dataesr/scientific-paragraphs-categorization huggingface dataset:
Cybersecurity is becoming vital as industries are gradually moving from automating physical processes to a higher level automation using cyber physical systems (CPS) and internet of things (IoT). In this context, security is becoming a continuous process that runs in parallel to other processes during the complete life cycle of a system. Traditional threat analysis methods use design models alongside threat models as an input for security analysis, hence missing the life-cycle-based dynamicity required by the security concern. In this paper, we argue for an attacker-aware systems modeling language that exposes the systems attack surfaces. For this purpose, we have designed Pimca, a domain specific modeling language geared towards capturing the attacker point of view of the system. This study introduces the formalism along with the Pimca workbench, a framework designed to ease the development and manipulation of the Pimca models.
In such situations, a domain specific language (DSL) that highlights the attack surface, i. e. the sum of different points where an attacker can interact on the system  (Manadhata and Wing, 2011) , improves the communication between stakeholders, the comprehension, and the rationalization of the system architecture. In this article, we present Pimca framework along-side its DSL for highlighting the security concerns of a system. Pimca can be seen as a securityfocused systems modeling language, which captures the coarse-grain architecture of large-scale systems (like CPS, enterprise systems and system of systems) to exhibit the security concerns (e. g. attack surfaces, attack scenarios, etc. ) while abstracting away the internal architectural details. The Pimca DSL was originally developed by the Directorate General of Armaments (DGA), French ministry armed forces.
In this context, it was used to facilitate preemptive attack surface analysis, attack impact analysis, attack routing analysis. Apart from the DSL, this study introduces an open-source security modeling framework 1  , based on Pimca, that offers (i) A graphical modeling language (ii) A methodology, and (iii) The associated tools focused on the following objectives: 1. System Modeling with the Intent of Highlighting the Attack Surface. A language that incorporates the basic system analysis and the understanding of system functions to model the system from the perspective of cyber threat analysis (CTA). 2. Security Concerns Modeling using a Graphical Language, Geared towards Automation. This helps support both manual and automated analysis. Furthermore, a human-readable model facilitates communication between multiple stakeholders.
3. Analysis Independent Modeling. Analysis techniques are often tightly coupled with certain DSLs, resulting in a plethora of languages. With the separation of concerns, we look forward to enabling different security analysis based on the same modeling language. Section 2 details the Pimca modeling language. Section 3 introduces the tooling and implementation of the proposed framework. Section 4 illustrates the capabilities of the framework on two use-cases from the CPS and enterprise network domains. The related works are over-viewed in Section 5 before concluding the paper in Section 6.
This concept is vital in CTA because it characterizes the attack surface as highlighted by  (Theisen et al. , 2018)  and  (Manadhata and Wing, 2011). Interfaces are the only concepts in the Pimca language that can be of compound dimension i. e. , they can access both physical and virtual spaces simultaneously. However Pimca model also allows for interfaces to be defined between two dimensions of the same type. To show directionality, the interfaces are specialized into entryPoint and exitPoint. From a virtual system perspective, a keyboard is an entry-Point (a physical to virtual bridge) while a screen is an exitPoint (a virtual to physical bridge). The workstation can be modeled as an interface between the physical space of the technician and the virtual space of the LAN network. • Checkpoint represents an element checking for a specific resource before granting passage.
). The Pimca workbench, illustrated in Fig. 3 , uses the diagramming adapter and the FML core language of Openflexo, which provides a satisfactory solution for our second requirement (the creation of a graphical language editor for Pimca). The graphical workbench is composed of multiple panels as follows: The project structure explorer (1) contains the Pimca XML model and the diagrams exportable in several graphical formats. Each model can be viewed through the modeling editor (3). The Pimca model explorer (2) lists all the instances of machinery, resource and The model explorer can also be used to instantiate, to delete, to show, and/or hide the language concepts. The modeling editor shows the graphical representations associated to each Pimca model. We can explore the different levels of granularity of the model for a composite node, we can zoom in to view the subnodes and the relationships between them.
