Milan Stepanov
email: milan.stepanov@l2s.centralesupelec.fr

M Umair Mukati

Giuseppe Valenzise
email: giuseppe.valenzise@l2s.centralesupelec.fr

Søren Forchhammer

Frédéric Dufaux
email: frederic.dufaux]@l2s.centralesupelec.fr

Learning-based lossless light field compression

Keywords: light field, lossless coding, deep learning

We propose a learning-based method for lossless light field compression. The approach consists of two steps: first, the view to be compressed is synthesized based on previously decoded views; then, the synthesized view is used as a context to predict probabilities of the residual signal for adaptive arithmetic coding. We leverage recent advances in deep-learningbased view synthesis and generative modeling. Specifically, we evaluate two strategies for entropy modeling: a fully parallel probability estimation, where all pixel probabilities are estimated simultaneously; and a partially auto-regressive estimation, in which groups of pixels are predicted sequentially. Our results show that the latter approach provides the best coding gains compared to the state of the art, while keeping the computational complexity competitive.

I. INTRODUCTION

Light fields are immersive visual representations that store the intensity of light rays of a scene. Techniques to capture light fields include using an array of cameras or inserting an array of microlenses (lenslets) in front of a camera sensor to acquire a collection of images from different viewpoints. Given the sheer amount of data this acquisition entails, light field coding is essential to practical applications.

In recent years, many methods for lossy compression of light fields have been proposed [START_REF] Conti | Dense light field coding: A survey[END_REF]. One of the main approaches consists of using view synthesis to radically reduce the number of views that need to be encoded by utilizing geometry information to generate remaining views at the decoder. Furthermore, by incorporating a residual signal, additional gains can be obtained.

In this work, we follow this paradigm and employ a recently proposed learning-based view synthesis technique to generate a high-quality prediction for lossless compression of light fields captured using LF cameras, e.g., Lytro. In contrast to typical lossless compression methods that use small local contexts for arithmetic coding, our approach can leverage a larger context obtained from several views. In addition, we estimate the symbol probabilities using a deep generative model [START_REF] Salimans | PixelCNN++: A pixelcnn implementation with discretized logistic mixture likelihood and other modifications[END_REF]. While initially proposed to generate images, deep generative models, and in particular auto-regressive methods that explicitly compute the data likelihood, have been recently shown to provide accurate probability estimates for lossless coding of pictures [START_REF] Mentzer | Practical full resolution learned lossless image compression[END_REF], [START_REF] Mentzer | Learning better lossless compression using lossy compression[END_REF] and 3D point clouds [START_REF] Nguyen | Learningbased lossless compression of 3D point cloud geometry[END_REF]. To our knowledge, this is the first work applying deep conditional probability estimation in conjunction with view synthesis for This project has received funding from EU's H2020 ITN programme, under the MSCA grant agreement No 765911 (RealVision). coding of light fields. Our results show more than 2% improvement in bitrate compared to the best performing state-of-the-art method while being competitive in terms of execution time.

The rest of the paper is organized as follows. Section II cites the related work on lossless LF image compression with the emphasis on the approaches proposed for decoded lenslet modality. We also mention relevant generative models and recently proposed learning-based approaches for lossless image compression. Section III describes the proposed method. In Section IV, variants of the proposed method are compared, along with a comparison to state-of-the-art methods in terms of bitrate and execution time. Section V concludes the presented work and outlines possible research directions.

II. RELATED WORK

In this paper, we focus on light fields obtained by lenslet cameras. Lossless compression of light fields can be broadly divided into two groups. The first group of methods tackles the problem of coding the raw lenslet image [START_REF] Perra | Lossless plenoptic image compression using adaptive block differential prediction[END_REF]- [START_REF] Tabus | Lossless compression of plenoptic camera sensor images[END_REF]. The other group of methods deals with rectified light field images, i.e., light fields obtained from raw sensor image through decoding procedure [START_REF] Dansereau | Decoding, calibration and rectification for lenselet-based plenoptic cameras[END_REF]. Helin et al. [START_REF] Helin | Sparse modelling and predictive coding of subaperture images for lossless plenoptic image compression[END_REF] first encode the center view and its quantized depth map. Then, the center view is partitioned based on the depth values, and for each partition, disparities with respect to side views are computed and transmitted to partition the side views. Residuals, predictor coefficients, and binary sparsity masks are encoded. In a follow-up work [START_REF]Minimum description length sparse modeling and region merging for lossless plenoptic image compression[END_REF], the compression performance is improved by segmenting the central view using a complex color-and depth-based segmentation approach, while variable length coding is used to encode the prediction mask and the coefficients of the sparse predictor. Santos et al. [START_REF] Santos | Lossless light-field compression using reversible colour transformations[END_REF] conduct a study on the impact of reversible color transformations and data arrangements for pseudo-sequence generation for lossless light field coding. The study shows the superiority of the forward reversible multiple component transform (RCT) [START_REF]Information technology -JPEG 2000 image coding system: An entry level JPEG 2000 encoder[END_REF]. Schiopu et al. [START_REF] Schiopu | Lossless compression of subaperture images using context modeling[END_REF] propose a local, context-based method for lossless compression. Each pixel is adaptively predicted from close co-located pixels in the reference frame based on the edge information. The prediction is subtracted from input values, and three matrices are generated: small residual, high residual, and error sign. The small residual matrix is encoded using context modeling defined by the regions obtained from image segmentation. In [START_REF] Santos | Lossless coding of light field images based on minimum-rate predictors[END_REF]- [START_REF] Santos | Lossless coding of light fields based on 4D minimum rate predictors[END_REF], minimum-rate predictors are employed that obtain competitive performance, but with a high computational cost. Another line of works achieve interesting results with a complexity similar to standard coding tools by adapting the CALIC codec [START_REF] Wu | Context-based, adaptive, lossless image coding[END_REF] to operate on the Epipolar Plane Image (EPI) representation of light field [21] [22].

Recently, deep learning has been applied to lossless compression of light fields [START_REF] Schiopu | Macro-pixel prediction based on convolutional neural networks for lossless compression of light field images[END_REF]- [START_REF] Schiopu | Deep-learning-based macro-pixel synthesis and lossless coding of light field images[END_REF]. Schiopu et al. [START_REF] Schiopu | Macro-pixel prediction based on convolutional neural networks for lossless compression of light field images[END_REF] design a deep convolutional neural network that uses a neighborhood of six macro-pixels to predict the current macro-pixel. Then, the residual is encoded using a modified version of the CALIC codec. Kaya et al. [START_REF] Kaya | Corner view disparity estimation for lossless light field compression[END_REF] propose CEPINET, a variant of EPINET [START_REF] Shin | EPINET: A fully-convolutional neural network using epipolar geometry for depth from light field images[END_REF], which estimates disparity maps of corner views. Textures and disparity maps of corner views and the center view are encoded. Then, a disparity map is generated for each target view by warping the closest reference disparity map to the target view location. The disparity map is divided into connected regions, and for each region, the index of the best reference view (that minimizes MSE over the region) is assigned. Finally, the residual image is also computed and encoded. In [START_REF] Schiopu | Deep-learning-based macro-pixel synthesis and lossless coding of light field images[END_REF], view synthesis and prediction methods based on macro pixels are proposed. The authors also study the influence of the size of the reference image set and modify the CALIC codec's binary mode to utilize different causal neighborhoods.

Recently, deep learning-based auto-regressive models have been successfully applied to lossless image compression [3] [4]. Their emergence is not surprising, considering their aim is to explicitly model the distribution of underlying data utilized for entropy coding. These tools achieve similar or better performance than state-of-the-art image codecs such as BPG and FLIF. Mentzer et al. [START_REF] Mentzer | Practical full resolution learned lossless image compression[END_REF] propose a hierarchical approach with learned feature extractors which generate latent representations transmitted to the decoder. At the decoder, latent representations are processed by decoding blocks and are used to estimate the probability distributions necessary for decoding the latents at the higher level. In [START_REF] Mentzer | Learning better lossless compression using lossy compression[END_REF], the authors design a twolayer lossless compression method by leveraging the standard coding tool BPG. The input image is first encoded with BPG. Then the residual between the input and its processed variant is entropy coded using the probability distribution estimated by a neural network. The network provides parameters of the probability distribution for each pixel allowing efficient, parallelized processing.

