Augusto Celentano

Emmanuel Dubois
email: emmanuel.dubois@irit.fr

An Analytical Frame for Describing Multi-* Interactive Systems

Keywords: Interaction design, Distributed interaction, Multi-device, Software engineering

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We introduce the term multi-* to denote such interactive scenarios. In such systems the user changes task and location and uses several devices according to the varying needs of a complex procedure. Interaction develops along dynamic sequences that depend on the technology used and the environmental context. The information accessed changes in content, detail, representation and deployment on devices. While the goals and applications domains of such systems are wide and differentiated, to delimit the area of discussion to a reasonable size we focus on information access in such scenarios.

A set of relevant issues comes immediately to our attention, in the form of questions concerning both the analysis of the domain and the design of applications and interactive experiences. First, how to analyze such situations, i.e., how to identify the components of such complex user experiences, and their relations and connections? And then, since the design involves complex issues due to the large variance of the technical solutions and the wide background needed to compare and evaluate them, how can we match the design goals with the multi-* environment?

The ultimate aim of this work is to contribute to the identification of the most appropriate interaction solutions supporting such scenario: (1) by taking into account the specificities and constraints of a multi-* system perspective, as they are more numerous and variable due to the multiple nature of the environment;

(2) by taking advantage from experience, models, tools and practices in other fields of complex systems design and development.

The contribution of this paper is the proposal of an analytical frame of reference for reasoning about multi-* interactive systems, based on a 3-dimensional space <Actions, Information, Envi-ronment>. The dimensions characterize, respectively: (1) the user activity and its structure, (2) the information accessed and processed, and the way it is organized and viewed, (3) the (physical) environment in which the user moves and acts. We call such space the user experience space. Points and regions in it describe different aspects concerning the user experience at different levels of detail. Links connecting points and regions describe the dynamics of the user experience during the execution of an activity.

Introduction

The scenarios in which complex interactive applications are developed and used have notably changed in the last decade. The growth of digital applications in a large spectrum of domains, the spread of embedded systems, ubiquitous networks of sensors, and ambient augmentation have produced an increase of information available both for direct processing within dedicated applications and indirectly, as a context to users for personalized experiences. The rapid evolution of mobile systems, perceptual interfaces, The choice of the three dimensions <Actions, Information, Envi-ronment> is not casual. As established in the Norman's Action theory [START_REF] Norman | The Design of Everyday Things[END_REF][START_REF] Norman | Human-centered design considered harmful[END_REF] when using an interactive systems, any user is looking to reach a goal and will necessary have to form an intention and perform actions to reach this goal: the granularity considered to describe these actions leads to the identification of different potential points of view that are addressed through the Action dimension. The second dimension, Information, is tightly coupled to the focus of our work, i.e., accessing a rich set of information: given the huge potential variety (origin, nature, type, etc.) of information, better understanding which part of the set the user is dealing with is covered by this dimension. Finally, as the collected data and the access to it is distributed on several devices possibly located in various physical places, the Environment dimension identifies the different parts of the user's environment that play a role in the interactive experience.

The three dimensions are orthogonal in principle, but specific applications and domains can show dependencies and constraints among an action, the location in which it is executed and the way data is accessed. The user experience space is able to capture such dependencies and constraints which appear as non-homogeneous distributions of points and regions describing the development of a specific activity.

Each of the three dimensions is structured into subcategories, recalling principles and theories described in the literature and applied to the analysis and design of complex human systems. We present these refinements in the following subsections.

!e Actions Dimension

As an extension of the concept of action according to Norman, we refer to the Activity Theory (AT) as a more general framework that describes the human work in terms of mediated interaction between human beings and the world [START_REF] Bertelsen | Activity theory[END_REF][START_REF] Kuutti | Activity theory as a potential framework for human-computer interaction research[END_REF]. According to the AT the human activities are structured in tasks, which are composed of actions. In the context of this work we use the term operation to refer specifically to actions on interactive devices. A further level of detail is represented by the gestures that a user does for executing an operation, but we shall not enter into such a level of detail which is relevant only for the design and analysis of low level operations concerning interface implementation.

Activities, tasks and operations have different relations with the space, which is distributed (for activities), local, or devicebound (for operations). They also involve several levels of interaction: a high level (corresponding to the activity) supports a complex interactive experience and is driven by the user's motivations; an intermediate level (corresponding to the execution of a task) supports a goal-directed interaction; a low level (corresponding to the operation) is characterized by properties like speed, efficiency, ergonomics, etc. Such hierarchy is recognized also by other models of the human work, such as the Hierarchical Task Analysis [START_REF] Stanton | Hierarchical task analysis: Developments, applications, and extensions[END_REF], and is therefore a suitable dimension to describe a space of complex user experiences.

!e Information Dimension

The Information dimension describes the views through which a user can access and process information. In general, during a multi-* interactive experience the user accesses several information contents, at different levels of detail, with different representations, for different goals. Information is presented and processed in several steps, whose correspondence with the user activity, tasks and operations is established by the application design at some extent, and by the amount of user freedom in interaction. The technology and the environment add further elements of variability. The relations between the information content and its representation, the refinements in levels of detail, the association with the interaction functions become complex. The design choices are often left to the interaction designer's skill, but in a complex multi-* paradigm a more solid background is required.

In a previous paper [START_REF] Celentano | A layered structure for a design space dedicated to rich interactive multimedia content[END_REF] we discussed a design space for rich interactive multimedia content, organized into five layers based on the identification, definition and characterization of five categories of information, targeted to different knowledge goals and needs. The outermost category is the Universe, which defines a knowledge domain and encompasses the whole information processed by some interactive application. The innermost category contains the elementary data Items, the smallest elements of information accessible. Three more layers are the Scenario, the Region of interest and the Compound (an aggregation of elementary data items). We draw from that work such information layering, simplified and re-organized into the three layers Scenario, Region of Interest, and Data. We take apart the Universe and remove the distinction between Compound (a structured set of elementary data) and Item (an elementary data instance), which is relevant only for structured data and aggregate/element operations.

In our experience space the Scenario describes the widest view on information relevant for a user with respect to a specific goal, and subject to a uniform perceptual representation. In terms of an often adopted overview-detail hierarchy the Scenario is at the overview level. It can be examined, modified but the details of data are not accessible at this level.

The Region of interest (ROI) defines a view on the Scenario containing information subject to a specific exploration in the context of some activity or task. The ROI can be accessed by appropriate interaction techniques, possibly on devices different from those used for accessing the Scenario. Indeed, different ROIs represent different sets of information associated to different phases of the user work, or to different user's motivations and goals. A change of ROI can also correspond to a change in the information representation/interaction, or in the deployment on different devices.

The Data layer contains and exposes to the user the detailed information relevant for the user, presented and accessed according to visualization and interaction modalities appropriate for the information type.

The layered structure and the related terminology were conceived in [START_REF] Celentano | A layered structure for a design space dedicated to rich interactive multimedia content[END_REF] with reference to a spatial representation of a data domain, e.g., a map. It is, however, appropriate for any structured information domain, as it clearly appears if we replace the words Scenario, Region of interest and Data with, e.g., Database, View and Record.

!e Environment Dimension

The role and importance of the space in human activities has been recognized by many authors. According to [START_REF] Kuhn | Handling data spatially: spatializing user interfaces[END_REF], "space is fundamental to perception and cognition because it provides a common ground for our senses and actions". The association between the structure of the physical space and the interactive activities done in it has been qualified in [START_REF] Celentano | Agents for Distributed Context-Aware Interaction[END_REF][START_REF] Pittarello | Interaction locus: a Multimodal Approach for the Structuring of Virtual Spaces[END_REF] through the concept of Interaction Locus, an association of information and environmental properties. The concept supports the structuring of an interactive environment in a set of places (loci) each devoted to a specific and recognizable user activity.

In our experience space we refer to an interaction environment structured in a set of locations (not necessarily hierarchically layered), in which tasks are executed. We consider a task to be executed on a specific device or set of devices in a certain physical location or, in case of mobile devices, through a sequence of operations with the same device, possibly in continuously changing locations. Physical locations are constrained by the relations between the physical ambient and the application functions (e.g., a security area where specific procedures must be followed) and by the availability of suitable devices and resources (e.g., a desktop equipment). "Floating" locations can be dynamically instantiated and changed during the execution (e.g., mobile devices can be seamlessly used in different areas). A border situation that we do not face in this work is the ambient computing in which the perception of the locations fades into a continuum space surrounding the user.

