dataesr/scientific-paragraphs-categorization huggingface dataset:
sFlow sampling sFlow is a well-known monitoring approach relying on packet sampling. 11 An sFlow agent integrated into the switch is in charge of both sampling packets at a configurable rate  14  and transmitting their header to a collector, logically running near the controller. Many SDN equipment manufacturers have already integrated sFlow agents in their products. Moreover, one of the reference software switches, Open vSwitch (OVS), is also sFlow compatible. sFlow offers significant benefits when compared with the native OpenFlow approach, since the monitoring functionality is totally decoupled from forwarding; ie, it is not necessary to add flow rules to ensure both forwarding and monitoring of flows. On the basis of sampled packets, the sFlow collector allows performing DDoS detection by maintaining an up-to-date list of features and counters.
Scenario and parameters In VM-based experiments, host h1 replays legitimate traffic taken either from the "BigFlows" trace or from synthetic traces with fixed entropy. By using the tcpreplay tool, this approach makes it possible to mimic a much more complex topology than the one presented in Figure  5. BigFlows trace: The "BigFlows" trace (available at previous literature  17  ) contains 50 000 packets and has been chosen to include a significant number of hosts (679) that exchange diverse types of legitimate traffic (9605, 40 249, and 118 packets for UDP, TCP, and ICMP, respectively); the trace captures a real network traffic on a busy private network's access point to the Internet. The trace is indefinitely replayed at the speed of 3. 3 Mb/s. Fixed entropy traces: We crafted several synthetic traces having fixed entropy values using the Python library scapy.
The set of packet matching rules and the associated actions can be defined programmatically via the P4 language, 35 which is then compiled to a target platform-either a hardware or software implementation of the SDN switch. In principle, P4 allows describing stateful functions; nevertheless, limitations in its concurrency model reduce their field of applicability. 36 For instance, PISCES represents a high-performance software switch implementation built with the P4 language. 37 PISCES does not support any stateful processing, thus not being able to implement the functionalities required by StateSec. Another approach, named SoftFlow, addresses stateful processing by extending OVS and integrating arbitrarily complex stateful functions. 38 Differently from OpenState, which offloads the stateful operations directly to the switch, a SoftFlow action is a black box approach, acting similarly to a VM attached to the switch.
ACKNOWLEDGMENTS This is an extended version of the work appeared 39 in IEEE NetSoft 2017. The authors thank  Victor Pus  and  Pavel Benacek  from  CESNET  for their assistance during the real-world deployment. This work has been partly funded by the  EU  in the context of the  H2020   BEBA  project (grant agreement  644122 ) and  Celtic-Plus SENDATE-Tandem.
One axis feedback loop modelling One could note that modelling is not easy because the whole system consists of several heterogeneous parts. That requires a global approach. Among several possibilities, (analytic calculus, MATLAB software, SPICE simulator…) we propose hereafter, the building of a mixed SPICE modelling adapted to our electronic initial culture. It must take into account the electronic behaviour (Electronic circuits, non-linear sensors (LDR1 and LDR2), the mechanical behaviour (servomotor), and also the optical and geometrical aspects, due to the rotation of the spot light (simulating the sun). Preliminary step is to establish a block diagram representing the global feedback system. In order to simplify the approach, the range of rotation angle -i. e. 180° for a classical servomotor-is supposed covering a sweep from East to West.
The identification of communities, or modules, is a common operation in the analysis of large biological networks. The Disease Module Identification established a framework to evaluate clustering approaches DREAM challenge in a biomedical context, by testing the association of communities with GWAS-derived common trait and disease genes. We implemented here several extensions of the MolTi software that detects communities by optimizing multiplex (and monoplex) network modularity. In particular, MolTi now runs a randomized version of the Louvain algorithm, can consider edge and layer weights, and performs recursive clustering. On simulated networks, the randomization procedure clearly improves the detection of communities. On the benchmark, the results DREAM challenge strongly depend on the selected GWAS dataset and enrichment -value p threshold.
The challenge has been divided into two sub-challenges, to identify communities either i) from six biological networks independently, or ii) from all these networks jointly. The second sub-challenge, in particular, intend to test if some approaches can leverage complementary information from multiple networks jointly to define integrated communities. The clustering approaches proposed by the participants are assessed regarding their capacity to reveal disease communities, defined as communities significantly associated with genes implicated in diseases in GWAS studies  3, 4. The challengers proposed various strategies and clustering approaches, including kernel clustering, random walks or modularity optimization. We competed with an enhanced version of MolTi, a modularity-based software that we recently developed  5.
