dataesr/scientific-paragraphs-categorization huggingface dataset:
Arguments opposing the utilization of black boxes in medicine Considering ethical implications of AI in medicine, the possibility of biases may concern any artificial intelligence system. Biases generally fall into three main categories  [9]. The first category is when imbalanced or misrepresentative data is fed as training data to algorithms, that could completely ignore misrepresented classes, for example using datasets not featuring enough data about vulnerable groups. The second category is bias generated by a faulty algorithm. Finally, the third category is that related to human error, indeed the subtlest, as it is a result of long-held societal prejudices. In the case of black boxes however, biases appear to be much more difficult to identify and prevent. This has been highlighted very well by Obermeyer  [10] , evidencing that the U.
Last but not least, human language expressions are the most representative physical instances of semiotic objects. Moreover, as computational experiments which are possible with linguistic objects present a high degree of systematicity (of oppositions), in general, it is relatively easy to elucidate their dependence on the concepts underlying signs. This new semiotic theory or rather this new research program emerged as the fruit of experimentation and reflection on the application of data science tools elaborated within the frameworks of Rough Set Theory (RST), Formal Context Analysis (FCA) and, though only theoretically, Distributed Information Logic (DIL). The semiotic objects (s-objects) of this theory can be described in tabular datasets. Nevertheless, at this stage of formalisation of the theory, lattices (not trees) can be used as working representation structures for characterizing the components of concept systems and graphs for categories of each layer.
Still, this first step which consisted of the theoretical construction of an overall ontology failed in implementation. Person-ally, I believe that the next step on this way will need to pass through a period of rather long-lasting technological effort in research on human languages (and especially, on languages belonging to as divergent families as possible) using methods of data science (Knowledge Discovery in Databases -KDD), as an example see the research project "Computer-aided Acquisition of Semantic Knowledge -CASK  (Włodarczyk 2007) , including machine learning (neural networks). Usually, objects when perceived are considered to be denoted as cognitive objects. In the present theory, families of ordinary concepts (o-concepts) in the mind, which contribute to represent ordinary objects, are named noemata.
Rough Set Theory (RST -Pawlak 1982) which provides discerning for concept approximation 2. Formal Concept Analysis (FCA -  Ganter and Wille 1999; Wille 1982 Wille , 2005) )  which provides calibration for concept scaling 3. Distributed Information Logic (DIL -Barwise and Seligman 1997) which provides classification for concept integration Tables with objects and features (datasets) are basic data structures used in computer science. The research on their nature gave rise to what has recently been launched as data science that tries to integrate not only the investigations into the deep learning mechanisms using the neuromimetic intelligence technology but also the techniques of Knowledge Discovery in Databases (KDD) using management techniques of semantic/ontological databases. In computer science, there are a few approaches 5 to the problem of mappings between objects and their features as assembled in tabular datasets: (1) information systems (RST), (2) contexts (FCA) and (3) classifications (DIL).
For this reason, one way to model these processes might be the utilisation of the lower and upper approximation techniques. Thus, combining the power of approximation (RST) with that of scaling (FCA), it might become possible to process formal concepts associated with signs and, perhaps, even use them very successfully for modelling the underlying conceptual components of non-semiotic (ordinary) systems. 6   The aim and meaning of Formal Concept Analysis as mathematical theory of concepts and concept hierarchies is to support the rational communication of humans by mathematically developing appropriate conceptual structures which can be logically activated  (Wille 2005: 2). The Formal Concept Analysis actually provides the mathematical capacity for defining concepts and scaling datasets. In FCA, all kinds of realworld entities (objects), once registered in datasets, are treated as formal objects.
17 The discrimination of phonemes is possible solely when one takes into consideration the system of "phonological oppositions" which can be represented by datasets to which the dual function (Galois connection, see the schema 1 above) can be applied in order to find out the formal concepts consisting of extents (objects i. e. sound traces) and intents (the latter being known in linguistics as "distinctive features" of phonemes). Nevertheless, from the viewpoint of data science, given that every object has properties, specialists assume that it is always possible to build datasets using features. Phonemes are concepts which cannot be directly observed, what speakers and hearers experience are different realisations of phonemes as sounds in different speech acts and different languages. Let me address another important issue of structural linguistics: the definition of the category word.
"Control" Application The "control" application consisted in presenting a picture to the student, which depicts a person or a social scene (Figure  3 ). For each picture, a set of symbols has been defined to be briefly depicted on up to three points of social interest in the picture. Once the symbols disappeared, the student had to recall the nature and the number of depicted symbols. Three difficulty levels were defined based on the content of the pictures, with: 1) pictures of faces, portraits with one person; 2) simple social scenes, with two or three individuals and, 3) complex social scenes with more than three individuals. The set of pictures was selected from the socio-moral picture database, composed with pictures either in the public domain, or under open licenses  (Crone, et al. , 2018).