The lack of a spatial hierarchy is typical of distributed environments where the association between actions and locations depends on the deployment of the resources rather than on predefined structural properties of an activity, and distinguishes this dimension from the other two. It's possible to conceive hierarchical interaction spaces where locations are grouped to support sets of connected activities, tasks or actions. In our view such hierarchy does not add interesting properties to the ambient, and we shall not elaborate on this. Given these three dimensions, their composition in a user experience space is illustrated in Figure 1: the Information axis holds increasing levels of information detail from the Scenario to the elementary Data structures. On the Actions axis increasing levels of detail from the user activity down to the operations performed on a specific device are placed. On the Environment axis an unordered, non-hierarchical collection of locations is placed. The shaded regions in the figure represent the actions and information layering. In principle, all the combinations of actions and information layers are possible, but in practice some of them are not plausible. In particular, given the complexity and size of the information space, it is unlikely that a specific elementary data is perceivable and is manipulated at the activity level: data selection and manipulation are typically done in the context of tasks and operations. In the Environment dimension the large layers represent a multiplicity of locations for activities and complex tasks, while the small layers represent single locations at the operation level.

Use Of !e Experience Space

To give concreteness to the experience space use we present a usecase based on the neOCampus project developed at University of Toulouse (https://neocampus.univ-tlse3.fr). neOCampus is a multidisciplinary approach aiming at the definition of a smart, innovative and sustainable university campus. The project is based on a huge information system recording and storing instant and historical values about gas, electricity and water consumption, together with equipment technical data. Consumption data is collected at different levels, from buildings down to individual devices. A control room provided with a variety of devices (computers, mobile devices, large screens, interactive tables) hosts the control applications. For space reasons we cannot describe here the project details. We recommend the reader to consult a refined presentation of the project in [START_REF] Celentano | Interaction-in-the-large vs interaction-in-thesmall in multi-device systems[END_REF].

We address a simplified version of the experiment described in [START_REF] Celentano | Interaction-in-the-large vs interaction-in-thesmall in multi-device systems[END_REF]. Figure 2 shows the experiment setup, which is a lab replica of a part of the control room installation. The PC on the left is the console and is not used in this use-case description. The user activity consists in the examination of the energy consumption for selected buildings and rooms according to various representations, using several devices: a multimedia table (in the lower part of Figure 2) showing a map of the campus on which buildings and rooms can be selected; a large screen (top of Figure 2) showing energy consumption values; a workstation (on the left) for executing a simulation of the energy management systems; a mobile device (placed on the multimedia table) for controlling the data shown.

Figure 3 shows the main components and phases of the user experience. The Activity spans several devices distributed in several locations of the control room, and refers to the Scenario made of the campus map with associated cumulative energy consumption values on the Information dimension. The Activity ("A" in Figure 3) is split in three tasks: map navigation and building or room selection ("T1"); energy related data selection and examination ("T2"); modification of energy management parameters ("T3").

Task T1 moves (in several steps through operations not shown in Figure 3) from the Scenario to the ROI level by defining which building/room is selected. Task T2 moves from the ROI level to the Data level through operations that select the data to be shown (power, gas and water consumption, instant or cumulative values, historical series, etc.) and their representation (e.g., numerical vs graphical). Operations may apply to cumulative data, as represented in Figure 3 at the ROI level by "O1" and "O2": for example, one floor might be represented in red if the consumption is too high. Operations can also apply to individual values and types of representation, as shown in Figure 3 at the Data level by "O3" and "O4"; for example, the detailed amount of KWh consumed may be displayed for each individual device of a room.

Task T3 involves the execution of a simulation program that changes the energy management parameters, allowing the operator to examine how they affect the energy consumption. This is accomplished through a set of operations at Data level (collectively denoted by "O5" in Figure 3). Each set of operation, e.g., open a property bar, select an icon, enter a threshold, etc., has an impact on different dimensions of the simulation, hence contributes to the realization of separate user sub-tasks.

The operations that compose each task use different devices, hence are represented in Figure 3 in different locations: the multimedia table for the operations of Task 1, the mobile system and the large screen for Task 2, and the simulation workstation for Task 3. The Activity execution spans all the locations.

In Figure 3, albeit severely simplified for readability reasons, the flow of execution, the change of information level examined and the change of devices are clearly visible, describing in a synthetic way how the user experience evolves.

Matching So"ware Engineering Concepts To !e Experience Space

This analytical frame is a first brick towards the building of a theoretical support for reasoning about the growth of complexity in interfaces and interaction environments. However, ensuring the potential and validity of such a frame is not an easy task. As HCI is addressing more and more complex systems, we chose to rely on knowledge and principles well established in the domain of complex systems to reinforce the relevance and interest of our approach. In particular we have examined the Software Engineering (SE) domain as a source of inspiration and we discuss in the following how its principles match the structure of the user experience space introduced above, hence ensuring the ability of our approach to tackle the analysis and design of complex systems. The literature reports several studies about the integration of SE principles and HCI, examined from different points of view: the integration of usability studies into SE [START_REF] Daniel | Understanding UI integration: A survey of problems, technologies, and opportunities[END_REF][START_REF] Di Mascio | If usability evaluation and software performance evaluation shook their hands: A perspective[END_REF][START_REF] Seffah | HCI, usability and software engineering integration: Present and future[END_REF]; the comparison of the requirements of the two domains and the active role of the user in modifying the system behavior even in unanticipated ways [START_REF] Brown | HCI and requirements engineering: Exploring human-computer interaction and software engineering methodologies for the creation of interactive software[END_REF][START_REF] Coutaz | Architectural design for user interfaces[END_REF]; the interdisciplinary approach needed to join human related concerns to the technical design issues [START_REF] Chatty | Supporting multidisciplinary software composition for interactive applications[END_REF][START_REF] De Paula | Conveying human-computer interaction concerns to software engineers through an interaction model[END_REF].

Individual design principles, good practices and cross-relations between SE and the HCI domains have been discussed at some extent [24, see also the above cited references]. There is, however, no systematic discussion of the specific issues posed by the multi-* interactive systems, nor evidence of an effort to integrate them into a unique, comprehensive view of multi-* systems design at several levels of detail.

We aim to fill this gap: we seek to establish here that our analytical frame conforms to/reproduces/adopts the main principles forming the core of SE and contributing to support the design of complex systems from a technical point of view. Among the main principles of Software engineering, top-down design, design in-thelarge vs design-in-the-small and modularity have a primary role, contributing to ensure that a system can evolve and be maintained in a sustainable way. We claim that they can fit our experience space and be applied to multi-* HCI design.

Top-down Design

Interaction design should be layered in a sort of top-down fashion, starting from the design of the overall interaction environment concerning the different phases of the application and their interconnections, down to the choice of the relevant properties of the devices, their physical properties and the operations to be executed on them. Indeed, top-down design is the result of the systematic application of refinements to structured information, functions and interaction. That means to define a hierarchy of data specifications, access functions and interaction techniques such that at each level of the hierarchy they can be combined and evolve almost independently.

Our user experience space ensures such an approach by: (1) refining the activity into tasks and operations according to the Activity Theory, and (2) refining the information space into a layered organization of categories related to the task-operation hierarchy.

Design In-the-large And In-the-small

In software design the intra-module and inter-module programming are distinct activities, each with own methodologies, languages and tools [START_REF] Coutaz | Architectural design for user interfaces[END_REF]. Interaction design should address with different perspectives and models the conception and organization of the phases of a distributed procedure wrt the details of a single interactive interface. Some authors have introduced the term interaction in the large to denote long lasting interactive activities that benefit from the integration of different HCI models [START_REF] Dix | Interaction in the large[END_REF][START_REF] Johnson | Interaction in the large: Developing a framework for integrating models in HCI[END_REF], but they have not discussed the issues of the different design goals at such level wrt the interaction on a local scale.

A split between local and distributed interaction in distributed systems has been proposed by the authors of this paper in [START_REF] Celentano | Interaction-in-the-large vs interaction-in-thesmall in multi-device systems[END_REF] through the concepts of interaction in-the-large vs in-the-small to describe the user's actions at different levels of granularity. The interaction in-the-large layer defines the sequence of tasks a user does moving from one phase to the next of a complex activity involving multiple devices and locations. The interaction in-thesmall layer defines the sequence of operations done by a user on local devices in a short and continuous span of time, with a limited and well defined goal.

This duality is supported in our approach through the identification of: (1) the different places in which a set of interactive resources is available, and (2) their relations to the other dimensions, actions and, mainly, information. In particular, information layering in Scenario, Region of interest and Data contributes to delimit the borders in which and across which local and global actions are executed.

