dataesr/scientific-paragraphs-categorization huggingface dataset:
Data access and data sharing An important recent field of application is that of data sharing. When different stakeholders want to exchange and share data, they need to agree on a common policy, usually referred to as a data sharing agreement (DSA). Recent works  [26, 27]  have applied an argumentation approach within the Gorgias framework for data access and sharing in the health sector. The MEDICA 9   [50]  system was developed for the purpose of determining the level of access to patients' data records, according to an EU country's law on medical data access. There are six different access levels, ranging from full access to various types of limited access and to restricted or no access. All decisions on the level of access reached by the MEDICA system are explained to the user by reference to the relevant articles of legislation, which are, in fact, the basis for the object and priority level arguments that support the reached decision.
The approach is sufficiently general to allow the development of applications in any, of the many existing argumentation frameworks (e. g. ,  [15, 38, 39] ), that support conditional and hierarchical forms of preference. The approach can be improved in several ways. An important development of the authoring tool of Gorgias-B is the fully automated extraction of the Gorgias argumentation theory directly from the tables of scenario-based preferences so that while these tables are filled the argumentation code can be produced and tested. Given the current investor interest that we have in this we hope to develop a new interface tool for professional use in software companies. We can also integrate within the application development approach various methods of Machine Learning. For example, scenario-based preferences can be extracted by data analytics on past cases of the application without the need for their explicit stipulation by the domain expert/user.
Gorgias-B (http://gorgiasb. tuc. gr) runs under the minimum requirements of a Windows or Linux OS, SWI-Prolog version 7. 0 or later, and Java version 1. 8 or later. The tool employs Eclipse EMF (http://eclipse. org) technology (with the xtext extension) for managing low level Prolog code and the methodology's high level concepts and transformations from one to the other.
, 2012) , which applies the mean-shift algorithm to resampled edges. In comparison to previous work  (Peysakhovich et al. , 2015) , we provide additional uses of the ADEB framework and open eye tracking datasets for which this technique helps to understand recorded data. Furthermore, we have taken Peysakhovich et al. 's work further by using the underlying computed gradient map (flow direction map) presented in the next section. The method is similar to the procedure described in the Section "Fixation clustering", except for resampling the lines (saccades) that connect fixation points and computing the density map taking into account all these resampled points (see Figure  1 , bottom, Fixation density map vs. Saccade density map). ADEB also introduced the flow direction mapsvector fields generated similar to density maps by weighting a unit vector tangent to the saccade curve with a bivariate radial kernel.
Nevertheless, while exploring two separate components can be intuitive when the flows are parallel to the components (i. e. purely vertical or horizontal, as in the square scanpath dataset), it is more troublesome in cases of diagonal or circular flows where both components are non-null. In the scientific visualization domain a variety of methods exist that can depict a vector field in a single 2D image  (Post et al. , 2002). Flow visualization techniques include direct flow visualizations using arrow glyphs, geometric flow visualizations using streamlines, feature-based flow visualizations using topological information, and dense, texture-based flow visualizations using repetition of a texture according to the local flow vector  (Laramee et al. , 2004). The texturebased flow visualization is among the most versatile and effective methods, and is easy to implement.
Illustrations Datasets We considered three use cases: a square scanpath, a visual search task and an art perception task. A participant's gaze position was recorded at 500 Hz with a remote SMI RED eye tracker (SensoMotoric Instruments GmbH, Germany). A 9-point calibration was performed in the beginning of the acquisition. The calibration was validated with four additional fixation points until the precision was below 1°. The participants had a viewing distance of approximately 60 cm from the 22-inch LCD monitor with 1680 × 1250 pixels screen resolution. The fixations and saccades were detected using the Event Detector 3. 0. 20 by SMI using default settings. The software that generated the illustrations using the described visual aggregations algorithms was implemented in C#. All the datasets, containing x and y coordinates of the start and end fixation points of each saccade and their timestamp, are available in supplementary files.
Square scanpath. In this example, the participant followed a small black circle on the screen for one minute. The circle moved from corner to corner of the square, each side of which has a length of 200 pixels. During the first half of the trial, the circle moved in the clockwise direction, during the other half it moved anticlockwise. The resulting dataset contains 90 saccades. Visual search task. During this task, the participant had to find all the numbers from 1 to 90 in the correct order. This test was used in the Soviet Union to test children's attentional capabilities. We considered the first minute of the task recording. The resulting dataset contains 595 saccades.
