dataesr/scientific-paragraphs-categorization huggingface dataset:
A similar, more extended application of this approach can be found in the work of  Ogas and Gaddam (2012) , who analyzed 400 million search-engine queries in order to unveil the 'billion wicked thoughts' of its users. In this article we present the methods used to acquire our datasets and their main characteristics, and go on to focus on the underlying classification systems and the structural differences they imply. Online content categorization has been the focus of many studies of online interaction and collaboration  (Guy and Tonkin 2006; Cattuto et al. 2009 ). We recall one of their major structural elements, namely the highly skewed distribution of the categories: a large proportion of items are covered by a very small number of almost universal categories, while a long tail of more specific categories still gather a large variety of content  (Anderson 2006).
Classifying one's desire: dataset acquisition and description Online porn is available in numerous forms. Because of their small size, plain text stories, picture galleries and comics were probably the first types of porn content to be widely diffused on the web. Audio and video files came later, with video the main medium during the 2000s, largely due to the wider availability of broadband internet connections and better streaming technologies that have enabled us to view, upload and host videos easily. However, video-hosting platforms are in competition with other kinds of services  (Ogas and Gaddam 2012)  that enable direct interaction between pornographic actors and viewers. For example, LiveJasmin. com, a webcambased interaction platform, is ranked as the third most visited website in the adult category.
1 Webcam communities broadcast unstructured contentoften streamed video and chatwhich is unarchived and has little metadata. Despite the importance of this growing medium of online pornography, the lack of structure in the data means it is outside the scope of our study. Video-hosting platforms, on the other hand, present well-structured data. Every video belongs to a page, with a specific URL, a list of associated keywords and various other metadata such as the number of views, upload date, comments, votes, descriptions, and so on. This information is publicly available to any user, and the method we used to collect our data differs from that used by a regular user only in its systematic approach. According to several website popularity rankings, 2 we identified the two most popular pornographic video hosting platforms -XNXX and XHamster.
We created a dedicated computer program to carry out the navigation and data collection tasks required to gather the metadata for all available videos on both websites without downloading any videos. The datasets are available online 3 and are released under a Creative Commons License. 4 As shown in Tables  1  and 2 , a variety of data is attached to each entry. The last column indicates how much of the dataset's entries are provided with the data described in each row. The XNXX and Xvideos 5 domains are the oldest among the most popular porn platforms, dating from 1997. In July 2013 the websites claimed to host more than 3. 5 million videos. We gathered information for 1,166,278 videos that were uploaded before March 2013. XNXX releases very little data about the videos it hosts. As shown in Table  1 , only the title, keywords and comments are available to the public.
Information about uploaders and the number of views is hidden or not logged by the platform maintainers. Our interest in this dataset lies primarily in its tags. When someone uploads a video, they can attach any number of keywords to their file. These keywords are meant to describe the video and highlight its specificities in order to help the user find it more easily, by anticipating the words used in a search query targeting this content. By allowing uploaders to index their videos with numerous keywords, XNXX possesses a corpus of over 70,000 tags. Among the most common pornographic platforms, XNXX is the only one to have such a corpus of descriptive keywords. XHamster is a recent platform dating from 2007, and probably for this reason hosts fewer videos. All of the videos can be accessed, and our dataset includes all of the videos hosted by the platform since its creation and still available when we collected the data in February 2013.
This represents 786,121 entries in the format described in Table  2. The presence of a timestamp on 99% of the videos permits analyses of changes through time. 6 To avoid taking incomplete years into account while considering metadata evolution, years 2007 and 2013 are omitted. An anonymized identifier links the uploader to their video clips. This permits us to track the repetition of videos among uploaders and the relations between uploaders with specific content categories or video characteristics (e. g. runtime, comments, views). 7  As two of the most important pornographic platforms, XNXX and XHamster offer a representative sample for studying online pornography. Moreover, the structure of their data is significantly different, which makes them amenable to a comparative approach.
The latterknown as 'folksonomy'-is a key feature in the development of content diversity and, in our case, in the tracking of contemporary porn diversification  (Attwood 2007). The substantial difference in the range of semantic possibilities for uploaders and viewers impacts the number of dimensions indexed by the platforms and is therefore observable in our study. However, despite the two platforms having different categorization systems, there are some strong similarities between the datasets, which suggests a possible generalization to other pornographic platforms. One structural similarity is that whatever the number of categories available, a very small number of tags allows one to access most of the content. For instance, on XNXX the top 5% of the most From frequency to network Behind this structure lies a 'long tail' of less common sexual scripts and descriptors, calling for finer-grained approaches.
