Elies Gherbi
email: eliesgherbi@gmail.comelies.gherbi@irt-systemx.fr

Blaise Hanczar
email: blaise.hanczar@univ-evry.fr

Jean-Christophe Janodet
email: jeanchristophe.janodet@univ-evry.fr

Witold Klaudel
email: witold.klaudel@irt-systemx.fr

Wee Sun Lee

Taiji Suzuki

An Encoding Adversarial Network for Anomaly Detection

Keywords: Anomaly Detection, Deep Learning, Encoding Network, Adversarial Learning, Generative Adversarial Networks, Intrusion detection, week supervised learning, Unbalanced datasets

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

Introduction

Anomaly detection is a well-established topic in Machine Learning, with many applications in domains such as fraud detection, cybersecurity, video surveillance, and predictive maintenance [START_REF] Chandola | Anomaly detection: A survey[END_REF][START_REF] Ravi Kiran | An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos[END_REF][START_REF] Chalapathy | Deep learning for anomaly detection: A survey[END_REF]. Moreover, the recent advances in Artificial Intelligence bring insight into future applications, as autonomous transportation like self-driving cars. Many cyber-security threats can impact the usability of those systems. The problem of anomaly detection highlights many risks related to those threats and can help the standard security systems to face new threats.

We can formulate the anomaly detection problem as follows. Let D be a data set containing a large number of normal examples (the normal states of the system) X n , and a relatively small number of anomalous examples X a . A model M must learn the distribution function p X over the normal data during training. Then, given any test example x, it must determine whether x deviates from the learned distribution p X by using an anomaly score function a(x).

Following [START_REF] Chalapathy | Deep learning for anomaly detection: A survey[END_REF], there exist three settings for anomaly detection. The first is the unsupervised case, where an algorithm has to discover intrinsic properties of the data to detect the anomalies without any label guidance [START_REF] Ricardo | Hierarchical density estimates for data clustering, visualization, and outlier detection[END_REF]; The training set contains both normal and anomalous examples, but the labels are not available. The second is the supervised case, where an algorithm must usually face unbalanced datasets, with significant rates of normal data and few anomalous examples.

The third is the one-class classification, where the algorithm has access only to a large set of normal data. Unlike the unsupervised case, there are no anomalous examples in the training set. In this paper, we focus on both the supervised and one-class anomaly detection problems. Notice that even though few anomalous examples may exist in the data, supervised learning algorithms are still challenging, because the set of anomalies often does not form a homogeneous class, that is, a set of examples that shares some semantics.

A large number of papers propose machine learning-based anomaly detection models [START_REF] Chandola | Anomaly detection: A survey[END_REF][START_REF] Hodge | A survey of outlier detection methodologies[END_REF][START_REF] Marco | A review of novelty detection[END_REF]. The recent advances in the field of deep learning have made it possible to revise this problem [START_REF] Chalapathy | Deep learning for anomaly detection: A survey[END_REF][START_REF] Ravi Kiran | An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos[END_REF] and in many application domains like intrusion detection system [START_REF] Galinina | State of the Art Literature Review on Network Anomaly Detection with Deep Learning[END_REF]. In particular, Generative Adversarial Networks (GANs) (Goodfellow et al., 2014a,b), that were proved very efficient in many application fields (Creswell et al., 2018), have also been adopted in recent works on anomaly detection [START_REF] Schlegl | Unsupervised anomaly detection with generative adversarial networks to guide marker discovery[END_REF]Zenati et al., 2018;[START_REF] Akcay | Ganomaly: Semi-supervised anomaly detection via adversarial training[END_REF][START_REF] Golan | Deep anomaly detection using geometric transformations[END_REF][START_REF] Sabokrou | Adversarially learned one-class classifier for novelty detection[END_REF][START_REF] Deecke | Image anomaly detection with generative adversarial networks[END_REF].

In this article, we investigate several problems related to GAN-based anomaly detection methods. We propose a new method, called AnoEAN (Encoding Adversarial Network for Anomaly Detection). The principle of AnoEAN is to learn a function (Encoder) that projects the original dataset into a small dimension latent space so that the normal examples are projected in a restricted region of the latent space and the anomalies outside this region. This latent representation allows us to identify anomalies directly in the latent space by using a Mahalanobis distance on the distribution induced by the normal examples. We thus eliminate all the problems related to the reconstruction loss function. To do so, we develop a new approach that trains an encoder by adversarial learning. We assume that we have a large amount of normal data and a small number of anomalies, which is a common framework for anomaly detection. We finally conduct a series of experiments proving that AnoEAN performs better than conventional anomaly detection techniques, including those based on GANs, using both the MNIST base of handwritten digits [START_REF] Lecun | The MNIST database of handwritten digits[END_REF] and two standard network intrusion detection databases (KDD'99, NSL-KDD).

Related works

According to the taxonomy of [START_REF] Golan | Deep anomaly detection using geometric transformations[END_REF], there are two leading families of methods. The first category, called Representation learning, consists of learning a representation of the normal data, which is used to trigger an alarm when deviant behavior is detected. Many machine learning techniques have been used to construct such models, including one-class SVM (OCSVM) [START_REF] Schölkopf | Support vector method for novelty detection[END_REF]. The problem returns to find a hypersphere with a small radius that encloses the majority of the normal data and keeps anomalies outside. Contrary to the deep learning approaches, OCSVM and other classic machine learning techniques require a specific feature preprocessing. The kernel methods also suffer from quadratically scaling, and require the storage of the support vectors which can be restrictive for an embedded system. Notice that [START_REF] Ruff | Deep one-class classification[END_REF]; [START_REF] Chalapathy | Anomaly detection using one-class neural networks[END_REF] has proposed a deep learning adaptation of one-class classification that does not suffer from these limitations.

TThe second category is based on a Reconstruction loss anomaly score. The model constructs a compact representation of the characteristics of the normal examples and learns to reconstruct the examples from this representation. These methods are based on the assumption that it is more difficult to reconstruct an anomaly than normal examples. The autoencoders were the first to be developed in this framework [START_REF] Xia | Learning discriminative reconstructions for unsupervised outlier removal[END_REF][START_REF] Xu | Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications[END_REF], but the adversarial learning techniques became more and more popular, and several GAN variants have been adapted to tackle anomaly detection problems [START_REF] Schlegl | Unsupervised anomaly detection with generative adversarial networks to guide marker discovery[END_REF]Zenati et al., 2018;Zenati et al.;[START_REF] Akcay | Ganomaly: Semi-supervised anomaly detection via adversarial training[END_REF][START_REF] Golan | Deep anomaly detection using geometric transformations[END_REF][START_REF] Sabokrou | Adversarially learned one-class classifier for novelty detection[END_REF][START_REF] Deecke | Image anomaly detection with generative adversarial networks[END_REF].

A Generative Adversarial Network (GAN) is composed of two neural networks: a generator G, that transforms a vector z drawn from simple prior distribution (latent space) into an artificial data space (same dimension as a real data space), and a discriminator D whose role is to differentiate artificial data from real data. One drives such a system competitively: the generator must deceive the discriminator by implicitly learning to approximate the distribution of real data (Goodfellow et al., 2014a). In the context of anomaly detection, AnoGAN [START_REF] Schlegl | Unsupervised anomaly detection with generative adversarial networks to guide marker discovery[END_REF] and its variants (Deecke et al.), learn a GAN from the normal data. Then, for a given test example x, the algorithm looks for a point z in the latent space such that G(z) ≈ x; if it fails, then the example x is considered as an anomaly. In other words, the GAN-based approaches work upon the inversion of the generator: AnoGAN tries to compute z = G -1 (x) under the assumption that G is invertible only for normal examples. Nevertheless, AnoGAN does not explicitly invert the generator: a reconstruction loss function is defined, typically L r = x -G(z) , and the techniques consist in finding the point z which minimizes L r withe respect to z. The process is very time-consuming because a gradient descent is performed for each test example. Notice that [START_REF] Creswell | Inverting the generator of A generative adversarial network (II)[END_REF] improved this idea by inverting many images at once. The GANs also suffer from the mode collapse [START_REF] Salimans | Improved techniques for training gans[END_REF]: The generator may have a limited diversity during the inference phase regardless of the input. In practice, it means that there exists a single point that the generator thinks as the most optimal point to generate, whatever the input. See Fig. 1c,1d.