Data analyses The data analyses were performed using SPSS version 22. Mean comparison tests were performed on internal and external measures for examining pre-post effects and between-group differences. Pre-post differences were assessed using a mean comparison test for paired samples, and between-group differences with a mean comparison test for independent samples. As the prerequisites for parametric testing were violated, we selected their non-parametric counterparts (i. e. , Mann-Whitney test for independent samples, Wilcoxon test for paired samples). Even if parametric analysis is relatively robust to normality assumption violations, non-parametric tests have more power than their parametric counterparts, when there are both unequal small sample size (N < 30 per group) and nonnormal data distribution  (Pett, 2015).
Acknowledgements: The authors express their gratitude to the children, their families and school staffs who participated to the study. The authors gratefully acknowledge the academy and the rectorate of Bordeaux as well as the head of the special education sector, for their support in the project and the recruitment of special education classrooms. The authors also acknowledge the financial support provided by the  Orange Foundation and Inria Bordeaux Sud-Ouest. Finally , the authors thank  Alexander Ten  for his help in the final revision of the article.
Human activity recognition can help in elderly care by monitoring the physical activities of a subject and identifying a degradation in physical abilities. Vision-based approaches require setting up cameras in the environment, while most body-worn sensor approaches can be a burden on the elderly due to the need of wearing additional devices. Another solution consists in using smart glasses, a much less intrusive device that also leverages the fact that the elderly often already wear glasses. In this article, we propose UCA-EHAR, a novel dataset for human activity recognition using smart glasses. UCA-EHAR addresses the lack of usable data from smart glasses for human activity recognition purpose. The data are collected from a gyroscope, an accelerometer and a barometer embedded onto smart glasses with 20 subjects performing 8 different activities (STANDING, SITTING, WALKING, LYING, WALKING_DOWNSTAIRS, WALKING_UPSTAIRS, RUNNING, and DRINKING).
However, and to the best of our knowledge, there is no available and usable dataset for human activity recognition based on smart glasses. Moreover, data would vary from one device to another due to sensors having different orientations, ranges, accuracy and sampling rates. In this article, we present a new dataset  [5]  called UCA-EHAR with data collected from Ellcie Healthy's smart glasses  [6]. Our dataset provides raw data collected from an accelerometer, a gyroscope and a barometer for 8 classes of activity performed by 20 subjects. Additionally, for privacy, connectivity and latency reasons, all the data processing related to human activity recognition is performed directly on the smart glasses. Therefore, the machine learning algorithm performing the classification task is executed on the smart glasses' microcontroller.
In previous works, we presented our MicroAI framework for end-to-end training, quantization and deployment of deep neural networks on microcontrollers  [7]. This framework is now available as open-source  [8]. In this work, the MicroAI framework is used to deploy a deep neural network model performing human activity recognition on the smart glasses. Quantization with 8-bit and 16-bit fixed-point representations is used to optimize the memory footprint and the inference time, thus reducing the power consumption as well. Section 2 gives an overview of some of the available datasets and approaches for human activity recognition. Section 3 presents the smart glasses used for collecting data and performing live inference. Section 4 details the dataset and the protocol used to collect the data. Section 5 describes the deep neural network architecture used to classify activities from our dataset as well as the training phase.
State of the Art Datasets for human activity recognition using various modalities have been flourishing for the past decade  [9]. In this article, we mainly focus on body-worn sensors since visionbased or other environmental sensor approaches are significantly different compared to the smart glasses approach. The most iconic dataset for human activity recognition using an inertial measurement unit is likely the Human Activity Recognition dataset hosted by the University of California Irvine, commonly dubbed UCI-HAR  [2]. This dataset is built from a 3-dimensional accelerometer and a 3-dimensional gyroscope sampled at 50 Hz, embedded into a smartphone attached to the subject's waist. The acceleration signal is filtered to create an additional signal without gravity. Therefore, there is a total of nine channels of sensor data.
The data are windowed over 2. 56 s with 50% overlap to create windows of 128 samples. The data are provided in two forms: vectors of 128 samples for each of the nine sensor channels, and vectors of 561 features computed from the 128 × 9 values. A total of 30 subjects participated in the experiments, performing 6 activities: WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LYING. A total of 21 subjects are used for training while the 9 others are used for testing, representing 7352 and 2947 vectors, respectively. As it will be seen further, some aspects of our dataset are inspired by UCI-HAR such as some classes and the window duration. The UCI-HAR dataset was extended in  [10]  to provide the transitions between static activities: STAND_TO_SIT, SIT_TO_STAND, SIT_TO_LIE, LIE_TO_SIT, STAND_TO_LIE, LIE_TO_STAND.
