Hakim Djeriouat
email: hakim.djeriouat@univ-lyon2.fr

Nadine Matton

Damien Mouratille

Does hostile intent cause physiological changes? An airport security check simulation experiment

Keywords: Deception, Hostile intent, Heart Rate Variability, Cognitive load, mental stress, aviation security

The present research was aimed at investigating in a simulation experiment whether the initiation of a hostile project in an environment akin to airport security checkpoints would translate in variation of cardiac activity. Twenty-three participants (eight women) enrolled as mock passengers had to make several traverses of a security checkpoint while carrying luggage containing either a neutral or a falsely dangerous item. The traverses with the falsely dangerous item were associated with an elevation of heart rate and higher drops of heart rate variability than the traverses with the neutral item. These effects were more salient for the first traverses. Implication of results for security management and the role of arousal and mental workload in threat detection are discussed.

Introduction

Air transport activities and airport settings have a long history of deadly terror attacks -The world's deadliest airline disaster being the September 11 attacks. Although terrorism perpetrations at airports or in context of air transports are rare, many transport administrations have developed dispendious security programs designed to prevent as much as possible their occurrences. For example, in the aftermath of the transatlantic aircraft plot in 2006, the USA's transport and security administration has deployed the screening passengers through observation technique (SPOT) program to all the United States (Weinberger, 2010).

Behavioral deception sciences have largely influenced these programs (Weinberger, 2010) which mostly rest upon Darwin's hypothesis (1872) that the automatic components of emotional expressions cannot be voluntarily concealed when triggered (Ekman & Sullivan, 2006). If it could be acknowledged that leakage of some observable behaviors is barely unavoidable, their use for detecting deception is problematic because, in realistic settings, both guilty and innocent suspects may exhibit similar cues (e.g., stress, fear of being detected). As a result, it is not striking that behavior detection programs have spawned oftenheated debates about its scientific validity [START_REF] Ormerod | Finding a needle in a haystack: toward a psychologically informed method for aviation security screening[END_REF]Weinberger, 2010), despite the advocacy that results from independent scientific scrutiny could hardly be made public.

What lies at the core of behavior detection programs at airport is the prevention of terrorism or unlawful actions through identification of markers of imminent pre-attack or preassault cues (National Commission on Terrorist Attacks Upon the United States, 2004). Preassault cues highlight the centrality of the notion of intention. Intention is a goal-directed mechanism that works towards an end-state [START_REF] Malle | The Folk Concept of Intentionality[END_REF][START_REF] Gollwitzer | Implementation intentions: strong effects of simple plans[END_REF].

Intention of perpetrating criminal or terrorist actions might have some physiological consequences because of the attentional resources devoted to the goal pursuit and to the concealment of believed suspicious behaviors [START_REF] Mann | Suspects, lies, and videotape: an analysis of authentic high-stake liars[END_REF][START_REF] Sporer | Moderators of nonverbal indicators of deception: A meta-analytic synthesis[END_REF][START_REF] Vrij | Detecting Lies and Deceit: Pitfalls and Opportunities[END_REF]. In this respect, previous findings have linked up hostile intent to changes in various physiological markers. For instance, it was evidenced that instructing people to transport mock explosives lead to higher subjective experience of hostile intent, higher levels of anxiety and inhibitory control than people from the non-hostile group [START_REF] Wijn | Telling friend from foe: Environmental cues improve detection accuracy of individuals with hostile intentions[END_REF]. Similarly, hostile intent in public crowded spaces were found to translate in various forms of bodily responses, including higher frequency of respiration, an overall significant change in salivary cortisol level, an increased heart rate, and a slight elevation of body temperature relative to control conditions [START_REF] Eachus | Hostile intent in public crowded spaces: a field study[END_REF]. Note that in this study, hostile intent was induced by having participants take photos without being detected. The magnitude of hostile intent was manipulated by instructing participants to cross a shopping mall faster than previous sessions and by warning them of the presence of closed-circuit television and plain-clothes security agents and of the fact that some individuals were allegedly caught in the same previous conditions. Additional research used eyeblink measurements as a proxy of arousal and cognitive load and revealed a lower frequency and duration of eyeblinks under the hostile intent condition in an interview, suggesting that nurturing hostile intent is more cognitively demanding [START_REF] Marchak | Detecting false intent using eye blink measures[END_REF]. Strikingly, diminutions in eyeblink rates were stronger when it came to lying about terrorist intent (i.e., explosive transportation) rather than about less serious unlawful actions (i.e., drug transportation), underscoring that the physiological effects of false intent are more elevated in high-stake cases. In a more recent work, it was observed that higher electrodermal activities were found to coincide with deceptive intents, indicating that the mere intent to deceive is sufficient to significantly modulate the sympathetic nervous system [START_REF] Ströfer | Deceptive Intentions: Can Cues to Deception Be Measured before a Lie Is Even Stated[END_REF].

Psychophysiology of Hostile Intent

Should individuals be about to execute a hostile project within a scrutinized place, they will need to feign inconspicuous behaviors [START_REF] Depaulo | Cues to deception[END_REF] and vigilantly monitor their surrounding environment [START_REF] Koller | What Is Suspicious When Trying to be Inconspicuous? Criminal Intentions Inferred From Nonverbal Behavioral Cues[END_REF]. Not only are these actions stressful to most individuals [START_REF] Eachus | Hostile intent in public crowded spaces: a field study[END_REF], but they also engage higher attentional resources and executive control [START_REF] Wijn | Telling friend from foe: Environmental cues improve detection accuracy of individuals with hostile intentions[END_REF]. These actions have consequences on both the sympathetic and parasympathetic branches of the autonomic nervous system. An increase in HR is expected due to the sympathoexcitatory effects of the stressful situation. According to the neurovisceral integration model [START_REF] Thayer | A model of neurovisceral integration in emotion regulation and dysregulation[END_REF], parasympathetically mediated HRV is able to provide insights into the complex relationship between the autonomic nervous system, the subcortical and the cortical regions. To allow an organism to generate emotional and cognitive functioning tailored to fulfill adaptive behaviors, the prefrontal cortex exerts an inhibitory control over the subcortical parts of the brain. This prefrontal-subcortical inhibitory circuit is linked to the cardiac vagal tone [START_REF] Thayer | Heart rate variability, prefrontal neural function, and cognitive performance: the neurovisceral integration perspective on self-regulation, adaptation, and health[END_REF]. "Inhibition involves the suppression of prepotent, irrelevant, or interfering stimuli or impulses associated with concomitant excitatory processes" (Thayer et al., 2009, p. 145). If higher order cortical functions are linked to parasympathetic activations in the nervous system, then individual differences in vagally-mediated HRV at rest should selectively align with performances in executive tasks. In this matter, [START_REF] Hansen | Vagal influence on working memory and attention[END_REF] have shown that high HRV people showed significantly better performances in an executive task than low HRV people.

However, individual differences in HRV did not yield distinct performances in a nonexecutive task, suggesting that HRV is closely related to higher levels of cognitive functions.

