dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2019)  that with the ISBI 2017 dataset it provided an IoU score of 77. 73%. Besides, a full-resolution convolutional network (FrCN) was introduced in  (Al-Masni et al. , 2018)  yielded an IoU score of 77. 11% on the ISBI 2017 dataset. Regarding skin lesion segmentation, several methods based on GANs have been proposed. For instance,  (Xue et al. , 2018)  has presented a GAN-based model that depened on ResNet blocks with skip connections. With the ISBI 2017 test dataset, it achieved an IoU score of 78. 50%. In  (Bisla et al. , 2019) , a two-stream deep convolutional generative adversarial network with the ResNet-50 model, was proposed to jointly segment and classify skin lesions. In this study for removing the artifacts from the lesion images, the authors have used different pre-processing techniques.
Attention mechanisms help DCNNs to pay more attention to the components with more enriched information. To that so, using trainable layers, the current convolutional features are weighted by multiplying with learned soft weight vectors to produce channel-wise features, which yield useful features and suppressed the redundant ones. As the attention mechanism focuses on relevant information in the collection of extracted features, it encourages DCNNs to learn faster with merely a small training dataset. In  (Chen et al. , 2016) , an attention mechanism for softly weighting the multiscale features at each pixel location has been proposed. This attention mechanism is able to capture the key features at different scales and positions. In  (Schlemper et al. , 2018) , the use of a generalized self-gated soft-attention mechanism allows the convolutional layers to contextualize local features.
g. , mobiles). To address this issue, we propose MobileGAN, which is a lightweight and efficient GAN-based model. The number of parameters of any segmentation model depends on several factors, such as the number of convolutional layers used, image size of the input layer, fully connected layers. Unlike the related work, the MobileGAN model proposes a novel layer, factorized-attention module (FCM), which combines two branches: residual 1-D factorized kernel convolution (factorized layer) and channel attention module. In the FCM, convolution is computed in a fashion that reduces the overall number of computational parameters. We employ a multiscale aggregation mechanism to extract skin lesions-relevant features at different scales to be able to segment of skin lesions of various shapes and sizes.
Thus, the rank-1 constraint can be rewritten, f i , as a linear combination of 1D filters: f i = K ∑ k=1 σ i k vi k hi k T , (5) where σ i k is a scalar weight and K is a rank of f i , in turn the length of vectors, vi k and hi k T is d. Thus, the ith output of the decomposed layer, a 1 i , is expressed as a function of its input a 0 * : a 1 i = ϕ b h i + L ∑ l=1 hT il * ϕ b v l + C ∑ c=1 vlc * a 0 c , (6) where the function, ϕ(. ), can be represented by non-linearity of the 1D decomposed filters, which can be implemented with ReLU. Finally, to get the final representation of the FCM, the output feature maps from 1-D kernel factorized layer is fed to the channel attention module. the architecture of the factorized-attention module used in MobileGAN is shown in Figure  4.
MobileGAN has a small number of parameters (2. 35 million parameters) compared to all other methods except ENet. Although ENet is a lightweight model (0. 36 million parameters), its segmentation results are worse than MobileGAN. Regarding the ISIC 2018 validation dataset, we compared MobileGAN with the FCN, U-Net, SegNet, FrCN, GAN-FCN, Rcnn-superpixels, Mask R-CNN models, as shown in Table  2. Note that the ISIC 2018 dataset includes 100 images for validation  (i. e. , ResNet)  (He et al. , 2016) , noting that the number of parameters of ResNet-50 is In other words, the number of parameters of these models is higher than MobileGAN. In turn, Figure  5  and Table  2  show that the Mask R-CNN  (Sivanesan et al. , 2019)  and Rcnn-superpixels  (Mishra and Daescu, 2019 ) models achieve JSC th a bit higher than MobileGAN, however the authors of Mask R-CNN  (Sivanesan et al.
