dataesr/scientific-paragraphs-categorization huggingface dataset:
The primary predictor is the effect of the intervention measured in 3-month intervals throughout the study period, comparing to the FCHV standard-of-care control at baseline. Calendar month will be included as a separate covariate to control for the overall secular trend. Variables will be considered as both nominal and continuous (linear effect) predictors, and the generalized linear model framework will be used to estimate effect of time-varying repeated measure intervention implementation over the several steps of the wedged design. Differential impact from time of intervention will be evaluated with test of month × intervention interaction. Models will be fit using generalized estimating equations, e. g. , using SAS Proc Genmod, to calculate valid standard errors in the presence of repeated measures over time and possibly correlated outcomes at the village cluster level.
Each step is measured immediately prior to implementation, which is likely a stronger approach than historical controls. Perhaps the most important aspect of the chosen evaluation is that it is built within a developing health care system, and conducting research within this system strengthens data systems and quality for non-research purposes. There are limitations, however, to this "quasi-experimental" approach. When there is no concurrent control group, causal inferences are difficult to make. Lack of randomization can lead to both measured and unmeasured biases. When examining implementation, our study will likely be affected by the baseline variation that exists in the large geography we are covering. We will measure and account for distances to health care facilities, socioeconomic variation, and health care personnel quality.
Acknowledgements We wish to express our appreciation to the  Nepal Ministry of Health  for their continued efforts to improve the public sector healthcare system in rural Nepal. We wish to give our thanks to  Sharon Rising  and colleagues at  Centering Pregnancy , to the  Population Council for developing the Balanced Counseling Strategy , and to our technology partners ThoughtWorks,  Dimagi , and Simprints. Lastly, we are indebted to the community health workers whose commitment to serving our patients and dedication to improving reproductive, maternal, newborn, and child health outcomes in rural Nepal continues to inspire us.
We analyze economic perspectives of forest adaptation to risk attributes, caused mostly by climate change. We construct a database with 89 systematically chosen articles, dealing simultaneously with climate, adaptation, risk and economy. We classify the database with regard to 18 variables bearing on the characteristics of the paper, the description of the risk and the adaptation strategy, the topic and the corresponding results. To achieve a "high level-of-evidence", we realize a multiple correspondence analysis (MCA) to identify which variables were found in combination with one other in the literature and make distinct groupings affecting adaptive decisions. We identify three groups: (i) profit and production; (ii) microeconomic risk-handling; and (iii) decision and behavior. The first group includes economic costs and benefits as the driver of adaptation and prioritizes simulation, and a mix of theoretical and empirical economic approach.
Data Our strategy to collect articles was based on two main methods. In the first method, we conducted a literature research using the search engines Google Scholar and Web of Knowledge. Each research combined four types of keywords as follows: - In the second method, we used the bibliographies of the collected papers to add relevant articles to our database. Those papers whose bibliographies were used as an additional source of literature were Refs. [6, 8, 26]. The search was restricted to articles published in English. We thus collected and used 89 papers in our analysis (see the supplementary material for an exhaustive list of the bibliographical references). We classified the content of these papers into 18 variables with 3 possible modalities, with four sets of variables. The choice of the variables and associated modalities emerges from a thorough investigation of each paper's content.
Statistical Analysis Our dataset consisted solely of non-linear nominal variables with more than two modalities and therefore a multiple correspondence analysis was conducted  [27] , which enabled the determination of combinations of variables and modalities which were most represented in our meta-analysis. The outputs of an MCA are eigenvalues (or inertias) and percentage of inertia (or explained variance) for each relevant combination of variables and modalities, called dimensions. There are two main methods for carrying out the MCA, one involves the use of an indicator matrix and the other of a Burt matrix, obtained from an initial indicator matrix. Our MCA was carried out using an adjusted Burt matrix method, as it provided the best explanation of the variation in our data  [28, 29]. The analysis was carried out using the R statistical software with the package "ca"  [20].