In order to get around these problems, other algorithms like EGBAD and ALAD (Zenati et al., 2018;Zenati et al.) learn Bidirectional GANs (BiGANs) [START_REF] Donahue | Adversarial feature learning[END_REF][START_REF] Vincent Dumoulin | Adversarially learned inference[END_REF][START_REF] Li | ALICE: towards understanding adversarial learning for joint distribution matching[END_REF] rather than GANs. Their architectures are composed of three networks: a generator G and a discriminator D as before, and a third network called an encoder E, whose role is to project the data to the latent space with E = G -1 . The encoder is learned alongside the GAN. Moreover, in this setting, finding a point z such that G(z) ≈ x is immediate since we have z = E(x). However, training an additional encoder network may encourage over-fitting, resulting in poor reconstruction (Creswell and Bharath, (a) 2018). Moreover, in practice, the generator and the encoder are rarely exact inverses of each other: they are inverse from a theoretical standpoint, after convergence. Furthermore, the BiGANs may suffer from non-identifiability issues, as shown by [START_REF] Li | ALICE: towards understanding adversarial learning for joint distribution matching[END_REF]: a single instance z can potentially correspond to many possible values of x (see Fig. 1a,1b). To deal with this problem, ALICE [START_REF] Li | ALICE: towards understanding adversarial learning for joint distribution matching[END_REF] uses conditional entropies to control the uncertainty of a pair (z, x). More precisely, the authors bound the conditional entropy by using the criterion of cycle-consistency (Zhao et al.). Most of the GAN-based approaches work upon the assumption that the generator is invertible. The inverse of the generator is obtained either by driving another network which simulates the inverse of the generator or via an optimization process. However, it has been shown experimentally that in both cases, the approximated function is not the inverse of the generator everywhere (see Fig. 1a and1b). Thus, applying the generator again on the antecedent of a datum x generally fails to reconstruct x, even though datum x was a normal example. In practice, this phenomenon dramatically increases the rate of false alarms.

AnoEAN algorithm

Formulation

We consider a problem in which we monitor the data x ∈ R p describing the state of a system with p variables. Our goal is to trigger an alert when the data shows that the system is out of normal behavior. For this, we construct an anomaly score a(x) : R p → [0, +∞[ measuring the degree of anomaly of an example x, the normal example must have a score close to 0. To learn this function, we have a training set containing N examples corresponding to a normal behavior {x

n i | i = 1..N } and M examples corresponding to anomalies {x a i | i = 1..M } with M N .
Notice that unlike normal data, the anomalies do not form a homogeneous class. Our method, named AnoEAN for Anormaly Detection by Encoding Adversarial Network, consists in projecting the examples x n and x a into a small space, called decision space, in which the distribution of normal examples is Gaussian. We call encoder, the neural network that projects the examples from the original space into the decision space E(x) : R p → R d with d p. Let p z be a Gaussian distribution N (0, I) in the decision space. The purpose of the encoder is to project the normal examples in p z and the anomalies outside p z . For this, in the same way as the GANs, we use a second network called discriminator D(z) : R d → R which receives as input a vector of the decision space and predicts if this vector comes from p z or from the encoder. D(z) returns the probability that the vector z comes from p z . The encoder must therefore both misleads the discriminator on the normal example projection E(x n ) to make it believe that it comes from p z and help the discriminator differentiate p z from the projection of the anomalies E(x a ). The architecture of our AnoEAN model is shown in Fig. 2. The discriminator and the encoder must respectively minimize the following L D and L E loss functions:

L D = -E z∼pz [log D(z)] -E xn∼px n [log(1 -D(E(x n )))] -E xa∼px a [log(1 -D(E(x a )))] (1) L E =E xn∼px n [log(1 -D(E(x n )))] + E xa∼px a [log(D(E(x a )))] (2)
In Eq. ( 1) and ( 2), the two first terms of loss function L D , and the first term of loss function L E are similar to the basic loss functions of a GAN. Instead of a noise vector in the input of the generator of the GAN, in AnoEAN a vector representing a normal example is in the input of the encoder. By maximizing D(z) and minimizing D(E(x n )), the decoder learns to differentiate vectors drawn from p z and projections of normal examples E(x n ). By maximizing D(E(x n )) the encoder gets the distribution of the projections of normal examples E(x n ) closer to p z . If we do not add the terms D(E(x a )) in L D and L E then there are no constraints on the projections of the anomalies, and the encoder tends to project both normal examples and anomalies on p z . The consequence is that we can not differentiate normal examples from anomalies in the decision space. On the contrary, by minimizing D(E(x a )), we get that (i) the decoder learns to differentiate vectors drawn from p z and projections of normal examples E(x a ), and (ii) the encoder also minimizes the overlap between the distribution of projections of the anomalies E(x a ) and p z . Once the model is trained, the prediction of the anomalies requires only the use of the encoder (Fig 4). The anomaly score for an example x is the distance between its projection in the decision space E(x) and the distribution of the projection of the normal examples. This distribution is supposed to tend to p z = N (0, I) during the learning of the model. We could therefore use the E(x) standard deviation as an anomaly score. However, because of the finite size of the training set, our experiments showed that the projection distribution of normal examples could diverge slightly from p z . Therefore, at the end of the learning, we represent this distribution by a Gaussian distribution N (µ, Σ) whose parameters are assessed with a validation base. The anomaly score is finally the Mahalanobis distance between E(x) and µ:

a(x) = (E(x) -µ) T Σ -1 (E(x) -µ)
(3)

Theoretical analysis

In this section, we develop the loss functions L D and L E (see Eq. ( 1) and ( 2)) in order to identify the optimal discriminator and encoder. We begin with the discriminator. The formulas of L D can be rewritten by introducing the integrals instead of the expectations:

L D = z -p z (z) log D(z)dz - x p xn (z) log(1 -D(E(x)))dx - x p xa (x) log(1 -D(E(x)))dx = z -p z (z) log D(z) -p zn (z) log(1 -D(z)) -p za (z) log(1 -D(z))dz
where p zn (resp. p za ) is the distribution of the projection of normal (resp. anomalous) examples into the decision space. We can express the optimal discriminator D given the encoder E. Let f (D(z)) be the function in the integral above such that L D = z f (D(z))dz.

To find the discriminator that minimized L D , we set the derivative of f (D(z)) to 0 :

∂f (D(z)) ∂D(z) = - p z (z) D(z) + p n (z) 1 -D(z) + p a (z) 1 -D(z) = 0 ⇒ D(z) = p z (z) p z (z) + p zn (z) + p za (z) (4)
if p z (z) + p zn (z) + p za (z) = 0, we can compute the second derivative to check that this extremum is a minimum :

∂f (D(z)) ∂D(z) = p z (z) D(z) 2 + p n (z) (1 -D(z)) 2 + p a (z) (1 -D(z)) 2 > 0 (5)
Hence the optimal discriminator is D * (z) = pz(z) pz(z)+pz n (z)+pz a (z) . It corresponds to the Bayes classifier predicting if z comes from the distribution p z or from the encoder.

Let us see what the encoder does when the optimal discriminator is plugged into loss function L E . We have: implies that the encoder tends to the solution where the three distribution are equal p z = p zn = p za . By maximizing KL (p za ||p z ), the encoder maximizes the divergence between p za and p z i.e. it tries to project the anomalies outside p z . To maximize KL p zn pz n +pz a 2

L E = z p zn (z) log(1 -D * (z)) + p za (z) log(D * (z))dz = z p zn (z
, the encoder has to maximize the divergence between p zn and p za , thus it aims at separating the projection of the normal examples from the projection of the anomalies. By optimizing all of these four divergences together the encoder tries to project the normal examples into p z and to project the anomalies outside p z and p xn .