We focused on the subchallenge dedicated to the identification of communities from multiple networks as MolTi was initially developed to cluster multiplex networks, i. e. , networks composed of different layers of interactions. Molti extended the modularity measure to multiplex networks and adapted the Louvain algorithm to optimize this multiplex-modularity. We have demonstrated that this multiplex approach better identifies the communities than approaches merging the networks, or performing consensus clusterings, both on simulated and real biological datasets  5. Grounded on these initial results, we here extended and tested our MolTi software, both on simulated data and on the DMI challenge framework. We improved MolTi with the implementation of a randomization procedure, the consideration of edge and layer weights, and a recursive clustering of the classes larger than a given size.
With simulated data, we observed that considering more than one network layer improves the detection of communities, as already noted in Didier et al. , 2015  5  , but also that communities are better detected with the randomization procedure. With the DMI benchmark, we pointed to a great dependence on the GWAS dataset used for the evaluation and on the FDR threshold defined, but, overall, randomizations and edge and layer weights increase the number of disease communities detected.
MolTi-DREAM: communities from multiplex networks We detected communities with an extended version of MolTi  5  , a modularity-based software. Although MolTi was specifically designed for multiplex networks, (i. e. , networks composed of different layers of interactions), it deals with monoplex networks (i. e. single-layer network) by considering them as multiplex networks composed of a single layer. All the networks are here considered undirected. The new version of MolTi, MolTi-DREAM, and the scripts used for the DMI DREAM challenge are available at https://github. com/gillesdidier/MolTi-DREAM.
sparse) networks, while mixed networks contain both sparse and dense networks. Multiplex networks with missing data are obtained by randomly removing half of the vertices (and the edges involving them) of the multiplex networks simulated from SBMs. The relevance of a community structure is assessed by computing the adjusted Rand index 10 between the detected communities and the ones used to simulate the multiplex networks. The Disease Module Identification challenge benchmark Biological Networks. The DMI challenge provided six human biological networks: two protein-protein interactions, one literature-curated signaling, one co-expression, one network linking genes essential for the same cancer types, and one network connecting evolutionary-related genes. These six networks have various sizes and edge densities (Table  1 ).
All networks have weighted edges, and all networks but the signaling network are undirected. However, we considered the signaling network as undirected. Evaluations with GWAS data. The communities identified by the different challengers were evaluated according to the associations of their member genes with GWAS data, using the PASCAL tool described in  Lamparter et al. , 2016 4. The procedure leverages the SNP-based p-value statistics obtained from 180 GWAS datasets, covering common diseases and traits. The communities are associated with p-values, then corrected for multiple testing, and an FDR threshold is used to determine the number of significant disease communities in a given partition  3, 4. We used three datasets: the "Leaderboard" (76 GWASs) and "Final" (104 GWASs), which were used during the challenge, and their union in a "Total" dataset (180 GWASs).
Finding disease modules with MolTi We applied the improved MolTi to the networks provided by the DMI challenge (Methods). We focused on the sub-challenge 2, which was dedicated to the identification of communities from multiple networks. We considered the six DMI biological networks as layers of a multiplex network, and applied the recursion procedure to obtain communities in the required size range. The significant disease communities were selected regarding their enrichments in GWAS-associated genes (Methods). We observed first that the number of detected disease communities is strongly dependent on the GWAS dataset and FDR threshold used (Figure  2 ). For the FDR threshold used during the challenge, i. e. , FDR lower than 0. 05, the number of significant disease modules detected slightly increases after randomization (Figure  2 ).
Evaluation of the edge and layer weighting. All the six biological networks used in the DMI challenge have weighted edges. We compared the number of disease modules obtained by considering or not considering these weights in the MolTi partitioning, for different FDR thresholds (Table  2 ). We observed that intra-layer edge weights only has a slight effect on the number of significant disease modules identified, except for the very low significance threshold of 0. 01, where it seems pertinent to use these weights. MolTi-DREAM allows assigning weights to each layer of the multiplex network, for instance to emphasize the layers known to contain more relevant biological information. Given the results obtained on individual networks, we decided to test a combination of weights that would lower the importance of the 5-cancer and 6-homology network layers.
Discussion and conclusion We applied here the MolTi software and various extensions to identify disease-associated communities following the DMI challenge benchmark. The new version of MolTi, MolTi-DREAM, runs a randomization procedure, takes into account edge and layer weights, and performs a recursive clustering of the classes that are larger than a given size. We finished tied for second in the challenge. However, even if we obtained higher scores than monoplex approaches, the difference was not significant and the organizers of the DREAM challenge declared the sub-challenge 2 vacant. In the simulations, all the networks are randomly generated from the same community structure. These networks can thereby be seen as different and partial views of the same underlying community structure. Combining their information in a suitable way is thereby expected to recover the original structure more accurately.