Art perception As in the visual search task example, the visualization of the art perception datasets reveals the scan strategy used by the participant viewing the masterpieces. Figure  7A  shows that the participant explored the Vincent van Gogh painting in a triangle between the café terrace, the night sky and the shop on the street corner. The line width encoding according to the bundle density also tells us that the least seen element was the corner shop, and the majority of transitions were between the blue sky and the yellow terrace. Figure  7B  uncovers the main transitions between the eyes and the lips of the peasant and the cow. Small transitions to the figures of two peasants on the top of the painting are also easily visible in the proposed scanpath representation. Figure  8  shows the bundled layout and different color encodings of the gaze of the participant exploring the Michelangelo masterpiece.
Conclusion and Future Work In this paper, we illustrated the use of different visual aggregation techniques to obtain non-cluttered visual representations of scanpaths. Fixation clustering and saccade bundling simplified the scanpath representation and allowed the scan strategy of the participant to be read. Flow direction maps generated using edge bundling can be further represented as a single image to explore the transitions and can be compared using cosine similarity maps. Used together, these techniques provide an efficient support for a visual analysis of the scanpaths and informative illustrations of the eye movement data. We also provide the example datasets in the supplementary material so that other researchers can test their visualization methods on the data and compare it with our results.
The proposed approach follows the theoretical foundation of non-stationary Gabor frames  [20, 45]  and their application to TF transforms with a variable TF resolution  [46] [47] [48]. This report extends the work reported in  [20]  (Section 5. 1) by providing a full theoretical and practical development of the Audlet. The manuscript is organized as follows. The next section briefly recalls the basics of non-uniform FBs, frames, and auditory frequency scales. Section 3 describes the theoretical construction of the Audlet framework. The practical implementation issues are discussed in Section 4 and Section 5 evaluates important properties and capabilities of the framework.
Code We provide code for performing an Audlet analysis/synthesis as part of the Matlab/Octave "large time-frequency analysis toolbox (LTFAT)" toolbox  [61, 62]  available at http://ltfat. github. io/. The analysis filters are generated by the function audfilters. The function allows to construct at will uniform or non-uniform Audlet FBs with integer or rational downsampling factors, thus offering flexibility in FB design. Rational downsampling factors can be achieved in the time domain by properly combining upsamplers and downsamplers (e. g. ,  [19] ). In LTFAT the sampling rate changes are directly performed in the frequency domain by periodizing and folding the Y k (z)'s, then performing an inverse DFT  [63]. This technique allows to achieve rational downsampling factors at low computational costs.
Example scripts to perform Audlet analyses/syntheses in various FB settings are provided as Supplementary Material (see Archive S1). The supplementary material also demonstrates the realization of iterative reconstruction. Note that for real-time implementations using macro blocks like in  [46] , the overall redundancy depends also on the overlap between the blocks. For analysis or processing purposes, the sub-bands can be combined in an overlap-add manner closely approximating the true non-blocked sub-bands. The perfect reconstruction property within the blocks is preserved.
The framework is implemented in the free Matlab/Octave toolbox LTFAT  [61, 62]. Supplementary Materials: Supplementary material available online at www. mdpi. com/2076-3417/8/1/96/s1 is provided by the authors. Archive S1: Matlab functions and test audio files to perform Audlet analyses/syntheses in various FB settings and reproduce all results presented in the manuscript. The archive, about 2. 6 MB in size, also includes a brief documentation. Figure  S2 : Frequency response and impulse response of two gammatone filters computed using the proposed framework.
Acknowledgments: The authors would like to thank  Damián Marelli  for insightful discussions and help on the theoretical development on non-uniform FBs. This work was partly supported by the  Austrian Science Fund (FWF) START -project  FLAME  (" Frames and Linear Operators for Acoustical Modeling and Parameter Estimation "; Y  551-N13 ), the  French-Austrian ANR-FWF  project  POTION ("Perceptual Optimization of Time-Frequency Representations and Audio Coding ;  I 1362-N30 "), and the  Austrian-Czech FWF-GA   ČR  project  MERLIN  (" Modern methods  for the restoration of lost information in digital signals; I  3067-N30 "). Open access publication costs were covered by  FWF.
An overview of active object languages is provided by  [5] , focusing on languages which have a stable implementation and a formal semantics. ASP  [7]  is an active object language that was implemented as the ProActive Java library  [3]. Deterministic properties were proven in ASP when no two requests can arrive in a different order on the same active object. Several extensions to the active object model enable controlled parallelism inside active objects  [12, 8, 2]. Multi-active objects is an extension of ASP  [12]  where the programmer can declare that several requests can run in parallel on the same object. This solution relies on the correctness of program annotation to prevent data-races but provides efficient data-parallelism inside active objects. Parallel combinators of Encore  [8]  also enable some form of restricted parallelism inside an active object, mostly dedicated to the coordination of parallel tasks.
