Franc ¸ois Lecellier Hermine Chatoux

Christine Fernandez-Maloigne

Comparative Study of Descriptors with Dense Key points

A great deal of features detectors and descriptors are proposed every years for several computer vision applications. In this paper, we concentrate on dense detector applied to different descriptors. Eight descriptors are compared, three from gradient based family (SIFT, SURF, DAISY), others from binary category (BRIEF, ORB, BRISK, FREAK and LATCH). These descriptors are created and defined with certain invariance properties. We want to verify their invariances with various geometric and photometric transformations, varying one at a time. Deformations are computed from an original image. Descriptors are tested on five transformations: scale, rotation, viewpoint, illumination plus reflection. Overall, descriptors display the right invariances. This paper's objective is to establish a reproducible protocol to test descriptors invariances.

I. INTRODUCTION

Over the last two decades, several descriptors were created with different purposes: wide base line matching [START_REF] Schaffalitzky | Multi-view matching for unordered image sets, or how do i organize my holiday snaps?[END_REF], [START_REF] Tola | DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo[END_REF], object recognition [START_REF] Alahi | Freak: Fast retina keypoint[END_REF], [START_REF] Levi | LATCH: Learned Arrangements of Three Patch Codes[END_REF], image retrieval [START_REF] Ke | Pca-sift: A more distinctive representation for local image descriptors[END_REF], [START_REF] Calonder | Brief: Binary robust independent elementary features[END_REF], [START_REF] Rublee | ORB: an efficient alternative to SIFT or SURF[END_REF], texture recognition [START_REF] Lazebnik | A sparse texture representation using affine-invariant regions[END_REF], [START_REF] Florindo | Locally enhancing fractal descriptors by using the non-additive entropy[END_REF] or shape matching [START_REF] Belongie | Matching shapes[END_REF], [START_REF] Ma | Sketch retrieval via local dense stroke features[END_REF].

To sort all these new techniques, Mikolajczyk et al. proposed a comparison method for detectors and descriptors with [START_REF] Mikolajczyk | A Comparison of Affine Region Detectors[END_REF], [START_REF] Mikolajczyk | A performance evaluation of local descriptors[END_REF]. In these papers, authors introduce a new dataset with a human created ground truth. To compare descriptors [START_REF] Mikolajczyk | A performance evaluation of local descriptors[END_REF], they count the number of correct matches over the total number of matches. One of the tested descriptors, SIFT developed by Lowe in [START_REF] Lowe | Object recognition from local scale-invariant features[END_REF], [START_REF]Distinctive image features from scale-invariant keypoints[END_REF], is based on gradient location and orientation histograms. In spite of its efficiency, SIFT remains relatively slow to compute, so, attempts were made to improve it, such as Ke and Sukthankar with PCA-SIFT [START_REF] Ke | Pca-sift: A more distinctive representation for local image descriptors[END_REF] which select the descriptor's strongest component. Another improvement, GLOH, introduced in [START_REF] Mikolajczyk | A performance evaluation of local descriptors[END_REF], uses a log polar location instead of a linear one before PCA. The other descriptors presented in this paper were: invariant moments [START_REF] Mindru | Moment invariants for recognition under changing viewpoint and illumination[END_REF], shape context [START_REF] Belongie | Matching shapes[END_REF], spin images [START_REF] Lazebnik | A sparse texture representation using affine-invariant regions[END_REF], steerable filters [START_REF] Freeman | The design and use of steerable filters[END_REF], differential invariant [START_REF] Koenderink | Representation of local geometry in the visual system[END_REF], and complex filters [START_REF] Schaffalitzky | Multi-view matching for unordered image sets, or how do i organize my holiday snaps?[END_REF].

Misksik and Mikolajczyk updated these articles in [START_REF] Miksik | Evaluation of local detectors and descriptors for fast feature matching[END_REF], with others detectors and descriptors tested together to determine detector/descriptor dependences. They compare mostly binary descriptors that will be detailed in the next section [START_REF] Calonder | Brief: Binary robust independent elementary features[END_REF], [START_REF] Rublee | ORB: an efficient alternative to SIFT or SURF[END_REF], [START_REF] Leutenegger | BRISK: Binary robust invariant scalable keypoints[END_REF]. For matching results, the best descriptors are LIOP, MRRID and MROGH proposed by Fan et al. in [START_REF] Wang | Local intensity order pattern for feature description[END_REF], [START_REF] Fan | Rotationally invariant descriptors using intensity order pooling[END_REF]. These descriptors are rotation invariant through a watershed on the key point region.

Other comparison articles dedicated to a specified task were published as [START_REF] Gauglitz | Evaluation of Interest Point Detectors and Feature Descriptors for Visual Tracking[END_REF] specialised in visual tracking, a real time task. Descriptors compared are SIFT, SURF [START_REF] Bay | Surf: Speeded up robust features[END_REF] (introduced by Bay et al. relying upon sums of Haar wavelet), random trees [START_REF] Lepetit | Keypoint recognition using randomized trees[END_REF] and ferns [START_REF] Ozuysal | Fast Keypoint Recognition Using Random Ferns[END_REF]. The last two descriptors belong to the binary descriptors family inspired from LBP, but binary comparisons are done sparsely. Difference between trees and ferns is a classification improving ferns' time efficiency. These descriptors are tested with various detectors and algorithms on several databases.

Mandal in [START_REF] Mandal | Performance evaluation of local descriptors and distance measures on benchmarks and firstperson-view videos for face identification[END_REF] compares six descriptors applied to face recognition with different distances. When applied to face identification, features are more important than distance, contrary to face recognition. Mainly, the best descriptor for facial application is BSIF created by Kannala and Rahtu in [START_REF] Kannala | Bsif: Binarized statistical image features[END_REF].

For class matching, Hietanen in [START_REF] Hietanen | A comparison of feature detectors and descriptors for object class matching[END_REF] compares several binary descriptors and SIFT with different detectors, including a dense grid. SIFT preformed better than other descriptors and dense grid responds very well. This result leads us to test dense detector for different descriptors. Compared to others methods, our approach is not based on manual key points pairing. Thus descriptors will be applied on computationally transformed images. An image from database leads to several transformed images. Hence every pixel's correspondence to original image is calculable.

The next section is dedicated to a detailed presentation of compared descriptors. The third one lays out the experimental protocol, images transformations and measuring results formulae. Fourth section introduces results. The last part is reserved for conclusion, perspective and acknowledgement.

II. DESCRIPTORS

Dense grid is chosen as detector, therefore we are only interested in the descriptor part of all detector/descriptors combination. We will present in this section all descriptors we intend to compare. Table I summarizes invariance assumed by each descriptors.

A. Gradient based descriptors 1) SIFT: As stated in introduction, SIFT was introduced by Lowe et al. in [START_REF] Lowe | Object recognition from local scale-invariant features[END_REF] and enhanced for scale invariance few years later [START_REF]Distinctive image features from scale-invariant keypoints[END_REF]. During detection, SIFT estimates key points' location, scale and orientation and uses these data to create the descriptor. SIFT, founded on a human vision behaviour, favours gradients and orientations to slight shift location in order to recognize objects. This method calculates gradients 3) DAISY: To the best of our knowledge, DAISY, created by Tola in [START_REF] Tola | DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo[END_REF], is the only descriptor originally designed for dense utilisation. Consisting in histogram of oriented gradients, measures are made a around key point with different orientations and radii. Then, different-orientation gradients are estimated and positives directions are kept. The descriptor is designed as a flower with overlapping areas. Each area's centre is convolved with a Gaussian kernel proportional to key point's distance. Hence, series of smoothed-gradient histograms are estimated. Each histogram is normalized independently to ensure lightness invariance.

The three previous descriptors use Euclidean distance to measure the difference between two key points. At the same time, another faster method appeared: binary descriptors.

B. Binary descriptors

The principle is to compare two pixels leading to a boolean response. These comparison are made locally around a key point. The five following descriptors rely on this method.

