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Huotari, M., Malhi, A. & Främling, K. (2024). Machine learning applications for smart building energy utilization: a survey. Archives of Computational Methods in Engineering
Öppna denna publikation i ny flik eller fönster >>Machine learning applications for smart building energy utilization: a survey
2024 (Engelska)Ingår i: Archives of Computational Methods in Engineering, ISSN 1134-3060, E-ISSN 1886-1784Artikel i tidskrift (Refereegranskat) Epub ahead of print
Abstract [en]

The United Nations launched sustainable development goals in 2015 that include goals for sustainable energy. From global energy consumption, households consume 20–30% of energy in Europe, North America and Asia; furthermore, the overall global energy consumption has steadily increased in the recent decades. Consequently, to meet the increased energy demand and to promote efficient energy consumption, there is a persistent need to develop applications enhancing utilization of energy in buildings. However, despite the potential significance of AI in this area, few surveys have systematically categorized these applications. Therefore, this paper presents a systematic review of the literature, and then creates a novel taxonomy for applications of smart building energy utilization. The contributions of this paper are (a) a systematic review of applications and machine learning methods for smart building energy utilization, (b) a novel taxonomy for the applications, (c) detailed analysis of these solutions and techniques used for the applications (electric grid, smart building energy management and control, maintenance and security, and personalization), and, finally, (d) a discussion on open issues and developments in the field.

Ort, förlag, år, upplaga, sidor
Springer Nature, 2024
Nationell ämneskategori
Annan data- och informationsvetenskap
Forskningsämne
data- och systemvetenskap
Identifikatorer
urn:nbn:se:umu:diva-220506 (URN)10.1007/s11831-023-10054-7 (DOI)001156366200002 ()2-s2.0-85184197546 (Scopus ID)
Forskningsfinansiär
Wallenberg AI, Autonomous Systems and Software Program (WASP), 570011220
Tillgänglig från: 2024-02-05 Skapad: 2024-02-05 Senast uppdaterad: 2024-02-21
Yousefnezhad, N., Malhi, A., Keyriläinen, T. & Främling, K. (2023). A comprehensive security architecture for information management throughout the lifecycle of IoT products. Sensors, 23(6), Article ID 3236.
Öppna denna publikation i ny flik eller fönster >>A comprehensive security architecture for information management throughout the lifecycle of IoT products
2023 (Engelska)Ingår i: Sensors, E-ISSN 1424-8220, Vol. 23, nr 6, artikel-id 3236Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

The Internet of things (IoT) is expected to have an impact on business and the world at large in a way comparable to the Internet itself. An IoT product is a physical product with an associated virtual counterpart connected to the internet with computational as well as communication capabilities. The possibility to collect information from internet-connected products and sensors gives unprecedented possibilities to improve and optimize product use and maintenance. Virtual counterpart and digital twin (DT) concepts have been proposed as a solution for providing the necessary information management throughout the whole product lifecycle, which we here call product lifecycle information management (PLIM). Security in these systems is imperative due to the multiple ways in which opponents can attack the system during the whole lifecycle of an IoT product. To address this need, the current study proposes a security architecture for the IoT, taking into particular consideration the requirements of PLIM. The security architecture has been designed for the Open Messaging Interface (O-MI) and Open Data Format (O-DF) standards for the IoT and product lifecycle management (PLM) but it is also applicable to other IoT and PLIM architectures. The proposed security architecture is capable of hindering unauthorized access to information and restricts access levels based on user roles and permissions. Based on our findings, the proposed security architecture is the first security model for PLIM to integrate and coordinate the IoT ecosystem, by dividing the security approaches into two domains: user client and product domain. The security architecture has been deployed in smart city use cases in three different European cities, Helsinki, Lyon, and Brussels, to validate the security metrics in the proposed approach. Our analysis shows that the proposed security architecture can easily integrate the security requirements of both clients and products providing solutions for them as demonstrated in the implemented use cases.

