dataesr/scientific-paragraphs-categorization huggingface dataset:
The generic modelling approach we promote in EMULSION both has advantages and drawbacks. The gathering of various model features within a homogeneous software tool, which can be picked up and combined according to the modeller's needs with little or even no additional code, reduces development time, ensures that model assumptions and processes are well implemented, and allows optimized code generation as a mid-term perspective. As a common software limitation, potential bugs in the generic engine could impact all models developed with EMULSION: but its diffusion as an opensource software among a broad community of users is the best way to get feedback on potential malfunctions, identify their origin and correct them rapidly. To our knowledge, EMULSION is currently the only software which addresses all major computing issues in the design of mechanistic models in epidemiology.
Acknowledgments This work was carried out with the financial support of the  French Research Agency (ANR) ,  Program Investments  for the  Future , project  ANR-10-BINF-07  (MIHMES), and the  European Union  through the  European fund for the regional development (FEDER) of Pays-de-la-Loire. It was also funded by the  French Research Agency (ANR)  through project  CADENCE  ( ANR-16-CE32-0007-01 ), the  Animal Health Division of the French National Institute for Agricultural Research (INRA) , and the  INRA meta-programme  " Sustainable management of animal health " through project  FORESEE. We are also very grateful to  Thibaut Morel-Journel  for his careful reading and relevant comments.
Only one Cardinium OTU (OTU26) was detected, occurring in seven B. tabaci cryptic species. One Fritschea OTU (OTU29) was found, restricted to New World 1  13. One Hamiltonella OTU (OTU24) was found, occurring in six B. tabaci cryptic species. Singh, et al. 31  and Bing, et al. 15  reported Hemipteriphilus/OLO(OTU27) from specimens of China 1 (collected from China) and Asia I and Asia II 1, both collected from north India. However, OTU27 was found in Asia II 3, Asia II 6, China Indian Ocean and SubSaharan Africa 6 in our study. Two OTUs were detected from each of the two remaining S-endosymbionts: Rickettsia (OTU3 and OTU19) and Wolbachia (OTU10 and OTU21). Through comparing our data to that of previous studies (Table  S3 ), although the strains from different studies are incomparable, a relatively higher strain diversity was reflected by PacBio and several newly discovered symbionts (OTU7, OTU11, OTU3, OTU28 and OTU19) were detected.
S3 ). To overcome this limitation and find an alternative marker, we looked for markers with sufficient sequence divergence through scanning the three available P-endosymbiont genomes from MEAM1, MED and Asia II 1 B. tabaci ("data under submission, DanTong Zhu" for the corresponding author of that study). Briefly, a sliding window approach was applied to determine the local identities of the three well-aligned Portiera genomes using a custom Perl script (available from the senior author on request). The well-aligned regions with relative high divergence among the three Portiera genomes were selected for further analyses. Totally, five regions with a relatively high evolutionary rate (RER), which were located between non-coding regions, were picked to generate PCR clones from each cryptic species.
Discussion By using PacBio sequencing platform, 28 bacterial OTUs were discovered in the B. tabaci cryptic species complex for the first time, which enriches the bacterial reference database of whiteflies. In comparison, in whitefly research, only the 454 pyrosequencing platform had been used once to investigate the bacterial communities  30. In that research, fewer than 10 bacterial OTUs were reported through screening representatives of seven cryptic species and three field populations. Apparently, this study adds more samples across the species complex of B. tabaci with a promising PacBio technology, which resulted in a reservoir of new information on the bacteria diversity  of B. tabaci. This study significantly revolutionizes our knowledge on the bacteria diversity of an important economical pest.
Known symbionts. As a primary endosymbiont, Portiera reads were assigned to two different OTUs, one from the 21 B. tabaci species and a second from B. afer-most probably due to the significant length of time since these clades diverged  48. The nucleotide identity of the two Portiera OTUs representatives was 96%, just outside our range for OTU cutoff (3% divergence). OUT 12 was compared against all Portiera 16S rRNA sequences in GenBank, which were all less than or equal to 96% identical. These results suggest that the Portiera 16S rRNA As regard to the secondary endosymbionts, previously, a total of two Cardinium spp. 11, 30 , three Rickettsia spp. (one unpublished was deposited in the GenBank_LN 8296931)  31, 49  and three Wolbachia spp. 12, 16, 18 have been discovered in B. tabaci. In this study, more novel OTUs were assigned to Arsenophonus, Rickettsia and Wolbachia resulting in at least two Cardinium spp.