1) BRIEF: Binary Robust Independent Elementary Features presented by Calonder et al. in [START_REF] Calonder | Brief: Binary robust independent elementary features[END_REF], has no associated detector. Based on random difference of pixels selected around a key point, description is made in two steps. First, image is blurred. Next, pixel's pairs (x, y) are randomly picked around a key point. Selection follows Gaussian distribution centred on the key point. Each pair of pixels are compared with:

b = 1 if p(x) < p(y) 0 else . (1)
The pixels difference has no influence on the descriptor calculation which ensures the invariance to contrast and brightness.

However, this descriptor is sensitive to rotation.

2) ORB: Oriented fast and Rotated Brief was introduced by Rublee in [START_REF] Rublee | ORB: an efficient alternative to SIFT or SURF[END_REF]. As the name indicates, the descriptor principle is the same as BRIEF but is oriented with the angle calculated in the FAST detection process. In our case, orientation is given by the transformation. To take orientation into account, a rotation matrix is applied to all pixel's pairs coordinates before computing binary comparison. To improve discrimination, an algorithm trained pairs to compare.

3) BRISK: proposed by Leutenegger in [START_REF] Leutenegger | BRISK: Binary robust invariant scalable keypoints[END_REF], Binary Robust Invariant Scalable Key points need an information on scale prior to description. Contrary to BRIEF, all pairs are fixed and uniformly extracted around key points. Pixel's smoothing size varies with the distance to the key point. The set of pairs is divided in two sets, L and S, corresponding to large and small distances between two elements of a pair, respectively. L is used to determine orientation of key point:

g x g y = 1 L (pi,pj )∈L I(p i , σ i ) -I(p j , σ j ) p i -p j 2 x i -x j y i -y j , (2)
where L is the cardinal of L, p a pixel, (x, y) its coordinates and g the resulting vector of equation. From g, orientation is extracted. S set elements location is modified by the angle estimated: S θ . The elements of S θ are compared to one another to obtain the descriptor.

4) FREAK: presented by Alahi in [START_REF] Alahi | Freak: Fast retina keypoint[END_REF], Fast Retina Key point relies upon human retina and analysis perception. Similar to retina, the surrounding of the key point is separated in three areas, fovea, para foveal and peri foveal. Ganglion cells' number diminish with the distance to the centre, so applied Gaussian depends on area observed (Standard deviation grows with the distance). Pixels are uniformly selected on these areas. Orientation is computed with the same formula as BRISK but fewer pairs are picked and are chosen symmetrically. Results concurs with human processing vision, coarse to fine analysis.

5) LATCH:

Learned Arrangements of Three Patch Codes is one of the latest descriptor published, proposed by Levi and Hassner in [START_REF] Levi | LATCH: Learned Arrangements of Three Patch Codes[END_REF]. Gaussian kernels causes a loss of information for most binary descriptors. To solve the problem, LATCH uses a "3×3 patches" for its binary comparison. Three patches are selected, one is classified as anchor, and Frobenius distances between anchor and the other two pixels are calculated. By comparing both distances, we obtain a binary element. The best patches triplets are learned, then selected to avoid high correlation.

To be able to compare descriptors, once the distances are calculated, we normalized the results using infinite norm for all binary descriptors. As they are constituted of binary elements, the distance to use is Hamming's counting different bits' numbers between two key points. All descriptors have been introduced, now we will detailed the transformations applied to initial image.

III. EXPERIMENTAL PROTOCOL

To estimate invariance of the previously detailed descriptors, all images from the photo CV KODAK base (768 × 512 sized images) are used. Descriptors used are only lightness, so photos are immediately transformed in grey level images. All images are positioned at the center of a squared image with zero-padding to keep it whole when it rotates. Descriptors are computed with OpenCV: SIFT, SURF, DAISY, BRIEF, ORB, BRISK, FREAK and LATCH using recommended parameters values except for key point size when necessary. Five types of transformations are applied: scale, rotation, shearing, lighting and reflection. The proposed transformations are recapitulated in Table II.

A. Scale

Image's size is divided by 1.5 to 5 with a step of 0.5. We do not perform image extension since this may lead to interpolation of pixels value. In this case, key points size is adapted accordingly on the transformed image.

B. Rotation

For the rotation transformation, we apply the following matrix for every angle between 10 • and 350 • with a step of 10 • :

x θ y θ = cos(θ) sin(θ) -sin(θ) cos(θ)

x y , (3)
where (x θ , y θ ) and (x, y) are pixels coordinates and θ is the rotation angle. Origin coordinate is in the image's centre. To assess angle estimation impact on descriptor invariance, the measure is performed twice, one without any information on angle, the second with the known angle.

C. Viewpoint

Shearing projects the image as if rotated toward depth axis, with either left or bottom side fixed. The projection observer is set at the image's centre. As for the rotation the step is 10 • from 10 • to 80 • . Transformations matrices are for vertical shearing:

  x θ y θ p θ   =   D cos(θ) 0 DCol(cos(θ)-1) 2 0 D 0 sin(θ) 0 D + Col sin(θ) 2     x y 1   , (4)
and for horizontal:

  x θ y θ p θ     D 0 0 0 D cos(θ) DRow(cos(θ)-1) 2 sin(θ) 0 D + Row sin(θ) 2     x y 1   , (5)
where p θ is the depth of the resulting pixel. 3D coordinates are then projected to obtain a 2D image. θ stands for the angle toward the z axis, Col and Row the column and row of the original image size respectively, and D is the observer viewing distance. Here D is chosen for c to be the usual 80 • of human vision. Note that matrices (4) and ( 5), as for rotation, is calculated for origin coordinate at the image's centre. As for scaling, key points are adapted to forbid overlapping. One may consider a rotation on key points but as it is not uniformed, angle is left unchanged.

D. Illumination

We want to measure invariance to illumination changes, so modifications are applied to pixels intensity and not on their position. We chose to make a linear change with saturation when intensity exceeds maximum. This transformation represents well what would happen if camera exposition is incorrectly set while taking pictures. Lightness coefficient varies from 0.2 to 3 with a 0.2 step. New grey level follows:

G = αG if αG < 1 1 else, ( 6
)
where G is a grey level and α a linear coefficient to apply to the lightness transformation.

E. Reflection

This case is a bit different since we do not want this transformation to be invariant for our descriptors. One can easily see that horizontal or vertical flip may lead to some modification in the image interpretation (e.g. 2 and 5).

Cumulating these five alterations, 76 transformations are obtained. We choose not to measure recognition rate for blur and jpeg compression. In our field of experimentation and with digital-archiving improvement, we can assume our pictures to be necessarily sharp and uncompressed.

The next operation consists in estimating key points position for the transformed images. To perform this task, we densely create an x pixels sized grid on image. At each transformation, the new position of each key point is measured with the corresponding transformation matrix. Therefore, theoretically we should obtain the same descriptors for all transformations. Once key points are estimated for all transformations, descriptors are computed for each transformed image with the appropriated set of key points.

IV. RESULTS AND DISCUSSION

Each transformation is matched to the original image using nearest neighbour algorithm. Since no key points detection is done, the number of correspondences is identical to the number of matches. We then compute matching rate:

r = | T | | C | , (7)
where T corresponds to the set of correct matches and C is the set of possible correspondences.

In this experiment we also test different key points size to assess the more efficient one for invariance. Test results are only presented on scale invariance, but they lead to the same conclusion for others invariances.

A. Scale invariance

First experiment realised on scale invariance was to estimate key points size influence. Descriptors tendencies are summarized in the three graphics of Figure 1. One can easily see that for all descriptors, key points size influences results. The larger it is, the more information is stacked in descriptors, hence a better matching rate. Only three descriptors are represented on Figure 1 since all tendencies are summarized with these. In Figure 1a, SIFT and SURF results are similar and decrease regularly with key point size. BRIEF, ORB, DAISY and LATCH (Fig. 1b) decrease very quickly regardless the size. It is consistent with their non invariance to scale presented in Table I. Figure 1c displays BRISK's results, FREAK's are very close. For 10 pixels key points, results collapse and no correct match is made for a scaling superior to 2. This size is smaller than regions used to estimate key points inducing information overlap in the descriptor. However, for 50 and 100 pixels, the matching is almost perfect.

For the rest of the experiment we have used a 50 pixels diameter key point, to be consistent with the previous results. On Figure 3a, we plot matching rates of all descriptors. We confirm scale invariance for SIFT, BRISK and FREAK with advantage for the latter ones. Others descriptors are strongly influenced by scale.

