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Vogel, Bahtijar
Publikasjoner (10 av 24) Visa alla publikasjoner
Flores, C., Gonzalez, J., Kajtazi, M., Bugeja, J. & Vogel, B. (2023). Human Factors for Cybersecurity Awareness in a Remote Work Environment. In: Proceedings of the 9th International Conference on Information Systems Security and Privacy ICISSP: . Paper presented at 9th International Conference on Information Systems Security and Privacy (ICISSP 2023), Lisbon, Portugal, 22–24 February 2023 (pp. 608-616). SciTePress, 1
Åpne denne publikasjonen i ny fane eller vindu >>Human Factors for Cybersecurity Awareness in a Remote Work Environment
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2023 (engelsk)Inngår i: Proceedings of the 9th International Conference on Information Systems Security and Privacy ICISSP, SciTePress, 2023, Vol. 1, s. 608-616Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The conveniences of remote work are various, but a surge in cyberthreats has heavily affected the optimal processes of organizations. As a result, employees’ cybersecurity awareness was jeopardized, prompting organizations to require improvement of cybersecurity processes at all levels. This paper explores which cybersecurity aspects are more relevant and/or relatable for remote working employees. A qualitative approach via interviews is used to collect experiences and perspectives from employees in different organizations. The results show that human factors, such as trust in cybersecurity infrastructure, previous practices, training, security fatigue, and improvements with gamification, are core to supporting the success of a cybersecurity program in a remote work environment.

 

 

 

sted, utgiver, år, opplag, sider
SciTePress, 2023
Serie
ICISSP, E-ISSN 2184-4356
Emneord
Cybersecurity, Trust, Human Factors, Awareness, Employees, Remote Work Environment
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-64247 (URN)10.5220/0011746000003405 (DOI)2-s2.0-85176343851 (Scopus ID)978-989-758-624-8 (ISBN)
Konferanse
9th International Conference on Information Systems Security and Privacy (ICISSP 2023), Lisbon, Portugal, 22–24 February 2023
Tilgjengelig fra: 2023-12-11 Laget: 2023-12-11 Sist oppdatert: 2023-12-11bibliografisk kontrollert
Ouhaichi, H., Spikol, D. & Vogel, B. (2023). Research trends in multimodal learning analytics: A systematic mapping study. Computers and Education: Artificial Intelligence, 4, 100136-100136, Article ID 100136.
Åpne denne publikasjonen i ny fane eller vindu >>Research trends in multimodal learning analytics: A systematic mapping study
2023 (engelsk)Inngår i: Computers and Education: Artificial Intelligence, ISSN 2666-920X, Vol. 4, s. 100136-100136, artikkel-id 100136Artikkel, forskningsoversikt (Fagfellevurdert) Published
Abstract [en]

Understanding and improving education are critical goals of learning analytics. However, learning is not always mediated or aided by a digital system that can capture digital traces. Learning in such environments can be studied by recording, processing, and analyzing different signals, including video and audio, so that traces of actors’ actions and interactions are captured. Multimodal Learning Analytics refers to analyzing these signals through the use and integration of these multiple modes. However, a need exists to evaluate how research is conducted in the emerging field of multimodal learning analytics to aid and evaluate how these systems work. With the growth of multimodal learning analytics, research trends and technologies are needed to support its development. We conducted a systematic mapping study based on established systematic literature practices to identify multimodal learning analytics research types, methodologies, and trending research themes. Most mapped papers presented different solutions and used evaluation-based research methods to demonstrate an increasing interest in multimodal learning analytics technologies. In addition, we identified 14 topics under four themes––learning context, learning process, systems and modality, and technologies––that can contribute to the growth of multimodal learning analytics.