Motivation and Objectives The SPMD programming model in general and BSP are well adapted for the implementation of specific algorithms, but composing different such algorithms in a single application requires coordination capabilities that are not naturally provided by the SPMD approach. Such coordination is especially difficult to implement in BSPlib because a program starts with all processes in a single synchronization group. For any communication to occur, all processes of the application need to participate in the same synchronization barrier, making it difficult to split a program into parallel tasks with different synchronization patterns. The PUB library can split communication groups to synchronize only some of the processes, but still lacks high level libraries for coordinating the different groups.
On the other hand, asynchronous message sending of active objects is appropriate for running independent tasks, but inefficient when there are many exchanged messages inside a given group of process or following a particular communication pattern. In this paper, we use active objects for wrapping BSP tasks, allowing us to run different BSP algorithms in parallel without requiring them to participate in the same synchronization. Active objects provide coordination capabilities for loosely coupled entities and can be used to integrate BSP algorithms into a global application. To our knowledge, this is the first model using active objects to coordinate BSP tasks. Among related works, programming languages based on stream processing, like the StreamIt  [16]  language, feature data parallelism.
While splitting a program into independent tasks could be considered similar to our approach, stream processing languages do not feature the strong synchronization model of BSP. They are also less convenient for service-like interaction between entities, particularly when those sending queries are not determined statically. In summary, ABSP features an interesting mix between locally constrained parallelism using BSP (with fixed number of processes and predefined synchronization pattern), and flexible service oriented interactions featured by active objects (more flexible but still with some reasonable guarantees). This makes our approach quite different from StreamIt and other similar languages.
Example In this section we show an example written using our C++ library under development. This library uses MPI for actor communications and reuses the BSPonMPI library for BSP communications. We chose C++ because we put higher priority on the efficiency of BSP dataparallel code, C++ allows us to re-use BSP implementations written in C while allowing objects and more transparent serialization. An implementation mixing incompatible languages would, at this point, yield unnecessary complexity in our opinion. We chose a motivating example based on this implementation instead of the formal language of the next section to show the re-use of existing code and because we think it is more convincing. This example shows how an active object can encapsulate process data and how its function interface can act as a parameterized sequential entry point to a parallel computation.
We also show the result of a call being used to call another active object to do another computation, which is not shown. The IPActor class interfaces the inner product implementation included in the BSPedupack software package  [4]. We only show parts of the code we deem interesting to present our model. Object variables begin by ' ', their declarations are not shown. We assume bsp nprocs() divides v1. size(). In the main function, the MPI process of pid 0 creates two active objects with two processes each, see the parameter of the createActor primitive. Then the ip function of the first one is called with vector v as the two parameters. This asynchronous call returns with a future f 1. The ip function of this active object is then called sequentially. Using BSP primitives, the input vectors are split among the processes of the active object.
We chose to model DRMA-style communications although message passing also exists in BSPlib; modelling messages between processes hosted on the same active object would raise no additional difficulty. P ::= Act{T x M } {T x s} program T ::= Int | Bool | Act | Fut < T > type M ::= T m(T x) {T x s} method s ::= skip | x = z | if v { s } else { s } | s ; s | return v | BSPrun(m) | sync | bsp put(v, v,.
The breadcrumbs we leave behind when using our mobile phones-who somebody calls, for how long, and from where-contain unprecedented insights about us and our societies. Researchers have compared the recent availability of large-scale behavioral datasets, such as the ones generated by mobile phones, to the invention of the microscope, giving rise to the new field of computational social science. With mobile phone penetration rates reaching 90% 1 and under-resourced national statistical agencies 2 , the data generated by our phones-traditional Call Detail Records (CDR) but also high-frequency x-Detail Record (xDR)-have the potential to become a primary data source to tackle crucial humanitarian questions in low-and middle-income countries. For instance, they have already been used to monitor population displacement after disasters 3 , to provide real-time traffic information, and to improve our understanding of the dynamics of infectious diseases 4.
Recent studies have, however, shown that pseudonymization and standard de-identification are not sufficient to prevent users from being re-identified in mobile phone data. Four data points-approximate places and times where an individual was present-have been shown to be enough to uniquely re-identify them 95% of the time in a mobile phone dataset of 1. 5 million people 6. Furthermore, re-identification estimations using unicity-a metric to evaluate the risk of.
re-identification in large-scale datasets  6  -and attempts at k-anonymizing mobile phone data  7  ruled out de-identification as sufficient to truly anonymize the data. This was echoed in the recent report of the [US] President's Council of Advisors on Science and Technology on Big Data Privacy which consider deidentification to be useful as an "added safeguard, but [emphasized that] it is not robust against near-term future re-identification methods". The limits of the historical de-identification framework to adequately balance risks and benefits in the use of mobile phone data are a major hindrance to their use by researchers, development practitioners, humanitarian workers, and companies. This became particularly clear at the height of the Ebola crisis, when qualified researchers (including some of us) were prevented from accessing relevant mobile phone data on time despite efforts by mobile phone operators, the GSMA, and UN agencies  8  , with privacy being cited as one of the main concerns.