60. Processing of sequencing data generated from PacBio platform. For PacBio 16S datasets, raw data processing was carried out using the protocol RS_ReadsOfInsert. 1 available in SMRT Portal version 2. 7 as described by Hou, et al. 61. Operational Taxonomic Units (OTUs) were clustered with 97% similarity cutoff using UPARSE (version 7. 1 http://drive5. com/uparse/), and chimeric sequences were identified and removed using UCHIME. The phylogenetic affiliation of each 16S rRNA gene sequence was analyzed by the tool of RDP Classifier (http://rdp. cme. msu. edu/) against the SILVA 62 16S rRNA reference database using a confidence threshold of 70% 63. To show the distribution of the OTUs along various cryptic species, network analyses was done by Cytoscape  64. Diversity analyses. Rarefaction, Chao, ACE and Shannon diversity indices were performed using a set of Mothur tool  65.
To examine dissimilarities in the composition of bacterial communities, a Principal Coordinate Analysis (PCoA) was performed with an operating principle of using a distance matrix to plot n samples in (n-1)-dimensional space  66. Sequence alignments and phylogenetic tree for each bacterium. Sequences for the toward full-length 16S rRNA, specific region of P-endosymbionts and their corresponding B. tabaci hosts were each aligned using default parameters in MUSCLE  67  as implemented in MEGA 5  68  or ClustalW-MPI  69. Alignments were inspected, and ends were trimmed, resulting in 14,599 base pairs (bp) for the B. tabaci mitogenome sequences and 795 bp for the Portiera genome region. The 16S rRNA gene data sets of published bacterial communities were downloaded from GenBank for phylogenetic analyses.
Verification of the presence and distribution of one of the new micro-organisms in the MED B. tabaci species. Halomonas 72 has been found in the gut of pine weevils and it was detected in our PacBio data in more than 13 species, including MED, with more than five reads per sample. To determine whether this bacterium was present in the gut of B. tabaci, we conducted two subsequent experiments, which were illumina midguts -target sequencing and Fluorescence in situ hybridization (FISH) analyses. Twenty mid-guts of adults were dissected at different times post-emergence (0-day, 0-1 day, 4-5 days) from both lab colony of MED and Asia II 1 (where Halomonas reads were absent)  73, 74. DNA was extracted from the midgut and used for sequencing the universal V4-V5 hyper variable region (515F/907R primer set)of 16S rRNA genes  75  using Illumina Hiseq2500 platform with PE250 mode at Biozeron (Shanghai, China).
To aid distinguish samples after sequencing, sample-specific 12-bp barcodes were added to the reverse primers. Different from PacBio sequences processing pipeline, V4-V5 region sequences were analyzed with QIIME software package (Quantitative Insights Into Microbial Ecology) following UPARSE pipeline  76. OTUs were identified with UCLUST at the 97% sequence similarity level  77  and a representative sequence from each OTU was aligned using PyNAST  76. To visualize the presence or absence of the Halomonas in the gut, FISH analyses were performed on dissected midguts as described by Ghanim, et al. 78  with a slight modification. To target the 16S rRNA of the new Halomonas micro-organism, the probe Ha-Cy5 (5′-Cy5-TCACCAACTAGCTAATCCGACAT-3′) for the Halomonas strain was designed using Primer3 software (http://fokker.
To corroborate the phylogenies, as determined through BI, the ML P-endosymbiont and B. tabaci phylogenies were generated and the substitution model GTR with CAT approximation was used to incorporate heterogeneity rate across sites. The B. tabaci Japan 2 species was chosen as an outgroup owing to this species having a more distant genetic relationship with the other members of the B. tabaci species. Matrices of parasite and host distances were calculated from host and parasite phylogenies with an additional matrix of host-parasite links. Next, ParaFit analyses were performed in R using the package "ape"  84  with 999 permutations to implement a global test as well as individual links. Each B. tabaci species and P-endosymbiont interaction was determined to be significant if either its ParaFitLink1 or ParaFitLink2 P-value ≤ 0.