Modularity

In SE modularity comes naturally from the layering of different levels of refinement in a top-down design approach and from information structure design. In the HCI context the layering of actions and information of a multi-* system should result in the identification of interaction modules which, as software modules, encapsulate the detail of the interaction for a specific phase of execution. The difference is that an interaction module transforms an initial state of a part of the system into a final state according to the variable user behavior, not only according to an autonomous and predefined algorithm. In HCI the design of interaction is still largely the result of a creative process. Hence how modularization is designed is not dominated by the functional specifications of the application but involves also a number of non-functional requirements. These are defined, among other factors, by the foreseen interaction model, the device technology, the environment, the user profile, etc.

Like traditional software modules, an interaction module should include a description of the internal behavior (the implementation of interaction model of a task or activity), as well as an interface with other modules and with the environment (location, device properties, etc.) to assure not only a correct exchange of parameters for a correct execution but also the necessary continuity and coherence in the interaction across modules. This concepts and requirements needs to be further refined but are already compatible with our approach.

Conclusion

Facing the growing complexity of interactive systems, in particular those devoted to data exploration, we have introduced the concept of multi-* interactive system. In order to help reasoning about such systems, we have proposed a 3-dimensional space <Actions, Information, Environment> and refined its structure on the basis of well-established principles taken from the design of complex systems. Extending these principles to the context of multi-* interactive system and linking them to our user experience space confer to it an initial proof utility for the design of such complex system. Future work will be targeted to a refinement of the interaction module concept, supporting the design of a multi-* interactive system as a set of interconnected and coordinated modules covering both the interaction and the logic functions of a set of tasks. Such an approach is similar to the concept of GUI widgets, modules defined at a software level supporting short-term interactions; interactive modules will play the same role but at the experience level.

Figure 1 . 5 +Figure 2 .

 152 Figure 1. The user experience space

Figure 3 .

 3 Figure 3. An example of activity decomposition


Marc Moreaux
email: mr.moreaux@gmail.com

Michael Garcia Ortiz

Isabelle Ferrané
email: ferrane@irit.fr

Benchmark for Kitchen20, a daily life dataset for audio-based human action recognition

Frederic Lerasle

LAAS-CNRS 3 Univ. de

II. MOTIVATIONS AND JUSTIFICATION FOR KITCHEN20

There are three main reasons for assembling this dataset. Firstly, given this context in which smart systems should understand human actions in their houses based on audio streams, we figured that kitchen-related activities would be interesting as it is a closed environment with characteristic noises. Furthermore, skilled robots could rely on this recognition to provide relevant help to humans, making it a very interesting domain of study. Secondly, we wanted a rawaudio dataset to gain flexibility on the algorithms that would be applied to it. In fact many deep learning models have emerged [START_REF] Kim | Samplelevel cnn architectures for music auto-tagging using raw waveforms[END_REF][START_REF] Van Den Oord | WaveNet: A Generative Model for Raw Audio[END_REF][START_REF] Tokozume | Learning environmental sounds with end-to-end convolutional neural network[END_REF][START_REF] Tokozume | Learning from Between-class Examples for Deep Sound Recognition[END_REF] in the later years using raw-audio instead of hand crafted features. Also illustrating this interest, new conferences based on raw-audio inputs have emerged such as DCase 1 that have seen its attendance double within 2 years, reaching 150 attendees in 2018. Thirdly, to the best of our knowledge, the literature does not provide raw-audio datasets containing that many classes related to kitchen sounds. For these reasons, a kitchen related audio dataset with raw-audio samples is created.

To have a better grasp on the novelty of Kitchen20 we compare it with the literature and found that Kitchen20 could be compared to (1) a subset of AudioSet like DCASE 2018 Task 4 (DCase 4): Large-scale weakly labeled semisupervised sound event detection in domestic environments [START_REF] Serizel | Large-Scale Weakly Labeled Semi-Supervised Sound Event Detection in Domestic Environments[END_REF]; (2) a subset of FreeSound like DCASE 2018 Task 2 (DCase 2): General-purpose audio tagging of Freesound content with AudioSet labels [START_REF] Fonseca | General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline. Detection and Classification of Acoustic Scenes and Events[END_REF]; and (3) another subset of FreeSound like A human quantitave appreciation of the audio samples contained in Kitchen20 is provided as well as several machine learning benchmarks on both Kitchen20 and ESC-70.

I. INTRODUCTION

Companion robots could serve for monitoring and assisting isolated persons, particularly elderly people. Robots which act and perceive in a human environment must rely on embedded sensors of different modalities in order to perceive and recognize human activities, and in order to provide help if necessary. Vision is often used as the principal modality in performing such recognition tasks [START_REF] Richard | NeuralNetwork-Viterbi: A Framework for Weakly Supervised Video Learning[END_REF][START_REF] Wu | Watch-n-Patch: Unsupervised Learning of Actions and Relations[END_REF] but audio can contribute and help in these challenging situations by providing additional information. Moreover, a robot does not always have a clear line of sight, and could thus rely on audio to help understand what the human is currently doing. Both the robotic and the audio communities can benefit from a systematic method to assess the performances of their smart devices using an audio dataset that is quantitatively augmented with challenging sounds coming from human interactions with objects of their daily environment. This paper aims at presenting a new dataset that is going in that direction. This new dataset shall be opensource, extensively tested with several state of the art machine learning techniques, and be easily available to the public for further testing. We acknowledge that similar datasets exist but they are not fully attending our requirements.

Thereafter, section II presents our motivation for building Kitchen20, section III provides an extensive description of the dataset, section IV describes the benchmark implementations, section V shows how the results obtained with this dataset compare with state of the art datasets, and finally, section VI concludes on the work. ESC-50 [START_REF] Karol | Dataset for environmental sound classification[END_REF]. A summary of the differences in terms of (a) number of classes, (b) number of samples, (c) sample lengths, (d) audio origin, and (e) label strenght between these datasets is given in Table I.

As specified in its documentation, DCase 4, consists of many 10s audio samples extracted from Youtube videos that are weakly labeled. In fact, by listening to the audio clips, one may notice that more actions than the labeled ones may be happening in a sample, for example, people may be speaking while they are extracting elements from the microwave oven. In addition, this dataset only has four classes closely related to kitchen actions which are Dishes, Frying, Blender, and Running water. In DCase 2, the samples are extracted from FreeSound, and, in the verified part of the dataset, each sample is strongly (accurately) labeled. Yet the samples of DCase 2 have varying audio-clip timespans ranging from 30ms to 30s and, as DCase 4, there are very few classes specific to kitchen environments with only Microwave oven, and Drawer open or close.

Finally, ESC-50, is an environmental dataset providing 50 classes of environmental sounds, strongly labeled, comprised of raw-audio samples, and carrying consistent audio clips of 5s. But, the dataset does not include kitchen labels. A subset of ESC-50, composed of 10 classes, has been proposed by the same authors to easily test new algorithms and is called ESC-10.

Seeing that a dataset like ESC-50 matched our expectations in terms of samples per class, audio-types, and audio-lengths, it was decided to build a new dataset called Kitchen20 that would resemble ESC-50 in its file structure and would have our kitchen oriented classes. In contrast to DCase 4, Kitchen20 is more specific in the sense that, by listening entirely to a sample, an annotator can be confident in the label of the clip he is listening to. In contrast to DCase 2, all the samples in Kitchen20 last 5s. In the end, Kitchen20 have 14 classes specific to the kitchen environement. The remaining 6 classes are related to kitchen but could appear in other environements. Because it is built on top of ESC-50, Kitchen20 and ESC-50 can be merged together in a new dataset that we refer to here as ESC-70 2 II, together with a detailed description of their corresponding actions.

In Kitchen20, every class is represented by 40 raw-audio samples. Each of these samples are 5s long. Each sample is attributed to one of 5 folds that are later used, in the learning process, to perform cross validation. All the samples were recorded at least at 44.1KHz and downrated to 44.1KHz when needed. The audio samples were either extracted from 2 Named as an extension of ESC-50 with the permission of the author of [ FreeSound or recorded in various real kitchen environments when samples found on FreeSound were scarce. Overall, 662 samples originated from FreeSound and 138 samples were recorded in 9 kitchens by 8 people using various modern mobile phones. Different non-overlapping samples extracted from the same original audiotrack are clustered together in the same fold when possible. The compatibility of Kitchen20 with ESC-50 accounts for all the similarities in audio-lengths, audio-rates, audio sources, and dataset folding.

Because Kitchen20 was gathered from FreeSound and individual kitchens, there is no overlapping samples between Kitchen20 and public audio-visual datasets such as YouTube-8M [START_REF] Abu-El-Haija | YouTube-8M: A Large-Scale Video Classification Benchmark[END_REF], or the Epic-Kitchens Dataset [START_REF] Damen | Scaling Egocentric Vision: The EPIC-KITCHENS Dataset[END_REF]. As a result, Kitchen20 may be used as an auxiliary task while learning any of these audio-visual datasets.