g. , by setting up an external ethics committee to review data uses. We will now review the four models, emphasizing their applicability to different data uses, pros and cons, and implementation challenges. We will also discuss potential threats and resulting information leaks for each model. Limited release is the closest model to traditional sharing of data. A mobile phone dataset is transformed in-house and a copy of the data is given to third-parties under a legal contract. The transformation aims at both adding technical difficulties to attempts at re-identifying individuals and at limiting the amount of information that could be uncovered if the data were to be re-identified. The transformed data are, however, still fairly close to the raw data. Transformations typically consist of 1) data sampling and longitudinal resampling with new identifiers -either using correspondence tables, properly salted hashes or the use of key-hash function -as well as 2) limited data coarsening along the temporal axis and Voronoi translation of antennas (spatial axis)  10.
If the data are distributed (across users or pieces of information), tools such as secure multiparty computation can be used to compute aggregated results  27  or to run statistical analysis such as correlations  28. We see the need for open-source software and practical privacy mechanisms to be the main challenges to the implementation of the question-and-answer model. Since the use of the data is tightly controlled, we consider the server being compromised to be the main privacy threat. However, as for the remote access model, we do not consider this risk to be significantly higher than the risk of any places where the data would be digitally stored (server, laptops, etc. ) to be compromised. While the likelihood of an attacker being able to infer information about a specific re-identified user through the QA API is not null (these attacks served as motivation for mechanisms such as differential privacy  26  ), we consider this risk to be moderate when combined with defense-in-depth mechanisms.
Acknowledgements S. G. is supported by a  Discovery Grant  and a  Discovery Accelerator Supplement Grant  from  NSERC  as well by the  Canada Research Chair program , J. E. S. by the  Bill & Melinda Gates Foundation  ( OPP1106936 ) and  Belgian Federal Science Policy Office (BELSPO) , S. P. by  MIT Media Lab Consortium :  2746036 , Y. -A. dM. , N. dC. , E. L. and S. P. are supported by a grant from the  Agence française de développement (AFD) , L. R. by the  Belgian Fund for Scientific Research (F. R. S. -FNRS ), M. G. -H. by the  UNICEF Venture Fund.
An attempt at quantifying the form-meaning mapping of sign locations was made by  Börstell and Östling (2017)  by linking the manually annotated Swedish Sign Language dictionary to a semantic dictionary. This showed that signs of certain semantic domains (e. g. , "think, " "see, " "eat") were more prominent in certain locations (forehead, eyes, and mouth/belly, respectively) than signs in general. In this paper, we aim to investigate the number of hands and location in signs and their association with specific semantics. We do this using automated methods on a large dataset with 120,000 videos from a sample of 31 different sign languages, using the parallel sign language dictionary Spread the Sign 2012 4. We specifically hypothesize that (1) the iconic mapping strategy of articulatory plurality, specifically using two-handed sign forms to represent plural meanings, is employed across languages, and (2) sensory and body part-related meanings will be iconically articulated at their associated locations on the body across languages.
MATERIALS AND METHODS We use data from the online parallel dictionary Spread the Sign 2012, which contains a total of 31 sign languages with sign entries,   (cf. Brentari, 2010; Jepsen et al. , 2015). A basic attempt at a classification is found under the Sign Language family in the Glottolog database, according to which several of the languages in our dataset are categorized as part the same language group (e. g. , Swedish Sign Language and Finnish Sign Language, or German Sign Language and Polish Sign Language)  (Hammarström et al. , 2017). In this study, we are not concerned with the potential relatedness between languages. The aim is to explore iconic properties of any language in the visual modality and the possible patterns that may be discerned. However, it should be noted that some patterns of metaphorical iconicity found here may be influenced by having a European/Western-biased language sample.
Data Processing Video files were downloaded from the Spread the Sign public website, along with metadata on the language used, as well as the name of the concept and the concept's category (in English)  5. As the first step in our processing chain, we used the body pose estimation model of  Cao et al. (2016)  to identify the position of wrists and elbows for each video frame. We used the model file published by the authors, which has been trained on the COCO dataset  (Lin et al. , 2014)   6. While their model can identify the poses of multiple humans in a frame and thus is much more general than needed here, we found that it is highly accurate in identifying the required body parts. This step required about two months of computing time on a single GPU. Since the body pose estimation model is not directly trained to detect hands, we estimate their location by extrapolating the elbow-wrist line outwards by half the distance between the elbow and wrist.