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Bouguelia, Mohamed-RafikORCID iD iconorcid.org/0000-0002-2859-6155
Publications (10 of 10) Show all publications
Ali Hamad, R., Salguero Hidalgo, A., Bouguelia, M.-R., Estevez, M. E. & Quero, J. M. (2020). Efficient Activity Recognition in Smart Homes Using Delayed Fuzzy Temporal Windows on Binary Sensors. IEEE journal of biomedical and health informatics, 24(2), 387-395
Open this publication in new window or tab >>Efficient Activity Recognition in Smart Homes Using Delayed Fuzzy Temporal Windows on Binary Sensors
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2020 (English)In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 24, no 2, p. 387-395Article in journal (Refereed) Published
Abstract [en]

Human activity recognition has become an activeresearch field over the past few years due to its wide applicationin various fields such as health-care, smart homemonitoring, and surveillance. Existing approaches for activityrecognition in smart homes have achieved promisingresults. Most of these approaches evaluate real-timerecognition of activities using only sensor activations thatprecede the evaluation time (where the decision is made).However, in several critical situations, such as diagnosingpeople with dementia, “preceding sensor activations”are not always sufficient to accurately recognize theinhabitant’s daily activities in each evaluated time. Toimprove performance, we propose a method that delaysthe recognition process in order to include some sensoractivations that occur after the point in time where thedecision needs to be made. For this, the proposed methoduses multiple incremental fuzzy temporal windows toextract features from both preceding and some oncomingsensor activations. The proposed method is evaluated withtwo temporal deep learning models (convolutional neuralnetwork and long short-term memory), on a binary sensordataset of real daily living activities. The experimentalevaluation shows that the proposed method achievessignificantly better results than the real-time approach,and that the representation with fuzzy temporal windowsenhances performance within deep learning models. © Copyright 2020 IEEE

Place, publisher, year, edition, pages
Piscataway: Institute of Electrical and Electronics Engineers (IEEE), 2020
Keywords
Activity recognition, fuzzy temporal windows, deep learning, temporal evaluation
National Category
Computer Systems
Identifiers
urn:nbn:se:hh:diva-41633 (URN)10.1109/JBHI.2019.2918412 (DOI)2-s2.0-85079094027 (Scopus ID)
Funder
EU, Horizon 2020
Note

Other funding: Marie Sklodowska-Curie EU Framework for Research

Available from: 2020-02-10 Created: 2020-02-10 Last updated: 2020-02-26
Bae, J., Helldin, T., Riveiro, M., Nowaczyk, S., Bouguelia, M.-R. & Falkman, G. (2020). Interactive Clustering: A Comprehensive Review. ACM Computing Surveys, 53(1), Article ID 1.
Open this publication in new window or tab >>Interactive Clustering: A Comprehensive Review
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2020 (English)In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 53, no 1, article id 1Article in journal (Refereed) Published
Abstract [en]

In this survey, 105 papers related to interactive clustering were reviewed according to seven perspectives: (1) on what level is the interaction happening, (2) which interactive operations are involved, (3) how user feedback is incorporated, (4) how interactive clustering is evaluated, (5) which data and (6) which clustering methods have been used, and (7) what outlined challenges there are. This article serves as a comprehensive overview of the field and outlines the state of the art within the area as well as identifies challenges and future research needs. © 2020 Copyright held by the owner/author(s).

Place, publisher, year, edition, pages
New York, NY: ACM Digital Library, 2020
Keywords
Clustering, Interactive, Interaction, User, Evaluation, Feedback, Survey, Machine Learning, Data Mining
National Category
Computer Systems
Identifiers
urn:nbn:se:hh:diva-41634 (URN)10.1145/3340960 (DOI)
Funder
Knowledge Foundation, BIDAF 20140221Swedish Research Council, EXPLAIN VR 2018-03622
Available from: 2020-02-10 Created: 2020-02-10 Last updated: 2020-02-18Bibliographically approved
Farouq, S., Byttner, S., Bouguelia, M.-R., Nord, N. & Gadd, H. (2020). Large-scale monitoring of operationally diverse district heating substations: A reference-group based approach. Engineering applications of artificial intelligence, 90, Article ID 103492.
Open this publication in new window or tab >>Large-scale monitoring of operationally diverse district heating substations: A reference-group based approach
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2020 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 90, article id 103492Article in journal (Refereed) In press
Abstract [en]

