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  • 1.
    Adewumi, Tosin
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Foteini
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    State-of-the-Art in Open-Domain Conversational AI: A Survey2022In: Information, E-ISSN 2078-2489, Vol. 13, no 6, article id 298Article, review/survey (Refereed)
    Abstract [en]

    We survey SoTA open-domain conversational AI models with the objective of presenting the prevailing challenges that still exist to spur future research. In addition, we provide statistics on the gender of conversational AI in order to guide the ethics discussion surrounding the issue. Open-domain conversational AI models are known to have several challenges, including bland, repetitive responses and performance degradation when prompted with figurative language, among others. First, we provide some background by discussing some topics of interest in conversational AI. We then discuss the method applied to the two investigations carried out that make up this study. The first investigation involves a search for recent SoTA open-domain conversational AI models, while the second involves the search for 100 conversational AI to assess their gender. Results of the survey show that progress has been made with recent SoTA conversational AI, but there are still persistent challenges that need to be solved, and the female gender is more common than the male for conversational AI. One main takeaway is that hybrid models of conversational AI offer more advantages than any single architecture. The key contributions of this survey are (1) the identification of prevailing challenges in SoTA open-domain conversational AI, (2) the rarely held discussion on open-domain conversational AI for low-resource languages, and (3) the discussion about the ethics surrounding the gender of conversational AI.

  • 2.
    Lemlouma, T.
    et al.
    University of Rennes 1, Rennes, France.
    Laborie, S.
    University of Pau and Adour Countries, Anglet, France.
    Rachedi, A.
    University of Paris-Est Marne la Vallée, Champs-sur-Marne, France.
    Santos, A.
    Vestas, Leça do Balio, Porto, Portugal.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Special issue on selected papers from e-health pervasive wireless applications and services 20172019In: Information, E-ISSN 2078-2489, Vol. 10, no 2, article id 52Article in journal (Other academic)
  • 3.
    Padyab, Ali Mohammad
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems. School of Informatics, University of Skövde, Skövde.
    Habibipour, Abdolrasoul
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems.
    Rizk, Aya
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems.
    Ståhlbröst, Anna
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems.
    Adoption Barriers of IoT in Large Scale Pilots2020In: Information, E-ISSN 2078-2489, Vol. 11, no 23, p. 1-23Article in journal (Refereed)
    Abstract [en]

    The pervasive connectivity of devices enabled by Internet of Things (IoT) technologies is leading the way in various innovative services and applications. This increasing connectivity comes with its own complexity. Thus, large scale pilots (LSPs) are designed to develop, test and use IoT innovations in various domains in conditions very similar to their operational scalable setting. One of the key challenges facing the diffusion of such innovations within the course of an LSP is understanding the conditions in which their respective users decide to adopt them (or not). Accordingly, in this study we explore IoT adoption barriers in four LSPs in Europe from the following domains: smart cities, autonomous driving, wearables and smart agriculture and farming. By applying Roger’s Diffusion of Innovation as a theoretical lens and using empirical data from workshops and expert interviews, we identify a set of common and domain specific adoption barriers. Our results reveal that trust, cost, perceived value, privacy and security are common concerns, yet shape differently across domains. In order to overcome various barriers, the relative advantage or value of using the innovation needs to be clearly communicated and related to the users’ situational use; while this value can be economic in some domains, it is more hedonic in others. LSPs were particularly challenged in applying established strategies to overcome some of those barriers (e.g., co-creation with end-users) due to the immaturity of the technology as well as the scale of pilots. Accordingly, we reflect on the theoretical choice in the discussion as well as the implications of this study on research and practice. We conclude with providing practical recommendations to LSPs and avenues for future research

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    fulltext
  • 4.
    Rouchitsas, Alexandros
    et al.
    Luleå University of Technology, Department of Health, Education and Technology, Health, Medicine and Rehabilitation.
    Alm, Håkan
    Luleå University of Technology, Department of Health, Education and Technology, Health, Medicine and Rehabilitation.
    Ghost on the Windshield: Employing a Virtual Human Character to Communicate Pedestrian Acknowledgement and Vehicle Intention2022In: Information, E-ISSN 2078-2489, Vol. 13, no 9, article id 420Article in journal (Refereed)
    Abstract [en]