In other words, tonic (resting) cardiac vagal activity is associated with cognitive and emotional self-regulatory capacity. Alternatively, experiencing stressful and cognitively demanding situations are more likely to translate into a decrease of parasympathetically mediated HRV to respond to immediate demands. This represents a reduction of phasic (reactivity) cardiac vagal activity or withdrawal of cardiac vagal control, which reflects defensive responses in a challenging environment [START_REF] Thayer | Autonomic characteristics of generalized anxiety disorder and worry[END_REF]. There is evidence that parasympathetically mediated HRV is reduced whenever individuals drive attentional focus toward threatening stimuli [START_REF] Thayer | Autonomic characteristics of generalized anxiety disorder and worry[END_REF], or else during a stressful speech task (Hughes and Stoney, 1996). In addition, some simulation studies incorporating tasks requiring situation awareness (e.g., Police shooting) have revealed that a decrease of HRV in the preparatory and execution phases of the task, as compared to the baseline and post recovery phases, was observed in untrained (but not in trained) police agents, suggesting that they experienced higher difficulty handling a rapidly changing dynamic events [START_REF] Saus | The Effect of Brief Situational Awareness Training in a Police Shooting Simulator: An Experimental Study[END_REF]. Conversely, in the absence of cognitive load, augmentation of phasic cardiac vagal activity has been evidenced to reflect self-regulatory efforts (see [START_REF] Segerstrom | Heart rate variability reflects self-regulatory strength, effort, and fatigue[END_REF]. When the mental resources are strained, no enhancement of phasic HRV is observed, even for people with higher tonic HRV [START_REF] Park | When tonic cardiac vagal tone predicts changes in phasic vagal tone: the role of fear and perceptual load[END_REF]. Diminution of sympathetically mediated HRV as reflecting cognitive workload has also been evidenced in various domains like educational cognitive performance (e.g., [START_REF] Croizet | Stereotype threat undermines intellectual performance by triggering a disruptive mental load[END_REF], mental tracking and arithmetic tasks (Ryu and Myung, 2005), or realistic flying simulations [START_REF] Hidalgo-Muñoz | Cardiovascular correlates of emotional state, cognitive workload and time-ontask effect during a realistic flight simulation[END_REF]. Interestingly, in a complex cognitive task implying additional auditory threat before and during the experiment, [START_REF] Mandrick | Neural and psychophysiological correlates of human performance under stress and high mental workload[END_REF] showed that increase in HR was impacted by both threat and task difficulty while decrease in HRV was uniquely impacted by the level of task difficulty. The authors suggested that the absence of detrimental effects of stress on cognitive performance was handled by cognitive strategies and compensatory efforts. In this respect, concomitant inclusion of both HR and HRV are needed, especially when a situation entails both an emotional (stress) and a cognitive component (mental effort) (see [START_REF] Mandrick | Neural and psychophysiological correlates of human performance under stress and high mental workload[END_REF][START_REF] Riese | Mental Fatigue after Very Severe Closed Head Injury: Sustained Performance, Mental Effort, and Distress at Two Levels of Workload in a Driving Simulator[END_REF]. In addition, according to Thyer et al. (2009), cognitive, affective, and physiological regulation are linked to vagally mediated cardiac function as indexed by both HR and HRV. Heart rate variability decreases during episodes of mental stress [START_REF] Madden | Effects of mental state on heart rate and blood pressure variability in men and women[END_REF]. Also, changes in mere heart rate HR and HRV are produced in conditions requiring cognitive load [START_REF] Porges | Research methods for measurement of heart rate and respiration[END_REF][START_REF] Roscoe | Evaluation of mental workload with a combined measure based on physiological indices during a dual task of tracking and mental arithmetic[END_REF]. Nonetheless, HRV is documented as more accurately reflecting variations in cognitive workload than HR, irrespective of the complexity of the task (see [START_REF] Wilson | Applied use of cardiac and respiration measures: practical considerations and precautions[END_REF].

As hostile intent in a scrutinized environment requires transient attentional control, situation alertness, and mental efforts to appear inconspicuous, it would momentarily trigger cardiac vagal control withdrawal and elevation of sympathetic activity to respond to demands.

Just like the [START_REF] Saus | The Effect of Brief Situational Awareness Training in a Police Shooting Simulator: An Experimental Study[END_REF] study, having hostile intent in an exceptional context would create higher difficulty handling environmentally challenging events, especially regarding the fact that individuals are untrained. As such, a peripheral index such as parasympathetically mediated HRV should provide insightful information beyond that of HR because it would indicate a defensive action to cope with a stressful situation.

Overview of experiment

The present experiment adds to the previous works on hostile intent by connecting direct behaviors with more acute measurements of cardiac activities. In order to emphasize more thoroughly the physiological effects of hostile intent that might potentially occur at airport settings, we designed a laboratory experiment mirroring the condition of airport security checkpoint. Physiological effects of hostile intents are here appraised using A within-design approach was favored to limit the imputation of eventual differential responses to individual differences among participants. The positive side of experimental method, which is related to the establishment of ground truth, is tempered by the problem of reproducing the stake reflecting real criminal situations [START_REF] Vrij | Detecting Lies and Deceit: Pitfalls and Opportunities[END_REF]. Herein, the stake was established by creating an environment of security surveillance in which the passengers are scrutinized by an agent and threatened to hear an aversive sound if suspected to carry prohibited items. In this experiment, the nonverbal interactions uniquely involved the agent and the passenger. Deceivers would have a greater level of stress and higher levels of circumspection or awareness, which could be attested by some variations in physiological responses (see [START_REF] Segerstrom | Heart rate variability reflects self-regulatory strength, effort, and fatigue[END_REF]. Therefore, it was posited that attempting to pass a security checkpoint undetected while carrying a prohibited item would be related to concomitant higher heart rate and a lower heart rate variability than attempting to pass with a non-prohibited item.

Method

Participants

All potential participants were contacted via email sent to the entire university campus with the description of the project attached. By the end, twenty-three participants (eight women, Mage = 36.26, SDage = 12.32) volunteered for the experiment and signed a consent The participants were all healthy and none of them had reported antecedents of heart disease or any other diseases affecting the nervous system. The screening was made with straightforward questions asked as inclusion/exclusion criteria prior to conducting the study and no medical professionals were involved in this scrutiny. These questions were as follows : Do you live with a condition that affects your cardiac activity or your nervous system? Are you under a medical prescription that could affect your daily cardiac activity? A research ethics committee has oversighted the experimental program prior to the recruitment stage. All participants received a laser pointer or an indoor fragrance for their participation.

Design

Each subject had to make seven traverses: An initial "familiarization traverse" referred to as the baseline passage were made without scrutiny and was also designed to make participants more familiar with the itinerary and the environment ; three traverses with the neutral item and three with the apparently dangerous item. The study used a within-subject design 2 (Neutral item vs. falsely dangerous item) × 3 traverses. Although all the participants had to make three hostile traverses and three non-hostile traverses, the passing number of all the six traverses was randomly arranged across participants. As a result, after the familiarization phase, some participants initiated the first traverse with the prohibited item while some others with the neutral item and the type of successive traverses also randomly appeared for the participants.

Materials

2.3.1

Setting. An improvised security checkpoint akin to a typical airport security checkpoint was constructed for the purpose of the experiment. In a 142 m² room (length= 13.88 m², width = 10.26 m²), we marked out a path by using retractable rope barriers forming a corridor until a transportable security gate. Two meters behind the security gate, a HD Pro video camera was front-positioned to take semi-close shots while participants went through the gate. At the left of the security gate, a fake security agent wearing an authentic security agent uniform was posted to observe each passenger and manually check the luggage when necessary. The mock agent did not appear in the camera field of view.

Audio.