, 2019)  and Rcnn-superpixels  (Mishra and Daescu, 2019)        bileGAN decreases the number of false positives from the segmented mask remarkably. In Figure  7 , we present a couple of difficult samples from the ISBI 2017 dataset. We present the DSC and JSC scores on each mask, noting that D and J refer to DSC and JSC, respectively. The BCE+L1+Jaccard loss provides a JSC score of 95. 74% and 97. 71% with the upper and lower examples of Figure  7 , respectively. This analysis reveals the proposed loss function (BCE+L1+Jaccard loss) yields a better improvement at pixel-level in the segmentation result. Comparing to the state-of-the-art, the number of parameters of MobileGAN has been significantly reduced, with only 2. 35 million parameters. In future work, we will im-.
for skin lesion segmentation with a Dice score of 84. 26 and a Jaccard score of 74. 81 on ISBI 2017 dataset. However, these scores have considered much lower than the results of the state-of-the-art, such as SLSDeep that achieved a Dice score of 87. 80 and a Jaccard score of 78. 20. In turn, in (Paszke et al. , 2016), an efficient deep neural network named ENet for real-time segmentation with small number of parameters of 1 million have been introduced. Enet has produced a Dice score of 82. 70 and a Jaccard score of 74. 10 with ISBI 2017.
), There are two terms to compute the binary cross-entropy (BCE) loss using two images: the termlog(D(x, y)) for ground truth images, and the other one log(1 -D(x, G(x, z)) for the predicted image. The optimizer fits D by maximizing the loss values for the ground-truth images and minimizing the loss values for the predicted images. We assume that the expected class for ground truth and generated images are 1 and 0, respectively 4. Experiments Datasets: The efficacy of the proposed model, MobileGAN, is assessed on two publicly available datasets of dermoscopic images for skin lesion analysis: IEEE International Symposium on Biomedical Imaging, (ISBI 2017) and Skin Lesion Analysis Towards Melanoma Detection, grand challenge datasets (ISIC 2018) 2. ISBI 2017 dataset was divided into training, validation, and testing sets with 2000, 150, and 600 images, respectively.
In turn, the ISIC 2018 dataset includes 2,594 images with the corresponding ground truth masks annotated by expert dermatologists. The validation and testing sets contain 100 and 1,000 images, respectively, without ground truth (evaluated on the ISIC2018 validation leaderboard 3 only). In our experiments, we used 80% of the training set of the ISIC 2018 dataset for training the segmentation models and 20% for validation, as proposed in (Al-Masni et al. , 2018). Note that we trained, validated, and tested MobileGAN individually on the ISBI 2017 and ISIC 2018 datasets. Evaluation metrics: In this paper, we use five evaluation metrics to evaluate the performance of MobileGAN. With the ISBI 2017 dataset, we use Jaccard similarity coefficient (JSC), Dice similarity coefficient (DSC), accuracy (ACC), specificity (SPE).
models did not mention the number of parameters of each model. As shown, both Mask R-CNN (Sivanesan et al. , 2019)  and Rcnn-superpixels (Mishra and Daescu, 2019)  exploit the ResNet 101 backbone. Since the number of parameters of ResNet 101 is 44. 5 million, and the Mask R-CNN and Rcnn-superpixels models are much heavier than MobileGAN. As shown, MobileGAN is a lightweight model and achieves competitive skin segmentation results when compared to state-of-the-art segmentation models. Qualitative segmentation results of the MobileGAN model with some examples of the ISBI 2017 test dataset are shown in Figure 6. As shown in Figure 6 (left), although the tested images have a high similarity between the color of the lesion and the skin regions, fuzzy boundaries and even tiny lesions, the MobileGAN model accurately segments the boundary of each skin lesion with an accuracy of about 95%.
lightweight and efficient GAN model for skin lesion segmentation, called Mo-bileGAN, has been proposed in this paper. MobileGAN has been built by adapting a GAN model that comprises 1-D kernel factorized networks, multiscale aggregation, position, and channel attention mechanisms. MobileGAN has been assessed on the ISBI 2017 test and ISIC 2018 validation datasets. On the ISBI 2017 test dataset, it yields precise segmentation results with accuracy, sensitivity, specificity, Dice coefficient and Jaccard index, of 97. 61%, 87. 81%, 99. 92%, 90. 63%, and 81. 98%, respectively. Mobi-leGAN achieves a threshold JSC score of 78. 4% with the ISIC 2018 validation dataset.
