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  • 1.
    Erickson, Ingrid
    et al.
    School of Information Studies, Syracuse University.
    Lewkowicz, Myriam
    Université de Technologie de Troyes.
    Light, Ann
    Malmö universitet, Fakulteten för kultur och samhälle (KS), Institutionen för konst, kultur och kommunikation (K3). University of Sussex.
    Ciolfi, Luigina
    Sheffield Hallam University.
    Krischkowsky, Alina
    Center for Human-Computer Interaction, University of Salzburg .
    Muller, Michael
    AI Experiences, IBM Research.
    Envisioning Futures of Practice-Centered Computing2019Inngår i: Proceedings of the 17th European Conference on Computer-Supported Cooperative Work - Demos and Posters, European Society for Socially Embedded Technologies (EUSSET) , 2019Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    In this panel, we will engage with the conference's membership and friends to consider directions for the possible futures of practice-centered computing. This panel is not targeting or aiming to result in a single, agreed "universal” vision, nor to ask for a shared vision among the panelists and the audience. Rather, we offer several and diverse vision statements by distinguished and innovative ECSCW scholars, being experts in their specific domain or context of research. These statements will be necessarily incomplete until the ECSCW membership has joined the discussion, offering their own, additional visions of the futures of the field. With this, the panel aims to engage in a discussion that foresees exciting future research directions for the field of ECSCW but likewise also unveils potential hurdles the community might face.

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  • 2.
    Eriksson, Tom
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Känsloigenkänning och röstigenkänning i realtid gjort i en web-baserad videomötes-applikation2021Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgave
    Abstract [en]

    The world is undergoing a digitization process in the ways in which people communicate with each other. As a result of the Covid-19 pandemic, certain parts of this process have accelerated and it is possible to see increased use of meeting applications that facilitate communication where physical presence is not possible. Despite certain disadvantages, the digital meeting has an advantage, namely that it generates data. This data could be analyzed and visualized to the user in order to improve the interaction between the meeting participants, all during the conversation in real-time.

    The goal of this thesis is to explore how to build a web-based video conferencing application that can record and visualize meeting participants' facial emotions but also transcribe the conversation between meeting participants in real-time. Such an application has therefore been built and subsequently tested to examine whether the application meets the requirements in the definition of a real-time system. The test investigates the system's RTF (real time factor) by examining the time for recording facial emotions and speech utterances but also the time from recording to the data being rendered in the browser. The results show that the system's RTF for facial emotion recognition and automatic speech recognition is greater than 1 in all tests. Since the data is displayed to the user as soon as it is available but also within a reasonable time, the system can classifies as a real-time system. The conclusion to be drawn is that it is the described event-driven architecture that enables the system to achieve the requirements from the definition of a real-time system.

  • 3.
    Fredriksson, Teodor
    et al.
    Chalmers University of Technology, Hörselgången 11, 417 56, Gothenburg, Sweden.
    Mattos, David Issa
    Chalmers University of Technology, Hörselgången 11, 417 56, Gothenburg, Sweden.
    Bosch, Jan
    Chalmers University of Technology, Hörselgången 11, 417 56, Gothenburg, Sweden.
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Data Labeling: An Empirical Investigation into Industrial Challenges and Mitigation Strategies2020Inngår i: Product-Focused Software Process Improvement: 21st International Conference, PROFES 2020, Turin, Italy, November 25–27, 2020, Proceedings / [ed] Maurizio Morisio; Marco Torchiano; Andreas Jedlitschka, Springer, 2020, s. 202-216Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Labeling is a cornerstone of supervised machine learning. However, in industrial applications, data is often not labeled, which complicates using this data for machine learning. Although there are well-established labeling techniques such as crowdsourcing, active learning, and semi-supervised learning, these still do not provide accurate and reliable labels for every machine learning use case in the industry. In this context, the industry still relies heavily on manually annotating and labeling their data. This study investigates the challenges that companies experience when annotating and labeling their data. We performed a case study using a semi-structured interview with data scientists at two companies to explore their problems when labeling and annotating their data. This paper provides two contributions. We identify industry challenges in the labeling process, and then we propose mitigation strategies for these challenges.