III. PROPOSED METHOD

Our approach takes inspiration from learning-based lossless image compression [START_REF] Mentzer | Learning better lossless compression using lossy compression[END_REF] and image generation [START_REF] Reed | Parallel multiscale autoregressive density estimation[END_REF] [START_REF] Kolesnikov | PixelCNN models with auxiliary variables for natural image modeling[END_REF], which are combined with view synthesis [START_REF] Navarro | Learning occlusion-aware view synthesis for light fields[END_REF] to enable the prediction of views from a set of reference views. The overall design of the proposed method is presented in Fig. 1. At the encoder side, reference views and the input view index are provided to the View Synthesis module, which predicts the input view. Then, the prediction is subtracted from the input view. The obtained residual signal is entropy coded using an arithmetic coder (AC) provided with probability distributions computed on a per-pixel basis by the Entropy Modeling block given the prediction. At the decoder, the prediction from the View Synthesis module is used to estimate probability distributions for decoding, and the decoded residual signal is added to the prediction to reconstruct the input view. View Synthesis and Entropy Model are briefly described in the following subsections.

A. View Synthesis

To estimate a novel view, Navarro et al. [START_REF] Navarro | Learning occlusion-aware view synthesis for light fields[END_REF] propose three distinctive blocks, a feature extractor, a disparity estimator and a per-pixel view selector. Given the set of corner views I c of a light field image L(x, u), where x and u denote spatial and angular positions of light rays, and the position of a view to be synthesized ũ, the feature extraction network F e computes the feature map of each corner view I ci independently:

F ci = F e (I ci , ũ). (1)
The features are then concatenated in a vector F together with the position of the novel view ũ and provided to the disparity estimation network F d to estimate the vector of disparity maps D of the novel view:

D = F d (F, ũ). (2)
Initial estimates of the novel view W ci are obtained by backward warping the corner views using the corresponding disparity maps,

W ci (x) = I ci (x + D ci (x) • (c i -ũ)). (3)
In the final stage of the scheme, the selection network F s computes the contribution M ci for each initial estimate

W ci M = F s (W, D, ũ). (4)
where M and W are concatenated vectors of merging maps M c and initial estimates W c . The final estimate of the novel view Îũ is obtained by merging initial estimates as a weighted sum

Îũ = L(x, ũ) = ci∈c M ci (x)W ci (x). (5)

B. Entropy model

Mentzer et al. [START_REF] Mentzer | Learning better lossless compression using lossy compression[END_REF] propose a Residual Compressor (RC) which takes the decoded input image, obtained through BPG coding, and estimates a set of parameters which model the probability mass function of the residual signal r x between the input pixel value x and its decoded version x, i.e., p m (r x |x), which is used to encode/decode the residual. The probability mass function is modeled with a mixture of logistic functions

p(r x ) = K k=1 π k p L (r x |µ k , σ k ), (6)
where

p L (x|µ, σ) = e -rx-µ σ σ(1+e -rx-µ σ ) 2
and K is the number of mixtures. Furthermore, the method encodes/decodes pixels channel-by-channel and conditions the current color channel on previously decoded channel(s):

μk c = µ k c + ci∈cprev λ k ci r x,ci . (7)
We utilize the same architecture as proposed in [START_REF] Mentzer | Learning better lossless compression using lossy compression[END_REF] while changing the number of filters, i.e., C f = 64, and number of residual blocks to 8. More importantly, we use the synthesized view Îũ as input and compute the residuals with respect to it. The weights of the RC network are learned by minimizing the cross-entropy loss

J(θ) = - x∈Iũ log 2 (p m (r x |x)). (8)
The architecture of [START_REF] Mentzer | Learning better lossless compression using lossy compression[END_REF], which we denote as Base in the following, estimates the pixel distribution for every pixel in a single pass, allowing parallel and efficient processing. On the other hand, to improve the estimation locally, one could use neighboring, previously decoded pixel values as it is typically done in lossless compression schemes. In order to improve performance, we propose to divide pixels into coding groups and to use previously decoded groups to estimate the parameters for the following groups. Notice that a similar approach has been considered in a recent work for image generation [START_REF] Reed | Parallel multiscale autoregressive density estimation[END_REF] and point cloud coding [START_REF] Nguyen | Multiscale deep context modeling for lossless point cloud geometry compression[END_REF].

The simplest version of this approach consists of using a group for each pixel, which corresponds to PixelCNN [START_REF] Salimans | PixelCNN++: A pixelcnn implementation with discretized logistic mixture likelihood and other modifications[END_REF]. By increasing the size of the groups and by assuming that pixels within each group are conditionally independent, it is possible to estimate in parallel all the pixels in a group from previously 0 3 2 1 2 3 0 3 3 3 3 3 3 3 2 3 2 3 2 3 2 1 3 3 1 3 3 1 2 3 2 3 2 3 2 3 3 3 3 3 3 3 0 3 2 1 2 3 0 2 1 2 0 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 0 2 1 2 1 2 0 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 0 2 1 2 0 4 x 3 x 1 x 3 x 4 x 0 4 x 4 x 5 + 4 x 5 + 4 x 4 x 3 x 5 + 3 x 5 + 3 x 5 + 3 x 1 x 4 x 5 + 2 + 5 + 4 x 1 x 3 x 5 + 3 x 5 + 3 x 5 + 3 x 4 x 4 x 5 + 4 x 5 + 4 x 4 x 0 4 x 3 x 1 x 3 x 4 x 0 Fig. 2. Hierarchical levels for prediction of views with Corner arrangement (left) and Cross arrangement (middle). At each level i, views can be predicted from decoded views from previous levels, i.e. iprev < i. Prediction scenarios applied in Hybrid variant (right). The number denotes the prediction level i while the superscripts denote the arrangement of the reference views used for the prediction, i.e. x and + denote Corner and Cross arrangements. decoded groups. In our experiments, we use four groups (each containing N/4 pixels), where each group contains every other pixel in the horizontal and vertical directions, similar to a checkerboard pattern.

C. Training details

We train our models on 3323 LF images of size 7 × 7 × 376 × 541 from the Flowers dataset [START_REF] Srinivasan | Learning to synthesize a 4D RGBD light field from a single image[END_REF]. First, the view synthesis method for both arrangements, Corner and Cross, are pretrained. Here we used the same settings as done in [START_REF] Mukati | Improved deep distributed light field coding[END_REF]. Then, the view synthesis network parameters are fixed, and we train the residual estimation network. The input LFs are cropped along angular dimensions by selecting center 8 views. At each iteration, a 4D patch of size 7 × 7 × 128 × 128, the positions of the reference views and the target view are randomly selected. Then, we randomly perturb color channels to increase the training dataset's color diversity and finally apply gamma correction selected from an interval [0.4, 1.0]. We use a batch size of 16 and Adam optimizer [START_REF] Kingma | Adam: A method for stochastic optimization[END_REF] to update weights.

IV. RESULTS

A. Dataset

The proposed method is evaluated on 12 LF images proposed in [START_REF] Rerábek | ICME 2016 grand challenge: Light-field image compression -call for proposals and evaluation procedure[END_REF] from the EPFL dataset [START_REF] Rerábek | New light field image dataset[END_REF]. We use Lytro Power Tool1 (LPT) to decode raw LFs, we apply a gamma correction of 0.45 and quantize to 8 bits. Moreover, LFs are cropped in angular and spatial dimensions to 7 × 7 views (we use top-left 7 × 7 crop of center 8 × 8 crop) and 320 × 512 pixels, respectively. Note that the outer views of decoded LFs are corrupted with vignetting artifacts and thus cropped to a smaller size.

B. Prediction variants

In the view synthesis task, typically, the whole LF is synthesized from a fixed set of reference views. On the other side, in lossless compression, as views become available after decoding, it becomes possible to dynamically select reference views and leverage reduced baseline between views for improved prediction. However, a potential drawback could be reduced random access capabilities as later views depend on previously encoded/decoded views. Here we compare both cases, namely the Single method, which operates on a single level and predicts views from the corner references, and the Hierarchical where three levels are incorporated as depicted on the left part of Figure 2. In the first level, the method processes the corner views and the middle views on the boundary of the LF, i.e., between corner views. Then, in the second level, a subset of views in each quadrant is estimated. Finally, the rest of the views are predicted from their neighboring four views in the third level. Besides utilizing corner views as reference views, we also examine cross arrangement, which proved to be superior to the corner arrangement in recent work [START_REF] Mukati | Improved deep distributed light field coding[END_REF]. We note that the corner approach is more flexible in selecting reference view as the cross approach demands odd angular size in order to select reference views. For instance, in an LF of 4 × 4 angular size, in order to utilize cross arrangement, it would be necessary to operate on LFs of 3 × 3 angular size. Conversely, the cross arrangement provides superior prediction. Thus, we finally consider a Hybrid approach which benefits from the qualities of both arrangements. The approach is illustrated on the right part of Figure 2 where the numbers denote hierarchical levels while the superscripts denote arrangements. The reference views are encoded independently using the L3C method, but any method can be utilized in general. Table I shows a comparison of the proposed variants. The columns marked as Single present the performance of the proposed method where four reference views are independently encoded and used to estimate all the other views. This setup is the most efficient from a random access perspective for the given view synthesis module. Better performance of Cross arrangement can be noted compared to Corner arrangement. These results are also aligned with previous research [START_REF] Mukati | Improved deep distributed light field coding[END_REF].