Indicator Matrix MCA is used to analyze a set of observations described by a set of non-linear nominal variables. Each nominal variable comprises several modalities, and each of these modalities is coded as a binary variable. There are K nominal variables, each nominal variable has J K modalities and the sum of the J K is equal to J. There are I observations and the I × J indicator matrix is denoted as X. Our dataset consisted of 18 nominal variables (K = 18) comprising 3 modalities (J K = 3; J = 54). The number of observations is equal to 89 articles. The columns represent the variables (and modalities) while articles are expressed in rows. The indicator matrix X is then a matrix 89 × 54, i. e. , 4806 cells containing either 0 or 1. An MCA may be performed on this indicator matrix. However, in our case a Burt matrix MCA was conducted because the eigenvalues obtained from this analysis provides an optimized approximation of the inertia of the eigenvalues of X by rescaling the Burt matrix solution  [29, 30].
3 indicates a strong correlation, according to the measure of the relationship between nominal values in Ref. [31] , and was determined as the threshold to which enough of the variance was deemed to have been explained to continue the analysis. This was calculated by summing the eigenvalues and finding their square root. The correlation value, based on our data, was 0. 348. The dimensions quantified using MCA represent combinations of multiple variables and modalities as a method of reducing the information in our dataset to a simpler form for ease of interpretation. One dimension is constructed from the explained variation of the variable combinations used in the analysis, with their position relative to the axis being based on their level of correlation with that axis. The number of dimensions for our statistical analysis was chosen by finding the point of inflection from the scree plot of the eigenvalues (see Figure  A1  and Table A1 in Appendix A) generated from the MCA  [32].
Acknowledgments: This work was supported by a grant overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  ( ANR-11-LABX-0002-01 ,  Lab of Excellence ARBRE). The  UMR Economie Forestière  is supported by a grant overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  ( ANR-11-LABX-0002-01 ,  Lab of Excellence ARBRE ).
When this information is unavailable we rely on the word order: The closest function word is the SUD governor of the lexical head, the next one is the SUD governor of the first one, and so on. The conversions (UD → SUD and SUD → UD) we proposed are encoded in a rule-based system. The rules are organized by means of a separation of a universal core rule set and a language specific rule set, which for the time being has only been implemented for French. We use the Grew software (http://grew. fr) based on a computational Graph Rewriting Model. Each conversion is encoded as a graph rewriting system (GRS): a set of rules and a strategy describing how the rule applications must be ordered. Below, we give an example of an UD → SUD rule for the inversion of mark: We have evaluated the results of the double conversion (from UD to SUD first and then from SUD back to UD) against the original UD annotation with the 122 corpora of version 2.
Conversely, UD might be a better entry point to the semantic content of the sentence. The lower dependency length gives psycholinguistic support to SUD treebanks. Possibly related is the fact that various experiments on parser performance also consistently give an advantage to function-wordheaded structures (Schwartz et al. 2012, Silveira  and Manning 2015, Kirilin and Versley 2015,  Rehbein et al. 2017) 15 which provides another raison d'être for parallel SUD treebanks. The whole UD 2. 2 database, with its 122 treebanks, has been converted into SUD and is already accessible at https://gitlab. inria. fr/grew/ SUD. We would like to see this alternative to be distributed on the UD website as soon as possible and hope that the new scheme will benefit from discussions with the whole community and evolve in parallel to the UD scheme.
Effectively using full syntactic parsing information in Neural Networks (NNs) to solve relational tasks, e. g. , question similarity, is still an open problem. In this paper, we propose to inject structural representations in NNs by (i) learning an SVM model using Tree Kernels (TKs) on relatively few pairs of questions (few thousands) as gold standard (GS) training data is typically scarce, (ii) predicting labels on a very large corpus of question pairs, and (iii) pre-training NNs on such large corpus. The results on Quora and SemEval question similarity datasets show that NNs trained with our approach can learn more accurate models, especially after fine tuning on GS.