Ort, förlag, år, upplaga, sidor
MDPI, 2023
Nyckelord
Internet of things (IoT), information management, security architecture, product lifecycle information management (PLIM), identity and access management (IAM)
Nationell ämneskategori
Systemvetenskap, informationssystem och informatik
Forskningsämne
data- och systemvetenskap
Identifikatorer
urn:nbn:se:umu:diva-205821 (URN)10.3390/s23063236 (DOI)000959436000001 ()36991946 (PubMedID)2-s2.0-85151184689 (Scopus ID)
Forskningsfinansiär
Wallenberg AI, Autonomous Systems and Software Program (WASP), 570011220EU, Horisont 2020, 856602
Tillgänglig från: 2023-03-20 Skapad: 2023-03-20 Senast uppdaterad: 2023-09-05Bibliografiskt granskad
Malhi, A. & Främling, K. (2023). An evaluation of contextual importance and utility for outcome explanation of black-box predictions for medical datasets. In: Luca Longo (Ed.), Explainable artificial intelligence: First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part I. Paper presented at 1st World Conference on Explainable Artificial Intelligence, xAI 2023, Lisbon, Portugal, July 26-28, 2023 (pp. 544-557). Springer Nature
Öppna denna publikation i ny flik eller fönster >>An evaluation of contextual importance and utility for outcome explanation of black-box predictions for medical datasets
2023 (Engelska)Ingår i: Explainable artificial intelligence: First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part I / [ed] Luca Longo, Springer Nature, 2023, s. 544-557Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Contextual Importance and Utility (CIU) is a model-agnostic method for producing situation- or instance-specific explanations of the outcome of so-called black-box systems. A major difference between CIU and other outcome explanation methods (also called post-hoc methods) is that CIU produces explanations without producing any intermediate interpretable model. CIU’s notion of importance is similar as in Decision Theory but differs from how importance is defined for other outcome explanation methods. Utility is also a well-known concept from Decision Theory that is largely ignored in current Explainable AI research. CIU is here validated by providing explanations for the two popular medical data sets - heart disease and breast cancer in order to show the applicability of CIU explanations on medical predictions and with different black-box models. The explanations are compared with corresponding ones produced by the Local Interpretable Model-agnostic Explanations (LIME) method [17], which is currently one of the most used post-hoc explanation methods. The paper’s main contribution is to provide new CIU results and insights on several benchmark data sets and showing in what way CIU differs from LIME-based explanations.

Ort, förlag, år, upplaga, sidor
Springer Nature, 2023
Serie
Communications in Computer and Information Science book series (CCIS), ISSN 1865-0929, E-ISSN 1865-0937 ; 1901
Nyckelord
Explainable AI, Contextual Importance, Contextual Utility, Multiple Criteria Decision Making, Heart disease, Breast cancer data
Nationell ämneskategori
Människa-datorinteraktion (interaktionsdesign)
Forskningsämne
datalogi
Identifikatorer
urn:nbn:se:umu:diva-217312 (URN)10.1007/978-3-031-44064-9_29 (DOI)2-s2.0-85176960967 (Scopus ID)978-3-031-44063-2 (ISBN)978-3-031-44064-9 (ISBN)
Konferens
1st World Conference on Explainable Artificial Intelligence, xAI 2023, Lisbon, Portugal, July 26-28, 2023
Forskningsfinansiär
Wallenberg AI, Autonomous Systems and Software Program (WASP), 570011220
Tillgänglig från: 2023-11-29 Skapad: 2023-11-29 Senast uppdaterad: 2023-12-04Bibliografiskt granskad
Främling, K. (2023). Counterfactual, contrastive, and hierarchical explanations with contextual importance and utility. In: Davide Calvaresi; Amro Najjar; Andrea Omicini; Reyhan Aydogan; Rachele Carli; Giovanni Ciatto; Yazan Mualla; Kary Främling (Ed.), Calvaresi, D., et al. (Ed.), Explainable and transparent ai and multi-agent systems: 5th international workshop, EXTRAAMAS 2023, London, UK, May 29, 2023, revised selected papers. Paper presented at AAMAS 2023 (pp. 180-184). Paper presented at AAMAS 2023. Springer Nature, 14127
Öppna denna publikation i ny flik eller fönster >>Counterfactual, contrastive, and hierarchical explanations with contextual importance and utility
2023 (Engelska)Ingår i: Explainable and transparent ai and multi-agent systems: 5th international workshop, EXTRAAMAS 2023, London, UK, May 29, 2023, revised selected papers / [ed] Davide Calvaresi; Amro Najjar; Andrea Omicini; Reyhan Aydogan; Rachele Carli; Giovanni Ciatto; Yazan Mualla; Kary Främling, Springer Nature, 2023, Vol. 14127, s. 180-184Kapitel i bok, del av antologi (Refereegranskat)
Abstract [en]