Kitchen20 is available on GitHub3 together with a Pytorch accessor to the dataset. This accessor creates files that are retro compatible with the original implementation of both EnvNet and EnvNetV24 . These models are presented in section IV. Overall, the python module can retrieve Kitchen20, ESC-10, ESC-50, and ESC-70 datasets with helper classes. An example of the module is shown in Listing 1.

Together with the dataset, a human based study was conducted to understand the perception difficulties found in Kitchen20. The dataset has been evaluated by a panel of 16 participants consisting of 5 women and 11 men aged from 22 to 45. All of the participants were new to the dataset, and were asked to classify 75 to 80 different sounds each. During the experiments, the participants were allowed to listen to each sample as many times as they wanted prior to classifying them. Before moving to the next sample, the participants were shown The results of this experiment by our panel shows an accuracy of 78.9% on 996 randomly sampled sounds. The confusion matrix obtained from this experiment is displayed on Figure 1. From this matrix we observe that the classes Eating, Book and Cutlery are the easiest to figure out whereas Fridge, Microwave and Juicer are the hardest. We explain the confusion by (1) the fact that our panel was not used to using the type of juicers present in the dataset, (2) the fact that the fridge slightly sounds like a cupboard when both are closing, and (3) a microwave sounds like a stove-fan when it is running. From these observations, one might conclude that this dataset is not trivial.

IV. BENCHMARK CLASSIFIER IMPLEMENTATION

This section presents two types of classification baselines evaluated on both Kitchen20 alone and jointly with ESC-50.

The first type of baseline relies on classical machine learning approaches. The methods used are K-Nearest-Neighbor (KNN), Random-Forest (RF) and Support Vector Machine (SVM) [START_REF] Christopher | Pattern recognition and machine learning[END_REF][START_REF] Breiman | Random Forests[END_REF] using the implementations from Scikit-Learn 5 . All three methods use hand-engineered features that are based on the zero-crossing rate (ZCR) [START_REF] Gouyon | On the use of zero-crossing rate for an application of classification of percussive sounds[END_REF] and the Mel-Frequency Cepstrum Coefficients (MFCC) [START_REF] Louis | Spectral analysis and identification of Dutch vowels in monosyllabic words[END_REF] of the audio at 44.1KHz. To be more specific, the features are (a) the mean and The hyperparameters of each method are finetuned using a Bayesian Optimization [START_REF] Močkus | On bayesian methods for seeking the extremum[END_REF] maximizing the accuracy of each method over the 5 folds. The set of parameters concerned by the optimization are a subset of those available on Scikit-Learn. The KNN is optimized over its number of neighbors. The RF is optimized over the number of features to consider when looking for the best split (Max-features), the number of trees estimators in the forest, the maximum depth of the trees (Depth-tree), and the criterion measuring the quality of a split. Finally, the SVM is optimized over the penalty parameter C, the kernel coefficient γ and the kernel type. Each Bayesian Optimization is performed using the Python module Hyperopt7 called with the Tree Parzen Estimator (TPE).

The second type of baseline are two deep learning models: EnvNet and EnvNetV2. These networks were presented with ESC-10 and ESC-50 [START_REF] Karol | Dataset for environmental sound classification[END_REF] and form a good baseline for raw-audio classification problems. EnvNet is a 7-layer-deep convolutional-neural-network; it takes as an input a 1.5s rawaudiofile at 16KHz; in its core, the network uses a mechanism to change its time-based convolutions to frequencybased convolutions; and, the network ends with a probability distribution over each classes in the dataset it is trained on. EnvNetV2 is an upscaled version of EnvNet. The core mechanism of the network and the probability distribution at the end of the network are the same, but EnvNetV2 is a 13-layer-deep convolutional-neural-network with 1.5s rawaudiofiles at 44.1KHz.

The training procedure of this network relies on feeding Strongly Augmented data [START_REF] Tokozume | Learning from Between-class Examples for Deep Sound Recognition[END_REF] and learning with a Between Classes strategy [START_REF] Tokozume | Between-class Learning for Image Classification[END_REF]. Strongly Augmented data implies having input clips that are (1) normalized, (2) padded with zeros, and (3) randomly stretched or contracted. Between Classes learning implies feeding the network with two superposed sounds randomly picked from two classes and predicting the gain ratio between these two inputs. Regarding the hyperparmeters of these models, the number of epochs, the learning rate (LR), and the learning schedule are set to the same default values as the ESC-10 learning scheme present in the original code 8 [START_REF] Karol | Dataset for environmental sound classification[END_REF] because the default values of the ESC-50 learning led to an exploding gradient. All these hyperparameters are reported on Table III in Section V.

V. CLASSIFICATION RESULTS

This section presents the results of the experiments starting with Kitchen20 and followed by ESC-10, ESC-50 alone or merged with Kitchen20. In either cases, the hyper-parameters optimization results are presented first. They are followed by the accuracies of the models and general remarks.

After 200 steps of Bayesian Optimization on each of the classical machine learning algorithms, applied on Kitchen20, it is found that the best KNN is reached when the algorithm uses a single neighbor; the best RF is reached with 765 estimators, 8 features max, a maximum depth tree of 16 and the entropy criterion; and the best SVM is reached with a linear kernel, a C value of 0.023 and γ equals 4.202.

KNN Neighbors: 1 RF Estimators: 765 -Max-features: 8 -Depth-tree: 16 Criterion: Entropy SVM Kernel: linear -C: .023γ: 4.202 EnvNet nEpochs: 1200 -LR: 0.01 -Schedule: .5, .7, .9 EnvNetV2 nEpochs: 2000 -LR: 0.01 -Schedule: .3, .6, .9 Given these hyperparameters, the KNN achieves 35.3% accuracy, RF achieves 49.5%, SVM achieves 44.5%, EnvNet achieves 71.8%, and EnvNetV2 achieves 79.1%. All the results of these classifiers are summarized, per folds, on Figure 2, and throughout the folds on Table IV. For further understanding, the confusion matrix obtained with the best baseline system, namely EnvNetV2, is shown on Figure 3.

Similarly to what has been observed in image classification, deep neural networks based on raw-audio outperform classical approaches based on extracted features. Also, a network properly trained with more parameters such as EnvNetV2 is more accurate than its lower dimension counterpart, namely EnvNet. 8 Looking at the confusion matrix displayed in Figure 3, we see that the classes Eating, Book and Cutlery are the easiest to figure out whereas Fridge, Microwave and Juicer are the hardest.

Jointly looking at the human and the neural confusion matrices, we notice that many confusions made by humans are reflected on the neural learning. For instance, in both cases, microwaves are mistaken with stove-fans, blenders with juicers, clean-dishes with water-flowing, and dishwashers with both stove-fans and boiling-water.

Examining the sole EnvNetV2 confusion matrix, one can also notice the low true-positive-rate of the microwave and the stove-fan. When listening to the corresponding audio-clips, one may notice that the audio from both classes only differs from one another by a triggering sound in the clip, like a door closing for the microwave or a button switched for the stove-fan. Finally, Clean-dishes is another interesting class as its predictions get mixed-up with water-flowing, plates and cutlery which truly represent what clean-dishes is: a mix of plates and cutlery manipulated under water-flowing.

ESC-70:

Now looking at the combination of Kitchen20 with ESC-50, after 200 steps of Bayesian Optimization on each of the classical machine learning algorithms, it is found that the best KNN is reached when the algorithm uses 10 neighbors; the best RF is reached with 980 estimators, 3 features max, a maximum depth tree of 17 and the Gini criterion; and the best SVM is reached with a linear kernel, a C value of 0.023 and γ equals 1.766. All of these values are summarized in Table V. Given the hyperparameters shown in Table V, the KNN achieves an accuracy of 23.5%, the RF achieves 37.6%, the SVM achieves 33.0%, EnvNet achieves 71.3%, and EnvNetV2 achieve 78.1%. These accuracies are reported on Table IV. A summary of the accuracies achieved by these machine learning methods applied on different datasets, namely ESC-10, ESC-50, ESC-70, and Kitchen20, is shown in Figure 4.

The dominance of the algorithms observed on Kitchen20 globally stays the same throughout ESC-10, ESC-50, and ESC-70. For every dataset presented, the neural networks are performing better than their traditional machine learning counterparts in terms of accuracy. All baseline approaches have an accuracy increase from ESC-50 to Kitchen20, but both neural networks that show a relative accuracy drop. We hypothesize that this last drop comes from the fact that the hyperparameters used for training EnvNet and EnvNetV2 are the default values found in the original code and are not specific to Kitchen20.