Learning algorithm

In Algo. 1, we show the training procedure of our model. We take random examples x n and anomalies x a from the training set and project them into the decision space with the encoder (i.e., we calculate each E(x a ) and E(x n )). We add random vectors from the p z distribution to these projected examples, and get the batch that is presented at the input of the discriminator. The discriminator is modified by a gradient descent to minimize L D (Eq 1); the encoder is frozen during this step. Then, it is the encoder's turn to be modified in order to minimize L E (Eq 2), the discriminator being frozen during this step. This procedure is iterated until convergence. Notice that to seed up the gradient descent, we modify the loss function of the encoder :

L E = -E xn∼px n [log(D(E(x n )))] - E xa∼px a [log(1 -D(E(x a )))
]. The minimization of this function is theoretically equivalent of the previous one, but it has larger gradient for bad encoders.

(i) n } mn ∼ P xn , {x (i) a } ma ∼ P xa , {z (i) } m ∼ N (µ, σ 2 )). for k = 0, ..., n D do δ w ← ∇ w [-1 m m i=1 log D w (z (i) ) -1 mn mn i=1 log(1 -D w (E θ (x (i) n ))) -1 ma ma i=1 log(1 - D w (E θ (x (i) a )))] w D ← Adam(δ w , α, β) end for δ θ ← ∇ θ [-1 mn mn i=1 log D w (E θ (x (i) n )) -1 ma ma i=1 log(1 -D w (E θ (x (i) a )))] θ G ← Adam(δ θ , α, β) end for

Experimentations

In this section, we describe our experimental protocol, the algorithms used to compare our model, the datasets, and the implementation details of our technique. Our results demonstrate the efficiency of our model compared to state of the art.

Experimental setup

In our experiments, we used three datasets: MNIST, KDD99, and NSL-KDD.

MNIST [START_REF] Lecun | The MNIST database of handwritten digits[END_REF] is a database of handwritten digits that is commonly used for image classification problems. Despite that MNIST does not initially contain normal classes and anomalies, it is often used in anomaly detection to analyze the behavior of algorithms and visualize their predictions. In our experiments, we used this dataset in two ways: 1) 1 against all: we consider that a certain number represents the normal class and the remaining nine numbers represent the anomaly class. For the OCSVM, AnoGAN, ALAD, EGBAD methods, the training set consists of 5000 normal data; the test set includes 1700 examples of which 80% of normal data and 20% of anomalies randomly selected among the other nine classes. For AnoEAN and SVM, we additionally inject 10% anomalies into the learning set; these are chosen among four classes out of nine so that certain figures (anomalies) do not appear during the learning. 2) n against m: it is based on the same principle as the "1 against all", the only difference being that the normal class is composed of several digits. We group n classes of digits into one normal class and treat the remaining m digits as abnormal examples. We use the same sample apportionment in the learning and test sets as in "1 against all". The objective is to analyze the behavior of the algorithms in the case where the normal class is heterogeneous and can be separated into several subclasses.

The other datasets KDD99 and NSL-KDD are commonly used to evaluate the performance of anomaly detection algorithms, the objective being to detect intrusions into monitored networks. We use the same sample apportionment in the learning and test sets as before.

We compare our method with two kernel methods and three GAN-based methods. OCSVM. The One-Class Support Vector Machine (OCSVM) is a classical kernel-based technique for novelty detection. It is usually used with the RBF or linear kernel function [START_REF] Schölkopf | Support vector method for novelty detection[END_REF]. In our experiments, the RBF kernel gives the best results. The OCSVM learns a decision boundary around normal examples, containing most of the learning samples. Samples residing outside the borders are considered as abnormal. We implemented two tests, one where we feed OCSVM with normal data (fixing ν = 0.0001), the other where we inject 10% anomalous example in train data and a soft margin of 10% (that is, ν = 0.1). The latter did not bring any improvement to the results.

SVM. Althought the SVMs often produce efficient solutions for balanced data sets, they are sensitive to unbalanced data sets [START_REF] Veropoulos | Controlling the sensitivity of support vector machines[END_REF][START_REF] Wu | Adaptive feature-space conformal transformation for imbalanced-data learning[END_REF][START_REF] Akbani | Applying support vector machines to imbalanced datasets[END_REF]. The main reason is that the objective function assigns the same cost C for positive and negative classification errors in terms of penalty [START_REF] Veropoulos | Controlling the sensitivity of support vector machines[END_REF]. We used a cost-sensitive learning solution by adding a weight for each class that penalizes errors (giving a higher weight to the least frequent class corresponding to the class of the anomalies).

AnoGAN was the first published anomaly detection method based on GAN [START_REF] Schlegl | Unsupervised anomaly detection with generative adversarial networks to guide marker discovery[END_REF]. The model learns a generator able to project random points from a lowdimensional multivariate Gaussian distribution (latent space) to the distribution of the training data set. The model adversarially learns a discriminator that must separate the generated data from the real data. After the training stage, for each element of the test set, we sample a latent space variable using a generator, followed by gradient descent on this latent variable, to estimate the inverse projection. The anomaly criterion is a combination of the reconstruction loss and the feature match loss (distance computd with the last hidden layer of the discriminator). For each test element, if the optimization cannot find a latent variable that minimizes the criterion, we obtain a high score.

EGBAD takes advantage of the BiGAN/ALI network structure. EGBAD has an encoder that projects the data into in the latent space [START_REF] Donahue | Adversarial feature learning[END_REF][START_REF] Vincent Dumoulin | Adversarially learned inference[END_REF]. The adversarial training process is driven through a discriminator that takes as an input the pair (data, latent variable) and must determine which pair constitutes a real pair consisting of a sample of real data and its coding (x, E(x)), or a false data sample and the corresponding latent space input of the generator (G(z), z). For a given test input x, EGBAD (Zenati et al., 2018) uses the encoder to infer the latent variable z = E(x) that will be used as input for the generator to reconstruct the test input G(E(x)). The anomaly criterion is the same as that of [START_REF] Schlegl | Unsupervised anomaly detection with generative adversarial networks to guide marker discovery[END_REF].

ALAD is a bidirectional GAN, based on the ALICE architecture [START_REF] Li | ALICE: towards understanding adversarial learning for joint distribution matching[END_REF]. It directly infer the reconstruction of a data in the test phase using an encoder and the generator G(E(x)). In ALAD (Zenati et al.), the authors regularize the conditional distribution by adding another discriminant D xx (x, G(z)) to approximate a conditional entropy constraint. The aim is to enhance EGBAD by explicitly forcing the encoder and generator during the training so that both are the inverse of each other. The authors also apply the spectral normalization [START_REF] Miyato | Spectral normalization for generative adversarial networks[END_REF] to regularize the training. The anomaly score is the L 1 reconstruction error in the discriminant space G xx between the actual data and the sample generated using the discriminant hidden layer before the logit layer. In Fig. 7, we show the results on MNIST (n against m). We are interested in the problem of identifying anomalies in a dataset where the normal class is heterogeneous (composed of several digits). In this configuration, AnoEAN dramatically outperforms the state of the art, even if its performance deteriorates with the increasing heterogeneity of the normal class. Notice that the performances of reconstruction-based approaches (ALAD and EGBAD) collapse. Indeed, for them to work, the generator and the encoder must necessarily be inverse from one another, what is particularly difficult to guaranty when the normal class is heterogeneous. On the other hand, AnoEAN succeeds in encoding complex distributions because it does not need to calculate the inverse of the encoder. We present the results on NSL-KDD and KDD99 datasets in Tables 1 and2. We obtain these results by running the train and test with ten different seeds, for the initialization of the weights and shuffling before splitting data into train and test. The values represent the mean and standard deviation confidence intervals, to show a calibrated performance of the results. These tables show several measures of performances: the area under the precisionrecall curve (AUC), the F1-measure (F1), the ROC curve, and the accuracy. Since the number of anomalies is much lower than the normal examples, AUC represents our primary measure of interest. Notice that ANOEAN has a better AUC than state of the art and also a better score on the other metrics on NSL-KDD. On the KDD dataset, ANOEAN is competitive with SVM method on F1 score and accuracy. Notice that KDD dataset contains more than 400 000 single connection vectors and even with 10% of anomalies, it remains a large amount of data; This is why the SVM has good results in that case. In Fig. 8a, we reduce the amount of anomalies in the training set, and we verify that ANOEAN keeps on average the same performances, whereas those of the SVM decreases when the rate of anomalies decrease. At last, in the Fig. 8b, we check the impact of the latent space Z dimension on the AUC. Clearly, AnoEAN performs better than the other GANs based approach (EGBAD/ALAD) whichever the dimension of Z.

