dataesr/scientific-paragraphs-categorization huggingface dataset:
This expectancy of precision from the software and the information system around the field of LCA reveal the trust that young students put in engineering software. This expectancy is coherent with the current metaparadigm defined by Hilbert which is about having easily available data and knowledge. The pedagogical situation was a project group which leads to collaborative work. Students faced the difficulty of working collaboratively on a LCA software. This difficulty was about the type of HCI that has been implemented in it. It is a man to computer interaction without any social activity needed on the software. This conclusion cannot be generalized without a similar training on other LCA software. Students were looking for a data in a LCA database to complete their LCA. "We don't find the corresponding data in EcoInvent"; "There is no data, not even close [to the one you are looking for], how do you do it?" 2 Students were looking for the correct way to insert data into their model.
"Should we create a new data or should we select one directly in the database?" 3 Students were trying to reproduce a LCA from a LCA report found on the Internet. "It was not precised in the report"; "We lack this information" 4 Students were reading the description of each data and were trying to understand if this data was the closest to what they were looking for. "The information is here but we do not understand it" 5 Students were trying to understand the logic of modeling in openLCA. "How do we model the end of life?" 6 Students were trying to understand the logic of modeling in openLCA. "How do we model transport processes?".
CONCLUSION In this paper we underlined that the socio-technical tool openLCA does not provide a satisfying user experience for a category of young designers. This digital tool appeared when human-computer interaction was mainly reduced to a text editing activity and calculation work. All those information allow us to conclude that openLCA software has been stuck in a man-computer paradigm specific to the 1970s and has not yet entered into a new paradigm (or not entirely in another paradigm). We don't know yet how this result can be generalized to all LCA software, but we have seen that a few LCA software provide multi-user exchange within the technology interaction. As openLCA has remained mainly in a man-computer paradigm, we can also add that the HCI analysis of this environmental tool has highlighted the "individual-consumer" approach  (Heitlinger et al.
Characteristics described in the literature: The forum ask. openlca. org can be used by new practitioners to get information about openLCA functioning. "Most packages do offer some form of graphical output" (Rice et al. , 1997) "A facility available on some of the software packages is that of network connection. This allows several users access to the same database simultaneously. " (this concerns Ecobalance UK's TEAMTM, SimaPro 3. 1, PIA and LMS Eco- Inventory). "Both "Boustead and Pira offer a site license for the their software, allowing multiple users, but the packages are not designed for network operation. " (Rice et al. , 1997) Social computing (we based our research on the 3 main characteristics given: "the disappearing computer;.
Since its publication in 2019  (Lecarpentier et al. ) , WALTer has undergone some changes aiming at improving the performances of the simulations, enhancing its realism and improving its ability to simulate mixtures of varieties, reusing several models distributed in the OpenAlea platform  (Pradal et al. , 2008 (Pradal et al. , , 2015)). Thus, in the new version of WALTer, the representation of the leaves is based on the model by  Fournier and Pradal (2012). The shape of the blades is based on the equations described in  Dornbusch et al. (2009)  and it is now possible to represent curved leaves based on the formalism described by  Perez et al. (2016). The formalism for the dimensions of the leaves remains unchanged. In addition, the discretization of the sky was optimized by the use of a TURTLE sky  (den Dulk, 1989)  as implemented in the Python package  Alinea.
astk [package python]. (version 2. 1. 0, 2019). Furthermore, the option to consider an infinite periodic canopy, which is integrated in the radiative model  (Chelle et al. , 1998) , was implemented in WALTer. Thus, it is no longer necessary to simulate additional plants to discard border effects. The computational cost of the simulations was further reduced by removing all dispensable (i. e. , non-visible) organs from the 3D representation of plants. Moreover, a project manager, that serializes parameters, inputs, and outputs for each simulation, has been designed to distribute the computation on high-performance computing infrastructure. Modifications were also implemented to WALTer to allow the simulation of complex, heterogeneous canopies, such as varietal mixtures and populations, in which plants can differ from each other in many parameters.