05. The recently developed, "Procrustean Approach to Co-phylogeny(PACo)" program  85  , implemented in R using packages ape and vegan  86  , was used to obtain and potentially corroborate, comparable global goodness-of-fit statistics with ParaFit global values. Although some specimens belonged to the same species, each terminal taxon was treated as an independent evolutionary unit as is common in co-speciation literature  87.
Acknowledgements This work was supported by the  China Postdoctoral Science Foundation  (Grant number  517000-X91502 ), the  China Postdoc Science Special Foundation  (Grant number  517000-X91609 ), the  National Natural Science Foundation of China  (Grant number  31501878 ) and by grants from the  Bill & Melinda Gates Foundation  (African cassava whitefly project,  OPP1058938 ). Grants ( AGL2013-48913-C2-1-R  and  AGL2016-75819-C2-2-R ) from the  Ministerio de Economía y Competitividad  ( MINECO, Spain ), co-financed by  ERDF. E. F. O. is a recipient of a ' Juan de la Cierva-Incorporación ' postdoctoral contract from  MINECO.
1, China 2, Cryptic species Location Host plant GenBank accession number of 16S GenBank accession number of RER region Asia I India Gossypium sp. (cotton) MG837029 MG063856 Asia II 1 Jiande, Hangzhou, Zhejiang, China Gossypium sp. (cotton) MG837043 MG063871 Asia II 3 Yuhang, Hangzhou, Zhejiang, China Glycine max (soybean) MG837051 MG063860 Asia II 5 India Manihot esculenta (cassava) MG837042 MG063870 Asia II 6 Baise, Guangxi, China Ipomoea batatas (sweet potato) MG837033 MG063861 Asia II 7 Guangzhou, Guangdong, China Codiaeum variegatum (variegated laurel) MG837045 MG063873 Asia II 9 Shaoyang, Hunan, China Ipomoea batatas (sweet potato) MG837044 MG063872 Australia Bargara, Queensland Euphorbia cyathophora MG837046 MG063874 Australia_E Kununurra, Western Australia Emilia sonchifolia MG837030 MG063857 Bemisia afer_Africa Entebbe, Uganda Manihot esculenta (cassava) Bemisia afer_China LinYi, China Chrysanthemun coronarium China 1 Yuhang, Hangzhou, Zhejiang Ipomoea batatas (sweet potato) MG837031 MG063858 China 2 Gaoyao, Zhaoqing, Guangdong, China Cucurbiat moschata (pumpkin) MG837032 MG063859 Indian Ocean Saint Pierre la Réunion Gossypium sp.
In recent years, there is an ongoing computational shift from the data center to the network edge. Due to the increased hardware capabilities of devices, the edge can also benefit from the dynamic and scalable services provided by the virtualization technologies. In turn, the edge computing brings low-latency and reduced network traffic, location-awareness and local caching. However, the new capabilities unlock new challenges in terms of security, data and workload location. In this work, we focus on the threats caused by the heterogeneous and distributed nature of the edge infrastructure. We build a trusted edge based on the hardware isolation of ARM TrustZone. Moreover, we use it as a secure foundation to perform location-aware virtual machine deployment utilizing the dispersed nature of the infrastructure.
As with any other new technology, edge computing brings new challenges together with the new opportunities. Undoubtedly, security is among the most important ones, though often neglected. Various surveys exist, analyzing cloud and edge security threats in different aspects and contexts  [2] -  [5]. The dispersed edge infrastructure often relies on wireless connectivity which makes it vulnerable to man-in-the-middle attacks. Remote or difficult to secure locations also increase the risk of tampering or replacement, therefore, extra measures need to be taken to verify the authenticity and integrity of the edge devices. Another challenge is related to the data and workload location. In a cloud environment, the actual location of the servers running the workloads and storing the data is known only to the cloud owner or provider.
