dataesr/scientific-paragraphs-categorization huggingface dataset:
• An EM-based on-line learning algorithm is adopted to SemiTMC-GMM for making the algorithm work on-line. The remaining of the paper is organized as follows. Section 2 depicts the state-of-the-art works in the field of activity recognition using wearable sensors. Section 3 gives the definition of conventional TMC model and gradually extends the model to SemiTMC-GMM. Then, how to apply the proposed model to recognize lower limb locomotion activities is presented in detail at the end of this Section. Section 4 depicts both batch mode and on-line mode parameter learning for the proposed model. In Section 5, the proposed recognition algorithm is tested on two datasets: one is the public dataset  [10] , and the other is our own dataset. In addition, the performance of the proposed algorithm is discussed compared to the competitive works.
Related Works Numerous works have investigated human activity recognition (HAR) in the last decade. The methodologies used recently can generally be classified into two dominant categories: (i) traditional classifiers; (ii) deep learning methods. For the first category, numerous classiffiers have been investigated. Parri et al. [11]  proposed a fuzzy-logical classifier to identify lower limb locomotion mode, with the assistance of gait phases. The authors developed a lower limb wearable robot system that can help impaired people to perform locomotion activity. Chen et al. [12]  proposed a robust activity recognition algorithm based on principal component analysis (PCA) and on-line support vector machine (OSVM), the algorithm obtained a robust recognition accuracy over a smartphone dataset collected in six different orientations.
The work  [18]  proposed convolutional recurrent neural network to recognise daily activity; their algorithm gained an improvement of 6% compared to the state-of-the-art works. Recently, as reported in  [19] , transfer learning and semantic approach have raised great research interest. Bao  [20]  and Rokni  [21]  used transfer learning to automatically construct model for newly added wearable sensors; they obtained an accuracy enhancement between 9. 3-10%. However, the recognition accuracy highly depends on the performance of labeling from source devices, thus it still requires a reliable method for recognition on a single sensor. Some other methods can also be applied to dedicated applications and obtain good results. Schneider et al. [22]  proposed an automatic extraction and selection method of highly relevant features, the method was tested on eight datasets and obtained a general accuracy over 90%.
For simplification, the dimension of L ( ) is set to 9. This value was determined by our experience, a too small value will make the results of SemiTMC-GMM no difference from that of TMC-GMM, while a too large value will cost too much time for running the code. The performance of different GMM components number (κ) is evaluated on two datasets, as depicted in Section 5. The observation Y is the feature extracted from the sensor readings. The utilized features are the sliding mean value and standard deviation, by calculating the mean value and standard deviation of acceleration and angular rate within a sliding window length. Since IMUs measure 3-dimensional acceleration and angular rate, then the dimension of the observation Y (w) equals to 12. The initialization of the hidden states is the same as the one in our previous work  [7] , so it will not be repeated here.
Experimental Results Two datasets are used to validate the proposed algorithm. The first dataset is the Sports and Daily Activities (SDA) dataset  [10] , in which eight subjects were enrolled to perform 19 daily and sports activities while wearing five Xsens MTx (Details of Xsens MTx can be found in http://www. xsens. com/en/general/mtx)) IMUs on their torso, left arm, right arm, left thigh, and right thigh, all the sensors measure the acceleration and angular rate of the body parts where the sensors are placed. The five sensor placements are determined in this dataset because the involved activities are not only lower limb locomotion activities but also include static activities and upper limb activities, such as sitting, lying, rowing, and playing basketball. So they used the 5 sensors to collect the motion data from different parts of the body.
These 11 locomotion activities of SDA dataset are referred to as D1A1 to D1A11 in the remaining of this paper. In the dataset, the subjects performed each activity for about 5 min separately, and each activity was divided into 60 segments of 5 s. Therefore, there are 60 × 8 = 480 segments for each activity. In order to make the dataset available for our algorithm, we firstly combined the 60 segments of one activity from one subject to recover the 5 min activity, then combined the data of the same activity from all the subjects to form 40 min for each activity, the final data was obtained by combining the 11 activities. Thus, the duration of the data is 11 × 40 = 440 min. There are only 7500 samplings for each experiment of SDA, and the data length is not long enough to use on-line EM recognition.