Finally, a fitness module has been implemented to allow the computation of the total fitness of the plot (F tot ) for each simulation. This new output is used as a proxy of the number of kernels produced by the plot and is 1 The GAI is the ratio of photosynthetic surface of a plant to ground area. computed by WALTer as the sum of the fitness of all the axes in the plot. The computation of the fitness of an axis is based on the following equation: F axis = PAR i45 T m45 where F axis is the fitness of the axis (expressed in molPAR• • Cd -1 ); PAR i45 is the mean daily amount of PAR intercepted by the axis during the 45 days preceding flowering (in molPAR•d -1 ); and T m45 is the mean temperature during the same period (in • C). This equation is an adaptation of the photothermal quotient  (Nix, 1976) , which has been demonstrated to be linearly correlated to the number of grains produced in wheat  (Midmore et al.
To build the Kriging metamodel, we used a set of WALTer runs following a 10 dimensions maximin latin hypercube sampling (LHS;  McKay et al. , 1979; Johnson et al. , 1990)  of 5,000 simulations generated with the DiceDesign R package  (Dupuy et al. , 2015)  with the ranges detailed in Table  1. For each output (N ears , L perc and F tot ), a Kriging approximation was generated using the DiceKriging R package  (Roustant et al. , 2012). The sensitivity R package  (Iooss et al. , 2017)  was then used to estimate the Sobol indices. To be able to compute the overyielding for every mixture, another set of WALTer runs was generated from a five dimensions maximin LHS of 1,000 simulations. This second LHS only includes simulations of pure stands (no trait differential in the stand) and the ranges explored for each input parameter corresponded to the minimum and maximum values detailed in Table  1.
For this output, the highest values of OY are obtained for the extreme values of L B max -diff and GAI c -diff by mixing plants with large leaves and high values of GAI c with plants with small leaves and low GAI c values. For N ears , the highest values of OY are also obtained for the extreme values of GAI c -diff, but for values of L B max -diff close to 0. For mixtures with a differential in both L B max and GAI c , the OY of N ears is more important when the cultivar with the largest leaves is also the one with the highest value of GAI c , as is the case for the OY of L perc. In contrast, the OY of F tot is the highest when mixing plants with large leaves and low GAI c values with plants with small leaves and high GAI c values. Interestingly, despite the observed differences between the combinations of parameters leading to the highest OY for N ears and F tot , N ears is positively correlated to F tot in the overall dataset (data not shown).
Indeed, diversity in phenology could lead to temporal complementarity in addition to the spatial complementarity offered by differences in traits of the aerial architecture. Several studies have emphasized the impact of phenological traits on the performance of wheat mixtures  (Borg et al. , 2018; Montazeaud et al. , 2020). However, these traits were not yet considered in our study because it would require the integration of a specific module to WALTer to ensure that modifications in phenology and their implication on plant development are reliably simulated. Future work could also include the exploration of the effects of some agricultural practices. We simulated balanced mixtures (with 50% of each genotype) sown at a density of 200 plants/m². However, it is expected that both sowing density and the proportions of the two cultivars in mixture should have an impact on the results (Grace and  Tilman, 1990).
Proposed solutions To investigate these research questions, we propose to analyze and compare the ability of the main families of RSs to provide diversified recommendations. To this aim, we compare seven different recommendation models representing the four different types of RSs on three real-world datasets (related to movies, books and anime). For non-personalized recommenders, we consider the item popularity-based approach  (Ferrari Dacrema et al. , 2019) ; For CBF recommenders, we consider a Linked Open Data based approach  (Di Noia et al. , 2012) ; For CF recommenders, we consider a memory-based approach  (Sarwar et al. , 2001) , a model-based approach  (Koren et al. , 2009)  and a state-of-the-art approach based on deep neural networks  (He et al. , 2017)  and for hybrid recommenders we consider an approach based on knowledge graph embeddings  (Palumbo et al.
Top popular items approach (TopPopular) Item popularity is known as an important factor of recommendation  (Abdollahpouri, 2019). As a non-personalized approach, the model provides each user with the most popular items yet to be rated. Formally, the popularity of a given item  is defined as the number of users  in the user set  who have rated it, with   being the set of items rated by : 𝑃 𝑜𝑝(𝑖) = |{𝑢 ∈ 𝑈 |𝑖 ∈ 𝐼 𝑢 }| (1) In spite of its simple approach, a recent study  (Ferrari Dacrema et al. , 2019)  has shown that the TopPopular model could lead to more accurate recommendations on some datasets than some recently proposed deep neural network-based models, such as CMN  (Ebesu et al. , 2018).