The hegemony of high frequencies Word frequency in titles All of the videos possess one title describing their content. Some recurring archetypes (such as 'boss', 'secretary', 'maid', 'brother's best friend', etc. ) can be identified in the datasets. The words 'mom' or 'mother' are present in 37 of the 100 most seen videos in XHamster. Therefore, while our study focuses on more structured aspects such as categories, we have released a tool 8 for plotting and comparing word frequencies over time in video titles from the XHamster dataset (Figure  1 ). The fact that titles are unstructured sequences of characters poses challenges for conducting a systematic analysis. Spelling and typing errors, abbreviations, uses of plural and conjugated forms can all result in significant biases. For word frequencies in XHamster's titles, our algorithm strips out dashes and catches any occurrence of the query in the title; for example, 'blow' catches 'blowing', 'blowjobs', and so on while leaving biases from typing errors ('blwjob') and abbreviations ('bj').
These categories may point to content for which demand surpasses what is offered by uploaders. User reaction ranking tends to increase the average number of comments per video of the given category. This uncovers viewers' reactions and interactions around the video's content. Without reading the actual comments, it is difficult to determine whether, for example, the reactions are simply descriptive or not. However, some videos may trigger comments and discussion. Table  3  only provides the top 10 tags for each of the rankings, but we have released the dataset for all tags to permit further studies to be carried out. 9 Ranking tags allows us to isolate the various properties of specific porn content descriptors compared with the others. However, this focus tends to mute the high number of tags that, while not among the most frequent, still have significant levels of popularity in terms of number of videos.
Taking tags into account, co-occurrences provide a far finer-grained tool for analysis, as detailed below. Porn semantics as a network Link over-representations: 'blowjob' does not make it 'funny' The majority of videos in our dataset are attached to more than one category. If we consider the presence of several categories for the same videos as a link between each of these keywords, then we can build a global 'semantic' network. Categories are nodes connected through an edge (link) when two categories are significantly 'close' to one another. Such an analytical framework, known as network analysis and coming from the study of social relationships  (Scott and Carrington 2011) , has become very popular in many fields  (Easley and Kleinberg 2010; Newman 2010 ). As we have observed, tag frequencies are highly heterogeneous.
Porn semantic network Figure  2  helps visualize the whole network obtained from the XHamster dataset. Only edges whose strengths are above a given threshold have been represented. An algorithm has automatically determined this threshold such that the final network is as sparse as possible but still composed of one unique connected component. We applied a community detection method, often referred to as the Louvain algorithm  (Blondel et al. 2008) , to identify cohesive subsets of tags in the corpus. These 'clusters' gather densely connected tags that are relatively disconnected from the rest of the network and may form semantically coherent units. In Figure  2  each node is coloured according to the clusters to which it belongs. As well as the statistical measures available for network analysis, one can also sketch qualitative observations from visualization to characterize the network's structure and the relations between the nodes.
On Category Nicheness and Dataset Limits We observed on the previous network that some nodes have high degrees (i. e. many links) and occupy relatively central positions in the network, while others are only connected to a few other tags and seem more peripheral in the general picture. To measure such a property more rigorously we designed a so-called nicheness coefficient. The nicheness coefficient is built upon the global matrix of mutual information between pairs of tags. We simply define the nicheness score of a tag as the sum of the preferential links connecting this tag to its relevant neighbours. The rationale behind such a measure is that tags with a 'niche' behaviourthat is, tags compatible with only few other tagswill be connected by very strong edges. Conversely, tags that may be used in conjunction with any other tags are likely to have many weakly connected neighbours and a degree of distribution that is close to random, thus resulting in a very low nicheness score.
It is not shifts in which perversions are put on/scene that form the basis of this specialization of niches, but rather specialization within major and minor sexual practices and identities  (Penley 2004). Online pornography consumers are unlikely to be immobile in the landscape of niches described by Figure  3. Some niches bring users to other niches; some of them might even attract newcomers, while others might repel viewers from porn. The paths of users within the search space should exhibit patterns relevant to understanding their 'careers' as porn consumers. Structured computer traces and other data from hundreds of millions of consumers would provide material to study pornography on an unprecedented scale. However, due to the fact that the traces left by users (mainly identification and geolocalization) on the platforms' servers are possessed by the owners of the hosting sites and are not publicly available, our dataset does not include data directly linked to users' behaviours.
By modelling and visualizing these data, we enabled qualitative assessments to be made of tags' positions in networks and the links between categories, and therefore of how practices, nationalities, places and techniques are staged in the pornographic landscape. Large datasets and tools permit more statistical explorations and validation, but also allow a qualitative approach to be taken with respect to their numerical and visual outputs. A small-scale approach to large-scale results is likely to provide richer and more detailed information on specific communities and users. Our study reverse-engineers users'' tastes and colours' through the analysis of platform structures and uploaders' behaviours. While highly relevant for both website maintainers and content diffusers in devising strategies to target users, users' practices are not well understood because their traces are owned and kept by the websites.