B. Rotation invariance

Results on rotation invariance are summarised in Figure 3b. The matching rates are computed with an a priori on rotation's angle. BRIEF and DAISY are the worst invariant descriptors, allowing no correct match for angles between 50 • and 300 • . SURF and FREAK measure the angle in the descriptor chain and not in the detector one, so pre-giving an angle has no effect on their results. On Figure 2, we display results with and without a priori for SIFT, ORB and LATCH since on these case, orientation is computed on the detection phase. We can clearly see that for SIFT, ORB and LATCH the descriptor needs information about the angle on rotation to keep their rotation invariance. Thus, when computing these descriptors, one must always provide an orientation information to ensure a correct description. This brought two issues: how to estimate a good orientation angle and does orientation need to be measured within the detector or descriptor part. In [START_REF] Wang | Local intensity order pattern for feature description[END_REF], [START_REF] Fan | Rotationally invariant descriptors using intensity order pooling[END_REF], Fan et al. could provide an answer as their descriptors are free from angle by computing watershed on key point region.

For almost all descriptors, some angle rotation has an effect on distance. Gradients directions are computed in histogram and Haar wavelets are summed, so SIFT and SURF are invariant to 90 • rotation. For ORB, approximation error is caused by precomputed rotated pairs. Results are repetitive every 90 • . We can see a symmetric period. The largest distance is reached Comparison results on scale invariance with different key points size. All descriptors have been tested for different sizes of key points. All descriptors are sensitive to key points size. A larger patch, contains more information. Scale invariance diminish with the key point size in 1a. Despite large key point giving better results, 1b shows this descriptor is very sensitive to scale. In 1c, the descriptor looses its scale invariance when key point is too small. every 90 • plus 45 • (for rotation invariant descriptors). All descriptors are consistent with results expected by Table I.

C. Viewpoint invariance

Vertical and Horizontal shear results are summarised in Figure 3c and 3d respectively and display similar results. The best results are obtained by FREAK which keeps its rate above 80 % until 60 • . SIFT, BRISK and SURF are slightly worse but remain above 60 % on almost all angles. Since BRISK was not set to viewpoint invariance, it yields surprisingly good results almost as good as SIFT. The four other ones are decreasing rapidly and do not present shear invariance.

D. Illumination invariance

Results on lightness are recapitulated in Figure 3e. Apart from DAISY which will be discussed later, all other descriptors display a decreasing matching rate as lightness coefficient pulls away from 1. Our method to change illumination leads to saturate areas when coefficient is larger than 1 which explain why curves decrease rapidly over 2. However, when change remains relatively small, all descriptors maintain matching rate higher than 80 %. DAISY gives poor results less than 60 % correct matching if factor is under 0.5 or over 1.5. Tola, in [START_REF] Tola | DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo[END_REF], mentioned that normalisation of each histogram separately could induce strange effects for other application than baseline matching.

E. Reflection invariance

DAISY and LATCH's matching rate differ a lot between horizontal and vertical flip. 20 % and 29 % for DAISY's horizontal and vertical respectively, 2 % and 6% for LATCH. Apart from DAISY and LATCH, all descriptors responds similarly to both reflections. SURF rate is slightly above 20 %. Both BRISK and FREAK are around 9 % and the last three are below. Globally all descriptors match poorly to reflection which was our expectation.

V. CONCLUSION

To summarize, descriptors respond as excepted to transformations. Their invariance capacities match Table I information. Nevertheless, each descriptor has a range of efficiency. The matching rate decreases outside the interval. Computation time responds as expected: gradient based descriptors are slower than binary ones. There is an inversion, SURF is faster than LATCH which measures several Frobenius norms, hence its slowness. Overall, flip transformations give the expected results and images do not match.

In this paper we present only key points with 50 pixels size but tests have been conducted in different sizes and may be enhanced to a complete study on key point size effect on matching rate. We also intend to conduct an experiment on the grid size to assess influence of overlapping in key points description. Our initial data set is composed of coloured images. Therefore, an experiment on colour descriptor invariances is currently in progress with all these transformations and coloured ones. Tests were made as if manipulating the cover of a book. To complete the analysis, a new set of data will be realized. A real 3D scene is created and will be shot in the same conditions as the previously described transformations. It will allow to obtain true occlusions and deformations from 3D observation.

  Fig.1. Comparison results on scale invariance with different key points size. All descriptors have been tested for different sizes of key points. All descriptors are sensitive to key points size. A larger patch, contains more information. Scale invariance diminish with the key point size in 1a. Despite large key point giving better results, 1b shows this descriptor is very sensitive to scale. In 1c, the descriptor looses its scale invariance when key point is too small.

Fig. 2 .

 2 Fig.2. Comparison results on rotation invariance with and without a priori. When the correct orientation is provided, matching rate is almost perfect for every angle. If this is not set, angles between 40 • and 310 • do not match any point.

Fig. 3 .

 3 Fig. 3. Comparison results. All descriptors results have been regrouped per transformation. There is scale changes in 3a, then orientation shift in 3b. The following (3d and 3c) displays viewpoint change. Last, 3e shows lightness variation.

ACKNOWLEDGEMENT

We would like to thank the Aquitaine-Limousin-Poitou-Charentes Region and European Union for supporting the project ESALHI which is financially supported by FEDER.


Guilhem Grimaud
email: guilhem.grimaud@ensam.eu

Nicolas Perry

Bertrand Laratte

Reducing Environmental Impacts of Aluminium Cable Recycling Process with Life Cycle Assessment Methodology

Keywords: Life Cycle Assessment, Recycling, Aluminium, metal refining, Process optimisation

Life cycle impact of European generic primary and secondary aluminium are well defined. However specific recycling processes are not available in literature. In this study, the environmental assessment of cable recycling processing is examined using the Life Cycle Assessment (LCA) methodology. The data come from a recycling plant (MTB Recycling) in France. MTB process relies only on mechanical separation and optical sorting processes on shredder cables. The LCA results demonstrate huge environmental benefits for aluminium recycled in comparison with primary aluminium.

This work was done firstly to document specific environmental impact of MTB recycling process in comparison with traditional aluminium recycling smelting. Secondly, to provide an environmental overview of the process steps in order to reduce the environmental impact of this recycling pathway. Using the identified hotspots from the LCA for the MTB specific recycling process for aluminium cables, we were able to provide help for designers to carry on reducing the environmental impact of the technologies used during the recycling pathway. This paper focuses on LCA results and implementations on the process for reducing the environmental impact.

Introduction

Today, the life cycle environmental assessment of European generic primary and secondary aluminium are well defined through the work of the European Aluminium Association (EAA) [START_REF]European Aluminium association[END_REF]. However specific recycling processes are not available in literature. In this study, the environmental assessment of cable recycling processing is examined. The data come from a recycling plant (MTB Recycling) in France. The specific and innovative process was developed by MTB Recycling engineers and is sold as a process solution in different countries. The specificity of MTB process relies on the absence of fusion for metal refining. Nevertheless, it reaches standard aluminium purity up to 99.6%. This performance is obtained using only mechanical separation and optical sorting processes on shredder cables. Environmental impact assessment is done using ILCD Handbook recommendations [START_REF]Int. Ref. Life Cycle Data Syst. Handb. - First[END_REF]. Three systems are compared: European primary aluminium data from EAA aggregated in Ecoinvent 3.1, secondary aluminium from European remelter data from EAA aggregated in Ecoinvent 3.1 and MTB cable recycling process.

The European demand for aluminium has been growing over the past few decades at a rate of 2.4% per annum [START_REF]Organisation of European aluminium Refineers and Remelters (OEA)[END_REF]. This increase is mainly supported by the rise of recycling which growth was in the same time about 5% per annum [START_REF]Organisation of European aluminium Refineers and Remelters (OEA)[END_REF]. The abundance and the versatility of aluminium in various applications have made it one of the top solutions for lightweight metal strategy in various industries [START_REF] Liu | [END_REF]. In the cable industry, substitute copper for aluminium can considerably reduce the linear weight without degrading too much the electrical properties [START_REF] Bruzek | Eco-Friendly Innovation in Electricity Transmission and Distribution Networks[END_REF]. To obtain optimal electrical conductivity, aluminium use for cables has purity above 99.7% [START_REF] Goodwin | Springer Handb. Condens. Matter Mater. Data[END_REF]. Because secondary aluminium does not meet the quality requirements for aluminium cables manufacturers; only primary aluminium is used for the aluminium cables supply chain. Nevertheless, improvement in recycling could help reach quality targets, by using new sorting technologies.

