dataesr/scientific-paragraphs-categorization huggingface dataset:
The next step will consist in testing these thermal sensors on real cases, that is to say, installed on an autonomous vehicle and circulating on road infrastructure. In addition to this, for quantifying YOLO detection, the detection rate was used as a parameter (the total number of frames with some detection over the total number of frames). As an extension of this research, we propose to measure not only the number of detections but also their intermittency-e. g. , how many detections are there in a one-second video? This will allow a better understanding of the operability limits regarding the use of detection software. The global results are very encouraging. However, a more intensive evaluation is needed to have definitive conclusions on the resilience of the sensor in the face of extreme foggy situations: more scenarios, with more static or moving objects.
A more extensive evaluation plan is also needed. In terms of processing, there are several possibilities: either we use ADASKY's proprietary software and test the hardware+software combination in foggy conditions, or we continue to use YOLO but by enriching the learning base with thermal images because the COCO base currently used by YOLO does not contain any. Furthermore, the thermal camera's configuration can also be tuned to improve observability for the use-cases and conditions tested.
To cover this use case, statistical methods and artificial intelligence approaches were explored. The former is a rule-based procedure that computes thresholds based on statistical indicators of the signals; it demands prior knowledge about the data and expertise in signal processing to be held. The latter is data-oriented and does not necessarily require deepened knowledge about the signals, since these supervised learning models gain knowledge from the data and can be adapted to the furnished dataset. So, lately, these methods [7,  9, 10]  are often used to do the classification tasks, since they are more flexible and can be improved through different means, that is, by processing the data, modifying the model's architecture, or tuning its hyperparameters. In this work, we explore configurations of data transformations, input formatting, and deep neural architectures that will allow us to achieve higher scores of accuracy in detecting foot-to-ground contact sequences.
Recent works highlight different architectures that are adapted to temporal data such as Long Short-Term Memory (LSTM) and others that enable extracting spatial patterns from signals, namely, Convolutional Neural Networks (CNN). Hybrid solutions such as ConvLSTM models were also used for similar assignments, since they benefit from both LSTM and CNN characteristics and allow more rich and valuable information extraction from the data. Therefore, we are comparing different combinations of our three parameters of interest: the trained model, signal discrete transformations, and the size of windows of data that we create. All in order to study the impact of each parameter and judge each model's robustness and its capacity of learning on raw data better than transformed data. This paper will, firstly, identify the works in relation to our problematic through the first section of related works.
Related Works 2. 1. Context and Hypotheses Within the vast applications of HAR, detecting foot-to-ground contact phases is fundamental to outline the characteristics of a person's gait. Specific data to this task, such as step width, cadence, gait irregularities, etc. , can only be obtained by classifying each timestamp of our signal into one class of gait analysis. While this task can have a multi-label classification enabling the determination of phases of pre-swing, initial swing, mid-swing, and terminal swing, we adopted the second form of gait cycle that is split into only two primary phases: namely, stance phase and swing phase. Since our work is based on data provided from IMU sensors, we are dealing with a timestamp-level classification process for time series. In that case, many architectures were developed in order to achieve high accuracies on this type of task.
Method In this section, we present the pipeline that we followed in order to hold this study. First, we will start by giving insights about our dataset and the pre-processing that we performed on it. Second, we will list the different neural architectures that we have chosen for our study. Next, we will discuss the evaluation process that we used to compare the performance of the different trained models. Finally, we will give more details about tuning the model with the highest accuracy. Before going through the steps of the development pipeline, we will introduce the workflow that this project followed during the experimental phase. We adopted an iterative and an adaptive life-cycle for this project in order to be able to improve constantly different aspects of the study. We listed several of research focus points, such as data representation, adapted model architectures, and evaluation process, to improve on each iteration.
Pipeline We held this study in accordance with a prepared and structured pipeline that allowed us to cover the different aspects of tests and comparisons that we are aiming to get by the end of the work. The first step was to explore the data in order to comprehend its structure and its distribution. Second, we thought about the different pre-processing methods that can be compared to raw data and then filtered them to get only the most suited for temporal data. Third, we selected different data representations that contain valuable patterns and combinations of signal components for the models. Fourth, after reviewing the state-of-theart applied models for this use case, we selected different architectures that were adapted for the type of the data as well as the selected representations. Most of these models have the capacity of extracting spatial, temporal, or both patterns from the data.
Finally, we evaluate these different models in order to compare their performance. Then, the best model receives further improvements by optimizing its hyperparameters. Figure  2  shows these different steps by giving the different possibilities for each step of this pipeline that were tested and compared. Deep Neural Networks (DNN), Random Forest, and XGBoost models were also tested, and they followed the same pipeline. Their only exception is that they do not accept twodimensional data for training. Instead, we used a dimension reduction algorithm, Principal Component Analysis (PCA), in the data formatting phase in order to get a one-dimensional array as an input for these models.
