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  • 301.
    John, Meenu Mary
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Chalmers University of Technology.
    AI on the Edge: Architectural Alternatives2020Ingår i: Proceedings 46th Euromicro Conferenceon Software Engineering and Advanced Applications SEAA 2020 / [ed] Antonio Martini, Manuel Wimmer, Amund Skavhaug, IEEE, 2020, s. 21-28Konferensbidrag (Refereegranskat)
    Abstract [en]

    Since the advent of mobile computing and IoT, a large amount of data is distributed around the world. Companies are increasingly experimenting with innovative ways of implementing edge/cloud (re)training of AI systems to exploit large quantities of data to optimize their business value. Despite the obvious benefits, companies face challenges as the decision on how to implement edge/cloud (re)training depends on factors such as the task intent, the amount of data needed for (re)training, edge-to-cloud data transfer, the available computing and memory resources. Based on action research in a software-intensive embedded systems company where we study multiple use cases as well as insights from our previous collaborations with industry, we develop a generic framework consisting of five architectural alternatives to deploy AI on the edge utilizing transfer learning. We validate the framework in four additional case companies and present the challenges they face in selecting the optimal architecture. The contribution of the paper is threefold. First, we develop a generic framework consisting of five architectural alternatives ranging from a centralized architecture where cloud (re)training is given priority to a decentralized architecture where edge (re)training is instead given priority. Second, we validate the framework in a qualitative interview study with four additional case companies. As an outcome of validation study, we present two variants to the architectural alternatives identified as part of the framework. Finally, we identify the key challenges that experts face in selecting an ideal architectural alternative.

  • 302.
    John, Meenu Mary
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Chalmers University of Technology.
    Architecting AI Deployment: A Systematic Review of State-of-the-art and State-of-practice Literature2020Ingår i: Software Business: 11th International Conference, ICSOB 2020, Karlskrona, Sweden, November 16–18, 2020, Proceedings / [ed] Eriks Klotins; Krzysztof Wnuk, Springer, 2020, s. 14-29Konferensbidrag (Refereegranskat)
    Abstract [en]

    Companies across domains are rapidly engaged in shifting computational power and intelligence from centralized cloud to fully decentralized edges to maximize value delivery, strengthen security and reduce latency. However, most companies have only recently started pursuing this opportunity and are therefore at the early stage of the cloud-to-edge transition. To provide an overview of AI deployment in the context of edge/cloud/hybrid architectures, we conduct a systematic literature review and a grey literature review. To advance understanding of how to integrate, deploy, operationalize and evolve AI models, we derive a framework from existing literature to accelerate the end-to-end deployment process. The framework is organized into five phases: Design, Integration, Deployment, Operation and Evolution. We make an attempt to analyze the extracted results by comparing and contrasting them to derive insights. The contribution of the paper is threefold. First, we conduct a systematic literature review in which we review the contemporary scientific literature and provide a detailed overview of the state-of-the-art of AI deployment. Second, we review the grey literature and present the state-of-practice and experience of practitioners while deploying AI models. Third, we present a framework derived from existing literature for the end-to-end deployment process and attempt to compare and contrast SLR and GLR results.

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  • 303.
    John, Meenu Mary
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Chalmers University of Technology.
    Developing ML/DL Models: A Design Framework2020Ingår i: Proceedings 2020 IEEE/ACM International Conferenceon Software and System Processes ICSSP 2020, ACM Digital Library, 2020, s. 1-10Konferensbidrag (Refereegranskat)
    Abstract [en]

    Artificial Intelligence is becoming increasingly popular with organizations due to the success of Machine Learning and Deep Learning techniques. Using these techniques, data scientists learn from vast amounts of data to enhance behaviour in software-intensive systems. Despite the attractiveness of these techniques, however, there is a lack of systematic and structured design process for developing ML/DL models. The study uses a multiple-case study approach to explore the different activities and challenges data scientists face when developing ML/DL models in software-intensive embedded systems. In addition, we have identified seven different phases in the proposed design process leading to effective model development based on the case study. Iterations identified between phases and events which trigger these iterations optimize the design process for ML/DL models. Lessons learned from this study allow data scientists and engineers to develop high-performance ML/DL models and also bridge the gap between high demand and low supply of data scientists.

  • 304.
    John, Meenu Mary
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Chalmers Univ Technol, Dept Comp Sci & Engn, Gothenburg, Sweden..
    Towards an AI-driven business development framework: A multi-case study2023Ingår i: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 35, nr 6, artikel-id e2432Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Artificial intelligence (AI) and the use of machine learning (ML) and deep learning (DL) technologies are becoming increasingly popular in companies. These technologies enable companies to leverage big quantities of data to improve system performance and accelerate business development. However, despite the appeal of ML/DL, there is a lack of systematic and structured methods and processes to help data scientists and other company roles and functions to develop, deploy and evolve models. In this paper, based on multi-case study research in six companies, we explore practices and challenges practitioners experience in developing ML/DL models as part of large software-intensive embedded systems. Based on our empirical findings, we derive a conceptual framework in which we identify three high-level activities that companies perform in parallel with the development, deployment and evolution of models. Within this framework, we outline activities, iterations and triggers that optimize model design as well as roles and company functions. In this way, we provide practitioners with a blueprint for effectively integrating ML/DL model development into the business to achieve better results than other (algorithmic) approaches. In addition, we show how this framework helps companies solve the challenges we have identified and discuss checkpoints for terminating the business case.

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  • 305.
    Johnsson, Magnus
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Perception, Imagery, Memory and Consciousness2022Ingår i: Filozofia i Nauka, E-ISSN 2545-1936, Vol. Zeszyt specjalny, nr 10, s. 229-244Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    I propose and discuss some principles that I believe are substantial for percep- tion, various kinds of memory, expectations and the capacity for imagination in the mammal brain, as well as for the design of a biologically inspired artificial cognitive architecture. I also suggest why these same principles could explain our ability to represent novel concepts and imagine non-existing and perhaps impossible objects, while there are still limits to what we can imagine and think about. Some ideas re- garding how these principles could be relevant for an autonomous agent to become functionally conscious are discussed as well.

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  • 306.
    Johnsson, Magnus
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Perceptions, Imagery, Memory, and Consciousness in Man and Machine2022Ingår i: The 2021 Summit of the International Society for the Study of Information, MDPI, 2022, Vol. 81(1)Konferensbidrag (Refereegranskat)
    Abstract [en]

    I propose a number of principles that I believe are substantial for various faculties of the mammalian brain, such as perception, expectations, imagery, and memory. The same principles are also of interest when designing an artificial but biologically inspired cognitive architecture. Moreover, I discuss how the same principles may lie behind the ability to represent new concepts and to imagine fictitious and impossible objects, while also giving us reasons to believe that there are limits to our imagination and to what it is possible for us to think about. Some ideas regarding how these principles could be relevant for an autonomous agent to become functionally conscious are discussed as well.

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  • 307. Jonsson, Håkan
    et al.
    Olsson, Carl Magnus
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    User privacy attitudes regarding proximity sensing2018Ingår i: ARES 2018: Proceedings of the 13th International Conference on Availability, Reliability and Security, ACM Digital Library, 2018, artikel-id 25Konferensbidrag (Refereegranskat)
    Abstract [en]

    User attitudes on privacy with respect to location data has been extensively studied. However, user attitudes of privacy in relation to proximity sensing is still lacking. We present the results from a survey conducted on users of a proximity sensing application we developed and diffused by handing out phones with the proximity sensing application pre-installed, with 31 respondents. The results compare this type of application to location sensing in general, as well as positions our respondents in relation to previous studies in terms of general privacy policies. Four results stand out in particular: One, our respondents are more aware of and care about privacy policies than in previous studies. Two, trust is reported as being based more on the specific data access asked for, than EULA or similar text based policies. Third, the respondents are willing to allowing having proximity data about them sensed, as long as they are in control of who can sense it. Finally, our results indicate that there is no perceived difference in sensitivity between location and proximity sensing.