VI. CONCLUSION

In this paper, a new dataset called Kitchen20 was introduced. Its objective was to leverage the use of the audio modal- ity in a context of helping elderly people in their houses with a robot. Our motivations for building this dataset was (1) the fact that both appliances sounds and sounds based on human interactions in a kitchen are characteristic and (2) the fact that companion robots could be helpful in such environment. Taking into account these motivations, we figured the literature showed a lack of strongly annotated, raw-audio, open-source datasets related to human actions in a kitchen and decided to build one called Kitchen20. Kitchen20 can convienienltly be merged with an existing dataset from the literature called ESC-50 such that a more challenging dataset issued from this merging would be available to the community.

To understand the consistency and what one could expect of Kitchen20, the dataset was evaluated by humans. Going further, many machine learning benchmarks are tested and compared both on Kitchen20 alone and paired with ESC-50. Together they reflect the intra-class similarities that make these datasets learnable.

In later work, we want to analyse to which extend a robot could rely on the audio modality together with the vision modality to understand a human activity in a kitchen environment. To some extend, the Epic-Kitchen Dataset may be used to test such purpose.

VII. ACKNOWLEDGMENT

  . III. KITCHEN20 DESCRIPTION Kitchen20 contains 800 sound samples equally split into 20 different classes. The 20 classes are themselves split into (a) 10 classes related to kitchen appliances and (b) 10 classes related to human manipulations. All 20 classes are listed in Table

7 #

 7 1 i m p o r t t o r c h 2 from t o r c h i m p o r t nn 3 from t o r c h . u t i l s . d a t a i m p o r t D a t a L o a d e r 4 from k i t c h e n 2 0 i m p o r t e s c 5 i m p o r t k i t c h e n 2 0 . u t i l s a s U 6 Get a t r a i n i n g s e t a t 16KHz , 8 # w i t h on-t h e -f l y d a t a a u g m e n t a t i o n 9 k 2 0 t r a i n = e s c . K i t c h e n 2 0 ( 10 f o l d s = [ 1 , 2 , 3 , 4 ] , 11 a u d i o r a t e =16000 , 12 t r a n s f o r m s = [ 13 U . p a d d i n g ( i n p u t L e n g t h / / 2 ) , 14 U . r a n d o m s c a l e ( 1 . 2 5 ) , 15 U . r a n d o m c r o p ( 2 4 0 0 0 ) , # 1 . 5 s 16 U . n o r m a l i z e ( f l o a t ( 2 ** 16 / 2 a v a l i d a t i o n s e t a t 16KHz 20 k 2 0 v a l = e s c . K i t c h e n 2 0 ( 21 f o l d s = [ 5 ] , 22 a u d i o r a t e =16000 , 23 t r a n s f o r m s = [ 24 U . r a n d o m c r o p ( 2 4 0 0 0 ) , # 1 . 5 s 25 U . n o r m a l i z e ( f l o a t ( 2 ** 16 / 2 ) ) 26 ] ) 27 28 # P y t o r c h D a t a L o a d e r s 29 t r a i n l o a d e r = D a t a L o a d e r ( k 2 0 t r a i n , 30 b a t c h s i z e =32 , s h u f f l e = T r u e ) 31 v a l i d l o a d e r = D a t a L o a d e r ( k 2 0 v a l , 32 b a t c h s i z e =3 2) 33 Listing 1. Creating train and validation sets with the Kichen20 library the ground-truth of the sample they had just classified but they did not have the possibility to change their annotation.

Fig. 1 .

 1 Fig. 1. Human confusion matrix obtained by a panel of 16 participants on Kitchen20.

Fig. 2 .

 2 Fig. 2. Accuracies achieved on 5 fold testing of Kitchen20 by K-Nearest-Neighbor, Random-Forest, SVM, EnvNet, and EnvNetV2 approaches.

Fig. 3 .

 3 Fig. 3. EnvNetV2's confusion matrix obtained from the validation sets of the 5 folds of Kitchen20. KNN Neighbors: 10 RF Estimators: 980 -Max-features: 3 -Depth-tree: 17Criterion: Gini SVM Kernel: Linear -C: .023γ: 1.776 EnvNet nEpochs: 1200 -LR: 0.01 -Schedule: .5, .7, .9 EnvNetV2 nEpochs: 2000 -LR: 0.01 -Schedule: .3, .6, .9

Fig. 4 .

 4 Fig. 4. Mean and variance of the accuracies achieved on cross folded learning of ESC-10, ESC-50, Kitchen20, and ESC-70 by five distinct machine-learning classifiers.

TABLE I COMPARISON

 I OF SELECTED RAW-AUDIO DATASETS. IN THIS TABLE FS STANDS FOR FREESOUND, IND. FOR INDIVIDUALS, AND AS FOR AUDIOSET. *UNBALANCED DATASET.

						Toulouse
					Toulouse, France
					lerasle@laas.fr
		# c l a s s	# s a m p l e s	s a m p l e l e n g t h o r i g i n	l a b e l
	Kitchen20	20	800	5s	FS/Ind. strong
	ESC-50	50	2000	5s	FS	strong
	DCase 2	41 9500* 300ms/3s	FS	strong
	DCase 4	10 2244*	10s	AS	weak

  Abstract-This paper introduces a new raw-audio, environmental, kitchen-related, non-vocal dataset to fill what we consider as a gap in the context of audio datasets. Our so-called Kitchen20 dataset is compared to other datasets such as ESC-50 and shown that both datasets can be merged together in what we call ESC-70.

1 http://dcase.community/

  [START_REF] Karol | Dataset for environmental sound classification[END_REF]

	Appliances
	Dishwasher A dishwasher running
	Microwave A microwave being set up and running
	Blender A blender blending and stopping
	Fridge Opening and closing a fridge
	Juicer Using an electric juicer
	Stove-fan Switching and running a stove-fan
	Frying-pan Cooking oily food in a pan
	Stove-burner Switching on and off gaz on a stove-burner
	Boiling-water Water boiling in a kettle or a pan
	Water-flowing Water flowing from a tap
	Human Manipulations
	Clean-dishes Cleaning dishes and cutlery with water
	Chopping Chopping vegetables
	Cupboard Opening and closing cupboards
	Drawer Opening and closing drawers
	Cutlery Manipulating cutlery with noise
	Plates Manipulating plates with noise
	Sweep Sweeping a floor
	Book Opening, closing and going through a book
	Peel Peeling vegetables
	Eat Chewing chips and other foods

TABLE II THE

 II 20 AUDIO CLASSES PRESENT IN THE KITCHEN 20 DATASET.

  https://github.com/mil-tokyo/bc learning sound/blob/master/opts.py

		KNN	RF	SVM EnvNet	EnvNetV2
	Kitchen20	35.3	49.5	44.5	71.8	79.1
	ESC-70	23.5	37.6	33.0	71.3	78.1

TABLE IV MEAN

 IV ACCURACIES ACHIEVED ON CROSS FOLDED LEARNING OF KITCHEN20, AND ESC-70 BY FIVE DISTINCT MACHINE-LEARNING CLASSIFIERS.

TABLE V HYPERPARAMETERS

 V USED FOR TRAINING GIVEN ALGORITHMS ON ESC-70.

https://github.com/marc-moreaux/kitchen20

https://github.com/mil-tokyo/bc learning sound

https://librosa.github.io/librosa/index.html

https://github.com/hyperopt/hyperopt

We thank the authors of ESC-50 for both approving the merging of Kitchen20 with their dataset and for approving the reuse of their naming convention leading to ESC-70.

This work has been carried out under the funding of an industrial doctorates fellowship from National Association for Research and Technology, France

This work is supported by a collaboration between SoftBank


domain: spi phys math info

text:
Florent Bollon

Anne-Lise Marchand

Nicolas Maille

Colin Blättler

Laurent Chaudron

Jean-Marc Salotti

Interpersonal trust to enhance cyber crisis management

In the field of cyber-security, software performance optimization is a major focus of research to better prevent cyber threats. However, once threats are detected, they have to be managed by a human operator or more often by human operators' joint actions. The purpose of this study is to show that in these collaborative situations, the interpersonal trust level between these actors shapes their handling of the threat. Forty-five participants performed, with twenty-eight different fictive teammates, a collaborative counting task that included aleatory phases of jamming. Each fictive teammate was described through two adjectives selected to induce a predefined level of interpersonal trust (low or high). The subject and his collaborator worked on different systems with different objects to count and different jamming phases. Nevertheless, each participant had the possibility of supervising his teammate's work by checking out his task and modifying his answers (number of targets and jamming events reported) if required. The subject was responsible for validating the team's final result. The experimental data show that, in this type of collaborative task, the interpersonal trust level has indeed an influence on the supervision strategy used and the team performance.

