Cecilia Lindig-Leon
email: cecilia.lindig-leon@inria.fr

Sébastien Rimbert
email: sebastien.rimbert@inria.fr

Oleksii Avilov
email: lesha.avilov@gmail.com

Laurent Bougrain
email: laurent.bougrain@loria.fr

Scalp EEG Activity During Simple and Combined Motor Imageries to Control a Robotic Arm

In this article, we study how combined motor imageries can be detected to deliver more commands in a Brain-Computer Interface for controlling a robotic arm. Motor imageries are a major way to deliver commands in BCI. Nevertheless only a few systems use more than three motor imageries: right hand, left hand and feet. Combining them allow to get four additional commands. We present an electrophysiological study to show that i) simple motor imageries have mainly an electrical modulation over the cortical area related the body part involved in the imagined movement and that ii) combined motor imageries reflect a superposition of the electrical activity of simple motor imageries. A shrinkage linear discriminant analysis has been used to test as a first step how a resting state and seven motor imageries can be detected. 11 healthy subjects participated in the experiment for which an intuitive assignment has been done to associate motor imageries and movements of the robotic arm with 7 degrees of freedom.

I. INTRODUCTION

Motor imagery (MI) is the ability to imagine performing a movement without executing it [1]. According to Jeannerod [2], MI represents the result of conscious access to the content of the intention of a movement, which is usually performed unconsciously during movement preparation [3]. MI has two different components, namely the visual-motor imagery and the kinesthetic motor imagery (KMI) [START_REF] Neuper | Imagery of motor actions: Differential effects of kinesthetic and visualmotor mode of imagery in single-trial EEG[END_REF]. KMI generates an event-related desynchronization (ERD) and an event-related synchronization (ERS) in the contralateral sensorimotor area, which is similar to the one observed during the preparation of a real movement (RM) [START_REF] Pfurtscheller | Event-related EEG/MEG synchronization and desynchronization: basic principles[END_REF]. More precisely, compared to a resting state taken before a motor imagery, several power modulations are observed in in the alpha [START_REF] Fok | An eeg-based brain computer interface for rehabilitation and restoration of hand control following stroke using ipsilateral cortical physiology[END_REF][START_REF]Brain-Computer Interfaces: Principles and Practice[END_REF][START_REF] Medical | World medical association declaration of Helsinki: ethical principles for medical research involving human subjects[END_REF][START_REF] Renard | Openvibe: An open-source software platform to design, test and use brain-computer interfaces in real and virtual environments[END_REF][START_REF] Friedman | Regularized Discriminant Analysis[END_REF][START_REF] Pedregosa | Scikit-learn: Machine learning in Python[END_REF][START_REF] Blankertz | Optimizing spatial filters for robust EEG single-trial analysis [revealing tricks of the trade[END_REF] and in the beta (18-25 Hz) bands of the electroencephalographic signal measured over the sensorimotor area corresponding to the body part involved in the motor imagery. Firstly there is a gradual power decrease in the alpha and in the beta bands, called ERD. Secondly, a low power level is maintained during the movement. Finally, from 300 to 500 milliseconds after the end of the motor imagery, there is a power increase called ERS or post-movement beta rebound with a duration of about one second. Although several studies showed an activity uniquely in the contralateral area [START_REF] Pfurtscheller | Motor imagery and direct braincomputer communication[END_REF], other studies showed that ERD and ERS are also in the ipsilateral area [START_REF] Fok | An eeg-based brain computer interface for rehabilitation and restoration of hand control following stroke using ipsilateral cortical physiology[END_REF].

Emergence of ERD and ERS patterns during and after a MI has been intensively studied in the Brain-Computer Interface (BCI) domain [START_REF]Brain-Computer Interfaces: Principles and Practice[END_REF] in order to define detectable commands for the system. BCI systems consist of a combination of modules that allow recording the user brain activity in order to analyze it and find specific patterns indicating that the person is executing a particular mental task, which in turn is associated to a specific command for controlling the function of certain devices, such as computers, robotics, wheelchairs, prostheses, orthoses, etc. In this way, BCIs replace the natural nervous system pathways by artificial ones, which allow people with a severe motor impairment to interact with their environment by using only their brain activity. Most of BCI applications aim at improving the quality of life of their users, who have lost certain function(s) due to a degenerative disease or an accident, in which case the system will attempt to restore the missing ability.

In order to explore new solutions for overcoming the existing limitations of EEG-based neuroprostheses, we explored new alternative that have been implemented and motivated by a JACO 3-fingers robotic arm. This study consists to control the three body parts of this robotic arm in increasing the number of MI commands. Indeed, we will present a 8class database; which comprises EEG signals from 11 healthy subjects inducing the eight mental states that it is possible to generate by the motor tasks involving the use of the right hand, left hand, both feet together, and rest. We will present a qualitative analysis on the ERD patterns that are found in the EEG signals during the eight different tasks and the classification performance for each subjects.

II. MATERIAL AND METHODS

A. The JACO 3-fingers robotic arm

The proposed approach has been motivated by a commercial device model JACO 3-fingers by Kinova TM1 , which affords a full 3D control through fourteen predefined motions; including six reaching movements along the three axis, six rotations around the three axis, and a grasping function. These functions are distributed in three main articulations-like that resemble the forearm, with which the device is able of approaching objects, the wrist, with which it is possible to turn the hand towards any direction, and the fingers, which allows to open and close the hand (Figure 1). Regarding the use of the fingers, even when the device allows controlling each pair separately, we have decided to consider them all together. In this way the number of required commands decreases without loss of functionality, since the grasping action by using two or three of the fingers has almost the same effect. Fig. 1. 3-axis operation mode. The robotic arm affords a full 3D control through fourteen predefined motions; including six reaching movements along the three axis (A), six rotations around the three axis (B), and a grasping function (C). These functions are distributed in three main articulations-like that resemble the forearm, the wrist, and the fingers. Note that we have decided to use all fingers together, so that the number of required commands decreases without loss of functionality, since the grasping action by using two or three of the fingers has almost the same effect.