A well-understood prior model for a District Heating (DH) substation is rarely available. Alternatively, since DH substations in a network share a common task, one can assume that they are all operationally homogeneous. Any DH substation that does not conform with the majority is an outlier, and therefore ought to be investigated. However, a DH substation can be affected by varying social and technical factors. Such details are rarely available.  Therefore, large-scale monitoring of DH substations in a network is challenging. Hence, in order to address these issues, we proposed a reference-group based monitoring approach. Herein, the operational monitoring of a DH substation, referred to as a target, is delegated to a reference-group which consists of DH substations experiencing a comparable operating regime along with the target. The approach was demonstrated on the monitoring of the return temperature variable for atypical\footnote{Here, "atypical" means that while it does not fit the definition of a normal operation, it is not faulty either and may also have some context.}  and faulty operational behavior in $778$ DH substations associated with multi-dwelling buildings. No target substation specific information related to its normal, atypical or faulty operation was used. Instead, information from the target's reference-group was leveraged to track its operational behavior. In this manner, $44$ DH substations were found where a possible deviation in the return temperature was detected earlier compared to models assuming overall operational homogeneity among the DH substations. In addition, six frequent patterns of deviating behavior in the return temperature of DH substations were identified based on the proposed reference-group based approach, which were then further corroborated by the feedback from a DH domain expert. © 2020 Elsevier Ltd

Place, publisher, year, edition, pages
Oxford: Elsevier, 2020
Keywords
District heating substations, Return temperature, Reference-group based operational monitoring, Fault detection, Outlier detection
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-40962 (URN)10.1016/j.engappai.2020.103492 (DOI)2-s2.0-85078822459 (Scopus ID)
Funder
Knowledge Foundation, 20160103
Available from: 2019-11-16 Created: 2019-11-16 Last updated: 2020-02-18
Holst, A., Karlsson, A., Bae, J. & Bouguelia, M.-R. (2019). Interactive clustering for exploring multiple data streams at different time scales and granularity. In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019: . Paper presented at 1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019. Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Interactive clustering for exploring multiple data streams at different time scales and granularity
2019 (English)In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, Association for Computing Machinery (ACM), 2019Conference paper, Published paper (Refereed)
Abstract [en]

We approach the problem of identifying and interpreting clusters over different time scales and granularity in multivariate time series data. We extract statistical features over a sliding window of each time series, and then use a Gaussian mixture model to identify clusters which are then projected back on the data streams. The human analyst can then further analyze this projection and adjust the size of the sliding window and the number of clusters in order to capture the different types of clusters over different time scales. We demonstrate the effectiveness of our approach in two different application scenarios: (1) fleet management and (2) district heating, wherein each scenario, several different types of meaningful clusters can be identified when varying over these dimensions. © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2019
Keywords
Clustering, Interaction, Time scales, Time series, Fleet operations, Gaussian distribution, Time measurement, Application scenario, Different time scale, Gaussian Mixture Model, Multiple data streams, Multivariate time series, Time-scales, Data mining
National Category
Other Computer and Information Science Computer Systems
Identifiers
urn:nbn:se:hh:diva-41537 (URN)10.1145/3304079.3310286 (DOI)2-s2.0-85069762696 (Scopus ID)9781450362962 (ISBN)
Conference
1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019
Available from: 2020-02-04 Created: 2020-02-04 Last updated: 2020-02-04Bibliographically approved
Bouguelia, M.-R., Nowaczyk, S., Santosh, K. C. & Verikas, A. (2018). Agreeing to disagree: active learning with noisy labels without crowdsourcing. International Journal of Machine Learning and Cybernetics, 9(8), 1307-1319
Open this publication in new window or tab >>Agreeing to disagree: active learning with noisy labels without crowdsourcing
2018 (English)In: International Journal of Machine Learning and Cybernetics, ISSN 1868-8071, E-ISSN 1868-808X, Vol. 9, no 8, p. 1307-1319Article in journal (Refereed) Published
Abstract [en]