By making our datasets, analysis and tools publicly available, we hope to make this approach more accessible to those wishing to extend this approach and/or to focus more specifically on particular communities and practices, or on other aspects of porn. Notes 1. http://www. alexa. com/topsites/category/Top/Adult. Accessed August 27, 2013. 2. Alexa and Netcraft rankings, accessed in August 2013. 3. http://pornstudies. sexualitics. org/#datasets. Accessed August 28, 2013. 4. https://creativecommons. org/licenses/by/3. 0/deed. en_US. Accessed August 28, 2013. 5. XNXX and Xvideos are two interfaces to the same corpus of videos. 6. For instance, the average runtime has been multiplied by seven. Also, runtime varies a lot between categories (23 minutes for 'double penetration' and four minutes for 'men').
7. Our dataset covers the contributions of 90,000 uploaders; one-half of them being one-time uploaders only, representing only 10% of the videos. 8. http://porngram. sexualitics. org/. Accessed August 28, 2013. 9. http://pornstudies. sexualitics. org/#catrank. Accessed August 28, 2013. 10. More precisely, denoting n(i) as the number of videos featuring tag i and n(j) as the number of videos in which j is mentioned. The edge strength is defined as the ratio between observed and theoretical values of videos using both i and j, which can be computed as s(i,j)=[ n(i,j)N]/[ n(i)n(j)], where N is the total number of videos. 11. The full dataset is available online: http://pornstudies. sexualitics. org/#link. Accessed August 28, 2013. 12. PornMD released an interface to explore the 10 most queried tags by country: http://www.
Description of XHamster dataset. Downloaded by[82. 227. 164. 151] at 06:46 15 June 2014 popular tags covers more than 90% of the videos. On both XHamster and XNXX the most frequent categories, respectively 'amateur' and 'blowjobs', target 30% of all entries. To further explore the datasets beyond the identification of the few dominant widespread categories, we designed several other methodological tools.
In this work, we provide the implementation and analysis of a cognitive transceiver for opportunistic networks. We focus on a previously introduced dynamic spectrum access (DSA) -cognitive radio (CR) solution for primary-secondary coexistence in opportunistic orthogonal frequency division multiplexing (OFDM) networks, called cognitive interference alignment (CIA). The implementation is based on software-defined radio (SDR) and uses GNU Radio and the universal software radio peripheral (USRP) as the implementation toolkit. The proposed flexible transceiver architecture allows efficient on-the-fly reconfigurations of the physical layer into OFDM, CIA or a combination of both. Remarkably, its responsiveness is such that the uplink and downlink channel reciprocity from the medium perspective, inherent to time division duplex (TDD) communications, can be effectively verified and exploited.
These radios are composed of two parts: a motherboard and one or two daughterboards. The motherboard controls the RF, universal serial bus (USB) circuitry and sampling. The adopted daughterboard is the RFX 2400, operating at 2. 4 GHz industrial, scientific and medical (ISM) band. In this regard, we note that the default hardware configuration of all these devices/blocks is adopted for all our implementations and experiments (see  [19]  for specifications). Therefore, no hardware modification is performed or proposed in this work to implement the reconfigurable transceiver. In fact, all the customizations and modifications are performed at software level. In particular, no additional filtering operation is performed before or during the baseband signal processing. The software toolkit adopted to perform the baseband signal processing is the so-called GNU Radio library  [20].
This popular library offers a flexible way to create baseband designs, by connecting ready-made signal processing basic blocks using python or by creating such blocks in C and C++. Afterwards, a flow graph made of these blocks can be visually prepared and executed by means of the graphical tools provided with the library. In practice, first, the developer implements signal processing blocks and transceiver chains as a toolbox of GNU Radio. Then, after the compilation and installation of this toolbox, corresponding flow graphs of the transceiver chains are created by properly connecting these modular blocks. The signal processing blocks and the transceiver chains described in Section 5 have been implemented following this approach. A simplified approach to the baseband design is adopted in this work, to guarantee perfect synchronization of CIA and OFDM signals at RX1 and satisfy a fundamental condition to ensure the effectiveness of CIA  [3].
Conclusions In this contribution, we provided a reconfigurable SDR transceiver implementation for interference management in CR networks. The adopted SDR framework is composed by the GNU Radio toolkit and a set of single antenna USRPs. The baseband operations are performed at software level, whereas the USRP's RF hardware is responsible for the actual transmissions. A simplified strategy to guarantee perfect synchronization of the primary and secondary signals at the primary receiver has been adopted, yielding a hybrid transceiver design, integrating both primary and secondary transmitter. The proposed architecture permits the implementation of transceivers adopting different physical layer strategies, e. g. , OFDM and CIA. The performance of this scheme has been studied under an interference cancelation constraint at the secondary system.
