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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
Stoyanov, T., Krug, R., Kiselev, A., Sun, D. & Loutfi, A. (2018). Assisted Telemanipulation: A Stack-Of-Tasks Approach to Remote Manipulator Control. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): . Paper presented at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, October 1-5, 2018. IEEE Press
Open this publication in new window or tab >>Assisted Telemanipulation: A Stack-Of-Tasks Approach to Remote Manipulator Control
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2018 (English)In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE Press, 2018Conference paper, Published paper (Refereed)
Abstract [en]

This article presents an approach for assisted teleoperation of a robot arm, formulated within a real-time stack-of-tasks (SoT) whole-body motion control framework. The approach leverages the hierarchical nature of the SoT framework to integrate operator commands with assistive tasks, such as joint limit and obstacle avoidance or automatic gripper alignment. Thereby some aspects of the teleoperation problem are delegated to the controller and carried out autonomously. The key contributions of this work are two-fold: the first is a method for unobtrusive integration of autonomy in a telemanipulation system; and the second is a user study evaluation of the proposed system in the context of teleoperated pick-and-place tasks. The proposed approach of assistive control was found to result in higher grasp success rates and shorter trajectories than achieved through manual control, without incurring additional cognitive load to the operator.

Place, publisher, year, edition, pages
IEEE Press, 2018
Series
IEEE International Conference on Intelligent Robots and Systems. Proceedings, ISSN 2153-0866
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-71310 (URN)10.1109/IROS.2018.8594457 (DOI)
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, October 1-5, 2018
Available from: 2019-01-09 Created: 2019-01-09 Last updated: 2019-01-18Bibliographically approved
Längkvist, M., Jendeberg, J., Thunberg, P., Loutfi, A. & Lidén, M. (2018). Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks. Computers in Biology and Medicine, 97, 153-160
Open this publication in new window or tab >>Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks
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2018 (English)In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 97, p. 153-160Article in journal (Refereed) Published
Abstract [en]

Computed tomography (CT) is the method of choice for diagnosing ureteral stones - kidney stones that obstruct the ureter. The purpose of this study is to develop a computer aided detection (CAD) algorithm for identifying a ureteral stone in thin slice CT volumes. The challenge in CAD for urinary stones lies in the similarity in shape and intensity of stones with non-stone structures and how to efficiently deal with large high-resolution CT volumes. We address these challenges by using a Convolutional Neural Network (CNN) that works directly on the high resolution CT volumes. The method is evaluated on a large data base of 465 clinically acquired high-resolution CT volumes of the urinary tract with labeling of ureteral stones performed by a radiologist. The best model using 2.5D input data and anatomical information achieved a sensitivity of 100% and an average of 2.68 false-positives per patient on a test set of 88 scans.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Computer aided detection, Ureteral stone, Convolutional neural networks, Computed tomography, Training set selection, False positive reduction
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:oru:diva-67139 (URN)10.1016/j.compbiomed.2018.04.021 (DOI)000435623700015 ()29730498 (PubMedID)2-s2.0-85046800526 (Scopus ID)
Note

Funding Agencies:

Nyckelfonden  OLL-597511 

Vinnova under the project "Interactive Deep Learning for 3D image analysis"  

Available from: 2018-06-04 Created: 2018-06-04 Last updated: 2018-08-30Bibliographically approved
Banaee, H., Schaffernicht, E. & Loutfi, A. (2018). Data-Driven Conceptual Spaces: Creating Semantic Representations for Linguistic Descriptions of Numerical Data. The journal of artificial intelligence research, 63, 691-742
Open this publication in new window or tab >>Data-Driven Conceptual Spaces: Creating Semantic Representations for Linguistic Descriptions of Numerical Data
2018 (English)In: The journal of artificial intelligence research, ISSN 1076-9757, E-ISSN 1943-5037, Vol. 63, p. 691-742Article in journal (Refereed) Published
Abstract [en]

There is an increasing need to derive semantics from real-world observations to facilitate natural information sharing between machine and human. Conceptual spaces theory is a possible approach and has been proposed as mid-level representation between symbolic and sub-symbolic representations, whereby concepts are represented in a geometrical space that is characterised by a number of quality dimensions. Currently, much of the work has demonstrated how conceptual spaces are created in a knowledge-driven manner, relying on prior knowledge to form concepts and identify quality dimensions. This paper presents a method to create semantic representations using data-driven conceptual spaces which are then used to derive linguistic descriptions of numerical data. Our contribution is a principled approach to automatically construct a conceptual space from a set of known observations wherein the quality dimensions and domains are not known a priori. This novelty of the approach is the ability to select and group semantic features to discriminate between concepts in a data-driven manner while preserving the semantic interpretation that is needed to infer linguistic descriptions for interaction with humans. Two data sets representing leaf images and time series signals are used to evaluate the method. An empirical evaluation for each case study assesses how well linguistic descriptions generated from the conceptual spaces identify unknown observations. Furthermore,  comparisons are made with descriptions derived on alternative approaches for generating semantic models.