Here, 16 men, 10 women, and 1 not identified person carried those experiences. The mean age of this population is around 61 ± 12 years old, weighing 77 ± 17 kg and a mean height of 171 ± 10 cm. Their capacity to walk varies from one person to another, and it is evaluated thanks to the Berg score; here, this score varies from 33 to 56 with a mean of 49 ± 5. The lower this score, the harder walking can be for a person. Those patients suffer from a post-stroke left or right hemiparesis, and most of them use support tools such as walking sticks to do these exercises. Table  1  gives an overview of the composition of the dataset. As mentioned in Table  1 , the total number of observations is quite interesting with consideration of the number of participants. Thus, we had a good amount of data to elaborate this study.
Thus, the fact that each participant performed one or more types of activities enriches the distribution of the data. Furthermore, the target variable was created based on calculus and other sources of data, such as pressure data originating from connected soles. By analyzing these data and computing thresholds on data reflecting the amount of force applied on the sole, the moment of contact between the foot and the ground was assigned as a new variable to the dataset. It was then calibrated and post-processed in order to denoise it and remove abnormal values. During the recordings, two IMU sensors are placed around the participants' ankles (on both feet) measuring the acceleration and the angular velocity. The collected data result in 12 components, namely axial acceleration and angular velocity according to X-axis, Y-axis, and Z-axis for both left and right feet.
In other words, we detect two phases of gait: namely, stance and swing phases for each foot. Since our input data are composed of acceleration and angular velocity components of both feet at each time step, we figured that the extraction of spatial and temporal patterns of these signals would be interesting, making model architectures such as CNN and LSTM the best candidates to test. Furthermore, since the convolutional architectures can also capture spatial dependencies not only between each time step but also between the different signals and their respective axial components, we added this type of architecture to the list of models to test. In addition, we have chosen LSTMs instead of the traditional vanilla RNNs because of the vanishing gradient problem, rendering it ineffective for extracting long-term temporal patterns.
For the sake of this study, we selected a few classic, yet performing, models to have a complete scheme of comparison. Still, there are more architectures that were developed and achieved high accuracies in HAR in general. Refs. [41, 42]  used an attention mechanism aiming to detect human activities using sensors. Others  [43]  used autoencoders for a better feature extraction, since it is best suited for learning complex data representations. In order to evaluate these architectures, we created 6 different models. We also made sure to have two models for each data representation that we discussed earlier. Next, we will give details about each one of these architectures, including our new model, based on the format of their inputs.
Concatenated Data In that case, as explained earlier, we concatenate the 6 different components of our signals side to side (Acc X , Acc Y , Acc Z , Gyro X , Gyro Y , Gyro Z ) where Acc data are signals acquired from the accelerometer, whereas Gyro data originated from the gyroscope. Then, in terms of rows, we apply a windowing without overlapping to create small batches of data as single inputs, which leaves us with each input of a size of a 2D matrix (window size × 6). The window size is also one of the parameters we are studying for this specific set of data. More importantly, our first model is based on a deep CNN architecture. It has three consecutive blocks that are made from a 2D convolution layer and a batch normalization layer followed by a ReLU activation layer. At the end of these three blocks, we use a 2D global average pooling to provide an adapted input to the decision layer, as illustrated in Figure  5.
LOSO Evaluation Many possibilities are offered to evaluate the performance of our models. The most common approach is by splitting the data into 70% for the training data and 30% for the test. This split suggests that the model will use the first portion of data in order to compute its new weights, and the remaining portion would not be seen by the model but only evaluated. These ratios can change from one scenario to another depending on the size of the data. In some configurations, another subset of the data, named the validation set, is also taken into account to help the model find its best hyperparameters. The choice of which observations to include in each set of data can be done in the given order of the original dataset, by random sampling or any customized method, although it should respect the fact that the distribution of data should be almost the same in these sub-datasets.
71 GHz, 8 GB memory, and an NVIDIA GeForce GTX 950M GPU. The whole pipeline of pre-processing and formatting the data, training the models, and evaluating them was implemented using Python 3. 8. We used pandas (version 1. 2. 4) to read and merge the data, scipy (version 1. 6. 2) for pre-processing, numpy (version 1. 19. 0) to format the data, and TensorFlow (version 2. 4. 1), Keras (version 2. 4. 3), and scikit-learn (version 0. 24. 1) to create, train, and evaluate models.