  • 308. Jordaan, Coert
    et al.
    Malekian, Reza
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Design of a monitoring and safety system for underground mines using wireless sensor networks2019Ingår i: International Journal of Ad Hoc and Ubiquitous Computing, ISSN 1743-8225, E-ISSN 1743-8233, Vol. 32, nr 1, s. 14-28Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this paper, a mine safety system using a wireless sensor network (WSN) is implemented. Investigations are done into design of sensors and wireless communication to profile the underground mining environment. The information is used to design and implement a robust hardware-based sensor node with standalone microcontrollers that sample data from six different sensors, namely temperature, humidity, airflow speed, noise, dust and gas level sensors, and transmit the processed data to a graphical user interface. The system reliability and accuracy is tested in a simulated mine and provided linear and accurate results over nearly a month of daily testing. It is observed that critical success factors for the wireless sensor node is its robust design, which does not easily fail or degrade in performance. The node also has strong, self-adaptive networking functionality, to recover in the case of a node failure.

  • 309. Joy, Mike
    et al.
    Sinclair, Jane
    Boyatt, Russell
    Yau, Jane Yin-Kim
    Malmö högskola, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap (DV).
    Cosma, Georgina
    Student perspectives on source-code plagiarism2013Ingår i: International Journal for Educational Integrity, E-ISSN 1833-2595, Vol. 9, nr 1, s. 3-19Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Prevention and detection of plagiarism has formed the basis of much research, but student perceptions on plagiarism are arguably not well understood. This is particularly the case in the computing disciplines. This paper considers two aspects of the student experience: (i) the types of plagiaristic activity that students engage in, and (ii) the specific understanding of what plagiarism means for students who write computer programmes. In a recent study, data were collected from published material (books, published papers, websites), and online formative quizzes and questionnaires used by universities to test student knowledge of what constitutes plagiarism. Facet analysis was used to classify the data into four initial categories (sources, actions, material, extrinsic). Further analysis suggested a refinement to six categories and 23 sub-categories which directly relate to the computing disciplines. In a further study a large-scale online questionnaire was carried out to obtain the perceptions of students on source-code plagiarism. Data were collected from 770 students studying at 21 higher education institutions in the UK and overseas. This study’s results suggest that certain types of plagiaristic activity are poorly understood. This paper summarises and compares the results of these two studies and reflects on the implications for educating computing students about how they should avoid plagiarism.

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  • 310. Jönsson, Karl-Ebbe
    et al.
    Ornstein, Kajsa
    Christensen, Jonas
    Malmö universitet, Fakulteten för hälsa och samhälle (HS), Institutionen för socialt arbete (SA).
    Eriksson, Jeanette
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    A reminder system for independence in dementia care: a case study in an assisted living facility2019Ingår i: PETRA '19 Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments, ACM Digital Library, 2019, s. 176-185Konferensbidrag (Refereegranskat)
    Abstract [en]

    By 2050, the population share aged over 80 will double. Considering the number of older adults and the wide range of chronic conditions, health systems need to assure that care is adapted to the individuals' different needs and enables the elderly to be active and involved. This article is about introducing a food reminder system into the domain of dementia care. The aim is to explore and describe whether and how such a system, built with existing technology, can be valuable, and how caregivers adopted it in a real-world environment. The system is built with Android® tablets and is called iRemember.® The research is done as a case study at a care facility in Simrishamn, Sweden. Domain knowledge is gained by reviewing previous research and by conducting interviews with professionals at the facility. The system is developed, deployed, and evaluated at a care facility home for persons with dementia. Data about usage and perception of the value of the system is collected through observations and interviews. Results indicate that a food reminder system can be valuable to and empower people with dementia. They also indicate that caregivers can readily adopt such a solution, including both management and people working directly with persons with dementia.

  • 311.
    Kadish, David
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Sarkheyli-Hägele, Arezoo
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Font, Jose
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Hägele, Georg
    Autonomous Operations and Services, Husqvarna AB, Sweden.
    Niehorster, Diederick C.
    Lund University Humanities Lab and Department of Psychology, Lund University, Sweden.
    Pederson, Thomas
    School of Business, Economics and IT, University West, Sweden.
    Towards Situation Awareness and Attention Guidance in a Multiplayer Environment using Augmented Reality and Carcassonne2022Ingår i: CHI PLAY '22: Extended Abstracts of the 2022 Annual Symposium on Computer-Human Interaction in Play, ACM Digital Library, 2022, s. -9Konferensbidrag (Refereegranskat)
    Abstract [en]

    Augmented reality (AR) games are a rich environment for researching and testing computational systems that provide subtle user guidance and training. In particular computer systems that aim to augment a user’s situation awareness benefit from the range of sensors and computing power available in AR headsets. The main focus of this work-in-progress paper is the introduction of the concept of the individualized Situation Awareness-based Attention Guidance (SAAG) system used to increase humans’ situating awareness and the augmented reality version of the board game Carcassonne for validation and evaluation of SAAG. Furthermore, we present our initial work in developing the SAAG pipeline, the generation of game state encodings, the development and training of a game AI, and the design of situation modeling and eye-tracking processes.  

     

  • 312.
    Kadish, David
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). IT Univ Copenhagen, Digital Design, Copenhagen, Denmark.
    Stoy, Kasper
    IT Univ Copenhagen, Comp Sci, Copenhagen, Denmark.
    BioAcoustic Index Tool: long-term biodiversity monitoring using on-sensor acoustic index calculations2022Ingår i: Bioacoustics, ISSN 0952-4622, E-ISSN 2165-0586, Vol. 31, nr 3, s. 348-378Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Acoustic indices are valuable tools for measuring and tracking changes in biodiversity. However, the method used to collect acoustic index data can be made more effective by recent developments in electronics. The current process requires recording high-quality audio in the field and computing acoustic indices in the lab. This produces vast quantities of raw audio data, which limits the time that sensors can spend in the field and complicates data processing and analysis. Additionally, most field audio recorders are unable to log the full range of contextual environmental data that would help explain short-term variations. In this paper, we present the BioAcoustic Index Tool, a smart acoustic index and environmental sensor. The BioAcoustic Index Tool computes acoustic indices as audio is captured, storing only the index information, and logs temperature, humidity, and light levels. The sensor was able to operate completely autonomously for the entire five-month duration of the field study. In that time, it recorded over 4000 measurements of acoustic complexity and diversity all while producing the same amount of data that would be used to record 3 minutes of raw audio. These factors make the BioAcoustic Index Tool well-suited for large-scale, long-term acoustic biodiversity monitoring.

  • 313.
    Kajtazi, Miranda
    et al.
    Lund University.
    Kurti, Erdelina
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Conceptualizing the Impact of Digital Business Models on Privacy Concerns2023Ingår i: 36th Bled eConference – Digital Economy and Society: The Balancing Actfor Digital Innovation in Times of Instability: June 25 – 28, 2023, Bled, Slovenia, Conference Proceedings / [ed] Andreja Pucihar, Mirjana Kljajić Borštnar, Roger Bons, Guido Ongena, Marikka Heikkilä, Doroteja Vidmar, Maribor: University of Maribor University Press , 2023, s. 721-735Konferensbidrag (Refereegranskat)
    Abstract [en]

    Digital technologies have enabled novel forms and reconfigurations of value creation, delivery, and capture. These new reconfigurations challenge the conventional notion of value creation with digital business models. On that premise, the widening of privacy concerns, alert us that organizations of the elite digital, like Netflix, Amazon, and Spotify, design technology to feed on personal data, based on algorithmic profiling capabilities. Then, privacy itself becomes their digital business model. In this paper we conceptualize the impact of digital business models on privacy concerns, by presenting a focused literature review that presents 4 waves of research on understanding privacy from the context of digital business models. With our initial findings, we recommend that future technological development should pay central attention to privacy-preserving digital business models, by making it possible that data privacy is envisioned with the right safeguards, targeting 'invisibility' of the user.

