dataesr/scientific-paragraphs-categorization huggingface dataset:
Subsequently an automated procedure, which exploits a heuristic optimization strategy, generates a set of YARA rules to cover newly identified malware with an acceptable generalization capability yet minimizing false positives. Experimental results on a dataset of 1. 5 million distinct Android applications confirm the effectiveness of the proposed system, both in the identification of new malware samples and in the generation of new family signatures in the form of YARA rules. Finally, the proposed approach has been deployed in January 2018 and, since then, it is in use on Koodous, the mobile antivirus platform developed by Hispasec. ANDREA ATZENI received the M. Sc. and Ph. D. degrees in computer engineering from Politecnico di Torino. He is currently a Senior Research Assistant at the TORSEC Security Group, Politecnico di Torino.
In the Internet of Things (IoT), many strong constraints have to be considered when designing the connected objects, including low cost and low power, thus limited resources. The confidentiality and integrity of sensitive data must however be ensured even when they have to be processed in the cloud. Security is therefore one of the design constraints but must be achieved without the usual level of resources. In this paper, we address two very different examples showing how embedded hardware/software co-design can help in improving security in the IoT context. The first example targets so-called "hardware attacks" and we show how some simple attacks can be made much more difficult at very low cost. This is demonstrated on a crypto-processor designed for Elliptic Curve Cryptography (ECC). A very lightweight countermeasure is implemented against Simple Power Analysis (SPA), taking advantage of the general processor usually available in the system.
Simpler functions are in general implemented in software and executed on an embedded general-purpose processor. We will show why such electronic devices must be protected against security attacks, on the basis of real-life examples e. g. ,  [2, 3]. The protections must in many cases come at low cost, at least to thwart the easiest attacks. Proposing a new approach for such cheap protections is the goal of the first part of our contribution. We will demonstrate how hardware/software partitioning can be exploited. In the global evolution briefly sketched above, another aspect noticeably changed in only a few years, with an increasing use of cloud computing, shared data and world-wide cooperation through shared outsourced databases, fed by many distributed sources (or sensors). However, preserving data confidentiality is of upmost importance in many domains such as genomic analysis, healthcare or other applications using very private data.
However, Figure  3  illustrates the usual form of the records obtained on the oscilloscope for such executions. The difference between the NOP consumption level and the consumption when repeating a different instruction is clearly visible and the characterization of the ISA can thus be automated. The loop control (execution of branch instructions) is similar in all cases, so it just induces some noise, a non-significant perturbation when comparing the power consumption between instructions. All the measures presented in this paper were obtained using the specific power-consumption measurement facilities of the Sakura-X board, connected to a Hall-effect probe combined with a 5-spire connection, and a digital oscilloscope. In practice, the characterization of each instruction depends not only on the addressing mode, but also on the data used during the computations.
Automated SPA As explained in  [18] , a SPA attack has been automated using Matlab in order to demonstrate (1) that the naïve implementation can easily be broken, and (2) the protected implementation cannot be broken in the same way. Figure  4  illustrates the principle used to discriminate point doublings and point additions during a scalar product computation. A similar approach may be used e. g. , to discriminate operations during a RSA computation. Basically, thresholds are defined to identify which operation was carried out when there is a peak in the current trace. Figure  4  shows that different thresholds can be used with similar results since the power consumption is very different from one operation to the other. The Matlab input file is directly generated from the oscilloscope records.
Proposed Approach: Results for the Modular Polynomial Multiplier We will present here some results obtained with the general approach proposed in the previous section. Some other results and comparisons with other published implementations can be found in  [26]. Results were obtained for several types of hardware platforms, including in particular Spartan 6 and Virtex 7 in order to find some partial comparisons with previously published works. With our approach, changing the platform is very easy. Our main prototype was implemented on a Zybo board, with a Zynq-7000 component including a dual-core Cortex-A9 ARM processor (only one core was used) and programmable logic equivalent to an Artix-7 FPGA. Between the embedded processor and the programmable logic, coefficients of the operand and result polynomials were transmitted through the high-performance AXI buses HP0-HP3 and control data were sent through the general purpose AXI buses (GP0-GP3).
