dataesr/scientific-paragraphs-categorization huggingface dataset:
It requires the specification of a threshold to compute the final BioCode. Thus, by combining the high confidence of the key to the biometric data, the inter-class variation increases while the intra-class distance is preserved. Hence, good performances can be achieved, see  (Belguechi et al. , 2013a)  for example. We insist on the fact that BioHashing is privacypreserving. Indeed, with BioHashing algorithm, as well as other cancelable biometrics techniques, original raw biometric data does not have to be stored. Therefore the users privacy is guaranteed. Moreover, revocability property is also automatically ensured: if the transformed template is compromised, the secret random key can simply be replaced to generate a new BioCode. Concerning the remaining properties among the aforementioned ones, unlinkability is also guaranteed together with the diversity, since distinct secret keys can be chosen for distinct applications, with no link between the generated BioCodes.
Client Architecture Smartphones have a great penetration into the market (Sophos, ) and possesses heavy sensing capabilities. Within those sensors, we can find: accelerometers, gyroscopes, light sensors, GPS and many others. In addition, features can be extracted using the software capabilities of the phone such as the call duration or the applications usage. In this work, we focus on the association of the location with call information. Location can be computed from the networks informations, the wifi networks available and the GPS of the mobile phone. Call information can be extracted from the software part of the phone and from hardware part too. For instance, on the Android system it is possible to extract: • The duration of the call • The position of the phone (accelerometer, gyroscope) • The location of the user • The phone number of the callee • Noise informations from the environment For this article, we have the following information available in an dataset described in section 4: • The location of the cell from where the call as been made • The phone number of the callee Those data were chosen because they already offer interesting performances in the literature  (Li et al.
, 2013). The generated Biocode size is dependent of the input vector (see section 3. 1. As a consequence, the longer the input vector is, the better it is to avoid collision between Biocodes. In this paper we only use geolocation and phone number data because of the available dataset. In a real use case, additional data can be used. The verification process is done online. Before being sent to the server, the content of the data must first be anonymized. This step is performed using the Biohashing algorithm. The privacy protection depends on the security of the secret key used to perform the Biohashing. To ensure this security, the key is stored inside the secure element of phone. It can either be an online secure element or a physical one. Depending on the computing capabilities of the secure element, the Biohashing algorithm could be computed directly inside it.
Server Architecture The server receives BioCodes continuously each time an user calls. The first step is to store the BioCode in the database. This database can be centralized because each user have its one key. The stored data permits to construct a template. When enough data are enrolled for an user, the server switch to verification mode. This step is described in figure  2. When in verification mode, the server keeps receiving BioCode from the mobile phone. When receiving a BioCode, the server uses a classifier to determine whether the received sample is genuine or not. In the next section, we describe the experiments we lead to evaluate the proposed approach.
Dataset The collected dataset is a private real dataset containing the communication behavior of 100 users during one month extracted from the network information. The available data are: • The latitude and longitude of cell We use those properties to perform a static and transparent authentication. The idea is to use the behavioral data extracted from a phone communication to authenticate a user. To perform this authentication, we choose not to use impostors data. This constraint reflects real life industrial datasets. Even if those datasets can contain millions of customers, impostors data are not necessarily inside the dataset. Each entry of the dataset is composed of a call or a text message associated to the position of the user. Table  1  presents the general statistics of the dataset.
The theoretical entropy of a perfect BioCode is equal to its length. When computing the real entropy a dataset of BioCode, we observe that the results are inferior to the theoretical entropy. The difference between these two measures is called the privacy leakage and express how much information is available concerning the original data. This value is expressed in bits. Two classifiers are evaluated. The first classifier used is a Classical Support Vector Machine  (Boser et al. , 1992)  answers to two class classification problems. In the studied case, we model each user's behavior with a one-class SVM. As a consequence, we use a One Class Support Vector Machine. The aim of a One Class SVM is to decide if new samples are within the previously enrolled ones. The implementation used the libSVM  (Chang and Lin, 2011)  for Matlab.
Additionally, we compute the sum of the distance to the distance of the nearest entries to the samples collected in the user template. The approach was preferred over the Kmeans algorithm because of the dataset, because the geolocation are already gathered by cell. In order to evaluate the performance of the dataset, we first compute the verification without any privacy protection. In this first case, the data are sent in clear. Then, we use the best case scenario where impostors try to perform a zero effort attack. In this attack, they simply try to authenticate with their own BioCodes. Finally, the worst case scenario is envisaged, in this case, the phone is stolen and the attacker try to perform usual phone calls. Those results are exposed in the following section.