Aluminium properties are not deteriorated by recycling. However, in most cases aluminium parts are mixed together at the end of life step without considering their provenance and use. According to this, the 7 series of aluminium are mixed together in waste treatment plant. All aluminium series do not have the same purity and alloying elements pollute aluminium. When aluminium series are mixed together, the cost-effective solution for refining use furnaces. As the metal is molten, the separation is done by using the difference of density and buoyancy (decantation methods, centrifugation, filtration, flotation, etc.). [START_REF] Rombach | Resour. Anal. Met. Raw Mater., Matter and Materials[END_REF] Despite the technology optimisation, a fraction of metal is un-recyclable [START_REF] Development | Global Aluminium Recycling: A Cornerstone of Sustainable Development[END_REF]. Some alloying elements are lost in the process [START_REF] Paraskevas | [END_REF] and this results in a drop of quality which is akin to a downcycling [10]. The solution lies in a better separation of aluminium series upstream from the recycling chain. This strategy should enable products to be guided through the best recycling path and maintain the quality of alloys. The cable is composed of an aluminium core cable (a), covered with a polymer thick layer (b) as illustrated in Fig. 1. Additional metallic materials (c) are coaxial integrated in order to finish the definition of this composite product. These cables are manufactured by extruding together all the materials that compose it.

Aluminium in cables represents between 35 and 55% of the total weight. Other metals are mainly steel, lead, copper, zinc. The variety of plastics contained in the sheath is even stronger than for metals: silicone rubber, Polyethylene (PE), crosslinking PE (xPE), Polychloroprene (PCP), vulcanised rubber, Ethylene Vinyl Acetate (EVA), EthylenePropylene Rubber (EPR), flexible PVC, etc. [START_REF]Union technique de l'électricité (UTE), UTE C30-202 -Insulated Cables and Flexible Cords -System for Cable Designation[END_REF] The Table 1 shows the mass proportion of materials contained in cables. Mass proportions are extracted from MTB monitoring data of cables recycled at the plant between 2011 and 2014. Aluminium in cables represents between 35 and 55% of the total weight. Other metals are mainly steel, lead, copper, zinc. The variety of plastics contained in the sheath is even stronger than for metals: silicone rubber, Polyethylene (PE), cross-linking PE (xPE), Polychloroprene (PCP), vulcanised rubber, Ethylene Vinyl Acetate (EVA), Ethylene Propylene Rubber (EPR), flexible PVC, etc. [START_REF]Union technique de l'électricité (UTE), UTE C30-202 -Insulated Cables and Flexible Cords -System for Cable Designation[END_REF] Although aluminium cables represent about 8% of aluminium products in Western Europe [START_REF]European Aluminium association[END_REF]. The inherent purity of aluminium used for cables justifies differentiate recycling channels to optimise processing steps and improve cost efficiency. At the end of life, the challenge concerns the separation of materials from each other. The most economical way to separate different materials rely on a smelting purification.

An alternative process for cables recycling uses only mechanical steps instead of thermal and wet separation, as developed for several years by MTB Recycling. The aluminium obtained by recycling cables is specially appreciated by the smelter. Its high purity makes it easy to produce a wide variability of aluminium alloys. Recycled aluminium can then be used in a large number of aluminium products and not only in applications requiring high alloy aluminium.

Numerous studies were conducted concerning the sustainability of aluminium recycling in comparison with primary aluminium. Outcomes about global and local environmental impacts show decrease up to 90% by using recycled aluminium [3.13]. However, systems modelling always relate to the standard melting solution for recycling aluminium.

In contrast this study focuses on the environmental assessment of cable recycling in MTB specific process.

On the one hand, the study demonstrates huge environmental benefits for aluminium recycled in comparison with primary aluminium. On the other hand, the results show the harmful environmental influence of the heat refining by comparison with cold recycling process. The study demonstrates the interest of recycling by sector rather than in blend. The data collection method does not allow the use of the results for other cables recycling processes. The results are representative only of recycling solutions developed by MTB.

Although the starting point of the study was to document the environmental impact of a specific recycling pathway; the results of this study have allowed to identify several hotspots of the recycling process. Thus it leads the development of effective fixes to reduce the overall impact of the recycling pathway. The purpose of this article is to explain how Life Cycle Assessment (LCA) methodology help MTB company to reduce environmental impacts of the aluminium cable recycling processes.

Analytical framework 2.1 Functional unit proposal

As part of this study, the functional unit used is as follows: producing one ton of aluminium intended for end-user applications, with a purity of> 97% using current industrial technologies (annual inbound processing> 10,000 t) located in Europe. The matching quality of the compared products can meet the same function as a high purity aluminium can be used for producing a large number of alloys without refining.

We selected three scenarios that meet all the conditions of the functional unit:  Scenario 1 or primary: primary aluminium, resulting from mining. The primary aluminium production is used as a reference for guidance on the quality of production. Foremost, our analysis is intended to compare methods of recycling. Comparison with scenario 1 should help translate environmental benefits of recycling.

Presentation of the Study Scope

This study is based on a life cycle approach, in accordance with the standards of International Organisation for Standardisation (ISO 14,010/44) [14.15]. The Fig. 2 presents the study scope used for the life cycle analysis of MTB recycling process, the boundaries are based on the Ecoinvent modelling. The boundaries include cradle to exit gate stages [16.17]. Life in use of aluminium in the products are not included in our study scope. The study only focuses on transformation steps of aluminium. As shown on the Fig. 2 by-products are included in environmental impacts calculation, but no benefit of by-products recycling is integrated into the study.

Sources of Data for the Life Cycle Inventory

The evaluation is designed by modelling input output flows that describe different systems of aluminium recycling with the software SimaPro (8.04 [18.19]. All the flows are based on from Ecoinvent 3.1 library [START_REF] Wernet | Ecoinvent User Meet[END_REF]. The systems are developed according to the local context of Western Europe. To allow comparison all the inventory elements are compiled based on the Ecoinvent database boundaries and data quality check [21.22]. Once modelling were done, the characterisation is conducted according to International Reference Life Cycle Data System (ILCD) Handbook [START_REF]Int. Ref. Life Cycle Data Syst. Handb. - First[END_REF] recommendations. The following sections detail key aspect of the methodology.

This study compares two different modelling systems. Both systems modelling using Ecoinvent data. Scenarios 1 and 2 using available data in Ecoinvent library without any modifications. And scenario 3 using Ecoinvent data to model MTB recycling process, the inventory data set was done using the inventory data sets recommendations from JRC [23]. The three modelling rely on the same system boundary.

Scenario Development

Aluminium recycling from remelter (scenario 2) is used as a baseline to evaluate the MTB alternative pathway (scenario 3). The baseline scenarios refer to the Western European aluminium average consumption. The scenario 2 and scenario 3 are based on Ecoinvent unit processes modelling. Ecoinvent database uses the EAA Life Cycle Inventory (LCI) [START_REF] Althaus | [END_REF]. For Ecoinvent v3.1 [25.26], the Aluminium processes are built with data collected by EAA in 2013 [27.28]. The Ecoinvent modelling using the average technology available on the market for Western Europe [START_REF] Weidema | Overview and Methodology. Data Quality Guideline for the Ecoinvent Database Version 3 Ecoinvent[END_REF].

Scenario 2: Conventional Aluminium Recycling

Scenario 2 provides the modelling of the traditional aluminium recycling solution. This scenario is based on shredding steps and melting purification step made by refiners. As for scenario 1, the scenario 2 is based on average values of European smelters. The data was compiled by the EAA and provided in Ecoinvent database. The electricity mix used in the modelling is equivalent to the electricity mix provide by the European Network of Transmission System Operators for Electricity. It is mainly fossil fuel (48.3%), nuclear power (28.1%) and renewable energy (23.6%). The distance of transport takes into account for the scenario 2 is 322 km (20 km on water, 109 km by train and 193 km by road).

