Morten Hertzum
email: hertzum@hum.ku.dk

Torkil Clemmensen

Barbara Rita Barricelli
email: barbara.barricelli@unibs.it

Pedro F Campos
email: pedro.campos@iti.larsys.pt

Frederica Gonçalves
email: frederica.goncalves@iti.larsys.pt

José Abdelnour Nocera

Ganesh Bhutkar
email: ganesh.bhutkar@vit.edu

Arminda Guerra Lopes
email: aguerralopes@gmail.com

Pilot Implementation: Testing Human-Work Interaction Designs

Keywords: Pilot implementation, Field test, Human-work interaction design

Pilot implementations are field tests of properly engineered, yet unfinished, systems. In contrast to lab tests, the users in a pilot implementation use the system for performing real work. In contrast to full-scale implementations, the objective of a pilot implementation is to learn. The workshop on pilot implementation aims to (a) help mature this technique for evaluating human-work interaction designs during the process of their development and implementation, (b) collect case studies that analyze experiences with conducting and learning from pilot implementations, and (c) formulate a research agenda for future work on pilot implementations -addressing their strengths, limitations, conduct, impact, and so forth. The target audience for the workshop is researchers and practitioners working on topics related to work analysis, interaction design, system-organization fit, organizational implementation, benefits realization, and in-the-wild evaluation.

Introduction

The integration of work analysis and interaction design methods is pivotal to the successful development and implementation of systems for pervasive and smart workplaces [START_REF] Nocera | Human work interaction design: Work analysis and interaction design methods for pervasive and smart workplaces[END_REF]. With this workshop, the IFIP TC13.6 working group on Human Work Interaction Design (HWID) aims to create a forum for investigating and discussing how pilot implementations can -and cannot -contribute to this integration. The workshop builds on prior research documenting the value of in situ methods in working to ensure and improve system usefulness [e.g., [START_REF] Bannon | From human factors to human actors: The role of psychology and humancomputer interaction studies in system design[END_REF][START_REF] Fields | In use, in situ: Extending field research methods[END_REF][START_REF] Nocera | An approach to the evaluation of usefulness as a social construct using technological frames[END_REF]. A pilot implementation is "a field test of a properly engineered, yet unfinished system in its intended environment, using real data, and aiming -through real-use experience -to explore the value of the system, improve or assess its design, and reduce implementation risk" [START_REF] Hertzum | Pilot implementation: Learning from field tests in IS development[END_REF]. This definition positions pilot implementation in between predictive usability tests and full-scale implementation. In contrast to usability tests, which are conducted in the lab to evaluate mockups or prototypes, pilot implementations are conducted in the field and necessarily involve users performing their real work using the pilot system. In contrast to full-scale implementation, which is intended to result in continued use, pilot implementations are temporary and aim to feed information about the finalization and implementation of the system back into the development process.

Pilot implementations recognize that systems are sociotechnical and that their social, organizational, and contextual qualities can only be evaluated thoroughly in the field. This recognition of a need for extending the iterative design process beyond the lab is not exclusive to pilot implementations. It is also at the fore in, for example, design in use [START_REF] Bjögvinsson | Design things and design thinking: Contemporary participatory design challenges[END_REF], living labs [START_REF] Alavi | The five strands of living lab: A literature study of the evolution of living lab concepts in HCI[END_REF], technology probes [START_REF] Hutchinson | Technology probes: Inspiring design for and with families[END_REF], digital twins [START_REF] Nikolakis | The digital twin implementation for linking the virtual representation of human-based production tasks to their physical counterpart in the factory-floor[END_REF], and breaching experiments [START_REF] Crabtree | Taking technomethodology seriously: Hybrid change in the ethnomethodologydesign relationship[END_REF]. However, some of these other methods are mainly used by researchers who study technology use. In contrast, pilot implementations are widely used by practitioners within the resource limitations of development and implementation processes.

Workshop Objectives

The HWID workshop on pilot implementation has three main objectives:

 To help mature pilot implementation as a technique for evaluating human-work interaction designs during the process of their development and implementation.  To collect case studies that analyze experiences -good and bad -with conducting and learning from pilot implementations.  To formulate a research agenda for future work on pilot implementations -addressing their strengths, limitations, conduct, impact, and so forth.

Pilot Implementation

Prior work emphasizes that a pilot implementation is not just the period during which a system is in pilot use. Hertzum et al. [START_REF] Hertzum | Pilot implementation: Learning from field tests in IS development[END_REF] propose that pilot implementation involves five interrelated activities:

 Planning and design, during which the pilot implementation is defined. This activity involves deciding where and when the pilot implementation will take place, how lessons learned during the pilot implementation will be collected, and so forth.

 Technical configuration, during which the pilot system is configured for the pilot site. As part of this activity, data must be migrated to the system and interfaces to other systems at the pilot site must be developed or simulations set up.  Organizational adaptation, during which the pilot site revises work procedures to benefit from the pilot system. This activity also involves user training and, possibly, temporary safeguards against pilot-system breakdowns.  Use, during which the system is used for real work at the pilot site. This activity involves striking a balance between integrating the system in normal procedures and maintaining a focus on the system as an object under evaluation.  Learning, which is the overarching objective of pilot implementation. This activity runs in parallel with the four other activities and collects data and insights about the system, its use, and the associated organizational changes.

The three first activities are preparations, which may consume more time than the period of pilot use. During the period of pilot use, the consequences of the system become salient to the users because it begins to influence their daily work and requires them to change their practices. This salience motivates the users to voice their concerns [START_REF] Wagner | Moving beyond user participation to achieve successful IS design[END_REF]. They will often be more motivated to participate at this stage than during requirements specification and usability tests, which may be experienced as a distraction that takes time away from getting their daily work done. Finally, learning occurs during the preparations as well as during the period of pilot use. The collection and analysis of the learnings require work. This work may involve measuring whether specified benefits are realized, interviewing users about their user experience, and having managers discuss additional visions for changes associated with the system.

Pilot implementations create room for innovative experimentation [START_REF] Bakker | Managing the project learning paradox: A set-theoretic approach toward project knowledge transfer[END_REF], help align the stakeholders on whom system adoption is dependent [START_REF] Mønsted | A socio-temporal perspective on pilot implementation: Bootstrappng preventive care[END_REF], provide valuable feedback about system finalization [START_REF] Hertzum | Pilot implementation: Learning from field tests in IS development[END_REF], identify important discrepancies in how different user groups perceive a system [START_REF] Winthereik | The project multiple: Enactments of systems development[END_REF], and create a decision point for discontinuing systems that face too many obstacles [START_REF] Hertzum | Grappling with the future: The messiness of pilot implementation in information systems design[END_REF]. These strengths are important arguments for conducting pilot implementations. However, multiple challenges have also been identified, including the following [START_REF] Hertzum | Pilot implementation: Learning from field tests in IS development[END_REF][START_REF] Hertzum | Grappling with the future: The messiness of pilot implementation in information systems design[END_REF][START_REF] Korn | Looking ahead -How field trials can work in iterative and exploratory design of ubicomp systems[END_REF][START_REF] Mønsted | A socio-temporal perspective on pilot implementation: Bootstrappng preventive care[END_REF][START_REF] Winthereik | The project multiple: Enactments of systems development[END_REF]:  Pilot implementations tend to be either too early or too late. When they are too early, the system and organization are not yet ready. When they are too late, the benefits to be realized from full-scale implementation are unduly delayed.  The boundaries of pilot implementations are continuously negotiated because any choice comes with difficult-to-handle interactions across the boundary between the pilot site and its surroundings, which have not yet implemented the system.  The focus on learning is difficult to maintain in the midst of getting the daily work done. Some users may see the pilot implementation as the first stage of full-scale implementation and fail to notice the learning objective altogether.  Pilot implementations involve special precautions to safeguard against critical errors and to encourage system use. There is a tension between the presence of these precautions and their absence when the system is released for full-scale use.

 The learning may be situated and difficult to transfer from the pilot site to other sites. Specifically, it is difficult to distinguish consequences that are local to the pilot implementation from consequences that are inherent in the system and its use.  Pilot implementations are not final statements about the fit between system and organization. They add realism (compared to lab tests), but the realism increases salience rather than terminates discussion about what using the system will be like.  Pilot implementations are generative; they are not merely tests. A pilot implementation fosters alignment, reveals tension, shifts power, and otherwise affects users and organizations. All these effects must be contended with post pilot.

Pilot implementations are recognized among practitioners for their contributions to system finalization, organizational implementation, and project de-escalation. However, the extant research on pilot implementations leaves lots of open questions about common experiences with conducting pilot implementations, about how best to organize them, about their pitfalls, and about which questions the research on pilot implementation needs to address.

Expected Outcomes

The workshop will produce a research agenda for studying pilot implementations and how best to conduct them. The aim of this research agenda is to stimulate further research interest and provide direction for research on pilot implementation. At the workshop, the organizers will invite the workshop participants to co-author a paper that presents the research agenda and discusses it on the basis of the cases and insights contributed by the participants.

Target Audience

The target audience for the HWID workshop on pilot implementation includes researchers and practitioners working on topics related to work analysis, interaction design, system-organization fit, organizational implementation, benefits realization, and in-the-wild evaluation. Because pilot implementations are common in practical development and implementation projects, we pay special attention to attracting practitioners from various work environments to discuss real-life case studies. Participation in the workshop requires the submission and acceptance of a position paper, which is limited to a maximum of four pages. Early-stage researchers and PhD students are encouraged to submit papers describing work in progress.