As illustrated by the presented results, we easily reached parameters (degree or coefficient size) that were not considered in previous works. We are also able to very quickly adapt an implementation with respect to new requirements. Table  2  shows some of the obtained results in terms of resource requirements and computation performance, compared with some of the most up-to-date published results. As illustrated by these figures, our approach is very efficient in terms of hardware complexity, in addition to its flexibility. As illustrated by the comparison with  [7] , we were able to generate solutions oriented towards either performance or low-cost implementation. Compared with full software implementations, our solutions can lead to a significant optimization factor in terms of performance with respect to the software libraries (HElib and FLINT).
Acknowledgments: Work done by  Asma Mkhinini  has been partially supported by the  French research project "SPICA" (FUI)  and a grant from the  University of Sousse (Tunisia). The authors wish to thank  S. Pontié ,  T. Backenstrass  and  M. Blot  for their experimental contributions on the protected ECC implementation and evaluation. We also thank the reviewers for their helpful comments.
The capabilities of these software to recover images from optical/infrared long-baseline interferometric data have been shown several times in the community through the "Interferometric Imaging Beauty Contest" [see e. g. , 36]. Additionally, these software have been used repeatedly to recover images from Fizeau interferometric data in Sparse Aperture Masking observations. For example,  [45]  used MACIM to recover the first images of Pinwheel nebulae; while  [35]  used BSMEM to recover images of several bow shocks around WR-stars in the central parsec of our Galaxy. However, the standard use of several of these software packages was limited to recover monochromatic images. With the consolidation of the spectrointerferometric capabilities of the second generation instruments, GRAVITY  [8]  and MATISSE  [17] , at the Very Large Telescope Interferometer (VLTI), some of the aforementioned software have been updated, and new methods are being developed to obtain chromatic images in a systematic way.
Going one step further An enormous effort has been made in the community to develop reconstruction methods one step further. For example, a project named POLCA funded by the French ANR (Agence Nationale de la Recherche) made the following advances on the subject: -Statistical analysis of AMBER data showed that the interferometric data do not follow the usual assumptions of uncorrelation and Gaussian (Normal) noise distribution. This algorithm uses a joint spectro-spatial regularisation, and thus will be applicable to objects with components exhibiting different spectra. -The power of combining chromatic model-fitting and "grey" image reconstruction was demonstrated by  [15]  for low-spectral resolution datasets. The software "SPARCO" was developed to demonstrate this. The potential of this technique is great and allows one to perform "numerical coronagraphy" on the interferometric data, by removing the (main) contribution from the central star.
On the other hand, Fabien Baron, at Georgia State University, has developed a chromatic imaging algorithm called SQUEEZE  [1, 2, 3]. This software uses a MCMC as engine for the minimization. The code is able to use Simulated Annealing and Parallel Tempering with Metropolis-Hasting moves. SQUEEZE allows us to use (i) visibilities (amplitudes and phases), (ii) powerspectra, (iii) bispectra, or combination of them as input observables. One of the key characteristics of this software is the large number of regularization functions offered, like the L0-norm, the L2-norm, Entropy, Total Variation, Wavelets, etc. All these software efforts have had a tremendous impact in the development of chromatic imaging, and they could lead to a new generation of image reconstruction software. For example, combining the new core algorithms with the chromatic model-fitting features of "SPARCO" and self-calibration techniques could produce a leading edge image reconstruction suite suitable to reduce both archival AMBER data, and the new data being taken with the new spectro-imaging interferometric instruments GRAVITY, and MATISSE at the VLTI or MYSTIC  [42, 43]  at CHARA.