A wireless in-ear headphone was used to broadcast two different sounds to participants according to experimental conditions. The first sound was a Fingernails-onblackboard sound effect used as a painful signal broadcasted at the end of the traverse depending on the situation. The second was a Harp sound effect used as a pleasant signal also broadcasted at the end of the traverse depending on the situation.

Transported items.

We prepared a rolling suitcase packed with travel essentials.

Within the suitcase, the participants had to transport as a function of the experimental process two types of items; either a neutral item (i.e., a diary) or an apparently dangerous prohibited item (false improvised explosive device, I.E.D).

State anxiety measure. A 6-item version of the state anxiety scale of the

Spielberger State-Trait Anxiety Inventory (STAI, [START_REF] Marteau | The development of a six-item short-form of the state scale of the Spielberger State-Trait Anxiety Inventory (STAI)[END_REF] was administered to participants immediately after each passage. This instrument taps anxious states felt at the moment and is known to be particularly sensitive to short-term stressors (see [START_REF] Marteau | The development of a six-item short-form of the state scale of the Spielberger State-Trait Anxiety Inventory (STAI)[END_REF]). An example is " I feel tense". They indicated their levels of agreement in a four-point scale (1 = "Not at all", 4 = "very much"; Total Cronbach's alpha = 0.75, mean inter-item correlation = 0.36).

Physiological recording and data reduction.

Throughout the experiment, the participants were equipped with a professional wireless ambulatory Holter ECG monitor (Faros 360 ®). To reduce invasiveness as much as possible, we had participants place the monitor using a chest strap with an easy step-by-step illustrated guide. ECG was acquired at a sampling frequency of 250 Hz. ECG data, timestamps and events were stored within the monitor during the experimental session and extracted with Matlab 2017a (© The Mathworks, Inc., MA, USA). Participants kept the monitor during all the experimental sessions and episodes were created from the moment the participants entered the room until they crossed the gate (MTraversetime = 18.19 seconds, SDTraversetime = 7.27 seconds). A 1 st order zero-phase low-pass Butterworth filter was applied on signals with 15 Hz cut-off frequency in order to reject high-frequency noise and baseline wander artefacts. Pan-Tompkins algorithm [START_REF] Pan | A real-time QRS detection algorithm[END_REF] was used to detect QRS complex in the ECG signal. All filtered signals were visually checked. Occasional artifact noises were automatically replaced with the interpolated adjacent RR interval values, three participants were excluded due to several interpolated RR intervals greater than 5% [START_REF] Peltola | Role of editing of R-R intervals in the analysis of heart rate variability[END_REF], leaving a final sample of 20 participants.

As the durations of episodes were ultra-short, we circumscribed the analysis to the time domain only (see [START_REF] Nussinovitch | Reliability of ultra-short ECG indices for heart rate variability: Ultra-short HRV reliability[END_REF]. Heart rate (HR) corresponds to the ratio between 1 second and the average duration of RR interval, multiplied by 60. Heart rate variability was measured as the root mean squared of the successive RR interval differences (RMSSD: Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996). RMSSD is thought of as a commonly used measurement of the vagally-mediated changes predominantly featuring parasympathetic modulations [START_REF] Nussinovitch | Reliability of ultra-short ECG indices for heart rate variability: Ultra-short HRV reliability[END_REF]. According to [START_REF] Nussinovitch | Reliability of ultra-short ECG indices for heart rate variability: Ultra-short HRV reliability[END_REF] and confirmed by [START_REF] Munoz | Validity of (Ultra-)Short Recordings for Heart Rate Variability Measurements[END_REF], RMSSD is the most reliable time domain indicator of HRV when dealing with ultrashort slices (10 s to 1 minute). In addition, respiratory frequency differences can be affected by the experimental conditions and can have some consequences on heart rate. Since RMSSD has been evidenced to be much less affected by respiratory frequency [START_REF] Penttilä | Time domain, geometrical and frequency domain analysis of cardiac vagal outflow: effects of various respiratory patterns[END_REF], its inclusion will help alleviate as much as possible this confounding variable.

Procedure

Initial phase.

Upon arrival at the preparatory room located near the experimental setting, the participants signed the consent form and were screened for medicine use and medical issues. They were explained that they would participate in a behavioral screening passenger study. All the instructions were made orally. Once the Holter ECG monitor was placed, they made a first traverse meant to familiarize themselves with the exercise; they did not put something inside the rolling suitcase. The participants were explicitly said that the first traverse was meant to make them familiar with the environment and the route. They were warned that an agent would be in place, but we made them sure that their behavior would not be scrutinized for this familiarization phase. At the end of each traverse, they filled out the short STAI questionnaire.

Stake manipulation.

After the first traverse, and once the participants indicated they understood the principle, they had to make three additional traverses with the neutral items and three others with the apparently dangerous item. The I.E.D was said to be nonfunctional and incomplete and it was clearly reminded that transporting such an object is highly prohibited and is legally punishable in real circumstances.

To elicit more evaluative pressure, the participants were tricked into thinking that the agent was a handpicked talented behavior detector enrolled to keep watching over them during the traverses while they were carrying the rolling suitcase. They were informed that if the agent estimated that they were carrying the apparently dangerous object, the agent would broadcast via their in-ear headphone a painful sound after having crossed the gate.

Conversely, if the agent thought that they were carrying the neutral item, a pleasant sound would be broadcasted. The participants did not get acquainted with pleasant and the aversive sounds prior to starting the traverses.

The participants were said that their objective was to successfully pass the security gate undetected by this agent. Each time the unpleasant sound was broadcasted, the agent manually checked the suitcase. To make the agent skills credible, the experimenter surreptitiously sent a vibratory signal to the agent during the experimental process to indicate that the passenger is carrying the falsely dangerous device. Everything was organized such that the agent made only one false positive decision (i.e., signaling to the agent while the participant was transporting the neutral item) and only one false negative decision (i.e., not signaling the presence of I.E.D). The passing number of the traverses was counterbalanced across participants. The false positives and misses appeared randomly.

A suspicion check questionnaire was administered at the end of experiment and no suspicion of trickery was reported.

Results

Data Analysis

Statistical analyses were computed in R (R Core Team, 2017) using the ez ANOVA package (Lawrence, 2011). Post hoc analyses were carried out with the AFEX and the Lsmeans packages (R Core Team, 2020). Statistical significance for multiple comparisons and contrasts was adjusted with the Bonferroni method and the Kenward-Roger's method was used for adjustment of degrees of freedom. We reported an effect size calculation that is more suitable to within-subject designs, the generalized eta squared (η²G) [START_REF] Bakeman | Recommended effect size statistics for repeated measures designs[END_REF] and also provide partial eta squared in parentheses. The data and R scripts (and an SPSS file version) are publicly available on the open science framework platform (https://osf.io/yf4cx/).

The descriptive statistics of the studied variable are presented in Table 1.

Manipulation check

We first tested the differences in self-reported state anxiety between each condition (i.e., neutral item vs. falsely dangerous item). Relative indexes were calculated by subtracting the baseline scores from those of the condition scores. Baseline scores reflected state anxiety measured after the familiarization phase. No statistically significant differences were observed between the first and the second traverses. The interaction effect between the condition and the passing number of the traverses was not significant, F (2, 44) = 2.63, p = 0.08, η²G = 0.01(η²p = 0.10).