sted, utgiver, år, opplag, sider
Elsevier, 2023
Emneord
Multimodal learning analytics, Mapping study, Learning technologies, Artificial intelligence
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-64288 (URN)10.1016/j.caeai.2023.100136 (DOI)2-s2.0-85151456109 (Scopus ID)
Tilgjengelig fra: 2023-12-12 Laget: 2023-12-12 Sist oppdatert: 2023-12-12bibliografisk kontrollert
Ouhaichi, H., Spikol, D. & Vogel, B. (2023). Rethinking MMLA: Design Considerations for Multimodal Learning Analytics Systems. In: L@S '23: Proceedings of the Tenth ACM Conference on Learning @ Scale: . Paper presented at Conference on Learning @ Scale, Copenhagen, Denmark, July 20-22, 2023 (pp. 354-359). ACM Digital Library
Åpne denne publikasjonen i ny fane eller vindu >>Rethinking MMLA: Design Considerations for Multimodal Learning Analytics Systems
2023 (engelsk)Inngår i: L@S '23: Proceedings of the Tenth ACM Conference on Learning @ Scale, ACM Digital Library, 2023, s. 354-359Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Designing MMLA systems is a complex task requiring a wide range of considerations. In this paper, we identify key considerations that are essential for designing MMLA systems. These considerations include data management, human factors, sensors and modalities, learning scenarios, privacy and ethics, interpretation and feedback, and data collection. The implications of these considerations are twofold: 1) The need for flexibility in MMLA systems to adapt to different learning contexts and scales, and 2) The need for a researcher-centered approach to designing MMLA systems. Unfortunately, the sheer number of considerations can lead to a state of "analysis paralysis," where deciding where to begin and how to proceed becomes overwhelming. This synthesis paper asks researchers to rethink the design of MMLA systems and aims to provide guidance for developers and practitioners in the field of MMLA.

sted, utgiver, år, opplag, sider
ACM Digital Library, 2023
Emneord
Multimodal Learning Analytics, System Design, Internet of Things, Scalability
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-63744 (URN)10.1145/3573051.3596186 (DOI)2-s2.0-85167870433 (Scopus ID)9798400700255 (ISBN)
Konferanse
Conference on Learning @ Scale, Copenhagen, Denmark, July 20-22, 2023
Tilgjengelig fra: 2023-11-20 Laget: 2023-11-20 Sist oppdatert: 2024-02-01bibliografisk kontrollert
Munir, H., Vogel, B. & Jacobsson, A. (2022). Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision. Information, 13(4), Article ID 203.
Åpne denne publikasjonen i ny fane eller vindu >>Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision
2022 (engelsk)Inngår i: Information, E-ISSN 2078-2489, Vol. 13, nr 4, artikkel-id 203Artikkel, forskningsoversikt (Fagfellevurdert) Published
Abstract [en]

The use of artificial intelligence and machine learning techniques across all disciplines has exploded in the past few years, with the ever-growing size of data and the changing needs of higher education, such as digital education. Similarly, online educational information systems have a huge amount of data related to students in digital education. This educational data can be used with artificial intelligence and machine learning techniques to improve digital education. This study makes two main contributions. First, the study follows a repeatable and objective process of exploring the literature. Second, the study outlines and explains the literature's themes related to the use of AI-based algorithms in digital education. The study findings present six themes related to the use of machines in digital education. The synthesized evidence in this study suggests that machine learning and deep learning algorithms are used in several themes of digital learning. These themes include using intelligent tutors, dropout predictions, performance predictions, adaptive and predictive learning and learning styles, analytics and group-based learning, and automation. artificial neural network and support vector machine algorithms appear to be utilized among all the identified themes, followed by random forest, decision tree, naive Bayes, and logistic regression algorithms.