Two main industry technologies are implementing a TEE: • Intel SGX or Intel Software Guard Extensions  [11]  is an Intel architecture extension with protected areas of execution in the memory, called enclaves. The developers can specify parts of their application which run isolated into the enclaves, protected from other applications, the operating system (OS) or even the firmware. • ARM TrustZone  [12]  provides a hardware isolation for the parallel execution of a secure environment and a rich OS, together with a mechanism for the context switch between the two. The latter is concentrated on the remote attestation of a device as an additional measure of trust. A formal definition of the process is given in  [8]  while a popular open-source implementation is the OpenAttestation project  [13].
A. ARM TrustZone-based edge nodes The key component is the edge infrastructure based on ARM TrustZone-enabled nodes running VOSYSMonitor  [16]. This mixed-critical infrastructure provides each node (host) with an isolated environment for storing and running sensitive data and applications. Each device has two execution environments, called Normal World and Secure World. The Normal World hosts a rich operating system, KVM-enabled Linux in the case of this work, where the virtualization services and the workloads are running. Meanwhile, the Secure World provides a secure environment for the critical tasks of the Normal World as well as a secure storage for cryptographic keys and other critical data. The context switch between the two worlds is done by VOSYSMonitor -a thin monitoring software layer developed by Virtual Open Systems.
In addition, the edge nodes make use of secure boot to propagate the trust to the upper software layers. It is accomplished by a vendor-specific mechanism relying on a hardware root of trust and is propagated to the Linux user-space by U-Boot's verified boot  [17]. The boot process is multi-staged and starts from the boot read-only memory (ROM) and the first-stage boot loader (FSBL). After the FSBL is determined as secure, it loads the open source boot-loader U-Boot which on its turn verifies the signature of the Linux kernel image and only then loads the operating system.
C. OP-TEE Secure services Open Portable Trusted Execution Environment (OP-TEE) is an open-source implementation of a TEE meeting the Global Platform TEE specifications. OP-TEE is designed to run into the Secure World in parallel with a Rich Execution Environment (REE) which is, in this case, a Linux OS running in the Normal World. The underlying hardware isolation is based on ARM TrustZone  [19]. We implement several services providing the security of the edge nodes as OP-TEE trusted applications inside the Secure world where they cannot be affected by software running in the Normal World. We use the OP-TEE secure storage to keep the geolocation of the edge nodes in a place where it cannot be maliciously modified. 1) Edge node authentication: The edge nodes authentication is initiated by the OpenStack Controller and is performed before the start of the provisioning.
Moreover, it contains a nonce that makes ineffective reply attacks. • Signature: acting as a Message authentication code (MAC) it guarantees the authenticity and integrity of the packet. By following this protocol, we achieve a secure communication secure channel that allows end-to-end encryption between the OpenStack Controller and the OP-TEE operating system. Even if the Linux kernel on the edge node is comprised, an attacker cannot alter this communication channel. The only possible attack is the interruption of the channel, but this can be already interpreted as a "red flag" by the OpenStack Controller. 2) Linux kernel integrity check: Detecting all signs of infection is a task hard to achieve. Having said that, this implementation intends to serve as a proof of concept and to demonstrate the feasibility of the work and measure its performance.
In order to interfere with the execution of a VM a malicious software (commonly referred to as malware) has to gain high privileges. Usually, an infection of the kernel space is needed. The main malware category that aims at this is the so-called "rootkits". One common technique used by rootkits is to tamper the kernel code to perform malicious actions. A valid countermeasure is a run-time integrity check of the kernel code memory. Although this doesn't prevent the execution of a rootkit but only the detection of it, this information is valuable to mark the infected host as not trusted and take countermeasures. We define our main attack vector as a rootkit and implement the Linux integrity check by monitoring two essential tables present in the Linux kernel: Thanks to OP-TEE it is possible to verify at run-time that these tables are not modified.
To perform this it is sufficient to compute the digest of the tables and verify that it is exactly as the one expected. OP-TEE embeds in its code the digests of the tables computed offline using the verified kernel image. Figure  2  summarizes the steps of this integrity check. 3) Location-aware VM deployment: The location-aware VM deployment can be split into two phases. The geo-tag provisioning phase when we load and store the geolocation to the edge compute nodes and the actual deployment phase. The components that play a part during the first phase are the geo-tag database and a tagging tool. Since the acquisition of the edge location is not the goal of this research, we use pre-defined static geolocation information and leave the dynamic geolocation for future works. The tagging tool has the role of provisioning the geo-tag to the trusted edge node.