Knowledge graph embeddings-based approach (KGE) Knowledge graphs (KG) are data structures that allow to represent both human-readable and machine-understandable ontological domain knowledge. KGs are typically composed of ⟨, , ⟩ triples in which  and  are subject and object entities while  is the predicate indicating the corresponding semantic relation of  toward  (e. g. , ⟨Brad_Pitt, starring_of, 12_Monkeys⟩). Famous largescale KGs such as DBpedia and Google Knowledge Graph 3  have been successfully applied to real-world applications. Although they are effective in representing heterogeneous information, the handling of such KGs remains difficult due to their underlying graph structure, notably for large-scale KGs. Knowledge graph embedding is a technique that makes the handling of a KG more flexible.
In the past few years, knowledge graph embedding models have been proven highly effective for recommender systems  (Palumbo et al. , 2020 (Palumbo et al. , , 2018a (Palumbo et al. , , 2018b)). In the RS context, two knowledge graphs can be constructed: ICKG (Item Content Knowledge Graph) and UPKG (User Preference Knowledge Graph). The former contains triples describing knowledge related to item contents (e. g. the director of a movie) while the latter represents knowledge related to user preferences (e. g. the movies liked by a user). As proposed in  Palumbo et al. (2018b) , the KGE recommender that we consider in this study is based on the whole knowledge graph (denoted as HybridKG) combining ICKG and UPKG, in which user preference data (user ratings) and item knowledge are hybridized. Specifically, entities within HybridKG are made of users, items and item property objects while relations within HybridKG are made of user preferences (i.
Though related, the authors discussed that calibrated recommendations are quite different from the relative diversity perspective. The main difference is that calibrated recommendations are restricted to a particular feature such as a movie's genre, whereas for the diversity measurement, it might be better not to use such a restriction since items (films) have more than one feature (e. g. directors, actors etc. ). One may also consider computing the KL divergence between multivariate attribute vectors, for instance. Nevertheless, it is often difficult to gather exactly the same attribute data for all items (e. g. missing values in the knowledge graph), and the KL computation is not straightforward.
Datasets and preprocessing Our experiments are based on three real-world datasets in different recommendation domains: MovieLens-1M 4  (the movie domain), Anime 5  (the Japanese manga domain) and LibraryThing 6  (the book domain). MovieLens-1M is a well known benchmark dataset containing 1 million ratings from 6,040 users on 3,900 movies. The Anime dataset is a public Kaggle dataset containing 1,597,830 ratings from 37,100 users on 12,294 anime. LibraryThing is a book recommendation dataset that contains 626,000 ratings of 7,112 users on 37,231 books. The DBpedia knowledge base is an indispensable support for our study as we used it to build the CBF recommender, to construct the ICKG and UPKG knowledge graphs (see their definitions in Section 2. 6) for the KGE recommender and to compute item semantic similarities for the estimation of recommendation diversity (cf.
Eq. (  7 )). To leverage this gigantic source of knowledge, items need to be mapped with corresponding DBpedia entities that are identified through their URIs. Items in the MovieLens-1M and the LibraryThing datasets have been mapped by previous work  (Di Noia et al. , 2012)  and the mappings are publicly available. The DBpedia knowledge graph has been evolving since these mappings were done. Some of them are now invalid, pointing to entity URIs that no longer exist. So, we corrected these mappings manually. For the Anime dataset, we first extracted all DBpedia entities of the dbo:Manga type and their English labels by using SPARQL queries through the DBpedia endpoint. 7 We then compared manga labels of DBpedia entities and anime names provided in the dataset's meta-data to define a similarity between Anime items and  The original anime ratings contain implicit preference values i.
e. -1, which means that the user watched the anime without rating it. These ratings were excluded from our experiments. DBpedia entities. An Anime dataset item was then mapped to a DBpedia entity when the normalized Levenshtein similarity between their labels was larger than 0. 95. The mapping procedure allowed us to obtain 3,301 items of the movie database (85%); 656 items of the anime database (5%) and 11,695 items of the book database (31%) successfully mapped to DBpedia entities. We then removed unmapped items, and the associated ratings, from the corresponding datasets. It is worth noting that the dataset preprocessing leads to removing many items (especially from the anime dataset); however, most of the removed items have few ratings anyway. The most famous (manga, book, movie) items are often those present in DBpedia and having received the most ratings; whereas the less known items, often missing from DBpedia, tend to have few ratings.