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  • 314.
    Karie, Nickson M.
    et al.
    Edith Cowan Univ, Dept Comp Sci, Joondalup, Australia..
    Kebande, Victor R.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Ikuesan, Richard A.
    Qatar Community Coll, Dept Comp Sci, Doha, Qatar..
    Sookhak, Mehdi
    Illinois State Univ, Sch Informat Technol, Normal, IL 61761 USA..
    Venter, H. S.
    Univ Pretoria, Dept Comp Sci, Pretoria, South Africa..
    Hardening SAML by Integrating SSO and Multi-Factor Authentication (MFA) in the Cloud2020Ingår i: 3RD INTERNATIONAL CONFERENCE ON NETWORKING, INFORMATION SYSTEM & SECURITY (NISS'20) / [ed] Mohamed, B Abdelhakim, BA Said, R Dirss, LM Alaoui, EA, ACM Digital Library, 2020, artikel-id 56Konferensbidrag (Refereegranskat)
    Abstract [en]

    Even though the cloud paradigm and its associated services has been adopted in various enterprise applications, there has been major issues with regard to authenticating users' critical data. Single Sign on (SSO) is a user authentication technique through which a server authenticates and allows a user to use a single aspect of login credentials, for example, to access multiple services in the cloud. Even though SSO reduces the number of logins that are needed over heterogeneous environments, the risk that might be associated with the security of SSO might be detrimental if, for example, a Man-in-the Middle (MITM) attacker manages to gain control of the SSO credentials. It is also possible to get the identity of the users who have logged into Active Directory or intranet and this identity can easily be used to log into other web-based applications, and this requires the use of the Security Assertion Mark-up Language (SAML). SAML is basically a standard that allows users to be logged into applications as per their sessions. The problem that this paper addresses is the lack of a proactive technique of hardening cloud-based SAML while combining SSO with a Multi-Factor Authentication (MFA) at the time of writing this paper. The authors have, therefore, proposed an effective approach that unifies SSO with MFA in this context. Based on the base score index conducted over Common Vulnerability Scoring System (CVSS), the architecture proves to be reliable, feasible and with better performance.

  • 315. Karie, Nickson M
    et al.
    Kebande, Victor R.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Venter, H S
    Diverging deep learning cognitive computing techniques into cyber forensics2019Ingår i: Forensic science international. Synergy, ISSN 2589-871X, Vol. 1, s. 61-67Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    More than ever before, the world is nowadays experiencing increased cyber-attacks in all areas of our daily lives. This situation has made combating cybercrimes a daily struggle for both individuals and organisations. Furthermore, this struggle has been aggravated by the fact that today's cybercriminals have gone a step ahead and are able to employ complicated cyber-attack techniques. Some of those techniques are minuscule and inconspicuous in nature and often camouflage in the facade of authentic requests and commands. In order to combat this menace, especially after a security incident has happened, cyber security professionals as well as digital forensic investigators are always forced to sift through large and complex pools of data also known as Big Data in an effort to unveil Potential Digital Evidence (PDE) that can be used to support litigations. Gathered PDE can then be used to help investigators arrive at particular conclusions and/or decisions. In the case of cyber forensics, what makes the process even tough for investigators is the fact that Big Data often comes from multiple sources and has different file formats. Forensic investigators often have less time and budget to handle the increased demands when it comes to the analysis of these large amounts of complex data for forensic purposes. It is for this reason that the authors in this paper have realised that Deep Learning (DL), which is a subset of Artificial Intelligence (AI), has very distinct use-cases in the domain of cyber forensics, and even if many people might argue that it's not an unrivalled solution, it can help enhance the fight against cybercrime. This paper therefore proposes a generic framework for diverging DL cognitive computing techniques into Cyber Forensics (CF) hereafter referred to as the DLCF Framework. DL uses some machine learning techniques to solve problems through the use of neural networks that simulate human decision-making. Based on these grounds, DL holds the potential to dramatically change the domain of CF in a variety of ways as well as provide solutions to forensic investigators. Such solutions can range from, reducing bias in forensic investigations to challenging what evidence is considered admissible in a court of law or any civil hearing and many more.

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  • 316.
    Katterfeldt, Eva-Sophie
    et al.
    University of Bremen, Germany.
    Cukurova, Mutlu
    University College London, United Kingdom.
    Spikol, Daniel
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Disciplinary literacy and inclusive teaching.
    Cuartielles, David
    Arduino Verkstad AB.
    Physical computing with plug-and-play toolkits: Key recommendations for collaborative learning implementations2018Ingår i: International Journal of Child-Computer Interaction, ISSN 2212-8689, E-ISSN 2212-8697, Vol. 17, s. 72-82Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Physical computing toolkits have long been used in educational contexts to learn about computational concepts by engaging in the making of interactive projects. This paper presents a comprehensive toolkit that can help educators teach programming with an emphasis on collaboration, and provides suggestions for its effective pedagogical implementation. The toolkit comprises the Talkoo kit with physical computing plug-and-play modules and a visual programming environment. The key suggestions are inspired by the results of the evaluation studies which show that children (aged 14–18 in a sample group of 34 students) are well motivated when working with the toolkit but lack confidence in the kit’s support for collaborative learning. If the intention is to move beyond tools and code in computer education to community and context, thus encouraging computational participation, collaboration should be considered as a key aspect of physical computing activities. Our approach expands the field of programming with physical computing for teenage children with a focus on empowering teachers and students with not only a kit but also its appropriate classroom implementation for collaborative learning.

  • 317. Katz, Dmitri
    et al.
    Arsand, Eirik
    Dalton, Nick
    Holland, Simon
    Martin, Clare
    Olsson, Carl Magnus
    Malmö högskola, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap (DV).
    Price, Blaine A.
    Designing, Developing, and Evaluating the Future Internet of Personal Health2016Ingår i: UBICOMP'16 Adjunct: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ACM Digital Library, 2016, s. 1068-1073Konferensbidrag (Refereegranskat)
    Abstract [en]

    Ubiquitous computing technologies have the potential to revolutionize the support of chronic health conditions: improving quality of life, reducing costs and optimizing health outcomes. Wearable networks of connected devices and sensors offer the prospect of personalized support and contextually aware advice, for those with specific chronic health conditions. However, there are many obstacles and concerns that need to be addressed before the full potential can be realized. This workshop aims to bring together those interested in developing ubiquitous health management and related personal decision support systems to identify how gaps in knowledge can be addressed and design practices can be improved to better support key communities and contexts of use in this rapidly growing field.

  • 318.
    Kebande, Victor R.
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Alawadi, Sadi
    Uppsala Universitet.
    Awaysheh, Feras
    University of Tartu.
    Persson, Jan A.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Active Machine Learning Adversarial Attack Detection in the User Feedback Process2021Ingår i: IEEE Access, E-ISSN 2169-3536, E-ISSN 2169-3536, Vol. 9Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Modern Information and Communication Technology (ICT)-based applications utilize currenttechnological advancements for purposes of streaming data, as a way of adapting to the ever-changingtechnological landscape. Such efforts require providing accurate, meaningful, and trustworthy output fromthe streaming sensors particularly during dynamic virtual sensing. However, to ensure that the sensingecosystem is devoid of any sensor threats or active attacks, it is paramount to implement secure real-timestrategies. Fundamentally, real-time detection of adversarial attacks/instances during the User FeedbackProcess (UFP) is the key to forecasting potential attacks in active learning. Also, according to existingliterature, there lacks a comprehensive study that has a focus on adversarial detection from an activemachine learning perspective at the time of writing this paper. Therefore, the authors posit the importance ofdetecting adversarial attacks in active learning strategy. Attack in the context of this paper through a UFPThreat driven model has been presented as any action that exerts an alteration to the learning system ordata. To achieve this, the study employed ambient data collected from a smart environment human activityrecognition from (Continuous Ambient Sensors Dataset, CASA) with fully labeled connections, where weintentionally subject the Dataset to wrong labels as a targeted/manipulative attack (by a malevolent labeler)in the UFP, with an assumption that the user-labels were connected to unique identities. While the dataset’sfocus is to classify tasks and predict activities, our study gives a focus on active adversarial strategies froman information security point of view. Furthermore, the strategies for modeling threats have been presentedusing the Meta Attack Language (MAL) compiler for purposes adversarial detection. The findings fromthe experiments conducted have shown that real-time adversarial identification and profiling during the UFPcould significantly increase the accuracy during the learning process with a high degree of certainty and pavesthe way towards an automated adversarial detection and profiling approaches on the Internet of CognitiveThings (ICoT).