In Ecoinvent, there are 2 data collections. One data collection was done for production scraps (new scrap) and the other one for post-consumer scrap (old scrap). The processes used for recycling new and old scraps are not the same. New scrap needs less operation than old scraps. The inbound logistics is also different because some of the waste is recycled directly on production plants. For the study the ratio between old and new scrap is based on European aluminium mix [START_REF]Reg. Mass Flow Model. - Part A[END_REF]. In 2013, old scrap represents 46.3% of aluminium recycled in Europe and new scrap 53.7%. After the recycling process, there are 2 outlets possible: wrought or cast aluminium. For the study, the choice falls on wrought aluminium because it has sufficient purity required by the functional unit (97%). The data chosen for the study is Aluminium, wrought alloy {RER} | Secondary, production mix [START_REF] Moreno | Ecoinvent User Meet[END_REF]. The Fig. 3 presents aluminium recycling as modelled in the Ecoinvent dataset. The modelling is divided in 5 steps: 4 mechanical separation steps (in red on the figure) and 1 thermal steps (in blue on the Fig. 3).

Scenario 3: MTB Cables Recycling

The Fig. 4 shows all the steps take into account in the modelling of scenario 3. For this scenario, the distance of transport takes into account is 540 km for old scraps and 510 km for new scrap from various cable manufacturers. The intrinsic aluminium quality reaches at least 99.6% of aluminium purity (average quality check during the period 2012-2014). An intensive inventory analysis was developed during an internal survey conducted in collaboration with EVEA consultants firm at MTB Recycling plant during autumn 2014. Foreground data are based on measurement and on stakeholder interviews. Collection of background data comes from Ecoinvent 3.1 or relevant literature. For this scenario, the distance of transport takes into account is 540 km for old scraps and 510 km for new scrap from various cable manufacturers. The intrinsic aluminium quality reaches at least 99.6% of aluminium purity (average quality check during the period 2012-2014).

MTB Recycling has an environmentally friendly strategy at a strategic level. As a consequence, they subcontracted with an energy provider that ensures an electricity mix from renewable energy source. Electricity comes almost exclusively from hydroelectric power (6.62% from alpine reservoirs and 2.4% from run of the river). The remaining electricity comes from waste to energy plants (0.51%) and from cogeneration plants (0.17%).

Life Cycle Assessment Results

Impact Assessment Method

The Table 2 presents the models of selected indicator of the Life Cycle Impact Assessment (LCIA) method. The two models in italics in the Table 2 are models replace compared to the recommended ILCD 2011 impact assessment methodology [START_REF]Characterisation Factors of the ILCD Recommended Life Cycle Impact Assessment Methods - EUR 25[END_REF], which was used throughout the study. The ILCD method is used with 2 modifications on calculation factors:



Human toxicity, non-cancer effects  Water resource depletion For human toxicity indicators, USEtox (recommended + interim) v1.04 (2010) [START_REF] Huijbregts | [END_REF] model was implemented to improve our characterisation method with latest calculation factors as recommended by UNEP and SETAC [32]. First results on water resource depletion with default calculation factor from Ecoscarcity [33], show anomalies. These anomalies are all related to the transportation modelling in Ecoinvent which involves electricity mix of Saudi Arabia. For the water resource depletion indicator, the Pfister water scarcity v1.01 (2009) [34] calculation factor was implemented in our characterisation method. The Table 2 presents the models of selected indicator of the Life Cycle Impact Assessment (LCIA) method. The two models in italics are models replace compared to the recommended ILCD methodology. A sensitivity analysis on the characterisation method was conducted using the ReCiPe Midpoint v1.1 method and CML IA Baseline v3.01 methods. This analysis has not yielded conflicting results.

Recycling Scenario Comparison

Our LCA show that secondary aluminium reaches approximately 10% of the impact of the primary aluminium scenario. And MTB aluminium shot is close to 5% of the primary aluminium impact on all the set of indicators. Those results correspondent to evaluation already done and meet the values given by the Bureau of International Recycling (BIR) for aluminium recycling benefits [36] and results from other studies publish in the International Journal of life Cycle Assessment [START_REF] Huber | [END_REF].

The Fig. 5 gives the opportunity to compare more specifically the two recycling scenarios. On the Fig. 5, the impacts are presented using the specific electricity mix for the 2 recycling scenarios. On the set of indicators, the impact of scenario 3 does not exceed the impact of scenario 2. In addition, the impact of MTB recycling scenario represents between 2% and 46% of the impact of recycling by melting. The average impact of the solution is halved.

MTB Aluminium Environmental Impacts Assessment

LCA results allow us to establish a hierarchy between environmental recycling solutions for aluminium cables. Whatever the electricity mix used by the recycling plant, the MTB mechanical recycling process is the most environmentally friendly. In this last part of the article, we focus only on the MTB recycling pathway.

The Fig. 6 shows the results for the characterisation of the MTB aluminium shot, with the specific renewable electric mix used by MTB. The values used for representation in Fig. 6 given on the figure. The results show a very strong contribution from the upstream transport for the collection of waste in the total impact of the scenario. On the set of indicators, the MTB recycling steps represents between 11.4% and 79.7% of the total impact. The average of the 11 indicators brings up an average impact of 36.1% and a median of 33.0%.

Besides the influence of transport, the study identified a major contributor to the impact of the shredding step: the steel consumables used for the blades and screens.

When the European electricity mix is used for the characterisation, all the recycling stages of MTB scenario represent on average 50% of the total impact on the set of indicators.

All plastics from the cable sheaths are not recycled. The plastic mixture and the presence of aluminium dust greatly complicates mixture recycling. Plastics waste management appears as a huge impact hotspot. Indeed, this step represents about 5 to 10% of the overall environmental impact of the MTB pathway while it is only transport (25 km) and landfill.

Discussion and Conclusion

LCA results demonstrate that recycling when driven without loss of quality is a relevant alternative to mining. However, LCA also allowed showing the shredder consumables (steel blades and screens) as elements with a high impact in proportion to their mass. MTB has launched an ecodesign approach in collaboration with subcontractors to identify more durable steel alloys for shredding blades. The tests carried out with news blades demonstrate an increase of 30 to 60% of the lifetime.

This study highlights the need to develop green recycling processes for mixture of plastics. Following this study, MTB has initiated a development approach to sort and recycle the plastic mixture. A first prototype was developed in late 2015. The synoptic of plastic processing method is shown in Fig. 7. The separation is always based on simple mechanical steps that achieve a uniform separation. Be noted that the process is modified according to the stream of plastics produced by the aluminium separation step.

The shredding steps are on average 2 times more impacting than mechanical separation steps. Work on the efficiency of the shredder is necessary to reduce the electricity consumption of this step. For now, no solution emerges to reduce this impact.

Nevertheless, the energy recovery solutions and new electric motors are studied.

Our team is now working on automating this ecodesign in order to propose a roadmap for the designer and an assessment tool available for recycling pathways.

Fig. 1

 1 Fig. 1 Section of a cable having multiple beams of aluminium.



  Scenario 2 or secondary: secondary aluminium from recycling by melting.  Scenario 3 or MTB: MTB aluminium, from recycling using the MTB processes.