HR analyses

To test the hypothesis of the differential levels of HR between the conditions, we have calculated HR change scores by subtracting HR scores of the conditions from that of the baseline condition corresponding to the familiarization phase. We computed a 2 × 3 repeated measure ANOVA with the passing number of the traverses and the condition as factors and HR as dependent variable.

The analysis detected a significant main effect of the condition, F (1, 19) = 8.01, p = 0.011, η²G = 0.02, (η²p = 0.30), showing that the level of HR in the neutral condition was lower (M = 8.08, SE = 2.52) than that of the false I.E.D condition (M = 12.002, SE = 2.92).

The analysis also detected a significant main effect of passing number , F (

HRV analyses

The same procedure as detailed above concerning statistical analyses of HR was applied to HRV with RMSSD values. RMSSD values were subjected to a 2 × 3 repeated measures ANOVA. The analysis yielded, as predicted, a significant drop of HRV across the conditions, F (1, 19) = 6.22, p = 0.022, η²G = 0.05, (η²p = 0.25). Effect size is medium and consistent with sensitivity analysis. The traverses with the false I.E.D were associated, as predicted, with higher drops of HRV (M = -5.83, SE = 2.12) than the traverses with the neutral item (M = 0.65, SE = 2.65). No statistically significant passing number effect, F (2, 38) = 1.22, p = 0.30, η²G = 0.02, (η²p = 0.06), nor interaction effect were detected, F (2, 38) = 0.48, p = 0.61, η²G = 0.003, (η²p = 0.02).

Discussion

The present research was aimed at testing the premise that hostile intent would predict differential physiological responses in the context of a controlled experiment. To our knowledge, there was no research examining heart rate variability of nonverbal hostile intent.

Our data support the hypothesis that the nature of the object to be transported triggered higher levels of both state anxiety and physiological responses in passengers. The results provide a novel insight into the previous work by showing that hostile intent relates to transient physiological change in a controlled experiment. More specifically, using HRV measures indicated that attempting to get around a security checkpoint undetected prompted significantly more physiological stress in participants. These results provide a new groundwork for the idea that intent to deceive predicts different neurophysiological responses usually due to attempted behavioral control to convey good impression [START_REF] Vrij | Detecting Lies and Deceit: Pitfalls and Opportunities[END_REF]. From a psychophysiological point of view, it is suggested that a hostile maneuver in a context of surveillance imposes a threat, which triggers a defensive response akin to a 'fight-or-flight' response [START_REF] Jansen | Central command neurons of the sympathetic nervous system: basis of the fight-orflight response[END_REF]. This response is indicated by the withdrawal of cardiac vagal control under pressure (Thyer et al., 1996;[START_REF] Park | When tonic cardiac vagal tone predicts changes in phasic vagal tone: the role of fear and perceptual load[END_REF]. In this kind of situation, the prefrontal inhibitory regulation is attenuated and the sympathoexcitatory subcortical pathway is exacerbated. The attribution of the reduction of phasic HRV in terms of increased cognitive load is a possibility that could not be guaranteed since the situation possibly conflates stress and cognitive demand, and phasic HRV does not constitute in itself and by itself a selective measure of cognitive load.

Interestingly, building upon HR results, the effects seemed to vanish as the passengers performed more traverses. This effect was accompanied by a decrement in the degree of selfreported anxiety. One explanation is that the participants became progressively accustomed to the experimental design and that the stake and the object to be transported would no longer have an impact on them. A psychophysiological interpretation would afford the idea that participants lately managed to exert emotional self-regulation, which would be a favorable condition to self-presentational strategies in deceptive situations [START_REF] Depaulo | Cues to deception[END_REF]. There is evidence that an increase [START_REF] Thayer | Autonomic characteristics of generalized anxiety disorder and worry[END_REF] or even a smaller decrease in cardiac vagal control reflects a better executive control under pressure as compared to a larger cardiac vagal control withdrawal (e.g., [START_REF] Laborde | The tale of hearts and reason: the influence of mood on decision making[END_REF][START_REF] Laborde | Is the ability to keep your mind sharp under pressure reflected in your heart? Evidence for the neurophysiological bases of decision reinvestment[END_REF]. If this observation is not only related to the features of experimental conditions, an important inference can be drawn on the alleviating effects of training and mental preparation. Cognizant that passengers' behaviors would be scrutinized for, it is possible that individuals with malicious intent who are planning an attack at the airport undergo an intensive phase of mental preparation enabling them to better cope with this stressful situation and enhance the inhibitory activity of the prefrontal cortex (elevated tonic HRV) to engage more cognitive and emotional adaptivity [START_REF] Thayer | Heart rate variability, prefrontal neural function, and cognitive performance: the neurovisceral integration perspective on self-regulation, adaptation, and health[END_REF]. As such, the occurrence of expected changes could be less noticeable, and the process of detection could therefore be hampered.

Our finding is consistent with the [START_REF] Eachus | Hostile intent in public crowded spaces: a field study[END_REF] Although this project falls into the domain of hostile intent in nonverbal communication context, the results should be discussed in the perspective of psychophysiological measures of deception detection to shed the light on potential issues with research on hostile intent. Like much deception works relying on measures of the activity of autonomic nervous system (ECG, GSR, BP) and central nervous system (EEG, and fMRI), differential responses in deceptive behaviors were reported (e.g., [START_REF] Furedy | Differentiation of deception as a psychological process: a psychophysiological approach[END_REF][START_REF] Gödert | Psychophysiological differentiation of deception: the effects of electrodermal lability and mode of responding on skin conductance and heart rate[END_REF][START_REF] Meijer | Memory detection with the Concealed Information Test: A meta analysis of skin conductance, respiration, heart rate, and P300 data[END_REF]. These elements are sometimes the only indicators upon which deception is inferred. Such a process can suffer from a drawback that neuroscientists are familiar with when it comes to interpret physiological data: the reverse inference fallacy [START_REF] Poldrack | Can cognitive processes be inferred from neuroimaging data?[END_REF]. Bluntly put, it refers to the proclivity to reason backwards from physiological or neuroimaging data to infer a specific state or cognitive activity without direct measurement of them. If deceptive conducts triggered differential physiological responses, relying on such responses could not be retained as necessary nor sufficient condition for inferring deceptive conducts in the future [START_REF] Meijer | Deception detection with behavioral, autonomic, and neural measures: Conceptual and methodological considerations that warrant modesty[END_REF]. The reasonable empirical evidence for the validity of the premise should not translate into an affirmation of the consequent leading to impart empirical credential to behavior detection. A single physiological response might originate from many types of intentions as well as a single intention might be related to many different types of physiological states (see Burgoon et al., 2005Burgoon et al., , 2009)). This research project emphasizes the necessity of performing crime simulation protocols with physiological measurements without discarding the utilization of self-report measures. The main advantage of resorting to physiological measurements beyond self-report is linked to the possibility to capture the richness of mental processes at work within a specific context of simulated crime attempts. This line of reasoning is consistent with a recent evidence in an experimental study of fear of crime which suggests a discrepancy between physiological and self-report results (see [START_REF] Castro-Toledo | Influence of environmental variables on fear of crime: Comparing self-report data with physiological measures in an experimental design[END_REF].

Some limitations need to be addressed. Even though results yielded as hypothesized, using HRV is not sufficient in itself and by itself to disentangle the implication of cognitive load and arousals. We may simply concur that mental workload might have been operant

while being cautious about its relative impact during a mock criminal intent. Secondly, in the design, the choice that has been made to elevate the experienced stake can be a limitation. The signaling sent to the agent about the exact moment when the luggage inspection needed to be made may have impacted the way the agent treated the participant, making it less clear about whether the observed effects were only due to the hostile intent.