1) Implementation challenges: The appropriate use of this robotic arm implies several challenges that must be addressed from different perspectives. For instance, users' training is one of the most difficult aspects. Even when powerful feature extraction and classification techniques are implemented, the control of this kind of devices becomes unsatisfactory if users are not able of properly modulating their brain activity. In this sense, the proposed paradigm adds a greater difficulty in comparison to the standard protocols, since the movement imagination of more than one body part at the same time requires a higher degree of coordination. Furthermore, training sessions can be long and tedious for users, specially if the task is not well accomplished, which might cause frustration and make the training process even more difficult. It must be mentioned that this aspect represents one of the main areas of BCI research, which was beyond of the scope of the present project and, therefore, it remains to be addressed as part of the related feature work.

2) Multiple commands: As shown in Figure 1 this device offers several predefined motions, which on the one hand facilitates the interaction with the subject, but on the other hand demands a considerable number of different brain states to be properly induced and classified. In this regard there are at least two aspects to be considered; first, the number of body parts with which it is possible to offer an intuitive control and whose related sources are separated enough to distinguish the induced activity is limited, and second, the classification task becomes more difficult as the amount of classes increases, which can lead to misclassified responses that hinder the correct use of the device. Because of these considerations we have decided to explore the use of combined motor imageries, since in contrast with the standard scheme, this approach has the advantage of considerably increasing the amount of afforded commands while using the same number of body parts (in order of 2 P compared to P , where P is the number of body parts). However, even when the use of combined movements is considered, there might be not enough commands to control devices as the one we are describing. For instance, in the present work we have decided to consider the use of the left hand, right hand and both feet together. Given that these body parts are intuitively associated with movement. It is worth mentioning that feet are considered together given the proximity of the two sources, which may be difficult to distinguish.

Under this scheme users are afforded only with eight commands if the rest condition is also considered (i.e., left hand, feet, left hand in combination with feet, right hand, both hands together; right hand in combination with feet; both hands together with feet, and rest), which are not sufficient to control the fourteen available motions. To overcome the insufficiency of commands we have designed a smart switching-mode scheme that allows controlling different actions by using the same command.

The combination of the three body parts included in the paradigm together with the rest condition, afford users with eight different commands. However, since it is essential to keep the rest condition unassigned to any movement, only seven classes remain to control the device. To this end, an implementation based on three modes has been proposed: one mode for controlling the arm position (see Figure 1-A), another one for the wrist (see Figure 1-B), and the third one for opening and closing the fingers (see Figure 1-C). Each mode assigns a specific class to a specific motion. The combination of the three motor imageries (both hands and feet) has been established to be used as a switch that allows changing from one mode to the next one (arm, wrist, fingers, arm...). In this way it is possible to manage different actions by using the same mental command to control the fourteen available motions (see Figure 2).

B. Participants

Eleven right-handed healthy volunteer subjects took part in this experiment (8 men and 3 women, 19 to 43 years old). They had no medical history which could have influenced the task. All experiments were carried out with the consent agreement (approved by the ethical committee of INRIA) of each partici- pant and following the statements of the WMA declaration of Helsinki on ethical principles for medical research involving human subjects [START_REF] Medical | World medical association declaration of Helsinki: ethical principles for medical research involving human subjects[END_REF].

C. Protocol

Subjects were seated in a comfortable chair with the arms at their sides in front of a screen showing the task cue to be performed, which consisted of one of the eight kinesthetics motor imageries that it is possible to generate with all combinations including the use of the right hand, left hand, both feet together, and rest (i.e., right hand; left hand; feet; both hands together; left hand in combination with feet; right hand in combination with feet; both hands together with feet, and rest). The whole session consisted of 4 runs, containing each one 10 trials per task, which totals 40 trials per class (320 trials considering the 8 classes). For the stimulation, three panels were simultaneously displayed on the screen, which were associated from left to right, to the left hand, feet and right hand. Each trial was randomly presented and lasted for 12 seconds, starting at second 0 with a cross at the center of each panel and an overlaid arrow indicating for the next 6 seconds the task to be performed: an arrow pointing to the left side on the left panel for left hand, an arrow pointing down on the central panel for feet, an arrow pointing to the right side on the right panel for right hand, and the simultaneous combination of all of them for the corresponding combined tasks (see Figure 3 and column 2 of Figure 2). The rest condition was also considered and it was indicated by the absence of arrows. After second 6, the task cue disappeared and the crosses were remaining for the next 6 seconds indicating the pause period before the next trial started.

D. Electrophysiological data

EEG signals were recorded by the OpenViBE [START_REF] Renard | Openvibe: An open-source software platform to design, test and use brain-computer interfaces in real and virtual environments[END_REF] platform from fiftenn right-handed healthy subjects at 256 Hz using a commercial REFA amplifier developed by TMS International TM . The EEG cap was fitted with 26 passive electrodes, namely Fp1; Fp z ; Fp2; F z ; FC5; FC3; FC1; FC z ; FC2; FC4; FC6; C5; C3; C1; C z ; C2; C4; C6; CP5; CP3;CP1; CP z ; CP2; CP4; CP6 and P z , re-referenced with respect to the common average reference across all channels and placed by using the international 10-20 system positions to cover the primary sensorimotor cortex.

E. ERD/ERS patterns

To evaluate more precisely the modulation which appeared during the two different time windows, we computed the ERD/ERS% using the "band power method" [START_REF] Pfurtscheller | Event-related EEG/MEG synchronization and desynchronization: basic principles[END_REF] with a Matlab code. First, the EEG signal was filtered in the mu frequency band (7-13 Hz) for all subjects using a 4th-order Butterworth band-pass filter. Then, the signal was squared for each trial and averaged over trials. Then it is smoothed using a 250ms sliding window with a 125 ms shifting step. The averaged power computed for each window was subtracted and then divided by the averaged power of a baseline corresponding to a 2s window before each trial. Finally, the averaged power computed for each window was subtracted and then divided by the averaged power of a baseline corresponding 2s before each trial. Trials were considered 2 seconds before the task cue appeared on the screen and 6 seconds after it disappeared, so that each segment corresponds to a 14s-length signal segment over 26 electrodes (Figure 5, Figure 6 and Figure 7).