This SBHAR dataset was used to evaluate the Transition-Aware Human Activity Recognition  [11]  system along with two other datasets: PAMAP2 and REALDISP. Instead of using a single smartphone with an accelerometer and a gyroscope, the PAMAP2 dataset  [4]  rather uses dedicated IMU devices called Colibri Wireless from Trivisio. One device is placed on the wrist, another one on the chest and a last one on the ankle. Each device contains a 3-dimensional accelerometer, a 3-dimensional gyroscope and a 3-dimensional magnetometer, along with a temperature sensor, all sampled at 100 Hz. Additionally, one heart-rate monitoring device is sampled at 9 Hz. In this dataset, nine subjects performed 12 to 18 activities. This setup is much more intrusive than UCI-HAR as multiple dedicated devices are used at specific location, making this approach harder to use in real conditions for live human activity recognition.
The REALDISP  [12]  dataset has an even more complex setup, using 9 IMU devices from Xsens sampled at 50 Hz, each with a 3-dimensional accelerometer, a 3-dimensional gyroscope, a 3-dimensional magnetometer. The IMU devices also provide orientation estimates in quaternion format (4D)  [13]. This dataset contains more classes performed by more subjects than PAMAP2, 33 classes and 17 subjects, respectively. Its purpose was to study the impact of sensor placement. Other popular human activity recognition datasets include UniMiB SHAR  [14]  containing accelerometer samples captured from a smartphone, Real-Life HAR  [15]  also collected from a smartphone but focusing on real-life situations (for example inactive, active or driving) rather than a laboratory setting, and OPPORTUNITY  [16]  that uses many sensors of different modalities.
Apart from these datasets using data collected from smartphones or specific devices, there are few other datasets based on wearables available from the market. We can cite WISDM  [3]  using a combination of a smartphone and a smartwatch (LG G Watch) to collect data from 51 subjects performing 18 activities. Other datasets for human activity recognition, such as  [17]  relying on a Microsoft Band 2, have been created from consumer smartwatches. However, these datasets have not been released so far. More specifically, smart glasses are still not a popular device to use for human activity recognition. Nonetheless, prior works have been done to build a dataset for smart devices including smart glasses in  [18]. This dataset makes use of Jins MEME smart glasses as well as a smartphone and a smartwatch to collect data from different sensors.
The smart glasses provide data from an embedded IMU. This dataset has however some noticeable drawbacks. First, only one subject participated in the experiment. Moreover, there is no well-defined set of activities or well-defined protocol, which makes it difficult to evaluate or to extend. Some efforts have been made in  [19]  to develop a system for activity recognition using smart glasses (Google Glass Explorer Edition  XE 22). The authors compare the classification performance of a Support Vector Machine (SVM) between data collected either from a smartphone or smart glasses for 4 activities (Biking, Jogging, Movie Watching, and Video Gaming). Their system can perform inference on the Android smartphone but not on the smart glasses themselves. However, and as it has been said in the introduction, each dataset will have its own characteristics depending on which device has been used.
UCA-EHAR Dataset UCA-EHAR is our proposition of a dataset to address the lack of usable data for human activity recognition using smart glasses. In order to build the UCA-EHAR dataset, we have enrolled 20 adult subjects, 8 women and 12 men (30. 6 y. o average; 12 y. o standard deviation). Excluded were adults or children below 1. 60 m of height, people with disabilities such as limping or backache. UCA-EHAR contains 8 distinct classes to classify: STANDING, SITTING, WALKING, LYING, WALKING_DOWNSTAIRS, WALKING_UPSTAIRS, RUNNING, and DRINKING. The choice of activities has been inspired by the UCI-HAR dataset as presented in Section 2. Additionally, these activities are simple to perform, common and relevant for elderly activity monitoring. STANDING, SITTING, and LYING are static activities where the subject stays in the same position for a given duration.
However, the subject does not need to stay completely still, but rather be natural as long as they keep either a STANDING, SITTING or LYING position. WALKING, WALKING_DOWNSTAIRS, WALKING_UPSTAIRS and RUNNING are dynamic activities associated to mobility. The RUNNING activity is closer to walking fast than a sprint. DRINKING is an activity that has been specifically added because we believe dehydration can be a risk for the elderly. The DRINKING activity is performed by drinking from a glass or a bottle, sip by sip. The composition of the dataset can be seen in Appendix A.
Data Collection Protocol Each subject was given a table stating the guidelines of the recording. One voice recording per session was acquired. The entire signal recorded during a session can contain multiple status and transition classes as shown in Table  1. Each data recording corresponds to one session as described in the table. Each session is described with 2 lines that must be read from left to right. The first line indicates the activity, while the second line gives the expected activity duration. Each session is a succession of activities. In order to provide a compact representation of sessions, an activity can be replaced by "repeat x times". In that case, no duration is indicated, it is rather replaced by the activity number to start again from. Subjects did not necessarily repeat the activities as many times as recommended due to time constraints or physical conditions.