    Pedestrians base their street-crossing decisions on vehicle-centric as well as driver-centric cues. In the future, however, drivers of autonomous vehicles will be preoccupied with non-driving related activities and will thus be unable to provide pedestrians with relevant communicative cues. External human–machine interfaces (eHMIs) hold promise for filling the expected communication gap by providing information about a vehicle’s situational awareness and intention. In this paper, we present an eHMI concept that employs a virtual human character (VHC) to communicate pedestrian acknowledgement and vehicle intention (non-yielding; cruising; yielding). Pedestrian acknowledgement is communicated via gaze direction while vehicle intention is communicated via facial expression. The effectiveness of the proposed anthropomorphic eHMI concept was evaluated in the context of a monitor-based laboratory experiment where the participants performed a crossing intention task (self-paced, two-alternative forced choice) and their accuracy in making appropriate street-crossing decisions was measured. In each trial, they were first presented with a 3D animated sequence of a VHC (male; female) that either looked directly at them or clearly to their right while producing either an emotional (smile; angry expression; surprised expression), a conversational (nod; head shake), or a neutral (neutral expression; cheek puff) facial expression. Then, the participants were asked to imagine they were pedestrians intending to cross a one-way street at a random uncontrolled location when they saw an autonomous vehicle equipped with the eHMI approaching from the right and indicate via mouse click whether they would cross the street in front of the oncoming vehicle or not. An implementation of the proposed concept where non-yielding intention is communicated via the VHC producing either an angry expression, a surprised expression, or a head shake; cruising intention is communicated via the VHC puffing its cheeks; and yielding intention is communicated via the VHC nodding, was shown to be highly effective in ensuring the safety of a single pedestrian or even two co-located pedestrians without compromising traffic flow in either case. The implications for the development of intuitive, culture-transcending eHMIs that can support multiple pedestrians in parallel are discussed.

    Download full text (pdf)
    fulltext
  • 5.
    Zhou, Huiquan
    et al.
    Department of Social Work and Social Administration, Faculty of Social Sciences, The University of Hong Kong, Hong Kong.
    Luo, Hao
    Department of Social Work and Social Administration, Faculty of Social Sciences, The University of Hong Kong, Hong Kong.
    Lau, Kevin Ka-Lun
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Architecture and Water.
    Qian, Xingxing
    School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong.
    Ren, Chao
    Department of Architecture, Faculty of Architecture, The University of Hong Kong, Hong Kong.
    Chau, Puihing
    School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong.
    Predicting Emergency Department Utilization among Older Hong Kong Population in Hot Season: A Machine Learning Approach2022In: Information, E-ISSN 2078-2489, Vol. 13, no 9, article id 410Article in journal (Refereed)
    Abstract [en]

    Previous evidence suggests that temperature is associated with the number of emergency department (ED) visits. A predictive system for ED visits, which takes local temperature into account, is therefore needed. This study aimed to compare the predictive performance of various machine learning methods with traditional statistical methods based on temperature variables and develop a daily ED attendance rate predictive model for Hong Kong. We analyzed ED utilization among Hong Kong older adults in May to September from 2000 to 2016. A total of 103 potential predictors were derived from 1- to 14-day lag of ED attendance rate and meteorological and air quality indicators and 0-day lag of holiday indicator and month and day of week indicators. LASSO regression was used to identify the most predictive temperature variables. Decision tree regressor, support vector machine (SVM) regressor, and random forest regressor were trained on the selected optimal predictor combination. Deep neural network (DNN) and gated recurrent unit (GRU) models were performed on the extended predictor combination for the previous 14-day horizon. Maximum ambient temperature was identified as a better predictor in its own value than as an indicator defined by the cutoff. GRU achieved the best predictive accuracy. Deep learning methods, especially the GRU model, outperformed conventional machine learning methods and traditional statistical methods.

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