Without Privacy Protection The first testing scenario is to authenticate without using any privacy protection on the data. This permits to evaluate the basic performance of classical algorithms on our dataset. We use the caller number as an identifier to verify our result. The only difference in this scenario is that the biohashing algorithm is not applied on the data. We evaluate two classifiers to determine the best approach for the current data. The first one is a One Class SVM with nu = 0. 0001 and the second one is based on the KNN classifier. Results are summarized in tables 2 and 3. Results using the KNN classifier are close to those we can found in the literature  (Li et al. , 2013). We observe a small difference that can be explained by the fact only one class is used for classification.
Best Case Scenario The best case scenario represents the normal utilization of the authentication protocol. In this scenario, a zero effort attack is performed. The Biohashing algorithm was applied as follows : the latitude and longitude were wrote in digits using respectively a 8 digits precision and a 6 digits precision, the phone number was concatenated to obtain a 24 digits tuple. This tuple is then converted to a 104 bits binary vector. This vector is then Biohashed to obtain a 100 bits Biocode. Table  4  and table 5 summarize the results obtained using a OCSVM and KNN. The privacy protection is evaluated by looking at how much bits of information are leaked when using this protection. The evaluation process measures the difference between a perfect dataset of random vectors and our results.
Worst Case Scenario In the worst case scenario, the attacker knows the key. The only possible solution is that the attacker has stolen the mobile phone because the key is stored inside a secure element. The performances are shown in table 6 and table 7. Even in this scenario, the secret key is not accessible to an attacker. This protects the data contained in the dataset. Assuming an attacker possess both the mobile phone and the dataset, an attacker can only guess location to see if the Biocodes are similar to obtain information about the location of an user. Of course because the attacker possess the mobile phone, he has access to the call history and the called numbers.
CONCLUSION AND FUTURE WORKS The ROC curves for the three evaluated scenarios are exposed in figure  3. We observe a great improvement when using the Biohashing algorithm in the nominal case (best case scenario). A limitation to this study is the number of available samples. But even with this limitation due to the available datasets, it shows great opportunity in privacy preserving mobile authentication. In the performance evaluation we focus on the call and location information. Those information can also be used combined with any other sensors and also    (Kayacik et al. , 2014). In this case, a similar approach to both allow revocation and privacy protection can be applied. In a future work, we wish to combine this modality with others to add new modality to a Single Sign On environement.
Electronics The data acquisition hardware is provided by CAEN s. p. a. , one of the partners of the collaboration. It consists of two CAEN 16-channels digitizers that can acquire data with 14-bit resolution at a sampling rate of 500 MS/s and a Digital Constant Fraction Discriminator (DCFD) embedded in the firmware for precise timing measurements, five CAEN 6-Channel 4 kV/3 mA VME HV Power Supply Module, and optical fiber connections using the CAEN CONET2 protocol for fast acquisition. In the digitizer firmware each event is analyzed online with a DCFD. The temporal resolution that is achievable is less than a nanosecond.
DAQ software architecture A novel distributed data acquisition system (DiDAQ) was developed for the project. This new DiDAQ is composed of a network of separate processes (Fig. 1 ), also called servers. Each server manages a single task (e. g. data acquisition, data logging…), in order to simplify its development and debugging. Being a distributed architecture, the system is more flexible since any of its servers can stop without compromising the whole network. This feature is especially useful during development, as it is possible to stop, update and restart servers on deployed systems, without stopping the whole infrastructure. Communication is carried out through network sockets, which means that the servers can be hosted in several computers, depending on the needed performances. Every server broadcasts its status and data stream to every one of its clients, and listens for commands received on a dedicated socket.
4 MeV) were used to perform the measurements. The photomultiplier tube (PMT) anode signals were fed into a VME CAEN digitizer V1730 (500 MS/s, 14 bit). A CAEN HV power supply model V6533 and a CAEN V1718 USB Bridge completed the electronic chain. The analysis of the acquired data was performed using ROOT  (Antcheva et al. , 2009). To obtain the energy resolution as a function of the energy itself, one of the sources was placed in front of the widest face (5" × 10") of the detector, approximately at half-height, and its spectrum was measured. The source-detector distance was varied as a function of the intensity of the source, in order to get a suitable count rate for a measurement with enough statistics in a reasonable period of time. The energy resolution curves for the set of detector are shown in Error! Reference source not found.
Conclusions We presented here the status of the NaI(Tl) detectors and DAQ of the first Rapidly Relocatable Tagged Neutron Inspection System (RRTNIS) of the H2020 European C-BORD project, emphasizing the work performed prior to the integration of the first prototype. A new Distributed Data Acquisition System (DiDAQ) was developed for the project. This DiDAQ follows the approach of distributing the tasks on different processes, communicating through network sockets. The performance tests of the DiDAQ, showed that it is able to cope with the requirements of the project. A set of 20 large (5" × 5" × 10") NaI(Tl) detectors was characterized, in order to determine which ones have performances within the project specifications. The characterization analyzed both the energy resolutions, as a function of gamma energy and interaction location in the crystal, and the time resolution.