The terms Hierarchical denote variants of the proposed method, which allow dynamic selection of reference views in order to achieve better prediction and lower bitrate at the cost of reduced random access capabilities. The hierarchical levels of Corner, Cross and Hybrid sub-column are illustrated in Figure 2. First, the Corner variant shows better performance compared to the Cross variant. Interestingly, the result is opposite to the result observed for Single setup, which could be explained by the lower number of hierarchical levels and reduced prediction capabilities and implying the lack of flexibility of the Cross arrangement. Second, additional gains compared to the two arrangements can be achieved by combining them as presented in the column Hybrid. Finally, we also present the result of the Base method (without spatial grouping), which has a lower memory footprint and better execution time, but lower probability estimation accuracy. As the Entropy model of the Base method has four times less parameters compared to the proposed method, in order to verify that the gains in the proposed method are achieved via grouping and auto-regressive modeling, we also train the Base network with approximately the same number of parameters. The two Base models showed similar performance. Notice that all the proposed variants require a similar processing time, while they just differ in the views selection mechanism.

C. Comparison with state-of-the-art methods

In Table III, the proposed method is compared to stateof-the-art methods. We select a few general lossless schemes for image and video compression and an approach designed for lossless compression of LFs. HEVC denotes HM v16.22 implementation of High Efficiency Video Coding (HEVC) standard [START_REF] Sullivan | Overview of the High Efficiency Video Coding (HEVC) standard[END_REF]. In this case, an LF image is first reshaped into a pseudo-video sequence following a serpentine scan order and encoded in the lossless mode in addition to the Main-RExt profile. L3C [START_REF] Mentzer | Practical full resolution learned lossless image compression[END_REF] and RC [START_REF] Mentzer | Learning better lossless compression using lossy compression[END_REF] represent learning-based methods for lossless image compression. RC method is the most relevant work as the approach is incorporated in the proposed method. Furthermore, we also evaluate the test set on FLIF codec [START_REF] Sneyers | FLIF: Free lossless image format based on maniac compression[END_REF] and JPEG XL [START_REF] Alakuijala | JPEG XL next-generation image compression architecture and coding tools[END_REF]. In order to allow image codecs to exploit angular correlation, LFs are converted to lenslet representation before processing. For reference, we also report results for independent encoding of each view in Table II. Among the LF lossless coding approaches we select EPIC [START_REF]Epipolar plane image-based lossless and near-lossless light field compression[END_REF]. As EPIC was designed to operate independently on color channels in the YCbCr color space, we employ RCT [START_REF]Information technology -JPEG 2000 image coding system: An entry level JPEG 2000 encoder[END_REF] to input RGBs. We also evaluated the approach [START_REF] Schiopu | Lossless compression of subaperture images using context modeling[END_REF] with the publicly available software. However, as the results appear considerably higher compared to other methods (the code yielded an average bitrate of 9.32 bpp), we do not report them in Table III.

We notice that RC and L3C obtain worse performance compared to other approaches. This might be due to the domain shift between the original training data of these methods and the lenslet test data. On average, our method outperforms FLIF, JPEG XL, EPIC by 2.23%, 3.22% and 2.73%, respectively. Interestingly, FLIF and JPEG XL perform quite well compared to the other two methods, which are designed to exploit the correlation in LFs. It is highly likely that due to the small baseline between the views and small angular size, the lenslet format allows to exploit spatial and angular similarities efficiently. HEVC underperforms compared to later approaches. This result aligns with the literature results.

A per-content comparison shows that the proposed method outperforms other methods on most of the sequences. The exceptions are less natural sequences such as Ankylosaurus, Magnets, and the two charts. We note various potential reasons for this behavior. Sequences like charts are mainly flat, which is a challenging content for geometry estimation. Moreover, the noise is strongly present, and the color constancy between the reference views and target views is lacking. On the other side, object-rich sequences with diverse geometry content suit the proposed method exceptionally well, as it can be observed in the performance of Bikes, Fountain & Vincent and Friends.

In Table IV, we report the processing time of encoder and decoder with different codecs. Notice that by construction, the complexity in our approach is symmetric at the encoder and decoder side, and is especially efficient at encoding compared to conventional methods such as FLIF, JPEG XL and HEVC.

V. CONCLUSIONS

We have proposed a learning-based method for lossless compression of LF images. The method incorporates a view synthesis block that provides an initial estimate of the encoded view. This is later used as a context to build an entropy model, which estimates for each pixel a conditional probability to be used in adaptive arithmetic coding. By introducing partial auto-regressive relations among groups of pixels, the proposed method outperforms state-of-the-art methods in terms of bitrate while maintaining a low computational complexity.

Future work includes several directions. First, we plan to add an additional residual coding layer to improve the estimation of probabilities at the decoder side. Next, jointly training view synthesis and the entropy model should further improve performance. Last but not least, the evaluation should be extended to LFs with larger angular size and wider baseline. These data are more challenging and possibly more critical to address.

Fig. 1 .

 1 Fig. 1. The block diagram of the proposed method. Note, View Synthesis and Entropy Model are utilized at encoder and decoder side as they depend only on previously decoded information. AC and AD denote arithmetic coder and arithmetic decoder, respectively.

TABLE I THE

 I PERFORMANCE EVALUATION OF VARIANTS OF THE PROPOSED METHOD PRESENTED IN TERMS OF BITS PER PIXEL (BPP).

		Single			Hierarchical
	Sequence	Corner Cross Corner Cross Hybrid Hybrid (Base)
	Bikes	6.51	6.32	5.90	5.97	5.81	6.22
	Danger de Mort	7.23	7.02	6.51	6.59	6.39	6.89
	Flowers	7.21	6.96	6.42	6.53	6.31	6.79
	Stone Pillars Outside	6.81	6.58	6.08	6.15	5.98	6.33
	Vespa	6.49	6.21	5.88	5.91	5.79	6.10
	Ankylosaurus	5.14	5.06	4.86	4.90	4.82	4.84
	Desktop	6.84	6.63	6.24	6.30	6.15	6.62
	Magnets	5.16	5.08	4.90	4.94	4.86	4.90
	Fountain & Vincent	6.56	6.37	6.01	6.01	5.90	6.13
	Friends	6.07	5.90	5.54	5.60	5.45	6.04
	Color Chart	5.93	5.65	5.43	5.45	5.35	6.00
	ISO Chart	6.33	6.11	5.80	5.75	5.69	5.92
	Average	6.36	6.16	5.80	5.84	5.71	6.06

TABLE III THE

 III COMPARISON OF THE PROPOSED METHOD AND AVAILABLE METHODS FROM LITERATURE IN TERMS OF BITRATE (BPP).

			General lossless schemes		LF lossless schemes
	Sequence	HEVC L3C RC FLIF JPEG XL EPIC-RCT Proposed
	Bikes	6.69	7.28 7.54 6.19	5.95	6.14	5.81
	Danger de Mort	7.19	7.90 8.03 6.70	6.49	6.71	6.39
	Flowers	7.08	8.00 8.27 6.84	6.49	6.67	6.31
	Stone Pillars Outside	6.61	6.93 7.52 6.01	5.84	6.33	5.98
	Vespa	6.64	7.12 7.30 6.10	6.03	5.98	5.79
	Ankylosaurus	5.43	5.84 5.79 4.77	5.48	4.83	4.82
	Desktop	6.76	7.18 7.56 6.22	6.33	6.26	6.15
	Magnets	5.49	6.03 5.92 4.87	5.60	4.80	4.86
	Fountain & Vincent	6.71	7.48 7.35 6.14	6.14	6.03	5.90
	Friends	6.10	6.50 6.89 5.47	5.38	5.69	5.45
	Color Chart	6.00	6.57 6.57 5.35	5.69	5.37	5.35
	ISO Chart	6.42	6.47 6.22 5.39	5.37	5.59	5.69
	Average	6.43	6.94 7.08 5.84	5.90	5.87	5.71

https://github.com/kmader/lytro-power-tools, accessed on Nov. 10th,


Honeypots: Concepts, Approaches and Challenges

By Ansab Naeem

The need for cyber security is ever growing these days with individuals and companies alike. In the past the methods for security have been rather defensive, these days however the aggressive type of defenses are more common. one way of doing this is by using a honey pot. A honey pot is a defense system which works when compromised or attacked. In this paper I will portray an outlook on honeypots, providing examinations on various honeypots, their concepts as well as the approaches taken for the implementations [START_REF] Lutkevich | honeypot (computing)[END_REF].

