dataesr/scientific-paragraphs-categorization huggingface dataset:
yield an outlyingness score with unknown distribution (ex-343 cept for. ), an outlyingness threshold can be computed 344 from the resulting empirical distribution of the depth values 345 through a bootstrap procedure as in the univariate case [14]. 346 It can also be computed from a training dataset based on the 347 receiver operating characteristic (ROC) curve. 348 In the experimental study (Section 5), we use the 349 and. functional depths as baselines because they have 350 been demonstrated to be promising for outlier detection in 351 multivariate functional data by regarding outlyingness as a 352 curve-shape feature.
Geometry-based functional-outlier detection 354 Representing functional data in a geometric framework 355 is a recent idea, and few studies have considered such repre-356 sentations for outlier detection. Recently, in [48], Xie et al. 357 proposed detecting outliers in univariate functional data by 358 decomposing each univariate functional sample into three fea-359 tures: translation, phase, and amplitude. The authors defined 360 the translation of a functional sample by its mean over the 361 observation interval . Both the amplitude and phase compo-362 nents are functional data extracted from the original samples. 363 The amplitude component reflects the vertical variability of 364 the functional data, whereas the phase component reflects the 365 horizontal variability. Analogously to the functional boxplot 366 by Sun and Genton [42] computed on the original dataset.
was fixed on logarithmic scale in [-9, -1], with a 826 thickness of 0. 1. The grid search of was fixed at the 827 integers between 35 and 60, that is, for a given integer ∈ 828 [[35, 60]], the B-spline functions were regularly placed in 829 . 830 For the synthetic datasets, the grid search of 1 and 2 831 was fixed on logarithmic scale in [-9, -4], with a thickness 832 of 0. 1. The grid search of was fixed in [[20, 25]], that is, 833 for a given integer ∈ [[20, 25]], the synthetic data were 834 approximated by the first frequencies 2 × × 1, , , 2 × 835 ×. Then, for each variable, we retained the coefficient 836 vector associated with both the optimal regularization and 837 basis-size parameters to recover the smooth approximation 838 function ̃ = ( ̃ 1 , ̃ 2 ).
As we selected  = [0, 1] for all datasets and the 894 observation points are regular, the grid is a regular discretiza-895 tion { 1. } of  with a thickness of 1 ( 1 = 0 and for 896 > 1, = ). Hence, for the outlier-detection algorithms, 897 the data correspond to -dimensional numerical vectors that, 898 in turn, correspond to univariate functional data output by 899 a mapping function. We selected the thickness of the grid 900 as the original size of the time series for both the synthetic 901 and ECG datasets (ECG data set: = = = 86, PenDig 902 dataset: = ′ = 200, synthetic data sets: = = 200).
To allow 928 flexibility on the hypersphere boundary owing to the presence 929 of outliers in the training data, slack variables are introduced 930 in the objective function in addition to the two other variables. 931 The hyperparameter corresponds to an upper bound on the 932 a priori proportion of outliers in the training set. A sample is 933 declared as an outlier if it lies outside the fitted hypersphere. 934 We used the radial-basis-kernel version of OCSVM with 935 equal to the exact proportion of outliers in the training set. 936 The bandwidth hyperparameter of the radial basis kernel was 937 optimized by a 20-fold cross-validation procedure. 938 For the ECG and PenDig datasets, we set the number of 939 trees to 1000, and the subsampling size to 32 [27]. For the 940 synthetic datasets, we also set the number of trees to 1000, 941 and the subsampling size to 16.
We randomly split each 942 dataset into a training set and a test set. As in [8], the training 943 set represents 50% of the data for the ECG dataset. The train-944 ing set for the PenDig dataset consists of 75% of the entire 945 dataset. The training set contains 60% of the data for the 946 synthetic data. The training set was used to both fit the model 947 (iFor and OCSVM) and select an outlyingness threshold from 948 the ROC curve that discriminates inliers from outliers. We 949 then computed the outlyingness score of the test samples 950 and achieved detection using the previously computed out-951 lyingness threshold. Regarding OCSVM, we finetuned the 952 bandwidth hyperparameter of the radial basis kernel on the 953 training set through a 20-fold cross-validation procedure on 954 the grid {2 -25.