Contextual Importance and Utility (CIU) is a model-agnostic method for post-hoc explanation of prediction outcomes. In this paper we describe and show new functionality in the R implementation of CIU for tabular data. Much of that functionality is specific to CIU and goes beyond the current state of the art.

Ort, förlag, år, upplaga, sidor
Springer Nature, 2023
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 14127
Nyckelord
Contextual Importance and Utility, Explainable AI, Open source, Counterfactual, Contrastive
Nationell ämneskategori
Människa-datorinteraktion (interaktionsdesign)
Identifikatorer
urn:nbn:se:umu:diva-215057 (URN)10.1007/978-3-031-40878-6_16 (DOI)2-s2.0-85172205293 (Scopus ID)978-3-031-40877-9 (ISBN)978-3-031-40878-6 (ISBN)
Konferens
AAMAS 2023
Forskningsfinansiär
Wallenberg AI, Autonomous Systems and Software Program (WASP), 570011220
Anmärkning

Part of conference: 5th International Workshop, EXTRAAMAS 2023, London, UK, May 29, 2023.

Tillgänglig från: 2023-10-06 Skapad: 2023-10-06 Senast uppdaterad: 2023-10-09Bibliografiskt granskad
Patil, M. & Främling, K. (2023). Do intermediate feature coalitions aid explainability of black-box models?. In: Luca Longo (Ed.), Explainable Artificial Intelligence: First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part I. Paper presented at xAI 2023: Explainable Artificial Intelligence, Lisbon, Portugal, July 26-28, 2023 (pp. 115-130). Cham: Springer
Öppna denna publikation i ny flik eller fönster >>Do intermediate feature coalitions aid explainability of black-box models?
2023 (Engelska)Ingår i: Explainable Artificial Intelligence: First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part I / [ed] Luca Longo, Cham: Springer, 2023, s. 115-130Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This work introduces the notion of intermediate concepts based on levels structure to aid explainability for black-box models. The levels structure is a hierarchical structure in which each level corresponds to features of a dataset (i.e., a player-set partition). The level of coarseness increases from the trivial set, which only comprises singletons, to the set, which only contains the grand coalition. In addition, it is possible to establish meronomies, i.e., part-whole relationships, via a domain expert that can be utilised to generate explanations at an abstract level. We illustrate the usability of this approach in a real-world car model example and the Titanic dataset, where intermediate concepts aid in explainability at different levels of abstraction.