Therefore, we utilize the second dataset for the validation of the proposed on-line EM algorithm. This second dataset, described in  [7] , is called Locomotion of Foot-mounted IMU (LMFIMU) dataset (the dataset and its details are available on the website: https://github. com/unilee/TMC_LowerLimbActs). Ten subjects were enrolled to perform a specific experiment that lasts nearly 30 min with a Shimmer3 (Shimmer3 GSR+, details at the manufacturer's site http://www. shimmersensing. com/images/uploads/docs/ConsensysPRO_Spec_Sheet_v1. 1. 0. pdf) IMU mounted on the right shoe. The sensor is placed on the shoe in LMFIMU but not the thigh as in SDA dataset is because both feet and thighs have a periodic pattern when people are performing the lower limb locomotion activities. In fact, the sensor can be placed at anywhere of the lower limbs in this study.
So foot is chosen in the LMFIMU dataset to show that the proposed algorithm is not restricted to only one sensor position. Because of the enrolled 10 healthy subjects, left foot and right foot have the same behavior, then the sensor is placed on the right shoe of each subject. Each subject was asked to perform one experiment, which contained two identical sections of a sequence of four locomotion activities: walking, running, stair ascent and stair descent. Therefore, the performance of the second section will be improved compared to the first section, if the on-line algorithm can gradually learn the activity pattern of the subject. The four locomotion activities are referred to as D2A1 to D2A4 in the rest of this paper. The sensor sampling rate was set to 100 Hz, so the data length is long enough for the on-line EM algorithm.
SDA Dataset The batch mode recognition is tested by a leave-one-out cross-validation (LOOCV) strategy, i. e. , taking one subject for testing and the others for training, then make the test for all the subjects. The sliding window length of feature extraction is set to 5. The length is set based on our experience; normally it depends on the shortest gait or leg phase of the activities, and a window length larger than the shortest duration may conceal some information of the phase, while too small length may cause a badly calculated standard deviation. Both the SemiTMC-GMM and TMC-GMM model are involved in the validation, and the GMM mixture number κ is set to 1, 3, 6, and 9, respectively. Particularly when κ = 1, the conditioned observation density yields to the conventional Gaussian distribution.
The overall accuracy of batch mode recognition on SDA dataset is shown in Figure  4a. As it can be seen, SemiTMC-GMM achieves an accuracy improvement of about 2-3% compared to TMC-GMM. The proposed model reaches the highest accuracy of 86. 00% when κ = 6, while the one of TMC-GMM is 83. 76%. Meanwhile, TMC-HIST obtains the lowest accuracy of 77. 91%. Figure  4b  is the plot of Receiver Operating Characteristics (ROC) w. r. t. different κ. The ROC space takes the true positive rate (TPR) as vertical axis and false positive rate (FPR) as horizontal axis, a better recognition performance is more closer to the upper left corner. So we can see that SemiTMC-GMM (red markers) obtains a better performance than the one of TMC-GMM (cyan markers). Table  1  shows the sensitivity, specificity, F1 score, and Matthews correlation coefficient (MCC) of each individual activity.
Particularly for the sensitivity of each individual activity, it equals to the accuracy of corresponding activity. Activities D1A1 to D1A3 are recognized with relatively poor performance, it is because that these three activities are all walking and are very easily misclassified. Table  1. The sensitivity, specificity, F1 score, Matthews correlation coefficient (MCC) value of the batch mode recognition, for each activity of SDA dataset, using the sensor placed on right thigh. Up: TMC-HIST; middle: TMC-GMM when κ = 6; and down: SemiTMC-GMM when κ = 6. As reported in  [20] , the classifiers of kNN, SVM and decision tree are tested on SDA dataset using all the five sensors. The accuracies are 78. 97%, 84. 03%, and 84. 63%, respectively. In  [21] , the authors used SDA dataset and showed single sensor recognition accuracy of four classifiers: kNN, decision tree, discriminant analysis, and Naive Bayes.