sted, utgiver, år, opplag, sider
MDPI, 2022
Emneord
AI, ML, DL, digital education, literature review, dropouts, intelligent tutors, performance prediction
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-51752 (URN)10.3390/info13040203 (DOI)000786209900001 ()2-s2.0-85129306474 (Scopus ID)
Tilgjengelig fra: 2022-05-30 Laget: 2022-05-30 Sist oppdatert: 2024-02-05bibliografisk kontrollert
Serrano Iglesias, S., Spikol, D., Bote Lorenzo, M. L., Ouhaichi, H., Gómez Sánchez, E. & Vogel, B. (2021). Adaptable Smart Learning Environments supported by Multimodal Learning Analytics. In: Davinia Hernández-Leo, Elise Lavoué, Miguel L. Bote-Lorenzo, Pedro J. Muñoz-Merino, Daniel Spikol (Ed.), Proceedings of the LA4SLE 2021 Workshop: Learning Analytics for Smart Learning Environmentsco-located with the 16th European Conference on Technology Enhanced Learning 2021 (ECTEL 2021). Paper presented at EC-TEL 2021: Learning Analytics for Smart Learning Environments, September 21, 2021, Bolzano, Italy (pp. 24-30).
Åpne denne publikasjonen i ny fane eller vindu >>Adaptable Smart Learning Environments supported by Multimodal Learning Analytics
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2021 (engelsk)Inngår i: Proceedings of the LA4SLE 2021 Workshop: Learning Analytics for Smart Learning Environmentsco-located with the 16th European Conference on Technology Enhanced Learning 2021 (ECTEL 2021) / [ed] Davinia Hernández-Leo, Elise Lavoué, Miguel L. Bote-Lorenzo, Pedro J. Muñoz-Merino, Daniel Spikol, 2021, s. 24-30Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Smart Learning Environments and Learning Analytics hold promise of providing personalized support to learners according to their individual needs and context. This support can be achieved by collecting and analyzing data from the different learning tools and systems that are involved in the learning experience. This paper presents a first exploration of requirements and considerations for the integration of two systems: MBOX, a Multimodal Learning Analytics system for the physical space (human behavior and learning context), and SCARLETT, an SLE for the support during across-spaces learning situations combining different learning systems. This integration will enable the SLE to have access to a new and wide range of information, notably students’ behavior and social interactions in the physical learning context (e.g. classroom). The integration of multimodal data with the data coming from the digital learning environments will result in a more holistic system, therefore producing learning analytics that trigger personalized feedback and learning resources. Such integration and support is illustrated with a learning scenario that helps to discuss how these analytics can be derived and used for the intervention by the SLE.

    

HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-48217 (URN)978-3-030-86436-1 (ISBN)
Konferanse
EC-TEL 2021: Learning Analytics for Smart Learning Environments, September 21, 2021, Bolzano, Italy
Tilgjengelig fra: 2021-12-16 Laget: 2021-12-16 Sist oppdatert: 2023-07-04bibliografisk kontrollert
Gabrielsson, J., Bugeja, J. & Vogel, B. (2021). Hacking a Commercial Drone with Open-Source Software: Exploring Data Privacy Violations. In: 2021 10th Mediterranean Conference on Embedded Computing (MECO): . Paper presented at 2021 10th Mediterranean Conference on Embedded Computing (MECO), 7-10 June 2021, Budva, Montenegro (pp. 1-5). IEEE
Åpne denne publikasjonen i ny fane eller vindu >>Hacking a Commercial Drone with Open-Source Software: Exploring Data Privacy Violations
2021 (engelsk)Inngår i: 2021 10th Mediterranean Conference on Embedded Computing (MECO), IEEE, 2021, s. 1-5Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Drones have been discussed frequently in both governmental and commercial sectors for their normalization in the airspace. Nonetheless, drones bring diverse privacy concerns to users. In this paper, we explore the ramifications to data privacy from the perspective of drone owners. To investigate privacy in this context, four experiments targeting a commercial drone were conducted using open-source software. The experiments identified personal data (e.g., audio, video, and location) that are at risk of being compromised particularly through the execution of a basic deauthentication attack launched at a commercial drone. Our findings indicate the severity of risks affecting commercial drones. This makes the case for more effective privacy regulations and better guidelines suitable for securing drones.