Fig. 2

 2 Fig. 2 Study scope for the cable recycling system boundaries

Fig. 3

 3 Fig. 3 Standard end-of-life recycling scenario for aluminium cables

Fig. 4

 4 Fig. 4 MTB end-of life recycling process for aluminium cables

Fig. 5

 5 Fig. 5 Comparison of the characterisation of the 2 recycled scenarios using specific electricity mix

  are

  Photochemical ozone formation (kg NMVOC eq) Acidification (molc H+ eq) Freshwater eutrophication (kg P eq) Freshwater ecotoxicity (CTUe) Water resource depletion (m3) Mineral, fossil & ren resource depletion (kg Sb eq)

Fig. 6

 6 Fig. 6 Characterisation of MTB aluminium shot with purity of 99.6% specific electricity mix

Fig. 7

 7 Fig. 7 Presentation of processes added to the MTB pathway to separate the plastics mixture

Table 1

 1 Composition of recycled cables at the MTB plant (average for the period 2011-14)

	Material	Proportion
	Rigid aluminium	48.5%
	Plastics and rubber	40.5%
	Non-ferrous metals	4.5%
	Ferrous metals (steel and stainless steel)	4.0%
	Flexible aluminium	2.5%

Table 2

 2 Indicators selected for the life cycle impact assessment[START_REF]ILCD Handbook: Recommendations for Life Cycle Impact Assessment in the European Context[END_REF]

		Mineral, fossil & ren	CML 2002
		resource depletion
	Indicators	Model
	Climate change	Baseline model of 100 years of the
		IPCC
	Ozone depletion	Steady-state ODPs 1999 as in
		WMO assessment
	Human toxicity, non-cancer	USEtox model v1.04 [32]
	effects
	Particulate matter	RiskPoll model
	Ionising radiation HH	Human health effects model as
		developed by Dreicer
	Photochemical ozone	LOTOS-EUROS
	formation
	Acidification	Accumulated Exceedance
	Freshwater eutrophication	EUTREND model
	Freshwater ecotoxicity	USEtox model
	Water resource depletion	Pfister water scarcity v1.01 [33]

Acknowledgments

This work was performed within the help of Marie Vuaillat from EVEA Consultancy firm and with financial support from French Agency for Environment and Energy Efficiency (ADEME). We also want to thank MTB Recycling and the French National Association for Technical Research (ANRT) for the funding of the PhD study (CIFRE Convention N ° 2015/0226) of the first author.


Javier Mikel Olaso
email: javier-mikel.olaso@ehu.es

Pierrick Milhorat
email: milhorat@telecom-paristech.fr

Julia Himmelsbach
email: julia.himmelsbach@ait.ac.at

Jérôme Boudy
email: jerome.boudy@telecom-sudparis.eu

Gérard Chollet
email: chollet@telecom-paristech.fr

Stephan Schlögl
email: stephan.schloegl@mci.edu

María Inés Torres
email: manes.torres@ehu.es

A Multi-lingual Evaluation of the vAssist Spoken Dialog System. Comparing Disco and RavenClaw

Keywords: spoken dialog systems, dialog management, real-user experiments

vAssist (Voice Controlled Assistive Care and Communication Services for the Home) is a European project for which several research institutes and companies have been working on the development of adapted spoken interfaces to support home care and communication services. This paper describes the spoken dialog system that has been built. Its natural language understanding module includes a novel reference resolver and it introduces a new hierarchical paradigm to model dialog tasks. The user-centered approach applied to the whole development process led to the setup of several experiment sessions with real users. Multilingual experiments carried out in Austria, France and Spain are described along with the analyses and results in terms of both system performance and user experience. An additional experimental comparison of the RavenClaw and Disco-LFF dialog managers built into the vAssist spoken dialog system highlighted similar performance and user acceptance.

Introduction

The vAssist project [START_REF] Chollet | vassist: Le majordome des personnes dépendantes[END_REF] aims at providing specific voice controlled home care and communication services for two target groups of older persons: seniors suffering from chronic diseases and persons suffering from (fine) motor skills impairments. The main goal is the development of simplified and adapted interface variants for tele-medical and communication applications using multilingual natural speech and voice interaction (and supportive graphical user interfaces where necessary) [START_REF] Milhorat | Un systéme de dialogue vocal pour les seniors: Études et spécifications[END_REF][START_REF] Schlögl | Designing, building and evaluating voice user interfaces for the home[END_REF]. The vAssist consortium consists of research institutes and companies from Austria, France and Italy. Toward the end of the project, the University of the Basque Country was included so as to expand the perimeter to Spanish speaking users.

Related Work

A Spoken Dialog System (SDS) is a system providing an interface to a service or an application through a dialog. An interaction qualifies as dialog when it exceeds one turn. It requires to keep track of the dialog state, including the history of past turns, in order to select the appropriate next step.

Those systems do not usually consist of a single component but comprise several specialized programs combined in order to recognize the speech, extract the relevant information in the transcriptions, act on back-end services, decide on the best next step, generate system responses and synthesize speech.

JUPITER [START_REF] Victor Zue | JUPlTER: a telephone-based conversational interface for weather information[END_REF] was one of the first SDSs released to the public. The phonebased weather information conversational interface has received over 30 000 calls between 1997 and 1999. Earlier, researchers from Philips implemented an automatic train timetable information desk for Germany [START_REF] Aust | The Philips automatic train timetable information system[END_REF]. More recently, Carnegie Mellon University provided Olympus [START_REF] Bohus | Olympus: an open-source framework for conversational spoken language interface research[END_REF], which has been used to build systems like the Let's Go! Bus Information System [START_REF] Raux | Let's go public! taking a spoken dialog system to the real world[END_REF], leading to the biggest corpus of man-machine dialogs with real users publicly available today. Recent platforms for developing spoken dialogue systems include the Opendial toolkit [START_REF] Lison | A hybrid approach to dialogue management based on probabilistic rules[END_REF] and the architecture developed by the University of Cambridge [START_REF] Young | POMDP-based Statistical Spoken Dialog Systems: A review[END_REF] for its startup VocalIQ.

ELIZA [START_REF] Weizenbaum | ELIZA-A computer program for the study of natural language communication between man and machine[END_REF] is considered by many as the first dialog system. The core of the system was based on scripts, which associated a system's response by looking for a pattern in the input. Larsson and Traum argued that the state of the dialog, including its history, may be represented as the sum of the so far exchanged information [START_REF] Larsson | Information state and dialogue management in the TRINDI Dialogue Move Engine Toolkit[END_REF]. An Information State (IS) designer defines the elements of the information relevant to a dialog, a set of update rules and an update strategy. An example-based dialog manager (DM) [START_REF] Lee | A situation-based dialogue management using dialogue examples[END_REF] constructs a request to a database from the annotated input Dialog Act (DA). The database stores examples seen in the interaction data so that the algorithm looks for the most similar entry and then executes the system's associated action. On the other hand, plan-based DMs [START_REF] Rich | Building task-based user interfaces with ANSI/CEA-2018[END_REF][START_REF] Bohus | The RavenClaw dialog management framework: Architecture and systems[END_REF] require a pre-programmed task model.

On the stochastic side, Markov Decision Processes (MDPs) represent a statistical decision framework to manage dialogs [START_REF] Levin | Using Markov Decision Process for Learning Dialogue Strategies[END_REF][START_REF] Young | Probabilistic Methods in SpokenDialogue Systems[END_REF]. Here, the dialog state space contains all the states the dialog may be in and the transitions dependent on the user inputs. The behavior of a DM based on MDPs is defined by a strategy which associates each state to an executable action. Statistical methods used for dialog management also include Stochastic Finite-State models [START_REF] Griol | A statistical approach to spoken di-alog systems design and evaluation[END_REF][START_REF] Inés | Stochastic Bi-Languages to model Dialogs[END_REF][START_REF] Ghigi | Decision making strategies for finite state bi-automaton in dialog management[END_REF] and SemiMDPs [START_REF] Cuayáhuitl | Evaluation of a hierarchical reinforcement learning spoken dialogue system[END_REF]. Finally the state-of-the-art POMDP extends the MDPs hiding the states which emit observations according to a probabilistic distribution [START_REF] Williams | Partially observable Markov decision processes for spoken dialog systems[END_REF][START_REF] Jurčíček | Reinforcement learning for parameter estimation in statistical spoken dialogue systems[END_REF][START_REF] Young | POMDP-based Statistical Spoken Dialog Systems: A review[END_REF]. This additional layer encodes the uncertainty about the Natural Language Understanding (NLU) and, in the case of SDSs, the Automatic Speech Recognition (ASR). Within a theoretical framework the proposal of a global statistical framework, allowing for optimization, is highlighted by POMDP [START_REF] Williams | Partially observable Markov decision processes for spoken dialog systems[END_REF]. However, practical POMDP-based DMs are currently limited in the number of variables and by the intractability of the computing power required to find an optimal strategy [START_REF] Crook | Parallel computing and practical constraints when applying the standard pomdp belief update formalism to spoken dialogue management[END_REF][START_REF] Jurčíček | Reinforcement learning for parameter estimation in statistical spoken dialogue systems[END_REF][START_REF] Young | POMDP-based Statistical Spoken Dialog Systems: A review[END_REF].