Conclusion and Perspectives

We presented a new anomaly detection method that uses a projection in a latent space of small dimension in which normal examples and anomalies are separated. For this, we train an encoder by adversarial learning. The cost function enforces the encoder learning to projects the normal data into a Gaussian distribution and the anomalies in the tail of this distribution. For the prediction, our method only needs the encoder weights. It is crucial to have a small model for applications in embedded systems where the memory is costly. We have shown that our model could discover non-encountered anomalies during the learning stage. Besides, our technique performs better than the state of the art when faced with a heterogeneous normal class. Finally, compared to state of the art, our technique triggers few false alarms. The limitation of our method is that it needs an amount of anomalous example to restrain the projection of normal examples. In future works, we will adapt our method to the case where no anomalies are available in the training set, to achieve this goal, the idea is to complete the architecture with a new generator of artificial anomalies, that should train against the encoder and the discriminator in an adversarial way.

  Figure 1: (a) and (b) shows the non-identifiability issue: the generator performs well in reconstruction, but the encoder has difficulties to retrieve the correct pairs between the data space and latent space. (c) and (d) shows the mode collapse issue: the generator is more likely to project every latent point to digit (6), which proves that the generator lacks from diversity.

Figure 2 :

 2 Figure 2: The training phase of AnoEAN: the Encoder and the Discriminator are adversarially learnt.

Figure 3 :

 3 Figure3: A sketch of the projection from the data space to the decision space that is performed by a well-trained Encoder.

Fig. 3

 3 Fig. 3 shows a toy dataset illustrating the objective of the encoder. The 2-dimension dataset (left panel) corresponds of 550 training examples; The normal examples are represented with 500 red points, which are drawn from a normal distribution; The remaining 50 blue points are drawn from multiple other Gaussian distributions and scattered around to simulate anomalous examples. The 1-dimension decision space is represented in the right panel, where the green, red and blue curves represent respectively the distribution of p z ,E(x n) and E(x a ). The distribution of the E(x n ) fits p z and the overlap between the distribution of the E(x a ) and p z tends to 0. This clearly yields an efficient space in which the anomaly score can be computed.

Figure 4 :

 4 Figure 4: The inference (test) phase: only the Encoder is requested.

Algorithm 1

 1 AnoEAN. Adam hyper-parameters (α = 0.0002, β = 0.5), Learning rate 10 -3 Require: batch size m, discriminator step n D , m = m a + m n Initialize the discriminator parameters w D , the generator parameters θ G . for number of training iteration do sample batches of {x

Figure 6 :

 6 Figure 6: Model performance according to the AUC metric on each digit in the case of the MNIST problem (1 against all)

Figure 7 :

 7 Figure 7: Visualization of the performances on different methods with several degrees of complexity in the normal class (varying the heterogeneity of the normal class with several numbers).

  Figure 8: (a) shows the impact of reducing the percentage of anomalies in AnoEAN and SVM on NSL-KDD. (b) shows the impact of the latent space vector dimension Z on the AUC metric, concerning the Adversarial Network Based models (BIGAN/ALICE/AnoEAN).

Table 2 :

 2 KDD99

	AUC	F1	ROC	accuracy	Model
	81.3 ± 0.92 80.1±1.4 83.9±0.89 86.2±0.78 AnoGAN
	95.1 ± 0.74 89.6±1.5 97.9±0.31 95.9±0.51	EGBAD
	95.2 ± 0.77 90.2±0.81 98.0±0.39 96.1±0.28	ALAD
	81.1 ± 4.52 78.5±4.98 91.1±2.24 88.3±7.48	OCSVM
	97.9±0.26 95.2±0.63 98.8±0.28 98.3±0.25 AnoEAN
	94.8 ± 0.5	93.9±0.8 95.6±1.49 97.4±0.24	SVM
		Table 1: NSL-KDD
	AUC	F1	ROC	accuracy	Model
	82.7±2.72 85.9±1.97 85.8±0.79 88.1±1.71 AnoGAN
	95.1±3.67 96.4±3.02 98.9±0.88 98.5±1.39	EGBAD
	95.6±1.51 96.8±0.72 99.1±0.37 98.7±0.28	ALAD
	86.6±3.86 84.4±5.36 95±2.02 92.5±3.21	OCSVM
	99.2±0.08 97.3±0.32 99.7±0.09 98.8±0.14 AnoEAN
	98.7±0	98.5±0	98.6±0	99.3±0	SVM

The performances of the algorithms are assessed with the area under the precisionrecall curves (AUC). We also calculate the optimal F 1 measure, as well as the receiver operating characteristic(ROC curve) and associated accuracy. For each model, we select the learning parameters that maximize the AUC calculated from the anomaly score a(x) Eq. ( 3) established with a validation set.

AnoEAN has two neural networks, the encoder and the discriminator. In the case of MNIST, the encoder is a convolutional network (4 * 2 * 32, 4 * 2 * 64, 4 * 2 * 128, d)(d is the dimension of latent space) and the discriminator is a multi-layer network (32, 16, 1). For the NSL-KDD and KDD99 bases, the encoder and the discriminator are multi-layer networks (128, 64, 32, d) and (32, 16, 1) respectively. The size of the batch is 200. The number of epochs is fixed by early stopping and the gradient descent is performed with Adam (lr = 10 -3 ).

Results

In Fig. 5a, we show the curves of the loss function L D . The green and blue curves show that the discriminator is confused through the learning steps: it cannot differentiate between the distributions over z and E(x n ). Therefore, the encoder is getting better with respect to the approximation of P z . At the same time, we see that the orange loss curve keeps decreasing, which means that the distribution of anomalous examples E(x a ) diverges from P z . In Fig. 5b, we can see that both the loss functions L E and L D are decreasing with the iterations until convergence.

In Fig. 6, we present our results on MNIST (1 against all): each digit is successively considered as the normal class, the others being seen as anomalies. AnoEAN outperforms all other methods in 5 out of 10 cases; its performances are comparable to the best methods on the remaining 5 digits. Notice that all the methods have poor performances on Classes 2, 3, 4 and 5 because these classes have similar visual characteristics as those of the anomalies. We


Laredj Foitih

Benchikh

Keywords: Augmented Reality (AR), Kinematics model, 200iC Fanuc, OpenGL, User Inteface

Introduction

Augmented Reality (AR) allows the overlay of virtual images on the real world, there has been much research in the field of AR in last years, AR is used for a wide range of applications in computer vision such as computer-aided surgery, repair of complex machines, establishment modifications, interior or structural design. AR techniques can be used to help bring the right information to the right place at the right time [START_REF] Regenbrecht | Augmented reality projects in the automotive and aerospace industries[END_REF]. The principle of AR is to take real images to which one adds additional information for a better perception of reality.