Concluding remark

The simulation experiment had demonstrated that transporting a prohibited item generated a phasic cardiac vagal withdrawal. The observed effects appeared to be stronger during the first and second traverses, which raises the question of training and familiarity. It is unclear at this stage how transporting the prohibited item was conducive to these physiological effects. The stress might stem from the action of feigning normal appearance while concealing the item or from the simple action of transporting an evocative and symbolically dangerous item, or both. Future studies are needed to disentangle the effect of stress management and the effect related to mere transport of a prohibited item using a broader variety of physiological measurements.

  electrocardiographic measurement. HR and HRV analyses carried out in this study coincided precisely with the moment of hostile phases (vs. non-hostile phases). Applying HR would indicate potential change of the level of sympathetic excitation in case of increase while HRV would afford important insights into the role of inhibition of parasympathetic activity in the development of hostile intent. HR is influenced by both the sympathetic and the parasympathetic nervous systems. Sympathetic and parasympathetic nerves have antagonistic influences on heart rate. An increase in HR is indicative of a sympathetic stimulation through the release of norepinephrine at the SA node (see Klabunde, 2011).At rest, vagal influences on HR dominate. However, during episodes of stress, like trying to pass a security system with a prohibited item, an increasing sympathetic activity is expected. In other words, elevation of heart rate during the transportation of a prohibited item would mean that sympathetic stimulation outperforms the parasympathetic one.

  form. A selection of the 46 studies measuring HR or HRV from a recent meta-analysis of the use of cardiac measures of cognitive workload (Hughes et al., 2019, information extracted from supplementary material) revealed that the median sample size was 21,5 (M = 27,3, SD = 23.1). Thus, our sample size was in line with other neuroergonomics studies measuring HR and HRV. A sensitivity analysis has been computed using MorePower 6.0 (Campbell and Thompson, 2012) with a power of .80, an alpha of .05, and a sample size of 23. The results yielded a partial η² of 0.28, which falls within the medium effect size range according to some recent guidelines (See Morris and Fritz, 2013).

Figure 1 .

 1 Figure 1. Anxiety state change scores as a function of hostile intent condition. Each bar depicts the average mean of state anxiety relative to the baseline. Scores higher (lower) than zero reflect higher (lower) levels of anxiety states than the baseline. Error bars represent standard errors of the mean.The relative state anxiety scores were subjected to a 2 × 3 repeated measures

  = 5.11, p = 0.012, η²G = 0.02, ( η²p = 0.21). Linear contrasts have shown that the first traverses were globally related to higher HR (M = 12.73, SE = 3.19) than the third traverses (M = 8.81, SE = 3.19), t(38)= 2.84, SE = 1.46, adjusted p < 0.04, Cohens' d = .46, 95 % CI [0.12 : 0.79]. Finally, a significant interaction effect was observed, F (2, 38) = 3.5, p = 0.04, η²G = 0.013, (η²p = 0.16). Simple effects analysis have shown that the interaction was qualified by the difference in HR between the neutral item (M = 8.75, SE = 2.75) and the false I.E.D of the first traverses (M = 16.70, SE = 4.05), t(55)= -3.7, SE = 2.15, adjusted p < 0.008, Cohens' d = .50, 95 % CI [0.22 : 0.77] and by the difference in HR between the first passing (M = 16.70, SE = 4.05) and the third passing in the false I.E.D condition (M = 9.06, SE = 2.65), t(76)= 3.76, SE = 2.03, adjusted p < 0.005, Cohens' d = .43, 95 % CI [.19; .67].

Figure 2 .

 2 Figure 2. HR change scores as a function of the conditions and traverses. Each bar depicts the average drop in HR relative to the baseline. Error bars represent standard errors of the mean.

Figure 3 .

 3 Figure 3. Drops in HRV as a function of the conditions and traverses. Each bar depicts the average drop in HRV relative to the baseline. Error bars represent standard errors of the mean.

  From a more practical standpoint, only trusting machine-based pre-crime detector showing physiological manifestations and brain activity of passengers ˗ e.g., the future attribute screening technology, FAST (see[START_REF] Weinberger | Terrorist "pre-crime" detector field tested in the United States[END_REF] ˗ could be insufficient and counterproductive. Note that while methods designed to make high probabilistic inference about deception (e.g., the Control Question Test) differ in many important features from methods designed to detect concealed memories (e.g., the Concealed Information Test) (Ben-Shakhar, 2012), physiological responses (e.g., skin conductance, electrocardiogram, and respiratory activity) remain the only indicators of these procedures and could therefore be affected by this fallacy.

Table 1 . Descriptive statistics of state anxiety and cardiac features

 1

	Variable	Minimum	Maximum	Mean	Std. Deviation
	STAI	6	18	10.8	3.20
	Anxiety change scores	-8	13	1.85	3.55
	HR (bpm)	67.57	156.35	101.29	20.27
	RMSSD (ms)	4.64	95.66	19.48	15.11
	HR change scores (bpm)	-32.55	55.44	8.77	13.06
	RMSSD change score	-34.43	57.58	-1.95	15.28
	(ms)

Note:

HR = Heart rate in beat per minutes, RMSSD = Root Mean Square of successive RR interval differences expressed in milliseconds.

  study in which distinct physiological responses ̶ reflected by measurements of cortisol, stress pheromones, heart rate, respiration, and body temperature ̶ emerged by merely having participants perform hostile reconnaissance (inconspicuously taking photos in a shopping mall). Importantly, in the[START_REF] Eachus | Hostile intent in public crowded spaces: a field study[END_REF] study the examination of participants' behaviors recorded on CCTV did not confirm a difference between low intent and high intent. As such, physiological differences in the context of hostile intent do not guarantee that physiological signs of stress might stand out behaviorally. Behavioral screening programs are underpinned by the rationale that criminal engagement entails identifiable observable behaviors that would not be present in the absence of intention. Evaluating the core effectiveness of such programs implies the examination of this conjecture. If there is no scientific evidence that hostile intention is noticeable, then the implementation of behavioral programs could be unproductive. In malevolent individuals, hostile intent is likely deemed negative because of the awareness that the project constitutes an outright violation of basic norms of morality. The stakes associated with the criminal project modulate the intensity of the hostile intention, which could modulate physiological responses. For instance, discreetly taking photos might not be like trying to dissimulate explosives, even in a mock paradigm.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.


Adama Arama
email: a.arama@estia.fr

Eric Villeneuve
email: e.villeneuve@estia.fr

Christophe Merlo
email: c.merlo@estia.fr

Laura Laguna Salvado
email: l.lagunasalvado@estia.fr

An approach of decision support system for drift diagnosis in cyber-physical production systems

Keywords: Industry 4.0, cyber-physical production systems, drift concept, decision support system

Despite the development and application of new digital solutions in the production industry, the human operator is still essential in the production chain monitoring and control processes. In this context, some activities can be crucial for the human operator like, for example, drift diagnosis in production control process. It requires attention and experience and can be assisted by Decision Support System (DSS) to guide operators in decision-making in industrial production process control. Drift diagnosis process is a challenging problem in this context and artificial intelligence technologies are promising to tackle this issue. In this paper, we propose a new approach of DSS for drift diagnosis. The proposed approach is built upon a literature review on drift concept, drift detection methods and failure diagnosis approaches. This multi-model approach is designed to address all the diagnostics tasks of production systems and is based on Machine Learning (ML) algorithms to model the behavior of production systems, a knowledge-based model to integrate human experiences and a data-driven model to combine historical data from sensors. When the drift occurs, the proposed DSS can help human operator to determine drift causes and to suggest corrective actions. This article also provides guidelines about the design of a decision support system to support human operators in complex decision activities.