Organizing Committee

The workshop is organized by IFIP TC13 WG6 -Human Work Interaction Design (https://barbara-barricelli.unibs.it/HWID). The organizers are as follows:

Barbara

  Rita Barricelli is assistant professor at the Department of Information Engineering of Università degli Studi di Brescia, Italy. Her research interests lie in the field of Human-Computer Interaction, and specifically: End-User Development, Interaction Design, and Human Work Interaction Design. She is Chair of the IFIP TC13.6 working group on Human Work Interaction Design. Ganesh Bhutkar is assistant head (research) at Department of Computer Engineering at Vishwakarma Institute of Technology (VIT), Pune, India. He has a PhD from Indian Institute of Technology, Bombay. He has established a Centre of Excellence in HCI at VIT. His research mainly focuses on Assistive Technologies and Medical Usability. He is Vice-Chair of IFIP WG 13.6 on Human Work Interaction Design. Pedro F. Campos is associate professor at the University of Madeira, Portugal, and vice-president for research at ITI (Interactive Technologies Institute), LARSys. His research interests span human-computer interaction, persuasive computing, virtual reality, and human work interaction design. He is a co-founder of IFIP TC13 WG6. Torkil Clemmensen is professor at the Department of Digitalization, Copenhagen Business School, Denmark. His research interest is in psychology as a science of design. His research focuses on cultural and psychological perspectives on usability, user experience, and the digitalization of work. He contributes to Human-Computer Interaction, Design, and Information Systems. He is a co-founder of IFIP TC13 WG6. Frederica Gonçalves is assistant professor at University of Madeira and researcher at ITI/LARSyS, Portugal. Her research interests are in HCI, Creativity, Cognitive Augmentation, Persuasive Computing, Creativity Support Tools, and Human Work Interaction Design. She is Secretary Officer of IFIP TC13 WG6. Morten Hertzum is professor of Information Science at University of Copenhagen, Denmark. His overall research interest concerns how information technology supports, and otherwise affects, human activity. He pursues this interest within human-computer interaction, computer-supported collaborative work, healthcare informatics, and implementation studies. Arminda Guerra Lopes is professor at Polytechnic Institute of Castelo Branco, Portugal, and research fellow at ITI/LARSys, Portugal. Her research interests are in Human-Computer Interaction and research methodologies. The focus is on Social Informatics, Interaction Design, Human Work Interaction Design, Creativity and Innovation, Collaborative Work, and Quality of Life Technologies. José Abdelnour Nocera is professor in Sociotechnical Design and Head of the Sociotechnical Centre for Innovation and User Experience at the University of West London. He is the current Chair for the British Computer Society Sociotechnical Specialist Group. His interests lie in the sociotechnical and cultural aspects of systems design, development, and use.


Carmelo Ardito

Tommaso Di Noia

Eugenio Di Sciascio

Brain Computer Interface, Visual Tracker and Artificial Intelligence for a Music Polyphony Generation System

Keywords: EEG, Brain Computer Interface, Leap Motion, Slonimsky

In the Brain Computer Interface domain, studies on EEG represent a huge field of interest. Interactive systems that exploit low cost electroencephalographs to control machines are gaining momentum. Such technologies can be useful in the field of music and assisted composition. In this paper, a system that aims to generate four-part polyphonies is proposed. An artificial intelligence algorithm permits to generate polyphonies based on the N. Slonimsky's theory by elaborating data coming from a Leap Motion device, to detect user's hand movement, and a five-channel EEG signal detection device.

Introduction

Fig. 1. Techniques for inserting notes within interval axes

A music composer continuously develops music models that represent her idea of music. Such models can be intended as musical materials useful in the construction of the final piece. Thus, composers explore multiple combinations of pitches, rhythmic profiles, dynamics, and all the other parameters that represent sound in general. In 1947, the composer N. Slonimsky published a treaty entitled "Thesaurus of Pattern and Melodic Scale" [START_REF] Slonimsky | Thesaurus of scales and melodic patterns[END_REF], in which he presented a technique for constructing melodic patterns based on dividing one or more octaves into equal parts. According to Slonimsky, the construction of melodic patterns can be made systematic by inserting notes below, within and above a musical interval-axis (see Fig. 1). Once the type of interval division of the octave is chosen1 , notes can be inserted within (Interpolation), beyond (Ultrapolation) and below the division interval (Infrapolation). By combining these techniques for filling interval-axes, sinusoidal profiles of Infra-Interpolation, Infra-Ultrapolation and Infra-Inter-Ultrapolation are obtained. In this research work, a technological tool is presented that exploits Artificial Intelligence algorithms, a Leap Motion controller [START_REF] Ultraleap | Leap Motion Controller[END_REF], and a five-electrode Emotive headset [START_REF]Emotiv Insight 5-channel mobile EEG[END_REF] to support composers in exploring melodic profiles that represent the basis for composing new music pieces via the Slonimsky's Infra-Inter-Ultrapolation technique.

2

The polyphony generation system

The system architecture is schematized in Fig. 2. The Pitch Profile Generator module on the left side is based on an algorithm that transforms into note pitches the coordinates of the composers' fingers detected by the Leap Motion device. The result is a pitch profile, i.e. a sequence of notes without indications about their duration. The module on the right side is characterized by FeedForward Net Classifier that, with a Deep Network algorithm, analyzes the EEG signal coming from the five electrodes of the Emotiv headset worn by the composer and classifies her mental status as "focused" or "relaxed". Here, a mental status is intended a sequence of power spectrum values obtained from the EEG signal. The algorithm has been previously trained by the specific composer's mental status dataset. The Polyphonic Structure Generator: 1) receives the pitch profile, 2) multiplies it by four to have a draft polyphony, 3) according to the composer's mental state, gives a duration to the notes of the four pitch profiles, which are differentiated by means of a scramble method. Before using the system for composing, it has to be trained to the mental states of a specific composer, so that the underlying can adapt to the bandwidth variation characteristics, which is a specific function of each user. Such a "calibration" has to be done once for each user. The composer wears the BCI headset and samples herself in two different mental states, i.e., focused and consciously relaxed. Focused mental status means a higher amplitude of high frequencies in the range 13-40 Hz, which corresponds to the Beta and Gamma frequency bands. Consciously relaxed means a greater activation of low frequencies and specifically the Alpha band between 8-13Hz.

If the mental status is classified as Focus, the polyphony is generated with rhythmic figures such as quarter note, height notes, sixteenth notes, and their corresponding pauses. If, on the contrary, the mental status is Consciously relaxed, the polyphony generated has rhythmic values of short, whole notes, half notes, quarter notes and their corresponding pause values. In addition, each bandwidth value received in real-time from the BCI headset is used to assign a dynamic value to each generated note. Fig. 4 shows the system user interface, which is divided in three sections, as for the main phases of the polyphony composition process. Starting from the top, the first section is devoted to the two mental states training; a summary of the overall process is provided in the green box, which also acts as user guide. The second section is dedicated to generating the pitch profile using the Leap Motion visual tracker; a graph of finger positions is drawn in the black panel, while on the right the composer can see in real-time the pentagram with the sequence of generated notes. By means of the field on the right, the composer can possibly change the interval-axis. The third section shows how, in real-time, the pitch profile is transformed in the resulting polyphony according to the composer's EEG. A few further parameters can be set by using the other widgets in this section of the interface.

The generated polyphony can be saved in a format compatible with popular software tools, e.g. MAX/Msp [START_REF] Msp | Last visit[END_REF], that the composer can use for refining it and transform in the final piece of music.

Conclusions

In this paper we presented an approach aimed at generating complex polyphonic structures related to the theoretical model of Slonimsky, useful for the composer who wants to optimize her compositional process. The polyphonic material obtained is deliberately not complete in a strict musical sense, because we want to leave to the composer the possibility to work and perfect it according to her artistic needs. The creative process is a process that involves different technical and emotional aspects and in this sense, the final goal is to demonstrate that the BCI, in our opinion, could be useful as a support, and not as a substitute, tool for the composer. The system can be evolved by expanding the compositional theoretical models, thus providing a wider support to the work of contemporary musicians/composers.

Fig. 2 . 3 .Fig. 3 .

 233 Fig. 2. System architecture

Fig. 4 .

 4 Fig. 4. User interface of the system.

In Fig 1, the octave between the C note under the pentagram and the C note in the second upper space has been chosen and is represented in the Principal Tones section.

Acknowledgments. The authors acknowledge partial support of the following projects: PON Casa delle tecnologie emergenti di Matera "CTEMT" (CUP I14E20000020001), Servizi Locali 2.0, PON ARS01_00876 Bio-D, PON ARS01_00821 FLET4.0, PON ARS01_00917 OK-INSAID, H2020 PASSPARTOUT.


Stefan Sauer
email: sauer@uni-paderborn.de

Carmelo Ardito
email: carmelo.ardito@poliba.it

Keywords: Human-Centered Software Engineering, Context of Use, Change, Adaptive User Interfaces

The context of use plays an important role in Human-Centered Software Engineering (HCSE). Typically, user, environment, and platform are considered to make up the core aspects of the context of use. Changing the context of use has significant impact on how to design and develop with a user-centered perspective and how usage of these systems changed for example in the current pandemic situation with a focus on remote work. New kinds of work environments in industry providing different types of user assistance evolve, new settings for remote and distributed work styles are required to account for social distancing, or the trends towards autonomous and AI-based systems demand for new ways of thinking about socio-technical interaction and systems. This workshop promotes sharing of knowledge and experiences that address how to deal with evolving contexts of use in today's and future application domains and its influence on human-centered socio-technical system design and development practices. Beyond the traditional themes of IFIP Working Group 13.2 workshops, the main focus lies on theories, methods, and approaches for dealing with the context of use, its influence on HCSE, and factors for its change over time.