Specifically for the right leg sensor that is used in our paper, the four classifiers obtained accuracies of 81. 72%, 78. 78%, 87. 03%, and 76. 93%. Therefore, we can state that SemiTMC-GMM outperforms the generic classifiers like kNN, SVM, decision tree, and Naive Bayes, and obtains a similar performance of discriminant analysis. On the other hand, the authors in  [31]  used CNN to recognize human daily activities in OPPORTUNITY dataset  [32] , which contains activities, such as open (close) door, open (close) drawer, clean table, and drink cup. They obtained an accuracy of 85. 8% by using 23 body-worn sensors, 12 object sensors, and 21 ambient sensors. In addition, for the OPPORTUNITY dataset,  [18]  used CNN to obtain an accuracy of 77. 99% by using the body-worn sensors only, while in  [33] , CNN obtained an accuracy of 93.
LMFIMU Dataset For this dataset, the size of sliding window for computing features is set to 15, and it is also determined by our experience. Firstly, the batch mode recognition is performed using LOOCV strategy. Figure  5a  shows the recognition accuracy w. r. t. different κ. The accuracy of SemiTMC-GMM when κ = 9 is 95. 16%, while the one of TMC-GMM is 92. 57%. Meanwhile, the choice of κ has less impact on accuracy for SemiTMC-GMM. The recognition accuracy obtained by TMC-HIST is 80. 42%, which is lower than the ones of TMC-GMM and SemiTMC-GMM when κ > 1. The ROC shown in Figure  5b  implies that SemiTMC-GMM outperforms the TMC-GMM in a general view. Table  2  shows the sensitivity, specificity, F1 score, and MCC of each individual activity. By comparing the batch mode recognition shown in Tables  1  and 2 , both TMC-GMM and SemiTMC-GMM outperform TMC-HIST.
It means that considering the observation correlation improves the recognition performance. As a matter of fact, Figures 4a, 5a and 6a show that introducing semi-Markov structure into the TMC model can improve the accuracy. Meanwhile, using GMM with κ > 1 also improves the recognition significantly. But it does not mean that using a larger κ allows higher accuracy to be achieved. In Figure  4a , the accuracy when κ = 9 is slightly lower than that obtained when κ = 6, it is because the observation of SDA dataset is more closer to a GMM mixture of 6 densities. A too large value of κ may probably lead to an overfitting problem. It is sure that κ can be automatically acquired through the methods, such as BIC  [34]  and AIC  [35] , to make κ consistent with different activities. For simplification in this paper, we manually set κ to 6 for all the activities based on the experimental results.
Then, the on-line EM algorithm is performed to validate the adaptive on-line recognition performances. The proposed algorithm is implemented in MATLAB code, running on a 64-bit system computer with 3. 2GHz CPU and 32G RAM. In the dataset, the average experiment time is 32. 33 min, while the computing time of SemiTMC-GMM when κ = 1, 3, 6, 9 are 9. 72, 14. 72, 21. 53, and 27. 65 min, respectively. Thus, using on-line EM is applicable in on-line scenarios. The window length W l for updating the parameters is set to 1000, which means that parameters are updated every 10 s. Figure  6a  shows the recognition accuracy obtained by LOOCV strategy. The solid lines are higher than the dashed lines which means that the on-line EM algorithm can improve the recognition performance. In addition, the GMM with κ > 1 can significantly improve the accuracy.
Methods The first stage of this research consists of a qualitative review of public authorities' strategies. The results of the qualitative analysis are then compared, with the outcomes of the second stage of the research supported by the open-source software packages Gargantext  [19]  and Gephi (gephi. org, last access: 1 April 2019). As the authors show in a previous study  [33] , these tools can be used to extract quantitative data on the sociosemantic structure of the media debate, i. e. the social actors and issues that are visible in the digital press and social media texts, and the connections that exist between these actors and issues. According to this study, these quantitative data are particularly suitable for multidisciplinary studies and research on the connections between socio-economic systems and physical-environmental systems.