Acknowledgements This work was partially supported by  CAPES-COFECUB, Fatih University Scientific Research Fund  under the project number  P50061002 2 (1353 ), the  European Commission  in the framework of the  FP7 Network of Excellence in Wireless COMmunications NEWCOM#  (Grant agreement no. 318306 ), and by the  ERC   Starting Grant 305123 MORE (Advanced Mathematical Tools for Complex Network Engineering ).
Related Works In the literature, finite models for lambda-calculi are common. For example, Hillebrand analyzes databases as finite models of the simply-typed lambda calculus  [16]. Otherwise, Salvati presents a model based on finite sets  [27] , while Selinger presents models based on finite posets  [30]. Finally, Solovev  [31]  relates the equational theory of cartesian closed categories with the category of finite sets. More general than vector spaces, various categories of modules over semirings, as standard models of linear logic have been studied as computational models: sets and relations  [7] , finiteness spaces  [10] , probabilistic coherent spaces  [11] , etc. As models of linear logic, finite vector spaces are folklore  [25]  and appear as side examples of more general constructions such as Chu spaces  [26]  or glueing  [17].
Both balloon-borne electrochemical ozonesondes and MOZAIC (measurements of ozone, water vapour, carbon monoxide and nitrogen oxides by in-service Airbus aircraft) provide very valuable data sets for ozone studies in the upper troposphere/lower stratosphere (UTLS). Although MOZAIC's highly accurate UV-photometers are regularly inspected and recalibrated annually, recent analyses cast some doubt on the long-term stability of their ozone analysers. To investigate this further, we perform a 16 yr comparison (1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009) of UTLS ozone measurements from balloon-borne ozonesondes and MOZAIC. The analysis uses fully three-dimensional trajectories computed from ERA-Interim (European Centre for Medium-Range Weather Forecasts Re-analysis) wind fields to find matches between the two measurement platforms.
, 1998)  or the Swiss NOXAR program (Nitrogen Oxides and Ozone along Air Routes,  Brunner et al. , 2001). For data in both programs, European long-range airliners are equipped with accurate UV photometers to measure O 3 and other trace gases. MOZAIC data are available from August 1994 onwards, while NOXAR data are available for 1995-97. In a companion paper  (Staufer et al. , 2013)  we used both data sets to analyse ozonesonde data from Payerne (Switzerland) by using fully three-dimensional trajectories to find commonly sampled air masses. Comparison of the Payerne sonde data with MOZAIC showed mean differences of up to 20 % between 1994-1997, followed by differences of around 5-10 % in the subsequent years  (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009).
The comparison of sonde data with the NOXAR data, however, showed a smaller offset of around 15 % from 1995-1997. The question arises as to whether these discrepancies indicate a small drift in the MOZAIC calibration or whether they are a particular feature of the Payerne data series. To answer this question, the analysis of  Staufer et al. (2013)  is extended to various other soundings sites in Europe, America, Japan, and Africa.
At Canadian stations both SP and ES-Z sondes were flown before 2004. After 2004 mainly ES sondes were launched but the 1. 0 % solution strength was retained (see metadata at WOUDC, the World Ozone and Ultraviolet Radiation Data Centre). Recently, the ozonesonde data user community has been addressing how to account for changes in radiosonde instrumentation that have accompanied ozonesonde changes at a number of stations in the past 5 yr or so  (Stauffer et al. , 2014). The radiosonde changes propagate to each ozone measurement, but mostly at pressures < 100 hPa; newer radiosondes mostly affect ozone data after 2009. Thus, these influences are neglected here. The standard operating procedures (SOP) for the BM sondes are defined by  Claude et al. (1987)  and have been followed by MOHp and Payerne.
This, however, is neither supported by lab studies (e. g. Thornton and Niazy, 1982) , nor by the the study of  Reid et al. (1996) , who found a 7-8 % better agreement between ECC ozonesondes and an UV photometer for tropospheric O 3 concentrations when a constant background current was assumed. The recent assessment of ECC SOPs calls for a constant background current  (Smit et al. , 2011). The background current (ib) is measured three times during the prelaunch procedure: once the sondes are exposed to purified (ozone-free) air (ib 1 ), once after exposure to O 3 (ib 2 ), and just prior to flight (ib 3 ). We contacted the principle investigators (PI) in case the treatment of the background current could not be extracted from the different archives. Only De Bilt (the Netherlands), Huntsville, Legionowo (Poland), Madrid (Spain), OHP, Payerne, Sodankylä, and Uccle report their background current values without large data gaps for 1994-2009 (i.