Toward integrated tools for image reconstruction With the developments mentioned above, one can produce images, not only by using the closure phases but also the wavelength-differential phases. These developments have allowed us to recover simultaneously chromatic images, while, additional techniques like self-calibration have allowed us to perform imaging even with 2 telescopes. Nowadays, the number of image reconstruction packages available to the community is large, however each of these packages uses different interfaces and programming languages, making a comparison relatively difficult in practice. In this respect, there is a working group at JRA4 aiming at providing advances in easing the use of image reconstruction software through a webbased interface called OImaging 1  , in a similar way to what has been done for model-fitting and the LITpro 2  software at JMMC.
Fig. 3  The image shows the impact of µ on the image reconstruction process. Eight reconstructed images of the source are shown using the hyperparameter values shown in Figure  2. Notice that for the first three images (in the under-regularizer region) the general morphology of the object is recovered, but the reconstructed maps are clearly noisier than the one with the optimal value of µ = 3000. However, when the value of the hyperparameter is quite large, the structure of the source is completely erased and the code is not able to converge to reproduce the expected brightness distribution of the source. While the monochromatic images are crucial as first step to the reconstruction, one of the main goals of the new generation of infrared interferometers is to recover the morphological changes of the astrophysical objects along the bandpass of the observations.
B. 2 GENERATING SYNTHETIC DATA SETS WITH ASPRO2 ASPRO2 (A Software to PRepare Observations) is a program developed at the Jean-Marie Mariotti Center for Expertise in Interferometry to allow users in the community to prepare interferometric observations by simulating the response of different interferometric arrays. ASPRO2 uses a simple Graphical User Interface (GUI) in which the user can introduce his/her observing constraints, for example: • Users can search for their targets. The tool is connected to the SIMBAD database, so that the user can look for a desired target just by specifying a name. • Users can define the observational setup. ASPRO2 allows to select an observing date, the instrument and interferometric configuration. Once the configuration is selected, users are able specify the integration time and the number of position hours (uv coverage) required.
C. 2 INSTALLATION AND CONFIGURATION SQUEEZE is a stand-alone package that can be retrieved from the following GitHub repository: https://github. com/fabienbaron/squeeze. The code is written in C and it is compatible with OpenMP. The code requires gcc to be compiled. This compiler is native in most of the Linux distributions. Nevertheless, it has to be installed by the user in OSX. The GitHub repository contains all the necessary instructions to install these compiler. However, the most reliable ways to do it on an OSX system is through the macports 3  or the Homebrew 4  environments. According to the source repository, once the compiler and dependencies are satisfied, the SQUEEZE installation is relatively simple, a sequence of the following commands has to be executed:.
e. , with the lowest χ 2 ) in the MCMC chain. If the algorithm does not converge (i. e. , it does not pass the burn-in phase in a given number of iterations), SQUEEZE still returns the aforementioned three images but they might have a large number of artifacts, particularly "image ghosts" due to the misaligned frames at different chain iterations. The SQUEEZE output images are standard. fits files and the user could use his/her prefered software to inspect them. Additionally to these files, SQUEEZE can deliver the changes in the posterior probability at each one of the iterations in the chain. To habilitate this option, the user could run the following command as an example: squeeze example1. oifits -w 128 -s 0. 4 -fullchain The results of the probability changes are thus saved in a file called output.
fullprobs. This file contains the following information: • The number of chains and the number of iterations • The objective temperature  5 • The logarithmic likelihood (χ 2 ), the prior probability and the posterior probability for each one of the iterations in the different chains. Figure  11  displays the changes of the likelihood (in log space), prior probability and posterior probability for the previous example. Notice that, although the reconstruction did not use a prior function, the code produces a prior probability because by default SQUEEZE uses the field of view regularizer to move the image to its centroid position at each one of the iterations in the MCMC. In the previous example, it is clear that the effect  of the prior probability is almost negligible once the algorithm has converged.
For example, in this case, performing the reconstruction only with closure phases and squared visibilities makes difficult the convergence of SQUEEZE, and the minimum temperature reached is of ∼7. 0 with a χ 2 in the squared visibilities of ∼3700. The user can corroborate this, by running the following command: In contrast with the previous attempt, using the visibility amplitude and closure phases the algorithm converges reaching an annealing temperature of 1. 0 and a χ 2 of ∼3. 6. The user can check this reconstruction by running the following command: squeeze 2004dataset2. oifits -w 128 -s 0. 4 -tv 100 -nov2 -novisphi -not3amp  One of the main goals of the new generation of infrared interferometers is to recover the morphological changes of the astrophysical objects across the bandpass of the observations.