Ort, förlag, år, upplaga, sidor
Cham: Springer, 2023
Serie
Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937 ; 1901
Nyckelord
Coalition Formation, Explainability, Trust in Human-Agent Systems
Nationell ämneskategori
Människa-datorinteraktion (interaktionsdesign)
Forskningsämne
datalogi
Identifikatorer
urn:nbn:se:umu:diva-216079 (URN)10.1007/978-3-031-44064-9_7 (DOI)2-s2.0-85176954534 (Scopus ID)9783031440632 (ISBN)9783031440649 (ISBN)
Konferens
xAI 2023: Explainable Artificial Intelligence, Lisbon, Portugal, July 26-28, 2023
Forskningsfinansiär
Knut och Alice Wallenbergs Stiftelse, 570011440
Tillgänglig från: 2023-11-01 Skapad: 2023-11-01 Senast uppdaterad: 2023-11-27Bibliografiskt granskad
Främling, K. (2023). Feature importance versus feature influence and what it signifies for explainable AI. In: Luca Longo (Ed.), Explainable artificial intelligence: First world conference, xAI 2023 Lisbon, Portugal, july 26– 28, 2023 proceedings, part I. Paper presented at 1st World Conference on eXplainable Artificial Intelligence (xAI 2023), Lisbon, Portugal, july 26– 28, 2023. (pp. 241-259). Springer Nature, 1901
Öppna denna publikation i ny flik eller fönster >>Feature importance versus feature influence and what it signifies for explainable AI
2023 (Engelska)Ingår i: Explainable artificial intelligence: First world conference, xAI 2023 Lisbon, Portugal, july 26– 28, 2023 proceedings, part I / [ed] Luca Longo, Springer Nature, 2023, Vol. 1901, s. 241-259Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

When used in the context of decision theory, feature importance expresses how much changing the value of a feature can change the model outcome (or the utility of the outcome), compared to other features. Feature importance should not be confused with the feature influence used by most state-of-the-art post-hoc Explainable AI methods. Contrary to feature importance, feature influence is measured against a reference level or baseline. The Contextual Importance and Utility (CIU) method provides a unified definition of global and local feature importance that is applicable also for post-hoc explanations, where the value utility concept provides instance-level assessment of how favorable or not a feature value is for the outcome. The paper shows how CIU can be applied to both global and local explainability, assesses the fidelity and stability of different methods, and shows how explanations that use contextual importance and contextual utility can provide more expressive and flexible explanations than when using influence only.

Ort, förlag, år, upplaga, sidor
Springer Nature, 2023
Serie
Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937 ; 1901
Nyckelord
Explainable AI, Feature importance, Feature influence, Contextual Importance and Utility, Additive Feature Attribution
Nationell ämneskategori
Människa-datorinteraktion (interaktionsdesign)
Forskningsämne
datalogi
Identifikatorer
urn:nbn:se:umu:diva-216622 (URN)10.1007/978-3-031-44064-9_14 (DOI)9783031440632 (ISBN)9783031440649 (ISBN)
Konferens
1st World Conference on eXplainable Artificial Intelligence (xAI 2023), Lisbon, Portugal, july 26– 28, 2023.
Forskningsfinansiär
Wallenberg AI, Autonomous Systems and Software Program (WASP), 570011220Knut och Alice Wallenbergs Stiftelse
Tillgänglig från: 2023-11-14 Skapad: 2023-11-14 Senast uppdaterad: 2023-12-01Bibliografiskt granskad
Patil, M. S. & Främling, K. (2023). Improving Neural Network Verification Efficiency Through Perturbation Refinement. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (Ed.), Artificial Neural Networksand Machine Learning – ICANN 2023: 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, September 26–29, 2023, Proceedings, Part I. Paper presented at Artificial Neural Networks and Machine Learning – ICANN 2023, 32nd International Conference on Artificial Neural Networks Heraklion, Crete, Greece, September 26–29, 2023 (pp. 504-515). Springer, 14254
Öppna denna publikation i ny flik eller fönster >>Improving Neural Network Verification Efficiency Through Perturbation Refinement
2023 (Engelska)Ingår i: Artificial Neural Networksand Machine Learning – ICANN 2023: 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, September 26–29, 2023, Proceedings, Part I / [ed] Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C., Springer, 2023, Vol. 14254, s. 504-515Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This paper presents a novel approach to efficient neural network verification through the use of adversarial attacks and symbolic interval propagation. The proposed method leverages low-cost adversarial attacks to quickly obtain a rough estimate of the first set of bounds, and then utilizes symbolic interval propagation to compute tighter bounds. We demonstrate the effectiveness of our proposed method on the popular MNIST dataset, which contains hand-written digit images. The results show that the proposed method achieves state-of-the-art verification accuracy with significantly reduced computational cost, making it a promising approach for practical neural network verification.