(i) We randomly generated a train/test split. For the ECG data, the training set corresponds to 50% of the full dataset, for the PenDig data, the training set is 75% of 1026 the dataset, and for the synthetic data, the training set 1027 represents 60% of the full dataset. 1028 (ii) We then applied the proposed and the baseline meth-1029 ods. Except for. (baseline), which does not 1030 require outlyingness-threshold learning because the 1031 outlyingness score follows a known distribution (see 1032 Section 5. 4), the outlyingness threshold was learnt on 1033 the training set based on the ROC curve. 1034 (iii) We evaluated the performance in terms of the true 1035 detection rate ( ), false detection rate ( ), and 1036 on the test set. 1037 For the ECG dataset (resp. , PenDig dataset), steps (i) to 1038 (iii) were repeated 50 times for each case of the five contam-1039 ination levels (resp.
, for the 10 outlier classes) (see end of 1040 Section 5. 1), and 500 times for the synthetic data for each of 1041 the five models (Section 5. 2). 1042 The two real datasets are not used to assess the same 1043 properties of the proposed method. The ECG data are used 1044 to demonstrate the robustness of the proposed method with 1045 respect to different contamination levels for some given out-1046 liers, whereas the PenDig data are used to assess the detection 1047 performance for different outliers and a given contamina-1048 tion level. Thus, we only compare these two in terms of 1049 performance, in the comparison of the various methods in 1050 Section 5. 6. 4.
1370 and OCSVM columns) show the pairwise consistency be-1371 tween the iFor and OCSVM algorithms for each mapping 1372 function. The same holds for the synthetic data. Thus, for 1373 a given dataset and mapping function, iFor and OCSVM 1374 achieve statistically the same performance results. This im-1375 plies that the detection performance relies more on the out-1376 lying features provided by the mapping function than on the 1377 capacity of the outlier-detection algorithm to discover outly-1378 ing features itself.
Significance of the pairwise comparisons for the correct detection rate on the ECG (upper table) and PenDig (lower table) datasets. The statistical significance of the difference of two given methods at level 0. 1 is indicated by ≠ * , and by = otherwise. ≠ * indicates that the corresponding methods in the row and the column of the cell are significantly different at a level of 0. 1, and = indicates that they are not. The lower triangular part was replaced by dashes because it is equal to the upper part.
2 Figure 1 : 21 Figure 1: Example of a bivariate ( = 2) functional dataset (see the color version for greater clarity). (a) A dataset of 21 bivariate curves, with variables 1 ( ), 2 ( ), = 1. 21, is plotted along the variable and the ∈ [0, 1] axes. There are 20 inliers (black) and one shape outlier (red). (b)The dataset is projected along the axis; the red curve clearly shows an outlying relationship between its variables, resulting in a different shape. This is the "view" adopted in this study. In (c) and (d), the variables 1 and 2 are plotted as two univariate functions with respect to. Determining the degree of difference of the red curve without computing derived functions (e. g. , derivative(s)) is not simple. Moreover, if the dataset is very large, the red curve is totally mixed with the black curves, thus rendering visual detection difficult.
342 yield an outlyingness score with unknown distribution (ex-343 cept for. ), an outlyingness threshold can be computed 344 from the resulting empirical distribution of the depth values 345 through a bootstrap procedure as in the univariate case [14]. 346 It can also be computed from a training dataset based on the 347 receiver operating characteristic (ROC) curve. 348 In the experimental study (Section 5), we use the 349 and. functional depths as baselines because they have 350 been demonstrated to be promising for outlier detection in 351 multivariate functional data by regarding outlyingness as a 352 curve-shape feature.