C. 4. 3 THE RECONSTRUCTION To begin with this reconstruction, an initial brightness distribution of a Gaussian was used. To include a pre-defined image, in SQUEEZE the user can use the -i option. In this case, the data channel with the longest wavelength (from 12. 8076 to 13. 20 µm) and higher signal to noise was used. For this purpose the -wavchan was enabled. With this setup, a first monochromatic reconstruction was performed with the following command: squeeze 2016dataset2. oifits -w 135 -s 3. 0 -la 1000 -l0 1 -i initial. fits -novisamp -not3amp -novisphi -v2s 2. 0 -wavchan 0 1. 26e-5 13. 10e-6 -n 150 -chains 3 In this example, a Laplacian regularization µ Lap =1000 was used. Please notice that the aforementioned value was obtained by using the L-curve described in Sec. C. 3. 1. 3. In the command, we have also disabled the use of the visibility amplitudes (not included in the data set), the triple amplitudes (also not included in the data) and the visibility phases.
Contrary to the monochromatic reconstruction, here the differential phases were included. The complete command for the polychromatic reconstruction is the following: squeeze 2016dataset2. oifits -w 135 -s 3. 0 -la 1000 -l0 1 -ts 3 -fv 0 -i initial_monochromatic. fits -novisamp -not3amp -novisphi -wavauto -diffvis -n 150 -chains 3 Figure  22  displays the recovered images channel by channel, as well as the images of the model used to simulate the data. Notice how the asymmetry of the disk is recovered, together with the changes in size and brightness of the central source. However, the elongation of the disk and the internal gap were not recovered in the reconstruction.
D. 2 INSTALLATION AND CONFIGURATION D. 2. 1 REQUIREMENTS The GUI is packaged as a JavaWebStart 8  application then requires a Java runtime environment. The required command is javaws. Java runs on every operating systems with a great compatibility and efficiency. Users who don't have java on their machine yet, just have to install the java package of their dis- tribution or may also visit http://www. java. com. The website provides support in many languages to install the runtime environment. Image reconstruction softwares must be installed previously and present in the PATH of the GUI. Look at Help Menu / Report Feedback to JMMC. / Details / System Properties. Enhancement are planned to help the GUI finding some softwares.
E. 2 INSTALLATION AND CONFIGURATION These instructions describe local installation of BSMEM on your computer. Installation under Linux or Mac OS X is supported. OImaging can work with a local installation of BSMEM or by running BSMEM on a remote server; if using the latter option local installation is not needed. First, download the gzipped tar file of the source code 11. Check the README. md in the tarball for more up-to-date installation instructions. BSMEM has successfully been built on Linux (Ubuntu 14. 04, kernel 4. 4. 0 and CentOS release 6. 6, kernel 2. 6. 32) and OS X (10. 9 Mavericks). Any recent Linux distribution or OS X version should work. Building on Solaris is no longer supported.
When following a two-step process, a pixel scale suitable for the full data set must be used for both steps (the initial image for step 2, derived from the result of step 1, defines the pixel scale for the final reconstruction). A scale of 0. 4 mas per pixel gives 6. 01 samples per fastest fringe at 1. 521 µm and is therefore suitable for this example. With this sampling, a 512 × 512 pixel image is needed to reconstruct both stars. Since the second-step reconstructions were performed for each spectral channel separately, it was necessary to write a custom script to run BSMEM many times, selecting a different subset of the data for each run. E. 6. 1. 2 USING OIMAGING This section illustrates the use of OImaging to perform a similar two-step procedure for a single channel of the 2010 contest LRHK data (Mystery-Low_HK.