Hence, this pre-filtering of the datasets is not as drastic as it may seems. Even for the anime dataset, in which only 5% of the items are kept, 63% of the ratings are preserved. Table  1  summarizes the main characteristics of the three preprocessed datasets. The sparsity of a dataset represents the proportion of the missing values among the whole user-item rating matrix. Finally, we split each dataset into the corresponding training set (80% of ratings) and test set (20% of ratings).
Knowledge graph constructions For each dataset, we built two knowledge graphs, ICKG and UPKG. To build the three ICKG instances, we selected a subset of properties within the DBpedia ontology, as proposed in  Palumbo et al. (2018b). The properties selected for each dataset are listed in Table 2. To build the three UPKG instances, the relation ''likes" should be established between user and item entities. Following Di Noia et al. (  2016 ),  Palumbo et al. (2018b) , we considered that a user  likes an item  if the rating  , ≥ 4 for the MovieLens-1M dataset (where ratings vary between 1 and 5) and  , ≥ 8 for the Anime and the LibraryThing datasets (where ratings vary between 1 and 10). Table  3  illustrates the number of triples encompassed in the ICKG and UPKG instances built for each dataset.
Recommender constructions We followed the proposition of  Steck (2013)  that all the items in the catalog are eligible for recommendation. Hence, in our experiments, the () set contains all the items that  has not rated before, regardless of whether they are in the training set or in the test set. In our experiments, we compare seven recommenders of four different types that are listed in Table  4. For each dataset, we proceed as follows to construct the recommenders: (1) hyper-parameters tuning for each of the compared RSs toward accuracy and (2) models training with optimal hyper-parameters. (Ferrari Dacrema et al. , 2019)  Non-personalized Method based on item popularity CBF-TopPopular (Eq. (  6 )) Hybrid Method combining CBF and TopPopular IBCF  (Sarwar et al. , 2001)  Collaborative filtering Method based on item-item collaborative filtering SVD  (Koren et al.
, 2009)  Collaborative filtering Method based on latent factors (matrix factorization) KGE  (Palumbo et al. , 2018b)  Hybrid Method based on knowledge graph embeddings DNN (NeuMF)  (He et al. , 2017)  Collaborative filtering Method based on deep neural networks Hyper-parameters tuning. Tuning hyper-parameters of machine learning models is an optimization step aiming at finding the optimal combination of hyper-parameters that leads to the best performances. This is a crucial step for modern recommendation models as the behavior of recommenders could be strongly impacted by the used dataset and hyper-parameters  (Jannach et al. , 2015). We adopted the Bayesian Optimization (BO) approach  (Snoek et al. , 2012)  to tune hyper-parameters for the compared RSs. Compared with the random search and the grid search methods which are generally time-consuming, BO is an iterative procedure that takes into account the past choices made in order to consider the next set of hyper-parameters to evaluate, thus reducing largely the computation time.
e. CBF-TopPopular, IBCF, SVD, DNN and KGE, we ran 20 BO iterations on each of the three datasets, to tune each model's hyper-parameters on each dataset. To this end, we opted firstly to split each training set (representing 80% of the dataset) into two sets: a sub-training set (80% of the training set) and a validation set (20% of the training set). The BO process takes the sub-training sets to train models and the hyper-parameters are tuned on the corresponding validation sets, by optimizing (maximizing) the mean average precision (MAP) metric (cf. Section 4. 4). After the BO step, the resulting optimal hyper-parameter configurations were used to train each model on each entire training set (80% of the corresponding dataset) and the fine-tuned models were finally tested on each test set. The sets of hyper-parameters that we tuned for each model are the followings: • CBF-TopPopular: the weighting factor.
In Eq. (  6 ), we opted to normalize the popularity score of items, i. e. () into [0, 1] so that it has the same scale as  (, ). • IBCF: the number  of the target item 's neighbors i. e. |  | in Eq. (  4 ). • SVD: the number of factors    , the number of training epochs of stochastic gradient descent, the learning rate  and the regularization term. • KGE: we trained the TransE model on HybridKG (hybridization of ICKG and UPKG, cf. Section 2. 6) for the corresponding dataset. TransE is a state-of-the-art knowledge graph embedding model that has been shown to perform better than alternative translation models on recommendation tasks  (Palumbo et al. , 2018a). Thus, the hyper-parameters include the embedding dimension , the margin loss , the learning rate , the batch size  ℎ and the number of training epochs.