In the last few decades, the concern for information security has rocketed, so has the requirement for more aggressive defenses. One type of this is an intrusion protection that is decoy based is a honeypot. Honeypots are an ever-changing technology therefore they are hard to define, they can be used in a multitude of elements of security, these include information gathering, prevention and detection. For this specific paper the definition I got with will be 'honeypots are a security solution which work as intended when attacked and/or compromised.

A honeypot is a network decoys that is monitored closely, its many purposes include:

1) Attacker can be distracted by them from attacking something of more value on the same network. 2) Honeypots can give preemptive warning about attacks and trends of exploitation. 3) During and after an attack, they can provide a thorough examination

The value of this technology is based on the attackers attacking it. The concept in general is the same with all honeypots, no one needs to interact with the honeypot, making any interactions done unauthorized. A honeynet is the term used to describe anywhere honeypot(s) are used. A precise definition would be, a honeypot that is high interaction which has the purpose of capturing a lot of information, it facilitates a multitude of 'real systems', services as well as applications for attackers to have interactions with [START_REF] Ghafir | A Survey on Botnet Command and Control Traffic Detection[END_REF].

The organisation of this paper is as follows: section 1 will give an examination of the many types of honeypots. Section 2 will go over the overview of the concept behind them while also explaining the approaches for implementation. Section 3 will describe the challenges and legal issues around honeypots. Finally, the conclusion and opinions on future will be shown in section 4.

The types of honeypots

A honeypot's classification is based on its purpose which are honeytokens, production and research as well as their interaction level and those are low, medium and high.

1.1 The purpose of honeypots

Research honeypots

A research honeypot does not add anything of value to any organisation, its purpose is to obtain information from a community/group attacking it. They are utilised to collect any general threat information a company could face, thus helping the company to protect themselves against attacks. These types of honeypots almost study the attackers to see how they progress, figure out their types of attacks as well as understand patterns of an attacker's behavior and motivation behind said attacks [START_REF] Ghafir | Advanced Persistent Threat and Spear Phishing Emails[END_REF]. A research honeypot is arduous to implement and maintain, these can also take a lot of time to implement. The contributions made by a research honeypot are rather minute, however the information they obtain can help a lot as it can be put into application for when attacks need to be detected/prevented or responded to. Usually its educational institutions, militaries, governments, and huge corporations that use these as they are interested in researching and learning these attacks. The true value lies in this information as the honeypot can facilitate a platform on which cyberthreats can be studied. The actions of an attacker can be looked upon one step at a time as they probe and attack the system. The information gathered can be further used to hone the forensics and analysis skills of security enthusiasts and professionals alike.

Production honeypots

This type of honeypot is what comes to mind when people think about honeypots. These are used in companies to mitigate risks and help protect the company. They provide an immediate protection to a company's resources. They are usually easier to build and implement as they need less functionality as compared to research honeypots. Production honeypots can identify patterns of attack and what system the attacks are from but not who the attackers are, what tools they utilise and how well they're oganised [START_REF] Ghafir | Blacklist-based Malicious IP Traffic Detection[END_REF]. These honeypots mimic the networks of the companies they are implemented in thus attracting attackers to probe and pick at them, letting this happen allows the vulnerabilities in a network to be seen. Gathering this information can help admins reduce intrusions.

The information can also help improve defenses so threats in the future aren't as damaging. It's to be noted the difference lies in how a honeypot is used and not build.

Interaction level

Honeypots don't have just types, they also have levels of involvement between a system and its intruder. The categories being low, medium, and high interaction. The level depends entirely on what the honeypot is for.

low interaction honeypot

At this level the honeypot simply mirrors a service which cannot get hijacked to the point access over the honeypot is gained. On a low interaction level there isn't an operating system present for an attacker to tinker with. This often gets compared to passive IDS's as the network cannot be altered at all, they cannot interact with attackers either. This makes the honeypot a low risk one however the use of the honeypot becomes limited too [START_REF] Eltanani | Aerial Wireless Networks: Proposed Solution for Coverage Optimisation[END_REF]. These are mainly used for putting spammers under analysis and countermeasures for worms. A honeypot at this level is not difficult to deploy and maintain. One example of this type is 'honeyd', they can mimic huge networks on a single network host. They copy computers that are on unused IP addresses on the network giving the attacker a front which they can try attack.

Medium interaction honeypot

These are slightly more complex; just as low interaction ones an operating system is not present but the mimicry is more advanced. With these honeypots the chance of attackers finding security vulnerabilities increases its not likely that the system gets compromised. The illusion of an OS (operating system) are better on medium interaction honeypots as there is more to interact with for an attacker meaning attacks of higher complexity can be noted and put under analysis. An example of a medium interaction honeypot is 'nepenthes', that daemon can note any automated attacks, get malware binaries by extracting information then finally downloads the malware it needs automatically.

high interaction honeypot

These honeypots are the most complex ones, they are very advanced and takes a lot of time to design. These possess the most risk too as they have operating systems present within them [START_REF] Hammoudeh | Continuous Monitoring in Mission-Critical Applications Using the Internet of Things and Blockchain[END_REF]. They have OS's so the attacker has something to actually interact with, in this there are no fronts or restrictions. This means all interactions with the attacker are logged and analysed as more date is collected. These honeypots need to be monitored regularly to make sure it does not become a liability.

Honeytokens

These are basically digital entities which are not real, they haver various applications. Honeytokens can exist as different things, these can be things like PowerPoints, credit card numbers, excel spreadsheets, fake logins or even database entries. Because of this, honeytokens have the same pros as regular honeypots but their reach extends far beyond regular computers. No matter what medium is chosen as a honey token nobody should be interacting with it, that way if someone does interact it can be deemed at suspicious activity. These just like regular honeypots aren't meant to solve security issues, they are meant to ensure integrity of data, detect unauthorized access [START_REF] Piggin | Active defence using an operational technology honeypot[END_REF]. Other security measures should be used with honeytokens. A big pro with these is that cost is minimal as no new technology needs to be implemented.

Concepts and approach to implementations

A honeypot is used to bait attackers luring them away from the actual production systems. The more layers a honeypot has the more an attack can be slowed down thus increasing the chance of an attack getting detected. Logging/IDS applications can be placed inside the honeypot allowing it to log any unauthorised activity [START_REF] Ghafir | Advanced Persistent Threat Attack Detection: An Overview[END_REF]. There is no need to place information filters in a honeypot because no interaction is authorized in the first place, this means any logs made by the honeypot is suspicious interactions. The information can then be utilised to figure out how the attackers are operating so countermeasures can be put up.

Approach to implementations

Various factors need to be considered prior to implementing a honeypot, these are:

• The type of date that can be accessed through the honey pot In order for the honeypot to seem like a real system, authentic data needs to be utilised. This has drawbacks because if the honeypot is compromised sensitive company data will be too. Countermeasures are to be taken against this.

• Uplink liability is to be prevented When a honeypot is compromised, the other systems can be attacked too this is what uplink liability is. This is another thing which requires counter measures.

• Does it need to be built or not The proprietor needs to decide is they want to build a honeypot or purchase one. Money becomes an issue here as running/maintaining the requires professionals who know what they're doing

• Location of the honeypot

It is generally advised that the honeypot should be isolated from the rest of the production so uplink liabilities can be prevented.

legal issues/challenges

there can be certain legal issues that can make a honeypot a liability as well as various factors which can make a honeypot legal or illegal. These legal issues are:

Entrapment

An argument is made by the attackers claiming they were entrapped. This happens when the government causes defendants to admit they committed the crime. This does not affect private honeypot owners [START_REF] Ng | Why A Honeypot Is Not A Comprehensive Security Solution, varonis.com[END_REF]. An attacker that was not lured in by the government cannot claim entrapment in a lawsuit. This means the chances of a defendant claiming entrapment as defense are slim to none in a case where there wasn't any inducing done by the government.