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  • 319.
    Kebande, Victor R.
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Alawadi, Sadi
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bugeja, Joseph
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Persson, Jan A.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Olsson, Carl Magnus
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Leveraging Federated Learning & Blockchain to counter Adversarial Attacks in Incremental Learning2020Ingår i: IoT '20 Companion: 10th International Conference on the Internet of Things Companion, ACM Digital Library, 2020, s. 1-5, artikel-id 2Konferensbidrag (Refereegranskat)
    Abstract [en]

    Whereas data labelling in IoT applications is costly, it is also time consuming to train a supervised Machine Learning (ML) algorithm. Hence, a human oracle is required to gradually annotate the data patterns at run-time to improve the models’ learning behavior, through an active learning strategy in form of User Feedback Process (UFP). Consequently, it is worth to note that during UFP there may exist malicious content that may subject the learning model to be vulnerable to adversarial attacks, more so, manipulative attacks. We argue in this position paper, that there are instances during incremental learning, where the local data model may present wrong output, if retraining is done using data that has already been subjected to adversarial attack. We propose a Distributed Interactive Secure Federated Learning (DISFL) framework that utilizes UFP in the edge and fog node, that subsequently increases the amount of labelled personal local data for the ML model during incremental training. Furthermore, the DISFL framework addresses data privacy by leveraging federated learning, where only the model's knowledge is moved to a global unit, herein referred to as Collective Intelligence Node (CIN). During incremental learning, this would then allow the creation of an immutable chain of data that has to be trained, which in its entirety is tamper-free while increasing trust between parties. With a degree of certainty, this approach counters adversarial manipulation during incremental learning in active learning context at the same time strengthens data privacy, while reducing the computation costs.

  • 320.
    Kebande, Victor R.
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Baror, Stacey O.
    Department of Computer Science, University of Pretoria, South Africa.
    Parizi, Reza M.
    College of Computing and Software Engineering, Kennesaw State University, Marietta, GA, USA.
    Raymond Choo, Kim-Kwang
    Department of Information Systems and Cyber Security, University of Texas at San Antonio, San Antonio, TX 78249-0631, USA.
    Venter, H.S.
    Department of Computer Science, University of Pretoria, South Africa.
    Mapping digital forensic application requirement specification to an international standard2020Ingår i: Forensic Science International: Reports, ISSN 2665-9107, Vol. 2, artikel-id 100137Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A potential security incident may go unsolved if standardized forensic approaches are not applied during lawfulinvestigations. This paper highlights the importance of mapping the digital forensic application requirementspecification to an international standard, precisely ISO/IEC 27043. The outcome of this work is projected tocontribute to the problem of secure DF tool creation, and in the process address Software Requirements Specification(SRS) as a process of digital evidence admissibility.

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  • 321.
    Kebande, Victor R.
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Ikuesan, Richard
    Karie, Nickson
    Edith Cowan University Australia.
    Alawadi, Sadi
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Kim-Kwang, Raymond Choo
    University of Texas at San Antonio.
    Al-Dhaqm, Arafat
    Universiti Teknologi Malysia.
    Quantifying the need for supervised machine learning in conducting liveforensic analysis of emergent configurations (ECO) in IoT environments2020Ingår i: Forensic Science International: Reports, ISSN 2665-9107, Vol. 2, artikel-id 100122Artikel i tidskrift (Övrigt vetenskapligt)
    Abstract [en]

    Machine learning has been shown as a promising approach to mine larger datasets, such as those that comprise datafrom a broad range of Internet of Things devices, across complex environment(s) to solve different problems. Thispaper surveys existing literature on the potential of using supervised classical machine learning techniques, such asK-Nearest Neigbour, Support Vector Machines, Naive Bayes and Random Forest algorithms, in performing livedigital forensics for different IoT configurations. There are also a number of challenges associated with the use ofmachine learning techniques, as discussed in this paper.

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  • 322.
    Kebande, Victor R.
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Karie, Nickson
    Edith Cowan University, Australia.
    Ikuesan, Richard
    Qatar Community college.
    Real-time monitoring as a supplementary security component of vigilantism in modern network environments2021Ingår i: International Journal of Information Technology, ISSN 2511-2104, Vol. 13, s. 5-17Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The phenomenon of network vigilantism is autonomously attributed to how anomalies and obscure activities from adversaries can be tracked in real-time. Needless to say, in today’s dynamic, virtualized, and complex network environments, it has become undeniably necessary for network administrators, analysts as well as engineers to practice network vigilantism, on traffic as well as other network events in real-time. The reason is to understand the exact security posture of an organization’s network environment at any given time. This is driven by the fact that modern network environments do, not only present new opportunities to organizations but also a different set of new and complex cybersecurity challenges that need to be resolved daily. The growing size, scope, complexity, and volume of networked devices in our modern network environments also makes it hard even for the most experienced network administrators to independently provide the breadth and depth of knowledge needed to oversee or diagnose complex network problems. Besides, with the growing number of Cyber Security Threats (CSTs) in the world today, many organisations have been forced to change the way they plan, develop and implement cybersecurity strategies as a way to reinforce their ability to respond to cybersecurity incidents. This paper, therefore, examines the relevance of Real-Time Monitoring (RTM) as a supplementary security component of vigilantism in modern network environments, more especially for proper planning, preparedness, and mitigation in case of a cybersecurity incident. Additionally, this paper also investigates some of the key issues and challenges surrounding the implementation of RTM for security vigilantism in our modern network environments.

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  • 323.
    Kebande, Victor R.
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Karie, Nickson
    ECU - Security Research Institute, Faculty of Science, Edith Cowan University, Joondalup Campus, Joondalup, Western Australia, Australia.
    Ikuesan, Richard
    Department of Cybersecurity and Networking, School of Information Technology, Community College of Qatar, Doha, Qatar.
    Venter, H S
    DigiFORs Research Group, Department of Computer Science, University of Pretoria, Pretoria, South Africa.
    Ontology-driven perspective of CFRaaS2020Ingår i: WIREs Forensics Science, ISSN 2573-9468, Vol. 2, nr 5Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A Cloud Forensic Readiness as a Service (CFRaaS) model allows an environmentto preemptively accumulate relevant potential digital evidence (PDE) which maybe needed during a post-event response process. The benefit of applying a CFRaaSmodel in a cloud environment, is that, it is designed to prevent the modification/tampering of the cloud architectures or the infrastructure during the reactive pro-cess, which if it could, may end up having far-reaching implications. The authorsof this article present the reactive processasaverycostlyexercisewhentheinfra-structure must be reprogrammed every time the process is conducted. This mayhamper successful investigation from the forensic experts and law enforcementagencies perspectives. The CFRaaS model, in its current state, has not been pres-ented in a way that can help to classify or visualize the different types of potentialevidence in all the cloud deployable models, and this may limit the expectationsof what or how the required PDE may be collected. To address this problem, thearticle presents the CFRaaS from a holistic ontology-driven perspective, whichallows the forensic experts to be able to apply the CFRaaS based on its simplicityof the concepts, relationship or semantics between different form of potential evi-dence, as well as how the security of a digital environment being investigatedcould be upheld. The CFRaaS in this context follows a fundamental ontologyengineering approach that is based on the classical Resource Description Frame-work. The proposed ontology-driven approach to CFRaaS is, therefore, aknowledge-base that uses layer-dependencies, which could be an essential toolkitfor digital forensic examiners and other stakeholders in cloud-security. The imple-mentation of this approach could further provide a platform to develop otherknowledge base components for cloud forensics and security

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  • 324.
    Kebande, Victor R.
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Mlotshwa, Likhwa
    Karie, Nickson M.
    Botnet's Obfuscated C&C Infrastructure Take-down Approaches Based on Monitoring Centralized Zeus Bot Variant's Propagation Model2019Ingår i: 2019 Ist-Africa Week Conference (Ist-Africa), IEEE, 2019Konferensbidrag (Refereegranskat)
    Abstract [en]

    While botnets still pose a big threat, they have also developed to be the most dangerous dark applications over the web. They are able to compromise a multitude of computers under the Command and Control (C&C) infrastructure, that is mainly controlled by a botherder/botmaster. Normally, a botnet uses malicious code to achieve its objectives and usually the motivation is based on either financial gain or Denial of Service (DoS) attack. The problem that is being addressed in this paper is structured to explore how a botnet's C&C infrastructure can be taken down based on how the botnet propagates itself within a network. The authors have used Zeus Botnet (ZBot) propagation model as a basis for this study. The main objective is to identify ZBot propagation patterns in order to be able to propose the take down approaches of the C&C infrastructure which acts as botnet control point. It is imperative to note that, even though ZBot was mainly resilient to attacks because of its Peer-to-Peer (P2P) nature, still other Zeus variants were controlled or acted as centralized bots. The study is more inclined to exploring the centralized Zeus variants like GameOver Zeus (GOZ) and ICE-IX for purposes of identifying the approaches. Based on the ZBot attack study, the C&C infrastructure can effectively be infiltrated hence averting unwarranted botnet attacks.