Introduction

In order to prevent the increase in the number of cyber-attacks, States are setting up cyber operations centers (C2Cyb). The operators of these C2Cybs, who monitor the state of systems and the information flows, are collectively responsible for detecting, correlating and analyzing the various indicators that can make sense of a cyber crisis [START_REF] Boin | Building European Union capacity to manage transboundary crises: Network or lead-agency model?[END_REF]. These indicators, which are difficult to perceive but that predict perturbations in the system, are called weak signals [START_REF] Saritas | The big picture-trends, drivers, wild cards, discontinuities and weak signals[END_REF] and are discrete, ephemeral, distributed and difficult to interpret.

In a complex and highly interconnected cyberspace, the collection, detection, analysis and comprehension of weak signals requires aggregating information from various actors, both human and material, engaged in monitoring the global system. The amount and complexity of the information available in cyberspace makes it impossible for a single operator to compile all the information in a limited amount of time. The heterogeneous nature of the signals also increases the uncertainty of operators about how to interpret them. As a result, decisions made by the C2Cyb team leader are based on information that is usually unverifiable and transmitted by his/her teammate. This information can sometimes contradict the leader's information. A question therefore arises: how does the team leader in C2Cyb consider this contradictory information when making decisions in a situation of uncertainty?

The decision-making strategies studied in psychology and economics are sometimes based on theories that adopt probabilistic visions. In particular, the dual-process theory presupposes the existence of two distinct rationality processes [START_REF] De Neys | Dual Processing in Reasoning: Two Systems but One Reasoner[END_REF][START_REF] Evans | In two minds: dual-process accounts of reasoning[END_REF][START_REF] Evans | Dual-Process Theories of Reasoning: Contemporary Issues and Developmental Applications[END_REF][START_REF] Kahneman | Frames and brains: elicitation and control of response tendencies[END_REF] used in optimizing decision-making. According to this theory, two systems, called system 1 and system 2, coexist. System 1 is a fast, intuitive system that does not require the use of working memory [START_REF] Evans | Dual-Process Theories of Reasoning: Contemporary Issues and Developmental Applications[END_REF]. System 2 is used for tasks requiring thoughtful decisionmaking, and, by extension, a calculation of the probabilities of possible futures generated by the decision. System 2 is slower than system 1 and requires greater cognitive resources and task-specific access to working memory [START_REF] Evans | Dual-Process Theories of Reasoning: Contemporary Issues and Developmental Applications[END_REF]. Thus, when a person uses system 2, s/he performs a conditional probability calculation in order to make the best decision.

In the work underlying this theory, the probability distributions of the different options are usually clearly identifiable by the participant, assisting decision-making [START_REF] Kahneman | Prospect Theory: An Analysis of Decision under Risk[END_REF]. However, due to the abundance of information in cyberspace, no probability distribution seems to be applicable by the operator to analyze the veracity and the impact of weak signals. In fact, when a team leader has to make a decision, he can only do it based on his own information (the weak signals directly perceived) and the information transmitted by his teammates without being able to check it or to compare it with a probability distribution. In these cases, other mechanisms that facilitate decision-making should therefore come in play. Among these mechanisms, trust is often described as a uncertainty reducer [START_REF] Meyerson | Swift trust and temporary groups[END_REF] that facilitates decision-making [START_REF] Bell | Regret in Decision Making Under Uncertainty[END_REF]. This article proposes to study in environments with high uncertainty, what the role of trust is in the leader's decision-making when he cannot verify the data transmitted by his teammate and when these data are different from his own. [START_REF] Posten | Egocentric Foundations of Trust[END_REF] established a trust predictability function, i.e. trust would allow us to anticipate the possibilities by calculating their probabilities of occurrence. This is what Gambetta indicated (1988: p. 217) when he defined trust as "a particular level of the subjective probability with which an agent assesses that another agent or group of agents will perform a particular action, both before he can monitor such action (or independently of his capacity ever to monitor it) and in a context in which it affects his own action". Gambetta's definition and, more generally, the research conducted in economics [START_REF] Williamson | Calculativeness, Trust, and Economic Organization[END_REF] and sociology [START_REF] Coleman | Foundations of Social Theory[END_REF] link the phenomenon of trust to the notion of probabilistic evaluation and are thus in accordance with the dual-process theory approach. Trust can be considered as the calculation of the perceived cost-benefit [START_REF] Williamson | Calculativeness, Trust, and Economic Organization[END_REF] of a relationship. In this calculative approach, "Trust emerges when the trustor perceives that the trustee intends to perform an action that is beneficial" (Rousseau, Sitkin, Burt & Camerer, 1998, p.399). Indeed, trust can only occur in relationships that bring rewards to both parties [START_REF] Lewicki | Trust in relationships[END_REF] and can be summarized, from an economic perspective, by a probability calculation [START_REF] Williamson | Calculativeness, Trust, and Economic Organization[END_REF]. This notion of probability calculation is the link between the literature on trust and the literature on decision-making. In theory, the decision corresponds to "a choice or a set of choices drawn from the available alternatives" [START_REF] Bellman | Decision-Making in a Fuzzy Environment[END_REF]. Like trust, decision-making is the choice of the alternative that subjectively presents the best cost/benefit ratio. In this approach, decision-making is no more than the result of a probabilistic assessment of the consequences of different choices [START_REF] Lowenstein | The Role of Affect in Decision Making[END_REF]. In the decision-making process, the trust mechanism could therefore be seen as a readjustment of the probabilities perceived by an operator of the possible futures generated by different options, the option chosen by the operator being the option with the best cost/benefit ratio. This interpretation is consistent with Lewis and Weigert's (1985, p.969) definition of trust when they describe it as "to trust is to live as if certain rationally possible futures will not occur". In teams operating in uncertain environments such as cyberspace where operators cannot assign probabilities about future events generated by a decision [START_REF] Duncan | The characteristics of organizational environments and perceived environmental uncertainty[END_REF], trust may therefore facilitate decision-making. In cases where the leader cannot verify in situ the information transmitted by his teammate, and therefore assign a probability as to the accuracy of this information, the level of trust could be a determining factor in decision-making, in particular by facilitating acceptance by the leader of the information transmitted by his teammate. When the level of trust between a leader and his teammate is high, the information provided by the teammate should be perceived by the leader as probably more accurate than when the level of trust is low.

Hypothesis 1: For a team leader, a high level of trust in his teammate leads to a greater acceptance of the unreliable information that the teammate transmits.

In C2Cyb, weak signals reported by a teammate are often unverifiable by the leader. This impossibility of verifying the information means that it is impossible for the leader to assign an effective probability to these weak signals. When the leader cannot rely on actual probabilities, he has to assign a subjective probability [START_REF] Kahneman | Subjective probability: judgment of representativeness[END_REF] to these weak signals. To do this, he can only rely on his own information, particularly the evaluation of the weak signals that he has himself received. He can therefore compare the weak signals he has perceived directly with those communicated to him; if all these weak signals correspond, they will be considered consistent. In this case, the leader should perceive the information transmitted by his teammate as probably more reliable than in the case of nonconsistent signals.

According to the dual-process theory, in the case of weak consistent signals, decisionmaking is fast and intuitive (system 1). In the case of non-consistent signals, because of the necessary probability calculation, the response is slower (system 2) (Hypothesis 2). In this case, when the level of trust between team members is low, if the leader has not perceived any evidence of an attack "directly", he may judge as unlikely the elements in favour of an attack that are provided by the teammate. In other words, a leader will be more inclined to accept the contradiction if he trusts his teammate (hypothesis 3).

Material and procedure

Method

To test these hypotheses, it is necessary to create an experimental context similar to that faced by cyber leaders. This environment must offer the participant (here, a team leader) a main task and a supervision task on which can be grafted one or more weak signals directly perceived by the leader or transmitted by a teammate. Despite the "weak" character, these signals must be sufficiently detectable. The leader has to make a decision based on these weak signals that he cannot verify in situations where he has a variable level of trust in his teammate and where these signals are not always consistent.

The chosen task fulfils these conditions: it offers the participant a main task of counting aircraft on a photograph with the possibility of checking a similar task with a teammate. The teammate is fictional and only presented by a predefined and controlled level of trust [START_REF] Bollon | Cyber-attaques : Organiser la confiance[END_REF]. During this task, "jamming" (see Figure 1) constituting the weak signals may occur. The participant has to indicate the number of jamming events without being able to check the number indicated by his teammate. This consideration of the teammate's data corresponds to a "blind" decision. It is this decision that is analyzed in this study and not the decisions related to the main task that can be checked on the teammate's side.