F. Classification

Each subject performed 4 runs of motor imageries. Each run contains 10 trials for each class (Rest, Left Hand, Right Hand, Both hands, Feet, Left hand & Feet, Right hand & Feet, Both hands & Feet). Thus 40 trials per class are available for each subject. From each trial 26 features (one per channel) have been extracted from the filtered signal computing the logarithm of the variance of the amplitude values between 0.5 and 3.5s after the GO signal. For classification a 8-classes Shrinkage Linear Discriminant Analysis (sLDA) [START_REF] Friedman | Regularized Discriminant Analysis[END_REF] has been used via the python library scikit-learn [START_REF] Pedregosa | Scikit-learn: Machine learning in Python[END_REF]. The reason to use a shrinkage estimator is due to the small number of samples per class. For the same reason, a cross validation has been done using 3 runs for train and 1 run for test. Thus 240 trials for train and 80 trials for test for every step of a the cross validation.

III. RESULTS

A. Oscillatory power analysis

Figure 4 shows a topographic representation for one of the subjects along all conditions of the ERD/ERS% values obtained for all electrodes and averaged over the 4-second period starting one second after the task cue was presented. It can be observed that the lowest values appear on the regions associated to the limbs that are engaged at each motor task (broadly, right hand over electrode C3, feet over electrode C z , and left hand over electrode C4). If we take a closer look over the time course of ERD/ERS% over electrode C3 (i.e, the activity source associated with the use of the right hand) presented in Figure 5, we can observe that all motor imageries involving the use of the right hand (i.e., right hand, right hand in combination with feet, both hands, and both hands in combination with feet) present considerably lower ERD/ERS% values than those that do not include it (i.e., rest, left hand, feet, and left hand in combination with feet).

Similarly, Figure 6 presents the time course of ERD/ERS% over electrode C z (i.e, the activity source associated with the use of the feet). Over this region it can also be observed that the motor imageries involving the use of the feet (i.e., feet, left hand in combination with feet, right hand in combination with feet, and both hands in combination with feet) present lower ERD/ERS% values than those that do no it include it (i.e., rest, left hand, right hand, and both hands). However, in comparison to the contrast observed over electrode C3 between the tasks involving the use of the right hand and those that do not involve it, differences are not as significant. In fact, we can observe that the use of both left and right hands induce some desynchronization over the central region, possible because the associated sources are quite close to electrode C z , which might cause it to incorporate some of this activity.

Finally, the same comparison is illustrated in Figure 7 for the time course of ERD/ERS% generated over electrode C4 (i.e, the activity source linked to the use of the left hand). In this case, similarly to the analysis over electrode C3, it is possible to observe a significant contrast between the low ERD/ERS% values obtained from the activity induced by the motor imageries involving the use of the left hand (i.e., left hand, left hand in combination with feet, both hands, and both hands in combination with feet) from the high ERD/ERS% values associated to the tasks that do not involve the use of the left hand (i.e., rest, feet, right hand, and right hand in combination with feet). Fig. 5. ERD/ERS% time series within the mu range for all motor tasks over electrode C3 (i.e, the activity source associated with the use of the right hand). ERD/ERS values were estimated over the EEG trials from subject 2. Note that all motor imageries involving the use of the right hand (i.e., right hand, right hand in combination with feet, both hands, and both hands in combination with feet) present considerably lower values that those that do not include it (i.e., rest, left hand, feet, and left hand in combination with feet). Fig. 6. ERD/ERS% time series within the mu range for all motor tasks over electrode Cz. ERD/ERS values were estimated over the EEG trials from subject 2. Note that the motor imageries involving the use of the feet (i.e., feet, left hand in combination with feet, right hand in combination with feet, and both hands in combination with feet) present lower ERD/ERS% values than those that do no it include it (i.e., rest, left hand, right hand, and both hands). However, this contrast is not as significant as the one observed on electrode C3, possibly due to the fact that the regions associated to both the left and right hands are quite close to electrode Cz, which might cause it to incorporate some of this activity.

B. Statistical analysis

We present a series of box plots for comparing the power spectrum magnitudes at 12 Hz between all motor tasks over the regions associated to the main sources. Again, it is possible to observe that motor imageries involving the use of the limb associated to each electrode present similar low power values at these positions, whether those that are not engaged generate higher values. This is particularly visible from figure 8, which presents the corresponding analysis over electrode C3 (i.e., the region associated to the right hand). Note that the distribution of the mean values confirm that there is no significant difference between motor imageries including the use of the right hand over this region, independently from Fig. 7. ERD/ERS% time series within the mu range for all motor tasks over electrode C4. ERD/ERS values were estimated over the EEG trials from subject 2. Note that all motor imageries involving the use of the left hand (i.e., left hand, left hand in combination with feet, both hands, and both hands in combination with feet) present considerably lower values that those that do not include it (i.e., rest, right hand, feet, and right hand in combination with feet).

the fact of whether the motor imagery is simple or combined. In contrast there is a significant difference with all the other tasks that do not include the use of the right hand which, in turn, present a uniform distribution between them. The same analysis is presented for electrode C z (i.e., the region associated to the feet) and electrode C4 (i.e., the region associated to the left hand) in figures 9 and 10 respectively. Fig. 8. Box plots for subjet 2 comparing the power spectrum magnitude at 12 Hz within the time interval from [2-5 s] among all the considered conditions over electrode C3. The distribution of data shows that there is no significant difference between motor imageries including the use of the right hand over this region, independently from the fact of whether the motor imagery is simple or combined. In contrast there is a significant difference with all the other tasks that do not include the use of the right hand which, in turn, present a uniform distribution between them.