UC1 -Water Pumping Station System Description: The water pumping station system features a water tank, various actuators, sensors and a Programmable Logical Controller (PLC). Water flows in the system from the environment, through a motorized valve controlled by PLC. The water flows out of the system from the tank through a pumping mechanism also controlled by PLC. There is a manual valve between the tank and the pump. The water level is monitored by the PLC through a sensor. The PLC follows the commands entered through a keyboard. The commands describe that (i) the motorized valve must be activated and the pump must be shut down when the water reaches a low threshold, and (ii) the motorized valve must be closed and the pump must be activated when the water reaches a high threshold. PLC is connected to the plant network which also contains a supervisory control and data acquisition (SCADA).
Pimca is specifically designed for systems modeling for security analysis. Once the system is modeled, security analysis techniques can use this model  (Drouot and Champeau, 2019). This allows security analysts to tailor their analysis to specific security needs such as attack surface evaluation or attack scenarios exploration. STRIDE is a proactive security analysis paradigm  (Kohnfelder and Garg, 1999)  used in the context of software systems. Efforts have been made to extend STRIDE to non-software systems. In particular, Khan et al. introduce a STRIDE-based methodology adapted to cyber physical systems  (Khan et al. , 2017). However,  (Farrell et al. , 2019)  share the limitations of STRIDE for cyber physical systems (CPS) noting the difficulties to use its concepts for non-software nodes.
CONCLUSION We present a systems modeling framework, Pimca, with a domain-specific modeling language, methodol-ogy and associated tools in the context of cyber threat analysis. The proposed modeling language satisfies the intention of highlighting the attack surfaces in a system model. The framework includes a methodology for the development of Pimca models. The associated tools in the framework allow developing Pimca models and integrating then with other security analysis artifacts for the development of a security solution. The approach was evaluated using two use cases, which emphasized the system modeling along with the attack surface deduction and refinement enabled by the Pimca DSL and workbench. In future, we plan to extend the Pimca DSL with behavioral primitives which will enable dynamic attack scenario enactment based on vulnerability databases and realistic system configurations.
For instance, Macduff et al. [13]  and Mitsuhashi et al. [14]  used rPPG methods for stress/emotion recognition where the results showed an accuracy of 60%-85% for emotion prediction via remote PRV. But the errors between the remotely measured PRV and the ground truth were not studied and discussed. None of the work which adopted the low-cost RGB camera has been able to recognize the emotion states with the accuracy higher than 90%. Therefore, improving the remote PRV measurement and assessing it with quantitative ground truth is an important task. In this paper, we use the dataset generated by a low-cost 3-band web camera and adopted the newly proposed Periodic Variance Maximization (PVM) method  [15]  to extract the BVP signal. Then, we used an event-related Two-Window  [16]  algorithm for BVP peak detection to increase the accuracy of PRV measurement via rPPG.
Stress and Emotion Detection with rPPG Framework As explained, the rPPG can be used for remote detection of stress and emotion. Macduff et al. [13]  proposed a framework that adopted facial landmarks to get the region of interest (ROI) from the face, and used ICA to process and combine the RGB signals to get the BVP signal, and then obtained PRV based on the extracted BVP signal. They then utilized several frequency features of PRV such as High Frequency (HF) power, Low Frequency (LF) power and LF/HF to train the model on the dataset which included two emotion states, relaxed and stressed states. It turned out that PRV alone could achieve an accuracy of 70% to distinguish between these two states. Mitsuhashi et al. [14]  proposed a method that combined the two-layer (melanin and hemoglobin layers) model and singular valued decomposition in RGB space for pulse signal measurement from videos.