In [START_REF] Ibari | Remote control of mobile robot using the virtual reality[END_REF], graphical user interface is proposed to make a task easier and mechanisms for the operator and the robot to ex-change information at different levels of detail or abstraction and to verify the robot in Virtual Reality and the wireless remote control.

In the work reported by Rastogi and Milgram [14], an operator is provided with visual feedback of a virtual wireframe robot superimposed over an actual physical robot located at its remote working environment. The virtual robot will execute a task for evaluation by the operator, and if it is satisfactory, the task will be transferred to the real robot.

The other initiative, The ARITI system [START_REF] Otmane | Ariti: an augmented reality interface for teleoperation on the internet[END_REF] (Augmented Reality Interface for Teleoperation via Internet), one of the characteristics

Design and Manipulation in Augmented Reality of Fanuc 200iC Robot

Ibari Benaoumeur 1 , Bouzgou Kamel 1 , Ahmed-foitih Zoubir 1 , Benchikh Laredj 2 1 Laboratory of Power Systems Solar Energy and Automation L.E.P.E.S.A. USTOMB. Oran-Algeria 1 IBISC Laboratory. University of Evry France bouzgou_kamel@hotmail.fr, ibari_b@yahoo.fr, zfoitih@yahoo.fr, llaredj@gmail.com of project ARITI is the use of the AR for the teleoperation, which is a system of teleoperation allowing the control of a robot by using the techniques of AR, it constitutes an experimental system of teleoperation via the Internet. As an application using a robot to four degrees of freedom, a new version with a robot FANUC Robotics with six degrees of freedom was designed.

In this research work, we present an idea of AR system based on a multimodal user interface, this interface allow users to interact with system using multiple different modes of communication (keyboard, Cameras and joystick), which integrates the virtual robot directly with the real environment video, without building an environment model. Video images provide rich and highly contrasted information. The ARToolkits marker detection method and OpenGL are used for rendering [START_REF] Ibari | An application of augmented reality (ar) in the manipulation of fanuc 200ic robot[END_REF]. The virtual environment is established by VC++ and OpenGL.

Design of the AR System

The main goal of the system is to generate a path on virtual workpieces using a virtual robot file ( Virtual Reality Modeling Language) and computer generated models in AR environment.

Relationships between Frames

In order to calculate the position of the end-effector, ie the robot tool, the transition matrix between the camera and the robot tool (Tct, Rct) is determined using the relationships between Frames as see in figure 1. The translation Tct and the rotation Rct are given by:

The transition matrix between the camera and the robot base (Tcb and Rcb) is determined using the most popular algorithm in AR applications, the ARToolKit [START_REF] Kato | Virtual object manipulation on a table-top ar environment[END_REF].

ARToolKit includes several models of two-dimensional markers. It allows to find markers, to recognize and identify them. However, its performance in markers detection should be improved. In fact, often, it happens that markers are confused with each other or they are detected by error in foreground. ARToolKit marker is black border square surrounding a model which is compared to other pre-recorded models in ARToolKit matching template database (figure 2) [START_REF] Maidi | An evaluation of camera pose methods for an augmented reality system: Application to teaching industrial robots[END_REF]. ARToolKit gives the position of the marker in the camera coordinate system, and uses OpenGL matrix system for the position of the virtual object. ARToolKit defines different coordinate systems mainly used by the computer vision algorithm and the rendering.

Whereas The transition matrix between the robot base and the end-effector (Tbt and Rbt) is calculated by using the Geometric model of the FANUC 200iC robot .

The Direct kinematic Model

In this work we use LRMate 200iC Robot model, an industrial 6 DOF robot from Fanuc Robotics [START_REF] Fanuc Robot | 200ic, fanuc robot arc mate 50ic, mechanical unit, operator's manual[END_REF]. The direct geometric model (DGM) is the set of relations which express the position of the end-effector, i.e. operational coordinates of the robot, according * * (1)

(2)

T ct = Rcb Tbt + T cb Rct = Rcb Rbt
σ i α i d i θ i r i X i = f (θ i ) (3)
Where σ i , α i-1 , d i , θ i and r i are the Denavit-Hartenberg parameters [START_REF] Denavit | A kinematic notation for lower-pair mechanisms based on matrices[END_REF]. The link transformation matrix are given by the Denavit- Hartenberg and Table 1 [START_REF] Giovanni | A homogeneous matrix .approach to 3d kinematics and dynamics[END_REF]. The transition matrix of the LRMate 200iC Robot is calculated in [START_REF] Bouzgou | Geometric modeling and singularity of 6 dof fanuc 200ic robot[END_REF], it can be represented by the transformation matrix T k 0 .

Table 1. Modified geometric parameters D-H of the FANUC robot

T k = k i =1 T i i -1 q (i) 0
Realizing the composition of transformations of equation ( 3) we obtain:

(4)

T 6 = T 1 . T 2 . T 3 . T 4 . T 5 . T 6 0 01 2 3 4 5 T E = T 6 f 0 and f T E =

S x N x A x P x S y N y A y P y S z N z A z P z

The transformation matrix of tool frame in the end-effector frame is given by :

E = T E 0 2.

Robot Fanuc Modeling

The virtual environment includes the geometrical model of robot. the virtual geometric model is created using the real dimensions of the robot.The robot is designed by the use of the CAD environment SolidWorks [START_REF] Yuan | Precise modeling of virtual device in virtual reality based on solidworks-vrml[END_REF]. The SolidWorks source file is used to generate the geometrical model of robot. After the file converted into VRML file format, it can be read directly by the OpenGl Library. Then the robot model can be displayed in simulation scene. The virtual model of the FANUC 200iC robot is shown in figure 3.

(

) (6) 5
Figure 3. Fanuc 200iC robot modeling in virtual environment

Results and Analysis

In this work ,we use a camera Logitech Webcam C 905, The camera specifications are:

• Resolution: 16001200.

• Up to 8-megapixel photos(enhanced from Native 2-MP HD sensor.)

• FPS at full resolution: 30 • Hi-Speed USB 2.0 certifed.

The camera is calibrated with ARToolKit method "Two Step. Calibration" [START_REF] Abdullah | Camera self-calibration for the artoolkit[END_REF], and the intrinsic parameters are:

• Distortion factor (mm): (288,5 356,5 53,3 1,3456)

• Matrix intrinsic :

The User Interface

For the users, Graphic Interface is developed to interact with AR application allowing the visualization and localization of virtual objects. This interface contains 2 parts: The first one used for the application controls using the forward kinematics model by giving the robot necessary articulation to attain a desired position, The second part is to integrate the vision module to display real-time video stream from camera. It is used to show the simulation display of the virtual robot and the virtual environment.

The Interface Control

This interface provides all the necessary tools to ensure the control of the robot and to follow the position of the end-effector relative to the camera. The robot can operate according to Joint angles where it moves following its 6 rotations (θ 1 ,θ 2 ,θ 3 ,θ 4 ,θ 5 ,θ 6 ) Figure 4. The end-effector of robot can be shown in this view. The control panel is used to control the motion of the virtual robot by pressing the button on it.

The Vision System

The solidWorks software is used to generate the 3D model of the robot Fanuc 200iC, in this module the tracking process in 655,0883 0.00000 438.50000 0.00000 0.00000 759.23013 316.60000 0.00000 0.00000 0:00000 1:00000 0.0000 ( ) is used with ARToolKit and openGL, it is composed of two steps: object recognition and pose estimation. The positions and orientations of the markers ralatively to the camera are calculated, and the virtual objects are rendered in video frame as see in Figure 5.

Exemlpe

A simple miniature work cell was constructed using virtual objects as shown in Figure 6. The task is to move the robot using the intrface control to the virtual objects. As the user moves the end-effector, its tip coordinates relative to the camera's coordinate system are displayed in the interface control user for guidance.