Different configurations of MobileGAN with the ISBI 2017 test dataset (bold refers to the top values). BL+CAM). In the BL+CAM model, we added a CAM module after each factorized layer in the G network. We also added a CAM module after the second downsampling layer in the discriminator network. The BL+CAM model gives DSC and JSC scores of 87. 23% and 76. 65%, respectively. As we can see, the scores of BL+CAM are better than BL+PAM. Indeed, the addition of the CAM mechanism in the BL GAN model provides an efficient feature discriminability between skin lesion regions and normal skin region in the skin image. As shown in the last row of Table4, the addition of the multiscale, PAM, and CAM blocks to the BL GAN improves the DSC and JSC scores to 90. 63% and 81. 98%, respectively. This last variation is the proposed Mobi-leGAN model.
The performance of MobileGAN with different input image resolutions of the ISBI 2017 test dataset. 7279. 4986. 3698. 21theimportantdetails are lost leading to low segmentation accuracy. With image size 256 × 256, the last layer of MobileGAN generates a feature map of 32 × 32, which also extracts skin lesions-irrelevant features (i. e. , artifacts) that reduces the overall accuracy. In turn, the image size of 128 × 128 generates a 16 × 16 feature map at the last layer that retains skin lesions-relevant features and discards the irrelevant ones to achieve the best accuracy in terms of all metrics. Since our main goal is to achieve a lightweight model with keeping accurate skin segmentation results, we used the image size of 128 × 128 to train MobileGAN. Table6shows the effect of different loss function variations on the performance of MobileGAN.
Acknowledgment This research is funded by the program " Marti Franque"  under the agreement between  Universitat Rovira Virgili  and  Fundacio Catalunya La Pedrera , projects  RTI2018-095232-B-C2 ,  SGR 1742 ,  CERCA ,  Nestore Horizon2020   SC1-PM-15-2017  (n 769643),  EIT Health Validithi  project. The authors acknowledge the support of  NVIDIA Corporation  with the donation of several Titan Xp GPU used for this research. M. Abdel-Nasser  and  D. Puig  are partially supported by the  Spanish Government  under project  DPI2016-77415-R.
A lightweight and efficient GAN model for skin lesion segmentation, called Mo-bileGAN, has been proposed in this paper. MobileGAN has been built by adapting a GAN model that comprises 1-D kernel factorized networks, multiscale aggregation, position, and channel attention mechanisms. MobileGAN has been assessed on the ISBI 2017 test and ISIC 2018 validation datasets. On the ISBI 2017 test dataset, it yields precise segmentation results with accuracy, sensitivity, specificity, Dice coefficient and Jaccard index, of 97. 61%, 87. 81%, 99. 92%, 90. 63%, and 81. 98%, respectively. Mobi-leGAN achieves a threshold JSC score of 78. 4% with the ISIC 2018 validation dataset.
INTRODUCTION Motivation. Knowledge Bases (KBs) are becoming increasingly important with many industrial key players investing on this technology. For example, Knowledge Graphs (KGs)  [32]  have emerged as the main vehicle for representing factual knowledge on the Web and enjoy a widespread adoption  [48]. Moreover, several tech giants are building KGs to support their core business. For instance, the KG developed at Microsoft contains information about the world and supports question answering, while, at Google, KGs are used to help Google products respond more appropriately to user requests by mapping them to concepts in the KG. The use of KBs and KGs in such scenarios is not restricted only to database-like analytics or query answering: KBs play also a central role in neural-symbolic systems for efficient learning and explainable AI  [23, 36].
A KB can be viewed as a classical database B with factual knowledge and a set of logical rules P , called program, allowing the derivation of additional knowledge. One class of rules that is of particular interest both to academia and to industry is Datalog  [2]. Datalog is a recursive language with declarative semantics that allows users to succinctly write recursive graph queries. Beyond expressing graph queries, e. g. , reachability, Datalog allows richer fixed-point graph analytics via aggregate functions. LogicBlox and LinkedIn used Datalog to develop high-performance applications, or to compute analytics over its KG  [3, 46]. Google developed their own Datalog engine called Yedalog  [21]. Other industrial users include Facebook, BP  [10]  and Samsung  [40]. Materializing a KB (P, B) is the process of deriving all the facts that logically follow when reasoning over the database B using the rules in P.