Overview and Goals

The context of use plays an important role in Human-Centered Software Engineering (HCSE) and Human-Computer Interaction (HCI) research [START_REF] Abowd | Towards a Better Understanding of Context and Context-Awareness[END_REF] [START_REF] Dey | Understanding and Using Context[END_REF]. Typically, user, environment, and platform are considered to make up the core aspects of the context of use [START_REF] Calvary | A Unifying Reference Framework for multi-target user interfaces[END_REF]. Changing the context of use, for example due to unplanned circumstances like the current pandemic situation, has significant impact on how we use systems, and how we adapt and adopt them even if the systems were not designed for such usages. In HCSE research we have to account for this change, making interactive system development context-aware or design and develop in a way that systems can adapt for novel forms of usage. Recently, we observe developments that strongly change contexts of use. For example, in the area of industrial automation (Industry 4.0) work environments change, new kinds of user assistance evolve, and workers are going to be supported by innovative types of devices and digital assistance tools to accomplish their working tasks. Typical examples are augmented, virtual, and mixed reality (AR, VR, MR) applications in training or support situations. The COVID-19 pandemic has drastically changed where and the way how we work, particularly in collaboration with others to keep distance and increase personal safety. In contrast, the trend towards increasingly autonomous systems and systems that use and provide artificial intelligence gives rise to new kinds of interaction, particularly, human-machine interaction (HMI), in areas such as autonomous driving or human-robot collaboration in different domains such as industrial production, logistics, or health. These trends should be accounted for in the way we design and build such interactive systems, possibly coming to evolutionary or even revolutionary solutions. For example, expected or unforeseen changes of usage scenarios and their context of use may be accounted for by flexible and more resilient system solutions and be reflected in the development practices and technical frameworks. Specific kinds of interaction such as HMI, but also social and socio-technical interaction may demand for more prominent and explicit consideration. Quality aspects such as ubiquity, security, and safety may be seen in a different light. Changing contexts of use may even have an impact on the way we think about user motivation and user experience, away from short-term notions like emotions towards long-term traits like users' values. We want to specifically account for these developments in addition to the general concerns of HCSE. Discussions and interactive working sessions in the course of the HCSE@INTERACT 2021 workshop will particularly deal with these concerns.

In this workshop, we aim to broaden the traditional scope of the workshop series of IFIP Working Group 13.2 [START_REF] Winckler | IFIP WG 13.2 Workshop on User Experience and User-Centered Development Processes[END_REF][5] [START_REF] Ardito | Handling Security, Usability, User Experience and Reliability in User-Centered Development Processes[END_REF]. We focus on the study of context of use, its longterm evolutionary trends as well as its short-term design and management in a usercentered design process, from a social and user-centered methodological viewpoint as well as from a technical viewpoint. Our aim is to cover a large set of user interface perspectives, aspects, and properties and fuel new ideas and approaches for research and practice. The long-term perspective of this workshop is to foster the development of theories, methods, tools and approaches for dealing with the changing context of use and its impact on HCI and collaboration that should be taken into account when developing interactive and socio-technical systems.

This workshop is a follow-up of the successful workshops organized at INTERACT 2017 in Mumbai, India [START_REF] Winckler | Dealing with Conflicting User Interface Properties in User-Centered Development Processes[END_REF] and INTERACT 2019 in Paphos, Cyprus [START_REF] Ardito | Handling Security, Usability, User Experience and Reliability in User-Centered Development Processes[END_REF].

Target Audience and Expected Outcomes

This workshop is open to everyone who is interested in aspects of human-computer interaction from a user-centered perspective. Typical contributions to this workshop focus on user interface properties while designing and building interactive systems and study associated methods, processes and approaches. We expect a high participation of IFIP Working Group 13.2 members. We invited participants to present position papers proposing novel solution ideas and describing real-life case studies that illustrate the role of the context of use in HCI and its impact on the system design and use. Any perspective and related aspects of user interface design were welcome. However, we were especially interested in work that deals with current trends that change the way humans use, interact and collaborate with technical components in socio-technical systems. We were also interested in methods, theories and tools for managing context of use at design and run-time. Submitted position papers were reviewed and selected for presentation in the workshop by an international program committee comprising the organizers and selected members of IFIP Working Group 13.2 who are experts in the field. Accepted papers were made available through the workshop website. Furthermore, an extended version of selected papers was considered for inclusion in a Springer LNCS post-proceedings volume published in conjunction with the other INTERACT workshops organized by the IFIP TC13 Working Groups.

Workshop Contributions

Nine contributions have been accepted for presentation at the workshop. They are briefly described in the following.

How to Identify Changing Contexts of Use With Creativity

Workshops -An Experience Report, presented by Wasja Brunotte, Lukas Nagel, Kurt Schneider and Jil Klünder, reports their experiences with a multi-stakeholder requirements elicitation workshop method that combines the 6-3-5 Method with the Walt-Disney Method to ensure that a broad range of requirements can be elicited while ensuring equal contribution from different stakeholders and improving the number of perspectives on the future context of use of a software that is developed.

Describing Digital Work Environment Through Contextual Personas, presented by Ruochen Wang, Marta Larusdottir and Åsa Cajander, reports the results from a study involving student software developers using personas that were enriched to represent physical work context, organisational work environment, social work environment aspects as well as the cognitive load. Interesting insights regarding improvements are identified: the ability of representing diversity of user groups especially when it comes to aging populations or non-tech savvy user groups, and, more generally, personas that evolve with the project and over time.

Towards Flexible Interaction Design for Operator Assistance Systems, presented by Jan Van den Bergh and Florian Heller. The authors present an approach to assess the usability of Operator Assistance Systems, such as hoist, lifts, cranes, and robotic arms. Such systems help operators in handling components of various weights in order to reduce ergonomic stress and are currently evaluated based on ergonomic criteria. However, the acceptance of such systems by the operator does not only depend on the amount of support it generates, but also on basic usability factors. Based on the observations of a contextual inquiry, the authors designed a simple scoring sheet to evaluate and compare the usability aspects of such systems.

Creating a Post-Sedentary Work Context for Software Engineering, presented by Martin Hedlund, Cristian Bogdan and Gerrit Meixner, presents a research agenda on how to address the sedentary work style of software engineers by re-structuring tasks to be performed and supported by for example visual programming and providing associated technological solutions including virtual reality (VR), augmented reality, and haptic interaction within VR to perform these tasks. The research agenda addresses the possible study set-ups for such types of interventions and how to investigate aspects like program comprehension, code creation or novel types of interaction techniques.

Coping with Changing Contexts: A Healthcare Security Perspective, presented by Bilal Naqvi and Carmelo Ardito. The authors aim at stimulating a discussion how to cope with changing contexts while considering the threats posed by phishing and ransomware attacks in the context of healthcare infrastructures. Indeed, phishing has been the most prevalent attack mechanism on the healthcare infrastructures during the ongoing COVID-19 pandemic, which increased the pace of digitization of the healthcare industry. The authors propose three ways to cope with these threats in the perspective of healthcare, i.e., i) training and supporting developers at work; ii) initiating cross-disciplinary education and training mechanisms; iii) application of existing HCI principles.

Privacy Knowledge Base for Supporting Decision-Making in Software Development, presented by Maria Teresa Baldassarre, Vita Santa Barletta, Danilo Caivano, Antonio Piccinno and Michele Scalera. The authors propose an approach to support software developers in integrating security and privacy requirements at every stage of the software development cycle, which is critical to guarantee the confidentiality, integrity and availability of the system and consequently of the data. In particular, they define a Privacy Knowledge Base (PKB) that encompasses the key elements to support decision-making in privacy-oriented software development. An industrial case study shows preliminary results on how PKB provides the guidance needed for privacy-oriented software development.

bRIGHT -A Framework for Capturing and Adapting to Context for User-Centered Design, presented by Rukman Senanayake and Grit Denker, deals with user context modeling and adaptation. Particular challenges of creating and maintaining an accurate model of the user context are incomplete and uncertain information. The proposed HCI framework bRIGHT is capable of creating an accurate context model as the user engages with the computer system. In this paper, the authors discuss the new architectural design of bRIGHT for improving knowledge representation and base type system and its expected benefits.

Affordance-derived Declarative Interaction Models for Context Adaptation, presented by Cristian Bogdan, proposes an approach to achieving fully declarative interactive applications. A concrete user interface is sketched and the interaction function is derived from the UI's form through affordance mechanisms. As a result, a declarative model of the dynamic interaction aspects of the application is obtained. This model can be used to complement existing solutions for declaratively describing the static aspects of the user interface to make a fully declarative application. This is an important prerequisite for such an application to be automatically adapted to its context of use.

On the Consistency of UI Adaptations, presented by Kai Biermeier, Enes Yigitbas, Nils Weidmann and Gregor Engels, looks at employing Triple Graph Grammars (TGGs) as a sophisticated mechanism for controlling the consistency of user interface (UI) adaptations to the current context-of-use. To prevent the undesirable application of conflicting UI adaptations and ease the specification of UI adaptation rules, they introduce their notion of adaptation consistency based on TGGs augmented with a 0-1 priority system. TGGs are an established formalism for consistency maintenance in model-driven engineering. TGG semantics are extended with a priority system to control the rules' application order. The authors also outline the implementation of a prototypical TGG interpreter as a design and run-time solution for ensuring consistency of UI adaptations.