I. INTRODUCTION

The introduction of digital solutions to the industrial production processes have led to new needs like safety, reorganization and re-adaptation. These new technologies development has been a key factor for the migration toward the smart production industry. This evolution is commonly known as Industry 4.0 [START_REF] Henning | Recommendations for implementing the strategic initiative industrie 4[END_REF] and attracts much attention of the researchers these last few years. There are several definitions, see e.g. [START_REF] Castelo-Branco | Assessing industry 4.0 readiness in manufacturing: Evidence for the european union[END_REF], [START_REF] Zhou | Industry 4.0: Towards future industrial opportunities and challenges[END_REF], [START_REF] Zawadzki | Smart product design and production control for effective mass customization in the industry 4.0 concept[END_REF] related to Industry 4.0 in the literature. However, in [START_REF] Ana | A review of the meanings and the implications of the industry 4.0 concept[END_REF], the authors defined Industry 4.0 as a complex technological system that is being shaped fundamentally by connectivity, integration and production digitization, emphasizing the opportunities of integrating all elements in a value-adding system. The production system part of Industry 4.0 consists in a cyber-physical production system (CPPS) combined with cloud computing and Internet of Things (IoT) [START_REF] Sachin S Kamble | Sustainable industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives[END_REF]. In this context, CPPS are used to monitor and control industrial processes, including continuous production (cement, steel, chemicals, etc.). This monitoring and controlling activity is highly critical for the production industry. It allows for timely mitigating actions such as fault checking, drift diagnosis, predictive maintenance and system shutdown to be taken such that potential loss caused by system failures can be avoided [START_REF] Feng | Time series anomaly detection for cyberphysical systems via neural system identification and bayesian filtering[END_REF]. On the other hand, despite the technological evolution in the context of Industry 4.0 represented by increasing automation and autonomy of machines, human operators yet represent an important and vital factor in Industry 4.0 [START_REF] Longo | Smart operators in industry 4.0: A human-centered approach to enhance operators' capabilities and competencies within the new smart factory context[END_REF]. Nowadays, it is not possible to automate everything. Indeed, the technologies of Artificial intelligence (AI) are often not properly trained in some situations/contexts to perform efficient, effective, flexible or creative decision-making. In this case, human operators can play an important role in the production industry process, because they have all these characteristics [START_REF] Fazel Ansari | A knowledge-based approach for representing jobholder profile toward optimal humanmachine collaboration in cyber physical production systems[END_REF]. However, human operators will need a Decision Support System (DSS) due to the difficulty of understanding of such a complex system of production. Thus, in this context, DSS will help human operators to understand system behavior and to make decision accordingly. Usually, decision-making process is done collectively and it involves human operators who use their knowledge to manage a given problem. This paper is focused on drift diagnosis in continuous production process. Drift diagnosis process is a daily activity of collective decision-making for human operators, see e.g. [START_REF] Seguy | Characterisation of collaborative decision making processes[END_REF], [START_REF] Villeneuve | Decision support in precision sheep farming[END_REF]. In this scenario, using a DSS can help human operators to make the "best" decision in production control process and, thus to perform the "best" operation of the production industry. Eventually, the primary objective of this study is to provide a new approach of DSS for the drift diagnosis process.

The remainder of this paper is therefore organized as follows. We briefly introduce the background of study in the next section. Our proposition is presented in Section 3. Section 4 is focused on discussion and limitations of the proposed methodology. Lastly, Section 5 presents final conclusions and futures works.

II. BACKGROUND

Thanks to the advance of digital technology, production industry is becoming a global interconnected system in which machine, sensors, information systems, monitoring and other professional softwares, and products are constantly communicating. The work on which is based the presented study is conducted in the chemical field, in this context of digitalisation. The studied production industry is involved in in a process of liquid-liquid extraction or rare earths separation. This process is composed of three main sub-processes: ore treatment; separation process through batteries; and final preparation for customers.

The ore comes in solid form to the ore workshop. It is attacked with nitric acid which makes it liquid and forms a solid pulp which is a waste. The liquid part is recovered by the battery shop for separation or extraction. And then the final product is sent to customers. Basically, the activities of the studied production industry are focused on two mains functions (Figure 1).

• Transform raw material (RM) to liquid • Split liquid which consists to recover liquid from the first workshop in order to split in group or sub-group. After processing, rare earths are stored in tanks that the customers can use. So, this study is focused on the battery part and aims to improve the control of the batteries in the production process by the DSS for drift diagnosis.

A. Cyber-physical production system concept

A cyber-physical production system (CPPS) is a concept, which consist in an integrated system with computer system, control system and physical system [START_REF] Padmajothi | Review of machine learning and deep learning mechanism in cyber-physical system[END_REF]. Therefore, CPPS is key technology of the production industry sector digitalisation and modernization. The production system would control the progress and quality of the product being manufactured.

In [START_REF] Monostori | Cyber-physical systems in manufacturing[END_REF], authors present CPPS like a set of cyber-physical systems. This conducted CPPS to be considered as a system of systems (SoS) and one of the pillars of Industry 4.0. The use case studied here (rare earths extraction) is considered to be a CPPS. According to the definitions in [START_REF] Lee | A cyber-physical systems architecture for industry 4.0-based manufacturing systems[END_REF] and [START_REF] Ashford | Introduction to embedded systems: A cyber-physical systems approach[END_REF], the studied system is, indeed, a complex system made of autonomous and cooperative elements. On the other aspect, the digitization of the production industry allows access to both the variety and availability of data. Thus, there are new opportunities for innovative uses of data which allows to led towards the Industry 4.0. Among these opportunity, it enable the ability to detect and correct the drifts that appear sometimes in the production control process.

Drift concept is a phenomenon in which the statistical properties of a target domain change over time in an arbitrary way [START_REF] Lu | Concept drift detection via competence models[END_REF]. Drifts cause a lot of problems to the human operators and can lead to plant shutdown. In addition, the drift management involves the (i) drift detection, the (ii) diagnosis of causes and, the (iii) proposal of a corrective action, in a quick way. It represents one of the most challenging tasks in continuous production processes.

In this context, the focus of this study is on the diagnosis of drift causes which is a complex process for human operators. It requires a good understanding of the industrial process and of the available data, which are potentially very numerous and heterogeneous. Thus, a decision support system makes sense to assist human operators in their work. Fig. 2. Proposed generic CPPS model [START_REF] Arama | Modélisation de CPPS : proposition d'un modèle générique[END_REF] When studying the literature about CPPS, the observation was made that the human operator role in CPPS is often absent of the proposed models, or the models that consider human in the loop are difficult to apply in other contexts, see e.g. [START_REF] Gnidenko | Methodology and integrated modeling technologies for synthesis of cyber-physical production systems modernization programs and plans[END_REF], [START_REF] Monostori | Cyber-physical production systems: Roots, expectations and r&d challenges[END_REF] and [START_REF] Cimini | A human-in-the-loop manufacturing control architecture for the next generation of production systems[END_REF]. As mentioned above, human operator represents an important factor to the good functioning of CPPS. It is important to model CPPS with human in the loop to get a better understanding about both its behavior and operating way. Thus, on a previous research work in the field of complex systems modeling, we proposed a generic model of CPPS (Figure 2). This model is designed to get a better understanding of any CPS-based production system. It allows to consider humans operator as part of both physical production and decision-making systems. The main contributions of this model are the :

• consideration of human operator as a physical part of CPPS • definition of all human roles in the CPPS • definition of DSS role to help human operator in its decision-making to control industrial production process. Human operator takes an important place in this model and, thus, allows to correct the perceived shortcomings of the main competing models from the literature.