This is a shame as NNs are very flexible in general and enable an easy system deployment in real applications, while TK models require syntactic parsing and longer testing time. In this paper, we propose an approach that aims at injecting syntactic information in NNs, still keeping them simple. It consists of the following steps: (i) train a TK-based model on a few thousands training examples; (ii) apply such classifier to a much larger set of unlabeled training examples to generate automatic annotation; (iii) pretrain NNs on the automatic data; and (iv) fine-tune NNs on the smaller GS data. Our experiments on two different datasets, i. e. , Quora and Qatar Living (QL) from SemEval, show that (i) when NNs are pre-trained on the predicted data, they achieve accuracy higher than the one of TK models and (ii) NNs can be further boosted by fine-tuning them on the available GS data.
Although, assessing that NNs specifically learn such syntax should require further investigation, our results show that only the transfer from TKs produces improvement: this is a significant evidence that makes it worth to further investigate the main claim of our paper. In any case, our approach increases the accuracy of NNs, when small datasets are available to learn highlevel semantic task such as question similarity. It consists in (i) using heavier syntactic/semantic models, e. g. , based on TKs, to produce training data; and (ii) exploit the latter to learn a neural model, which can then be fine-tuned on the small available GS data.
Question Matching and Ranking Question similarity in forums can be set in different ways, e. g. , detecting if two questions are semantically similar or ranking a set of retrieved questions in terms of their similarity with the original question. We describe the two methods below: The Quora task regards detecting if two questions are duplicate or not, or, in other words, if they have the same intent. The associated dataset  (Wang et al. , 2017)  contains over 404, 348 pairs of questions, posted by users on the Quora website, labelled as duplicate pair or not. For example, How do you start a bakery? and How can one start a bakery business? are duplicated while What are natural numbers? and What is a least natural number? are not. The ground-truth labels contain some amount of noise. In the QL task at SemEval-2016  (Nakov et al.
Support Vector machines A top-performing model in the SemEval challenge is built with SVMs, which learn a classification function, f : Q × Q → {0, 1} , on the relevant vs. irrelevant questions belonging to the question set, Q. The classifier score is used to rerank a set of candidate questions q i provided in the dataset with respect to an original question q o. Three main representations were proposed: (i) vectors of similarity features derived between two questions; (ii) a TK function applied to the syntactic structure of question pairs; or (iii) a combination of both. Feature Vectors (FV) are built for question pairs, (q 1 , q 2 ), using a set of text similarity features that capture the relations between two questions. More specifically, we compute 20 similarities sim(q 1 , q 2 ) using word n-grams (n = [1,.
Learning NNs with structure To inject structured information in the network, we use a weak supervision technique: (i) an SVM with TK is trained on the GS data; (ii) this model classifies an additional unlabelled dataset, creating automatic data; and (iii) a neural network is trained on the latter data. The pre-trained network can be fine-tuned on the GS data, using a smaller learning rate γ. This prevents catastrophic forgetting  (Goodfellow et al. , 2013) , which may occur with a larger learning rate.
Data Quora dataset contains 384, 358 pairs in the training set and 10, 000 pairs both in the dev. and test sets. The latter two contain the same number of positive and negative examples. QL dataset contains 3, 869 question pairs divided in 2, 669, 500 and 700 pairs in the training, dev. and test sets. We created 93k 4  unlabelled pairs from the QL dump, retrieving 10 candidates with Lucene for 9, 300 query questions.