Workshop Format

This full-day workshop is designed to be held in hybrid format, i.e., allowing participants to either attend in person on-site or just remotely/virtually. It is organized around presentation of position papers and working activities in small groups. In the morning sessions, after welcoming the participants and setting the frame for the workshop, the selected position papers are presented. In addition, further position statements can be contributed by the workshop participants. Participants are also invited to comment on the propositions and case studies and to report similar experiences. Position papers and statements together are used to support the discussion that follows. The afternoon sessions are thus devoted to interactive sessions, where participants will be engaged to work in small groups, discuss and propose solutions to the identified challenges seen in the morning. Due to the hybrid workshop format, on-site and virtual group discussions are run in parallel. Solutions proposed by the participants are compiled and compared.

Based on the lessons learned, participants are encouraged to draft an agenda of future work that can be accomplished.


Carmelo Ardito
email: carmelo.ardito@poliba.it

Tommaso Di

Di Noia

Eugenio Di Sciascio
email: eugenio.disciascio@poliba.it

Domenico Lofù
email: domenico.lofu@poliba.it

Andrea Pazienza
email: andrea.pazienza@exprivia.com

Felice Vitulano
email: felice.vitulano@exprivia.com

Tommaso Di Noia
email: tommaso.dinoia@poliba.it

User Feedback to Improve the Performance of a Cyberattack Detection Artificial Intelligence System in the e-Health Domain

Keywords: User Interaction, User Feedback, Artificial Intelligence for Security, Internet of Medical Things, e-Health

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

In the e-Health sector, the protection of patient telemonitoring systems is essential to ensure that they follow their clinical path without any kind of external intrusion. In particular, Artificial Intelligence (AI) and Machine Learning (ML) have become critical technologies in information security, as they are capable of rapidly analyzing millions of events and identifying many different types of threats.

Intrusion analysts infer the context of a security breach by using prior knowledge to discover events relevant to the incident and understand why it happened [START_REF] Afzaliseresht | An explainable intelligence model for security event analysis[END_REF]. Although security tools have been developed that provide visualization techniques and minimize human interaction to simplify the analysis process, too little attention has been paid to humanizing the interpretation of security incidents. Simply reporting a cyberattack is not sufficient to allow the healthcare professional to correct the patient's clinical path. These data must be represented graphically, which can be understood by the healthcare professional. The detection of the cyberattack must therefore be supported by systems that provide different forms of visualization and interaction, according to the different endusers, and that allows them to have the possibility to interactively manipulate graphical representations based on Visual Data Mining (VDM) techniques.

This paper is organized as follows. Section 2 provides an overview of related work and technologies which were investigated as background knowledge. Section 3 provides the main contribution of the paper regarding the user interaction with a Cyberattack Detection System (CADS). Finally, Section 4 concludes the paper, outlining future works.

Background and Related Work

Cyberattack Detection System (CADS) is software that automates the cyberattack detection process and detects possible cyberattacks. Anomaly detection typically works on monitored network traffic data. Indeed, the integration of healthcare-based devices and sensors within the Internet of Things (IoT) has led to the evolution of the Internet of Medical Things (IoMT) [START_REF] Yang | A centralized cluster-based hierarchical approach for green communication in a smart healthcare system[END_REF]. In particular, ML-based anomaly detection systems are critical for ensuring security and mitigating threats such as bogus data injection attacks [START_REF] Luo | Deep learning-based anomaly detection in cyber-physical systems: Progress and opportunities[END_REF]. Therefore, designing a distributed security framework for distributed IoMT applications is a challenging task due to the dynamic nature of IoMT networks such as IoT devices, edge devices, and the cloud. The line of research in this way is moving towards building robust anomaly-based intrusion detection systems that efficiently distinguish attack and normal observations in the IoMT environment, consisting of interconnected devices and sensors. For this reason, works in [START_REF] Ardito | Towards a situation awareness for ehealth in ageing society[END_REF][START_REF] Pazienza | Adaptive critical care intervention in the internet of medical things[END_REF][START_REF] Pazienza | Artificial intelligence on edge computing: a healthcare scenario in ambient assisted living[END_REF] has dealt with a clinical and operational context to develop integrated solutions for continuous care in which AI and IoMT are used at the Edge, with a people-centered approach that fit the needs of healthcare professionals and is incorporated into their workflows.

Figure 1 depicts the architecture of the CADS proposed in [START_REF] Ardito | An artificial intelligence cyberattack detection system to improve threat reaction in e-health[END_REF]. It focuses on the security of data transmitted from IoMT sensors to three different interconnected processing modules: a Clinical Pathway Anomaly Detection (CPAD), an Explainer module, and an User Interaction Engine. The latter is made up of three sub-modules, i.e.: a Visualization Framework, an User Interface, and an User Feedback system. The activity of interacting with the data is carried out by means of the User Interaction Engine, which provides a dashboard through which to visually explore the data to get a clearer view of what happened over a period of time. In particular, thanks to the User Feedback module, it is possible to implement a continuous improvement of the classification performance, and consequently of the anomaly detection, thus obtaining a more effective identification of threats. In this way, CADS is increasingly accurate in identifying threats and therefore more robust from a security point of view. Below is a case study, where an e-Health telemonitoring system is, in turn, monitored by a CADS at the Edge. Let's consider a smart ECG device, which collects heartbeat information from a patient in remote assistance.

ECG User Interaction and User Feedback

The Interactive Machine Learning (IML) research domain incorporates human feedback into the model training process to develop high-performance ML models [START_REF] Fails | Interactive machine learning[END_REF]. It is experience research again momentum [START_REF] Koch | Imagesense: An intelligent collaborative ideation tool to support diverse humancomputer partnerships[END_REF].

Thanks to the use of examples provided by the system and human feedback [START_REF] Shi | Interactive context-aware anomaly detection guided by user feedback[END_REF][START_REF] Stumpf | Toward harnessing user feedback for machine learning[END_REF], it is possible to allow the algorithm to learn how best to behave when faced with a given ECG detection. In our case study, the contribution of the User Feedback to the system is the evaluation of the detected anomaly and the embedding of a doctor's feedback. The User Feedback module will generate for each detection ECG i a feedback coefficient φ i that represents the doctor's feedback on a given instance.

Definition 1 Let ECG be the set of the heartbeat detections received from a smart ECG monitoring end-device, ECG i be the i-th heartbeat detection. Then, the feedback coefficient is a function φ : ECG → {-1, 1} such that any i-th user feedback related to the heartbeat detection ECG i , is defined as follows:

φ i = +1 if ECG i is false positive or false negative -1 if ECG i is true positive or true negative (1)
Therefore, the User Feedback U F is a set of tuples such that, for any i-th pair of arguments (ECG i , φ i ), a single element U F i is defined as:

U F i = (ECG i , φ i ) (2)
In this way, the CADS will become more robust to external cyberattacks, since the User Feedback would report the opinion of the caregiver which will confirm or not whether the i-the ECG detection is abnormal or not. In the User Interaction Engine, the Visualisation Framework represents the data orchestrator, handling and visualizing processed data coming from the various modules. It uses algorithms of VDM [START_REF] Kreuseler | A history mechanism for visual data mining[END_REF] that allow, through different visualization techniques, to interactively group data in a more efficient way, improving the data insight process. Afterwards, the User Interface (UI) included in the User Interaction Engine allows the user to interact with the data. In the case study, the UI allows the caregiver to interact with the ECG instances. In the CADS architecture, an Explanation module displays useful classification information, with which it is possible to visually manage each ECG detection. For instance, one would be able to no longer consider an ECG instance as an anomaly, or, more specifically, to improve the classifier performances by indicating the correct class of anomaly when a wrong one has been predicted. The interaction with the user, in this case a doctor, helps the system to be more and more reliable, as well as secure from cyberattacks.

Through the integration of CPAD, Explainer, and User Interaction Engine modules, the Visualization Framework will be able to manage anomalies detected as threat insights. These will be appropriately displayed on the UI which, in addition to allowing interaction with the anomalous data (in this case the ECG detection), will be able to display the threat representation through a dashboard. Thanks to the threats graphical representation in the dashboard, the user's reaction to the threat is improved. The visual process, whereby the healthcare professional is able to mark a detection as true or not, is a key scenario in which ML methods are combined with human feedback through interactive visualization. This process enables the fast prototyping of the ML model that can improve both the performance of the algorithm and human feedback. It will be also able to complete tasks where anomaly identification was not yet possible. The system then uses User Feedback to refine detection results and guide further analysis. Caregiver Feedback is therefore used as an essential source of ever-improving anomaly detection of ECG, which means that labeling the local environment will trigger global updates and thus guide further analysis.

Conclusion

An e-Health telemonitoring system cannot always have a security expert managing the security of the system. The proposed User Interaction and, in particular, User Feedback modules introduced in an AI-based CADS provide a visual representation of the results to the expert user engaged in a feedback response, determining whether the detected anomalous data are truly atypical by assessing the detection with positive feedback.Otherwise, the user provides negative feedback. Such interaction with a CADS has an impact on the processed health data, adjusting the visualization reports with the corrected measurements, shown in a useful dashboard. Future works will focus on the development of a further visual interface in which, thanks to the use of ML and VDM algorithms, it is possible to graphically represent both machine capability and human intelligence.

Fig. 1 .