B. Drift concept

Drift management is a topic which attracts a lot of attention both in industrial and academic researches due to its potential benefits in terms of reliability, safety and maintenance costs [START_REF] Noronha Gemaque | An overview of unsupervised drift detection methods[END_REF]. Beyond drift detection, it is more necessary for a human operator to know the causes of the observed phenomenon in the production control process in order to act accordingly and to avoid the problem in the future. Consequently, in recent years, drift concept has been widely studied in the literature. The research involves the development of methodologies and techniques for drift detection, understanding and adaptation. In [START_REF] Lu | Learning under concept drift: A review[END_REF], authors describe drift concept as an unforeseeable changes in the underlying distribution of streaming data over time. Also, the changing of the production system behavior, called drifts, may be considered as an indication of malfunction beginning, see e.g. [START_REF] Selcuk | Predictive maintenance, its implementation and latest trends[END_REF], [START_REF] Zenisek | Machine learning based concept drift detection for predictive maintenance[END_REF].

In this work, we define a drift as a discrepancy in relation to parameter values. Regarding the case study, it is a gap between the value wanted by the human operators and the measured value. The drift is an usual problem encountered in production control process of batteries. When it occurs, it must be corrected as quickly as possible so that it does not spread to other floors. To do this, the batteries have to be slow down, which has an impact on production or worse, can stop it. The human operators are sometimes able to detect that problem visually.

Drift can have various natures. According to [START_REF] Tsymbal | The problem of concept drift: definitions and related work[END_REF], there are two kinds of concept drift that may occur in the real world. They are normally distinguished in the literature by (1) sudden (abrupt, instantaneous), and (2) gradual drift. Drift management can represent a challenging issue in industrial environment that can impact several application domains, see e.g. [START_REF] Noronha Gemaque | An overview of unsupervised drift detection methods[END_REF], [START_REF] Wang | Concept drift detection for streaming data[END_REF]. The downtime of tools, equipment, pump failures, or any other machines can cause a direct loss of revenue to production process. Forecasting such failures using sensor data can prevent or reduce the downtime [START_REF] Munirathinam | Drift detection analytics for iot sensors[END_REF]. Drift management in CPPS presents several challenges for human operators. We translate those into three main challenges for a DSS such as the (i) drift detection, (ii) drift diagnosis, i.e to determine its causes and (iii) proposed a corrective action.

Drift detection methods allow to detect drift quickly using sensor data and are based on strategies to identify suspicious behavior in data streams, emerging from condition monitoring. Detection methods can be divided in two categories [START_REF] Zenisek | Machine learning based concept drift detection for predictive maintenance[END_REF]:

• Classifying data streams as normal, drifting or defective based an a comparison with a large instance pool of formally under supervision categorized data sets. • Predicting the next values in a data stream with a regression method and comparing these estimations with actually sensed values; an increasing prediction error might indicate an underlying drift. Regression models are used both to detect and predict drifts. There are many detection methods in literature and only the significant proposals, chosen to illustrate the diversity of existing detection methods, are detailed above:

• Generic Model for Detecting Drifts (GMDD) [START_REF] Munirathinam | Drift detection analytics for iot sensors[END_REF].

Here, the authors explored three drift detection techniques that identifies drift, notifies the early warnings of the drift and helps operators to act proactively to fix the issues. • Drift Detection Method (DDM) [START_REF] Gama | Learning with drift detection[END_REF].

This drift detection method is based on statistics (using binomial distribution) to estimate the probability of errors in process and predict if a drift occurs. • Early Drift Detection Method (EDDM) [START_REF] Baena-Garcıa | Early drift detection method[END_REF].

The method EDDM has been developed to improve the detection in presence of gradual drift. At the same time, it keeps a good performance with sudden concept drift. The basic idea is to consider the distance between two errors classification instead of considering only the number of errors. As mentioned above, there are a lot of detection methods for drift situation but, in the best of our knowledge, very few methods for drift diagnosis [START_REF] Lu | Learning under concept drift: A review[END_REF]. Thus, the focus of next sections is on DSS to assist drift diagnosis process.

C. Decision support for drift diagnosis

Making decision in drift diagnosis process presents several challenges for a human operator. For better understanding the decision process in the production industry, a literature review has been made. Moreover, a field study has been made to identify actors involved and to characterize and model decision processes used by these actors.

To be fully operational and have a good performance, production systems need to be controlled [START_REF] Doltsinis | A decision support system for rapid ramp-up of industry 4.0 enabled production systems[END_REF]. For that, decision support systems remain essential in the production industry control process. According to the objectives and context, the decision support approaches can be different, see e.g. [START_REF] Villeneuve | Decision support in precision sheep farming[END_REF], [START_REF] Salama | A decision support system architecture based on simulation optimization for cyber-physical systems[END_REF]. While performing the field study, the main actors involved in drift diagnosis decision process have been identified (Figure 4). There are four decision actors in this process. At the strategic level, Day Foremen (DF), are top decision hierarchical level which interacts with all actors of production process. They are like a general manager of all production parts. The Shift Foremen (SF) constitute an intermediary between DF and the others actors of production.

At the operational level, Shopfloor Operators (SO), can act directly on the batteries. Then, there is the Control Room operators (CRO), which also interact with the batteries but mainly with the support of software. • Apply SF's instructions • Inspect the batteries room • Collect samples and send them to laboratory All these roles above are essentials to the operating of the studied production system. The SO plays an important role in the production control process, as he is the main operator that physically interacts with production process. All of these people are responsible for the daily control of the batteries. CRO and SO are keys actors of the drift diagnosis, because the drift is usually detected by them.

When a drift occurs, it must be corrected as quickly as possible so that it does not spread to other floors. For more understanding, Figure 3 shows the actual drift diagnosis mechanism and decision process. It shows also the interaction between the four actors involves in drift management. It allows to understand production decision process. To manage a drift the four actors can interact. This actual mechanism (Figure 3) of drift diagnosis can be summarized in four mains steps which are as following:

• Analyze drift This step comes once the drift is detected and confirmed by the human operators (CRO or SO). It consists in analyzing drift causes by the actors of operational level and including actors of the strategic level if necessary.

• Define action

There are several actions to be defined after analyzing process. These possible actions range from ignoring the drift to stopping completely the production system. Usually, the CRO refers to a document which contains predefined actions regarding some drifts. If the corrective action of a drift is not in this document, the CRO will consult SF or DF.

