dataesr/scientific-paragraphs-categorization huggingface dataset:
Face Analysis Face tracking provided by the Kinect SDK gives values for 6 action units (AUs) that are used to derive the probability that the user is smiling (in particular position of the upper lip and lip corners). In our tests we selected 4 of them as promising candidates for smile detection, namely upper lip raiser, jaw lowerer, lip  stretcher, lip corner depressor (see Figure  2 ). In order to evaluate these features test recordings were observed and analysed in Matlab. Plots of the features over time are visualized in Figure  3. Laughter periods are highlighted in green. We can see that especially the values received for upper lip raiser (1st graph) and lip stretcher (3rd graph) are significantly higher during laughter periods than in-between laughter periods; lip corner depressor, on the other hand, has a negative correlation, i.
Body Analysis Real-time processing of body (i. e. , trunk, shoulders) features is performed by EyesWeb XMI  [27]. Compressed (JPEG) Kinect depth image streams captured by SSI are sent on-the-fly via UDP packets to a separate machine on which EyesWeb XMI programs (called patches) detect shoulder movements and other body-movement measures, e. g. , Contraction Index. Additionally, color-based tracking of markers (green polystyrene balls) placed on the user's shoulders is performed by EyesWeb XMI and the resulting recognition data is sent back to SSI to be integrated in the following overall laughter detection and fusion process (Section 6. 3). The body detection algorithms we present in this report are an improvement and extension of the techniques developed for the Laugh Machine (eNTER-FACE'12)  [33].
The Planner poses a new question when neither of the human participants speak for at least 5 seconds. If the observer starts to speak, he probably poses a new question or a new sequence of questions. In that case, Laughter planner abandons its sequence of questions and starts a new one in the next turn. Also the set of laughter is predefined. The laughte episodes (audio and facial expressions) are pre-synthesized off-line (see Sections 7. 4 and 7. 5 for details) from the available data (AVLC corpus). Only the shoulder movements are generated in real time. For each episode of AVLC corpus, five different versions were created, each of them with different laugh burst duration and consequently also different durations. Thus each original sample can be played "quicker" or "slower" and also corresponding lip movement animation and shoulder movement animation are accordingly modified.
All the pre-synthesized laughs are divided into 3 clusters according to their duration and intensity. Additionally each cluster is divided into 5 subclusters according to the mean laugh burst velocity. While the choice of the episode is controlled with 2 input parameters sent by Dialog Manager (see Table  3 ), the 2 parameters sent by Mimicry Module are used to choose the correct velocity variation of the episode. In more details, the values of LAUGH DUR and LAUGH INT are used to choose a cluster of laugh episodes. Next, mimicry parameters are used to choose a subcluster of this cluster of episodes, i. e. a set of laughs of laugh bursts corresponding to the value sent by mimicry module. Finally, the laugh episode is chosen randomly from the subcluster and BML messages containing the name of episode as well as BML tags describing the animation over different modalities are sent to the Realizer.
Facial Animation As for the audio signal (Section 7. 4), our work is based on the AVLC data set  [52]. 24 subjects (9 women and 15 men) were recorded while watching humorous videos. This corpus includes 995 laughter examples: video, audio and facial motion capture data. Laughs were phonetically annotated  [51]. Automatic landmark localization algorithm was applied to all the laughter example videos for extracting the trajectories of Facial Animation Parameters (FAPs) (see  [39] ). In our model we use 22 lip FAPs as lip motion features, 3 head rotation FAPs as head features and 8 eyebrow FAPs as eyebrow features. Therefore, we have the lip, head and eyebrow motions and phonetic information of all the laughter examples included in AVLC. Lip movements play an important role in human voice production.
This relationship is learned by a statistical framework in our work. Then the learnt statistical framework is used to synthesize the lip motion from pseudo-phonemes and duration sequences. We used a Gaussian Mixture Model (GMM) to learn the relationship between phones and lip motion based on the data set (AVLC). The trained GMM is capable of synthesizing lip motion from phonetic sequences. One Gaussian distribution function was learnt to model the lip movements for each of the 14 phonetic clusters used for laughter synthesis. Therefore, the trained GMM was comprised of 14 Gaussian distribution functions. For synthesis, one phonetic sequence including the duration of each phone is taken as the input, which is used to establish a sequence of Gaussian distribution functions. The determined sequence of Gaussian distribution functions  [45]  is used to synthesize directly the smoothed trajectories.