It is well known that homogeneous classes can be of premium importance to reach a good accuracy for some neural network family. As a transition is by nature shorter in time compared to a status class, the number of transition signal samples is very small compared to the status classes' samples. Even tough the transitions are labelled in the dataset, they are not considered meaningful for classification in this article and are therefore filtered out for classification results. The recording process is performed using two mobile phones. One phone, running the so-called "research application" from Ellcie Healthy, is connected to the smart glasses through a Bluetooth Low Energy connection. The research application records the accelerometer, gyroscope and barometer samples sent by the smart glasses.
Data Format The accelerometer, gyroscope and barometer, respectively, have 3 values for acceleration, 3 values for the angular velocity and 1 atmospheric pressure value. The full sensitivity range is ±2g (g = 9. 81 m•s -2 ) for the accelerometer and ±2000 dps (degrees per second) for the gyroscope. The Ellcie Healthy glasses used in this experiment sample the 6 signals from the accelerometer and the gyroscope at a rate of 26 Hz, whereas the barometer is sampled at 6. 66 Hz. Before the labelling process, an interpolation routine has to be executed within the Matlab environment to provide the atmospheric pressure interpolated values for each accelerometer timestamp, so that a merged file containing one timestamp and 7 columns is produced. It is worth noticing that the barometer, the gyroscope and the accelerometer share the same sampling time origin.
The values are provided in m•s -2 , rad•s -1 and hPa. The voice recording and additional supporting Matlab routines are used to determine the right label for each sample. Files are provided in CSV format with a semicolon as the column delimiter. The files contain one line every 40 ms approximately, with nine columns labelled "T" for the timestamp, "Ax", "Ay" and "Az" for the accelerometer, "Gx", "Gy" and "Gz" for the gyroscope, "P" for the atmospheric pressure and "CLASS" for the activity label. All numeric values are provided with 2 decimals. Finally, the name of the file is a combination of the identifier of the subject and the session name. The identifier of the subjects is numbered T1 to T21; however, T11 is skipped due to not having performed enough activities. Some recordings have been performed in two sessions, in such a case "_1" or "_2" is appended to the filename.
Data Pre-Processing As the objective is to perform live inference directly on the smart glasses, the amount of computation done before entering the artificial neural network must be minimized. In consequence, only a windowing pre-processing task is performed. The neural network indeed requires time series, in other words a context around each data point. The windowing process uses windows of 64 time samples, each time sample containing a value for the three accelerometer and gyroscope axes. Each window is overlapped by 25% with the previous one. Since data are sampled at 26 Hz, each window has a duration of approximately 2. 46 s. This is close to the choice made by the authors of the UCI-HAR dataset  [2]. The raw data from the dataset have one label per time sample. Time samples in a window may have different labels.
Train/Test Split The dataset is split in two parts: one for training and one for testing. There are 14 subjects in the training set and six subjects in the testing set, representing approximately 77% and 23% of the total number of samples, respectively. Subjects number 5, 15, 17, 18, 19, and 20 have been chosen for the testing set since they have completed all activities. Moreover, these subjects have the lowest standard deviation on the percentage of samples for each class in the testing set. Therefore, as seen at the bottom of Appendix A, activities are balanced as much as possible between the training and testing sets. The total number of time samples in the training and the testing sets are 563,469 and 170,150, respectively. After windowing, the total number of vectors in the training and the testing sets are 35,213 and 10,631, respectively.
Artificial Neural Network Architecture A deep neural network is used as the machine learning algorithm. More specifically, a residual neural network has been used as it performed well on the UCI-HAR dataset in previous works  [7]. Moreover, this type of network is easy to scale down for embedded hardware by changing the number of filters per convolutional layer. In this work, a onedimensional ResNetv1-6  [20]  network is used to classify time series from our dataset. All convolutional layers have the same number of filters f. The ResNetv1-6 architecture is illustrated in Figure  3. The neural network is trained over 750 epochs using stochastic gradient descent (SGD) with momentum set to 0. 9 and weight decay set to 5 × 10 -4. The batch size is set to 768. Initial learning rate is set to 0.
Finally, the code is cross-compiled using the GCC compiler with -Ofast optimization level. MicroAI can optionally make use of the CMSIS-NN  [25]  library for faster 8-or 16-bit fixed-point inference, taking advantage of the so-called DSP instructions available in the ARMv7E-M instruction set architecture of the Cortex-M4 core. The inference time can then be measured directly onto the target by sending input vectors through the virtual serial port and waiting for the output of the deep neural network inference. Alternatively, the C inference library can be included into a third-party firmware, such as the firmware for Ellcie Healthy's smart glasses, in order to perform live inference with real data.