We carried out the experiment with 20 robot positions of Tx,Ty and Tz, we computed at the same time the transforma-tion relating the robot tool to the camera. From the figure 7, we can see that the measured values are fairly constant. It is the goal for the pose estimation between the camera and the end-effector robot.

Table 2 shows that the calibration presents the best performance in term of generalization error, the numerical results proved the effectiveness of this calibration to extend overlaying on other scene elements using a single target pose computation.

(a) Translation X Compared to the manipulation application in a virtual reality environment based on VRML (Virtual Reality Modeling Language) in [START_REF] Denavit | A kinematic notation for lower-pair mechanisms based on matrices[END_REF], The results from our AR system reveal that our new interface was successful in improving task efficiency, the AR system is added to perform a certain task using the ARToolkits algorithm

Conclusion

In this paper, we presented an AR system intended to ameliorate the interaction with the real world. For this purpose,the system vision has been developed using ARToolkits marker detection method and OpenGl library. The manipulation of 200iC FANUC Robot manipulator is solved using the kinematic model of the robot. For the use we created a Graphic User Interface to follow the evolution of virtual robot in the real environment. The analysis of results shows the performance of the system in term of accuracy for camera localization using AR targets. To evaluate the performances of localization, we can added different pose estimation methods (Dhome et al [START_REF] Dhome | Determination of the attitude of 3d objects from a single perspective view[END_REF], EKF [START_REF] Chai | Three-dimensional motion and structure estimation using inertial sensors and computer vision for augmented reality[END_REF], Hybrid EKF [START_REF] Maidi | A performance study for camera pose estimation using visual marker based tracking[END_REF]).

Figure 1 .

 1 Figure 1. Relationships between Frames

Figure 4 .Figure 5 .

 45 Figure 4. Fanuc 200iC robot interface control

Figure 6 .

 6 Figure 6. Example of the fanuc robot task

Figure 7 .

 7 Figure 7. The calibration of the end-effector position (Tct)

Table 2 .

 2 The numerical results of the calibration (Tct)

		Average value	Variance Standard deviation
	Tx (mm)	181.1198 1.9960	1.4128
	Ty (mm)	181.8311 11.0411 3.3228
	Tz (mm)	816.0435 4.3297	2.0808


Paul Chapman

Giuseppe Conte

Pierre Drap

Pamela Gambogi

Frédéric Gauch

Klaus Hanke

Luc Long

Vanessa Loureiro

Odile Papini

Antonio Pascoal

J Richards

D Roussel

VENUS, Virtual ExploratioN of Underwater Sites

Keywords: (according to ACM CCS): I, 3, 7 [Computer Graphics]: Virtual Reality

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Project Summary

The VENUS project aims to provide a virtual exploration of deep underwater archaeological sites. Virtual exploration of our underwater environment will permit both experts and the general public to study interesting archaeological sites in a safe, cost-effective and pedagogical environment. Precious underwater archaeological sites, for example ship-wrecks, are continuously jeopardized by activities such as trawling that destroys the crucial surface layer of the site. The generation of a thorough and exhaustive 3D record of these wrecks consequently gains importance daily. These sites can never be experienced first hand by the majority of archaeologists or the general public: our project will provide a faithful accurate 3D immersive reconstruction of the site providing virtual access to all. Our project is composed of five objectives: Initially we will define a series of best practices and procedures for collecting and storing data in an efficient, economic and safe way from the underwater archaeological site.

Our second objective will be to survey wrecks, (at various depths), using AUVs / ROVs (Autonomous Underwater / Remotely Operated Vehicles) and various techniques of data acquisition (sonar + photogrammetry). We will then provide archaeologists with software tools for signal, data and information processing and management. The generation of these tools is our third objective and will allow the extraction of digital models and management of confidence levels of the data collected from objective two. Our fourth objective is to generate virtual reality and augmented reality tools for the immersive interaction and visualization of the models created in objective three. These tools will provide archaeologists with an improved insight into the data and the general public with simulated dives to the site. Finally we will disseminate our results via a dedicated website and publications within the field of archaeology, sea exploration, photogrammetry and virtual reality.

Project Objective

The VENUS project aims at providing scientific methodologies and technological tools for the virtual exploration of deep underwater archaeology sites. This exploration relies on the accurate construction of a virtual submarine environment representing the site. The VENUS project is a pipeline of five steps:

• Data acquisition on the site; • Data processing and storage;

• Construction of a virtual model of the site;

• Plotting archaeological artefacts and information system management;

• Dissemination of the procedures adopted for data acquisition and processing, as well as software tools and endresults of the project via traditional and internet publication.

This pipeline process will provide a fully functional virtual environment for archaeologists and the general public. Each step raises interesting research problems and technical challenges.

Motivations

The studies on ground archaeological sites, as well as shipwrecks sites, have always aimed at obtaining very accurate graphical representations of reality. As soon as appropriate tools became available, there was also a trend towards obtaining 3D representations on graphic displays. Over recent years, the field of archaeology has witnessed increasing interest in Virtual and Augmented Realities, or more generally Mixed Reality. The technologies available have paved the way for the presentation (in a realistic manner) of reconstruction assumptions of archaeological sites, either for freely exploring these assumptions in a Virtual Reality framework, or by exploring the site itself mixed with a reconstruction assumption within an Augmented Reality framework. [START_REF] Chapman P | Visualization viewpoints: Real-time visualization in the offshore industry[END_REF] Underwater archaeology is a challenging issue for Mixed Reality since the environment may not be accessible to man and is often considered hostile. As a matter of fact, deep wrecks are out of reach for divers, (beyond 60m, traditional air diving is prohibited and diving beyond that limit requires gas mixtures and significant surface facilities). [START_REF] Bass | [END_REF].

Wreck preservation in the open sea is facilitated due to several factors such as darkness, low temperature and low oxygen rate. However, deep wrecks are now jeopardized by emerging deep trawling that destroys the surface layer of the sites and thus scrambles legibility. Indeed, the twenty year old assumption that deep wrecks would be protected from trawling is not true anymore as trawls can nowadays be deployed down to depths of 1000m. Therefore, many of theses wrecks are likely to be destroyed even before they can be studied. More generally, this project is oriented toward both kind of sites, those reachable by divers and those reachable only with submersible vehicles; in both cases the project will develop methodologies and techniques for data acquisition by means of unmanned vehicles, i.e., Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs). The use of AUVs and ROVs will be a great improvement in terms of efficiency, economy and safety and will permit high quality automatic data capture. Regardless of diver accessibility, an underwater site is out of physical and practical reach for the majority of archaeologists and for the general public: our project aims at drastically changing this situation, by making the site virtually accessible to everybody. Due to the complexity of this ambitious project we will have always two points of view: the first one is to offer to archaeologists best practice, efficient, safe and cost effective tools to collect and manage archaeological data from shipwrecks; the second point, is to show what can be achieved without the constraint of a tight budget (which is generally the case in archaeology). The budget has a particularly high influence on the technological aspect of this project: especially for underwater data acquisition and virtual reality immersion. In this project it is also planned to survey a deep shipwreck in order to apply the entire VENUS platform to a shipwreck in an excellent state of conservation and to show some possible immersive VR applications. We now present the five general objectives of this project and in addition, Objective Six, "Missions in open sea" which represents the three missions necessary to collect data and test and improve the methodologies adopted.

Objective one: Underwater exploration best practices and procedures

The first aim is to define a series of procedures and best practices for collecting data in an efficient, economic and safe way on underwater archaeological sites:

• Efficiency is related to the automatic collection of enough data, of sufficient good quality to allow for a satisfactory virtual reconstruction;

• Economy relates to the possibility of using, with suitable adaptations, "off-the-shelf" equipment, that archaeologists can easily get and employ;

• Safety has to do with the reduction of risks and inconvenience for human beings when operating at great depths and/or in a hostile environment.