  • 325.
    Kebande, Victor R.
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Mudau, Phathutshedzo P.
    DigiForS Research Group, Department of Computer Science, University of Pretoria, South Africa.
    Ikuesan, Richard A.
    Cyber and Network Security Department, Science and Technology Division, Community College of Qatar, Qatar.
    Venter, H.S.
    DigiForS Research Group, Department of Computer Science, University of Pretoria, South Africa.
    Choo, Kim-Kwang Raymond
    Department of Information Systems and Cyber Security, University of Texas at San Antonio, San Antonio, TX 78249-0631, USA.
    Holistic digital forensic readiness framework for IoT-enabled organizations2020Ingår i: Forensic Science International: Reports, ISSN 2665-9107, Vol. 2, s. 100117-100117, artikel-id 100117Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Internet of Things (IoT) are becoming commonplace in homes, buildings, cities, and nations, and IoT networks are also getting more complex and interconnected. The complexity, interconnectivity, and heterogeneity of IoT systems, however, complicate digital (forensic) investigations. The challenge is compounded due to the lack of holistic and standardized approaches. Hence, building on the ISO/IEC 27043 international standard, we present a holistic digital forensic readiness (DFR) framework. We also qualitatively evaluate the utility of the proposed DFR framework.

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  • 326.
    Kebande, Victor R.
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Venter, H. S.
    CFRaaS: Architectural design of a Cloud Forensic Readiness as-a-Service Model using NMB solution as a forensic agent2019Ingår i: African Journal of Science, Technology, Innovation and Development (AJSTID), ISSN 2042-1338, E-ISSN 2042-1346, Vol. 11, nr 6, s. 749-769Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The proliferation of cloud resources among organizations has had numerous benefits with regard to how business processes are conducted. However, despite the benefits, the cloud has not been very resilient due to how it is distributed and its open nature. Due to this, there have been numerous reports on how the security of organizational information has been compromised. In any organization, Digital Forensic Readiness (DFR) is employed as a pre-incident phase whose aim is to maximize the use of Potential Digital Evidence (PDE) while minimizing the cost of performing a Digital Forensic Investigation (DFI). Therefore, it is on this premise that this paper makes a contribution to the architectural design of a Cloud Forensic Readiness as-a-Service (CFRaaS) that uses a Non-Malicious Botnet (NMB) solution as a forensic agent. The authors argue that the architectural design of a CFRaaS is an important aspect, which brings out the requirements that are needed in order for the cloud to be forensically ready for digital investigations when a modified NMB acting as an Agent-Based Solution (ABS) is used. To support this claim, the authors have identified important dependencies and indicators that will provide a synergistic relationship while coming up with CFRaaS design decisions. The main objective of this paper is to present the requirements, design and implementation for achieving DFR in the cloud using a CFRaaS. This study complies with the ISO/IEC 27043: 2015 international standard which presents guidelines for Information Technology, Security Techniques and Incident Investigation Principles and Processes. The result of the study has indicated that it is possible to achieve DFR in the cloud environment using a botnet with modified functionalities.

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  • 327.
    Khorashadizadeh, Saeed
    et al.
    Univ Teknol Malaysia, Fac Comp, Skudai, Malaysia..
    Ikuesan, Adeyemi Richard
    Community Coll Qatar, Sch Informat Technol, Dept Cybersecur & Networking, Doha, Qatar..
    Kebande, Victor R.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Generic 5G Infrastructure for IoT Ecosystem2020Ingår i: Emerging Trends in Intelligent Computing and Informatics: Data Science, Intelligent Information Systems and Smart Computing / [ed] Saeed, F Mohammed, F Gazem, N, Springer, 2020, s. 451-462Konferensbidrag (Refereegranskat)
    Abstract [en]

    While the Internet of Things (IoT) is still gaining rapid adoption in an upward trajectory means across many smart areas in recent years, still, there is a need to develop a scalable ecosystem that is able to support future IoT implementations, given the heterogeneity and increased information flow among IoT devices. The lack of effective interoperability, availability, reliability, and performance in IoT are a few challenges that hinder the effectiveness of IoT deployment and communication, and this has acted as a stumbling block for the optimisation of IoT platforms. That notwithstanding, the advent of the Fifth Generation (5G) networks, has seen a significant shift on how the IoT-paradigm operates. This article introduces a discussion on the generic 5G infrastructure for IoT environment that can support future deployments. The article begins by conducting a technical review of the evolution of the First generation (1G) through 5G cellular networks. Thereafter, an illustration of the Fourth Generation (4G) architecture, stating its features as a precedent that shows to what extent 5G network may thrive or support IoT ecosystems, is given. Furthermore, the paper explores both the architectural requirements and futuristic vision of 5G infrastructure in the perspective of IoT applications. Most importantly, the paper has also explored the IoT market share and forecast on growth and how it affects different industrial sectors. The authors believe that the conclusions that have been made in this paper will act as a pacesetter and give a direction worth exploring once the 5G infrastructure for IoT ecosystems is implemented.

  • 328.
    Khoshkangini, Reza
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Mashhadi, Peyman
    Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Sweden.
    Tegnered, Daniel
    Volvo Group Connected Solutions, Gothenburg, Sweden.
    Lundström, Jens
    Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Sweden.
    Rögnvaldsson, Thorsteinn
    Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Sweden.
    Predicting Vehicle Behavior Using Multi-task Ensemble Learning2023Ingår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 212, s. 118716-118716, artikel-id 118716Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Vehicle utilization analysis is an essential tool for manufacturers to understand customer needs, improve equipment uptime, and to collect information for future vehicle and service development. Typically today, this behavioral modeling is done on high-resolution time-resolved data with features such as GPS position and fuel consumption. However, high-resolution data is costly to transfer and sensitive from a privacy perspective. Therefore, such data is typically only collected when the customer pays for extra services relying on that data. This motivated us to develop a multi-task ensemble approach to transfer knowledge from the high-resolution data and enable vehicle behavior prediction from low-resolution but high dimensional data that is aggregated over time in the vehicles. This study proposes a multi-task snapshot-stacked ensemble (MTSSE) deep neural network for vehicle behavior prediction by considering vehicles’ low-resolution operational life records. The multi-task ensemble approach utilizes the measurements to map the low-frequency vehicle usage to the vehicle behaviors defined from the high-resolution time-resolved data. Two data sources are integrated and used: high-resolution data called Dynafleet, and low-resolution so-called Logged Vehicle Data (LVD). The experimental results demonstrate the proposed approach’s effectiveness in predicting the vehicle behavior from low frequency data. With the suggested multi-task snapshot-stacked ensemble deep network, it is shown how low-resolution sensor data can highly contribute to predicting multiple vehicle behaviors simultaneously while using only one single training process.

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  • 329.
    Khoshkangini, Reza
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Rani Kalia, Nidhi
    Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Halmstad, Sweden.
    Ashwathanarayana, Sachin
    Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Halmstad, Sweden.
    Orand, Abbas
    Arriver Software AB, a Qualcomm Company, Linköping, Sweden.
    Maktobian, Jamal
    Information and Communication Technology, University of Tasmania, Hobart, Tasmania, Australia.
    Tajgardan, Mohsen
    Faculty of Electrical and Computer Engineering Qom University of Technology, Qom University.
    Vehicle Usage Extraction Using Unsupervised Ensemble Approach2022Ingår i: Proceedings of SAI Intelligent Systems Conference, Springer, 2022, s. 588-604Konferensbidrag (Refereegranskat)
    Abstract [en]

    Current heavy vehicles are equipped with hundreds of sensors that are used to continuously collect data in motion. The logged data enables researchers and industries to address three main transportation issues related to performance (e.g. fuel consumption, breakdown), environment (e.g., emission reduction), and safety (e.g. reducing vehicle accidents and incidents during maintenance activities). While according to the American Transportation Research Institute (ATRI), the operational cost of heavy vehicles is around 59%59% of overall costs, there are limited studies demonstrating the specific impacts of external factors (e.g. weather and road conditions, driver behavior) on vehicle performance. In this work, vehicle usage modeling was studied based on time to determine the different usage styles of vehicles and how they can affect vehicle performance. An ensemble clustering approach was developed to extract vehicle usage patterns and vehicle performance taking into consideration logged vehicle data (LVD) over time. Analysis results showed a strong correlation between driver behavior and vehicle performance that would require further investigation.