C. Classification

Table I reports the accuracy for 8 classes obtained by a shrinkage LDA for 11 subjects. The mean accuracy of 31.6 % is low even compared to a random assignment (12.5 %). However, it should be noted that two subjects have better performance (S2 and S11) compared with other subjects. It is interesting to see that the best subject in term of accuracy had also the powerful ERD during the MI-task (Figure 5, Figure 6 and Figure 7). Thus, in the next section, we will discuss possible solutions to improve the classification accuracy. Hz within the time interval from [2-5 s] among all the considered conditions over electrode C4. The distribution of data shows that there is no significant difference between motor imageries including the use of the left hand over this region, independently from the fact of whether the motor imagery is simple or combined. In contrast there is a significant difference with all the other tasks that do not include the use of the left hand which, in turn, present a uniform distribution between them.

Table II shows the main misclassifications. In particular, feet motor imageries have been confused with a resting state (Rest → Feet, Feet → Rest, Both hands → Feet, Left hand & Feet → Left hand, Right hand & Feet → Right hand, Both hands & Feet → Both hand). One explanation could be that the activity of the right hand (respectively the left hand and both hands) goes over the Cz location.

Each class is described by a few number of samples (30 for training and 10 for testing). Increase the number of samples for each class should improve the performance. The low classification rate could also be linked with the difficulty to properly imagine a kinesthetic movement. An incremental motor imagery learning using first real movements and then intented movements could be useful to train the user to produce a good motor imagery.

IV. CONCLUSION

To control a robotic arm with 14 commands using a EEGbased BCI, we presented a mapping of these commands with a set of simple and combined motor imageries. Our study shows that the electrical activity of combined motor imageries measured on the scalp is very similar to the electrical activity measured for each simple motor imagery taken individually. Thus an efficient classification should be able to distinguish them. The accuracy obtained by a shrinkage linear discriminant analysis is not good enough to allow a control of the robotic arm. Further works will focus on ensemble of binary classifiers to allow the use the efficient common spatial pattern algorithm [START_REF] Blankertz | Optimizing spatial filters for robust EEG single-trial analysis [revealing tricks of the trade[END_REF] as pre-processing to increase by linear combination of the channels the amplitude of the signal for one class and decrease it for the other one. Then one-versusone approach, one-versus-the-rest approach or a hierarchical approach should improve the detection of combined motor imageries.

Fig. 2 .

 2 Fig. 2. Arm control. The different MIs that users were asked to perform are shown in the first column with their associated cue stimuli in the second one.The last three columns present the different robotic movements associated to each mode. Note that the rest condition is not linked to any action, and that motor imagery including the combination of all body parts represents a switch to change from one mode to another.

Fig. 3 .

 3 Fig. 3. Time scheme. Each trial was randomly presented and lasted for 12 seconds. During the first 6 seconds, users were asked to perform the motor imagery indicated by the task cue, which was launched throughout three simultaneously displayed panels associated, from left to right, to left hand, feet and right hand. The use of each body part was indicated by the presence of arrows: an arrow pointing to the left side on the left panel for left hand, an arrow pointing down on the central panel for feet, an arrow pointing to the right side on the right panel for right hand, and the simultaneous combination of all of them for the corresponding compound motor imageries. After second 6, the task cue disappeared and the crosses were remaining for the next 6 seconds indicating the pause period before the next trial started.

Fig. 4 .

 4 Fig.4. Distribution of the relative oscillatory power along the sensorimotor cortex during the course of the different motor tasks for subjet 2. ERD/ERS% values were obtained for all electrodes within the mu frequency range and averaged over the 4-second period starting one second after the task cue was presented. Negative values indicate ERD% modulation, which is characteristic of a motor task execution that appears over the contralateral hemisphere of the body part used in the process (broadly, electrode C4 for left hand, Cz for feet, and C3 for right hand). Note that each topographic map is independently normalized to enhance the corresponding patterns.

Fig. 9 .

 9 Fig. 9. Box plots for subjet 2 comparing the power spectrum magnitude at 12 Hz within the time interval from [2-5 s] among all the considered conditions over electrode Cz. The distribution of data reflects what was observed from the ERD/ERS% time series, and confirms that EEG activity around the central region is influenced by the surrounding sources.

Fig. 10 .

 10 Fig.10. Box plots for subjet 2 comparing the power spectrum magnitude at 12 Hz within the time interval from [2-5 s] among all the considered conditions over electrode C4. The distribution of data shows that there is no significant difference between motor imageries including the use of the left hand over this region, independently from the fact of whether the motor imagery is simple or combined. In contrast there is a significant difference with all the other tasks that do not include the use of the left hand which, in turn, present a uniform distribution between them.

TABLE I ACCURACY

 I OBTAINED BY A SHRINKAGE LDA FOR EACH SUBJECT FOR 8-CLASS OF MOTOR IMAGERIES.

	Subject 8-class accuracy
	1	21.9% ± 3.4%
	2	58.1% ± 5%
	3	34.7% ± 2.7%
	4	28.4% ± 6.6%
	5	26.9% ± 4%
	6	23.7% ± 1.8%
	7	27.8% ± 6.6%
	8	26.6% ± 3.5%
	9	20.3% ± 2%
	10	35.3% ± 3%
	11	43.4% ± 5.2%
	AVG	31.6% ± 10.5%

  [1] L. Avanzino, A.Giannini, A. Tacchino, E. Pelosin, P. Ruggeri, and M. Bove, "Motor imagery influences the execution of repetitive finger opposition movements," Neuroscience Letters, vol. 466, no. 1, pp. 11 -15, 2009. [2] M. Jeannerod, "Mental imagery in the motor context," Neuropsychologia, vol. 33, no. 11, pp. 1419-32, Nov 1995. [3] M. Lotze and U. Halsband, "Motor imagery," J Physiol Paris, vol. 99, no. 4-6, pp. 386-95, Jun 2006.

http://www.kinovarobotics.com/assistive-robotics/products/manipulation/


Elena Epure
email: elena.epure@malix.univ-paris1.fr

Rebecca Deneckere

Camille Salinesi
email: camille.salinesi@univ-paris1.fr

Benjamin Kille
email: benjamin.kille@dai-labor.de

Jon Espen Ingvaldsen

Devising News Recommendation Strategies with Process Mining Support

News media is in a digital transformation, disrupting their existing business models. Many news media houses are looking into recommender systems as a part of their digital strategies. However, the social role of journalism, existing publishing platforms and news as a continuous data stream infer particular challenges for applying standard recommender technologies. This paper explores how news recommendation can go beyond popularity and recency and take advantage of content quality metrics and interaction patterns. This knowledge is derived through adapting process mining for usage with web logs. The proposal is evaluated on real event logs from a German news publisher, revealing encouraging results.