The key technology we propose to exploit in pursuing this objective is based on the extensive use of Uninhabited Underwater Vehicles (UUVs), both remotely and autonomous vehicles (ROVs/AUVs). UUVs, equipped with acoustic, magnetic and optical sensors of various kinds, are becoming the tools "par excellence" in underwater archaeology as the technology offers ever increasing user-friendly and economical solutions, in particular because they allow the exploration and preservation of sites located at

Objective Two: Underwater 3D survey merging optic and acoustic sensors

The second objective aims at defining practical procedures for merging optical and acoustic data, acquired during the surveying of underwater sites, into a coherent representation. [START_REF] Chapman P | Visualizing underwater environments using multi-frequency sonar[END_REF]. The project will witness the application of advanced techniques for underwater data acquisition and processing to the mapping of selected archaeological sites. An Autonomous Underwater Vehicle (AUV) and a Remotely Operated Vehicle (ROV), equipped with acoustic and vision sensors (for sonar mapping + photogrammetry), will be used to acquire relevant data in an efficient, systematic, and safe manner at the sites, including one that is out of reach for divers. [START_REF] Drap | Photogrammétrie et archéolo-gie sous-marine profonde. le cas de lâ Ȃ Źépave étrusque grand ribaud f[END_REF].

The need of geo-referencing the gathered data will be considered. Steps will be taken to endow the vehicles with navigation systems capable of providing information on their position with the accuracy that is required for the applications envisioned. [START_REF] Bechaz | Gib system: the underwater gps solution[END_REF], [START_REF] Conte | Rov depth control[END_REF]. Simplified underwater photogrammetric techniques will be developed to allow the use, after appropriate image processing, of software originally conceived for terrestrial photogrammetry. A semi-automatic surface densification technique will be developed in order to automatically generate a large amount of 3D point data based on multiple image correlation and to facilitate the time consuming photogrammetric process during the plotting phase (in the laboratory).

A convenient tool for digitalization will be developed, offering to the archaeology community a convenient way to make accurate 3D measurements based on the product of the merging operation: a 3D mesh and a set of oriented photographies in the same reference system. [DDPL05], [START_REF] Drap | Archaeological 3d modelling using digital photogrammetry and expert system. the case study of etruscan amphorae[END_REF].

We plan to make at least three experimental surveys, two in shallow water (on wrecks already studied) with archaeologists and divers and the final one in deep water to enhance the innovative aspect of this project. These surveys will be performed by conforming to the data collection guidelines specified in objective one. Measure of success: Data gathering by means of robotic tools and the integration of acoustic and photogrammetric data at a sufficiently small scale will make virtual access, exploration and study of underwater archaeological sites possible. Access to these sites are currently beyond reach for the large majority of archaeologists and, in many cases, for the general public. The demonstration that i) archaeological surveys can be automated by using underwater robots that are easy to program seamlessly, and ii) sufficient navigational accuracy can be achieved so as to meet the precision requirements compatible with the scale dimensions envisioned, will provide quantitative indicators of the results obtained and a measure of success.

Objective Three: managing and revising archaeological knowledge

Objective three aims to provide underwater archaeologists with software tools for signal, data and information processing and management. These tools will allow for the extraction of digital models and management of confidence levels of the data collected from objective two. [START_REF] Aceres | Inference services for description logics[END_REF] In the last decade, the tools developed for managing archaeological data have only focused on the geometric aspects; however, in order to integrate the archaeologist's knowledge and designing tools managing both data and knowledge, an appropriate representation of the archaeological knowledge is required. [START_REF] Baader | From tableaux to automata for description logics[END_REF]. Another important factor is the management of this knowledge. We deal with pieces of information of different nature that also originate from different sources characterized by various degrees of confidence. The methods of acquisition differ and provide data of unequal quality. Moreover, most of the time, the pieces of information are incomplete, uncertain, or inaccurate and have the potential to conflict with each other and hence may need the definition of fusion operations. Different cases arise according to the nature of the data used for fusion.

[JP00] The proposed work for this particular objective starts with a case study of archaeologist knowledge and procedures in order to define data management systems and a virtual universe.

We plan to investigate how artificial intelligence methods and tools could be used to represent the archaeological information and to perform revision and fusion according to the following points:

• to design and build specific ontology: by analyzing the relevance of the objects which can be handled in a particular archaeological application, independently of the expected data; to carefully examine their definition and the kind of implicit knowledge it carries; to compare how the data, with their quality depending on the observation process, and the recording process, can fit with the object definition; [START_REF] Seinturier | Fusion rÃl'versible : application Ãȃ l'information archÃl'ologique[END_REF] • To design which knowledge representation formalisms are suitable to represent semi-structured information, paying attention to the specificity of the archaeological data: 3D spatial reference, topological relationships and spatial correlations, specific expert knowledge and specific ontology;

• To select which fusion strategy is best suited to the representation formalism chosen according to the nature of the situation, and the availability of relevant data, and their degree of quality and reliability. For instance, revision is relevant if one data set is strongly reliable;

• To assess the tractability of the fusion: How can the nature of the data help in defining tractable classes of problems for merging? How should existing general algorithms be adapted for merging? How can heuristics be defined that stem from the specificity of the nature of data to speed up algorithms?

• To define reversible fusion operations. This stems from the fact that, in general, the existing fusion operations developed in the field of artificial intelligence are not reversible; this is in contrast with the fact that reversibility is mandatory when facing real scale applications.

[NS97], [BBJ * 05].

Measure of success:

The first draft of the "archaeological ontology" description, in Task 3.2, must be able to encompass most of the various features, properties, processes or behaviours collected during the first months of discussion between the computer scientists and the archaeologists of the consortium. A "typical" archaeological campaign will be summarized and described at the beginning of the project, as a baseline test bed for the further representation formalisms.

A confrontation of this baseline to external expert will permit to measure its relevance, and therefore, to measure the relevance of the representation formalisms against it.

Objective four: Mixed reality modelling

Archaeologists need to explore and make an inventory of deep wreck sites unreachable by divers as these sites may be jeopardized by deep trawling in the next few years. The digital preservation aspect should also be addressed by this objective. [START_REF] Chapman | Constructing immersive virtual environments for the visualization of underwater archaeological sites[END_REF].

The main goal of objective four is therefore to immerse archaeologists inside a virtual universe depicting a reconstructed archaeological site, for example a shipwreck, and allow them to work on this site as naturally as possible. The digital model generated in the previous section will then be used, with the help of virtual reality and mixed reality, for constructing immersive, virtual environments that enable archaeologists and general public to experience an accurate and fully immersive visualization of the site. Archaeology is a challenging issue for Virtual and Augmented Reality (and more generally Mixed Reality) as these techniques can offer a realistic rendering of reconstruction assumptions either within the framework of Virtual Reality for exploring theses assumptions or within the site itself by using Augmented Reality. [START_REF] Acevedo | Archaeological data visualization in vr: Analysis of lamp finds at the great temple of petra, a case study[END_REF], [ATP * 04].