  • 330.
    Khoshkangini, Reza
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP). Halmstad Univ, Ctr Appl Intelligent Syst Res CAISR, S-30118 Halmstad, Sweden..
    Tajgardan, Mohsen
    Qom Univ Technol, Fac Elect & Comp Engn, Qom 151937195, Iran..
    Lundström, Jens
    Halmstad Univ, Ctr Appl Intelligent Syst Res CAISR, S-30118 Halmstad, Sweden..
    Rabbani, Mahdi
    Univ New Brunswick UNB, Canadian Inst Cybersecur CIC, Fredericton, NB E3B 9W4, Canada..
    Tegnered, Daniel
    Volvo Grp Connected Solut, S-41756 Gothenburg, Sweden..
    A Snapshot-Stacked Ensemble and Optimization Approach for Vehicle Breakdown Prediction2023Ingår i: Sensors, E-ISSN 1424-8220, Vol. 23, nr 12, artikel-id 5621Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Predicting breakdowns is becoming one of the main goals for vehicle manufacturers so as to better allocate resources, and to reduce costs and safety issues. At the core of the utilization of vehicle sensors is the fact that early detection of anomalies facilitates the prediction of potential breakdown issues, which, if otherwise undetected, could lead to breakdowns and warranty claims. However, the making of such predictions is too complex a challenge to solve using simple predictive models. The strength of heuristic optimization techniques in solving np-hard problems, and the recent success of ensemble approaches to various modeling problems, motivated us to investigate a hybrid optimization- and ensemble-based approach to tackle the complex task. In this study, we propose a snapshot-stacked ensemble deep neural network (SSED) approach to predict vehicle claims (in this study, we refer to a claim as being a breakdown or a fault) by considering vehicle operational life records. The approach includes three main modules: Data pre-processing, Dimensionality Reduction, and Ensemble Learning. The first module is developed to run a set of practices to integrate various sources of data, extract hidden information and segment the data into different time windows. In the second module, the most informative measurements to represent vehicle usage are selected through an adapted heuristic optimization approach. Finally, in the last module, the ensemble machine learning approach utilizes the selected measurements to map the vehicle usage to the breakdowns for the prediction. The proposed approach integrates, and uses, the following two sources of data, collected from thousands of heavy-duty trucks: Logged Vehicle Data (LVD) and Warranty Claim Data (WCD). The experimental results confirm the proposed system's effectiveness in predicting vehicle breakdowns. By adapting the optimization and snapshot-stacked ensemble deep networks, we demonstrate how sensor data, in the form of vehicle usage history, contributes to claim predictions. The experimental evaluation of the system on other application domains also indicated the generality of the proposed approach.

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  • 331. Klinger, Ulrike
    et al.
    Svensson, Jakob
    Malmö universitet, Data Society. Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    The Power of Code: Women and the making of the digital world2023Ingår i: Women in the Digital World, Routledge, 2023Kapitel i bok, del av antologi (Refereegranskat)
  • 332. Klugl, Franziska
    et al.
    Davidsson, Paul
    Malmö högskola, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap (DV). Malmö högskola, Internet of Things and People (IOTAP).
    AMASON: Abstract Meta-model for Agent-based Simulation2013Ingår i: Multiagent System Technologies, Springer, 2013, s. 101-114Konferensbidrag (Refereegranskat)
    Abstract [en]

    The basic prerequisite for methodological advance in Multi-Agent Based Modelling and Simulation is a clear, ideally formally-grounded, concept of our subject. A commonly accepted, implementation-independent meta-model may improve the status of MABS as a scientific field providing a solid foundation that can be used for describing, comparing, analysing, and understanding MABS models. In this contribution, we present an attempt formalizing a general view of MABS models by defining the AMASON meta-model that captures the basic structure and dynamics of a MABS model.

  • 333. Knapen, Luk
    et al.
    Holmgren, Johan
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Identifying bicycle trip impediments by data fusion2020Ingår i: Procedia Computer Science, E-ISSN 1877-0509, Vol. 170, s. 195-202Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A set of GPS traces for bicyclists and a set of notifications by bicyclists of problematic situations (spots identified by GPS records) had been collected independently. The data collection periods did not coincide but overlapped and none was contained in the other one. The aim is to use both datasets to determine an optimal action plan for problem solving given a limited budget. First, problematic locations are clustered. Each cluster corresponds to an impediment. Impediments are then associated with trips using a distance function. The aim is to find out which impediments to solve under a given budget constraint in order to maximize the number of impediment free trips. Thereto the trip set is partitioned by matching each trip with the largest set of its affecting impediments. Solving all impediments in such set induces a cost and makes the associated part of trips impediment free. An optimizer is presented and evaluated. (C) 2020 The Authors. Published by Elsevier B.V.

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  • 334.
    Knapen, Luk
    et al.
    Hasselt University, Belgium.; VU Amsterdam, Netherlands..
    Holmgren, Johan
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Optimal bicycle trip impediments resolution by data fusion2021Ingår i: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145, Vol. 12, s. 103-120Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We propose a method, whose purpose is to combine a set of GPS traces collected by bicyclists with a set of notifications of problematic situations to determine an optimal action plan for solving safety related problems in a traffic network. In particular, we use optimization to determine which problem locations to resolve under a given budget constraint in order to maximize the number of impediment free trips. The method aims to suggest a priority of impediments to resolve, which would be manually infeasible. The proposed method consists of two steps. First, problematic locations are clustered, where each cluster corresponds to a so-called impediment. Each impediment is associated with trips nearby using a distance function. The trip set is partitioned by matching each trip with the largest set of its affecting impediments. Solving all impediments associated with such a part induces a cost and makes the associated part of trips impediment free. The second step aims to find the set of impediments that can be solved with a given budget and that makes the maximum number of trips impediment free. A branch-and-bound optimizer for the second step is presented and evaluated. The clustering parameters affect the set of identified impediments and the extent of each of them. In order to evaluate the sensitivity of the result to the clustering parameters a technique is proposed to consistently estimate the impediment resolution cost. Our study aims to support the interactive urban designer to improve the urban bicycle road infrastructure. By providing a method to prioritize between impediments to resolve, it also aims to contribute to a safer and more attractive traffic situation for bicyclists.

  • 335.
    Koch Svedberg, Gion
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Peters, Anne-Kathrin
    Uppsala universitet, Institutionen för informationsteknologi.
    Om utbildningens möjlighet att förändra den mansdominerade teknikkulturen.2021Ingår i: Genus och professioner / [ed] Cecilia Franzén; Despina Tzimoula, Lund: Studentlitteratur AB, 2021, 1:1, s. 223-241Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [sv]

    Vi har utvecklat en förenklad systemmodell för att visa på de komplexa sambanden mellan teknik, samhälle och maskuliniteter, som de beskrivs i litteraturen. Sambanden illustreras genom empiriskt material från en studie om högre utbildning inom datavetenskap. Utifrån systemmodellen diskuterar vi slutligen universitetens möjligheter att förändra den mansdominerade teknikkulturen.

  • 336.
    Kock, Elina
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Sarwari, Yamma
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Russo, Nancy
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Johnsson, Magnus
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). AI Research AB, Sweden.
    Identifying cheating behaviour with machine learning2021Ingår i: 33rd Workshop of the Swedish Artificial Intelligence Society, SAIS 2021, Institute of Electrical and Electronics Engineers Inc. , 2021Konferensbidrag (Refereegranskat)
    Abstract [en]

    We have investigated machine learning based cheating behaviour detection in physical activity-based smart-phone games. Sensor data were acquired from the accelerometer/gyroscope of an iPhone 7 during activities such as jumping, squatting, stomping, and their cheating counterparts. Selected attributes providing the most information gain were used together with a sequential model yielding promising results in detecting fake activities. Even better results were achieved by employing a random forest classifier. The results suggest that machine learning is a strong candidate for detecting cheating behaviours in physical activity-based smartphone games.