Shoulder Movement Previously the analysis of the motion capture data of the Multimodal Multiperson Corpus of Laughter in Interaction (MMLI)  [31]  has shown regularities in the shoulder movements during the laughter. In more detail, 2D coordinates of the shoulders' positions were processed using the Fast Fourier Transform (FFT). The results showed peaks in the frequency range  [3, 6] Hz. Interestingly, from the analysis of acoustic parameters we know that similar frequencies were observed in audio laugh bursts  [1, 40]. Both these sources of information were used to generate shoulder movements that are synchronized with the synthesised audio (see Section 7. 4). The shoulder movements in the Greta agent are controlled by BML tags sent by Laughter Planner. The tag shoulder specifies the duration of the movement as well as its two additional characteristics: period and amplitude.
These parameters are chosen by the Laughter Planner (see Section 7. 2). In particular the period of the movement corresponds to the mean duration of the laugh burst in the laughter episode to be displayed. The amplitude of the shoulder movement corresponds to the amplitude of the movements detected within the Mimicry Module. If the detected movements are large then also the amplitude of the agent movements is higher, and conversely. Next, the shoulders' BML tags with all these parameters are turned into a set of frames. The vertical position of the shoulder joints is computed for each frame by using the following function: X(t) = Amplitude * cos(2 * P I * f requency * t -75. 75) (4) where the amplitude and frequency are parameters of the BML.
Living Actor TM The Living Actor TM module includes a 3D real-time rendering component using Living Actor TM technology and a communication component that constitutes the interface between the Living Actor TM avatar and the ActiveMQ messaging system. This version is based on sample animations created by 3D artists and combines "laughter" faces (facial expressions associated with visemes that are mouth movements corresponding to synthesized laughter sounds), "laughter" body animations corresponding to several types of movements (backward bending, forward bending, shoulder rotation) and "laughter" intensities. The main animation component is related to the trunk and arms that are combined with additional animations of head and shoulders. The prepared trunk animations are later connected to form a graph so the avatar changes its key body position (State) using transition animations.
Conclusions The Laugh when you're winning project was designed in the framework of the EU Project ILHAIRE, and its development took place during the eNTERFACE 2013 Workshop, where several partners joined to collaborate for the project setup. Further, the participation in the eNTERFACE Workshop allowed researchers to recruit participants for the testing phase. Tests showed that virtual characters laughter capabilities helped to improve the interaction with human participants. Further, some participants reported that they perceived whether the virtual character was mimicking their behavior. Several critical points emerged from the project set up and testing and will be addressed in the future: the fused detection module is more robust than the one developed in eNTER-FACE'12, but on the other hand its reaction time is slightly longer (1-2s) which can cause disturbing delays in the agent's actions; in particular, the agent should not speak simultaneously to the participants but would do so due to the introduced delay; this will be adressed in the future by consulting a low-delay voice activity detection feature when to decide if the agent can speak; the cheap microphones used were insufficient for the desired open scenario (agent audio rendered by loudspeakers), which created long laughter loops by the agent; high-quality directional microphones must be used in the future, or the audio of the agent should be rended through headphones; the open-source speech synthesis system used with the Greta agent was not intelligible enough, which, in addition to bad timing of some reactions, lead some users to neglect the agent; a professional speech synthesis system will be used in the future to limit this problem; more voice/face/body features must be detected or improved; in parallel, the detected features should be synthesised by the virtual character; analysis of mimicry during human-human interaction is in progress on the data corpora recorded in the framework of the EU Project ILHAIRE; results will contribute to improved human-virtual character interaction.
Path Loss Models Due to the geometry of the human body and the multitude of different signal path trajectories which are possible, it can be quite difficult to fit a general distance related path loss model to the data obtained in on-body channels. For example, consider an on-body link that traverses (in line of sight) from the front right chest to the front right waist, now consider a link of the same length from the front right waist to somewhere along the waist on the posterior side of the body. This link, despite having the same length (when measured along the perimeter of the body surface), is nonline of sight (NLOS) and thus subject to very different propagation mechanisms and hence likely to experience much greater attenuation. This inability to apply a one-model-fits-all approach has led to the conclusion  [D'Errico and Ouvry, 2009; Smith et al.