In vAssist the development context, along with the difficulty to collect 'real' training dialogs, favored the use of a deterministic control formalism. This was also motivated by the overall requirements of the system it had to be integrated with.

Main Goals and Contributions

This paper describes the vAssist SDS and presents the results of the final system evaluation. The vAssist DM system is based on an open and adaptative software architecture that allows for an easy configuration of the DMs according to the targeted scenario, user needs and context. In accordance with [START_REF] Milhorat | An Open-source Framework for Supporting the Design and Implementation of Natural-language Spoken Dialog Systems[END_REF], the novelties of the vAssist SDS are the Semantic Unifier and Reference Resolver (SURR) defined in the natural understanding module and the Link-Form Filling (LFF) concept proposed to model the task (for both cf. Section 4.6). The vAssist prototype is based on the Disco plan-based DM and the LFF task model. For comparison purposes we have also integrated an alternative, plan-based DM, i.e. Ravenclaw (cf. Section 4.7).

The main contribution of this work is therefore a multilingual lab evaluation of the final vAssist assistive home care and communication service applications running on a smart-phone. Such was carried out with real users in Austria, France and Spain (cf. Section 5). As an additional contribution the evaluation has been carried out in terms of system performance and user experience (cf. Section 6). The final contribution of this work is the experimental comparison of the Disco-LFF DM and the Ravenclaw DM working within the same SDS architecture, dealing with the same task and language (i.e. Spanish), and interacting with the same users (also cf. Section 6).

System Description

The vAssist SDS extends the usual chained design. Components were split into modified sub-modules and new processes were integrated into a state-ofthe-art workflow chain. Fig. 1 shows the resulting SDS architecture.

Speech Recognition

The system uses the Google Speech API where an HTTP POST request transmits the signal segment to be recognized. The API returns the n-best hypotheses (note: n is a parameter of the request), as well as the confidence score for the best one. An empty result is returned when the segment cannot be recognized with enough confidence, i.e. when it does not contain speech.

Natural Language Generation and Text-to-Speech

A simple but effective solution to produce natural language utterances conveying the DM's messages was targeted. Input messages are Semantic Frames (SFs). The engine is fed with a set of templates that consist of a title (identical to an SF's goal) associated with an utterance, and whose parts may be replaced by slot names or slot name-value pairs. The result is a natural language utterance to be synthesized or displayed on a screen.

MaryTTS [START_REF] Schröder | The German text-to-speech synthesis system MARY: A tool for research, development and teaching[END_REF], an open-source speech synthesis framework maintained by the Cluster of Excellence MMCI and the DFKI, is used for synthesis. It offers pre-built voice models for different languages as well as tools to create and manipulate them. The MaryTTS module is a client connected to a generating server (hosted local or remote). A request containing the text to be synthesized with additional prosodic information is sent to the central server, which returns the speech stream. The text-to-speech module of the present platform is a basic client program embedded into an ActiveMQ wrapper.

Semantic Parsing

The semantic parser, which gets inputs from the ASR, associates semantic labels to text utterances (or parts of them). The most commonly used parsing techniques are based on context-free grammars or probabilistic context-free grammars, which are either hand-coded, based on the analysis of collected dialog data, or designed by experts.

Our semantic parser, integrates the algorithm proposed by [START_REF] Jurčíček | Transformation-based Learning for semantic parsing[END_REF], which is the application of the work from [START_REF] Brill | Transformation-based error-driven learning and natural language processing: A case study in part-of-speech tagging[END_REF]. Instead of matching whole sentences with parse structures, the algorithm looks for patterns in chunks of the textlevel utterance and in the temporary (i.e. currently assigned) SF. The module applies an ordered set of conditional rules, which is learned from data.

Semantic Unification and Resolution

The Semantic Unifier and Reference Resolver (SURR) holds a rather simplistic forest of nodes which is used to mine the dialog history, incorporate external information sources and add local turn context. It is the meeting point of the user's semantic frames, the external environment sensors and functions, the dialog history, and the links generated by the context catcher.

At its core the SURR embeds a forest structure. Trees consist of hierarchies of fully or partially defined SFs (some nodes are calls to external systems or services). When requested, the SURR may dynamically modify (remove/add) branches of the forest. The top node of a hierarchy defines the root.

The SURR algorithm tries to find a unique path from an input SF, i.e. from the parsed user input, to nodes of the forest, to a root node. Going up the trees, the algorithm applies the optional operations held on branches.

Reaching a root node equals the user input being contextualized [START_REF] Milhorat | An Open-source Framework for Supporting the Design and Implementation of Natural-language Spoken Dialog Systems[END_REF]. In case the algorithm cannot find such a path, i.e. the SURR fails to produce a suitable SF (given the current context and available knowledge), a "NoMap" SF is generated to signal a 'non-understanding' to consecutive components.

Dialog Act Mapping

As a last stage of the NLU processing, the dialog act mapping is performed. Once an input has been parsed, external and local references have been resolved, and the semantic level has been unified, the ultimate step is to convert the SF into a DA. Following an input the mapper retrieves a set of available DAs. Then it looks for a unique match between the SF and the set of DAs.

Dialog Management Based on Disco

The core of the implemented DM is based on Disco [START_REF] Rich | Building task-based user interfaces with ANSI/CEA-2018[END_REF], an open-source dialog management library, whose algorithm processes task hierarchy models. A dialog model is a constrained XML tree of tasks. The plan recognizer uses the recipes defined in the dialog models and this dialog state to select the best available plans for the tasks in the stack. Then the reasoning engine selects the most appropriate next step.

In an attempt to overcome the hurdles inherent to the specification of task models, the dialog modeling paradigm was shifted to a Linked-form-filling (LFF) one. Form-filling dialogs are based on structures containing sets of fields, which the user needs to provide a value for in order to trigger a terminal action. The order in which the DM asks for the values is not predefined. The user may define multiple field values within a single utterance/turn.

The LFF language offers to combine these properties with the ability to trigger actions at any point of the dialog and the inclusion of subforms. Furthermore, fields and subforms can be optional, i.e. either be ignored when unset or proposed to the user. Here, we use the unlimited depth of a task model to circle tasks while keeping a sequencing order; i.e. the link between two task nodes is a reference, hence a node can point to its 'parent' node.

The aim of the LFF language is to offer a somehow simpler design method to a powerful standard dialog modeling specification. Since it is also an XML based language we opted for XSLT to convert an LFF document into a compliant dialog model. A number of rules have been defined to create a wellformed LFF document. Doing this, the relative reduction in terms of code size and task hierarchy depth was 76% and 77%, respectively.

Dialog Management Based on RavenClaw

RavenClaw (part of the CMU Communicator system [START_REF] Bohus | The ravenclaw dialog management framework: Architecture and systems[END_REF]) is a task-independent DM. It manages dialogues using a task tree and an agenda.

The task tree is basically a plan to achieve the overall dialog task. At runtime, the tree is traversed recursively from left to right and from top to bottom. The execution of the dialog ends when the bottom-right node has been reached. During this process, loops and conditional control mechanisms may be added to the nodes in order to alter the normal exploration of the tree, allowing the definition of more complex dialog structures.

The second defining structure, the agenda, is an ordered list of agents that is used to dispatch inputs to appropriate agents in the task tree. It is recomputed for every turn and the current agent is placed on top of the stack. Inputs are matched to successive items on the agenda. When a match occurs the corresponding agent is activated with the matching concepts as inputs of the dialog. An agent may not consume all input concepts and thus remaining concepts are passed further down the agenda until agents can consume them.

In order to integrate RavenClaw in the architecture shown in Figure 1, the original Disco-LFF DM was substituted by a module responsible for translating the message format defined by RavenClaw to the message format defined by the Disco-based component and vice versa.