5 The new research, presented in this paper, aims to adapt the computer-aided analysis to a corpus of digital administrative texts. Gargantext is a software package for the exploration of unstructured sets of documents. It combines 'co-word analysis' with 'network representation'. Co-word analysis is a technique developed in the field of scientometrics to map the semantic structure in a database, on the basis of the statistics on the frequency of co-occurrences between pairs of terms. A 'network representation' is an abstract representation of a system, such as a text database, that facilitates a smart navigation. It is a mathematical structure that represents a collection of objects, called 'nodes', that are interconnected through the so-called 'edges'  [32]. The analysis of the network consists of exploring its nodes, its edges, and the pattern of interactions.
Gargantext automatically computes networks where each node represents a key term and each edge represents a co-occurrence relation between two terms. Each of the 12 documents of our dataset was divided into smaller meaning units, since Gargantext can analyse documents with the maximum length of a press article or a scientific abstract. A first list of key terms was extracted through automatic selection computed by Gargantext algorithms. The extraction is computed on the basis of each term occurrence and the other occurrences that characterise all Gargantext database corpora, and on the basis of the co-occurrences that characterise the specific corpus (iscpif. fr/gargantext/, last access: 18 March 2019). The term list was then refined through manual selection in order to keep only those terms that occur at least five times in the corpus and that refer to flood risk management solutions, stakeholders, or infrastructure at risk.
Link prediction and entailment graph induction are often treated as different problems. In this paper, we show that these two problems are actually complementary. We train a link prediction model on a knowledge graph of assertions extracted from raw text. We propose an entailment score that exploits the new facts discovered by the link prediction model, and then form entailment graphs between relations. We further use the learned entailments to predict improved link prediction scores. Our results show that the two tasks can benefit from each other. The new entailment score outperforms prior state-of-the-art results on a standard entialment dataset and the new link prediction scores show improvements over the raw link prediction scores.
, 2008) , DBPedia  (Lehmann et al. , 2015)  and Yago  (Suchanek et al. , 2007) , but they can also be applied to assertions extracted from raw text. In this paper, we explore the synergies between the two tasks. Current entailment graphs suffer from sparsity and noise in the data. The link prediction methods discover new facts that can be used to alleviate the sparsity issue. In addition, they can remove noise by filtering facts that are not consistent with the other facts. We propose a new entailment score based on link prediction ( §3. 1) which significantly improves over prior state-ofthe-art results on a standard entailment detection dataset (5. 1). For example, our method can discover that be elected president of entails run for presidency of by relying on the predicted links concerning the two relations (Figure  1 ).
g. , Obama-Hawaii), where the values are frequencies or pointwise mutual information (PMI) between the relations and the features  (Berant et al. , 2011 (Berant et al. , , 2012 (Berant et al. , , 2015)). While these methods currently hold state-of-the-art results on relation entailment datasets  (Hosseini et al. , 2018) , they suffer from low recall because the feature vectors are usually sparse and do not have high overlap with each other. The link prediction models, on the other hand, can predict the probability of any triple being in the knowledge graph. Using predicted probability scores can hugely alleviate the sparsity problem by increasing the overlap between feature vectors ( §3. 1).
Text Corpus Link prediction models are often applied to existing knowledge graphs such as Freebase  (Bollacker et al. , 2008) , DBPedia  (Lehmann et al. , 2015)  and Yago  (Suchanek et al. , 2007) ; however, we chose to experiment on assertions extracted from raw text. This is because we can then evaluate the predicted entailments on existing entailment datasets with examples stated in natural language ( §4. 3). We use the multiple-source NewsSpike corpus of  Zhang and Weld (2013). The NewsSpike corpus includes 550K news articles and is well-suited for finding entailment and paraphrasing relations as it includes different articles from different sources describing identical news stories. We use the triples released by  Hosseini et al. (2018)   6  who run the semantic parser of  Reddy et al.