Ort, förlag, år, upplaga, sidor
Springer, 2023
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 14254
Nyckelord
Perturbation Refinement, Neural Network Verification, Adversarial Robustness
Nationell ämneskategori
Systemvetenskap, informationssystem och informatik
Identifikatorer
urn:nbn:se:umu:diva-215665 (URN)10.1007/978-3-031-44207-0_42 (DOI)2-s2.0-85174612363 (Scopus ID)978-3-031-44206-3 (ISBN)978-3-031-44207-0 (ISBN)
Konferens
Artificial Neural Networks and Machine Learning – ICANN 2023, 32nd International Conference on Artificial Neural Networks Heraklion, Crete, Greece, September 26–29, 2023
Forskningsfinansiär
Wallenberg AI, Autonomous Systems and Software Program (WASP), 570011220
Tillgänglig från: 2023-10-24 Skapad: 2023-10-24 Senast uppdaterad: 2023-11-01Bibliografiskt granskad
Patil, M. S. & Främling, K. (2023). Investigating lipschitz constants in neural ensemble models to improve adversarial robustness. In: ICSRS 2023: 7th international conference on system reliability and safety. Paper presented at 7th International Conference on System Reliability and Safety, ICSRS 2023, Bologna, 22-24 November 2023 (pp. 434-438). Institute of Electrical and Electronics Engineers (IEEE)
Öppna denna publikation i ny flik eller fönster >>Investigating lipschitz constants in neural ensemble models to improve adversarial robustness
2023 (Engelska)Ingår i: ICSRS 2023: 7th international conference on system reliability and safety, Institute of Electrical and Electronics Engineers (IEEE), 2023, s. 434-438Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This work investigates the relationship between adversarial robustness and the local Lipschitz constant in ensemble neural network frameworks, namely bagging and stacking. Capitalising on this, we introduce an ensemble neural network design that improves both accuracy and adversarial resilience. We theoretically obtain the local Lipschitz constants for both ensembles, offering insights into their susceptibility to adversarial attacks and identifying architectures optimal for adversarial defense. Notably, our approach negates the need for specific adversarial attack and accommodates any number of pre-trained networks for an ensemble architecture. Evaluations on the MNIST and CIFAR-10 datasets against white-box attacks, specifically FGSM and PGD, show our approach is adversarially robust compared to standalone networks and vanilla ensemble architectures.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2023
Nyckelord
Adversarial Robustness, Certification, Ensemble Methods, Lipschitz constant, Neural Network
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:umu:diva-220754 (URN)10.1109/ICSRS59833.2023.10381066 (DOI)2-s2.0-85183474111 (Scopus ID)9798350306057 (ISBN)9798350306040 (ISBN)
Konferens
7th International Conference on System Reliability and Safety, ICSRS 2023, Bologna, 22-24 November 2023
Forskningsfinansiär
Wallenberg AI, Autonomous Systems and Software Program (WASP)Knut och Alice Wallenbergs StiftelseInterreg
Tillgänglig från: 2024-02-12 Skapad: 2024-02-12 Senast uppdaterad: 2024-02-13Bibliografiskt granskad
Malhi, A., Javed, A., Yousefnezhad, N. & Främling, K. (2023). IoT open messaging standards: performance comparison with MQTT and CoAP protocols. In: Proceedings - 2023 International Conference on Future Internet of Things and Cloud, FiCloud 2023: . Paper presented at 10th International Conference on Future Internet of Things and Cloud, FiCloud 2023, Hybrid, Marrakech, 14-16 august, 2023. (pp. 130-135). Institute of Electrical and Electronics Engineers (IEEE)
Öppna denna publikation i ny flik eller fönster >>IoT open messaging standards: performance comparison with MQTT and CoAP protocols
2023 (Engelska)Ingår i: Proceedings - 2023 International Conference on Future Internet of Things and Cloud, FiCloud 2023, Institute of Electrical and Electronics Engineers (IEEE), 2023, s. 130-135Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