353 2. 3. 23 Geometry-based functional-outlier detection 354 Representing functional data in a geometric framework 355 is a recent idea, and few studies have considered such repre-356 sentations for outlier detection. Recently, in [48], Xie et al. 357 proposed detecting outliers in univariate functional data by 358 decomposing each univariate functional sample into three fea-359 tures: translation, phase, and amplitude. The authors defined 360 the translation of a functional sample by its mean over the 361 observation interval . Both the amplitude and phase compo-362 nents are functional data extracted from the original samples. 363 The amplitude component reflects the vertical variability of 364 the functional data, whereas the phase component reflects the 365 horizontal variability. Analogously to the functional boxplot 366 by Sun and Genton [42] computed on the original dataset.
661 5. 5 Experimental study 662We conducted an experimental study on real and synthetic 663 datasets to demonstrate the effectiveness of the proposed map-664 ping functions in improving outlier detection in multivariate functional data. The detection performance was evaluated in terms of the true detection rate (i. e. , the proportion of outliers correctly detected), false detection rate (i. e. , the proportion of outliers falsely detected), and area under the ROC curve ().
825 and 2 2 was fixed on logarithmic scale in [-9, -1], with a 826 thickness of 0. 1. The grid search of was fixed at the 827 integers between 35 and 60, that is, for a given integer ∈ 828 [[35, 60]], the B-spline functions were regularly placed in 829 . 830 For the synthetic datasets, the grid search of 1 and 2 831 was fixed on logarithmic scale in [-9, -4], with a thickness 832 of 0. 1. The grid search of was fixed in [[20, 25]], that is, 833 for a given integer ∈ [[20, 25]], the synthetic data were 834 approximated by the first frequencies 2 × × 1, , , 2 × 835 ×. Then, for each variable, we retained the coefficient 836 vector associated with both the optimal regularization and 837 basis-size parameters to recover the smooth approximation 838 function ̃ = ( ̃ 1 , ̃ 2 ).
938 For the ECG and PenDig datasets, we set the number of 939 trees to 1000, and the subsampling size to 32 [27]. For the 940 synthetic datasets, we also set the number of trees to 1000, 941 and the subsampling size to 16. We randomly split each 942 dataset into a training set and a test set. As in [8], the training 943 set represents 50% of the data for the ECG dataset. The train-944 ing set for the PenDig dataset consists of 75% of the entire 945 dataset. The training set contains 60% of the data for the 946 synthetic data. The training set was used to both fit the model 947 (iFor and OCSVM) and select an outlyingness threshold from 948 the ROC curve that discriminates inliers from outliers. We 949 then computed the outlyingness score of the test samples 950 and achieved detection using the previously computed out-951 lyingness threshold.
• MN detachment In order to perform its topological anchor' functionalities, the LMA must maintain a database called Binding Cache (BC) in which it stores an entry for each MN located in the PMIPv6 domain. Thus, each entry represents a mapping between the MN and its MAG, with also a list of the assigned HNPs to this MN. As normal database, the LMA must perform the basic functions of persistent storage, known as CRUD (Create, Read, Update, Delete) operations, each time that a change is necessary in the BC. To perform these CRUD functions, PMIPv6 entities exchange messages as illustrated in figure  1.
This method consequently triggers the method IsUp() on each of the installed physical interfaces. The method IsUp() on physical interfaces is inspired from the ones proposed by the Media Independent Event Service (MIES)  [36, 37, 38]. Using the OR conjuction, it checks whether at least one interface is up (which means that its RSSI is enough to receive packets) at this mobile. Later, we will use the term of "Find remained Up interface" to refer to this selection and decision phases in the flowchart of our approach. Thus, the pseudo-code in algorithm 1 illustrates this process. If at least one interface is connected, therefore the mobile can receive data without wondering whether the corresponding physical interface is down or not. This is possible thanks to the possibility of establishing a reception tunnel between the logical interface and each up interface.
We designed and implemented the scenario illustrated by the figure 8 in NS3. Using the wireshark software and our PCAP logs, we have therefore measured and checked if all the PMIPv6 major functions and requirements presented in section 3. 1 are present and are well functioning. In fact, we firstly validated the the MN attachment detection. Then, the binding and registration function has been checked through PBU and PBA messages analysis. We analyzed also the establishment of the tunnel between the PMIP entities (MAGs and LMA). After that, we have also validated the implementation of the logical interface and MIVH by plotting the receiving interface per packet with MIVH and without it (figure  15a ), as well as the throughput (figure  15b ). Through figure  15a , it can be seen that the vertical handover takes place between the 15 th second and 20 th second in the simulation.