• DNN: the batch size  ℎ , the number of embedding factors    , the number of hidden layers in the MLP network   , the learning rate , the number of epochs and the number of negative samples for training. The implementation of these models was achieved with open-source packages. Specifically, Hug (2020)'s Surprise Python library 8 was used to tune and train the IBCF and SVD models. For the DNN model, we used an available PyTorch 9 implementation. 10 The learning of the KGE model was done with the Pykeen package  (Ali et al. , 2019) , i. e. a reference package for learning knowledge graph embeddings. The configuration details of the hyper-parameters tuning along with the found optimal hyper-parameters for each dataset is provided in Tables A. 7 and A. 8 in Appendix. All resources of our study (datasets, scripts, mappings, embeddings etc.
Chosen parameters for studying diversification within recommenders First, each of the tested RSs is supposed to recommend a list of  = 10 items with the highest predicted relevance scores. The recommenders are then evaluated in terms of accuracy and diversity (both absolute and relative). Second, to analyze the impact of the diversification post-processing on each RS, we ran the bounded greedy algorithm (Algorithm 1) with two different objective functions, i. e. the classic (Eq. (  11 )) and the personalized (Eq. (  12 )) ones. For the bounded greedy algorithm, the bound  was set to 100 ( 2 ), i. e. () contains the top-100 rated items. For different recommenders and datasets, the ranges of their predicted scores, i. e. the (⋅) term in the objective functions, are quite different. To avoid possible bias, we opted to normalize these (⋅) values.
𝑟𝑒𝑙(⋅)-𝑟𝑒𝑙 𝑚𝑖𝑛 𝑟𝑒𝑙 𝑚𝑎𝑥 -𝑟𝑒𝑙 𝑚𝑖𝑛 with   and   being the minimum and the maximum values of the (⋅) function for a specific method and dataset. Finally for the diversification factor , we tested 10 different values ranging in [0, 0. 9] with the step=0. 1. Last, as our study is fully based on offline experiments, we studied the impact of the bound size  of the bounded greedy post-processing on the RSs' performances. Indeed, larger bound sizes (e. g. > 100) may promote RSs to find more diversified items, but it would also sacrifice more accuracy. For each dataset and metric, the best score is bolded, the second-to-best is underlined and the third one is in italics.
Accuracy and diversity performances of the tested RSs Table  5  summarizes the accuracy metrics obtained by the seven tested RSs (of 4 different families) on the three considered datasets. Let us first consider the MovieLens and Anime datasets. RSs based on more recent techniques (i. e. KGE for knowledge graph embeddings and DNN for deep neural networks) are always more accurate than other standard models. Indeed, for all the 8 configurations (4 measures × 2 datasets), DNN is ranked the best model 7 times and KGE once. Item popularity-based models (TopPopular and CBF-TopPopular) are slightly less accurate than the best models with CBF-TopPopular being slightly better than TopPopular. Note that a simple, while fine-tuned, mix of CBF and TopPopular leads to 6 times (out of 8) the second most accurate model.
The SVD and the CBF models have comparable accuracy performances on these two datasets and the IBCF approach has the lowest accuracy. For instance, on the MovieLens dataset, the MAP is ∼ 0. 19 for DNN, KGE and CBF-TopPopular; around 0. 16 for TopPopular; ∼ 0. 09 for SVD and CBF and as low as 0. 04 for IBCF. The performances rank is unchanged when considering the MAP for the Anime dataset, while the difference between DNN (∼ 0. 43) and KGE/CBF-TopPopular (∼ 0. 35/∼ 0. 36) is larger. The same observation can be made for other metrics on the Anime dataset, e. g. ∼ 0. 25 for DNN v. s ∼ 0. 18 for KGE and CBF-TopPopular, considering F1-Measure. Surprisingly enough, the accuracy results are quite different on the LibraryThing dataset. There, recommendation approaches leveraging item knowledge, i.