Materialization is a core operation in KB management. An obvious use is that of caching the derived knowledge. A second use is that of goal-driven query answering, i. e. , deriving the knowledge specific to a given query only, using database techniques such as magic sets and subsumptive tabling  [8, 9, 13, 55]. Beyond knowledge exploration, other applications of materialization are data wrangling  [35] , entity resolution  [37] , data exchange  [26]  and query answering over OWL  [44]  and RDFS  [16]  ontologies. Finally, materialization has been also used in probabilistic KBs  [56]. Problem. The increasing sizes of modern KBs  [48] , and the fact that materialization is not a one-off operation when used for goal-driven query answering, make improving the materialization performance critical.
The chase, which was introduced in 1979 by Maier et al. [42] , has been the most popular materialization technique and has been adopted by several commercial and open source engines such as VLog  [58] , RDFox  [47]  and Vadalog  [10]. To improve the performance of materialization, different approaches have focused on different inefficiency aspects. One approach is to reduce the number of facts added in the KB. This is the take of some of the chase variants proposed by the database and AI communities  [11, 24, 49]. A second approach is to parallelize the computation. For example, RDFox proposes a parallelization technique for Datalog rules  [47] , while WebPIE  [59]  and Inferray  [54]  propose parallelization techniques for fixed RDFS rules. Orthogonal to those approaches are those employing compression and columnar storage layouts to reduce memory consumption  [34, 58].
The former TGs are computed exclusively based on the rules of the KB and allow us to reason over any possible instance of the KB making them particularly useful when the database changes frequently. In contrast, instancedependent TGs are computed based both on the rules and the data of the KB and, thus, support reasoning over the given KB only. We show that not every program admits a finite instance-independent TG. We define a special class, called FTG, including all programs that admit a finite instance-independent TG and explore its relationship with other known classes. As a second contribution, we propose algorithms to compute and minimize (instance-independent) TGs for linear programs: a class of programs relevant in practice. A program P not admitting a finite instance-independent TG may still admit a finite instance-dependent TG.
EVALUATION We implemented Algorithm 1, TG-guided reasoning over a fixed TG (Def. 5) and Algorithm 2 in a new opensource reasoner called GLog. GLog is a fork of VLog  [60]  that shares the same code for handling the extensional relations while the code for reasoning is entirely novel. We consider three performance measures: the absolute reasoning runtime, the peak RAM consumption observed at reasoning time, and the total number of triggers. The last measure is added because it reflects the ability of TGs to reduce the number of redundant rule executions and it is robust to most implementation choices.
The performance improvements are attributed to a more efficient strategy for filtering out redundancies: TGs allow filtering out redundancies after reasoning has terminated, in contrast to the chase, which is forced to filter out redundancies right after the derivation of new facts. This strategy is more efficient because we can use a single n-way join rather than multiple binary joins to remove redundancies. With regards to memory, GLog has similar memory requirements with VLog, while it is much more memory efficient than RDFox and the commercial engine COM. C2: The TG computation overhead is small. The time to compute and minimize a TG in advance of reasoning is only a small fraction of the total runtime, see Table  2. We argue that even if this time was not negligible, TG-guided reasoning would still be beneficial: first, once a TG is computed reasoning over it is multiple times faster than the chase and, second, the same TG can be used to reason over the same rules independently of any changes in the database.
Results on scalability We used the LUBM benchmark to create several KBs with 133M, 267M, 534M, 1B, 2B, 4B, 8B, and 17B facts respectively. Table  7  summarizes the performance with the Datalog program LUBM-L. Columns are labeled with the size of the input database. Each column shows the runtime, the peak RAM memory consumption, and the number of derived facts for each input database. We can see that GLog can reason with up to 17B facts in less than 40 minutes without resorting to expensive hardware. We are not aware of any other centralized reasoning engine that can scale up to such an extent.