We propose a new active learning method for classification, which handles label noise without relying on multiple oracles (i.e., crowdsourcing). We propose a strategy that selects (for labeling) instances with a high influence on the learned model. An instance x is said to have a high influence on the model h, if training h on x (with label y = h(x)) would result in a model that greatly disagrees with h on labeling other instances. Then, we propose another strategy that selects (for labeling) instances that are highly influenced by changes in the learned model. An instance x is said to be highly influenced, if training h with a set of instances would result in a committee of models that agree on a common label for x but disagree with h(x). We compare the two strategies and we show, on different publicly available datasets, that selecting instances according to the first strategy while eliminating noisy labels according to the second strategy, greatly improves the accuracy compared to several benchmarking methods, even when a significant amount of instances are mislabeled. © Springer-Verlag Berlin Heidelberg 2017

Place, publisher, year, edition, pages
Heidelberg: Springer, 2018
Keywords
Active learning, Classification, Label noise, Mislabeling, Interactive learning, Machine learning, Data mining
National Category
Signal Processing Computer Systems Computer Sciences
Identifiers
urn:nbn:se:hh:diva-33365 (URN)10.1007/s13042-017-0645-0 (DOI)000438855100006 ()2-s2.0-85050140726 (Scopus ID)
Available from: 2017-02-27 Created: 2017-02-27 Last updated: 2020-02-03Bibliographically approved
Bouguelia, M.-R., Nowaczyk, S. & Payberah, A. H. (2018). An adaptive algorithm for anomaly and novelty detection in evolving data streams. Data mining and knowledge discovery, 32(6), 1597-1633
Open this publication in new window or tab >>An adaptive algorithm for anomaly and novelty detection in evolving data streams
2018 (English)In: Data mining and knowledge discovery, ISSN 1384-5810, E-ISSN 1573-756X, Vol. 32, no 6, p. 1597-1633Article in journal (Refereed) Published
Abstract [en]

In the era of big data, considerable research focus is being put on designing efficient algorithms capable of learning and extracting high-level knowledge from ubiquitous data streams in an online fashion. While, most existing algorithms assume that data samples are drawn from a stationary distribution, several complex environments deal with data streams that are subject to change over time. Taking this aspect into consideration is an important step towards building truly aware and intelligent systems. In this paper, we propose GNG-A, an adaptive method for incremental unsupervised learning from evolving data streams experiencing various types of change. The proposed method maintains a continuously updated network (graph) of neurons by extending the Growing Neural Gas algorithm with three complementary mechanisms, allowing it to closely track both gradual and sudden changes in the data distribution. First, an adaptation mechanism handles local changes where the distribution is only non-stationary in some regions of the feature space. Second, an adaptive forgetting mechanism identifies and removes neurons that become irrelevant due to the evolving nature of the stream. Finally, a probabilistic evolution mechanism creates new neurons when there is a need to represent data in new regions of the feature space. The proposed method is demonstrated for anomaly and novelty detection in non-stationary environments. Results show that the method handles different data distributions and efficiently reacts to various types of change. © 2018 The Author(s)

Place, publisher, year, edition, pages
New York: Springer, 2018
Keywords
Data stream, Growing neural gas, Change detection, Non-stationary environments, Anomaly and novelty detection
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-36752 (URN)10.1007/s10618-018-0571-0 (DOI)000444383000003 ()2-s2.0-85046792304 (Scopus ID)
Projects
BIDAF
Available from: 2018-05-13 Created: 2018-05-13 Last updated: 2020-02-03Bibliographically 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)000430032000004 ()2-s2.0-85037072003 (Scopus ID)
Projects
BIDAF
Available from: 2017-12-01 Created: 2017-12-01 Last updated: 2020-02-03Bibliographically approved
Farouq, S., Byttner, S. & Bouguelia, M.-R. (2018). On monitoring heat-pumps with a group-based conformal anomaly detection approach. In: Robert Stahlbock, Gary M. Weiss, Mahmoud Abou-Nasr (Ed.), ICDATA' 18: Proceedings of the 2018 International Conference on Data Science. Paper presented at 2018 Internal Conference on Data Science (ICDATA’18), Las Vegas, NV, USA (pp. 63-69). CSREA Press
Open this publication in new window or tab >>On monitoring heat-pumps with a group-based conformal anomaly detection approach
2018 (English)In: ICDATA' 18: Proceedings of the 2018 International Conference on Data Science / [ed] Robert Stahlbock, Gary M. Weiss, Mahmoud Abou-Nasr, CSREA Press, 2018, p. 63-69Conference paper, Published paper (Refereed)
Abstract [en]