Privacy

There are laws in place that forbid monitoring users present on a system. Owners of systems have responsibilities for keeping a system secure however the rights for monitoring those systems have limitations. These limitations can be privacy/employment policies, state/federal statutes, service term agreements as well as other contracts. A company can be put under criminal sanctions or held liable based on the type of restriction and where it comes from I will now state a few of the limitations that are part of constitution/federal statutes.

• The 4th amendmentwhen an agency of the government is using a honeypot there is a chance this amendment can put limitations on the monitoring. This is because the amendment states a search warrant needs to be issued by a judge in order to search/seize evidence [START_REF] Ghafir | DNS query failure and algorithmically generated domain-flux detection[END_REF]. This does not affect a private origanisations honeypot only if they aren't following any government orders.

• Wiretap actthis act states no one is allowed to intercept communications apart from exceptions that are applied from the act itself. It is to be ensured that an organisation understands the exceptions of the statute whilst meeting the statutes requirements. The exceptions are as follows:

Computer trespasser exceptionthe government can monitor trespassers as stated in this exception. This happens when the operator of the honeypot authorization of the governments interception thus allowing monitoring. The rule they must follow is that communications from the trespasser must be part of the ongoing investigation [START_REF] Raza | An IoT and Business Processes Based Approach for the Monitoring and Control of High Value-Added Manufacturing Processes[END_REF]. Party consent exceptionhere, interception is allowed if a party that is communicating agrees to being monitored, a system banner is also installed so consent can be secured.

Provider exceptionmonitoring happens in this to an operators' rights/property can be protected. Honeypots are to be associated with production servers only whilst system admin task are to be kept separate from the investigative functions.

• Patriot actone segment of this act allows hackers to be monitored by the government without having a warrant, this is applicable to some situations [START_REF] Ghafir | Tor-based malware and Tor connection detection[END_REF]. It's mainly used when a honeypot is run directly by an entity of the government.

Hacker communications can be intercepted if:

-The operator of the network has given permission to intercept -The person(s) doing the intercepting are involved in the investigation -Reasonable theory suggests that the intercepted information will be beneficial to an investigation

liability

If an attacker succeeds in accessing the honeypot, they can then use the bandwidth and network to cause harm to many others. This is why honeypots that are neglected get used for illegal purposes. An example would be the honeypot becoming a dropsite for password files, stolen credit card information, trade secrets and other contraband. Another example would be turning it into a warez site. In a scenario where something like this happens, downstream victims can potentially file lawsuits. This is why it's crucial to watch over a honeypot once its deployed.

Disadvantages/Advantages

If we consider knowledge as power for the attacker, the same applies to the security professional. This is why knowing the disadvantages and advantages are crucial when it comes to honeypot. Once someone knows the risks they can use that information to mitigate and avoid disadvantages. Below I will highlight both:

disadvantages

There are several both disadvantages and risks. The amount of these isn't high but they still stop honeypots from fully replacing current security solutions.

• Vision limitationsactivity is only tracked and logged by honeypots if an attacker interacts directly with it. Any attacks conducted on different areas of the system will not be logged, only and only will they be recorded if the honeypot is also threatened [START_REF] Peter | A Practical Guide to Honeypots[END_REF].

• Fingerprinting and discoverythis is when an attacker figures out a honeypots identity because of the behaviour/characteristics they have. Something as miniscule as a spelling error in the emulation of a service can expose the fact that it's a honeypot [START_REF] Ghafir | A Survey on Intrusion Detection and Prevention Systems[END_REF].

• Takeover riskthe honeypot can be utilised to cause harm on other systems inside/outside an organisation if it's taken over. It can then be used to also house and share contraband.

advantages

Disadvantages aside, honeypots have very good advantages when compared to security mechanisms that are currently common:

• Small data setshoneypots are concerned only with the traffic that passes through them, they do not need to bother with the huge amounts of traffic that passes through or figuring out if the packets are genuine or not. This is why they log only a small amount of info [START_REF] Ghafir | Disguised Executable Files in Spear-Phishing Emails: Detecting the Point of Entry in Advanced Persistent Threat[END_REF]. The information they collect might be small but it's very valuable.

• Minimal resourcesdue to the fact that they log only suspicious activity the resources needed to run a honeypot can be minimal, low end or decommissioned systems can be utilised as honeypots.

• Simplehoneypots are about flexibility and simplicity. State tables, signatures that require to be updated/maintained nor complex algorithms development, none of that needs to be done.

• Discovery of new tactics/toolshoneypots log all interaction, meaning they can capture tactics and tools that are new to them.

Conclusions and outlooks for the future

Through this paper I have given brief outlook on what honeypots are as well as what they are useful/used for. I have discussed the various honeypot types, these included production, research and honeytokens. I have also provided an overview of the things that need to be taken under consideration when honeypots are to be implemented, an example being their interaction level depends entirely on what purpose the honeypot is intended for. Legal concerns surrounding them and examining their implementation. A vital thing to note is that honeypots should be used in conjunction with IDS's and other forms of security. Although honeypots are a rather new type of tech they are increasing more and more in popularity, this will only increase as commercial solutions to them become more readily available as they would be easier to implement and use. I believe honeypots can be a great tool used for investigations in digital forensics as they can collect information on attackers, their attack types as well as other threats.


Marie-Alix Deval

Sophie Hooge
email: sophie.hooge@mines-paristech.fr

Benoit Weil
email: benoit.weil@mines-paristech.fr

Understanding the emergence of unknown domains of expertise in established firms: learnings from Renault's collaboration with industrial management scholars since the 1950's

The paper studies the emergence of unknown domain of expertise in an established French technological firm with a strong organization of experts. The research is built on a quantitative and qualitative longitudinal research partnership with Renault (global car manufacturer). This research highlights 4 main results: Firstly, in the case of an industrial company like Renault, it took around 29 years to constitute the first domains of expertise, but since then, new domains have been formalized in just a decade. In the case of emergent domains of expertise, we do not yet know how long will be needed for them to be fully institutionalized. In this way (second result), external peer's community can acknowledge the expertise of a company, and not only of an expert. Thirdly, all the experts in industries are not strategic and exact & experimental scientific or technical expert, they can also be experts in social sciences, and strategic for the other experts instead of just for the company. Finally, research collaborations are a way to create and reinforce a domain of expertise, both for an expert and for the company.

Introduction

The role of an expert is well established in the literature: it is either to decide or to define a strategy [START_REF] Trépos | Handy Misunderstandings. Experts, Professionals and Laypersons Relationships[END_REF]. Thus, the knowledge and the skills of an expert are strategic for a firm. "Therefore, firms are constantly preoccupied with procuring required skills through recruitment or training, […] to accelerate the renewal of products and markets." (Hatchuel and Weil 2011, 3). In the context of hyper competitiveness [START_REF] Ilinitch | New Organizational Forms and Strategies for Managing Hypercompetitive Environments[END_REF], experts also contribute to the dynamic and strategic renewal of industrial expertise within their firm [START_REF] Cabanes | Technical Staff Management for Radical Innovation in Science-Based Organizations: A New Framework Based on Design Theory[END_REF]. This dynamic has intensified to face the modern challenges (energetic transition, pandemic, etc.), which require more scientific new knowledge and then more industrial explorations in the unknown (Le [START_REF] Masson | The expertise of the unknown: toward a form of non-destructive creation[END_REF]. To do so, some experts at Renault regularly work with external scholars, publish in specialized journals, or even supervise doctoral theses with university laboratories. Hence, the role of collaborations between experts in an industry and industrial management scholars, in the creation of knowledge for an expertise, imposes itself as a pertinent topic of research. As a result, the research question of the paper is the following: How to better master scientific research to innovate? Understanding the emergence of unknown domains of expertise in established firms by analyzing their research collaborations projects between internal expert and scholars. The paper starts with a literature review about the recognition of experts and domains of expertise in industries. The second section presents the settings of the collaborative research and our data collection methods. In section 3, we present our longitudinal case study. Section 4 discusses these results and presents the main conclusion for research on expertise in industries.

