Emerging 5G and future networks (5GFNs) will change the mobile communication business ecosystem by introducing multi-service wireless applications with diverse specifications. These smart and future-oriented wireless networks are intended to address not only consumer-based smartphone applications, but also the needs of various vertical industry markets (e.g., education, energy, healthcare, transportation, manufacturing, agriculture, and so forth). 5GFNs are aimed at attaining economic value for all key stakeholders, including customers, mobile network operators (MNOs), equipment vendors, public institutions, private enterprises, digital business start-ups and various third parties. This paper discusses business model options for 5GFNs from the MNOs’ perspective.  We describe current MNOs’ business models and their shortcomings. Thereafter, we present emerging business models for 5GFNs which MNOs should consider when rolling out these networks.  

Advancements in technology, such as smartphones and wearabledevices, can be used for collecting movement data through embeddedsensors. This paper focuses on linking Parkinson’s Disease severitywith data collected from mobile phones in the mPower study. As referencefor symptoms’ severity, we use the answers provided to part 2 ofthe standard MDS-UPDRS scale. As input variables, we use the featurescomputed within mPower from the raw data collected during 4 phonebasedactivities: walking, rest, voice and finger tapping. The features arefiltered in order to remove unreliable datapoints and associated to referencevalues. After pre-processing, 5 Machine Learning algorithms areapplied for predictive analysis. We show that, notwithstanding the noisedue to the data being collected in an uncontrolled manner, the regressedsymptom levels are moderately to strongly correlated with the actualvalues (highest Pearson’s correlation = 0.6). However, the high differencebetween the values also implies that the regressed values can not beconsidered as a substitute for a conventional clinical assessment (lowestmean absolute error = 5.4).

For acute medical conditions, for instance strokes, the time until the start of the treatment is a crucial factor to prevent a fatal outcome and to facilitate the recovery of the patient’s health. Hence, the planning and optimization of patient logistics is of high importance to ensure prompt access to healthcare facilities in case of medical emergencies. Computer simulation can be used to investigate the effects of different stroke logistics policies under realistic conditions without jeopardizing the health of the patients. The success of such policies greatly depends on the behavior of the individuals. Hence, agent-based simulation is particularly well-suited as it imitates human behavior and decision-making by means of artificial intelligence, which allows for investigating the effects of policies under different conditions. Agent-based simulation requires the generation of a realistic synthetic population, that adequately represents the population that shall be investigated such that reliable conclusions can be drawn from the simulation results. In this article, we propose a process for generating an artificial population of potential stroke patients that can be used to investigate the effects of stroke logistics policies using agent-based simulation. To illustrate how this process can be applied, we present the results from a case study in the region of Skåne in southern Sweden, where a synthetic population of stroke patients with realistic mobility behavior is simulated. 

Human creativity has been often aided and supported by artificial tools, spanning traditional tools such as ideation cards, pens, and paper, to computed and software. Tools for creativity are increasingly using artificial intelligence to not only support the creative process, but also to act upon the creation with a higher level of agency. This paper focuses on writing fiction as a creative activity and explores human-AI co-writing through a research product, which employs a natural language processing model, the Generative Pre-trained Transformer 3 (GPT-3), to assist the co-authoring of narrative fiction. We report on two progressive – not comparative – autoethnographic studies to attain our own creative practices in light of our engagement with the research product: (1) a co-writing activity initiated by basic textual prompts using basic elements of narrative and (2) a co-writing activity initiated by more advanced textual prompts using elements of narrative, including dialects and metaphors undertaken by one of the authors of this paper who has doctoral training in literature. In both studies, we quickly came up against the limitations of the system; then, we repositioned our goals and practices to maximize our chances of success. As a result, we discovered not only limitations but also hidden capabilities, which not only altered our creative practices and outcomes, but which began to change the ways we were relating to the AI as collaborator.  

Recommender Systems (RS) are used in many different applications such as ecommerce and for media streaming, including music. Recommenders not only help users discover new music, but they also help to create assemblages of songs into playlists. Intentionally or otherwise, playlists often manifest themes, that is, universal ideas that are expressed in particular songs, lyrics, or passages. In this paper we were interested to explore the capabilities of AI to introduce themes through generated playlists, them-selves seeded by the theme of plants. Taking a self-reflexive and user experience approach, we collaborated with AI to create four Plant Music playlists to subject ourselves to what came to refer to as a posthuman user experience.  

In this paper, we developed a power line communication (PLC) system design, power measurement sensor design, light sensor design, temperature sensor design, and the integration of these components into an advanced sensor network to allow for energy metering and environment monitoring. A power measurement sensor was implemented through a current and voltage sensing circuitry was interfaced multi-plug power adapter to allow for non-invasive measurement of power usage of appliances. The sensors produce signals corresponding to the drawn voltage and current, which are then sampled and processed to estimate power usage. The PLC communications operated at an average accuracy of 95%. The power measurement sensor had an accuracy of 92%, which made it appropriate for home user estimations. The light sensor had an accuracy of between 91-97%, which was suitable for home lighting measurement.

Forecasting in financial markets is challenging due to the inherent randomness of financial data sources and the vast number of factors that affect the market trends. Thus, it is essential to find informative elements within the vast number of available factors to enhance the performance of the predictive models in such a vital context. This makes the feature selection process an integral part of the financial prediction. In this paper, we propose a multi-objective evolutionary algorithm to reduce the number of features employed to predict the yearly performance of the US stock market. The primary idea is to select a smaller set of features with the slightest similarity and the best prediction accuracy. In this practice, we have utilized genetic algorithm, XGBoost and correlation in order to obtain a more diverse set of features which increases the performance. Experiential results show that our proposed approach is able to reduce the number of features significantly while maintaining comparable prediction accuracy.

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.  

The embodiment of Artificial Intelligence (AI) in everyday use products is raising challenges and opportunities for HCI and design research, such as human understandings of AI’s functions and states, passing back and forth of control, AI ethics, and user experi-ence, among others. There has been progress in those areas, such as works on explainable AI (XAI); fairness, accountability, and transparency (FAccT); human-centered AI; and the development of guidelines for Human-AI interaction design. Similarly, the in-terest in studying interaction modalities and their contributions to understandable and transparent AI has been also growing. How-ever, the tangible and embodied modality of interaction and more broadly studies of the forms of such everyday use products are relatively underexplored. This paper builds upon a larger project on designing graspable AI and it introduces a series of concept cards that aim to aid design researchers’ creative exploration of tangible and understandable AI. We conducted a user study in two parts of online sessions and semi-structured interviews and found out that to envision physicality and tangible interaction with AI felt challenging and “too abstract”. Even so, the act of creative exploration of that space not only supported our participants to gain new design perspectives of AI, but also supported them to go beyond anthropomorphic forms of AI.

Constructing simulation models can be a complex and time-consuming task, in particular if the models are constructed from scratch or if a general-purpose simulation modeling tool is used. In this paper, we propose a model construction framework, which aims to simplify the process of constructing discrete event simulation models for emergency medical service (EMS) policy analysis. The main building blocks used in the framework are a set of general activities that can be used to represent different EMS care chains modeled as flowcharts. The framework allows to build models only by specifying input data, including demographic and statistical data, and providing a care chain of activities and decisions. In a case study, we evaluated the framework by using it to construct a model for the simulation of the EMS activities related to acute stroke. Our evaluation shows that the predefined activities included in the framework are sufficient to build a simulation model for the rather complex case of acute stroke.