  • 337.
    Kong, Tianjiao
    et al.
    College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; Key Laboratory of Underwater Acoustic Signal Processing, Ministry of Education, Southeast University, Nanjing 210096, China.
    Shao, Jie
    College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; Key Laboratory of Underwater Acoustic Signal Processing, Ministry of Education, Southeast University, Nanjing 210096, China.
    Hu, Jiuyuan
    College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; Key Laboratory of Underwater Acoustic Signal Processing, Ministry of Education, Southeast University, Nanjing 210096, China.
    Yang, Xin
    College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; Key Laboratory of Underwater Acoustic Signal Processing, Ministry of Education, Southeast University, Nanjing 210096, China.
    Yang, Shiyiling
    College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; Key Laboratory of Underwater Acoustic Signal Processing, Ministry of Education, Southeast University, Nanjing 210096, China.
    Malekian, Reza
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    EEG-Based Emotion Recognition Using an Improved Weighted Horizontal Visibility Graph2021Ingår i: Sensors, E-ISSN 1424-8220, Vol. 21, nr 5, artikel-id 1870Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Emotion recognition, as a challenging and active research area, has received considerable awareness in recent years. In this study, an attempt was made to extract complex network features from electroencephalogram (EEG) signals for emotion recognition. We proposed a novel method of constructing forward weighted horizontal visibility graphs (FWHVG) and backward weighted horizontal visibility graphs (BWHVG) based on angle measurement. The two types of complex networks were used to extract network features. Then, the two feature matrices were fused into a single feature matrix to classify EEG signals. The average emotion recognition accuracies based on complex network features of proposed method in the valence and arousal dimension were 97.53% and 97.75%. The proposed method achieved classification accuracies of 98.12% and 98.06% for valence and arousal when combined with time-domain features.

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  • 338. Krzysztof, Okarma
    et al.
    Darius, Andriukaitis
    Malekian, Reza
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Sensors in Intelligent Transportation Systems2019Ingår i: Journal of Advanced Transportation, ISSN 0197-6729, E-ISSN 2042-3195, Vol. 2019, s. 1-2, artikel-id 7108126Artikel i tidskrift (Övrigt vetenskapligt)
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  • 339.
    Kurasinski, Lukas
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Mihailescu, Radu-Casian
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Towards Machine Learning Explainability in Text Classification for Fake News Detection2020Ingår i: 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, 2020Konferensbidrag (Refereegranskat)
    Abstract [en]

    The digital media landscape has been exposed in recent years to an increasing number of deliberately misleading news and disinformation campaigns, a phenomenon popularly referred as fake news. In an effort to combat the dissemination of fake news, designing machine learning models that can classify text as fake or not has become an active line of research. While new models are continuously being developed, the focus so far has mainly been aimed at improving the accuracy of the models for given datasets. Hence, there is little research done in the direction of explainability of the deep learning (DL) models constructed for the task of fake news detection.In order to add a level of explainability, several aspects have to be taken into consideration. For instance, the pre-processing phase, or the length and complexity of the text play an important role in achieving a successful classification. These aspects need to be considered in conjunction with the model's architecture. All of these issues are addressed and analyzed in this paper. Visualizations are further employed to grasp a better understanding how different models distribute their attention when classifying fake news texts. In addition, statistical data is gathered to deepen the analysis and to provide insights with respect to the model's interpretability.

  • 340.
    Kurasinski, Lukas
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Tan, Jason
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Malekian, Reza
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Using Neural Networks to Detect Fire from Overhead Images2023Ingår i: Wireless personal communications, ISSN 0929-6212, E-ISSN 1572-834X, Vol. 130, nr 2, s. 1085-1105Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The use of artificial intelligence (AI) is increasing in our everyday applications. One emerging field within AI is image recognition. Research that has been devoted to predicting fires involves predicting its behaviour. That is, how the fire will spread based on environmental key factors such as moisture, weather condition, and human presence. The result of correctly predicting fire spread can help firefighters to minimise the damage, deciding on possible actions, as well as allocating personnel effectively in potentially fire prone areas to extinguish fires quickly. Using neural networks (NN) for active fire detection has proven to be exceptional in classifying smoke and being able to separate it from similar patterns such as clouds, ground, dust, and ocean. Recent advances in fire detection using NN has proved that aerial imagery including drones as well as satellites has provided great results in detecting and classifying fires. These systems are computationally heavy and require a tremendous amount of data. A NN model is inextricably linked to the dataset on which it is trained. The cornerstone of this study is based on the data dependencieds of these models. The model herein is trained on two separate datasets and tested on three dataset in total in order to investigate the data dependency. When validating the model on their own datasets the model reached an accuracy of 92% respectively 99%. In comparison to previous work where an accuracy of 94% was reached. During evaluation of separate datasets, the model performed around the 60% range in 5 out of 6 cases, with the outlier of 29% in one of the cases. 

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  • 341.
    Kvist, Jonathan
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Ekholm, Philip
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Vaidyanathan, Preethi
    Eyegaze Inc., USA.
    Bailey, Reynold
    Rochester Institute of Technology, USA.
    Alm, Cecilia Ovesdotter
    Rochester Institute of Technology, USA.
    Dynamic Visualization System for Gaze and Dialogue Data2020Ingår i: Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: HUCAPP, SciTePress, 2020, s. 138-145Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    We report and review a visualization system capable of displaying gaze and speech data elicited from pairs of subjects interacting in a discussion. We elicit such conversation data in our first experiment, where two participants are given the task of reaching a consensus about questions involving images. We validate the system in a second experiment where the purpose is to see if a person could determine which question had elicited a certain visualization. The visualization system allows users to explore reasoning behavior and participation during multimodal dialogue interactions.

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  • 342.
    Lagergren, Ebba
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Packmohr, Sven
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Data Society.
    Enhancing the Digital Transformation of Sports Arenas2022Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    Developments within digital technology are redefining how spectators will experience sport in the future. Combined with current crises, it creates new demands on how sports arenas can generate visitors to their events. An alternative can be virtual arenas. Therefore, this study aimed to understand the visitor’s expectations of a virtual arena and identify key factors that affected potential spectators’ intentions to visit a virtual arena. This qualitative study collected empirical data through focus groups. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) was used as a theoretical foundation for the analysis. This study results in an enhanced hypothetical model arguing for additional elements affecting a spectator’s intention to visit a virtual arena. Our research contributes to helping shape future research on and practical implementation of virtual arenas.

  • 343.
    Larchen Costuchen, Alexia
    et al.
    Universitat Politècnica de València, Camí de Vera, s/n, 46022 València.
    Font Fernández, José María
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Stavroukalis, Minos
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    AR-Supported Mind Palace for L2 Vocabulary Recall2022Ingår i: International Journal: Emerging Technologies in Learning, ISSN 1868-8799, E-ISSN 1863-0383, Vol. 17, nr 13, s. 47-63Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    MnemoRoom4U is an AR (Augmented Reality) tool that uses a memory-palace strategy for foreign-language training. A memory palace helps information recall with the aid of object association in visualizations of familiar spatial surroundings. In MnemoRoom4U, paper or digital flashcards are re-placed with virtual notes containing L1 words and their L2 translations that are placed on top of real physical objects inside a familiar environment, such as one’s room, home, office space, etc. The AR-supported notes aid associative memory by establishing a relationship between the physical objects in the user’s mind and the virtual lexis to be retained in L2. Learners first set up a path through their familiar environment, attaching virtual sticky notes—each containing a target word to be memorized together with its corresponding source-language translation—to real-life objects (e.g. furniture in their homes or offices). They then take the same path again, reviewing all the words, and finally carry out a retention test. MnemoRoom4U is a technological artifact designed for specific didactic purposes in the Unity game engine with the ARCore augmented-reality plug-in for Android. This work takes a Design-Science approach with phenomenological, exploratory underpinnings tracking back to the efficiency of spatial mnemonics previously reported quantitatively and combines it with AR technology to effect L2 vocabulary recall.