Task and Experimental Scenarios

Our goal was to empirically evaluate the operation of the developed voicecontrolled application running on a smartphone under standardized conditions. For these experiments several scenarios were defined and implemented. This is the (translated) description of the scenarios as given to the participants:

The Prescription Management enables to monitor medical prescriptions and individual intake times. To evaluate this scenario, participants were asked to add a new predefined prescription to the application database and to set a reminder for it (AP). The app requests information regarding name of medication, quantity, dosage form, frequency, and time of intake. The Health Report (HR) provides an overview of physiological data. Participants filled in predefined glycaemia and blood pressure data. The Sleep Report (SR) monitors sleep quality. The following data was provided by the users: the time he/she went to bed, the time he/she fell asleep, and their wake-up times. Participants also reported awake periods at night and the total number of hours slept. Finally, users were asked to rate their well-being on a six-point scale. Furthermore, the evaluation included setting a reminder to remember completing the sleep report (SRR). Fitness Data Management consists of reporting daily activities (FD) and setting reminders for the reports. Within the evaluation, participants were asked to enter a new report including the duration of their fitness activity. The Communication Services include sending messages (SM) and initiating phone calls (PC). Participants were asked to test both functions.

Experimental Evaluation

Two series of experiments were carried out: We evaluated the vAssist system including the Disco-LFF engine in three languages: French, German and Spanish. Further, we compared the RavenClaw and Disco-LFF DMs built into the vAssist system with Spanish users.

Sixteen users took part in the experiments in each of the trial sites. In France, 14 male and 2 female persons between 65 and 90 years (Mn=77.0) participated in the study. In Austria, 8 male and 8 female participants between 60 and 76 (Mn=68.0) years old took part. The Spanish trial site included 12 males and 4 females between 26 and 69 (Mn=39.6) years.

Users were first shown the smartphone application, followed with a short demonstration and some usage advices. The experimental scenarios were then carried out without any other introduction than the simple description of the goal. It was up to the user to figure out how to perform each task.

The system's performance was measured in terms of Task Completion (TC), i.e. success rate, and Average Dialog Length (ADL), i.e. efficiency. TC evaluates the success rate of the system in providing the user with the requested information, based on the total number of dialogues carried out and the number of successful dialogues achieved for a specific task. ADL is the average number of turns in a successful task.

For the subjective measures, a set of standardized questionnaires was applied. The standard Single Ease Questionnaire (SEQ) [START_REF] Tedesco | A comparison of methods for eliciting post-task subjective ratings in usability testing[END_REF], the System Usability Scale (SUS) [START_REF] Brooke | SUS-a quick and dirty usability scale[END_REF] and the Subjective Assessment of Speech System Interfaces (SASSI) [START_REF] Hone | Towards a tool for the subjective assessment of speech system interfaces (SASSI)[END_REF] questionnaire were used to evaluate the vAssist system with the Disco-LFF DM. A custom set of questions was used to compare the Disco-LFF-based DM with the Ravenclaw-based DM. Results of the SEQ, SUS and SASSI are not given for Spanish, as for this language no localized mobile application interface was available.

System Performance

The first series of experiments was carried out in France, Austria and Spain, evaluating the vAssist system with the Disco-LFF DM. Table 1 shows the system performance evaluation in terms of TC and ADL values.

Table 1 reveals good TC rates, with the French version being the one generating the highest system performance and the Spanish version the one producing the lowest. Surprisingly, our results show that the vAssist system performance is not better for younger users (Spain: Mn=39.6 years) than for older ones (France: Mn=77 years). Language dependent modules, i.e. the ASR and, more importantly, the NLU, were more robust in French and German. Spanish results suffered from a less robust semantic parser and the missing mobile UI, leading to a higher number of turns to achieve the task goals.

Task Easiness and Usability

Besides performance, the perceived task easiness is considered an important factor influencing user experiences [START_REF] Venkatesh | Technology Acceptance Model 3 and a Research Agenda on Interventions[END_REF]. This aspect was measured right after each task via the SEQ using a 7-point semantic differential (very difficultvery easy). The analysis revealed a sufficient ease of use for each task; i.e mean ratings for the Prescription Management and for sending a message were 4.94. Initiating a phone call and the Health Report were rated 5.06.

To obtain insights regarding the prototype's usability, learnability, and intuitivity, the SUS was used. SUS scores fall between 0 and 100; the higher the score the better. The values for Austria and France were 68 (sd = 17.2) and 70 (sd = 11.5), respectively. Hence, even though the perceived easiness of single tasks was good, the overall system experience could still be improved.

Speech Assessment

The SASSI questionnaire was employed to examine the interaction quality. The analysis provides developers with an assessment of the system along several axes such as easiness, friendliness, speed, etc.

Results indicate that both "Response Accuracy" (Austria: 4.27, France: 3.99) and "Speed" (Austria: 4.64, France: 4.19) were judged neutral. The analysis of the French sample reveals that "Likeability" (4.9) and "Cognitive Demand" (5.15) were fair. In contrast, the Austrian participants rated these factors as good (Likeability: 5.28, Cognitive Demand: 5.15). Hence, participants liked the system and were not overwhelmed by its cognitive demands.

Disco-LFF and RavenClaw DM Comparison

The second series of experiments was carried out in Spanish only. Note that both DMs were integrated in the same architecture (Figure 1), i.e. only the task planification and the agent execution differed. Each user carried out the scenarios defined in Section 5 with either of the DMs. Table 2 shows the system performance achieved by both systems in terms of TC and ADL, for each of the defined subscenarios. Both metrics show similar behavior for the Disco-LFF and the Ravenclaw DM. A Z-test comparing the average TC proportions and the ADL means showed no statistically significant difference between the two DMs (p-value = 0.05). A detailed scenario-based analysis showed, however, differences between TC values in the AP and the SR scenarios, which correspond to longer dialogs in terms of the ADL metric. A previous series of experiments has furthermore highlighted a certain lack of robustness exhibited by the dependent modules of the Spanish vAssist version. This issue was more evident in longer dialogs (AP and SR).

As there was no mobile UI for the Spanish language, the user experience was evaluated trough a set of direct questions regarding the system efficiency, usability and user satisfaction. Task easiness received an average score of 3.00 for the Disco-LFF DM and 3.14 for the RavenClaw DM. The respective satisfaction scores were 3.57 and 3.43 and efficiency scored 3.28 and 3.14.

Conclusion

This paper had two objectives. First, we reported on the results of the final lab evaluation of the vAssist system, and second we compared the system's core DM implementation with a publicly deployed one.

Despite minimal differences between languages, the vAssist SDS performances proved to be sufficient for its target users, i.e. seniors suffering from chronic diseases and persons suffering from (fine) motor skills impairments.

The DM comparison showed similar performance and subjective experience for the system with the Disco-LFF DM and the one with RavenClaw, promoting the Disco-LFF as a valid alternative to existing DM approaches.

Acknowledgements

The presented research is conducted as part of the vAssist project (AAL-2010-3-106), which is partially funded by the European Ambient Assisted Living Joint Programme and the National Funding Agencies from Austria, France and Italy. It has also been partially supported by the Spanish Ministry of Science under grant TIN2014-54288-C4-4-R and by the Basque Government under grant IT685-13.

Fig. 1

 1 Fig. 1 Architecture of the platform

Table 2

 2 Comparing the Disco-LFF and RavenClaw DMs.

		French	German	Spanish		Disco-LFF DM RavenClaw DM
		TC	ADL	TC	ADL	TC	ADL		TC	ADL	TC	ADL
	AP	93.33% 8.00 88.88% 8.18 84.00% 13.62	AP	84.00% 13.62 94.40% 15.64
	HR	100.00% 3.15 93.33% 3.78 100.00% 4.41	HR	100.00% 4.41 100.00% 4.90
	SR	91.66% 7.81 100.00% 7.25 100.00% 10.18	SR	100.00% 10.18 83.30% 11.90
	SRR	83.33% 3.40 100.00% 3.50 87.50% 5.78	SRR	87.50% 5.78 75.00% 6.08
	FD	100.00% 3.00 66.66% 3.00 93.75% 4.53	FD	93.75% 4.53 92.80% 4.30
	SM	100.00% 3.86 100.00% 4.62 100.00% 6.21	SM	100.00% 6.21 100.00% 6.64
	PC	100.00% 1.92 100.00% 1.82 100.00% 2.00	PC	100.00% 2.00 100.00% 2.42
	Average 97.12% 4.44 95.18% 4.73 92.19% 6.21	Average 92.19% 6.21 89.90% 6.60
	Table 1 TC and ADL of the vAssist system using
	the Disco-LFF DM.