Results on Quora Second, CNNs pre-trained with the data generated by FV or in a self-training setting, i. e. , CNN(CNN-10k), and also fine-tuned do not improve 6 on the baseline model, i. e. , CNN-10K, (see the second part of the table). Third, when CNNs and LSTMs are trained on the data labelled by the TK model, match the TK model accuracy (third part of the table). Most importantly, when they are fine-tuned on GS data, they obtain better results than the original models trained on the same amount of data, e. g. , 1% accuracy over CNN-10k. Next, the fourth part of the table shows that the improvement given by our method is still present when training TK (and fine tuning the NNs) on 6 The improvement of 0. 5 is not statistically significant. less GS data, i. e. , only 5k. Additionally, the fifth section of the table shows a high improvement by training NNs on all available Quora data annotated by TK-10k (trained on just 10k).
Results on Qatar Living Table  2  reports the results when applying our technique to a smaller and different dataset such as QL. Here, CNNs have lower performance than TK models as 2,669 pairs are not enough to train their parameters, and the text is also noisy, i. e. , there are a lot of spelling errors. Despite this problem, the results show that CNNs can approximate the TK models well, when using a large set of automatic data. For example, the CNN trained on 93k automatically annotated examples and then fine tuned exhibits 0. 4% accuracy improvement on the dev. set and almost 3% on the test set over TK models. On the other hand, using too much automatically labeled data may hurt the performance on the test set. This may be due to the fact the quality of information contained in the gold labeled data deteriorates.
Conclusion In this work, we have trained TK-based models, which make use of structural information, on relatively small data and applied them to new data to produce a much larger automatically labeled dataset. Our experiments show that NNs trained on the automatic data improve their accuracy. We may speculate that NNs learn relational structural information as (i) TK models mainly use syntactic structures to label data and (ii) other advanced models based on similarity feature vectors do not produce any improvement. Indeed, the latter only exploit lexical similarity measures, which are typically also generated by NNs. However, even if our conjecture were wrong, the bottom line would be that, thanks to our approach, we can have NN models comparable to TK-based approaches, by also avoiding to use syntactic parsing and expensive TK processing at deployment time.
Side-channel attacks (SCA) exploit the reification of a computation through its physical dimensions (current consumption, EM emission,. ). Focusing on Elecromagnetic analyses (EMA), such analyses have mostly been considered on low-end devices: smartcards and microcontrollers. In the wake of recent works, we propose to analyze the effects of a modern microarchitecture on the efficiency of EMA (here Correlation Power Analysis and template attacks). We show that despite the difficulty to synchronize the measurements, the speed of the targeted core and the activity of other cores on the same chip can still be accommodated. Finally, we confirm that enabling the secure mode of TrustZone (a hardware-assisted software countermeasure) has no effect whatsoever on the EMA efficiency. Therefore, critical applications in TrustZone are not more secure than in the normal world with respect to EMA, in accordance with the fact that it is not a countermeasure against physical attacks.
Motivations: Our main objective is to expose the risks faced by users using embedded devices for critical operations when they are not protected against physical attacks. Most physical attacks are performed on low-end devices (microcontrollers and smartcards) and we investigate if they can be transposed easily to high-end devices. We verify that the TrustZone implementation does not offer any protection against side-channels leakages (TrustZone is only advertised as a software security solution by ARM, not hardware), and even multicore computations has no influence on these leakages. Contribution: Two different attacks have been experimentally realized on a device representing a modern smartphone. We will use two use-cases for that purpose: the secret-key cryptography key recovery, and a Personal Identification Number (PIN) recovery.
Trusted Execution Environment (TEE) Trusted Execution Environment Protection Profile (TEE PP) is a profile specified by GlobalPlatform in 2014  [1]. This TEE PP has been made specifically for mobile devices, the goal was to deal with new threats in the mobile world and to allow customers to have confidence in the TEE in order to deliver mobile wallets, or secure mobility solutions. A TEE is an execution environment that respect the TEE PP, meaning that it can be trusted by users. A Trusted Application (TA) is an application running in the TEE, it provides security related functions, and its assets include code, program data, cryptographic keys, etc. A TEE is designed to provide isolated execution, integrity of TA but also integrity and confidentiality of TA assets. In order to provide a secure environment, a TEE might be working alongside a Rich Execution Environment (REE).