Place, publisher, year, edition, pages
AAAI Press, 2018
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-70433 (URN)10.1613/jair.1.11258 (DOI)000455091500015 ()2-s2.0-85057746407 (Scopus ID)
Available from: 2018-12-04 Created: 2018-12-04 Last updated: 2019-01-23Bibliographically approved
Lidén, M., Jendeberg, J., Längkvist, M., Loutfi, A. & Thunberg, P. (2018). Discrimination between distal ureteral stones and pelvic phleboliths in CT using a deep neural network: more than local features needed. In: : . Paper presented at European Congress of Radiology (ECR) 2018, Vienna, Austria, 28 Feb.-4 Mar., 2018.
Open this publication in new window or tab >>Discrimination between distal ureteral stones and pelvic phleboliths in CT using a deep neural network: more than local features needed
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2018 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Purpose: To develop a deep learning method for assisting radiologists in the discrimination between distal ureteral stones and pelvic phleboliths in thin slice CT images, and to evaluate whether this differentiation is possible using only local features.

Methods and materials: A limited field-of-view image data bank was retrospectively created, consisting of 5x5x5 cm selections from 1 mm thick unenhanced CT images centered around 218 pelvis phleboliths and 267 distal ureteral stones in 336 patients. 50 stones and 50 phleboliths formed a validation cohort and the remainder a training cohort. Ground truth was established by a radiologist using the complete CT examination during inclusion.The limited field-of-view CT stacks were independently reviewed and classified as containing a distal ureteral stone or a phlebolith by seven radiologists. Each cropped stack consisted of 50 slices (5x5 cm field-of-view) and was displayed in a standard PACS reading environment. A convolutional neural network using three perpendicular images (2.5D-CNN) from the limited field-of-view CT stacks was trained for classification.

Results: The 2.5D-CNN obtained 89% accuracy (95% confidence interval 81%-94%) for the classification in the unseen validation cohort while the accuracy of radiologists reviewing the same cohort was 86% (range 76%-91%). There was no statistically significant difference between 2.5D-CNN and radiologists.

Conclusion: The 2.5D-CNN achieved radiologist level classification accuracy between distal ureteral stones and pelvic phleboliths when only using the local features. The mean accuracy of 86% for radiologists using limited field-of-view indicates that distant anatomical information that helps identifying the ureter’s course is needed.

National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:oru:diva-67372 (URN)
Conference
European Congress of Radiology (ECR) 2018, Vienna, Austria, 28 Feb.-4 Mar., 2018
Available from: 2018-06-20 Created: 2018-06-20 Last updated: 2018-06-20Bibliographically approved
Akalin, N., Kiselev, A., Kristoffersson, A. & Loutfi, A. (2018). Enhancing Social Human-Robot Interaction with Deep Reinforcement Learning.. In: Proc. FAIM/ISCA Workshop on Artificial Intelligence for Multimodal Human Robot Interaction, 2018: . Paper presented at FAIM/ISCA Workshop on Artificial Intelligence for Multimodal Human Robot Interaction (AI-MHRI), Stockholm, Sweden 14-15 July, 2018 (pp. 48-50). MHRI
Open this publication in new window or tab >>Enhancing Social Human-Robot Interaction with Deep Reinforcement Learning.
2018 (English)In: Proc. FAIM/ISCA Workshop on Artificial Intelligence for Multimodal Human Robot Interaction, 2018, MHRI , 2018, p. 48-50Conference paper, Published paper (Refereed)
Abstract [en]

This research aims to develop an autonomous social robot for elderly individuals. The robot will learn from the interaction and change its behaviors in order to enhance the interaction and improve the user experience. For this purpose, we aim to use Deep Reinforcement Learning. The robot will observe the user’s verbal and nonverbal social cues by using its camera and microphone, the reward will be positive valence and engagement of the user.

Place, publisher, year, edition, pages
MHRI, 2018
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-68709 (URN)10.21437/AI-MHRI.2018-12 (DOI)
Conference
FAIM/ISCA Workshop on Artificial Intelligence for Multimodal Human Robot Interaction (AI-MHRI), Stockholm, Sweden 14-15 July, 2018
Projects
SOCRATES
Available from: 2018-09-03 Created: 2018-09-03 Last updated: 2018-09-04Bibliographically approved
Köckemann, U., Tsiftes, N. & Loutfi, A. (2018). Integrating Constraint-based Planning with LwM2M for IoT Network Scheduling. In: : . Paper presented at Workshop on AI for Internet of Things (AI4IoT), Stockholm, July 15, 2018.
Open this publication in new window or tab >>Integrating Constraint-based Planning with LwM2M for IoT Network Scheduling
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper describes the design and implementationof a network scheduler prototype for IoT networks within the e-healthcare domain. The network scheduler combines a constraint-based task planner with the Lightweight Machine-to-Machine (LwM2M) protocol to be able to reconfigure IoT networks at run-time based on recognized activities and changes in the environment. To support such network scheduling, we implement a LwM2M application layer for the IoT devices that provides sensor data, network stack information, and a set of controllable parameters that affect the communication performance and the energy consumption.