 1 Fig. 1. Cyberattack Detection System Architecture

Acknowledgments

This work was partially funded by the European Union, Horizon 2020 research and innovation programme, through the ECHO project (grant agreement no 830943) and by the Italian P.O. Puglia FESR 2014 -2020 (project code 6ESURE5) SECURE SAFE APULIA.


Giuseppe Desolda
email: giuseppe.desolda@uniba.it

Vincenzo Deufemia
email: deufemia@unisa.it

Maristella Matera
email: maristella.matera@polimi.it

Fabio Paternò
email: fabio.paterno@isti.cnr.it

Fabiana Vernero
email: fabiana.vernero@unito.it

Massimo Zancanaro
email: massimo.zancanaro@unitn.it

International Workshop on Empowering People in Dealing with Internet of Things Ecosystems (EMPATHY)

Motivation and Objectives

The design and development of the so-called smart objects have become a huge trend in the last decade, thanks to the proliferation of low-cost technologies embedded with sensors and actuators. A major role in further fostering this tendency has been played by the Internet of Things (IoT), which connects the physical world with the Internet via ubiquitous sensors and actuators.

In this scenario, end-users who may often lack specific technical skills are provided with the opportunity to configure the behavior of their smart objects, as well as their interactions with other objects and online services, thanks to novel interaction paradigms. Depending on the solution adopted, "IoT ecosystems" can leave their users completely free and unsupported in their configuration task, exploit intelligent techniques to automatically determine how smart objects should behave, or implement an intermediate approach. To this respect, a prominent example is represented by social and humanoid robots, i.e., integrated sets of sensors and actuators which can exhibit human-like behavior, which can guide and support end users in their configuration tasks.

The technological landscape, therefore, appears extremely variegated, with existing solutions offering different levels of user control and automation. Such possibilities can be exploited in a wide variety of application domains that can have an impact on our daily life, such as industry 4.0, ambient-assisted living, retail, smart home, cultural heritage, and education. In this perspective, it is also important to understand how to combine methods and tools from the artificial intelligence field and those developed in the area of End-User Development (EUD) to help users understand, control and modify the automation available in the surrounding context of use.

Given the diffusion and the relevance that these approaches have been gaining, this workshop aims to serve as a venue for discussing ongoing research and sharing ideas for researchers and practitioners working on solutions to personalize the behavior of IoT ecosystems.

Topics

Workshop Format and Participants

It is a one-day workshop, oriented towards discussions, hands-on sessions, and presentations. The number of workshop participants is limited to 30, mainly from academia and companies. Participants were informed about the workshop via the most known international mailing lists (e.g., DBWORLD, CHI-Announcements, etc.).

Workshop papers are made available on CEUR Workshop Proceedings while workshop results are published on the workshop website (https://empathyws.github.io/2021/). 1 shows the workshop schedule. At the beginning of the workshop, participants are welcomed and introduced to the workshop's goals and organizers. Then the accepted papers are presented in two sessions. Participants are encouraged to provide short but provoking presentations that tackle questions in line with the workshop goals. They are granted 10/15 minutes for presentation and Q&A. The workshop thus continues with a discussion on the main challenges, future directions, and controversial aspects that characterize the current landscape for the EUD of IoT systems.

In the afternoon, after lunch, the organizers present the challenges and future directions that emerged during the previous discussion, inviting the participants to use them as a ground for a creative thinking session. This activity takes 60 minutes and participants are divided into small groups; participants are provided with material for the illustrations of their ideas. Due to the COVID-19 prevention procedures recommended by the World Health Organization (https://www.who.int/emergencies/diseases/novel-coronavirus-2019), interaction among participants is supported both online, thanks to virtual collaborative environments, and in presence, considering appropriate distances. Participants are provided with digital tools to collaboratively sketch their ideas and to prepare posters, videos, or 'Wizard-of-Oz' prototypes, or use storytelling or role-playing. After the coffee break, each group presents their solution to the workshop audience. Finally, the workshop organizers and participants consolidate results and ideas into the production of a structured map of topics to be addressed in future research.

The workshop is wrapped by identifying ways to move forward, including the initiation of joint publications, the organization of a new edition of the workshop (e.g., at CHI 2022, or IS-EUD 2023), and journal special issues on the topic.

Previous history

Looking at the recent history of significant workshops in the same research area, at CHI 2019 two workshops have been organized on IoT for non-technical people [START_REF] Fröhlich | Everyday Automation Experience: Non-Expert Users Encountering Ubiquitous Automated Systems[END_REF][START_REF] Fuentes | New Directions for the IoT: Automate, Share, Build, and Care[END_REF], while a workshop on the interaction with smart objects [START_REF] Müller | SmartObjects: Sixth Workshop on Interacting with Smart Objects[END_REF], which led to a TOCHI special on "End User Development for the Internet of Things", was organized at CHI 2018. A workshop on "Intelligent User Interfaces for Internet of Things" took place at IUI 2019. Last year at CHI 2020 a workshop on Automation experience across domains: designing for intelligibility, interventions, interplay and integrity was organized [START_REF] Fröhlich | Automation experience across domains: designing for intelligibility, interventions, interplay and integrity[END_REF]. The first EMPATHY workshop was held at AVI 2020, it was well attended, and the discussion was stimulating and interesting. Thus, we felt useful proposing a new edition that includes the same topics of the previous one, and addresses also further aspects that can raise interest and participation.

Organizers

The organizing team is a group of researchers with different disciplinary training and experience levels. They are involved in the EMPATHY project, which has been funded by the Italian Ministry of Education, Universities and Research (MIUR) troughs a three-year basis, research projects of national interest (PRIN). Their interdisciplinary nature allows the team to appreciate the diverse backgrounds of workshop attendees and to stimulate a critical discussion during the workshop.

Giuseppe Desolda is Assistant Professor at the Computer Science Department of the University of Bari "Aldo Moro" (email: giuseppe.desolda@uniba.it, website: http://ivu.di.uniba.it/people/desolda.htm). His research interests lie in HCI, specifically Interaction with Ubiquitous Systems, IoT, EUD, and Usable Security. Among others, he chaired the "Human-centered cybersecurity" (CHItaly 2019) and the "SERVE: Smart Ecosystems cReation by Visual dEsign" (AVI '16) workshops. Vincenzo Deufemia is Associate Professor at the Department of Computer Science of the University of Salerno (email: deufemia@unisa.it, website: http://docenti.unisa.it/vincenzo.deufemia). His research interests include data science, pattern recognition and Human-Computer Interaction, with emphasis on usable privacy and security for end users. He has been a member of the organizing committee of several international conferences.

Maristella Matera is Associate Professor at the Department of Electronics, Information and Bioengineering (DEIB) of Politecnico di Milano (email: maristella.matera@polimi.it, website: http://matera.faculty.polimi.it/). Her research focuses on aspects at the intersection between Web Engineering and Human-Computer Interaction, with emphasis on design methods and tools for Web application development. She has been a member of the organizing committee of several international eventsamong others, she was program co-chair of AVI 2016. She is an associate editor for the journals "Future Generation Computer Systems" (FGCS) and ACM Transactions on Web (TWeb). She is chair of the Italian chapter of the ACM SIGCHI.

Fabio Paternò is Research Director at CNR-ISTI, where he leads the laboratory on Human Interfaces in Information Systems (http://hiis.isti.cnr.it/Users/Fabio/). His research activity has mainly been carried out in the Human-Computer Interaction (HCI) field, with the goal to introduce computational support to improve usability, accessibility, and user experience for all in the various possible contexts of use. For this purpose, he has continuously led for several years numerous interdisciplinary and international projects. He has been co-organiser of several workshops in international conferences.

Massimo Zancanaro is a professor of Human-Computer Interaction at the Department of Psychology and Cognitive Science at the University of Trento (email: massimo.zancanaro@unitn.it; https://webapps.unitn.it/du/it/Persona/PER0004568/). His research interests are in the field on Human-Computer Interaction and specifically on the topic of Intelligent Interfaces for which he is interested in investigating aspects related to the design as well as to study the reasons for use and non-use. At present, he serves as vice-chair for the Italian Chapter of the ACM Special Interest Group in Computer-Human Interaction and as Associated Editor for the journal Behavior and Information Technology.

Fabiana Vernero is a Lecturer at the Department of Computer Science, University of Turin (email: fabiana.vernero@unito.it, website: https://www.unito.it/persone/fvernero). She is part of the PhD committee in Modeling and Data Science, University of Turin. Her research interests include intelligent user interfaces, recommender systems and persuasive technologies.

Table 1 .

 1 Workshop Schedule

	Time	Phase
	09.00 -09.15 Welcome and Introduction
	09.15 -10.30 Paper Presentations I
	10.30 -11.00 Coffee Break
	11.00 -12.30 Paper Presentations II
	12.30 -13.00 Future directions and wrap up
	13.00 -14.30 Lunch with all participants
	14.30 -15.00 Presentation of challenges
	15.00 -16.00 Creative thinking
	16.00 -16.30 Coffee Break
	16.30 -17.00 Result presentation
	17.00 -17.45 Agenda Definition & Wrap-Up
	Table

The primary contact person is Giuseppe Desolda. All the organizers plan to attend the workshop.


Keywords: Trustworthy AI, Financial Services, Credit Scoring, Fairness, Explainability, Human-centered computing

Decision-making systems have been widely used in the Financial Services domain. AI is bringing both many innovations and opportunities as well as new risks linked to ethical considerations. Customer trust is at the forefront of customer retention. To build trust, there is the need to make the decision process Interpretable, Understandable, and Trustworthy for the end-user. Since products offered within the banking sector are usually of an intangible nature, customer trust perception is crucial to maintain a long-standing relationship and to ensure customer loyalty. To this end, in this paper we propose more insightful and userfriendly explanations for decisions made by AI systems in the financial domain.