Literature Review

Duration of construction of the knowledge of an individual expertise

For the purpose of this paper, we choose the definition of expert from the handbook of Expertise and Expert Performance (Ericsson et al. 2018, 3) which describes an Expert as "someone widely recognized as a reliable source of knowledge, technique, or skill whose judgment is accorded authority and status by the public or his or her peers. Experts have prolonged or intense experience through practice and education in a particular field". "Expertise then refers to the characteristics, skills, and knowledge that distinguish experts from novices and less experienced people". Thanks to their expertise, experts exhibit superior performances to less experienced people. Thus, these definitions are focused on the appreciation of knowledge. Many studies focus on the knowledge acquisition of an expert, and agree that at least 10 years of experiences are required to become an expert (William G. [START_REF] Chase | The Mind'seye in Chess[END_REF][START_REF] Ericsson | The Road To Excellence: The Acquisition of Expert Performance in the Arts and Sciences, Sports, and Games[END_REF][START_REF] Hayes | Cognitive Processes in Creativity[END_REF][START_REF] Weisberg | Modes of Expertise in Creative Thinking: Evidence from Case Studies[END_REF]). This "10 years rule" became the golden rule for designing an individual expertise. This long expertise acquisition process does not end after 10 years, instead it has to continue, to be dynamic, because an expert, as a reference in their field, must master the latest advances.

If we now consider an expertise through a group of individuals, who collectively embody that concentration of knowledge and skills in the organization, then that expertise can be relative: the most informed, educated, and competent individuals can be considered experts. Levels of individual competence, from novice to expert, can thus be defined according to criteria of level of diploma, experience, seniority in a field [START_REF] Chi | Two Approaches to the Study of Experts' Characteristics[END_REF][START_REF] Hoffman | How Can Expertise Be Defined? Implications of Research from Cognitive Psychology[END_REF]. At least, this group embodies the expertise of their organization. If 10 years are needed to constitute an individual expertise, the time needed by an organization to constitute an expertise is unknown.

Knowledge nature of industrial experts

The word expert finds its origin in the Latin "expertus", which served to designate a knowhow acquired during experiments. However, the term "expertise" appears in the 19th century in the legal field and refers to the technical and scientific skills in the service of a jurisdiction, a public administration or a company [START_REF] Calafat | Expertise et compétences[END_REF]. From that point on, experts appeared in many kinds of domains: professional domains like medicine, transportation, software design, writing; art, sport and games domain [START_REF] Ericsson | The Cambridge Handbook of Expertise and Expert Performance[END_REF]; thus, a domain of expertise appears when knowledge and content of a field can be accumulated and organized. Expert in industries appeared in the 1940s-50s in the United States. In the 50's, to be competitive, science-based firms were dependent on technological innovations as instruments of competition [START_REF] Shepard | The Dual Hierarchy in Research[END_REF]. Shepard (1958, 177) highlights that "within the individual firm, power tends to shift to those who possess the skills most needed for survival and growth. Over the past half-century, this distinction has passed from manufacturing to sales and thence to research and development." However, the R&D presented a weakness in its skills organization: with its growth, the R&D department presented problems of "coordination, control, evaluation, program formulation, personnel maintenance, decision-making" (Shepard 1958, 178). Thus, a new managerial class emerged to exercise control over the scientific and engineering activities. By this way, all the technical men entering the managerial class were no longer devoted to technical topics. "When a good scientist is made a manager, a good scientist is lost" (Shepard 1958, 179). Simultaneously, having a high degree of technical competence in the laboratory is recognized as assuring better results. Hence, to remedy that loss, recognize the expertise of some scientists and engineers, and propose an alternative to the managerial career path, a dual ladder was developed to offer a technical career path [START_REF] Shepard | The Dual Hierarchy in Research[END_REF]. Thus, the technical and scientific experts appeared, and they aimed to secure a pool of technical and scientific talents for science-based organizations. Since that time, "firms are constantly preoccupied with procuring required skills through recruitment or training, […] to accelerate the renewal of products and markets." (Hatchuel and Weil 2011, 3). This dynamic has been accelerated to face the modern challenges (energetic transition, pandemic, etc.), which needs more breakthrough R&D for radical innovation and thus more explorations to unveil the unknown (Le Masson andWeil, 2020, O'connor, 2018). The amount of knowledge of an expert is not useful if the knowledge is not applied in the organization. [START_REF] Blackler | Knowledge, Knowledge Work and Organizations: An Overview and Interpretation[END_REF] insists on the principle of "knowing and doing", that is, putting into action the knowledge that the expert conveys through relations with others in an activity. Thus, to face the hyper competitiveness [START_REF] Ilinitch | New Organizational Forms and Strategies for Managing Hypercompetitive Environments[END_REF], technical and scientific experts contribute to the dynamic and strategic renewal of industrial domains of expertise within their firm [START_REF] Cabanes | Technical Staff Management for Radical Innovation in Science-Based Organizations: A New Framework Based on Design Theory[END_REF][START_REF] Maniak | Value Management for Exploration Projects[END_REF], and to foster the innovation capability of the firm [START_REF] Cabanes | Regimes of Expertise Creation: Innovation and Governance of Expertise in Industrial Organizations[END_REF][START_REF] Lelebina | Initiating and Managing Career Creativity of Corporate Professionals[END_REF][START_REF] Trépos | Handy Misunderstandings. Experts, Professionals and Laypersons Relationships[END_REF]. So, since the 1950's the knowledge of industrial experts is scientific or technical and strategic. However, few scholars focused on the evolution of the domain, or the evolution of nature of the knowledge contained in industrial domains of expertise.

Expertise acknowledgment

The role of an expert in industrial firms described in the literature for decades is not to design but to organize knowledge per strategic area. They make decisions or define the strategy (Trépos, 2014), by mobilizing their knowledge (Blaker, 1996) based on their 10 years of experience (William G. [START_REF] Chase | The Mind'seye in Chess[END_REF][START_REF] Ericsson | The Road To Excellence: The Acquisition of Expert Performance in the Arts and Sciences, Sports, and Games[END_REF][START_REF] Weisberg | Modes of Expertise in Creative Thinking: Evidence from Case Studies[END_REF], and with the acknowledgement by peers [START_REF] Shepard | The Dual Hierarchy in Research[END_REF][START_REF] Shanteau | Competence in Experts: The Role of Task Characteristics[END_REF][START_REF] Hoffman | How Can Expertise Be Defined? Implications of Research from Cognitive Psychology[END_REF]Ericsson 2006;[START_REF] Lelebina | Initiating and Managing Career Creativity of Corporate Professionals[END_REF][START_REF] Cabanes | Technical Staff Management for Radical Innovation in Science-Based Organizations: A New Framework Based on Design Theory[END_REF][START_REF] Ericsson | The Cambridge Handbook of Expertise and Expert Performance[END_REF]. "The identification of an expert implies a definition of the relationship between the validity of expertise and its acceptability in collective action." (Hatchuel and Weil 2011, 64). To validate an expertise, academic qualifications (such as graduate students vs. undergraduates), seniority or years performing the task, or consensus among peers are considered (Chi 2006, 23). By "consensus among peers", peers are understood as an institutionalized community in a scientific field. Institutionalization serves also to define who has access to these places where scientific expertise is developed and negotiated [START_REF] Shappin | Leviathan And the Air-Pump -Hobbes, Boyle, and the Experimental Life[END_REF][START_REF] Evetts | Professionalization, Scientific Expertise, and Elitism: A Sociological Perspective[END_REF]. These community of peers is constituted by scientists who write empirical or theorical considerations, and by colleagues working in similar domains involved in the validation of the scientist character of these considerations to be published in specialized journals [START_REF] Ericsson | The Road To Excellence: The Acquisition of Expert Performance in the Arts and Sciences, Sports, and Games[END_REF]Evetts, Mieg, and Felt 2006, 115). Thus, experts can validate their expertise and knowledge through scientific publications validated by the peers. The scientific publication is also a way to create and spread new knowledge.

In addition, the exploration of the unknown plays a role in the constitution of new knowledge (Le Masson and Weil 2020). It appears that the experts' activities can't be limited to strategic decisions. They must incorporate activities to explore the unknown, which can later reinforce the emergence of new expertise. But the literature on exploration of the unknown and experts does not offer such understanding.

Then, the question of the emergence of unknown expertise through scientific exploration activities seems to be relevant. More specifically, the research question is "How to better master scientific research to innovate? Understanding the emergence of unknown domains of expertise in established firms by analyzing their research collaborations projects between internal expert and scholars."

RESEARCH METHODS

Collaborative management research with two firms

This study is based on a collaborative management research [START_REF] Shani | Handbook of Collaborative Management Research[END_REF], with Renault, an established French technological firm with a strong organization of experts. The partnership has been led since 2019 by the first author, a PhD candidate who is both a researcher in design and innovation capability and an employee of Renault, and it has been supported by senior researchers in innovation management, as well as members of industrial expert organizations specialized in radical innovation management.