  • 4.
    Gabrielsson, Jonas
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Zaki, Maria
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Exploring Food Waste in Private Households in Skåne2022Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [en]

    In 2020, 200 million children under the age of 5 were reported to be malnourished and between 720 and 811 million people around the world faced hunger. Yet, the global food production have the potential to feed every human being twice the amount required. So what is happening with all that food? 1.3 billion tonnes of the global food supply is wasted every year, which accounts for one third of the food produced. In Sweden, private households stand for 70% of the total waste. Food waste has been a problem for some time now. So, the goal with this study is to investigate reasons that contribute to this high food waste and suggest a solution or guidelines to prevent/reduce that in private households in the Skåne county. To explore the topic, academic literature were reviewed and Nine semi-structured interviews were conducted with the target group for this study, i.e., families living in Skåne county with children living at home and both parents working. Additionally, 103 responses were gathered through an online questionnaire from the same target group. 

    The findings revealed that families struggled with planning properly before they entered a grocery store, which meant that they ended up buying much more than they needed. Moreover, it was revealed that people had the tendency to get sidetracked during shopping. These practices, in most instances, resulted in double and over buying, and impulsive shopping, which meant that more food was going to waste without ever being consumed in their respective households. 

    With these findings in mind, we have hypothesized that online shopping has the potential to prevent food waste in private households, as well as created a design on how to get more people feeling comfortable doing grocery shopping online based on a human centred design approach. 

    To conclude this thesis, we define the contributing factors of household food waste and argue that food waste can be reduced by a significant amount if people are shopping online and are adhering to some sort of food budget to control their spendings. 

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  • 5.
    Johnsson, Magnus
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Perception, Imagery, Memory and Consciousness2022Inngår i: Filozofia i Nauka, E-ISSN 2545-1936, Vol. Zeszyt specjalny, nr 10, s. 229-244Artikkel i tidsskrift (Fagfellevurdert)
    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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  • 6.
    Johnsson, Magnus
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Perceptions, Imagery, Memory, and Consciousness in Man and Machine2022Inngår i: The 2021 Summit of the International Society for the Study of Information, MDPI, 2022, Vol. 81(1)Konferansepaper (Fagfellevurdert)
    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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  • 7.
    Liu, Yongshuang
    et al.
    College of Computer, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China; High Technology Research Key Laboratory of Wireless Sensor Network of Jiangsu Province, Nanjing, 210023, China.
    Huang, Haiping
    College of Computer, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China; High Technology Research Key Laboratory of Wireless Sensor Network of Jiangsu Province, Nanjing, 210023, China.
    Xiao, Fu
    College of Computer, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China; High Technology Research Key Laboratory of Wireless Sensor Network of Jiangsu Province, Nanjing, 210023, China.
    Malekian, Reza
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Wang, Wenming
    College of Computer, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China; High Technology Research Key Laboratory of Wireless Sensor Network of Jiangsu Province, Nanjing, 210023, China; School of Computer and Information, Anqing Normal University, Anqing, 246011, Anhui, China.
    Classification and recognition of encrypted EEG data based on neural network2020Inngår i: Journal of Information Security and Applications, ISSN 2214-2134, E-ISSN 2214-2126, Vol. 54, artikkel-id 102567Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    With the rapid development of Machine Learning technology applied in electroencephalography (EEG) signals, Brain-Computer Interface (BCI) has emerged as a novel and convenient human-computer interaction for smart home, intelligent medical and other Internet of Things (IoT) scenarios. However, security issues such as sensitive information disclosure and unauthorized operations have not received sufficient concerns. There are still some defects with the existing solutions to encrypted EEG data such as low accuracy, high time complexity or slow processing speed. For this reason, a classification and recognition method of encrypted EEG data based on neural network is proposed, which adopts Paillier encryption algorithm to encrypt EEG data and meanwhile resolves the problem of floating point operations. In addition, it improves traditional feed-forward neural network (FNN) by using the approximate function instead of activation function and realizes multi-classification of encrypted EEG data. Extensive experiments are conducted to explore the effect of several metrics (such as the hidden neuron size and the learning rate updated by improved simulated annealing algorithm) on the recognition results. Followed by security and time cost analysis, the proposed model and approach are validated and evaluated on public EEG datasets provided by PhysioNet, BCI Competition IV and EPILEPSIAE. The experimental results show that our proposal has the satisfactory accuracy, efficiency and feasibility compared with other solutions. (C) 2020 Elsevier Ltd. All rights reserved.