• Execute action

Defined actions are executed automatically by the CRO or manually by SO. Sometimes, in order to execute action automatically, the CRO collaborates with the help. • Experience feedback Following action execution, experience feedback is done by SO. Thus, these different feedback are send to the actors of strategic level in order to improve the referred document. Nowadays, we noticed that this process is very collaborative and iterative. Knowledge required for making decisions depends mostly on the experience of human operators. Existing software tools only give basic information and experts knowledge is available inside a huge support document in paper format. Our aim is to propose a proactive environment (DSS) for helping human operators, and improve their decisions, workloads and mental load.

III. PROPOSITION

Proposal in this paper is based on an inductive research approach. Inductive, because both the problem and the solutions are based on field research, with the goal of creating a common general hypothesis. In this section we propose a methodology to drift diagnosis with a DSS.

A. Overall methodology

As a reminder, this study is interested in drift diagnosis process in the production industry control. In the case study, the roles of cyber-physical software consists to extract data about the physical state in order to inform some decisionmaking process in the production control process. This data is used to diagnosis processes, such as dashboards displaying the current status of a system or alarms which detect drift or errors within the system. In this context, DSS is needed to assist human operators in their decision-making process for the drift diagnosis. Decision support in the production control process has been summarized into four main steps (Figure 5). The different steps are described as following:

• Design process This first step consists to build both production industry model and knowledge base. Key parameters are extracted from data collected though IoT technologies installed on the physical components of CPPS. This is stored and constitute the historical Data Base (DB) to be used by Machine Learning (ML) technologies. Knowledge Base (KB) building is done by interviewing actors. All actors (DF, SF, SO and CRO) will be interviewed in order to extract the business rules. The KB includes also relevant pieces of information (e.g.: rules, object descriptions) in an application domain (case study here).

Technically, relevant pieces of information will be done using formalism, like logical representation or semantic network representation for example. Thus, the proposed DSS designed using a multi-model approach, combining data-driven model (based on DB) and knowledge based model (based on KB).

• Control process

Based on AI technologies, the DSS records and processes data from production. This second step consists in continuous monitoring of production system. The human operator (CRO here) makes the monitoring of the keys parameters and constantly update parameters until drift occurrence.

• Problem solving

When a drift is detected in the monitoring process, the CRO use DSS to solve the problem. The DSS suggest different solutions and the CRO makes the best choice in collaboration wither others human operators (DF, SO and SF).

• Update process

The last step is based on the experience feedback. After the application of each corrective action for a drift, experience feedback is made to improve both the KB and DSS globally.

B. Decision support system for drift diagnosis

As studied above, few drift diagnosis method are present in literature [START_REF] Lu | Learning under concept drift: A review[END_REF]. Thus, our proposal consists in a new DSS approach to address this issue. Once drift detected, DSS allows to diagnose the drift, i.e to determine its causes and propose corrective action. Figure 6 shows drift diagnosis process using the proposed DSS. When the drift is detected, human operator (CRO) use DSS. Some blocks of this process are not linked by arrow because actions are not sequential. Human operators often need to iterate some actions until the best action and until all actors have reached a consensus. The proposed decision support process for drift diagnosis allows, once drift detected, to suggest (i) probable causes, to propose (ii) corrective actions and to capitalize the problem solving process though experience feedback to improve DSS. Nowadays, ML is efficient mechanism to model the behavior of such complex systems for design and optimization. The DSS for drift diagnosis is based both on the ML technologies and on a multi-model approaches which allows to combine of data-driven model and knowledge-based model. We assume that the drift is already detected. The DSS will proceed directly to determine its causes in the first block (Determine causes). In DSS, the drift will be directly categorized using AI algorithms, K-Nearest Neighbors or Decision Tree for example. Depending on the category of the drift, the DSS uses relevant algorithms of AI to suggest drift causes with probability rate to inform human operators. The final decision is made by human operators using these suggestions.

Once the cause of drift is known, the second block of DSS (Corrective action) consists to propose a corrective actions to the human operator, which will choose the set of action. For that, DSS will proceed in the same manner as previously. The last step consists to capitalize experience feedback done by human operators (CRO and SO).

IV. DISCUSSION AND LIMITATIONS

In this paper, the crucial importance of the human operators in the CPPS and especially in the drift diagnosis process, is highlighted. Drift diagnosis process, i.e to determine drift's causes and to propose corrective actions, is a challenging problem for human operators and AI technologies in production industry scenarios. During studying the literature related to drift diagnosis methods, there are not enough methods able to identify drift causes. In addition, the research gaps that need to be addressed exclusively in the area of ML-based drift diagnosis (multi-approach) has not yet been covered in the existing literature.

The proposed approach of DSS for drift diagnosis allows to determine the causes of a drift and propose to corrective actions. Sensor data collected from monitored CPPS allows to model the data base. The presence of humans operators in the loop allows us also to interact with them in order to extract knowledge to build knowledge base. The multimodel approach (DB and KB) ML-based algorithms will be used to develop the DSS. This proposed study and methods are original on several aspects. Indeed, the study was fully conducted in the field, but considering very carefully the state-of-the-art from literature. The CPPS model proposed takes into account the shortcoming like human absence in CPPS's physical part. The originality of this new approach is also about the complementarity between human operator and DSS in the control process of production industry. Other particularity is also based on integrating the human operator at the heart of the drift diagnosis, and taking into account the human knowledge in DSS design process. The proposed methodology is a step forward in the drift diagnosis process, but it suppose that drift is detected before diagnosis process. As there are many existing drift detection methods in the literature, we plan to integrate into the proposed approach a drift detection methods in data streams scenario.

However, once drift is detected, DSS can assist human operator for the drift diagnosis. A prototype of this approach will be developed in the near future, after having designed knowledge base, which contains a proper formalization of human operators' experience and their behavior when interacting with the CPPS.

V. CONCLUSIONS AND FUTURE WORKS

In this paper, we propose and discuss an approach of DSS for drift diagnosis in CPPS context. It allows a human operator to determine the drift causes and to suggest him corrective actions in production control process. The main contributions of this work can be summarized as follows:

• modelling CPPS as a complex socio-technical system • definition of all human roles in the production system • description of the importance of human operator in CPPS • proposal of a DSS methodology for drift diagnosis.

We have provided a state-of-the-art on drift diagnosis research developments and guidelines about how to develop a decision support system, to support human operators in various decision activities, for drift diagnosis in production industry process.

Fig. 1 .

 1 Fig. 1. Main function

Fig. 4 .

 4 Fig. 4. Decision actors of the case study In the decision process, actors can have different roles : (a) Day Foremen's tasks (DF) • Provide instructions to SF (about product quality for example) • Comment results of Laboratory (via a software or voice) • Meet every afternoon with customers • Meet every week with SF (b) Shift Foreman's tasks (SF) • Transmit DF's instructions to its team • Receive team requests • Transmit to DF the requests of others • Visit shopfloor (c) Control room operator's tasks (CRO) • Monitor and control the production process using a software • Detect drift on the production line • Act on the batteries using a software • Communicate and send instructions to the shopfloor via walkie-talkie (or phone). (d) Shopfloor operator's tasks (SO)

Fig. 3 .

 3 Fig. 3. Present Drift Diagnosis Mechanism

Fig. 5 .

 5 Fig. 5. Global view of DSS process

Fig. 6 .

 6 Fig. 6. Drift diagnosis process using proposed DSS

ACKNOWLEDGMENT

This study was supported within the HyperCOG 1 project by the European Union's Horizon 2020 (H2020) research and innovation program under grant agreement N°869886. We would like to thank all the partners of the HyperCOG project for their help and involvement.