I. INTRODUCTION

The digital transformation has reshaped news media's ecosystem, conveying the adoption of web portals, mobile solutions, and recommendation technologies as part of their strategies. From a business perspective, news media has also been facing a higher competition in engaging their readers for maintaining advertising revenues. With datadriven technologies, news publishers have managed to target their content distribution to individual readers' interests.

However, devising an effective news recommendation strategy is still challenging because of various reasons: the very high rate at which new items are published (data sparsity); users' relatively unstable preferences influenced by breaking news and such; potentially unreliable implicit user feedback. These challenges are amplified for smaller news providers, where technical resources and knowledge are more limited [START_REF] Epure | Process mining for recommender strategies support in news media[END_REF]. Thus, grasping users' interests and their reading behavior is necessary for publishers to create a strategy addressing these challenges.

In this paper, we propose and adapt process mining [START_REF] Aalst | Process mining[END_REF], a suite of data mining techniques initially applied in the context of Business Informatics, to reveal visual flow models describing news reading behavior. In particular, we analyze users' interaction with news categories. The discovered knowledge drives the implementation of a hybrid recommendation strategy. The solution is evaluated in a near-to-online setup with a Berlin-based newspaper, which accommodates about 3 million visits per week on average. Although web logs have their limitations, we show that we are able to reveal valuable insights about how news categories are consumed and how they relate to each other.

Our proposed solution improves the recommendations over a popularity and recency-focused baseline in almost 90% of the cases. It also diversifies the suggestions.

II. PROCESS MINING FOR NEWS RECOMMENDATION STRATEGY

A. Process Mining Presentation

Process mining consists of a suite of techniques and tools, which has been effectively used to discover and visually represent complex process models from logged traces of information systems' users [START_REF] Aalst | Process mining[END_REF] and, more recently, of web users [START_REF] Poggi | Proceedings, chapter Business Process Mining from E-Commerce Web Logs[END_REF]. A process model can be formalized by a quadruple, P = (S start , S stop , A, T ), where each element is:

• A = {a n } N
n=1 the finite set of process activities, • S start = the set of initial process states,

• S stop = the set of final process states, • T ⊆ ((S start × A) (A × A) (A × S stop )) the finite set of transitions between activities, or activities and states.

Further, using the transition notation, several types of relations could be identified:

• Sequence: t ∈ T : t = a i ≺ a j or t = s start ≺ a k or t = a l ≺ s stop with s start ∈ S start , s stop ∈ S stop , a i , a j , a k , a l ∈ A and i = j . The interpretation of a i ≺ a j is that once the activity a i is finished, the activity a j follows.

The transitions s start ≺ a k and a l ≺ s stop are implicit transitions marking the start and the end of the process. • Loop: t ∈ T : t = a i ≺ a i where a i ∈ A. The interpretation is that once the activity a i is finished, it starts again.

• XOR-split: t m , t n ∈ T, t m = a i ≺ a j , t n = a i ≺ a k and t m ⊕
t n where a i , a j , a k ∈ A and j = k. The interpretation is that either t m or t n takes place; once the activity a i is finished, either the activity a j or a k follows.

More relations exist but only the presented ones are within the scope of this work. A process execution E = s start ≺ . . . ≺ a i ≺ . . . ≺ s stop represents a sequence in the process from one start state to one end state, with at least one activity in between. A process can contain multiple execution paths given the presence of loop and XOR-split relations.

In most of software application nowadays, either webbased or desktop-based, events are generated and logged during their usage. Process mining exploits these events for revealing process models in a bottom-up approach. Let e be such an event captured during the user interaction with an application. The minimum information about an event required by a process mining technique is: e i d the event's unique identifier, e t the event's timestamp, e a ∈ A the event's associated activity. The activity is usually inferred and attached to the event in a pre-processing phase, preceding the mining phase. This happens by reasoning on the existing event's meta-data. Example of activities include "access account", "add new customer", and "delete registration".

A specific process execution E by a user u ∈ U is logged as a trace τ = {e 1 , e 2 , . . . , e r }. Let L = {τ v } V v=1 be a log containing a finite set of traces. The process mining technique is a function γ that maps the log L on a process model P . For one specific log, multiple definitions of the function γ could be proposed as different algorithms. Also, variations of the same function γ could exist by parameter manipulation [START_REF] Aalst | Process mining[END_REF]. Often, these parameters can be changed interactively in the interface of the process mining tool.

Apart from discovering formalized process models, an effective representation of these models for enabling decision support is central to process mining. This principle represents in fact one major difference to data mining. A particular challenge that process mining must deal with is the "spaghetti processes'", the type of processes that are very complex and highly unstructured, resulting in unreadable graphical representations. For this, several techniques have been aimed not only at discovering the function γ but also at proposing ways of simplifying the visualization while keeping a fit representation of the log L. An example of algorithm in this direction is the Fuzzy Miner [START_REF] Gunther | Fuzzy mining: Adaptive process simplification based on multi-perspective metrics[END_REF], which follows an approach inspired from cartography. It mines processes and represents them in readable, visual models by leveraging the level of representative behavior through abstraction, emphasis, customization and aggregation. The level of details regarding a process is interactively set by the user using the interface of the process mining tool [START_REF] Gunther | Fuzzy mining: Adaptive process simplification based on multi-perspective metrics[END_REF].