It has been shown that interacting proteins tend to be coexpressed  11  , which could explain why the co-expression network also provides complementary information. In contrast, the cancer network is determined from processes operating at a very different level. Overall, these results show that the sources of biological information that are added as layers of a multiplex need to be evaluated thoroughly. Evaluating the relevance of the community structure detected from real-life datasets is a very complicated problem since the     actual structure is hidden and generally unknown. In this context, the only possibility for assessing the detected communities is to consider indirect evidence provided by some independent biological information. Different teams are thereby developing proxies to evaluate the communities, mainly based on testing the enrichment of genes contained in each community in Pathways or Gene Ontology annotations.
Grant information The project leading to this publication has received funding from the Centre National de la Recherche Scientifique (PEPS BMI IMFMG), the French "Plan Cancer 2009-2013", and the Excellence Initiative of Aix-Marseille University -A*MIDEX, a French "Investissements d'Avenir" programme. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The DMI DREAM Challenge itself is an important exercise addressing the non-trivial task of identifying biologically meaningful communities in molecular networks. This paper by Didier et al. takes on this challenge by treating the benchmark datasets as a multiplex network. While limited by the constraints of the challenge, I think the paper is an important contribution that offers an insight as to how multiplex methods fare on real-world biological networks of diverse and not necessarily related sources.
How many nodes did the final multiplex consist of? How many edges were on each layer? Is there any specific inter-layer link structure? 4) The authors use PASCAL to determine the disease-associated genes from GWAS, which are in turn used to determine the communities that are significantly enriched in disease signals. It is crucial at this used to determine the communities that are significantly enriched in disease signals. It is crucial at this point to convey to the reader very clearly what PASCAL does and how it's translated to "significant disease modules. " The authors should, with a couple of sentences, describe how PASCAL does the SNP-to-gene mapping, and then, more importantly, how the p-value weighted gene annotations from PASCAL are used in the enrichment to determine the significant disease modules.
A good description of this would facilitate the interpretations of Figure  2 3 4. 5) The Methods section on controlling the size of modules could be more informative. "We tested different pre-filters (pruning leaves), parameters (resolution parameter, recursions, combination of graph weights for multiplexes) and post-filters (density, size, pruning leaves) in each leaderboard round. " This sentence contains a lot of information without really getting into details. The authors could elaborate on each item. For instance, the resolution parameter does not really seem to come into play (it is even omitted from the definition of multiplex modularity since it is set to 1). Recursion was used to limit the size of the clusters, and while the authors mention tests conducted in leaderboard rounds, no data here is shown as to how varying the resolution parameter changes the results.
Here I think the distinction between the different GWAS datasets is important to discuss, if the authors are saying the results are sensitive to the datasets. What is it that makes the "Leaderboard" and "Final" datasets different? What are the diseases and traits included in the GWAS datasets? Perhaps the authors could comment on this. 9) Also for Figure  2  (I may be missing something obvious here) -are these results based on multiplex or monoplex? 10) "We hypothesize that the community structures of these networks (if they exist) are so unrelated that it is pointless to seek for a common structure by integrating them. " This is an interesting point that should remind us that the multiplex biological network should be constructed with a hypothesis in mind (e. g. various molecular levels from transcripts to proteins to metabolites) rather than piling up different networks into a multiplex structure.
Author We agree that our manuscript requires familiarity with the DMI DREAM challenge data and consortium manuscript: It is indeed not independant, and was specifically written as a companion paper to be published with papers from other participants for the F1000 dream challenge channel. We apologize for it's strong size constraints, and the fact that we tried to avoid any overlap with the main consortium paper, available in bioRxiv and considered for publication in a journal. We checked and tried to give the same amount of details as the other papers published in DREAM channels, in particular in the DMI channel.
We agree that more information about the individual networks can help deepening the understanding of some of the results presented in the article, but the networks are an important point and deeply described in the main DMI challenge consortium paper available on biorXiv. This challenge paper is still considered for publication in a journal, and we are expected to avoid any overlap. We however now state in the method section that all the data are human. 3) It is important to clarify how the multiplex network was constructed out of the six networks. For it to be a multiplex network, the same set of nodes should be represented on each layer, which likely requires the pruning of the six benchmark networks. How many nodes did the final multiplex consist of? How many edges were on each layer? Is there any specific inter-layer link structure? This is a very important and interesting point.