We present the ParisNLP entry at the UD CoNLL 2017 parsing shared task. In addition to the UDpipe models provided, we built our own data-driven tokenization models, sentence segmenter and lexiconbased morphological analyzers. All of these were used with a range of different parsing models (neural or not, feature-rich or not, transition or graph-based, etc. ) and the best combination for each language was selected. Unfortunately, a glitch in the shared task's Matrix led our model selector to run generic, weakly lexicalized models, tailored for surprise languages, instead of our dataset-specific models. Because of this #ParsingTragedy, we officially ranked 27th, whereas our real models finally unofficially ranked 6th.
). Here, the Universal Dependency (hereafter "UD") shared task introduced an endto-end parsing evaluation protocol where none of the usual stratification layers were to be evaluated in gold mode: tokenization, sentence segmentation, morphology prediction and of course syntactic structures had to be produced 1  for 46 languages covering 81 datasets. Some of them are low-resource languages, with training sets containing as few as 22 sentences. In addition, an out-of-domain scenario was de facto included via a new 14-language parallel test set. Because of the very nature of the UD initiative, some languages are covered by several treebanks (English, French, Russian, Finnish. ) built by different teams, who interpreted the annotation guidelines with a certain degree of freedom when it comes to rare, or simply not covered, phenomena.
Architecture and Strategies In preparation for the shared task, we have developed and adapted a number of different models for tokenization 4  and sentence segmentation, tagging-predicting UPOS and the values for a (manually selected, language-independent) subset of the morphological attributes (hereafter "partial MSTAGs")-and parsing. For each dataset for which training data was available, we combined different pre-processing strategies with different parsing models and selected the best performing ones based on parsing F-scores on the development set in the predicted token scenario. Whenever no development set was available, we achieved this selection based on a 10-fold crossevaluation on the training set. Our baseline pre-processing strategy consisted in simply using the data annotated using UDPipe (Straka et al.
, 2016) provided by the shared task organizers. We also developed new tools of our own, namely a tagger as well as a joint tokenizer and sentence segmenter. We chose whether to use the baseline UDPipe annotations or our own annotations for each of the following steps: sentence segmentation, tokenization, and tagging (UPOS and partial MSTAGs). We used UDPipe-based information in all configurations for XPOS, lemma, and word segmentation, based on an a posteriori character-level alignment algorithm. At the parsing level, we developed and tried five different parsers, both neural and non-neural, which are variants of the shift-reduce (hereafter "SR") and maximum spanning-tree algorithms (hereafter "MST"). The next two sections describe in more detail our different pre-processing and parsing architectures, give insights into their performance, and show how we selected our final architecture for each dataset.
Tagging Tagging architecture Taking advantage of the opportunity given by the shared task, we developed a new part-of-speech tagging system inspired by our previous work on MElt (Denis and Sagot,  2012), a left-to-right maximum-entropy tagger relying on features based on both the training corpus and, when available, an external lexicon. The two main advantages of using an external lexicon as a source of additional features are the following: (i) it provides the tagger with information about words unknown to the training corpus; (ii) it allows the tagger to have a better insight into the right context of the current word, for which the tagger has not yet predicted anything. Whereas MElt uses the megam package to learn tagging models, our new system, named alVW-Tagger, relies on Vowpal Wabbit.
5 One of Vowpal Wabbit's major advantages is its training speed, which allowed us to train many tagger variants for each language, in order to assess, for each language, the relative performance of different types of external lexicons and different ways to use them as a source of features. In all our experiments, we used VW in its default multiclass mode, i. e. using a squared loss and the one-against-all strategy. Our feature set is a slight extension of the one used by MElt (cf. Denis and Sagot, 2012). The first improvement over MElt concerns how information extracted from the external lexicons is used: instead of only using the categories provided by the lexicon, we also use morphological features. We experimented different modes. In the baseline mode, the category provided by the lexicon is the concatenation of a UPOS and a sequence of morphological features, hereafter the "full category".
In the F mode ("ms mode" in Table  1 ), only the UPOS is used (morphological features are ignored). In the M mode, both the full category and the sequence of morphological features are used separately. Finally, in the FM mode, both the UPOS and the sequence of morphological features are used separately. The second improvement over MElt is that alVWTagger predicts both a part-of-speech (here, a UPOS) and a set of morphological features. As mentioned earlier, we decided to restrict the set of morphological features we predict, in order to reduce data sparsity. 6 For each word, our tagger first predicts a UPOS. Next, it uses this UPOS as a feature to predict the set of morphological features as a whole, using an auxiliary model. 7 traction of morphological lexicons As mentioned above, our tagger is able to use an external lexicon as a source of external information.