To ensure effective decision making and knowledge extraction support within news organizations, data exploration should be visual, interactive and intuitive [START_REF] Huang | An empirical investigation of factors influencing the adoption of data mining tools[END_REF]. This is why process mining is explored for devising recommendation strategies. Also, encouraging results have been obtained by recent works in web mining using process mining [START_REF] Awatef | Process mining in the education domain[END_REF], [START_REF] Poggi | Proceedings, chapter Business Process Mining from E-Commerce Web Logs[END_REF].

B. Log Preparation for Mining News Reading Processes

Let's consider a user u that visits the web news portal and navigates to another page to read the article ar t by clicking its title advertised on the screen. In that moment, a click event, e c is generated for capturing the fact that a recommendation succeeded as the advertised news article was clicked. Further, once the requested article is rendered and shown in browser, an impression event, e i , is generated and stored with the following meta-data: the user's id u i d (the session's id), the article's id ar t i d , the article's text including the title and abstract ar t t ext , the set of categories associated with the article ar t cat = {c m } M m=1 , the timestamp θ, and other features collected by the tracking component such as the type of browser, operating system, etc. Each article could have multiple categories associated. In order to generate the event logs necessary for process mining, the following mapping are made:

• The session id (cookie-based) is associated with the unique identifier of the process execution's trace; • For each impression event, the categories of the read article are sorted, concatenated by commas, and associated with the activity of one trace event;

• For each impression event, its timestamp is associated with the timestamp of one trace event; • For each impression event, a unique identifier is generated during the pre-processing and associated with the id of one trace event.

The process activities are mapped on articles' categories because, in the literature, users' news reading interests are often represented by categories [START_REF] Das | Google News Personalization : Scalable Online[END_REF], [START_REF] Li | News Recommendation via Hypergraph Learning : Encapsulation of User Behavior and News Content[END_REF], [START_REF] Liu | Personalized News Recommendation Based on Click Behavior[END_REF]; hence, we associate the news reading behavior to a process of transitioning between news interests. In addition, this overcomes the data sparsity issue, especially prevalent in the news domain. The format chosen for the log files is "csv", character-separated values. Table I shows an example of processed web log events from news reading.

C. Process Mining Tools for News Reading Processes

Two process mining tools are reported to be extensively used by related works: ProM [START_REF] Van Dongen | The prom framework: A new era in process mining tool support[END_REF] and Disco [START_REF] Gunther | Disco: Discover your processes[END_REF]. ProM contains a more extensive variety of mining algorithms but is less suited for handling very big event logs. By contrary, Disco is a commercial tool that can handle big data. It is based mainly on the Fuzzy Miner technique [START_REF] Gunther | Fuzzy mining: Adaptive process simplification based on multi-perspective metrics[END_REF]. Considering these, we opt to use Disco in the current work as web logs from news reading contain millions of events per month.

Examples of mined models are presented in Figure 1. Each rectangle in Figure 1(b) represents an activity. We notice in Figure 1(b) the default states, start and stop, represented as circles, at the top and bottom of the model. The arrows mark the transitions from one activity to another. The size of the arrow and the color of activities give information about metrics. For example, the frequency of a transition is marked by the thickness of the arrow's line while the frequency of an activity is revealed by the color's intensity (darker, more frequent). Figure 1 also shows the explorative feature of process mining tools to maneuver from spaghettiness and noise to valuable flow relations and actionable insights. Figure 1 (a) shows how newspaper's event logs produce a complex "spaghetti" model when we visualize all activities Process mining tools provide process models enriched with various performance metrics. Figure 1 presents the activities and transitions enriched with frequency values (the numbers associated with the arrows and rectangles). Process mining also describes temporal aspects of the process flows such as: how much time an activity lasts on average, what is the average, minimum or maximum time between two activities [START_REF] Ingvaldsen | Industrial application of semantic process mining[END_REF]. Disco, the process mining tool, creates a view on the same model emphasizing on the temporal metrics. In our knowledge extraction phase, we often switched between the two perspectives of the same models with the help of the tool. Moreover, the tool provides these perspective for individual sessions, thus enabling us to make comparisons. This work omits such comparisons due to space limitations.

D. Revealing News Reading Processes and Related Knowledge

In the reading news processes, the frequency values, regarding activities and transitions, are a measure of popularity of standalone news categories and of relations between Fig. 2. Proportion of cases when common news categories are accessed after readings of a "Travel Tourism Navigation"-article Fig. 3. Average reading times for a subset of common news categories accessed after readings of a "Travel Tourism Navigation"-article news categories. In Figure 1(b), we can see the news category "Travel Tourism Navigation" is accessed 1356070 times. Readings of this news category is also observed to be followed by readings within the same category 879174 times (the loop transition). Mined models reveal that loops are common for all news categories. Figure 4 shows how many categories users read during both short and long reading sessions, for Berlin newspaper in September 2014. Here we can see that most short and long reading sessions contain a single category. This reveals that the next read article is likely to be within the same category as the previous article. When we look at the alternative paths from start to end in this model, we also see how different category-activities cluster and form sequential relationships. In Figure 1 (b), "Travel Tourism Navigation" has two outgoing transitions to other category-activities at the selected filtering levels. This is actionable insight that could be exploited in the definition of recommendation strategies.

Figure 2 shows some of the most common news categories following after readings of articles from category "Travel Tourism Navigation". In 80 % of the cases, the next article is related to the same category. The second most popular category, following "Travel Tourism Navigation", is "News Science Communication" with 2 % of the traffic. Although these frequency values can predict what the user will click next, they do not describe to which extent the users actually appreciate or engage with the content. When we manually analyzed the process models, we noticed that the frequency of transitions sometimes delivered insufficient information.