The tags are stored in a database with a JSON format. The tool extracts the needed tag from the database, calculates its hash digest and sends it to the nodes Linux part (Normal World). Then the control is transferred to the OP-TEE where the digest is stored. The proposed geo-tag structure consists of GPS coordinates and an additional text description: {"host": { "trusted": "true/false", "tag": "tag string", "geolocation": { "latitude" : 12. 345, "longitude": -67. 890 } }} Alternative geo-tag structures can consider regions, road names, cities blocks, buildings or any other geographical concept instead of GPS coordinates. After the tag is provisioned to the trusted edge compute nodes, the VM deployment can start. The OpenStack Flavors are updated to include the geo-tag attributes. Before VM instantiation, Nova (through the nova-scheduler and its filters) checks the DB for a tag with matching attributes, calculates the tags hash and attests the compute node.
The paper presents a generic approach to the supervised sentiment analysis of social media content in foreign languages. The method proposes translating documents from the original language to English with Google's Neural Translation Model. The resulted texts are then converted to vectors by averaging the vectorial representation of words derived from a pretrained Word2Vec English model. Testing the approach with several machine learning methods on Polish, Slovenian and Croatian Twitter corpora returns up to 86 % of classification accuracy on the out-of-sample data.
Introduction Sentiment analysis is gaining prominence as a topic of research in different areas of application (journalism, political science, marketing, finance, etc. ). In the last two decades, opinion-rich data sources are widely available because of webresources and social networks. While lexiconbased frameworks have long been investigated for sentiment analysis, deep learning methods with a vectorial representation of words are proving to deliver promising results. The integration of two types of methods is widely investigated as well. Thus, the sentiment analysis approaches usually require either fine-grained lexicon of most frequent words along with their polarity scores or the dataset large enough for supervised training of deep learning network, sufficient computational memory, etc.
Moreover, most of the open-source datasets for training sentiment models comprise Englishlanguage texts. The lexicons are not always available for other languages, and it remains a timeconsuming task to construct them. It motivates us to build on the existing approaches and test a rather general method to run a sentiment analysis for different languages without polarity dictionaries using relatively small datasets. We address the challenges for sentiment analysis in Slavic languages by using the averaged vectors for each word in a document translated in English. The vectors derive from the Word2Vec model pre-trained on Google news. Researchers tend to use the language-specific pre-trained Word2Vec models (e. g. , Word2Vec model pre-trained on Wikipedia corpus in Greek, Swedish, etc. ). On the contrary, we propose benefiting from Google's Neural Translation Model translating the texts from other languages to English.
Translated documents are then converted to the fixed-vectorial representation with Google Word2Vec model. 1  The supervised machine learning classifiers such as Gradient Boosting Trees, Random Forest, Support Vector Machines provide sufficiently high accuracy on the out-of-sample data converted to the aggregate vectors. The rest of the paper is structured as follows: Section 2 provides a brief review of related literature. Section 3 describes the methodology. Section 4 expands on data used. Section 5 presents the results from our experiments. Section 6 concludes with some observations on our findings and identifies directions of future research.
Corpus-based and Hybrid Methods with Vectorial Representation Embedding approaches usually rely on the semantic vector spaces derived from the neural networks. Their application in supervised experimental setups for polarity analysis often demonstrates superior performance to the lexicon-based methods  (Le and Mikolov, 2014; Severyn and Moschitti, 2015). As the reference point in our study we use the papers of  Giatsoglou et al. (2017)  and  Garten et al. (2018)  where authors meticulously employ Sentence2Vec in their methodological settings. Giatsoglou et al. (2017)  uses Sentence2Vec based on the Word2Vec model learned from the Wikipedia corpus in Greek. The performance is evaluated with the datasets of mobile phonesâ Ȃ Ź reviews in Greek. The model that exploits the vectors derived from the Wikipedia corpora+the reviews provides the highest accuracy of 70.