Improving the user experience and the trustworthiness of Financial Services

Giandomenico Cornacchia 1[0000-0001-5448-9970] , Fedelucio Narducci 1[0000-0002-9255-3256] , and Azzurra Ragone 2

Introduction

Artificial Intelligence (AI) has increasingly played a predominant role in the interaction with consumers, citizens, and patients in the last years. It has been capable of revolutionizing their daily lives and improving all those user-centered services that are expanding out from the back office into customer-facing applications 3 . However, users often interact with such systems without even knowing that life-changing decisions like mortgage grants, job offers, patients screenings, etc., are in the hand of an AI-based system. Sometimes, such decisions may result arbitrary or inconsistent and limit users' ability to access the opportunities for which they are indeed qualified [START_REF] Barocas | Fairness and Machine Learning[END_REF]. In particular, AI adoption is growing rapidly in the financial sector. Financial firms have widely used it to monetize data assets, improve customer experience, customize product and service offerings, drive business growth, and enhance operational efficiencies. On the other hand, AI has shown its weakness by emphasizing social and ethical issues such as gender and demographic discrimination [START_REF] Chouldechova | Fair prediction with disparate impact: A study of bias in recidivism prediction instruments[END_REF][START_REF] Cohen | Efficient candidate screening under multiple tests and implications for fairness[END_REF], and lack of interpretability and explainability. As these applications become key enablers and more deeply embedded in processes, financial services organizations need to cope with AI applications' inherent risks. This is true both from a compliance point of view (regulatory and ethical norms) and because the lack of trust is the leading barrier to AI adoption and acceptance by users. Building trust requires transparency and clear communication with internal stakeholders and customers, who should know when, how, and why AI is being used. This is especially true in a sector heavily regulated, such as the financial services one.

AI-based systems are increasingly attracting the attention of regulatory agencies and society at large, as they can cause, although unintentionally, harm. Indeed as reported by the Ethics guidelines for trustworthy AI from the European Commission's High-Level Expert Group on AI: "The development, deployment, and use of any AI solution should adhere to some fundamental ethical principles such as respect for human autonomy, prevention of harm, fairness, and explainability" [START_REF]on AI: Ethics guidelines for trustworthy ai[END_REF]. Moreover, the GDPR set off the right to explanation: users have the right to ask for an explanation about an algorithmic decision made about them. In the UK, the Financial Conduct Authority (FCA) requires firms to explain why a more expensive mortgage has been chosen if a cheaper option is available. The G20 has adopted the OECD AI Principles4 for a trustworthy AI where it is underline that users should not only understand AI outcomes but also be able to challenge them. In the financial sector, this is not an easy task to solve. As on one side, it is required to show how an outcome has been reached and whether it was fair and unbiased. On the other, not all the rationales behind a decision can be disclosed to prevent users from gaming the system.

In this paper, we propose an approach to provide more insightful explanations to make the interaction more user-friendly and, at the same time, to reinforce customer trust in the system.

What does it mean to be fair?

There is no single, globally accepted definition of fairness in AI [START_REF] Verma | Fairness definitions explained[END_REF]. However, being fair implies ensuring the same quality of service to all people, avoiding discriminating against minorities, and using protected characteristics like gender, nationality, age. AI-based systems should allocate opportunities, resources, or information fairly, thus avoiding societal or historical biases. The definition provided by Mehrabi et al. [START_REF] Mehrabi | A survey on bias and fairness in machine learning[END_REF] summarizes well these concepts: the absence of any prejudice or favouritism toward an individual or a group based on their inherent or acquired characteristics.

In our analysis, we refer to Credit Scoring (CS) systems that compute the probability of a customer to repay a loan. We use this case study since for credit loan the concept of equal opportunity is crucial, and it lies very often in the hands of ML algorithms. Indeed, governments have addressed demographic, gender, and racial discrimination as regulatory compliance requirements since the 1960s [START_REF]The truth in lending act[END_REF], [START_REF]Congress of the United States[END_REF], [8]. Since those norms were not set to prevent discrimination in not-human decision making (as in the case of ML algorithms), "Ethics guidelines for a Trustworthy AI" [START_REF]on AI: Ethics guidelines for trustworthy ai[END_REF] and "The White Paper" 5 were released to give guidelines for ethical and safe use of AI. Some critical keys requirements are "equity, diversity and not-discrimination" enclosed in the concept of fairness.

Going deeper with this analysis, the concept of fairness in CS could linked to one or more of these three criteria [START_REF] Kozodoi | Fairness in credit scoring: Assessment, implementation and profit implications[END_REF]: (i) Independence [START_REF] Dwork | Fairness through awareness[END_REF], (ii) Separation [START_REF] Hardt | Equality of opportunity in supervised learning[END_REF], and (iii) Sufficiency [START_REF] Chouldechova | Fair prediction with disparate impact: A study of bias in recidivism prediction instruments[END_REF]. The (i) Independence guarantees that the fraction of customer classified as good-risks is the same in each sensitive groups. Therefore, if the gender is considered as sensitive, both men and women should have the same percentage of good-risk classification. The (ii) Separation criterion is related to the concepts of misclassification. Accordingly, the errors in classifying will be the same both in sensitive and non-sensitive groups. Finally, the (iii) Sufficiency criterion states that the probability that an individual belonging to the good-risk class is classified as good-risk will be the same for both sensitive groups. In this case, if the algorithm shows a gender bias, for example, a woman that belongs to the good-risk customer could be classified in the bad-risk class.

Once defined the concept of fairness and described the dimensions it is based on, the next question is: how can the customer be sure that the decision made by the algorithm is fair? We introduce now the next step that allows the customer to realize that the decision is fair. In particular, the person accountable for the AI system should be able to explain their outcome to the customer. The following section will address what explanation means and how to reach this goal.

From model fairness to end-user explanation

Several definitions are provided in the literature on what explainable means when we talk about an ML algorithm. The most relevant one for our purpose is provided by Bracke et al. [START_REF] Bracke | Machine learning explainability in finance: an application to default risk analysis[END_REF] "explanations can answer different kinds of questions about a model's operation depending on the stakeholder they are addressed to". This definition introduces an interesting characteristic of the explanation that has to consider the point of view of a specific stakeholder. Accordingly, in a CS scenario, for example, the explanation for a given decision might be different if addressed to customers rather than to the risk management functions. From the customer's point of view, which is the most interesting in our analysis, the explanation should describe the motivations behind a decision in a way that is easy to understand. Naturally, as abovementioned, the decisions are made by algorithms thus, it is crucial to know how these algorithms work. The ML algorithms belong to two main classes: interpretable and uninterpretable. More specifically, the former implement a white-box model, the latter a black-box one. On this perspective, Sharma et al. [START_REF] Sharma | Building up explainability in multi-layer perceptrons for credit risk modeling[END_REF] distinguish model-agnostic and modelspecific explanations. Model-agnostic methods provide an explanation that is not dependent on the ML model adopted and are geneally used for black-box models. A surrogate model is thus implemented with the aim of simulating the behavior of the original algorithm.

Several methods have been proposed to explain black-box models. Two of the most important are LIME and SHAP. LIME trains local surrogate models explaining single data [START_REF] Ribeiro | Explaining the predictions of any classifier[END_REF]. It generates a perturbation of initial data creating a new dataset and observing how the prediction changes through training an interpretable model. The analysis of the outcome of the perturbated data allows to interpret the original model. SHAP [START_REF] Lundberg | A unified approach to interpreting model predictions[END_REF] is inspired by the cooperative game theory based on the Shapley Values. Each feature is considered a player that contributes differently to the outcome (i.e., the algorithm decision). SHAP does not compute all the possible combinations between all the features but performs only a random set of combinations for efficiency constraints. SHAP provides a ranked list of the features that contributed to the outcome ordered from the most to the less important. However, this explanation probably is not so clear for a customer who does not have experience with how an algorithm works. For this reason, if we want to improve the user's trust and general user experience with the system, we need to make the explanation more understandable. In that direction, we guess that an effective solution could be to transform the output produced by software like LIME or SHAP in a natural language sentence. We propose the pipeline described in Figure 1. Customer characteristics are the input, then the algorithm makes a decision, e.g. the computation of the CS, and shows using SHAP the features that contributed the most to the decision. At this point, another module takes as input the decision and the SHAP output and generate a natural-language explanation: e.g. Dear Giulio, your loan application has been rejected since you don't have an account with us, the credit amount you asked for is too high compared to your income, and the duration is too long. An interesting opportunity in this context could be provided by a counterfactual explanation that explains how the output of the algorithm could be changed [START_REF] Stepin | A survey of contrastive and counterfactual explanation generation methods for explainable artificial intelligence[END_REF]. For example, the system can add: In the case you decide to open an account with us, to reduce the credit amount to 10,000$, and to reduce the duration to 12 months, the application will be probably accepted. Conversely, model-specific explanations are based on the analysis of the structural information and the internal components of the algorithm that should be interpretable natively. From a technical perspective, these algorithms are easier to explain, but in this case, as well, most users will not be able to understand them. Therefore, the scenario is quite similar to the previous one and also here the exploitation of natural language can improve comprehensibility.