The timestamp differences between two events in a session can tell us how long the user spent reading a specific article. If, for instance, the time between two subsequent events with the categories "Sport" and "Economics" is short, this means that users did not read the sport-related article but they quickly navigated to the economics-related article. Contrarily, if the timestamp difference is too long, this is a signal that the user has been inactive and that the "Economics" reading is actually the first event in a new reading episode. To compare the alignment of popularity and reading time, we extracted and analyzed reading episodes with a maximum 5 minutes delay between events. We traced the sequential reading events in this dataset and calculated the average reading time for each of the next news categories observed. Figure 3 shows the average reading time on a subset of common categories following after a "Travel Tourism Navigation" article. We can see that the category "People Relationships" has the highest value with 27 s. When we compare the popularity and reading time statistics, we see that news articles of this category are only accessed 0.18 % of the cases, but when they are accessed users tend to appreciate and engage with the content. In summary, the analysis driven by process mining revealed multiple insights about the news reading processes:

• Loops: news article readings are likely to be followed by readings within the same news category. • Category relations: the process models reveal strong sequences and XOR-splits of specific news categories. • Reading time: there could be an inconsistency between frequently clicked categories and the time users spent engaging with those types of articles. • Loyalty: Users are loyal to few news categories in both short and long reading sessions.

III. NEWS RECOMMENDATION STRATEGIES

A. Baseline Strategy

A baseline strategy takes into account the recency and popularity of news articles. The recency refers to the property of an article to have been recently read by the crowd. The popularity refers to the number of times an articles has been read. The popularity and recency can be captured through a ring buffer implementation. We define a list L, which can take elements up to a fixed number k and the index i pointing to the next entry to be populated in L. Whenever the system observes a visitor reading a news article ar t , it adds the article to the list at the position i and moves the index to the next entry (if i is currently at position k, it is reset to 1 creating a circular pattern). In this way, the collective reading preferences are captured. Note that duplicates are allowed.

At recommendation, the system takes the current list and iterates backwards from the current index. The system returns the first article ar t j (or first N articles) that mismatches the article that the visitor is currently reading ar t i , i = j . Consequently, the baseline guarantees that visitors are likely to receive the most recent news. In addition, the probability of an article to be suggested is proportional to the article's popularity. This is ensured by allowing duplicates in the ring buffer. The baseline has proved to help the system to follow trends and distribute important news [START_REF] Lommatzsch | Advances in Information Retrieval: 36th European Conference on IR Research, ECIR 2014[END_REF].

B. Process-driven Strategy

Based on related literature, past users' reading interests, expressed as news categories, could affect the reading choices apart from popularity and recency. However, the relations between categories, thus the process over the reading interests, has been less investigated. As shown, the baseline strategy covers popularity and recency, but it does not consider the users' personal reading interests and habits to transition between those interests in reading. Process mining provides insights to narrow this gap. Process mining delivered a set of patterns describing readers' transitioning between categories of news and other knowledge valuable in data pre-processing such as selecting just transitions reflecting engaged reading. Let the set of transitions from a specific process model P be: T = {(c s , c d , p sd )|∃c s , c d , (c s ≺ c d ) ∈ P }. Therein, we denote the individual category or a combination of categories by c. We consider p sd the degree of expected belief with which the transition to c d from c s is observed. In order to maintain the popularity and recency constraints in recommendation, a separate ring buffer for each category c is created to store read articles from this category, similarly to the baseline version. Subsequently, the system has to determine to which ring buffer to delegate the recommendation request. We derive this information from the p sd values and consider the maximum degree of belief, given the source category c s .

The set of categories comprises relatively few elements compared to the immense number of articles. Nonetheless, editors struggle to assign a single category to a given article especially for those articles covering topics related to multiple domains. For instance, imagine an article about the economic implications of a major sports event. As a result, a majority of articles has multiple category labels assigned. This situation presents some challenges for the implementation of the proposed recommendation strategy. We could approach this by creating a ring buffer for all combinations of categories. However, even with few existing categories, this approach would require a large collection of ring buffers. For instance, 8 initial categories would result in 256 ring buffers needed (exponentially growing). Another problem with this solution is the fact that some final categories would contain very few or even a single article. Thus, the system would always suggest the same article in some cases.

We opt for another approach in order to circumvent these issues. We iterate over the collection of transitions T extracting all combinations of categories for each transition. This step is costly, but we pre-compute the links between categories only once in advance. Thus, repetition is avoided and the complexity at run-time remains unaffected. Having extracted all possible transitions, we aggregate p sd for all combinations of categories c s , c d . This resulted in a symmetric matrix M , where the rows and columns correspond to the categories, and the values to the probabilities of each transition. Note that each row, respectively column, refers to a single news category here. Whenever the system receive a request, we decode the contained categories to a binary vector. This vector has 1 for each present category and 0 for each absent category. The category whose ring buffer to use for suggesting a recommendation is determined by multiplying the request vector with M . Finally, the category corresponding to the maximum value in the resulted vector is chosen. If maximum values coincide for multiple categories, a category is selected at random.

IV. NEWS RECOMMENDATION EVALUATION

A. Evaluation Strategy and Metrics

In order to evaluate the performance of a recommendation strategy, two types of evaluations are possible: online and near-to-online. The online or live evaluation uses as performance metric the click-through rate (CTR). This metric calculates the proportion of click events to the total number of suggested articles. The near-to-online evaluation verifies how accurately a recommender algorithm predicts the articles interesting for the user. An article is interesting for the user if the user will read it within a short time frame. The recommender algorithm receives the sequence of impression events and the sequence of click events. For each impression event e i , the recommender algorithm provides a list of 4 recommendations R = {ar t r 1 , ar t r 2 , ar t r 3 , ar t r 4 }; 4 corresponds to the required number of recommendations for the newspaper under study.