Figure 1. The picture on the left is an example of a photograph used in the experiment; the picture on the right is the jamming that can occur at any time. In the event of jamming, the image on the right appears for one second before disappearing.

In order to test the impact of trust on acceptance of the information transmitted (hypothesis 1), it is necessary to induce different levels of trust in the participant, to check this induction and to test, for each level, the percentage of information transmitted by the teammate and accepted by the participant. The trust-level induction is an independent variable (IV) with two controlled levels (low and high) that will be called "trust-levels" in the following section of this article. The percentage of information transmitted by the teammate and accepted by the leader (in %) is a dependent variable (DV) collected during the experiment that will be called "decision" in the following section of this article.

In order to test the impact of consistency on the choice of decision system (system 1 or system 2) (hypothesis 2), it is necessary to induce consistent and non-consistent signals and to compare the time taken by participants to validate a decision according to these signals. The consistent or non-consistent nature of the signals is an IV which will be called "consistency" in the following section of this article. The consistency distribution is controlled by the occurrence of the weak signals transmitted. The time taken by participants to validate a decision (in ms) is a DV, called " time ", collected during the experiment.

In order to determine the impact of trust on decision-making during consistent and/or non-consistent events (hypothesis 3), the two IVs explained above as well as the DV "decision" are used.

Participants 45 people (46.6% women and 53.3% men) with an average age of 22.7 years (SD: 1.09%) participated in this study. All participants were second-year engineering students. No participants had any health problems; all had normal or corrected vision.

Protocol

Before the start of the experiment a briefing was carried out, and all participants completed an informed consent sheet. Following this, the participants carried out a 5minute training session before starting the experiment. The experiment was divided into 28 trials, each with 4 phases. For each trial the participant worked with a different teammate (computer simulated behaviour). 14 trials were performed with a trustworthy teammate (high trust) and 14 trials with a non-trustworthy teammate (low trust). In order to avoid an order effect, teammates' profiles were randomly drawn. All participants therefore worked with all teammate profiles but in a different order.

Participants performed the experiment in groups in computer rooms that did not allow them to see what was happening on the other participants' screens. At each trial, the participant thought s/he was doing the task in collaboration with one of the other participants in the room, although in reality all the teammates were fictitious. Each participant performed the task on an ordinary desktop computer using the keyboard and mouse. The screens of all participants were similar in terms of resolution and brightness.

The task was carried out in 4 phases. The first phase of each trial was designed to introduce to the participant the characteristics of his new teammate who was more or less trustworthy (IV "trust level"). The first display showed a pair of words characterizing this teammate (see Figure 2 "1"). This pair of words allowed the participant to induce a level of trust in his teammate, either low (thanks to rather negative elements: unreliable, disloyal, etc.) or high (thanks to rather rewarding elements: professional, organized, etc.) [START_REF] Bollon | Cyber-attaques : Organiser la confiance[END_REF]). These word pairs were obtained by following the protocol described by [START_REF] Bollon | Cyber-attaques : Organiser la confiance[END_REF] which uses social psychology methods to identify social representations of trust in given social groups. In order to ensure that the participant had taken the teammate's characteristics into account, he was asked, on a second display, to find these two characteristics among 8 distractors [START_REF] Bollon | Cyber-attaques : Organiser la confiance[END_REF].

Figure 2 . First display of phase 1. On this display the participant was informed of the instructions (similar throughout the experiment), the time allocated to the task (the time differed depending on the photograph) and the characteristics of his teammate (noted "1" on the image above). These characteristics induced a low or high level of trust in the participant.

The second phase corresponded to the completion of the aircraft counting and jamming counting tasks. The participant had a control display that allowed him to see the countdown of the remaining time as well as the sum of the aircraft counted in the two photographs. This screen contained 4 buttons that allow the participant to (see Figure 3):

1. Display the image on which s/he had to count the aircraft and jamming events 2. Display his teammate's image in order to check the count made by his teammate if necessary 3. Modify the total score, if the participant considered that the number of aircraft counted in the two photographs was not correct 4. Complete this task and move on to the next phase. This button was only active after the participant had validated the number of aircraft present in his photograph.

Figure 3. Main display of phase 2. On this display the participant can see the time remaining as well as the number of aircraft counted by his teammate. With the help of different buttons, the participant can access his own image (button "1"), access the image of his teammate (button "2"), modify the total score (button "3") or complete phase 2 (interlocutor "4")

On the display allowing him to perform his own counting task, the participant found his photograph, the remaining time (see Figure 4 "1") as well as 5 buttons that allowed him to:

-Increment or decrement the count by the number of aircraft (see Figure 4 "2"), -Increase the number of jamming events detected (see Figure 4 "3"), -Validate his count of the number of aircraft (see Figure 4 "4") -Return to the control display (see Figure 4 "5"). The teammate's display was exactly the same as the participant's one. However, on the teammate's screen the buttons were not clickable (except for the button used to come back to the control screen). On the teammate's display, the photograph was different from the one presented on the participant's screen and s/he had to do the aircraft and jamming counting tasks on this other photograph. Moreover, on the teammate's screen, it was impossible for the participant to see the jamming (jamming events were never displayed on the teammate's screen). In this experiment, the participant was not aware that it was impossible for him to see the jamming events occurring on the teammate's screen.

The participant had to count the aircraft in his photograph and validate the team's total result before the end of the time limit. If this was not the case, the trial was failed and an additional trial with a teammate of the same level of trust was added at the end of the session. The validation of the total score allowed the participants to move on to the next phase.

Figure 4. Display used by the participant in phase 2 to perform his counting task. On this display the participant can see the remaining time ("1"). Using different buttons, the participant can count the aircraft (button "2"), increment the interference counter (button "3"), validate his aircraft count (button "4") or return to the main display (button "5") (see

Figure 3).

The third phase was devoted to validation by the participant of the jamming events detected on the two photographs. The display showed the number of jamming events detected by the teammate (see Figure 5 "1") and the number of jamming events detected by the participant (see Figure 5 "2"). Because no real jamming was displayed on the teammate's screen, the participant could not see these jamming events and therefore could not assess the validity of the information transmitted by his teammate. Next to each of these numbers, there were 3 buttons to validate or invalidate the jamming (none, 1 or more). The participant had to make a decision on the number of jamming events to validate on the teammate's photograph (see Figure 5 "3") as well as the number of jamming events to validate on his own photograph. Once this was done, the participant could move on to phase 4.

The different DVs used to test the 3 hypotheses were collected in phase 3. The "decision" DV used to test hypotheses 1 and 3 corresponds to the percentage of jamming events transmitted by the teammate and not validated by the participant (in %). The "time" DV used to test hypothesis 2 corresponds to the time taken by the participant to validate this third phase (in milliseconds).

In order to control the IV "consistency", in this experiment, the jamming events presented to the participant were linked to the jamming events transmitted by the fictitious teammate in order to obtain the following 4 cases:

-No jamming was presented to the participant and the number of jamming events detected by the teammate was 0 (25% of cases) -1 or 2 jamming events were presented to the participant and the number of jamming events detected by the teammate was 1 or 2 (25% of cases) -No jamming was presented to the participant but the number of jamming events detected by the teammate was 1 or 2 (25% of cases) -1 or 2 jamming events were presented to the participant but the number of jamming events detected by the teammate was 0 (25% of cases)

Figure 5. Display used by the participant in phase 3. On this display the participant can see the number of jamming events detected by his teammate ("1") and the number of jamming events he had himself indicated ("2"). The participant had to make a decision on the number of jamming events to be validated for the participant (button "3") and for himself before he could complete phase 3 by clicking on the validation button (button "4").

The first two cases were the so-called consistent cases and the other two nonconsistent cases.

Finally, Phase 4 was the subjective assessment of the participant's level of trust in the results (number of aircraft) reported by his teammate. The purpose of this evaluation on non-segmented scales was to verify that the experimental trust induction equipment was working well and that the participant was working with teammates whom he perceived as trustworthy and others as less trustworthy [START_REF] Bollon | Cyber-attaques : Organiser la confiance[END_REF]. As a high level trust induction should lead to a higher subjective evaluation by the participant of his teammate's performance than a low level trust induction [START_REF] Dirks | The Role of Trust in Organizational Settings[END_REF], the smooth operation of the experimental protocol should therefore lead the participant to assign a high evaluation to teammates in whom he had high trust and a lower one to teammates in whom he had less trust.