They defined three different stress levels by the difficulty of the tasks. For instance, easy (mental arithmetic of 5*6), middle (mental arithmetic of 13*16), and difficult (mental arithmetic of 114*123). They used the KNN method to classify the stress modes. The results showed that the accuracy of the classification is around 66%-83% for different stress levels. Belaiche et al. [17]  used both micro-expressions and PRV to predict three emotion states, namely, happiness, disgust and anger. This study found that although the accuracy of the PRV based method is higher than the micro-expressions based method in emotion states recognition but the average accuracy is usually not higher than 60% for the dataset that contains sudden emotion change.
MMSE Dataset For the experiments, we used the MMSE dataset which consists of about 100 videos recorded by RGB cameras with the frame rate of 25 Hz. This dataset was chosen for the following reasons: first of all, the dataset is large enough for training and testing. Secondly, the dataset includes different peoples, such as Europeans, Middle Easterners, South Asians, South Americans and East Asians which potentially makes it more challenging for the RGB signal processing. Thirdly, the contact PPG sensor was used to record the pulse signal as the ground truth which was synchronized with the videos so that it can be used to quantitatively assess the results. Lastly, with emotion elicitation in the experiments, the volunteers showed some movement of faces and heads which make the dataset more complicated and closer to the realistic scenarios.
• MinPeakProminence: the minimum height of the peaks relative to the lowest bottom line within a certain range. To detect the peaks of the remotely measured BVP signal with findpeaks, we used the brute force search to determine the values of the parameters. The ground truth of peak locations are given by the contact PPG. We used the number of correctly detected peaks with regards to the ground truth, the number of false and missing peaks with regards to the ground truth, and the average errors of peak locations to optimize the parameters. With the the MMSE dataset, 37 videos were used to get the parameters, and the other 54 videos were used to test the methods. As a result, the MinPeakHeight was set as 0. 75 * |y| and the MinPeakProminence was 0. 3 * |y|, where |y| is the average absolute value of the detrended BVP signal.
To maximize SSF, the window size w should be approximately the same with the length of the rising phase of the original signal. Similar to the Local Maximum method described in Section 4. 2. 1, this window size and the minimum height were determined by the brute force search. The exact value of w was 0. 4 s. The threshold height was 0. 175 * |y| ss f , where |y| ss f is the average absolute value of the detrended new signal. The minimum peak distance was 0. 24 s. Figure  3b  shows an example of the pre-filtered BVP signal transformed by SSF. The black curve is the BVP signal generated from the MMSE dataset. The blue curve is the new signal after it got processed by SSF. It can be seen that after using the SSF, the upslope part is enhanced and it becomes sharper and more straightforward for peak detection.
• Errors of Root Mean Square of Successive Inter-Beat Interval Differences (RMSSD) (RMSSD er (s)). As before, this metric is calculated as the absolute difference between the RMSSD measured by rPPG and the RMMSSD measured by the ground truth. The RMSSD was defined as: RMSSD = 1 N -1 ( N-1 ∑ i=1 (IBI (i+1) -IBI i ) 2 ) (11) where IBI i is the ith peak interval value. The experimental results were the average values of the entire testing dataset (54 videos).
We tested the algorithm with the MMSE dataset which is recorded with the low-cost RGB camera and quantitatively compared this method with the Local Maximum method and Slope Sum Function method with ten metrics. The results showed that the Adaptive Two-Window method performed better than the other two methods so that our new algorithm can be potentially used in the realistic PRV applications. However, the results also showed that the relative precision of the PRV measurement is not higher than 90%. A possible approach to address the issues in the future is to study the physical properties of the noise, combine all the methods and propose detailed global rules for the BVP filtering and peak detection for different dataset so that the achievements can be used in realistic applications such as long-term health monitoring, lie detection, and so forth.
It is important to note therefore, that despite the rather linear discussion of our methods in this section, we needed to move back and forth between a qualitative analysis and a quantitative analysis in our research -as  Akrich (2019)  did in her analysis of a discussion forum on health issues. To perform a quantitative analysis of the forum we had to use various tools and we had to surmount a number of hurdles. First, the scraping of the forum was made via the program Extractify  (Vergnaud, 2020a). Extractify is an extension for Chrome, developed in JavaScript under Bracket, whose purpose is to scrap structured data on the web. It has been designed for collecting online comments or online conversations such as forums. Unfortunately, we could not access the whole corpus of messages on the forum via email, because users only receive messages from the date of their registration onwards.