sted, utgiver, år, opplag, sider
IEEE, 2021
Serie
Mediterranean Conference on Embedded Computing (New Jersey), ISSN 2377-5475, E-ISSN 2637-9511
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-47465 (URN)10.1109/MECO52532.2021.9460295 (DOI)2-s2.0-85114205880 (Scopus ID)978-1-6654-3912-1 (ISBN)
Konferanse
2021 10th Mediterranean Conference on Embedded Computing (MECO), 7-10 June 2021, Budva, Montenegro
Tilgjengelig fra: 2021-12-13 Laget: 2021-12-13 Sist oppdatert: 2024-02-05bibliografisk kontrollert
Ouhaichi, H., Spikol, D. & Vogel, B. (2021). MBOX: Designing a Flexible IoT Multimodal Learning Analytics System. In: Chang, M., Chen, NS., Sampson, DG., Tlili, A. (Ed.), IEEE 21st International Conferenceon Advanced Learning TechnologiesICALT 2021: . Paper presented at IEEE 21st International Conference on Advanced Learning Technologies, 12–15 July 2021 Online (pp. 122-126). IEEE
Åpne denne publikasjonen i ny fane eller vindu >>MBOX: Designing a Flexible IoT Multimodal Learning Analytics System
2021 (engelsk)Inngår i: IEEE 21st International Conferenceon Advanced Learning TechnologiesICALT 2021 / [ed] Chang, M., Chen, NS., Sampson, DG., Tlili, A., IEEE, 2021, s. 122-126Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Multimodal Learning Analytics (MMLA) provides opportunities for understanding and supporting collaborative problem-solving. However, the implementation of MMLA systems is challenging due to the lack of scalable technologies and limited solutions for collecting data from group work. This paper proposes the Multimodal Box (MBOX), an IoT-based system for MMLA, allowing the collection and processing of multimodal data from collaborative learning tasks. MBOX investigates the development and design for an IoT focusing on small group work in real-world settings. Moreover, MBOX promotes adaptation to different learning environments and enables a better scaling of computational resources used within the learning context.

sted, utgiver, år, opplag, sider
IEEE, 2021
Serie
IEEE International Conference on Advanced Learning Technologies, ISSN 2161-3761
Emneord
Multimodal Learning Analytics, CSCL, IoT, Interaction Design, Human Social Signal Processing
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-48140 (URN)10.1109/ICALT52272.2021.00044 (DOI)000719352000038 ()2-s2.0-85114887166 (Scopus ID)978-1-6654-4106-3 (ISBN)
Konferanse
IEEE 21st International Conference on Advanced Learning Technologies, 12–15 July 2021 Online
Tilgjengelig fra: 2021-12-15 Laget: 2021-12-15 Sist oppdatert: 2024-02-05bibliografisk kontrollert
Ferati, M. & Vogel, B. (2020). Accessibility in Web Development Courses: A Case Study. Informatics, 7(1), Article ID 8.
Åpne denne publikasjonen i ny fane eller vindu >>Accessibility in Web Development Courses: A Case Study
2020 (engelsk)Inngår i: Informatics, ISSN 2227-9709, Vol. 7, nr 1, artikkel-id 8Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Web accessibility is becoming a relevant topic with an increased number of people with disabilities and the elderly using the web. Numerous legislations are being passed that require the web to be universally accessible to all people, regardless of their abilities and age. Despite this trend, university curricula still teach traditional web development without addressing accessibility as a topic. To investigate this matter closely, we studied the syllabi of web development courses at one university to evaluate whether the topic of accessibility was taught there. Additionally, we conducted a survey with nineteen students who were enrolled in a web development course, and we interviewed three lecturers from the same university. Our findings suggest that the topic of accessibility is not covered in web development courses, although both students and lecturers think that it should. This generates lack of competence in accessibility. The findings also confirm the finding of previous studies that, among web developers, there is a low familiarity with accessibility guidelines and policies. An interesting finding we uncovered was that gender affects the motivation to learn about accessibility. Females were driven by personal reasons, which we attribute to females having an increased sense of empathy. Finally, our participants were divided in their opinions whether accessibility contributes to usability.

sted, utgiver, år, opplag, sider
Basel, Switzerland: MDPI, 2020
Emneord
accessibility, disability, education, curriculum, web development, web design, web programming, usability
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-17100 (URN)10.3390/informatics7010008 (DOI)000523665900004 ()2-s2.0-85083281088 (Scopus ID)
Tilgjengelig fra: 2020-04-21 Laget: 2020-04-21 Sist oppdatert: 2024-02-05bibliografisk kontrollert
Vogel, B., Kajtazi, M., Bugeja, J. & Varshney, R. (2020). Openness and Security Thinking Characteristics for IoT Ecosystems. Information, 11(12)
Åpne denne publikasjonen i ny fane eller vindu >>Openness and Security Thinking Characteristics for IoT Ecosystems
2020 (engelsk)Inngår i: Information, E-ISSN 2078-2489, Vol. 11, nr 12Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