Furthermore, we can notice that our approach have higher throughput values than the PMIP-HD solution. This is also due to the increase in the number of received packets in our approach than in the PMIP-HD. Which is also proved by the PDR. The obtained network topology was, then, loaded into the mobility simulator tool SUMO 6   [50] , which gives the network topology illustrated by the figure  21. From this exported network topology, we have generated the road traffic using the "ran-domTrips. py" script provided by SUMO. The simulation duration was set to 300 seconds. Then, we have exported the generated traffic flow into an XML trace file using the SUMO application and the "fcd-output" switch flag. Regarding the mobility model, we have used the IDM (Intelligent Driver Model 7  ) car-following model  [51]  in order to generate random trips.
Recordings captured by wearable microphones are a standard method for investigating young children's language environments. A key measure to quantify from such data is the amount of speech present in children's home environments. To this end, the LENA recorder and software-a popular system for measuring linguistic input-estimates the number of adult words that children may hear over the course of a recording. However, word count estimation is challenging to do in a language-independent manner; the relationship between observable acoustic patterns and language-specific lexical entities is far from uniform across human languages. In this paper, we ask whether some alternative linguistic units, namely phone(me)s or syllables, could be measured instead of, or in parallel with, words in order to achieve improved cross-linguistic applicability and comparability of an automated system for measuring child language input.
We discuss the advantages and disadvantages of measuring different units from theoretical and technical points of view. We also investigate the practical applicability of measuring such units using a novel system called Automatic LInguistic unit Count Estimator (ALICE) together with audio from seven child-centered daylong audio corpora from diverse cultural and linguistic environments. We show that language-independent measurement of phoneme counts is somewhat more accurate than syllables or words, but all three are highly correlated with human annotations on the same data. We share an open-source implementation of ALICE for use by the language research community, enabling automatic phoneme, syllable, and word count estimation from child-centered audio recordings.
This means that automatic or semiautomatic tools are essential for processing and analyzing such recordings (see  Casillas & Cristia, 2019 , for a review). One key measure of interest is the amount of linguistic input that a child hears within a given time period (e. g. , specific time of the day, within the full day, or in a specific environment such as daycare or at home). The existing standard solution to collecting and analyzing child-centered recordings is the widely adopted LENA system  (Xu et al. , 2008; Gilkerson & Richards, 2009). It consists of a physical recording device and associated software for automatically analyzing a number of variables from the data, including estimation of the number of words spoken by adults in the vicinity of the child, in addition to detecting child vocalizations and conversational turns.
However, LENA is proprietary and expensive, and its core technology is aging with respect to the cutting edge in automated speech processing. In addition, LENA is optimized for American English, which means that its absolute word count estimates tend to be more accurate for English than for other languages (though relative counts in within-corpus comparisons are still usually reliable; see  Räsänen et al. , 2019, for a summary). In order to address these shortcomings of LENA, an open-source alternative for automatic word count estimation was proposed by  Räsänen et al. (2019)  (hereafter referred to as "WCE-R"). Instead of using a single static model for word count estimation, WCE-R can be adapted to any target language of interest by using a few hours of orthographically transcribed child-centered audio.
As a result, WCE-R outperformed LENA on three out of four tested dialects of English, and achieved similar performance on multiple languages other than English (see  Räsänen et al. , 2019, for details). However, while the WCE-R adaptation procedure improves the cross-linguistic performance of the system, the requirement of transcribed domain-specific data also greatly limits the practical usability of WCE-R as a standardized tool for developmental research, not least since its performance ultimately depends on the quality and representativeness of the adaptation data used for the domain of interest. A fundamental challenge with both WCE-R and LENA is that accurate word count estimation in any given language necessarily requires at least some knowledge of the lexicon and phonology of that language.