Therefore, there is no need for an inter-layer link structure when constructing the multiplex network. We rephrased the corresponding method section to mention p-value and multiple testing corrections. However, we have for this companion paper a strong size constraints. The PASCAL tool is a complex approach deeply described in a publication (Lamparter et al. Plos Computational Biology), and its application and use in the context of the DMI dream challenge is fully described in Biology), and its application and use in the context of the DMI dream challenge is fully described in the consortium paper. A precise and correct description of the evaluation procedure, from SNP mapping to significant modules would be too long and overlapping with the consortium and PASCAL papers. Indeed, the resolution parameter was missing in the equation, and we now have included it.
1 Figure  1  For FDR = 0. 05 Figure  2  For FDR = 0. 01 Figure  3  6) The 100 cutoff is set by the challenge, but in other settings, is there a way to set this ad hoc limit more concretely? Could the authors comment on this? hoc limit more concretely? Could the authors comment on this? Defining the optimal module size is a complex task. One could imagine using the challenge benchmark to find the optimal module size. However, this is not feasible in practice as 1) the computation of PASCAL scores are time-consuming and cannot really be used to optimize the algorithms, 2) the sensitivity/volatility of the results to the number of submitted modules and GWAS data used make the interpretation on a fine-grained scale difficult. The consortium paper discuss this point and identify, in particular, that the average module size do not correlate with the number of significant modules identified by the different approaches (Figure  3C  in bioRxiv).
This point definitely deserves to be further investigated. 2  tells me is that if we simply relax the association criterion (FDR cutoff), we'll simply get more enriched communities, which is not entirely surprising. Here I think the distinction between the different GWAS datasets is important to discuss, if the authors are saying the results are sensitive to the datasets. What is it that makes the "Leaderboard" and "Final" datasets different? What are the diseases and traits included in the GWAS datasets? Perhaps the authors could comment on this.
8) What Figure The number of significant communities increases when the FDR cutoff is larger, as expected. However, the results are also quite sensitive to the dataset considered as pointed out by the reviewer. The different GWAS datasets were chosen by the DREAM challenge organizers, and splited into "Leaderboard" and "Final" manually. A detailed discussion of the diseases and traits included in the GWAS datasets, as well as their comparison, is proposed in the DMI consortium manuscript (in particular in Figures  1, 3  and 4 ) and cannot be reproduced here.
We used combinations of 2 to 6 networks, considered as multiplex networks of 2 to 6 layers. 12) I found the Discussion a bit too DREAM Challenge-oriented. I think the parts comparing the results with those of the top performer are unnecessary (but this may be a requirement of the challenge so ignore if that's the case). The authors should revise the discussion to recapitulate and interpret their results, and to highlight the advantages of discussion to recapitulate and interpret their results, and to highlight the advantages of using the multilayer approach. I think it is commendable that the authors chose to include all of the six datasets, regardless of how related they are. Even the fact that the addition of some (possibly orthogonal) layers is detrimental to the outcome is an important finding.
The authors did not have control over the DREAM challenge evaluation, but a reader might have a few questions regarding the performance evaluation. For example, if the judging criterion is the number of significantly GWAS associated modules, does the score improve by dividing one large disease associated module into two, if they both are still significant? This is a tricky question. Briefly, the DREAM evaluation uses the PASCAL tool to compute a score for each modules according to their enrichments in GWAS-significant genes. The number of significant disease module in a given partition is then computed according to an enrichment threshold, and considering correction for multiple testing. In this context, the total number of submitted valid communities is important, as submitting more modules will induce a stronger correction for multiple testing.
The authors provide source code for both the latest version, and also the version that was The authors provide source code for both the latest version, and also the version that was used for the DREAM challenge. It would be helpful if the authors also provide, if those are readily available, the scripts that were used to run the DREAM and simulated data for the results presented. This will help answer minor questions regarding details like filtering etc. The scripts used to run the DREAM challenge approaches are all available from the challenge webpages as a community resource.
subchallenge 2; they should either say this is what they are going to do up front or also mention their performance on subchallenge 1. We indeed focused on subchallenge 2. During the challenge leaderboard phase, our consortium tested many very different approaches on subchallenge 1, and we did not evaluate Molti thoroughly in this subchallenge. In addition, Molti was specifically designed to leverage multiplex networks. We added a sentence (highlighted in blue) to the manuscript to state this point more clearly. The paper is not self-contained, it already assumes some familiarity with the setup of the challenge; since this is being published in a collection on F1000 related to the dream challenge, perhaps that is appropriate, however I would have preferred if the authors had spent more time describing the challenge: i.