The tools that we had at our disposal for formatting and analysing the messages were therefore ineffective. The only way to access all messages was via the web page of the list (https://groups. google. com/forum/?fromgroups#!forum/diybio). While this page is freely accessible, and we could thus use Extractify, the web page's structure made it difficult for us to perform an automatic data extraction. The first difficulty we encountered is located on the main presentation page of the topics. As can be seen on the screenshot above (figure  2 ), the page does not immediately load all the messages, because the page's script adapts them to the screen size (i. e. on a laptop with a 13inch screen, the last 30 messages of the list will be loaded; on a desktop computer with a 27inch screen, there will be 60 messages).
To be able to read all the messages that do not open up (first red frame), the user must click on each message (second red frame) -a procedure that we had to mimic computationally. The second step was to import our corpus into the program L@ME  (Vergnaud, 2020b)  to provide first statistics. L@ME is a software for processing and analyzing electronic messages, developed in JAVA under Eclipse. It allows its user to: open locally electronic messages or download messages located on a PhpBB forum or import messages from an Extractify json file; view and obtain data for each message, author and discussion; group discussions according to several parameters; clean up authors and topics of messages; split and merge lists according to different parameters; obtain general statistics on the list (i. e.
number of speakers, of subjects, of messages), statistics on the dominant speakers of the list, as well as statistics on the collective subjects. L@ME allowed us to examine the kinds of issues discussed and the kinds of knowledge most valued on the forum -and thus to test our hypotheses 2 and 3. Third, we used iRaMuTeQ, a program for the multidimensional and statistical analysis of a corpus of text  (Ratinaud, 2020). iRaMuTeQ is based on the Max Reinert classification method  (Reinert, 1983 (Reinert, , 1986 (Reinert, , 1991)) , an analysis which is based on a hierarchical descending classification. While iRaMuTeQ offers three classification methods, we chose the double classification method based on the groupings of text segments, in particular to test hypothesis 4. To examine if and how the content of the discussions has evolved, we carried out, for each individual year, a classification analysis of the topics posted to the forum (see appendix D).
SCUBA-2 data at 850 µm showed apparently monolithic continuum sources  [6]  but NOEMA combined with the 30m revealed complex morphologies ranging from regions dominated by a single high-mass core to regions that fragment into up to 20 cores at 1. 37 mm with an angular resolution of 0. 3 -0. 4 (see Fig. 1 ). The fragmentation diversity between regions with few or only one massive core compared to those with many low-mass cores may be explained by different formation mechanisms that coexist, or even interplay, or by differences in the initial density profiles and/or magnetic field properties of the parent gas clump. Thanks to sample selection of CORE LP, evolutionary stage and turbulence can be ruled out as the main contribution for fragmentation diversity. Since the nearest neighbor separation peaks close to NOEMA's angular resolution, it is likely that further fragmentation takes place on smaller spatial scales, that could be investigated even at higher resolution.
Indeed, an apparently monolithic continuum source showed fragmentation signatures down to 0. 2 (∼ 500 AU) relying on kinematic analysis of HCN at 0. 8 mm  [5]. NIKA2 with its large FoV and fast mapping speed is well suited to discover high-mass candidates inside star forming regions to be followed up with NOEMA. Moreover, NOEMA will double its baseline lengths in 2020, pushing towards higher angular resolution and making possible to observe more distant star forming regions. Combining single-dish and interferometric continuum data can also reveal a column density enhancement in a dense core which has not previously traced by single-dish observations alone. This was the conclusion of a study combining IRAM MAMBO-2 and the Alma Compact Array (ACA) data that found a centrally concentrated sub-solar mass starless core in the Taurus L1495 filamentary complex  [7].