  • 8.
    Munappy, Aiswarya Raj
    et al.
    Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, 412 96, Gothenburg, Sweden.
    Bosch, Jan
    Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, 412 96, Gothenburg, Sweden.
    Olsson, Helena Homström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Data Pipeline Management in Practice: Challenges and Opportunities2020Inngår i: Product-Focused Software Process Improvement: 21st International Conference, PROFES 2020, Turin, Italy, November 25–27, 2020, Proceedings / [ed] Maurizio Morisio; Marco Torchiano; Andreas Jedlitschka, Springer, 2020, s. 168-184Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Data pipelines involve a complex chain of interconnected activities that starts with a data source and ends in a data sink. Data pipelines are important for data-driven organizations since a data pipeline can process data in multiple formats from distributed data sources with minimal human intervention, accelerate data life cycle activities, and enhance productivity in data-driven enterprises. However, there are challenges and opportunities in implementing data pipelines but practical industry experiences are seldom reported. The findings of this study are derived by conducting a qualitative multiple-case study and interviews with the representatives of three companies. The challenges include data quality issues, infrastructure maintenance problems, and organizational barriers. On the other hand, data pipelines are implemented to enable traceability, fault-tolerance, and reduce human errors through maximizing automation thereby producing high-quality data. Based on multiple-case study research with five use cases from three case companies, this paper identifies the key challenges and benefits associated with the implementation and use of data pipelines.

  • 9.
    Raj, Aiswarya
    et al.
    Chalmers Univ Technol, Gothenburg, Sweden..
    Bosch, Jan
    Chalmers Univ Technol, Gothenburg, Sweden..
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Wang, Tian J.
    Ericsson, Gothenburg, Sweden..
    Modelling Data Pipelines2020Inngår i: 2020 46TH EUROMICRO CONFERENCE ON SOFTWARE ENGINEERING AND ADVANCED APPLICATIONS (SEAA 2020) / [ed] Martini, A Wimmer, M Skavhaug, A, IEEE, 2020, s. 13-20Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Data is the new currency and key to success. However, collecting high-quality data from multiple distributed sources requires much effort. In addition, there are several other challenges involved while transporting data from its source to the destination. Data pipelines are implemented in order to increase the overall efficiency of data-flow from the source to the destination since it is automated and reduces the human involvement which is required otherwise. Despite existing research on ETL (Extract-Transform-Load) and ELT (Extract-Load-Transform) pipelines, the research on this topic is limited. ETL/ELT pipelines are abstract representations of the end-to-end data pipelines. To utilize the full potential of the data pipeline, we should understand the activities in it and how they are connected in an end-to-end data pipeline. This study gives an overview of how to design a conceptual model of data pipeline which can be further used as a language of communication between different data teams. Furthermore, it can be used for automation of monitoring, fault detection, mitigation and alarming at different steps of data pipeline.

  • 10.
    Tegen, Agnes
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Approaches to Interactive Online Machine Learning2020Licentiatavhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    With the Internet of Things paradigm, the data generated by the rapidly increasing number of connected devices lead to new possibilities, such as using machine learning for activity recognition in smart environments. However, it also introduces several challenges. The sensors of different devices might be of different types, making the fusion of data non-trivial. Moreover, the devices are often mobile, resulting in that data from a particular sensor is not always available, i.e. there is a need to handle data from a dynamic set of sensors. From a machine learning perspective, the data from the sensors arrives in a streaming fashion, i.e., online learning, as compared to many learning problems where a static dataset is assumed. Machine learning is in many cases a good approach for classification problems, but the performance is often linked to the quality of the data. Having a good data set to train a model can be an issue in general, due to the often costly process of annotating the data. With dynamic and heterogeneous data, annotation can be even more problematic, because of the ever-changing environment. This means that there might not be any, or a very small amount of, annotated data to train the model on at the start of learning, often referred to as the cold start problem.