Future directions and challenges

In the credit risk context, we analyzed which fairness metrics can better evaluate the ML model and which explanation tools can get a better insightful interpretation of the decision process. For decision-making systems, it is necessary to understand the causality of learned representations, and visualization tools need to be human-centered through natural language. It turns out that Shapley values can help scientists to understand something more on what features have more influence on the outcome. However, no explanation has been developed specifically for the knowledge domain of the end-user. One of the possible future directions concerns the development of intelligent conversational systems that can adapt the explanation to the type of user they interface with, guaranteeing the fairness of the treatment received and proposing counterfactual analyses of their characteristics. The explanation needs to be more Human-centered and more user-friendly without disclosing all the financial institution's decision criteria, risking adverse actions from unfair users.

Fig. 1 .

 1 Fig. 1. An example of generation of natural-language explanations

https://www.fca.org.uk/publications/research/research-note-machine-learning-ukfinancial-services

https://oecd.ai/ai-principles

https://ec.europa.eu/digital-single-market/en/news/white-paper-artificialintelligence-public-consultation-towards-european-approach-excellence


Federica Cena
email: federica.cena@unito.it

Amon Rapp
email: amon.rapp@unito.it

Claudio Mattutino
email: claudio.mattutino@unito.it

Noemi Mauro
email: noemi.mauro@unito.it

Liliana Ardissono
email: liliana.ardissono@unito.it

Simone Cuccurullo
email: simome.cuccurullo@unito.it

Stefania Brighenti
email: stefania.brighenti@unito.it

Roberto Keller
email: roberto.keller@aslcittaditorino.it

Maurizio Tirassa
email: maurizio.tirassa@unito.it

A Personalised Interactive Mobile App for People with Autism Spectrum Disorder

Keywords: Autism, Mobile App, Crowdsourcing, Recommender Systems

The PIUMA app aims at allowing users with autism to explore their city, finding new places that can be interesting for them but at the same time do not annoy them. Users can navigate the city through an interactive map that is both personalized and crowdsourced.

Introduction

Autism Spectrum Disorder (ASD) is characterized by the presence of persistent deficits in social communication and interaction, and deficits in social-emotional reciprocity, accompanied by the presence of restricted, repetitive behavior, interests or activities and hypo/hyper reactivity to sensory stimuli [START_REF] Tavassoli | Sensory over-responsivity in adults with autism spectrum conditions[END_REF]. Moreover, several studies [START_REF] Robertson | Sensory perception in autism[END_REF][START_REF] Rapp | Finding a secure place: A mapbased crowdsourcing system for people with autism[END_REF] reported that people with autism tend to avoid places that may negatively impact on their senses. Sight, smell and hearing are relevant with reference to mobility in urban environments. Such sensory aversions may result in anxiety, fatigue, disgust, sense of oppression or distraction [START_REF] Rapp | Finding a secure place: A mapbased crowdsourcing system for people with autism[END_REF].

In this paper, we describe the app resulting from the PIUMA project (Personalized Interactive Urban Maps for Autism) 4 , which aims to support ASD people in moving and living their city by means of an interactive map. The map guides everyday movements by providing tailored suggestions, i.e., by recommending items (Points of Interest -PoIs) suitable to ASD people according to their interests and aversions. Moreover, the map is crowdsourced. In other words, it is populated with PoIs, comments, and reviews by people with autism, and caregivers, as well as by anyone wishing to contribute to make ASD people's lives easier. The app provides the user with different modalities of content fruition. The user can navigate the map to find interesting places for her/him, or (s)he can use the search tool or apply filters for category and sensory features.

In the following, we discuss three main aspects of the app: the recommendations (Sec. 2), the crowdsourcing features (Sec. 3), and exploration modalities (Sec. 4). Section 5 concludes the paper with technical details.

Personalization

A key feature of our app is the possibility to provide the user with personalized recommendations (as you can see in Fig. 1a). We consider both user preferences, and item compatibility in the identification of the most relevant items for a user. We assume that the overall compatibility of a user with a PoI depends on her/his compatibility with each of the PoI's sensory features. Regarding preferences, we assume that the user's interest in a PoI depends on her/his preference for the category to which the item belongs [START_REF] Mauro | Personalized recommendation of PoIs to people with autism[END_REF]. We combine the compatibility (comp) and preference (pref ) evaluation of an item to estimate its rating (r) by means of a weighted model that balances these two components in a way that is personalized to the individual user. For this purpose we introduce the α parameter, which takes takes decimal values from 0 to 1:

r = α * comp + (1 -α) * pref (1)
In order to receive any recommendations, a user has to fill in a short questionnaire where (s)he declares which category of place (s)he likes, and which sensory features of places bother her/him. This step is not mandatory: if the user decides to skip it, (s)he is still able to use all the functions of the app, but without the personalized suggestions.

Crowdsourcing

The app enables users to extend the knowledge about places by adding evaluations of their sensory features. This is crucial for two very different reasons:

-The first one concerns user empowerment. Enabling users to provide their opinions does not only improve their life, favouring independent living, but also their participation in urban life. This can impact on the sense of selfefficacy and empowerment of persons with cognitive problems who, by contributing to the crowdsourced data collection, can actively work for a collective goal that may bring benefits to other people as well as to themselves. -The second reason is technical, i.e., in order to recommend places that are compatibile with the user's aversions, we need sensory information about items. As the Open Data made available by geo websites like OpenStreetMap5 lack such information, a possible solution is that of acquiring sensory features directly from people, a task that perfectly fits the previously discussed user empowerment goal.

The user interface of our app is thus designed to gather sensory features of places [START_REF] Rapp | Finding a secure place: A mapbased crowdsourcing system for people with autism[END_REF]. For each place, the user can rate each of its features, and in particular its level of i) brightness, ii) crowding, iii) noise, iv) smell and v) openness; see Fig. 1b. Ratings take values from 1 to 5. These features have been defined based on findings collected in a user study carried out by some of the authors of the present paper [START_REF] Rapp | Finding a secure place: A mapbased crowdsourcing system for people with autism[END_REF], and of state-of-art research [START_REF] Robertson | Sensory perception in autism[END_REF]. By interacting with the app, the user can also provide a global evaluation of the place. For each PoI, the system returns the average of the collected evaluations. Such information represents the domain knowledge base to be used as a source for the generation of personalized recommendations. Finally, people can insert new places. In the case of Torino (Italy), more than 200 markers were placed on the map by real users.

Filters

We provide users with different content fruition modalities. They can navigate the map to discover interesting places (by clicking on a marker of the map, the app shows information about the place, and the values of its sensory features), they can use the topside search box to find specific locations, or they can apply filters to select the PoIs to be displayed in the map. Users can customize the content of the map concerning PoIs that are near to their current location by applying filters. We provide two types of filters: by category and by sensory features. Using the category filter, the user can select the types of places to

Technical details

The PIUMA app has been developed to run on Android smartphones and tablets. The minimum supported Android version is 7.0 Nougat (API Level 24) that, according to the latest data from Google6 , can bring PIUMA to 73.3% of Android devices. Users' data are stored into a MongoDB server.

Fig. 1 :

 1 Fig. 1: User interface of the PIUMA app -personalized recommendations.

  (a) Categories (top) and sensory (bottom) filters. (b) Map with PoIs based on the filters of Fig. 2a.

Fig. 2 :

 2 Fig. 2: User interface of the PIUMA app -filters.

Supported by Compagnia di San Paolo.[START_REF] Tavassoli | Sensory over-responsivity in adults with autism spectrum conditions[END_REF] The project involves a collaboration among Computer Science and Psychology Departments of the University of Torino and Adult Autism Center of Torino, Italy.

https://www.openstreetmap.org/

https://developer.android.com/about/dashboards


Carmelo Ardito

Tommaso Di Noia

Eugenio Di Sciascio

A Biofeedback System to Compose Your Own Music while Dancing

Keywords: Brain Computer Interface, Biofeedback, Music

Brain Computer Interfaces can enable engaging interactions between different art forms such as music, dance, painting. Building on this, we present a demo of a biofeedback system: a dancer wearing a NeuroSky headset adapts her performance according to the music she listens to. The same music has been generated by a music-composition software depending on her own real-time mental status represented by different fluctuations of some EEG parameters.

Introduction

Fig. 1. An actual implementation of the proposed biofeedback system.

The relationship between various art forms is one of the fields of investigation that crosses all artistic disciplines, from musicians/composers to choreographers and theater or cinema directors. The interplay between music and dance has profoundly influenced composers of every era by creating new technical-compositional models both in the world of music and in the world of dance itself. At the same time, in Computer Science, the interest in Brain Computer Interfaces (BCIs) has significantly increased in recent years, because it represents a possibility for implementing a more intrinsic human-machine relationship. Also biofeedback systems [START_REF] Michael | Brainwaves and BioFeedback. A Brief History-Implication for Music Research[END_REF] are gaining momentum, characterized by the continuous loop between users wearing a BCI and computers.

In this paper, we propose the demonstration of a biofeedback system that puts in a choreographic/compositional relationship a dancer with a music composition software developed on purpose and two musicians. The dancer wears a NeuroSky MindSet device [START_REF]NeuroSky EEG device[END_REF] that detects some neurologic parameters, in particular her attention values [START_REF]Neurosky: NeuroSky's eSense™ Meters and Detection of Mental State[END_REF]. Depending on these values, the software generate a music polyphony that is presented on an electronic score to musicians, who play it. In turn, the music thus produced influences the mental status of the dancer, who will adapt her choreography according to what she hears, generating new polyphonic musical bars.

