unlabeled image data set with minimal guidance by a human teacher. We evaluate the proposed in- teractive learning system by comparing the labeled data set from the system to the human-provided labels. The results show that the learning system is capable of almost completely label an entire image data set starting from a few labeled examples provided by the human teacher.

National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-52116 (URN)
Conference
International Joint Conference on Artificial Intelligence (IJCAI), New York, USA, 9-15th July, 2016
Available from: 2016-09-12 Created: 2016-09-12 Last updated: 2018-01-10Bibliographically approved
Alirezaie, M., Klügl, F. & Loutfi, A. (2016). Knowing without telling: integrating sensing and mapping for creating an artificial companion. In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems: . Paper presented at 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2016), Burlingame, California, USA, October 31 - November 3, 2016 (pp. 11:1-11:4). New York, NY, USA: Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Knowing without telling: integrating sensing and mapping for creating an artificial companion
2016 (English)In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, New York, NY, USA: Association for Computing Machinery (ACM), 2016, p. 11:1-11:4Conference paper, Published paper (Refereed)
Abstract [en]

This paper depicts a sensor-based map navigation approach which targets users, who due to disabilities or lack of technical knowledge are currently not in the focus of map system developments for personalized information. What differentiates our approach from the state-of-art mostly integrating localized social media data, is that our vision is to integrate real time sensor generated data that indicates the situation of dfferent phenomena (such as the physiological functions of the body) related to the user. The challenge hereby is mainly related to knowledge representation and integration. The tentative impact of our vision for future navigation systems is re ected within a scenario.

Place, publisher, year, edition, pages
New York, NY, USA: Association for Computing Machinery (ACM), 2016
Keywords
Navigation, Sensors, Personal Travel Assistant, Ontology
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-54274 (URN)10.1145/2996913.2996961 (DOI)000403647900011 ()2-s2.0-85011016494 (Scopus ID)978-1-4503-4589-7 (ISBN)
Conference
24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2016), Burlingame, California, USA, October 31 - November 3, 2016
Funder
Knowledge Foundation, 20140033
Available from: 2017-01-04 Created: 2017-01-04 Last updated: 2018-02-02Bibliographically approved
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