Evaluation Datasets We discuss the datasets that we use to test the proposed methods for the entailment detection and the link prediction tasks. Entailment Detection Evaluation. For the entailment detection task, we evaluate on Levy/Holt's dataset  (Levy and Dagan, 2016; Holt, 2018). Each example in the dataset contains a pair of triples where the entities are the same (possibly in the reverse order), but the relations are different. The label of the examples are either positive or negative, meaning that the first triple entails or does not entail the second triple. For example Bartlett was interviewed on television, entails Bartlett appeared on television, but the latter does not entail the former. The dataset contains 18, 407 examples (3,916 positive and 14,491 negative). We use the split of the dataset into development (30%) and test sets (70%) chosen by Hosseini et al.
Comparison We compare the following entailment scores for evaluating on the entailment detection dataset. MC is the entailment score based on the Markov chain (3. 1), when the link prediction scores are computed only for the predicates we have seen in the corpus. While the link prediction method can assign scores to any possible triple, we report this results to check how the Markov chain model performs compared to the other scores that are directly computed for the triples in the corpus. Aug MC is our novel entailment score that is based on the Markov chain, but augments the matrix S of the MC model with new entries. We use the link prediction method to compute scores on the original set of triples as well as new predicted triples. For each triple (r, e 1 , e 2 ), we compute the score S r,e 1 ,e 2 for all candidate entities e 2 that have been seen with e 1 for any other relation r.
(2011). It computes local similarities and then learns global entailment graphs satisfying transitivity constraints by solving an Integer Linear Programming. We downloaded  Berant et al. (2011) 's entailment graphs and tested it on the Levy/Holt's dataset. 10 or all the above similarities, we report results both in the local setting, where the similarities are computed for each relation pair independent of the others and the global setting, where we apply the global soft constraints of  Hosseini et al. (2018). We apply two sets of global soft constraints: a) Cross Graph which transfers similarities between relations in different, but related typed graphs; and b) Paraphrase Resolution which encourages paraphrase relations to have the same patterns of entailment. We tune the parameters of the global soft constraints on the development set of the Levy/Holt's dataset.
Entailment Scores based on Link Prediction In this section, we compare the variants of our method to the previous state-of-the-art results on the Levy/Holt's dataset. We compute similarity scores and report precision-recall curve by changing the threshold for entailment between 0 and 1. In order to have a fair comparison with Berant's ILP method, we first test a set of rule-based constraints proposed by them  (Berant et al. , 2011). We also apply the lemma baseline heuristic process of  Levy and Dagan (2016)  before testing the methods. Figure  3  shows the precision-recall curve of all the methods in both local (A) and global (B) settings. From the SBOW methods, we only show the BInc score in the graphs as it got the best results on the development set. For Berant's ILP method, we only have one point of precision and recall, as we had access to their entailment graphs for only one sparsity level.
We report the results for all entities as well as infrequent entities, where in the latter case we have removed any triple with an entity in the top 20 most frequent entities. In each setting, the first row is the plain ConvE model. We then test how the different variants of our entailment scores change the results. We observe that adding the entail-ment scores improve the rankings of the correct triples. The value of MRR, Hits@1 and Hits@10 have increased after applying any of the methods for learning entailment scores. It is interesting to see that the improvements obtained by the different entailment scores are generally consistent with the results on the entailment detection task, i. e. , the scores with better results on the Levy/Holt's dataset, show more improvements on this task as well.
The change of the mean rank (MR) is more apparent. For example, MR has decreased about 50% when we apply our best method (Global Aug MC) to re-rank the link prediction scores. This means using entailment relations is more useful to improve the link prediction for harder examples. The results of all methods for infrequent entities are worse than the results on all entities; however, we observe the same trends among the different methods. Note that the amount of the data that is used for all the methods is the same. In particular, we have only used the triples from the NewsSpike corpus for both link prediction and entailment detection tasks and the gain in performance of the both tasks is merely because the two tasks learn complementary information. scores (>0. 95).