). Path loss measurements were performed at a frequency of 2. 36 GHz for everyday activities such as standing, walking, and running within an indoor environment. An exact description of the measurement setup, derivation, and data analysis can be found in  Miniutti et al. [2008]. The path loss model considered for this study had the form P dB ¼ P Tx À P Rx þ G amplifier À L cable ( 3 ) where P Tx represents the transmitted power, i. e. , incident on the transmitting antenna port, P Rx the RMS received power, i. e. , incident on the receiving antenna port, G amplifier , the amplifier gain and L cable , the cable loss, used in the test bed. Table  4  presents the results of fitting equation (3) to the experimental data obtained in this study, i. e. , the average path loss P dB. Analytical models based on electromagnetic theory have also been applied to the study of signal attenuation on human tissue  [Conway et al.
For a 20 dB threshold below the largest path, the mean number of paths in indoor environments is found to be within the range of 41 to 66 in Di Bari et al. [2013], in line with the value of 38 prescribed by IEEE 802. 15. 6 standard. Surprisingly, values in Table  6  vary significantly from one dataset to another, especially in the anechoic case as far as Γ is concerned. While some reference papers do not provide many details regarding the corresponding experimental setup, we could gather the following arguments. 1. On the one hand, values in  Chen et al. [2011]  are extremely large, while power delay profiles look quite unrealistic: these values should in our opinion be taken with great caution. 2. On the other hand, values in  Fort et al. [2006]  are much smaller: this can be explained by the fact that these values were extracted on the body wave only, removing all other paths by time gating.
[2010]  where diversity brought by Multiple Input Multiple Output (MIMO) technique is exploited not only for interference rejection but also for increasing channel capacity. Ray tracing methods have also been used recently in  Kouitas [2009]  for off-body and body-to-body communications. The 2-D scenario considered in this work does not take into account the environment around the bodies and the latter are modeled as closed cylindrical surfaces with constitutive parameters chosen based on the "muscle" model. This approach could represent a powerful building block in the perspective of modeling inter-BAN communications (i. e. , at least the part which does not involve an interaction with the environment). In  Cotton [2009f]  and  Cotton et al. [2010a] , the authors present an approach to simulate dynamic soldier-to-soldier signal propagation using a combination of animation-based technology, which takes into account real body movements (using the Poser7 software tool), a computer generated environmental model (using the AutoCAD software package from Autodesk), and a commercial ray-launching engine to perform channel prediction.
Off-Body Channels One of the earliest attempts at providing a complete insight into the characteristics of the off-body channel has been presented in  Ziri-Castro et al. [2004]. The authors performed a comparison between measured data and simulations of a narrowband 5. 2 GHz radio channel for two indoor environments (a corridor and an office). The measurements involved one fixed transmitter and one receiver placed on the hip of a subject walking toward (LOS conditions) and away (NLOS conditions) from the transmitting antenna, whereas the simulations were performed using a three-dimensional image-based propagation prediction technique. The use of a simulation tool seemed to be appropriate for the LOS case but revealed some inaccuracies in NLOS situations. A statistical analysis of the small-scale fading found that a combination of the Rayleigh (signal level below the mean) and lognormal (signal level above the mean) distributions provided the best fit for the corridor environment, whereas in the office case the lognormal distribution provided the optimum fit.
Off-body communications at 868 MHz were studied in  Cotton and Scanlon [2007b]. Here measurements were conducted by considering several receiving antennas placed on the subject's body, different environments (i. e. , anechoic chamber, open office, and hallway), LOS and NLOS conditions and a walking movement. An analysis of the channel data found that both antenna positioning and human body movement play an extremely important role in indoor propagation characteristics for wearable systems. Moreover, it has been Radio Science 10. 1002/2013RS005319 verified that the Nakagami-m distribution provided the optimum fit for the majority of the off-body propagation channels investigated in both anechoic and indoor multipath environments. This distribution turned out to be suitable for the considered scenarios as, differently from other distribution like Rice or Rayleigh, it does not assume scattered components of equal amplitude.
Different statistics were compared in order to find the one that best describes the received signal amplitude. Overall, it was found that the lognormal distribution provided the most reliable fit for both the considered bands. In order to evaluate the channel stability, the authors introduced a new parameter called channel variation factor and calculated the ratio between the standard deviation and the root-meansquare power of a channel response sequence. The off-body channel turned out to be quite stable, more so at 820 MHz than at 2. 36 GHz, with a coherence time in the order of tens of milliseconds. In this study it was also found that the on-body device position also played a significant role in the channel temporal stability. Considering the great advantages that the use of UWB could bring to the system performances in terms of reduction of multipath fading, the possibility of reaching high data rates and a reduction in operating power, some researchers have also focused on the characterization of the channel at UWB frequencies.