Keywords
LwM2M, Internet of Things, network scheduling, e-healthcare
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-71977 (URN)
Conference
Workshop on AI for Internet of Things (AI4IoT), Stockholm, July 15, 2018
Projects
E-care@home
Funder
Knowledge Foundation
Available from: 2019-01-31 Created: 2019-01-31 Last updated: 2019-02-04Bibliographically 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
Renoux, J., Köckemann, U. & Loutfi, A. (2018). Online Guest Detection in a Smart Home using Pervasive Sensors and Probabilistic Reasoning. In: Achilles Kameas, Kostas Stathis (Ed.), Ambient Intelligence: . Paper presented at 14th European Conference on Ambient Intelligence, Larnaca, Cyprus, November 12-14 (pp. 74-89). Springer, 11249
Open this publication in new window or tab >>Online Guest Detection in a Smart Home using Pervasive Sensors and Probabilistic Reasoning
2018 (English)In: Ambient Intelligence / [ed] Achilles Kameas, Kostas Stathis, Springer, 2018, Vol. 11249, p. 74-89Conference paper, Published paper (Refereed)
Abstract [en]

Smart home environments equipped with distributed sensor networks are capable of helping people by providing services related to health, emergency detection or daily routine management. A backbone to these systems relies often on the system’s ability to track and detect activities performed by the users in their home. Despite the continuous progress in the area of activity recognition in smart homes, many systems make a strong underlying assumption that the number of occupants in the home at any given moment of time is always known. Estimating the number of persons in a Smart Home at each time step remains a challenge nowadays. Indeed, unlike most (crowd) counting solution which are based on computer vision techniques, the sensors considered in a Smart Home are often very simple and do not offer individually a good overview of the situation. The data gathered needs therefore to be fused in order to infer useful information. This paper aims at addressing this challenge and presents a probabilistic approach able to estimate the number of persons in the environment at each time step. This approach works in two steps: first, an estimate of the number of persons present in the environment is done using a Constraint Satisfaction Problem solver, based on the topology of the sensor network and the sensor activation pattern at this time point. Then, a Hidden Markov Model refines this estimate by considering the uncertainty related to the sensors. Using both simulated and real data, our method has been tested and validated on two smart homes of different sizes and configuration and demonstrates the ability to accurately estimate the number of inhabitants.

Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11249
Keywords
probabilistic reasoning, smart home
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-71113 (URN)10.1007/978-3-030-03062-9_6 (DOI)2-s2.0-85056486514 (Scopus ID)
Conference
14th European Conference on Ambient Intelligence, Larnaca, Cyprus, November 12-14
Funder
Knowledge FoundationEU, Horizon 2020, 732158
Available from: 2019-01-07 Created: 2019-01-07 Last updated: 2019-01-18Bibliographically approved
Krishna, S. & Loutfi, A. (2018). Robotics for Successful Ageing. In: Eleonor Kristoffersson & Kerstin Nilsson (Ed.), Successful ageing in an interdisciplinary context: popular science presentations (pp. 29-35). Örebro, Sweden: Örebro University
Open this publication in new window or tab >>Robotics for Successful Ageing
2018 (English)In: Successful ageing in an interdisciplinary context: popular science presentations / [ed] Eleonor Kristoffersson & Kerstin Nilsson, Örebro, Sweden: Örebro University , 2018, p. 29-35Chapter in book (Other (popular science, discussion, etc.))
Abstract [en]

The main idea of the ongoing research is to use robotics to create new opportunities to help older people to remain alone in their apartments which can beachieved by using robots as an interacting tool between the elderly and theirfamily members or doctors. This can be done by building a system (software)for Mobile Robots to work autonomously (self-driving) and semi-autono-mously (controlled by the user) when necessary, depending on the situationand the surroundings. This system is integrated with social cues, particularlyproxemics, to know and understand human space, which is very importantfor social interaction. In conclusion, we are interested in having a Socially Intelligent Robot, which could use the social cues, proxemics, to have a natural interaction with people in groups.

Place, publisher, year, edition, pages
Örebro, Sweden: Örebro University, 2018
National Category
Robotics
Research subject
Human-Computer Interaction
Identifiers
urn:nbn:se:oru:diva-71874 (URN)978-91-87789-18-2 (ISBN)
Available from: 2019-01-28 Created: 2019-01-28 Last updated: 2019-01-28Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3122-693X

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