RELATED WORK One approach to improve the reasoning performance is to parallelize the execution of the rules. RDFox proposes a parallelization technique for Datalog materialization with mostly lock-free data insertion. Parallelization has been also been studied for reasoning over RDFS and OWL ontologies. For example, WebPIE encodes the materialization process into a set of MapReduce programs while Inferray executes each rule on a dedicated thread. Our experiments show that GLog outperforms all these engines in a single-core scenario. This motivates further research on parallelizing TG-based materialization. A second approach is to reduce the number of logically redundant facts by appropriately ordering the rules. In  [59] , the authors describe a rule ordering that is optimal only for a fixed set of RDFS rules.
We now turn our attention to the applications of materialization in goal-driven query answering. Two well-known database techniques that use materialization as a tool for goal-driven query answering are magic sets and subsumptive tabling  [8, 9, 53, 55]. The advantage of these techniques over the query rewriting ones, which are not based on materialization, e. g. ,  [6, 19, 29] , is the full support of Datalog. The query rewriting techniques can support Datalog of bounded recursion only. Beyond Datalog, materialization-based techniques have been recently proposed for goal-driven query answering over KBs with equality  [13] , as well as for probabilistic KBs  [56] , leading in both cases to significant improvements in terms of runtime and memory consumption. The above automatically turns TGs to a very powerful tool to also support query-driven knowledge exploration.
Historical approach A focus on the positive experience: Specific add-ones developed to communicate and exchange on the results but also to improve graphical representation and thus user experience. SimaPro 9. 1. 1. (Release in September 2020) provides a software as a service mode (PRé Sustainability, 2021). You don't need anymore to connect to Windows Server to get access to the SimaPro Database Server. It is thus easier to use that a client-mode. Building of communities: "This paper presents a novel process of developing the LCA Calculator with inputs from community members to support community infrastructure codesign. " This last point can also be considered as a part of cultural computing aspect for a sustainability HCI context as the method used brings confidents in the system.
, 2015). But even though the methodology is questioned, the way we use a LCA software is not actually a real discussion (the man-computer paradigm is still strongly present). Indeed, the humancomputer interaction has not moved for LCA specialists, even if some initiatives try to transform LCA as a collaborative software (example: last version of SimaPro, (PRé Sustainability, 2021)) to foster collaboration among life cycle analysts (cooperative paradigm). This lack of changes in LCA HCI might be an obstacle to the democratization of LCA among organizations. Also, it might not correspond to the interaction expect from the young generation of engineers. LCA has emerged as a methodology in a world which was starting to realize how human activities could impact our planet. Earth System Sciences have gained maturity since it was coined in 1983 and scientists are now conscient of human impacts.
Why future practitioners? We are not interested in the current practitioners because they are very much embedded in their daily professional habits. Instead, we are interested in the new generation of engineers and how this new generation conceives of human-technology interactions. The future engineers that we have observed have the characteristic of being interested in sustainability (specific choice of training and involvement in sustainable associative activities). This section is focused on an observation of a pedagogical situation. Future practitioners are being observed. This group was chosen because they are in a learning phase of the environmental analysis method and the openLCA tool. Taking individuals who are in a learning period allows us to see the problems that arise very quickly for people wishing to use life cycle assessment software.
Description of the situation and method The focus of the observation was the understanding of LCA of a class of 30 Master students (second year of master). By groups of 5 to 7, the students were involved in a LCA project. In this study, a specific focus is made on their expectations from a LCA software (openLCA in this case study). We observed the group of students during 3 tutorials of 2 hours each. During each tutorial we noted, questions of students on data, information on LCA and their remarks on the software. Also, following a qualitative analysis  (Lejeune, 2019) , we made an account, followed by a micro analysis, followed by a coding moment.
In front of a LCA software (openLCA) This part deals with expectations of students on the software. Table  3  presents the different students remarks expressed when they were learning how to handle LCA software. The situation of each quote is explained on the same line. The recurrence of the questions asked was not recorded at the time the observation was made. We can identify three different categories of issues: issues on data (situation N°1 and 2), issues on information (situation N°3 and 4), issues on modeling (situation N°5 and 6). Some students were actually disappointed to realize they could not represent the entire complexity of their product through the software. Also, some students were surprised that all of the data from LCA databases were not complete and that sometimes, they had to choose a data which does not reflect the exact reality of their product.