TrustZone TrustZone (TZ) is a specific implementation of a TEE proposed by ARM. This proposition is based on a few hardware-specific parts only. Security functions are mainly performed by software implementations or by optional pieces of hardware that can be added by each integrator. It respects GlobalPlatform specifications, and goes into TEE PP. TrustZone hardware architecture leads to specific software implementations, a ROS, a TOS and the entry point (managing accesses to the secure world). It consists in separating hardware resources into two worlds, Secure and Normal (Non-Secure) worlds. This is achieved by adding a security-related bit on the system bus (and therefore all bus accesses are impacted), and by adding new privilege levels. In that way each peripheral, each data or code section are separated between the two worlds and a core can only handle them if it is in the right privilege mode.
Algorithm 1 Comparison between candidate PIN and true PIN change the true PIN in her profiling device; obtain many traces on her profiling device. The attack starts with a measurement campaign on the profiling device. Template attacks use a divide-and-conquer approach, the digits of the true PIN are attacked separately. For each value of the pairs, candidate PIN and true PIN (one digit each), (v, k) in [[0, 9]] 2 , traces are collected. M v,k = xk (i,j) , i for trace, j for time. For effective computations, both for proper floating point arithmetic and to limit the amount of data to handle, a principal component (PCA) preprocessing is used on the data and the matrix M v,k is only considered here after this preprocessing step. After the PCA, our traces have always 10k points each. The covariance matrix S v,k = sk (j,j ) is computed.
Targeted device A chip which is representative of what can be found in modern smartphones is targeted. Unfortunately, the public documentation on modern SoC is often sparse. To ease our bare-metal development process, we chose a relatively open platform with some documentation (mostly thanks to the community around it): the Raspberry Pi 2 (RPi2). This board possesses a BCM2836 SoC from Broadcom and features: a quad-core Cortex-A7 processor (ARMv7) running at 900MHz, a VideoCore IV GPU running at 250MHz, -1GB of LPDDR2 RAM memory running at 400MHz (or 450MHz in Turbo mode), -TrustZone is available. The processor uses 2 levels of cache: L1 : 32KB, 4-way associative, separate data and instructions, L2 : 512 KB, 8-way associative, unified data and instructions, shared between GPU and CPU. The RPi2 board features GPIOs allowing to send electric signals directly from our code.
Both applications are always present in the final binary and one is selected at start-up with a proper command sent on the UART. Additionally, a configuration can be chosen at this step to enable or disable several features, namely the TrustZone activation, and the multicore execution. The following configurations have been tested: 1. Default (D): the applications are run in non-secure kernel mode on a single core. The other cores are trapped in an empty infinite loop. Data and instruction caches are enabled but not the MMU. 2. Multicore (M): same as default but now the other cores are active. They generate two pseudo random numbers (using the xorshift  [22]  algorithm) and compute their GCD in an infinite loop. This simulates an intense computation on all cores (asynchronously from our targeted applications).
Test Bench Our EM measurement test bench includes the following devices: -An EM probe from Langer either a RF-R 0. 3-3 or a RF-R 400-1 both with a 30MHz to 3GHz bandwidth (see below), a Langer PA 303 preamplifier (3GHz bandwidth) to amplify the signal from the probe, a DSOS404A oscilloscope from Keysight with a 4GHz bandwidth able to capture up to 20 GSamples per second. A control computer (Xeon E5-1603v3 @ 2. 80GHz, 4 cores, 40GB RAM) is used to orchestrate the measurements and perform the analysis with home-made tools. In particular, special care was needed when developing our tools to achieve a nice measurement speed, and be able to manage the vast quantity of data that resulted from the experiments. Around 7 hours and 100GB of free disk space are necessary to capture the needed traces for the template on our test bench, and 70 hours to perform an attack.