Results

Data from the 45 participants were included in the analysis. Before analysing the results required for hypothesis testing, the verification of the induction of trust in the experimental protocol was performed. The subjective evaluation data of the results transmitted by the teammate (recovered in Phase 4) show that when the trust level was high (M = 5.68, SD = 2.33) the subjective evaluation of the teammate's performance seem to be higher than when the trust level was low (M = 5.34, SD = 2.44). In order to validate these results, a one-way repeated measure ANOVA, with the IV "trust level" as a factor, has been carried out. The significant results (F(1,44) = 4.11, p = .04) validated the presence of two levels of trust (high and low).

In order to test the hypothesis that a high level of trust between team members leads to greater acceptance by the leader of the information transmitted by his teammate (hypothesis 1), the DV "decision" and the IV "trust level" were used. For each participant, the data obtained were averaged, for each level of trust. The data indicate (see Figure 6) that between the low trust level (M = 23.4%, SD = 35.7%) and the high trust level (M = 21%, SD = 33%) the performances are relatively similar. A one-way repeated measure ANOVA, with the IV "trust level" as a factor, has been carried out.

The insignificant results (F(1,44) = 1.1, p = .30) do not support hypothesis 1. In other words, trust does not seem to have a direct effect on the validation of the results reported by the teammate.

Figure 6 . Percentages of jamming events transmitted by the teammate and not validated by the participant according to the trust level

In order to test the hypothesis that consistent signals are processed faster by the leader than non-consistent signals (hypothesis 2), the "time" DV and the "consistency" IV were used. For each participant, the data obtained were averaged, for each level of consistency. The results show that when the jamming events were consistent (M = 3750.5 ms, SD = 1192.4 ms) the participants seem to validate phase 3 more quickly than when the jamming events were non-consistent (M = 4272.7 ms, SD = 1515.5 ms).

In order to validate these results, a one-way repeated measure ANOVA, with the IV "consistency" as a factor, has been carried out. The significant results (F(1,44) = 13.37, p <.001) validated hypothesis 2. It would seem that the participants had a different perception of the consistency of the signals.

In order to test the hypothesis that the trust level has an indirect effect on decision making through the modulation of the consistency consideration (hypothesis 3), the DV "decision", the IV "trust level" and the IV "consistency" were used. For each participant, the data obtained were averaged, for each trust level, according to their consistency. The data show (see Figure 7) that when the information transmitted by the teammate was in line with the event perceived as the most likely (consistent case) the leader seems to validate the information transmitted by his teammate, irrespective of whether the teammate was associated with a high (M = 21.3%, SD = 30.8%) or low (M = 18.5%, SD = 32%) trust level. However, when the information transmitted by the teammate supported an event perceived as unlikely (non-consistent cases), when the trust level was high (M = 20.6%, SD = 35.4%), the leader seem to validate the information transmitted by his teammate more easily than when the trust level was low (M = 28.4%, SD = 38.7%). In order to validate these results, a two-way repeated measure ANOVA, with the IV "trust level" and the IV "consistency" as a factor, has been carried out. The results of the ANOVA showed an interaction effect (F(1,132) = 4.86, p = .02 (eta-squared =.068))). A post hoc analysis performed with a Tukey HSD test indicated a significant difference in trust levels for non-consistent trials (p = .02) and no difference for consistent trials (p = .40).

Discussion

This study has investigated the relationship between interpersonal trust and decisionmaking in uncertain environments. On the basis of the dual-process theory [START_REF] De Neys | Dual Processing in Reasoning: Two Systems but One Reasoner[END_REF][START_REF] Evans | In two minds: dual-process accounts of reasoning[END_REF][START_REF] Evans | Dual-Process Theories of Reasoning: Contemporary Issues and Developmental Applications[END_REF][START_REF] Kahneman | Frames and brains: elicitation and control of response tendencies[END_REF], it is expected that decision-making can be supported either by a rapid and intuitive mechanism (system 1) that requires few resources or by a slower mechanism (system 2) [START_REF] Evans | Dual-Process Theories of Reasoning: Contemporary Issues and Developmental Applications[END_REF] involving an assessment of probabilities in relation to the possible situations, risks and benefits of certain alternatives. Applied in a micro-world resulting from cyber crisis management, the experiment aimed to better understand the impact of trust between operators and the consistency of the information exchanged on the decision-making mechanism (through the time taken to complete the task), but also on the decision itself (validation of the partner's response).

The results show that trust does not directly impact decision-making when it is made on unverifiable elements (hypothesis 1). This result seems to contradict existing models that link trust and decision-making [START_REF] Kim | A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents[END_REF]. However, the current literature studies trust in collaborative tasks where participants can at least access the teammate's work to assess it [START_REF] Bollon | Cyber-attaques : Organiser la confiance[END_REF][START_REF] Dirks | The effects of interpersonal trust on work group performance[END_REF], while the protocol presented here proposes a "blind" decision. It seems necessary to further study this type of situation and its impact on trust. On the other hand, the consistency of the information exchanged directly modifies the time taken to take the decision (hypothesis 2). In other words, the consistency of the elements exchanged between operators appears to be the primary criterion that determines the mechanism underlying the decision-making process. Once system 1 or 2 has been chosen, trust comes into the decision to the extent that the system 2 leader agrees more with the teammate's result when he or she has trust even if the information given is nonconsistent. Once system 1 or 2 has been chosen, trust becomes an important factor in the decision-making. In fact, the leader in system 2 accepts the teammate's result to a greater extent when he trusts him even if the information given is non-consistent.

(Hypothesis 3) (see Figure 8). When the trust level is high, the leader's decision-making is in line with the information provided by the teammate and when the trust level is low, the decision making is in line with the information he has himself perceived.

Thus, the study shows that the result of the decision, in terms of the acceptance or non-acceptance of the teammate's information, is linked both to the consistency of the information transmitted and to the level of trust between the operators. When the information received is consistent with the teammate's observations, decision-making is intuitive and not linked to the level of trust between operators and all information is accepted by the leader. On the other hand, when the information is non-consistent and the leader uses system 2 to make his decision, then the level of trust in the teammate who gave him the information can change the decision; the more trust the leader has in his teammate, the more inclined he is to accept his information, whether the latter confirms or invalidates his observations. The level of trust appears therefore to have a significant impact on the probability that the leader will associate with the information received, which the literature has suggested since [START_REF] Gambetta | Can we trust trust?[END_REF] work.

The direct impact of consistency is significant in the implementation of C2Cyb. Indeed, it is important in these safety-critical operations to better understand what can impact the way decisions are made. This can make it possible to adapt operator training by making them aware of the effect of consistency on their decision-making (rapid decision versus rational decision). These results can also contribute to a better understanding of how information is presented on the interfaces in order to help in better decision-making.

In terms of trust, the experiment shows that in the context of a decision made by assessing the risks or costs associated with each choice, trust in the source of the information changes the decision. This result is also important from an applicative point of view because it shows that some weak signals sent back to the decision-maker could be taken into account differently in the decision depending on the relationship between the people. Trust between people therefore changes the trust placed in the data itself. It will therefore be important for socio-technical systems such as C2s to take this dimension into account to optimize its effect on the functioning of the system.

One of the methodological contributions of this study is that we have confirmed experimentally the implementation of different decision-making mechanisms according to consistency, in accordance with the dual process theory. In other words, this micro-world may affect decision-making in either system 1 or system 2. However, the protocol used does not make it possible to check whether the time delay observed as a function of consistency corresponds to a probability calculation. A future study should make it possible to test this probability calculation by detailing how the decision-making process is carried out. It could use this micro-world to better understand the cognitive mechanisms really at work in each strategy.

This study considered two factors, consistency and trust, which combine to modulate the decision-making mechanism and decision content in collaborative activities. It would now be appropriate to investigate how these results are related to the interaction between human operators or whether they are more general. Are the mechanism and decision similar if the operator acts in cooperation with an automated system or artificial intelligence?

Hypothesis 2 :

 2 Consistent signals are processed more quickly by the leader than nonconsistent signals Hypothesis 3: The level of trust has an indirect effect on decision-making by modulating the consistency consideration

Figure 7 .

 7 Figure 7. Percentages of interference transmitted by the teammate and not validated by the participant according to the trust level and the consistent or non-consistent character of the tests

Figure 8 .

 8 Figure8. When the weak signals directly received by the leader and the weak signals transmitted by the teammate are perceived as consistent, decision-making is fast and intuitive (system 1) and is independent of the trust level. However, in the case of weak signals perceived as non-consistent, decision-making is slower (system 2) and involves the trust level. When the trust level is high, the leader's decision-making is in line with the information provided by the teammate and when the trust level is low, the decision making is in line with the information he has himself perceived.