Data collection process and research material

This article is based on two longitudinal [START_REF] Menard | Longitudinal Research[END_REF][START_REF] Pettigrew | Longitudinal Field Research on Change: Theory and Practice[END_REF]) case studies [START_REF] Yin | Case Study Research: Design and Methods[END_REF], that mixed the quantitative method of collecting data and the qualitative methods of the exploration and theory-building process with practitioners [START_REF] Eisenhardt | Theory Building from Cases: Opportunities and Challenges[END_REF]. This combination of qualitative and quantitative methods follows the process of mixed methods research or MMR [START_REF] Venkatesh | Bridging the Qualitative-Quantitative Divide: Guidelines for Conducting Mixed Methods Research in Information Systems[END_REF]. First, the authors reconstituted the history of the all the domains of expertise at Renault, with the creation date, all the expert-leaders nominated and sometime the cessation date of each domain. This set of data is essentially constituted by 57 semi-directive interviews (1 with a former human resource director dedicated to the experts in 2008, 2 with the expert-fellow in charge of all the experts, 1 with his executive secretary, 10 with expert-leaders in charge of a domain, 43 exchanges by Microsoft Teams or by e-mail with other expert-leaders), as well as by compiling relevant internal and historical documents since the creation of the domains of expertise (the nomination letter of the expert leader, some missions letters, and some PowerPoint presentations fromm 2008, as well as profiles searches on Linkedin). The second and central set of data on collaborations between Renault and industrial management scholars, is collected through 1 academic search engine. We put "Renault" as institution and got 2 589 results. We realized after, that we didn't get papers on Renault published by other institutions. However, among these 2 589 publications, some have been published with external institutions, from which we obtained the first 19 names. We also collected the name of the 20 more important authors at Renault.

Data analyses

For the history of the domains, we classified these 66 domains in 7 major themes and 21 minor themes. Then, we analyzed the apparition of these 66 domains in a matrix by minor themes, by major themes, and by the time of apparition.

To analyze the papers, we first deleted the papers without publication year, those published in 2021, and those related to medicine / healthcare. We classified the remaining 2 449 papers according to the 7 major and 21 minor themes, and the 66 domains. We created an analytic matrix with the time of the publication, the major and minor themes, and the related domain. We then compared the tow matrices and identified correlations. We also looked into the 20 most important authors at Renault, who were also experts.

Data

Historical strategic expertise domains creation at Renault

Before 2009, there was no formalized expert system at Renault, even if some experts already existed. Renault structured and formalized its expertise organization in 2009 and, in the process, also acknowledged the previously existing experts. The aim of such organization was and is still to make these experts innovate by proposing and promoting disruptive solutions, by promoting standards and by being actors in the development of the Expertise sector (coach, trainer, etc.). From 2009 to 2021, 66 strategic domains of expertise were created, and 6 disappeared. 779 experts were labelled as "Expert Leader", "Experts" or "Specialists" of the domain they are charged with, organized into 7 major and 21 minor themes. 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 3.2.1. Nature of knowledge : strategic, scientific, or technic ? Among the 66 strategic domains created, we consider that those in the minor themes of "Safety", "Business model" and "Tests & Simulations" are not totally dedicated to "hard «scientific or technical area, but to management sciences. Thus, 30 domains of expertise are dedicated to management fields. However, for all these fields, we count in total 2 442 scientific publications, which means validated by peers following a scientific review process. Thus, knowledge in these fields are recognized to be scientific enough to constitute an expertise for Renault. While attributing one domain of expertise to each paper was straightforward in most cases, 18 papers required a second domain of expertise, to fully encompass the scope of the themes. Many papers in "Numerical Modeling Simulation" deals with the numerical modelling of other domains, as if the "Numerical Modeling Simulation" domain was strategic for the other domains, and not directly for Renault. Others could not be link to one domain, as if they preexist the creation of the domain. Indeed, 12 papers were dedicated to Data management , and we know that some experts at Renault are working on this topic and considering the creation of a dedicated domain. By linking these 2 442 publications to the 66 domains of expertise, the keywords of the publication became the keywords of the domains of expertise. As a keyword describes one topic in a publication, by gathering all the keywords of the publications linked to the same fields, all the topics of the field can be identified. Counting how many times a keyword appeared helps to identify the main topics of the field. Thus, the knowledge of all expertise fields at Renault can be identified, described, and analyzed. As example, the most 1980As example, the most 1981As example, the most 1982As example, the most 1983As example, the most 1984As example, the most 1985As example, the most 1986As example, the most 1987As example, the most 1988As example, the most 1989As example, the most 1990As example, the most 1991As example, the most 1992As example, the most 1993As example, the most 1994As example, the most 1995As example, the most 1996As example, the most 1997As example, the most 1998

1955
ADAS 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 Engineering 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Quality 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Safety 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Industrial system 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Process Quality 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Tests & Simulations 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Materials Quality 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Business model 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 Safety 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Quality 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Business model 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Engineering 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Safety 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Tests & Simulations 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Business model 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 e-Powertrain 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Internal Combustion Engine 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 Engineering 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 0 Quality 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 2 Safety 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 1 4

Vehicle Performance

1999 ADAS 0 0 1 0 0 1 1 1 0 0 0 1 1 5 1 0 1 0 0 Engineering 0 0 1 1 0 0 0 0 0 0 1 0 0 0 1 2 0 1 1 Quality 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Safety 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 Industrial system 0 0 1 1 0 1 2 1 0 0 1 1 0 0 0 1 0 0 1 Process Quality 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 Tests & Simulations 0 1 0 0 0 0 1 1 1 1 2 2 2 6 9 6 6 2 8 Materials Quality 0 0 0 0 2 0 1 0 0 0 2 3 3 0 4 5 5 12 7 Business model 0 0 0 1 2 0 1 0 0 0 0 0 2 0 0 2 1 2 1 Safety 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 Quality 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 Business model 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 Engineering 0 0 0 0 0 0 1 1 0 0 2 0 0 0 0 2 2 2 0 Safety 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 Tests & Simulations 0 0 1 0 2 1 0 2 0 1 2 2 0 2 1 1 4 4 4 Business model 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 0 e-Powertrain 0 1 1 0 2 0 0 0 1 0 0 0 0 0 0 0 0 2 0 Internal Combustion Engine 0 0 1 0 1 1 1 1 1 1 1 1 1 0 0 1 3 6 8 Engineering 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Quality 0 0 0 1 0 1 0 2 0 1 4 0 0 1 0 1 2 1 4 Safety 0 1 2 2 3 0 0 1 0 0 0 1 3 1 0 1 2 2 1
Vehicle Performance

0 0 0 1 0 1 0 0 0 3 3 2 4 2 6 3 5 Industrial system 0 1 0 1 0 0 2 1 0 1 2 1 3 5 1 6 2 3 5 1 Process Quality 0 1 0 0 0 0 1 4 0 3 1 1 0 2 0 1 1 5 1 1 Tests & Simulations 1 3
3 2 3 1 0 1 1 2 3 5 2 1 1 4 5 7 4 4 0 Safety 0 2 0 0 0 1 0 0 1 1 0 0 0 0 2 0 0 0 1 1 Quality 0 0 0 0 0 2 0 0 0 1 2 1 0 2 0 1 5 1 1 2 Business model 1 1 0 0 1 1 0 0 0 0 1 0 0 0 3 0 1 0 1 2

External acknowledgment of expert or domain of expertise

Thanks to the scientific publication, experts at Renault can be recognized by different internal and external communities of peers. First all the papers are from the institution "Renault". It means that at least one author per paper was/is an employee at Renault. By publishing their new knowledge, the authors at Renault wanted their expertise to be validated by external peers. This helps them first to internally make their expertise validated by performing in scientific publications, and then by other internal peers. Today, among the 10 most important authors at Renault, 6 are at the level of Expert-Leader or Expert. These scientific publications also help them to build the Renault expertise as a whole, by turning the empirical or theorical considerations into standardized knowledge Among the 2 442 papers, some papers have a second institutional attachment. It means that some of the Renault authors worked with external authors, from other institutions. The following table shows the 19 most active institutions co-publishing with Renault. Among them 18 are universities, engineering, or business schools, and 1 is another car manufacturer: PSA Peugeot-Citroën. Thus, this external community of peers is composed as well by employees of concurrent companies, as by different kind of high education scholars. In this same database, around 2 700 other papers focus on Renault's activities, without being attached to the Renault Institution, but to other institutions. Among these scientific publications, they are PhD theses. Indeed, since the creation in France of the CIFRE (Industrial Training Agreement Through Research, in French "Conventions Industrielles de Formation par la Recherche") in 2000, Renault supervises around 100 theses per year with many academic laboratories, which lead to numerous scientifical publications being attached to these laboratories, while also being supervised by Renault's employees. Thus, research partnerships attached to external laboratories contribute to acknowledge the expertise of some experts. But it can also contribute to the construction and validation of the knowledge of a domain of expertise without any attached expert. In other words, external peers acknowledge that Renault have domains of expertise, sometime without necessarily a dedicated expert.