Subsequently, in the evaluation, it is checked if any of the suggested articles appears among the future impression events. A score of 1 is assigned to the requests if a match is detected and 0 otherwise. Note that this procedure differs from click-rate evaluation. The user is not actually required to click on the recommendation advertised on the news portal's web page. Instead, it is checked whether the user requests the recommended article in the session, the session being generally very short. We define a session, S = {ar t 0 , . . . , ar t N } as sequences of articles read correspond-ing to the impression events {e i 0 , . . . , e i N } and the user u. The scoring function is defined:

ξ(ar t i , R) = 1 if ∃ar t r ∈ R such that ar t r ∈ S >i 0 otherwise. ( 1
)
We denote all readings that occur after ar t i as S >i . Eventually, we obtain a normalized score for each session [START_REF] Ahmed | Fair and Balanced : Learning to Present News Stories[END_REF], which is bound to the closed interval [0, 1].

Q(S) = 1 |S| ar t i ∈S ξ(ar t i , R) (2)

B. Evaluation Objective and Data

The main objective is to evaluate and compare the proposed recommendation strategies in a near-to-online setup, using the metric previously introduced. We refer to the baseline as BaseRecmm and to the recommender enhanced with process mining knowledge as ProcessRecmm. Previous evaluations have shown that BaseRecmm yields results comparable to other more complex recommender algorithms [START_REF] Lommatzsch | Advances in Information Retrieval: 36th European Conference on IR Research, ECIR 2014[END_REF]. We used a data set with ≈ 1.8 million recommendation requests in 224 109 sessions from February 2015. The data set spanned ≈ 4 weeks. Both BaseRecmm and ProcessRecmm provided 4 recommendations for each request. This corresponds to the number of slots available on the publisher's news portal.

The past news reading behavior knowledge was extracted with process mining from September 2014. The rational for considering only one month was influenced by the capacity of the process mining tool. Being desktop-based, we had a memory constraint. The news publisher's monthly data amounts for 20 GB to 30 GB excluding articles' texts. After we extracted the information relevant for process mining, we were able to shrink the data to 4 GB to 5 GB, which allowed us to work on a 8 GB RAM computer for the data analysis with the process mining tool. September 2014 was chosen in particular because similar to February 2015 starts also on a Monday and the distance between them ensures that completely different users are concerned.

C. Results and Discussion

First, we analyzed to what degree both recommendation strategies provide identical suggestions. Initially, the algorithms fail to provide the requested number of suggestions. As soon as they collected enough information, they managed to respond with the designated number of items. Both systems provided 4 items in more than 99.9 % of the sessions used in the evaluation. Further, for each request, we determined how many common items have been recommended by both algorithms. In 50.2 % of the requests, all 4 items were identical. In 30.1 % of the cases, ProcessRecmm and BaseRecmm agreed on only 3 items. The algorithms delivered two identical items in 7.0 % cases. A single item matched 8.3 % of the time. Both recommenders completely disagreed in 4.3 % of requests. This showed that ProcessRecmm and BaseRecmm ensured differently the diversification of recommendations. Events per Session (a) Histogram events per session. >85 % of sessions have max 10 events.

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In summary, the recommendation strategy incorporating knowledge on news reading processes improved the recommendations significantly compared to a popularity and recency-based strategy and diversified more the suggestions. A more extensive analysis is required, especially with data from different months for both prediction and training.

V. RELATED WORK Billsus and Pazzani [START_REF] Billsus | Adaptive News Access[END_REF] describe various ways for visitors to receive personalized news. Their taxonomy includes news content personalization, adaptive news navigation, contextual access, and news aggregation. Das et al. [START_REF] Das | Google News Personalization : Scalable Online[END_REF] illustrate the news recommender system deployed at Google. Their solution exploits the transitions between articles merely through a covisitation metric; that stands for a popularity mechanism too. Existing works based on processes exist. Li et al. [START_REF] Li | News Recommendation via Hypergraph Learning : Encapsulation of User Behavior and News Content[END_REF] consider the process activities being users and articles. Ahmed et al. [START_REF] Ahmed | Fair and Balanced : Learning to Present News Stories[END_REF] tackled personalized news recommendation via a transition graph with three types of activities: views, clicks and documents. Compared to these, our approach is based on transitions between news categories. Liu et al. [START_REF] Liu | Personalized News Recommendation Based on Click Behavior[END_REF] introduced a follow-up work on Google News. Their frameworks model visitors' current interests as news categories. It is the closest to our work. Although, the category prediction is based on Bayesian networks and the recommendation of articles is different. Doychev et al. [START_REF] Doychev | An analysis of recommender algorithms for online news[END_REF] describe several approaches for news recommendation algorithms evaluated on CLEF-NewsREEL 2014 competition dataset. Their data analysis also shows that users tend to read news from the same category and a few dominant ones account for the majority of items that are read. They also found valuable patterns of activity between certain less popular categories and point out attempts to exploit these in future work.

VI. CONCLUSIONS

In this paper, we extract knowledge from news portals' logged data as dynamic models that capture the users' reading interests and behavior using process mining. We further, integrated this knowledge into a recommendation strategy together with articles' recency and popularity aspects. The proposed solution proves encouraging in an evaluation with one month worth of real data from a German news publisher. Further work aims at expanding the experiments for results' generalization. Compared to related works, we show how to devise a low-level recommendation strategy by being supported by a high-level, visual analysis. This could empower news organizations to conduct such analyses inhouse and draw informed strategy's decisions without a thorough level of technical knowledge.

Fig. 1 .

 1 Fig. 1. (a) Example of mined "spaghetti" model. (b) The same model showing the 10 % most frequent activities and 5 % most representative behavior. The model is zoomed in around the "Travel Tourism Navigation"

Fig. 4 .

 4 Fig. 4. Number of sessions vs. number of unique categories per session.

  Distribution of Q(RB) depending on Session Length(b) Boxplot BaseRecmm. Score relatively constant independent of session length.

  Distribution of Q(PMR) depending on Session Length(c) Boxplot ProcessRecmm. Scores much better than BaseRecmm for short sessions.

Fig. 5 .

 5 Fig. 5. Analysis of the proposed recommendation strategies: baseline and driven by news reading processes.