Results Since we are following the LOSO evaluation protocol, every model will be evaluated based on its performance in detecting the foot-to-ground contact for the same patient, namely, patient n°27, and for our best model, we will give more details about its performance for different activities to see how well it is adapted to the different scenarios of movements and test its agnostic capacity to descriptive data of the patients. These results include scores (e. g. , accuracy, precision, recall, etc. ) for each tested combination. Hence, 72 neural models were trained and evaluated and will be presented in the first section of this part. We will also show detailed results of the best model in order to have an overview of how well the model did for the different types of activities and for each foot.
The rest of the scores shows that for other window sizes, our model and the multi-input LSTM are the best models, and the latter could surpass the ConvLSTM model. It has also achieved the same best score using DCT transformed data, but since the point is to create a model that trains well on raw data, our model remains the best in this category. Other neural architectures that are based on LSTM layers  [15, 32, 47]  have also performed very well on our dataset, such as the multi-input LSTM followed by the DNN layers. Other than the configurations with FFT transforms, this model had a high ranking in most of the cases. Indeed, this architecture is actually the most suited for time series, especially if we are treating multiple dependent time series. The model learns temporal patterns from each signal by treating each one of them independently; then, the outputs of the LSTM layers are flattened and concatenated, and the resulting vector benefits from further learning by the dense layers.
Machine Learning and DNN Models In addition to the trained Deep Learning models, we have also trained Random Forest and XGBoost classifiers for large-scale comparison. Those models were also trained on windowed data (raw, transformed with FFT and DCT) and with different values of window size. Yet, these models do not accept 2D matrices as a single input, so we used PCA (Principal Component Analysis) to reduce the dimension of the input matrix and get a single dimension vector. While training the RF and the XGBoost models for the different sets of configurations, we noticed the same behavior as the neural network models, which is that the accuracy of detecting FTG contact phases increased while lowering the window size value. Yet, this progress reached a limit for values lower than 6, the accuracy has no longer been improving, and the recorded highest accuracy score was 95.
Comparison with the State-of-the-Art Compared to the works of  [35]  based on a confrontation of LSTM and Decision Tree approaches that resulted in initial and corrected results, our proposed configuration surpassed the output of their LSTM model in terms of the overall accuracy (95. 7%). Yet, after adding some corrections to the data, their LSTM model made significant progress and attained an overall accuracy of 99. 0%. Since our target variable is annotated based on computed thresholds, it may be one reason leading the model to errors besides inaccuracy in data acquisition from the IMU sensors. For other works that were based on local minima for motion data such as  [58] , our model surpassed this method in terms of accuracy of detection. In addition, ref. [45]  used a CNN-LSTM model to detect different phases of the gait process, and it achieved an accuracy of 92%, which is lower than this study's best score.
Our results are comparable to those of  [30]  except for the cases where the model is reprocessed on personal historical data, making the model more adjustable to each participant. Since we are seeking an agnostic model for this type of data and are able to detect the foot-to ground contact only based on the IMU sensors data, we have not tried similar approaches. Nevertheless, our best model underperformed the FootNet developed by  [59]  that achieved 99. 23% accuracy using an LSTM model on the kinematic data of a large dataset. In addition, ref. [33]  showed that with an LSTM model, they could precisely predict the moments of foot strike as well as the moments of foot-off with an accuracy of 99%. We can deduce that until now, LSTM models have the best performance for detecting foot-to-ground contact moments, since they are well suited for temporal signals.
Conclusions Throughout this work, we are trying to compare different methods in order to achieve the highest accuracy in detecting foot-to-ground sequences. These methods are focusing on three major aspects: namely, the input data representation, the impact of applying discrete transformations to the signal, and the different adapted architectures for our use case. In addition, the model will be trained on the IMU sensors data only without any descriptive data about the patients or the type of feet the signal belongs to. This helped us test how independent the model is from these descriptive data. Starting with the data formatting, we used three data representations; for each one of these representations, two models were adapted and able to train on. We applied windowing on data in order to have a classification value for each window.
Acknowledgments: We would like to express our sincere gratitude and appreciation to the reviewers for assessing our manuscript, especially for dedicating their valuable time and efforts to such a diligent task. In addition, we would like to thank  Alice Douchet  and  Mathilde André  from  Fondation Hopale  for their diligent work and assistance in carrying out and supervising the clinical trials and collecting the data; their expertise and professionalism were reflected in the quality of data that were acquired thanks to their commitment.
Data Availability Statement: The data that were used in this project can be found in the following drive: https://drive. google. com/drive/folders/1dWjOwrN1IAY7aRxPlRVoE8LFMApfmJm9?usp= sharing (accessed on 12 April 2022). In addition, the code has been structured in a way to launch all the discussed experiences in an automated way; it also can be found this Github repository: https://github. com/YounessElMarhraoui/CEA_Data/tree/master (accessed on 12 April 2022).