Acknowledgments This work was supported by the  U. K. Royal Academy of Engineering  and the  Engineering and Physical Research Council (EPSRC)  under Grant Reference EP/H044191/1 and by the  Leverhulme Trust, UK  through a  Philip Leverhulme Prize. Work by  CEA  has been partially funded by the  French ANR  project  CORMORAN  and the  FP7 European Project WiserBAN. Radio Science 10. 1002/2013RS005319.
MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one ''virtual'' derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus).
We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias.
This, as well as its extreme simplicity of use, has made MAXENT the most widely used SDM algorithm. In December 2013, 1886 citations of the article describing the method  [30]  were reported in Web of Science. MAXENT modeling, and SDM in general, is now commonly implemented in conservation-oriented studies  [31]. Regional or continent-wide studies are facilitated by the recent availability of global datasets. Environmental layers, such as the global climate variables developed in the WorldClim project  [32] , offer continuous description of very large areas  [33]. Similarly, the development of open biodiversity databases (see for example the Global Biodiversity Information Facility, GBIF, http://www. gbif. org) increases manifolds the spatial coverage of fieldwork observations that could have been collected by a single project.
Such databases usually provide presence-only data that can be handled by modeling methods like MAXENT. However, datasets derived from opportunistic observations or museum records rather than from planned surveys often exhibit a strong geographic bias  [34] , some areas being visited more often than others because of their accessibility  [35]  or their naturalistic interest. This unequal survey coverage of a species distribution is often referred as sampling bias, sample selection bias or survey bias. The quality of the model can be strongly affected if entire parts of the environmental space suitable to a species are absent or poorly represented in the survey dataset  [36, 37] , or alternatively, if some areas are overrepresented due to locally high sampling efforts. Several studies questioned the effect of sampling design  [38] , or the biased nature of museum and herbarium datasets  [39]  on the predictive performance of SDMs.
Surprisingly, the issue of quantifying and correcting sampling bias has been poorly addressed despite its crucial importance. Although authors pointed out that the distribution of locations in the geographical and/or ecological space may impact the reliability of the model  [35, 36, [40] [41] [42] [43] , the potential effect of the sampling bias in the dataset is usually poorly taken into account or not considered at all. However, very different SDM outputs can be generated that lead to contrasting conclusions whether sampling bias is corrected or not  [44] , making SDM studies that did not incorporate this issue highly doubtful. In regard to the considerable influence of sampling bias on SDM prediction ability, Araujo et al. [45]  considered the improvement of sampling designs as one of the five major challenges for future development of SDMs.
Therefore, the influence of the nature and intensity of bias on the capacity of various techniques to correct for sampling bias has not been investigated. This remains however a critical issue, especially for users who need robust and reliable SDM predictions such as conservation practitioners. The goal of this comparative study is to test the effect of bias type, bias intensity, and correction method on MAXENT model performance. Unlike the previous cited studies  [41, [52] [53] [54]  we assessed the performance of five bias correcting methods among the most frequently used under various conditions of bias type and intensity. We used a virtual species to generate four types of sampling biases and three bias intensities, and applied on these biased datasets different corrections. We quantified the relative correction performance across the range of bias conditions and across species.
The same framework was also applied on two real datasets. The full workflow on which analyses were based is sketched in Figure  1. Therefore, the present study provides for the first time a comprehensive multi-species evaluation of the most common methods of sampling bias correction under different scenarios of bias and intensities of bias. Intended for conservationists who use MAXENT on a regular basis, we expect this work to provide insights on the selection of the most suitable methods to produce reliable distribution models using biased datasets. Furthermore, it encourages modelers to develop improvements of techniques to correct for sampling bias suitable for the vast set of modeling methods available.
Species datasets In order to obtain a true unbiased dataset, we created a virtual species by randomly sampling a set of points in a given environment determined by a single categorical variable. A similar approach has been used formerly  [52]. We extracted 2000 random coordinates from the ''Closed to open shrubland'' category of the North American Globcover map (Globcover 2009, http://due. esrin. esa. int/globcover [56] ) to generate an unbiased dataset. The geographical extent of the virtual species was chosen to match the scale of the two real species ranges which are also both located in North America. In practice, the virtual species covered the larger part of western North America, including the lower valleys of the Rocky Mountains, Mojave Desert, Baja California peninsula, and Northern Mexico (Figure  2 ).