We therefore created a number of morphological lexicons for as many languages as possible, relying only on data and tools that were allowed by the shared task instructions. We compared the UPOS accuracies on the development sets, or on the training sets in an 10-fold setting when no development data was provided, and retained the best performing lexicon for each dataset (see Table  1 ). Each lexicon was extracted from one of the following sources or several of them, using an a posteriori merging algorithm: • The monolingual lexicons from the Apertium project (lexicon type code "AP" in Table  1 ); • Raw monolingual raw corpora provided by the shared task organizers, after application of a basic rule-based tokenizer and the appropriate Apertium or Giellatekno morphological analyzers (codes "APma" or "GTma"); • The corresponding training dataset (code "T") or another training dataset for the same language (code "Tdataset"); • The UDPipe-annotated corpora provided by the shared task organizers (code "UDP"); • A previously extracted lexicon for another language, which we automatically "translated" using a dedicated algorithm, which we provided, as a seed, with a bilingual lexicon automatically extracted from OPUS sentence-aligned data (code "TRsource language").
All lexical information not directly extracted from UDPipe-annotated data or from training data was converted to the UD morphological categories (UPOS and morphological features). For a few languages only (for lack of time), we also created expanded versions of our lexicons using word embeddings re-computed on the raw data provided by the organizers, assigning to words unknown to the lexicon the morphological information associated with the closest known word (using a simple euclidian distance on the word embedding space). 8 When the best performing lexicon is one of these extended lexicons, it is indicated in Table  1  by the "-e" suffix.
SB) at a non-whitespace boundary, a token (resp. sentence) boundary is created and "SpaceAfter=No" is added to the preceedings token; (iv) if it predicts We compared our tokenization and sentence segmentation results with the UDPipe baseline on development sets. Whenever the UDPipe tokenization and sentence segmentation scores were both better, we decided to use them in all configurations. Other datasets, for which tokenization and sentence segmentation performance is shown in Table  2 , were split into two sets: those on which our tokenization was better but sentence segmentation was worse-for those, we forced the UD-Pipe sentence segmentation in all settings-, and those for which both our tokenization and sentence segmentation were better.
Preprocessing model configurations As mentioned in Section 2, we used parsing-based evaluation to select our pre-processing strategy for each corpus. More precisely, we selected for each dataset one of the following strategies: 1. UDPIPE: the UDPipe baseline is used and provided as such to the parser. 2. TAG: the UDPipe baseline is used, except for the UPOS and MSTAG information, which is provided by our own tagger. 3. TAG+TOK+SEG and TAG+TOK: we apply our own tokenization and POS-tagger to produce UPOS and MSTAG information; sentence segmentation is performed either by us (TAG+TOK+SEG (available for datasets with "yes" in the last column in Whenever we used our own tokenization and not that of the UDPipe baseline, we used a characterlevel alignment algorithm to map this information to our own tokens.
A noop transition may also be used at the end of parsing paths to compensate differences in path lengths. Training is done with a structured averaged perceptron, using early aggressive updates, whenever the oracle falls out of the beam, or when a violation occurs, or when a margin becomes too high, etc. 15  Feature templates are used to combine elementary standard features: • Word features related to the 3 topmost stack elements s i=0   13  For surprise languages, the UDPipe baseline was trained on data not available to the shared task participants. 14 Because of a last-minute bug, we used the TAG configuration for trpud and ptpud although we used the UDPIPE configuration for tr and pt. We also used the TAG setting for fipud rather than the TAG+TOK+SEG setting used for fi. 15 By "violation," we mean for instance adding an edge not present in the gold tree, a first step towards dynamic oracles.
The 2 neural-based variants of DYALOG-SR and DYALOG-MST, straightforwardly dubbed DYALOG-SRNN and DYALOG-MSTNN, implement a similar architecture, the one for DYALOG-SRNN being a bit more advanced and stable. Moreover, DYALOG-MSTNN was only found to be the best choice for a very limited number of treebanks. In addition to these models, we also investigated a basic version of DYALOG-SRNN that only uses, in a feature-poor setting, its character-level component and its joint action prediction, and which provides the best performance on 3 languages. The following discussion will focus on DYALOG-SRNN. The architecture is inspired by Google's PARSEYSAURUS (Alberti et al. , 2017), with a first left-to-right char LSTM covering the whole sentence and (artificial) whitespaces introduced to separate tokens.