Ancillary dataset: geostationary satellite data We also employ stereoscopic 10-minute-resolution cloud top height retrievals to infer the chronology of the eruption. These data are derived at NASA Langley using the Pacific sector) and Himawari-8 (Western Pacific sector). For the Hunga plume, the spatial resolution of the product is about 6 km and its vertical accuracy typically lies between 0. 2-0. 4 km. Further description of the retrieval method is provided in Supplement S2.
Open Research Strateole  Text S1. As mentioned in Sect. 2. 1 of the paper, we attempt to correct for the effect of vertical motion of the balloon in the hydrostatic pressure gradient p = p l exp g R d T ζ -p (1) where p l is the raw (balloon-following) pressure and p the Eulerian pressure which would be measured in the absence of altitude variations. Without a high-frequency inertial measurement unit (IMU) onboard, 30-s vertical GPS position has to be interpolated at 1 s to compute ζ. The raw pressure and Eulerian (corrected) pressure spectra are shown in Fig. S1. The correction effectively cancels out balloon neutral oscillations and affects the spectra up to ∼17 mHz (half the sampling frequency of ζ ). It is likely that hydrostatic pressure flucatuations are still impacting data above that frequency, but the temporal resolution of the altitude dataset does not enable us to account for that effect.
Introduction Computing sentence semantic representations traditionally calls for a recursive compositional function whose structure is tree-shaped. There is a strong intuition in natural language processing that language has a recursive structure  (Chomsky, 1956; Shen et al. , 2019). Tree-based models should thus mimic the compositional effect of language and enable better generalization and abstraction. Yet, tree-based models need carefully handannotated data to be trained. Alternative methods such as recurrent neural network  (Hochreiter and Schmidhuber, 1997; Cho et al. , 2014)  or BERT  (Devlin et al. , 2019)  have gained increased popularity as they only require raw text as input. On the other hand, as many results suggest  (Linzen et al. , 2016; Jawahar et al. , 2019; Clark et al. , 2019) , these new models acquire some sort of tree structure.
Semantic textual similarity (STS) We first evaluate our model on the SICK-R downstream task  (Marelli et al. , 2014) , which is dedicated to assessing models' compositional properties. The dataset comprises 9,927 sentence pairs, distributed in a 4,500/500/4,927 train/dev/test split, annotated for semantic similarity on a 1 to 5 real range. It includes specific examples of variations on passive and active forms, quantifier and modifier switches, or negations 3. We use a similar training procedure as in Tai et al. (2015). We transform the target y from the SICK-R task into the distribution p defined by: p i =    y -⌊y⌋, i = ⌊y⌋ + 1 ⌊y⌋ -y + 1, i = ⌊y⌋ 0 otherwise We use a dedicated architecture to predict the similarity distribution from a pair of sentences. The similarity module takes as input a pair of sentence vectors h L and h R and computes their componentwise product h L ⊙ h R and their absolute difference |h Lh R |.
(2018)  that latent trees obtained from sole downstream supervision are not meaningful in syntax. However, PTB-All and PTB-100 are remarkably close; only a few PTB samples are needed to obtain intelligible linguistic parses with our setup. Regarding the PTB-100 configuration, we note an evolution of the parses when finetuning on the downstream task. We hypothesize that the model can adapt to the dataset's specificity. For the SNLI task, fine-tuning the parser deeply impacts the shape of the parses. Depending from the initialization, parses will converge to distinct structures. Indeed, the UAS between all configura-tions is very low. Moreover, when using a random initialization (PTB-∅), the standard deviation between UAS from various runs is very high: without fixed initialization, parses become unstable.
The hidden size of the similarity architecture is set to 50. The TREE-LSTM hidden size is set to 150. We use the Adagrad optimizer. We do not apply any dropout. We perform training for a maximum of 20 epochs and stop when no improvement was observed on the development set for 3 consecutive epochs. Regarding the vocabulary, we limit the size to 20,000 words and initialize the embeddings layer with 300-dimensional GloVe embeddings 7. The embeddings are not updated during training. Training infrastructure We trained all models on a single 1080 Ti Nvidia GPU. Training time for each epoch is approximately 1 minute. The model counts 13. 7M parameters. Data can be downloaded using the SentEval package 8.
It involves a wide range of empirical data collection tools like observation, interviews, focus groups, and personal narratives as documentary evidence. It has been criticized for its subjective nature and lack of scientific rigor, having a less solid basis for generalization of findings to larger groups and settings  (Crowe et al. , 2011) , but it can also be valuable for unique situations from which researchers could not collect data from more cases (e. g. , a very rare illness). Researchers should consider waiting until later stages of their academic HRI careers before conducting case studies because: New researchers in the area will have more trouble breaking into the field with a case study than with the more accepted empirical study that has a sizable sample. This is because of the history of considering case studies as an illegitimate, less rigorous research strategy, and because there are few case studies in user-centered HRI.