oifits). For the first step, the high spatial frequencies must be removed from the data. This can be done using the oifits-filter program that comes with OIFITSlib. Alternatively, a future OImaging release should allow setting of UV_MAX to achieve the same result. A 60 mas FWHM circular Gaussian is used as the default image. For this data set, MAXITER must be set to a large number as several hundred iterations are needed to converge. The resulting image is shown in fig. 27. The first reconstructed image is exported and processed outside of OImaging. The image is convolved with a Gaussian function, then all pixels below a specified threshold are set to zero. The FWHM of the blurring function is the finest fringe spacing in the filtered data, in this case 5. 6 mas. This has the effect of smoothing out all of the stellar surface structure.
For instance,. -pixelsize=0. 25mas -fov=100mas. -normalization=1 -min=0. is a must for processing OIFITS data. F. 3. 1. 5 REGULARISATION Because the algorithm has to deal with sparse interferometric data, the user has also to indicate which regularisation must be applied to interpolate missing data. The regularisation is the kind of prior imposed to the reconstructed image. MiRA accepts any regularisation, but it has several already implemented in the code (see, for e. g. , Thiébaut 2013, and rgl. i, inside MiRA folder, for details). For all implemented regularisations, the relative strength of the prior (compared to the data) is specified with the option -mu=µ, where µ 0. Of the available regularisations, the following are the most relevant: • Edge-preserving smoothness, which is selected with the options:.
G. 2 INSTALLATION AND CONFIGURATION The installer can be downloaded from the JMMC website at http://www. mariotti. fr/wisard_page. htm. Although WISARD consists in a set of IDL procedures, it is not necessary to buy this expensive software: the alternative, open-source, free IDL clone "GDL" is able to run WISARD. GDL is available on several distributions (Debian, FreeBSD but also MacOS and Windows). See http://gnudatalanguage. sourceforge. net/ for details. We recommend GDL with a version ≥ 0. 9. 6. WISARD uses several procedures and functions from the IDLASTRO library (http://idlastro. gsfc. nasa. gov) and C. Markwardt's library (https://www. physics. wisc. edu/~craigm/idl/). In the following, the terms "IDL" and "GDL" are equivalent. After unpacking the software (see below), you need to accept the license, which can be accessed under IDL by typing: dummy=wisard(/copyright).
G. 2. 1 DEPENDENCIES WISARD needs Éric Thiébaut's "OptimPack" library for IDL (OptimPack_IDL*. so) and its IDL frontend (op_*. pro files) to be installed. For convenience, the OptimPack IDL frontend and several pre-compiled OptimPack libraries for IDL (for Linux, Mac/OS and Solaris, on 32 and 64-bit architectures, and for Windows) are included in the lib/optimpacklib/ directory of the WISARD distribution, with their author's permission. WISARD selects the library automatically so this should be transparent. In case of trouble, refer to the complete documentation on the JMMC website.
G. 5 OUTPUT PARAMETERS WISARD in its standalone version outputs at the end of a minimization cycle the reconstructed image and an IDL structure, AUX_OUTPUT, described in the file wisard. pro of the distribution and containing all relevant information. The wisardgui procedure used in conjunction with the OImaging GUI reads and writes OIFITS files according to the interoperability norm described in the Interface to Image Reconstruction document (https://github. com/emmt/OI-Imaging-JRA/blob/master/doc/interface/OI-Interface.
H -CONCLUSIONS • Restoring milliarcsecond resolution interferometric images in the infrared will represent a key facility for the new generation of optical interferometers. During the last decade, the community has gain experience by developing a number of different softwares and innitiatives (e. g. , the Interferometric Imaging Beauty Contest). The packages, here described, are the result of an extensive collaborative effort and represent a reliable basis to properly recover images from interferometric data. • The current understanding of the image reconstruction problem together with today's development of software allow the user to perform both mono-chromatic and poly-chromatic image reconstructions of simulated and real interferomet-  ric data. However, algorithms to perform full poly-chromatic reconstructions are still under development and future improvements of the existing software should move towards this field.