While security is often recognized as a top priority for organizations and a push for competitive advantage, repeatedly, Internet of Things (IoT) products have become a target of diverse security attacks. Thus, orchestrating smart services and devices in a more open, standardized and secure way in IoT environments is yet a desire as much as it is a challenge. In this paper, we propose a model for IoT practitioners and researchers, who can adopt a sound security thinking in parallel with open IoT technological developments. We present the state-of-the-art and an empirical study with IoT practitioners. These efforts have resulted in identifying a set of openness and security thinking criteria that are important to consider from an IoT ecosystem point of view. Openness in terms of open standards, data, APIs, processes, open source and open architectures (flexibility, customizability and extensibility aspects), by presenting security thinking tackled from a three-dimensional point of view (awareness, assessment and challenges) that highlight the need to develop an IoT security mindset. A novel model is conceptualized with those characteristics followed by several key aspects important to design and secure future IoT systems.

sted, utgiver, år, opplag, sider
Basel, Switzerland: MDPI, 2020
Emneord
IoT, ecosystem, openness, security, privacy, awareness, assessment, challenges, security thinking, model, design
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-37515 (URN)10.3390/info11120564 (DOI)000601977000001 ()2-s2.0-85097033643 (Scopus ID)
Forskningsfinansiär
Knowledge Foundation, 20140035
Tilgjengelig fra: 2020-12-10 Laget: 2020-12-10 Sist oppdatert: 2024-02-05bibliografisk kontrollert
Vogel, B., Dong, Y., Emruli, B., Davidsson, P. & Spalazzese, R. (2020). What is an Open IoT Platform?: Insights from a Systematic Mapping Study. Future Internet, 12(4)
Åpne denne publikasjonen i ny fane eller vindu >>What is an Open IoT Platform?: Insights from a Systematic Mapping Study
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2020 (engelsk)Inngår i: Future Internet, E-ISSN 1999-5903, Vol. 12, nr 4Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Today, the Internet of Things (IoT) is mainly associated with vertically integrated systems that often are closed and fragmented in their applicability. To build a better IoT ecosystem, the open IoT platform has become a popular term in the recent years. However, this term is usually used in an intuitive way without clarifying the openness aspects of the platforms. The goal of this paper is to characterize the openness types of IoT platforms and investigate what makes them open. We conducted a systematic mapping study by retrieving data from 718 papers. As a result of applying the inclusion and exclusion criteria, 221 papers were selected for review. We discovered 46 IoT platforms that have been characterized as open, whereas 25 platforms are referred as open by some studies rather than the platforms themselves. We found that the most widely accepted and used open IoT platforms are NodeMCU and ThingSpeak that together hold a share of more than 70% of the declared open IoT platforms in the selected papers. The openness of an IoT platform is interpreted into different openness types. Our study results show that the most common openness type encountered in open IoT platforms is open-source, but also open standards, open APIs, open data and open layers are used in the literature. Finally, we propose a new perspective on how to define openness in the context of IoT platforms by providing several insights from the different stakeholder viewpoints.

sted, utgiver, år, opplag, sider
Basel, Switzerland: MDPI, 2020
Emneord
internet of things, IoT, open IoT platforms, openness, open-source, open standards, open API, systematic mapping study
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-17332 (URN)10.3390/fi12040073 (DOI)000533885000007 ()2-s2.0-85084682502 (Scopus ID)
Tilgjengelig fra: 2020-05-18 Laget: 2020-05-18 Sist oppdatert: 2024-02-05bibliografisk kontrollert
Prosjekter
Securing IOT Devices in a Dynamic Environment: The Case of Drones; Malmö universitet, Internet of Things and People (IOTAP)
Organisasjoner