Up to now, virtual reality has been used in archaeology only in the context of assumptions visualization which is already suitable for general public, the goal here is therefore to produce an immersive interface which could enable archaeologists to study a site reconstruction using the surveys, but also to generate reconstruction assumptions directly in virtual immersion. This project tackles the concept of augmented reality. More specifically, within the framework of underwater archaeology, the main difficulty of this type of exploration lies in the expensive deployment of heavy equipment to enable a team of archaeologists to explore, make an inventory and chart an underwater site before expressing the slightest assumption about the nature of a wreck or its cargo. Virtual Reality would also provide an overall view of a site and allow data exploration in a contextual way. Novel interaction and visualization techniques will be developed in order to meet the requirements of this objective. [LSB * 04] Completing this objective should offer the opportunity for archaeologists to explore a digital mock-up of a wreck site built upon the digital models mentioned in the previous objective. This exploration should be immersive in order to maintain the same framework as real dives (without any risk or constraints), but should also offer new opportunities to archaeologists. Ultimately this virtual environment should offer the same tools as during real dives (such as navigation, measures, annotations, etc.). This environment should also provide new tools for creating reconstructions assumptions of various artefacts discovered on the wreck site in order to enrich the database created from the site. Such a virtual exploration should also be performed by sev-eral researchers at the same time, by offering Collaborative Work facilities. [START_REF] Pansiot | New perspectives on ancient landscapes: A case study of the foulness valley[END_REF]. In addition, Collaborative work raises the problem related to hardware requirement for each of the researchers. Some of them might have access to high level hardware such as immersive platforms, whereas others may only have access to a simple desktop computer. The key idea would therefore be to build several demonstrators using the same engine:

• An immersive demonstrator using purely immersive devices such a VR helmet and data gloves for interacting with the site;

• A semi-immersive demonstrator using stereo large scale screen and 3D joystick (or data gloves) to interact with the site;

• And finally a low end platform using only the standard devices of a desktop computer. Each of these various platforms may require specific interaction models, but the goal is to keep the same functionalities on the different platforms. Measure of success: All the functionalities developed in these various demonstrators are based on a case study of archaeologists working modalities in order to define interaction modalities and available tasks within the virtual universe connected to the underlying archaeological database. This is why the last task of this objective is devoted to the demonstrator evaluation on the basis of the previous case study by archaeologists.

Objective five: dissemination

The ultimate objective of the project is scientific dissemination in the fields of archaeology, marine exploration, photogrammetry and virtual reality through the publication of best practice recommendations, a set of open source software tools, and online preservation of an exemplar archive. Dissemination will be organized in four stages:

1. to refine digital preservation techniques so that they can be readily applied to the unusual range of data formats captured during underwater archaeological investigation;

2. to ensure that partners within the consortium learn about digital preservation; 3. to promote best practice in digital preservation through publication of a short practical guide based on this shared experience; 4. to adopt and trial these best practice techniques within the project.

• The first objective will be achieved through completion of a desk-based study, supported by a number of data audits carried out at the start of the project. The principal investigator for this work package will be a digital preservation specialist who will spend time with active fieldworkers, surveying the techniques that they use and establishing the most appropriate methods for digital preservation associated with these file formats. This preservation path, based on an implementation of the OAIS reference model for digital preservation and the PREMIS metadata model, will provide a preservation manual for long term curation of digital data;

• The second objective will be met in part through the data audit process described above whereby the principal investigator will discuss issues of digital preservation informally with project partners, and report back to them the results of that research. The objective will be mainly met through the organization of a twoday project workshop on digital preservation to which senior members of each partner organization will be invited. This workshop will be managed by the Archaeology Data Service and will draw from existing curricula that the UoY-ADS offer on digital preservation;

• Participants at this workshop will be invited to form an editorial committee and nominate a peer review group who will assist the principal investigator in the writing of a short guide to good practice for underwater archaeology. This guide will join the existing guides in the much praised "Guide to Good Practice" Series (http://ads.ahds.ac.uk/project/goodguides/ g2gp.html). It will be distributed in print to all partners and key stakeholders, as well as being made available for free online for all readers;

• By the end of the project, partners will not only have adapted their tools to ensure digital preservation, but they will be in a position to apply that best practice in real life situations. An exemplar archive will be lodged with the UoY-ADS and preserved into perpetuity. This scholarly archive will also be disseminated online alongside other research archives maintained by the UoY-ADS. It will therefore be available for reuse and interrogation by all visitors to the UoY-ADS site and will stand as a worked example of good practice to demonstrate the project outcomes to the wider profession.

To avoid duplication of efforts in a rapidly changing field a "technology watch" will be maintained, in close collaboration with the EPOCH 6th Framework Network of Excellence. The project team will present reports on its activities at appropriate technology and humanities conferences. Finally, the 3D and virtual reality aspects of the project make it appropriate for wider public dissemination, which will be most effectively achieved via the Internet. Measure of success. The success of the workshop and subsequent Guide to Good Practice will be measurable by the extent to which the standards are endorsed and taken up by the sub-discipline of underwater archaeology. The number of Guides sold, and the number of visits to the on-line publication provides one measure of the visibility of the Guide. Previous Guides in the series, such as that for GIS, have sold out and have had to be re-printed. They have been widely and positively reviewed, and have been adopted by national and international standards bodies, and cited in undergraduate and postgraduate reading lists. The successful dissemination of the exemplar archive is also measurable, to same extent, by the number of visits to the site. This will be monitored by UoY-ADS standardized benchmarks for site visits, and can be compared against other online resources. The web site can be promoted through the UoY-ADS outreach programme and related publications, and qualitative feedback can also be collected.

The case study: The Mediterranean archaeological context

If the "first civilizations" were born in the Eastern Mediterranean from the Near East, this sea has been empty for millennia before becoming the main stage of shocks, conflicts and discovery of civilizations. From Marathon to Lépante, from Punic Wars to crusades, the Mediterranean space/area is full of historical reappearances and lights coming from the dead world. Beyond its current political divisions, it is divided into three cultural communities: Christian, Islamic and the Greek orthodox universe, that is an affiliation more or less linked to Rome, Carthage and Constantinople. One particular period interests us here. In ancient times, by imposing its will and political unification on the Mediterranean world, Rome did not erase the cultural differences but built its internal sea as a gigantic trading crossroad where oils and brines from Spain would cross with corn from Egypt, wines from Algeria and Rhodes, slaves from Nubia, ceramics from Gallia, marbles from Greece and bronzes from Italy. [ANS04], During the Roman Empire, among all these various products coming from the Mediterranean, a great quantity of Portuguese amphorae were sailing from Hercules Pillars to the Rhine frontiers and carried the famous Lusitanian fish sauce. Today underwater archaeology opens, from the deep past of the sea, a direct route to these shipwrecks, complex works that testify the wealth and the diversity of exchanges and of men. In this project, the methods of excavation, the systems of data capture and of the data's visualization and the coop-eration of archaeologists and multinational specialists, will enable historical restoration of these trades and to continue these faraway sailing journeys, brutally interrupted. We plan to make three experimentations in different archaeological contexts and under different sea conditions. The first one, leaded by MIBAC-SBAT in Tuscany will focuss on a very interesting archaeological site near the Pianosa island, and will be considered as a shallow water (-36m) mission.

The wreck site consists of a large area of mixed amphorae probably due to more than one wreck superposition. The sec-

Conclusion and future work

This paper has described a new project that focuses on the virtual exploration of underwater sites. Our international research group has expertise in a wide range of areas including archaeology and underwater exploration, marine robotics and instrumentation, knowledge representation and photogrammetry, virtual reality and digital data preservation. Research conducted throughout this project will contribute to all these disciplines. This European Community funded project began on the 1st July 2006 and will last for 36 months. A dedicated project website is available here: http://www.VENUS-project.eu/

Figure 1 :

 1 Figure 1: VENUS project architecture diagram.

Figure 2 :

 2 Figure 2: ROV and Diver, ISME.

Figure 3 :

 3 Figure 3: Autonomous Underwater Vehicle (AUV) of IST.

Figure 4 :

 4 Figure 4: Synoptic schema of integrated Virtual Reality in VENUS.

Figure 5 :

 5 Figure 5: A view of the site close to Pianosa, Photo Alessandro Parotti, 2000

Figure 6 :

 6 Figure 6: The "Calanque de Port Miou" photograph from ROV of COMEX.

Figure 7 :

 7 Figure 7: The submarine REMORA 2000 of COMEX during rhe Grand Ribaud F excavation, 2000. Photo Frederic Bassemayousse.

Acknowledgements

Work partially supported by the European Community under project VENUS (Contract IST-034924) of the "Information Society Technologies (IST) programme of the 6th FP for RTD". The authors are solely responsible for the content of this paper. It does not represent the opinion of the European Community, and the European Community is not responsible for any use that might be made of data appearing therein.