Integrating such knowledge into the system for all of the world's approximately 7000 languages (number from  Coupé et al. , 2019)  is simply not feasible. This is especially true for low-resource languages, for which orthographically transcribed data may be sparse or may not exist at all. LENA's only way to cope with this issue is to simply assume that all speech is American English, whereas WCE-R uses the data-driven adaptation discussed above. Arguably, neither of the approaches is ideal from the cross-corpus (cross-linguistic) analysis point of view, where sensitivity and accuracy of the unit count estimates would ideally be similar across different datasets. An independent, but equally important issue is whether words are the linguistic units that we should be counting in the first place.
As a part of this work, we present our open-source system, Automatic LInguistic unit Count Estimation (ALICE 1  ), and use it to investigate the feasibility of estimation for the different units on seven different child-centered audio corpora from various language environments. We start with theoretical and technical considerations for measuring syllables versus words (the next section. Following the earlier work on LENA  (Xu et al. , 2008)  and WCE-R  (Räsänen et al. , 2019) , we then describe our system for estimating phoneme, syllable, and word counts from acoustic speech (Methods). Performance of the system is then evaluated in within-and across-language linguistic unit count estimation tasks (Experiments), including a comparison and combination of different intermediate signal representations in the estimation tasks.
Measuring phone(me)s, syllables, and words The overall aim of this paper is to investigate which linguistic units one should be measuring for spoken input, when the ultimate goal of research is to characterize both (a) the language input that children hear in their daily environments, and (b) how that input relates to other variables of interest in the child's development. An ideal solution has all the following desiderata: 1) the quantity of the chosen units is highly relevant to the learning mechanisms linking linguistic input to language development, 2) the units have a meaningful and interpretable relationship to linguistic theory, 3) the units of interest can be measured automatically from child-centered daylong audio with comparable accuracy across languages, and 4) the automatic measurement system operates out-ofthe-box on any given dataset.
As far as we know, the relationship between measurable correlates of words (e. g. , automatically detected phone(me)s or syllables) and the corresponding word counts themselves is relatively systematic within language, especially at larger time scales, as demonstrated by LENA and WCE-R. However, the exact relationship is also necessarily dependent on the language in question, as there are no universal acoustic or perceptual cues for words or word boundaries. For instance, LENA AWCs are optimized for American English, but the absolute word count accuracy varies for other languages. On the other hand, LENA-based relative AWCs within a corpus are still quite systematic, and are therefore useful for analyses within the given corpus (see  Räsänen et al. , 2019, for a summary)  In contrast to words, syllables are potentially easier to detect across languages.
Methods Our system for ALUC estimation is called Automatic LInguistic unit Count Estimator (ALICE). The basic architecture of ALICE is shown in Fig. 1. ALICE is conceptually based on the earlier systems in  Räsänen et al. (2019) ,  Ziaei et al. (2016) , and the LENA system  (Xu et al. , 2008) , fusing many of their ideas into a common publicly available opensource pipeline. ALICE also extends the child-centered diarization pipeline called DiViMe described in  Le Franc et al. (2018) , but operates in a generic Python environment instead of a Linux virtual machine. ALICE also uses a more powerful speech detection and speaker diarization front-end than the earlier DiViMe-system. The ALICE pipeline consists of three main components: 1) a module responsible for speech detection and speaker diarization, 2) feature extraction from detected speech segments from talkers of interest, and 3) mapping of features into linguistic units of interest using a set of linear models, one model for each unit type.
The diarization system that ALICE uses is called voice type classifier  (Lavechin et al. , (in press ) submitted;  Lavechin, 2020) , which originates from large-scale collaborative development efforts carried out at the JSALT 2019 workshop  (Garcia et al. , 2020). Technically speaking, it is based on a neural network architecture called SincNet  (Ravanelli & Bengio, 2018) , and the model is trained on approximately 200 hours of daylong child-centered audio recordings from a number of languages, including data from Minnan Chinese, Ju|'hoan, Greek, Japanese, American English, French, Tsimane', and others (and excluding any data used in the present experiments). Implementation of the voice type classifier is based on the pyannote-audio library  (Bredin et al. , 2020). Since the diarizer operates directly on the audio waveform, it involves a large amount of computation.