The communication protocols are the foundation for Internet of Things (IoT) for the seamless integration of hundreds of thousands of devices enabling a lightweight IoT communication network. Further, interoperability is a major concern in regard with connecting the multitude of heterogeneous devices, sensors, actuators, agents etc. Open messaging standards are designed to overcome the problem of horizontal interoperability for providing peer-to-peer communication network and real-time interaction possible in heterogeneous systems. The current research focus on the performance analysis to review the applicability of the open messaging standards available for IoT communication. In this paper, we design and implement the experiments to analyze the protocols' behaviour with respect to two performance metrics; throughput and latency. Message Queue Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP) protocols are used for comparative analysis for the Open messaging standards. The evaluation is done by using various experimental scenarios to analyze performance results. It is observed that Open messaging standards performance outperforms when compared with MQTT and CoAP in IoT applications by considering various evaluation parameters. It has been analyzed that open messaging standards lead in the IoT domain which can be well explained by the obtained results.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2023
Nyckelord
CoAP, Internet of things, MQTT, Open standards
Nationell ämneskategori
Kommunikationssystem Datorteknik
Identifikatorer
urn:nbn:se:umu:diva-221394 (URN)10.1109/FiCloud58648.2023.00027 (DOI)2-s2.0-85184993576 (Scopus ID)9798350316353 (ISBN)
Konferens
10th International Conference on Future Internet of Things and Cloud, FiCloud 2023, Hybrid, Marrakech, 14-16 august, 2023.
Tillgänglig från: 2024-02-27 Skapad: 2024-02-27 Senast uppdaterad: 2024-02-27Bibliografiskt granskad
Calvaresi, D., Omicini, A., Najjar, A. & Främling, K. (2023). Preface. In: Davide Calvaresi; Amro Najjar; Andrea Omicini; Reyhan Aydogan; Rachele Carli; Giovanni Ciatto; Yazan Mualla; Kary Främling (Ed.), Explainable and transparent ai and multi-agent systems: 5th international workshop, EXTRAAMAS 2023, London, UK, May 29, 2023, revised selected papers (pp. VI-VI). Springer Science+Business Media B.V.
Öppna denna publikation i ny flik eller fönster >>Preface
2023 (Engelska)Ingår i: Explainable and transparent ai and multi-agent systems: 5th international workshop, EXTRAAMAS 2023, London, UK, May 29, 2023, revised selected papers / [ed] Davide Calvaresi; Amro Najjar; Andrea Omicini; Reyhan Aydogan; Rachele Carli; Giovanni Ciatto; Yazan Mualla; Kary Främling, Springer Science+Business Media B.V., 2023, s. VI-VIKapitel i bok, del av antologi (Övrigt vetenskapligt)
Ort, förlag, år, upplaga, sidor
Springer Science+Business Media B.V., 2023
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 14127
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:umu:diva-214984 (URN)2-s2.0-85172220251 (Scopus ID)978-3-031-40878-6 (ISBN)978-3-031-40877-9 (ISBN)
Anmärkning

Conference: 5th International Workshop, EXTRAAMAS 2023, London, UK, May 29, 2023.

Tillgänglig från: 2023-10-16 Skapad: 2023-10-16 Senast uppdaterad: 2023-10-16Bibliografiskt granskad
Organisationer
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-8078-5172

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