Because this core is very dense, with a high deuterium fraction it indicates that it should be at the very end of its starless phase. Hence, it may be the precursor of a brown dwarf or a very low-mass star. IRAM Telescopes will help to understand the very low mass star formation. First, NIKA2 will provide a unique opportunity to look for pre-brown dwarf candidates (see proceeding by B. Ladjelate) and to perform square-degree studies of Galactic clouds (e. g. the GASTON LP, see proceeding by Peretto et al. ). In a second step, NIKA2 and NOEMA continuum data could be directly combined by attributing pseudo-visibilities to NIKA2 short spacings.
Line contamination Because of its broad band, emission in NIKA2 bands is not coming only from continuum but also from lines. In particular, for nearby sources the 12 CO emission line at 230. 538 GHz lies towards the low frequency edge of the 1. 15 mm NIKA2 band (see Table  1 ). The typical contribution of 12 CO for galactic filaments was estimated to 1 -3% in NIKA studies  [8, 9]. Line surveys have shown forest of lines in nearby clouds  [12]  and galaxies  [13]  suggesting that CO is likely the dominant but not the only contamination inside NIKA2 wavebands. Moreover, CO and water line emission are also detected in strongly lensed galaxies e. g. by ALMA at z ∼ 3. 6  [10]  and by NOEMA at z ∼ 6. 5  [11]  suggesting that ancillary data may not be sufficient to properly estimate the line contamination to NIKA2 data.
[22] , a Gaussian source of 4 mJy/beam with a size of 3. 2 (FWHM) is also displayed in dashed blue. Top x-axis show that going up to 20 to 40 scales is suitable to retrieve the SZ signature in the Fourier domain. Right: Pressure profile of PSZ2 G144. 83+25. 11 (in blue) constrained by NIKA2 (blue circles) and Planck  [23]  which is compared to the Universal Pressure Profile  [24]  with an orange line. NIKA2 data and associated fit suggest a departure from the Universal Profile at small radii. SZ with interferometers present some advantages with respect to single dish imaging. In particular, with interferometry it is possible to disentangle the diffuse negative SZ emission from the small-scale positive point source contamination thanks to spatial filtering. Indeed, SZ effect is primarily present in short baselines only which is not the case of point sources (see Fig.
Mapped reads are then grouped by gene specificity, and saved into sample-and gene-specific FASTA files. Using a sequence alignment software, the FASTA files can be manually analyzed to genotype individual samples. While the TARGT pipeline can be applied to any kind of shotgun sequence data, we here present it in combination with a targeted DNA capture approach to analyze HLA polymorphisms in a comprehensive set of historical human samples. Target-enrichment by hybridization, also known as DNA capture, is one of the most widely used approaches for sequencing aDNA, because of its efficiency in increasing the sequence coverage of the endogenous DNA fraction  [19] [20] [21] [22]. In this work, we used a customized DNA capture approach previously developed for modern DNA and based on sequence information from 8,159 known HLA alleles (available at the IMGT/HLA database  9  )  23  , to enrich a set of DNA libraries from medieval Europeans for the most polymorphic classical HLA class I (HLA-A, -B, -C) and class II (HLA-DRB1, -DQB1, -DPB1) genes.
Results Success of the HLA target-enrichment for historical samples. One of the main purposes of this work was the accurate genotyping of HLA genes in aDNA samples. Thus, a previously described dataset of sixty-eight samples collected from the medieval cemetery of St. Jørgen (Denmark) was used to assess the success of a HLA target-enrichment approach and the subsequent performance of the TARGT pipeline. Owing to different post mortem degradation processes over time, DNA molecules retrieved from ancient organisms hold a high level of base pair modifications. Such DNA damages are the source of incorrect incorporation of nucleotides during DNA amplification, and might cause false SNP calling during the final step of sequencing data analysis. The majority of damage-derived miscoding lesions in aDNA sequences are caused by deamination of cytosine into uracil  [26] [27] [28].