    To be able to handle these issues, adaptive systems are needed. With adaptive we mean that the model is not static over time, but is updated if there for instance is a change in the environment. By including human-in-the-loop during the learning process, which we refer to as interactive machine learning, the input from users can be utilized to build the model. The type of input used is typically annotations of the data, i.e. user input in the form of correctly labelled data points. Generally, it is assumed that the user always provides correct labels in accordance with the chosen interactive learning strategy. In many real-world applications these assumptions are not realistic however, as users might provide incorrect labels or not provide labels at all in line with the chosen strategy.

    In this thesis we explore which interactive learning strategies are possible in the given scenario and how they affect performance, as well as the effect of machine learning algorithms on performance. We also study how a user who is not always reliable, i.e. that does not always provide a correct label when expected to, can affect performance. We propose a taxonomy of interactive online machine learning strategies and test how the different strategies affect performance through experiments on multiple datasets. The findings show that the overall best performing interactive learning strategy is one where the user provides labels when previous estimations have been incorrect, but that the best performing machine learning algorithm depends on the problem scenario. The experiments also show that a decreased reliability of the user leads to decreased performance, especially when there is a limited amount of labelled data.

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  • 11.
    Tegen, Agnes
    et al.
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö University.
    Davidsson, Paul
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Persson, Jan A.
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    A Taxonomy of Interactive Online Machine Learning Strategies2020Inngår i: ECML PKDD 2020: Machine Learning and Knowledge Discovery in Databases / [ed] Hutter F.; Kersting K.; Lijffijt J.; Valera I., Springer, 2020, s. 1-17Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In interactive machine learning, human users and learning algorithms work together in order to solve challenging learning problems, e.g. with limited or no annotated data or trust issues. As annotating data can be costly, it is important to minimize the amount of annotated data needed for training while still getting a high classification accuracy. This is done by attempting to select the most informative data instances for training, where the amount of instances is limited by a labelling budget. In an online learning setting, the decision of whether or not to select an instance for labelling has to be done on-the-fly, as the data arrives in a sequential order and is only valid for a limited time period. We present a taxonomy of interactive online machine learning strategies. An interactive learning strategy determines which instances to label in an unlabelled dataset. In the taxonomy we differentiate between interactive learning strategies when the computer controls the learning process (active learning) and those when human users control the learning process (machine teaching). We then make a distinction between what triggers the learning: active learning could be triggered by uncertainty, time, or randomly, whereas machine teaching could be triggered by errors, state changes, time, or factors related to the user. We also illustrate the taxonomy by implementing versions of the different strategies and performing experiments on a benchmark dataset as well as on a synthetically generated dataset. The results show that the choice of interactive learning strategy affects performance, especially in the beginning of the online learning process, when there is a limited amount of labelled data.

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  • 12.
    Tegen, Agnes
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP). Malmö University.
    Davidsson, Paul
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Persson, Jan A.
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Activity Recognition through Interactive Machine Learning in a Dynamic Sensor Setting2024Inngår i: Personal and Ubiquitous Computing, ISSN 1617-4909, E-ISSN 1617-4917, Vol. 28, nr 1, s. 273-286Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The advances in Internet of things lead to an increased number of devices generating and streaming data. These devices can be useful data sources for activity recognition by using machine learning. However, the set of available sensors may vary over time, e.g. due to mobility of the sensors and technical failures. Since the machine learning model uses the data streams from the sensors as input, it must be able to handle a varying number of input variables, i.e. that the feature space might change over time. Moreover, the labelled data necessary for the training is often costly to acquire. In active learning, the model is given a budget for requesting labels from an oracle, and aims to maximize accuracy by careful selection of what data instances to label. It is generally assumed that the role of the oracle only is to respond to queries and that it will always do so. In many real-world scenarios however, the oracle is a human user and the assumptions are simplifications that might not give a proper depiction of the setting. In this work we investigate different interactive machine learning strategies, out of which active learning is one, which explore the effects of an oracle that can be more proactive and factors that might influence a user to provide or withhold labels. We implement five interactive machine learning strategies as well as hybrid versions of them and evaluate them on two datasets. The results show that a more proactive user can improve the performance, especially when the user is influenced by the accuracy of earlier predictions. The experiments also highlight challenges related to evaluating performance when the set of classes is changing over time.

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