Discussion of the results

4.1. Duration of construction of the knowledge of an industrial expertise 10 years are required to become an expert, but how long does an organization need to construct its domains of expertise? In this case of study, between the first publication and the year of its dedicated domain of expertise formalization, the average time is 29 years. But we can also observe that it depends on the domain. Indeed, in our case study, the first publications deal with Vehicle Performance, and in 2010, 6 domains of expertise were created, which means that 55 years were needed to construct the expertise of the industry. On the contrary the first publication on Quality in EEEA appeared only in 2001, and one domain was created in 2011, which is only 10 years. Moreover, we observe that for the most recent domain of expertise, this database counts very few dedicated publications. But we also know that some papers are missing. Indeed, for the domain Innovation Pattern, created in 2018, we count only 7 papers, and we know that at least 9 other papers are missing.

Acknowledgement of an organization's expertise

When an expert builds their expertise with scientific publications, he also builds the expertise of their company. However, we have seen that the company can also build its expertise without any dedicated experts. This is underlines by all the scientific publication focusing on Renault without being written by a Renault employee. But it means that external peers wrote these publications, which were in turn validated by other external peers. So, a double external peers validation of the knowledge occurred, which acknowledged Renault's expertise. Thus, external peers can acknowledge an expertise to an organization, without any dedicated expert.

New knowledge nature of industrial experts

Among the 66 domains of expertise at Renault, around 30 are dedicated to management. It means that the knowledge of an expertise can be other than scientific or technical. By scientific we mean not exact & experimental sciences, as these 30 domains of expertise have scientific publications, so their contents are also scientific, but related to social science. Moreover, one of the 66 domains of expertise is cross disciplinary, as if it were more strategic for the other domains than for the company. In a paper to come, we also observe that the Innovation Pattern domain was dedicated to the other experts, and not directly for the strategy of Renault. It seems that a new kind of expertise is emerging, and then a new kind of knowledge.

Finally, we are now able to precisely model the knowledge of an expertise, and to study the past evolution through the years, by studding the keywords of the past publications.

Emergence of an unknown expertise

At Renault, the experts are in charge of promoting standard and developing the Expertise sector. The peer-reviewed scientific publications act as validated new knowledge, which then become standards, and allow to develop the expertise. In this way, after 2010, the number of scientific publications doubled: only 980 before 2010 vs 1500 after. Therefore, collaborations with industrial management scholars resulting in a scientific publication are a way to construct and develop an unknown domain of expertise, for an expert as well as for an organization.

Conclusion

This research highlights 4 main results: Firstly, in the case of an industrial company like Renault, it took around 29 years to constitute the first domains of expertise, but since then, new domains have been formalized in just a decade. In the case of emergent domains of expertise, we do not yet know how long will be needed for them to be fully institutionalized. In this way (second result), external peer's community can acknowledge the expertise of a company, and not only of an expert. Thirdly, all the experts in industries are not strategic and exact & experimental scientific or technical expert, they can also be expert in social sciences, and strategic for the other experts instead of just for the company. Finally, research collaborations are a way to create and reinforce a domain of expertise, both for an expert and for the company. However, more researches must be pursued to add the missing papers, and the papers on Renault but attached to other institutions, in order to reinforce these results. Another interesting point should be to focus on how experts or external researchers bring out a research study: what kind of unknown are they facing, how do their research work is a form of exploration of this unknown, and how publication formalizes an innovant solution to respond to this unknown.

Figure 1 -

 1 Figure 1-Figure 1 -Scientific publications by Renault per year and major theme

Table 1 -

 1 Major and minor themes of the total strategic domains at RenaultIn 2009, Renault analyzed the domains of expertise at Nissan, choose among them 57 strategic expertise area to internally formalized and could nominate only 36 experts between 2010 and 2012. Indeed, Renault did not have the 57 corresponding experts in house. At that R&D Management Conference 2021 -Innovation in an Era of Disruption Online -July 6th -8th, 2021 time the strategic expertise domains were essentially in Manufacturing & Supply Chain, Powertrain, and Vehicle Performance. From 2013 to 2021, 27 other domains of expertise have been created. We can identify 3 tendances in the creation of these domains: a huge increase in Market & Customer a stability in Electronics, Electrical Energy, Automatic (EEEA) Materials, and Operational Effectiveness, and a decrease in the Manufacturing & Supply Chain, Powertrain and Vehicle Performance.

	Major	Minor	Total
		ADAS
	Electronics, Electrical Energy, Automatic	Engineering Quality
		Safety
		Industrial system
	Manufacturing & Supply Chain	Process Quality
		Tests & Simulations
	Materials	Quality
		Business model	14
	Markets & Customer	Safety
		Quality
		Business model
		Engineering
	Operational Effectiveness
		Safety
		Tests & Simulations
		Business model
	Powertrain	e-Powertrain
		ICE
		Engineering
	Vehicle Performance	Quality
		Safety
	Total of strategic domains		66

Table 2 -

 2 Phases of appearance of the strategic domains at Renault, organized by major themes

Table 3 -

 3 Number of strategic domains created at Renault per major and minor themes and per year Duration of construction of the knowledge of an individual expertiseBy analyzing the scientific publication published from Renault's collaboration with industrial scholar since 1955, 4 periods were identified.-From 1955 to 1980 is the period that sees the appearance of the first scientific publications. Most of them concerns the Vehicle Performance expertise domains. For the 80s to the 90s, the number of scientific publications slowly increased, essentially in Material, Manufacturing & Supply Chain, Operational Effectiveness and Powertrain. At that point, almost each minor theme already counts a publication, except for the Quality in EEEA. -From 2000 to 2013, the period includes 10 years before the formalization of the experts' organization and the three first years of nominations. During this time, some minor themes see the number of publications hugely increased, such as Test & Simulation in Operational Effectiveness, Internal Combustion Engine in Powertrain, then ADAS in EEEA, Materials, and Quality in Vehicle Performance. The others showed no major increase or only slow growth in the number of publications. Over that time, we count 1311 publications in total.

	3.2. Renault's collaboration with industrial management scholars
	3.2.1.

-From 2013 to 2020, a new trend appears. The number of publications exploded in ADAS in EEEA, in Test & Simulation in Operational Effectiveness, other keep going slowly like in Quality in EEEA, Materials, e-Powertrain, and Quality in Vehicle Performance, and surprisingly, some decreased like in Internal Combustion Engine in Powertrain. At that time, we count 1131 publications in only 7 years.

Table 4 -

 4 Number of scientific publications from the Renault's collaboration with scholars per year and major and minor theme (the first publication is in red)

  important keywords of the Artificial Intelligence, Internal Combustion Engine, Marketing and Numerical Modelling are presented in the next tables.

	Electronics,
	Electrical Energy,
	Automatic
	Manufacturing &
	Supply Chain
	Markets &
	Customer
	Operational	Engineering	4	2	4	2	2	2	5	3	5	1	4	5	4	8	7	7	6	3		6	4
	Effectiveness	Safety	0	1	2	0	1	0	1	1	0	2	0	1	3	0	1	1	3	0		2	2
		Tests & Simulations	1	4	8	11	9	8	5	22	11	14	19	21	23	19	18	28	17	22	23	22	24
		Business model	1	3	1	0	2	0	3	2	1	0	3	3	3	2	1	6	5	2		1	2
	Powertrain	e-Powertrain	1	0	0	0	2	3	3	3	1	4	8	4	5	11	10	11	14	16		8	9
		Internal Combustion Engine 14	9	4	10	19	15	11	15	15	21	20	32	30	32	15	20	16	13	11	19	14
	Vehicle Performance	Engineering Quality Safety	2 4 0	1 5 6	0 8 1	2 5 2	1 6 1	0 7 2	1 11 1	1 12 1	1 10 1	0 17 1	0 15 4	2 12 3	0 8 4	2 15 1	1 19 4	1 14 4	0 16 5	1 15 5	17	0 18 4	0 8 9
								Marketing
		0 0,5 1 1,5 2 2,5 3 3,5 4 4,5

Table 5 ,

 5 6, 7 & 8 -20 most important keywords in the following domain of expertise at Renault

Table 9 -

 9 The 19 most important co-publishers with Renault