The ever increasing complexity of modern systems and equipment make the task of monitoring their health quite challenging. Traditional methods such as expert defined thresholds, physics based models and process history based techniques have certain drawbacks. Thresholds defined by experts require deep knowledge about the system and are often too conservative. Physics driven approaches are costly to develop and maintain. Finally, process history based models require large amount of data that may not be available at design time of a system. Moreover, the focus of these traditional approaches has been system specific. Hence, when industrial systems are deployed on a large scale, their monitoring becomes a new challenge. Under these conditions, this paper demonstrates the use of a group-based selfmonitoring approach that learns over time from similar systems subject to similar conditions. The approach is based on conformal anomaly detection coupled with an exchangeability test that uses martingales. This allows setting a threshold value based on sound theoretical justification. A hypothesis test based on this threshold is used to decide on if a system has deviated from its group. We demonstrate the feasibility of this approach through a real case study of monitoring a group of heat-pumps where it can detect a faulty hot-water switch-valve and a broken outdoor temperature sensor without previously observing these faults.

Place, publisher, year, edition, pages
CSREA Press, 2018
Keywords
group-based monitoring, nonconformity measure (NCM), martingale test
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-40961 (URN)1-60132-481-2 (ISBN)9781601324818 (ISBN)
Conference
2018 Internal Conference on Data Science (ICDATA’18), Las Vegas, NV, USA
Available from: 2019-11-16 Created: 2019-11-16 Last updated: 2019-11-18Bibliographically 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
Bouguelia, M.-R., Gonzalez, R., Iagnemma, K. & Byttner, S. (2017). Unsupervised classification of slip events for planetary exploration rovers. Journal of terramechanics, 73, 95-106
Open this publication in new window or tab >>Unsupervised classification of slip events for planetary exploration rovers
2017 (English)In: Journal of terramechanics, ISSN 0022-4898, E-ISSN 1879-1204, Vol. 73, p. 95-106Article in journal (Refereed) Published
Abstract [en]

This paper introduces an unsupervised method for the classification of discrete rovers' slip events based on proprioceptive signals. In particular, the method is able to automatically discover and track various degrees of slip (i.e. low slip, moderate slip, high slip). The proposed method is based on aggregating the data over time, since high level concepts, such as high and low slip, are concepts that are dependent on longer time perspectives. Different features and subsets of the data have been identified leading to a proper clustering, interpreting those clusters as initial models of the prospective concepts. Bayesian tracking has been used in order to continuously improve the parameters of these models, based on the new data. Two real datasets are used to validate the proposed approach in comparison to other known unsupervised and supervised machine learning methods. The first dataset is collected by a single-wheel testbed available at MIT. The second dataset was collected by means of a planetary exploration rover in real off-road conditions. Experiments prove that the proposed method is more accurate (up to 86% of accuracy vs. 80% for K-means) in discovering various levels of slip while being fully unsupervised (no need for hand-labeled data for training). © 2017 ISTVS

Place, publisher, year, edition, pages
Doetinchem: Elsevier, 2017
Keywords
Unsupervised learning, Clustering, Data-driven modeling, Slip, MSL rover, LATUV rover
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-35169 (URN)10.1016/j.jterra.2017.09.001 (DOI)000415782800008 ()2-s2.0-85029811187 (Scopus ID)
Available from: 2017-10-09 Created: 2017-10-09 Last updated: 2020-02-03Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-2859-6155

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