  • 344.
    Larsson, Andreas
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Ekblad, Jonas
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Alvarez, Alberto
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Font, Jose
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    A Comparative UX Analysis between Tabletop Games and their Digital Counterparts2020Ingår i: Extended Abstracts of the 2020 Annual Symposium on Computer-Human Interaction in Play, Association for Computing Machinery (ACM), 2020, s. 301-305Konferensbidrag (Refereegranskat)
    Abstract [en]

    As tabletop games are ported to digital versions to increase their accessibility, the expected User Experience (UX) might be degraded in the transition. This paper aims to understand how and why playing tabletop games differentiates depending on the platform. Seven tabletop games have been chosen from different genres with an official digital adaptation. Our approach has been to do a comparative analysis of both versions followed by a user study to analyze and measure the UX differences, measuring five key factors, Usability, Engagement, Social Connectivity, Aesthetics, and Enjoyment. Our results indicate that games that rely on imperfect information offer a much higher social connectivity and engagement when played around a table. Meanwhile, games relying on tile-placement offers higher usability and engagement when played digitally due to the assistance provided by the game. However, the physical versions got, in general, a higher rating than the digital versions in all key factors except slightly in the usability. Physical versions are the preferred options, but the digital versions' benefits, such as accessibility and in-game assistance, makes them relevant for further analysis.  

  • 345.
    Larsson, N. Jesper
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Run-Length Encoding in a Finite Universe2019Ingår i: String Processing and Information Retrieval: 26th International Symposium, SPIRE 2019, Segovia, Spain, October 7–9, 2019, Proceedings, Springer, 2019, s. 355-371Konferensbidrag (Refereegranskat)
    Abstract [en]

    Text compression schemes and succinct data structures usually combine sophisticated probability modes with basic coding methods whose average codeword length closely match the entropy of known distributions. In the frequent case where basic coding represents run-lengths of outcomes with probability p, i.e. geometric distribution Pr(i)=pⁱ(1-p), a Golomb code is an optimal instantaneous code, which has the additional advantage that codewords can be computed using only an integer parameter calculated from p, without need for a large or sophisticated data structure. Golomb coding does not, however, gracefully handle the case where run-lengths are bounded by a known integer n, where codewords allocated for the case i>n are wasted. While negligible for large n, this makes Golomb coding unattractive in situations where n is recurrently small, e.g., when representing many short lists, or when the range of n is narrowed down by a recursive algorithm.

  • 346.
    Larsson, Tinea
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Font, Jose
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Alvarez, Alberto
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Towards AI as a Creative Colleague in Game Level Design2022Ingår i: Proceedings of the 18th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AAAI Press, 2022Konferensbidrag (Refereegranskat)
    Abstract [en]

    In Mixed-Initiative Co-Creative tools, the human is mostly in control of what will and can be created, delegating the AI to a more suggestive role instead of a colleague in the co-creative process. Allowing more control and agency for the AI might be an interesting path in co-creative scenarios where AI could direct and take more initiative within the co-creative task. However, the relationship between AI and human designers in creative processes is delicate, as adjusting the initiative or agency of the AI can negatively affect the user experience. In this paper, different degrees of agency for the AI are explored within the Evolutionary Dungeon Designer (EDD) to further understand MI-CC tools. A user study was performed using EDD with three varying degrees of AI agency. The study highlighted elements of frustration that the human designer experiences when using the tool and the behavior in the AI that led to possible strains on the relationship. The paper concludes with the identified issues and possible solutions and suggested further research.

  • 347.
    Leckner, Sara
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Delning av invånardata från ett användarperspektiv2022Ingår i: Den (ut)forskande staden: En FoU-innovation i offentlig sektor / [ed] Anna-Karin Bergman; Magnus Andenskog, Helsingborg: FoU Helsingborg , 2022, 1, s. 193-217Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [sv]

    Som ett av fem hypoteslabb i projektet ”Den (ut)forskande staden” kartlade projektet Datalabbet förutsättningarna för att samköra stora datamängder invånardata från Helsingborgs stads olika förvaltningar och bolag under åren 2020–2022. Labbet har utgått från hypotesen att genom avancerad analys av samkörda data, bland annat med hjälp av artificiell intelligens, kan kommunen bättre förstå invånarnas varierande och komplexa behov och ta fram värdeskapande offentliga tjänster som bättre kan tillgodose dessa behov och öka invånarnas livskvalitet.

    Undersökningsmässigt har Datalabbet bestått av två delar: en del som undersökt förutsättningarna för delning av data från ett juridiskt perspektiv och en del som undersökt det utifrån ett användarperspektiv, som presenteras i det här kapitlet. Kapitlet bygger på resultat från en enkätstudie riktad till invånarna i Helsingborgs stad och deras inställning till kommunens hantering av invånardata och användning av datadriven teknologi. Inledningsvis beskrivs Datalabbets övergripande syfte och utmaningen med samkörning av invånardata. Därefter presenteras labbets upplägg och arbetsprocess med tyngdpunkt på användarperspektivet och enkätstudien. I efterföljande avsnitt presenteras resultat och analys av enkätundersökningen. Kapitlet avslutas med en sammanfattning inklusive rekommendationer till kommunen.

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  • 348.
    Leckner, Sara
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Ett etiskt perspektiv på datadelning: Kommuninvånarnas inställning till delning, hantering och användning av personliga data2023Ingår i: Kommunala myndigheters delning av invånardata: Slutrapport från Datalabbet i Helsingborgs stad / [ed] S. Leckner, J. Ledendal & A. Nilsson, Helsingborg: Avdelningen för innovation och transformation , 2023, s. 18-43Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [sv]

    Studien är en del i ett tvåårigt projekt som undersökt invånarna i Helsingborgs stads inställning till kommunens nuvarande och önskade dataanvändning och digitaliseringsarbete, specifikt vad rör AI­baseradsamkörning av förvaltningarnas invånardata. Målet har varit att ge Helsingborgs stad, liksom andra kommunder i Sverige, underlag för hur datadriven teknik kan användas på ett etiskt gott sätt, utifrån invånarnas perspektiv.

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    Leckner_Ett etiskt perspektiv på datadelning_2023
  • 349.
    Leckner, Sara
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Nya innovationer och gammal teknik: Att studera och förstå medieteknologisk utveckling2023Ingår i: Tekniska mediestudier: En introduktion till metoder och teknologier / [ed] Martin Berg, Maria Engberg & Sara Leckner, Lund: Studentlitteratur AB, 2023, 1, s. 27-53Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [sv]

    Vad är nytt med nya framväxande medieteknologier? Hur och när blir ny medieteknik gammal? Och går teknikutvecklingen verkligen så snabbt som vi upplever att den gör? Det är några av de frågor som vi kommeratt titta närmare på i det här kapitlet. Syftet är att du ska få inblick ihur du kan förstå och studera medieteknologisk utveckling på ett adekvat sätt. Efter att ha läst kapitlet kommer du kunna ta dig an frågor somrör medieteknologiers interaktion – konkurrens och samexistens – med varandra över tid, samt kritiskt kunna förhålla dig till de möjligheter och utmaningar som finns med nya innovationer och gammal teknik.

  • 350.
    Leckner, Sara
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Sceptics and supporters of corporate use of behavioural data: Attitudes towards informational privacy and Internet surveillance in Sweden2018Ingår i: Northern Lights, ISSN 1601-829X, E-ISSN 2040-0586, Vol. 1, nr 16, s. 113-132Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    With the growing use of the Internet, companies are increasingly collecting and using personal data for commodifying purposes, resulting in both benefits and privacy risks for users and raising the issue of corporate surveillance. The present article investigates people’s attitudes towards corporate collection of personal data, discusses possible reasons for attitude results in relation to self-regulation, trust and media context, and compares these findings with results from the previous year. The study is based on a survey using a large-n probability sample of the Swedish population. The results are in accordance with the suggested privacy paradox: the majority of the population, as in the previous year, have negative attitudes towards corporate collection of their data, largely independent of context. Nonetheless, they continue to share their data without making any great effort to secure their privacy. Whether this depends on inexperience, ignorance or resignation, everyday corporate surveillance does not meet expectations regarding the just governing of informational privacy. As the results showed that the more positive people were towards sharing their data in various contexts, the more positively this affected their attitudes towards the fact that the data were being used by the collecting companies for various purposes, balancing out the power differences online would benefit not only users but also companies to a great extent.

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