System Architecture and Operation

The hardware part of the system is based on the NeuroSky MindSet, i.e. a device that can detect the brain EEG signal through a dry electrode placed on the forehead at the Fp1 position (see [START_REF] Tyagi | A Review of EEG Sensors Used for Data Acquisition[END_REF] for further details). It translates electrical impulses related to brain neuronal activitỳ into digital numerical signals that can then be processed by a computer. The electrical signals can be measured by placing the electrode of the device on the forehead. The NeuroSky MindSet headset returns two parameters, namely attention (similar to concentration) and meditation (similar to relaxation) [START_REF]Neurosky: NeuroSky's eSense™ Meters and Detection of Mental State[END_REF], [START_REF] Bronzino | Principles of Electroencephalography[END_REF]. The software is provided with a graphical interface that permits to choose on which musical scales model the polyphony is generated (see Fig. 5). All possible scales that can be calculated within an octave interval can be selected. Finally, the generated rhythmic sequences are superimposed on the previously selected scalar model. The process is repeated for all four voices of the polyphony texture. The music score reporting the resulting polyphony is displayed and continuously updated on a screen, so that musicians can play it.

Conclusion

We presented a biofeedback system useful for the interaction between music and dance in a BCI context. A possible evolution of this work could be to implement emotion recognition techniques to further improve the interaction between different art forms.

Fig. 2 .

 2 Fig. 2. Dancer wearing the BCI headset during a live performance.1

Fig. 3 . 4 .

 34 Fig. 3. The NeuroSky MindWave Mobile 2 used in the system.

Fig. 4 .

 4 Fig. 4. System workflow.

Acknowledgments.

  The authors acknowledge partial support of the following projects: PON Casa delle tecnologie emergenti di Matera "CTEMT" (CUP I14E20000020001), Servizi Locali 2.0, PON ARS01_00876 Bio-D, PON ARS01_00821 FLET4.0, PON ARS01_00917 OK-INSAID, H2020 PASSPARTOUT.

Fig. 5 .

 5 Fig. 5. User interface of the Polyphony Generator module.

The cables connected to the dancer's wrists, as well as the light strips on the headset are only for choreographic reasons and do not have any further technical implication.


Joseph Aneke
email: joseph.aneke@uniba.it

Carmelo Ardito
email: carmelo.ardito@poliba.it

Giuseppe Desolda
email: giuseppe.desolda@uniba.it

Making Warning Messages Personal: A Big 5 Personality Trait Persuasion Approach

Keywords: Personalized messages, Big 5 personality trait, Social Network Sites (SNSs)

Several mitigation strategies in form of warning messages against phishing attacks have continued to fail largely due to user negligence. Thus, it is important for researchers to focus not only on the accuracy of the provided recommendations but also on other factors that influence the acceptance of recommendations and the extent to which these recommendations are convincing or persuasive. In this paper, we present our ongoing approach that leverages on the Big 5 Personality trait model and users digital traces harvested from their social networks, thereafter, transformed into a personalized warning message. We argue that stimulating users through personal recommendations evokes an understanding of the implications of their actions or inaction.

Introduction

Recipients of a warning message in the event of a phishing attack are more likely to heed a warning if they believe they are the intended recipient [START_REF] Baker | Hurricane evacuation behavior[END_REF] and perceive the content as applicable to their unique personality. In light of this, the issue of personalization becomes key in presenting warnings. In this work we are guided by the following research questions: RQ1: How can we deploy human-related theories to enhance users' acceptance of warning recommendations? RQ2: How can unique digital traces combined with persuasiveness as intrinsic characteristics be integrated into the design of a warning mechanism system to increase users' acceptance of the warning recommendations?

The impact of the Big 5 personality traits with reference to digital traces on social networks has been a subject of interest to researchers with a possible positive correlation been envisaged in recommendations [START_REF] Winter | The effects of trait-based personalization in social media advertising[END_REF]. For instance, authors in [START_REF] Baker | Hurricane evacuation behavior[END_REF] through Facebook likes by users demonstrated that Computer-based personality judgments were more accurate and reliable than those made by humans or close friends.

In developing our personalized warning messages, we developed a framework consisting of two key approaches. First, there was the refinement of the warning message text to appeal to recommended individual personality trait preferences. Secondly, we exploited harvesting unique digital traces from already existing users' relationships in SNS to reinforce the acceptance of the warning advice. Thus, a variant of the warning text tailored to a user's personality trait, combined with inputs (e.g., profile picture) of a Phishing attack expert, makes the message personalized and easier to accept.

The rest of the paper is structured as follows: in Section 2 we present a background to related work, Section 3 discusses our conceptual framework. Conclusions and future work are provided in Section 4.

Background of related work

Personalization or customization of messages is a term used to describe different styles of tailoring strategies [START_REF] Houghton | Who needs social networking? An empirical enquiry into the capability of Facebook to meet human needs and satisfaction with life[END_REF]. Its applications could be found in websites, emails, health campaigns, etc. [START_REF] Kocaballi | The Personalization of Conversational Agents in Health Care: Systematic Review[END_REF]. The research community has tried to understand, among others, its structure and user interconnection, as well as interactions with Big 5 personality traits, in keeping persons glued for so long to messages [START_REF] Viswanath | On the evolution of user interaction in facebook[END_REF].

The Big 5 Personality Traits model was developed by the psychologists Paul Costa and Robert McCray and it is one of the most common models used in modern research into information security behavioral analysis [START_REF] Wang | The relationships among the Big Five Personality factors, self-esteem, narcissism, and sensation-seeking to Chinese University students' uses of social networking sites (SNSs)[END_REF]. It consists of five factors that represent personality traits: conscientiousness, agreeableness, extroversion, neuroticism, openness to experiences [START_REF] John | The Big-Five trait taxonomy: History, measurement, and theoretical perspectives[END_REF].

With respect to information security, studies have shown that there is a significant relationship between conscientiousness personality and agreeableness personality as it pertains to security policy [START_REF] Franke | Cyber situational awareness-a systematic review of the literature[END_REF].

Design of Warning Text

In order to address RQ1, we adopted the Big 5 personality trait spectrum. In this spectrum, each of the individual personalities may fall anywhere either from Low (Negative), Neutral, or High (positive) [9], as depicted in Fig. 1. Hence, our designed warning texts were done to represent several variants that returned all possible emotional values within the spectrum range. The generation of the explanations proposed in the text of our warning messages is based on a generic pattern purposely defined in [START_REF] Aneke | Help the user recognize a phishing scam: The design of explanation messages in warning interfaces for phishing attacks[END_REF][START_REF] Aneke | Designing an Intelligent User Interface for Preventing Phishing Attacks[END_REF][START_REF] Aneke | Unpacking Warning Messages: Towards Mitigating Phishing Attacks[END_REF] to instantiate each warning message. For example, for the Time life security indicator, a phishing warning message text reads as: "This website was created recently. This is typical of fraudulent websites.

Do not disclose private information on it.".

According to the warning guidelines indicated in [START_REF] Bravo-Lillo | Improving computer security dialogs[END_REF], we produced five variants of the above text message (see Table 1), which had the same objective but were expressed in different ways. We then subjected these messages to sentiment analysis and readability measures evaluations. The messages returned all possible spectrum values (-ve, Neutral and +ve), as reported in the sentiment analysis column.

Warning message variant across spectrum range

Message Variant Sentiment Analysis

Reach (%)

Word count

Smog Index

This website was created recently. This is typical of fraudulent websites. Do not disclose private information on it.

-

Integration of Digital Traces in Warning Messages

In order to address RQ2, we adopted Cialdini's persuasion principles [START_REF] Cialdini | The science of persuasion[END_REF]. It states that when people are faced with a lifeline decision, they defer to experts or similar others, they are most likely inclined to accept requests made by somebody they like in a way, and usually have the tendency to commit to their previous or reported opinion or behavior.

The conceptual framework, presented in Fig. 2, comprises of two stages: Identification and pre-processing and customization. At the Identification and pre-processing phase, three key activities take place: 1) the defiled security indicator is identified from the pool; 2) the personality traits of the user and the spectrum rage values for the warning text are marked; 3) the list of social networks that the user has subscribed to is also identified and ranked. At the Customisation phase, the best fit warning text and the most persuasive image (e.g., profile picture) selected from users frequently used social network is selected. The most persuasive image is determined through a possible relationship to a friend or friend of friends. Once a relationship graph has been derived, the shortest path is selected, and the resultant friends' picture is selected and uploaded on the tray for the warning message.

Fig. 2. Conceptualization of the proposed framework

Fig. 3 shows how the mechanism has been implemented as a splash-screen displayed as soon as the user tries to access a deceptive website. On the left side, one of the variants of the warning message is shown, while the right panel aims at persuading the user to not proceed since the users' friends Joseph, Lucia, and Andrea strongly recommend that he should not proceed to the fake URL (www.bank.0f.america.com).

Conclusion

In this paper, we presented a mechanism that aims at drawing users' attention to phishing warning messages and help them to take the right decision about proceedings towards a website or not. Our contribution is novel in that we proposed an approach that tailors the warning text to suit the Big 5 personality trait. Also, the personalized warnings included referrals and pictures harvested from a user's SNS and recommendations from experts in security domains implicit or explicit to them. As a next step, we would evaluate our design with real users. Also, it could be extremely interesting to study the possible use of the multiple SNSs which users do not subscribe to.

Fig. 1 .

 1 Fig. 1. Big 5 Personality Trait Spectrum

Fig. 3 .

 3 Fig. 3. Implementation of the personalized warning message mechanism