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9,152 results for “Networking”
Dataset for "Secure and Dependable Multi-Cloud Network Virtualization"
<p>Processed data from experiments using Sirius, the SUPERCLOUD network hypervisor</p> <p>Type of data: processed data</p> <p>Hardware/software used:</p> <p>We use one public (Amazon EC2 in Germany – Frankfurt) and one private (the private cloud is our datacenter in Portugal – Lisbon) clouds in the evaluation. In Amazon EC2 we use t2.medium as gateway VMs and t2.micro as normal VMs. The private cloud is based on a rack of Dell R420, with 2 Intel Xeon E5520 quad-core, 2.2 GHz, and 32 GB RAM. VirtualBox managed the VMs, which were configured</p> <p>with 1 CPU and 2GB RAM. The VMs run Ubuntu with Docker (1.13.1) and OvS (2.5.0). The containers were also based in Ubuntu. The hypervisor was hosted in the server of the private Datacenter.</p> <p>Data Format: Text files</p> <p>Source: Simulations and experiments</p> <p>Number of samples: 10 samples in each experiment</p> <p>Total size of samples:</p> <p>Time to create and destroy containers: 1KB</p> <p>Time to setup a virtual network: 1KB</p> <p> </p>
KC0P 40 m SEQP Reverse Beacon Network Log of Spots
<p>Log of the Spots generated by the Reverse Beacon Network of the 40 m transmissions of KC0P during the</p> <p>SEQP on August 21, 2017.</p>
Fig. 1. NeighborNet split network for the 104 in Scandinavian Oncophorus (Bryopsida, Oncophoraceae): species, cryptic species, and intraspecific variation
Fig. 1. NeighborNet split network for the 104 specimens studied, based on ITS (A) and the chloroplast markers trnG and rps4 (B): Oncophorus crispifolius (O.c., 2 specimens), O. dendrophilus (O.d., 2), O. elongatus (24), O. rauei (O.r., 2), O. virens (22), O. wahlenbergii (22), O. integerrimus Hedenäs sp. nov. (26), and outgroup (light grey boxes: C.s. = Cynodontium strumiferum, 2; R.f. = Rhabdoweisia fugax, 2). Haplotype networks based on all samples of O. elongatus, O. virens, O. wahlenbergii, and O. integerrimus Hedenäs sp. nov. are shown distal to 'branches' identified in the split network to show the full resolution among haplotypes. Circle size in the haplotype networks is proportional to the number of sampled populations of a certain haplotype, and sample numbers are explained in Table 1. Circles connected by a line differ in a single mutational difference; black dots indicate 'missing' haplotypes. Jacknife support values of 75–94.99 and 95–100 are indicated by transverse grey and black lines, respectively.
Data from: Prediction of Pedestrian Speed with Artificial Neural Networks
<p>Corridor data are trajectories of pedestrians in a closed corridor of lenght 30m and width 1.8m. The trajectories are measured on a section of length 6m. Experiments are carried out with N=15, 30, 60, 85, 95, 110, 140 and 230 participants.</p> <p>Bottleneck data are trajectories of pedestrian in a bottleneck of lenght 8m and width 1.8m. Experiments are carried out with 150 participants for bottleneck widths w=0.7, 0.95 1.2 and 1.8m.</p> <p>See http://ped.fz-juelich.de/experiments/2009.05.12_Duesseldorf_Messe_Hermes/docu/VersuchsdokumentationHERMES.pdf page 20 and 24 for details (in German). The data are part of the online database http://ped.fz-juelich.de/database.</p> <p>Column names of the file are: ID FRAME X Y Z.</p> <ul> <li>ID is the pedestrian ID.</li> <li>FRAME is the frame number (frame rate is 1/16s).</li> <li>X Y and Z are pedestrian position in 3D.</li> </ul>
Modelling the influence of parental effects on gene network evolution : Data and Program
<p>The C++ software "Simevolv" is a population genetics tool able to simulate the evolution of complex genetic architectures.</p> <p>This version is a development version from which the results of the manuscript "Modelling the influence of parental effects on gene network evolution" (in prep) have been obtained.</p> <p>The "Data" file corresponds to the simulation results used in the above mentioned article.</p>
Visualizing linguistic variation in a network of Latin documents and scribes
<p>Gephi project files (for Gephi 0.8.x) and Sigma.JS -visualizations.</p>
Packet time delivery on ad hoc network
<p>This dataset represents the 90,000 simulations of package delivery on an ad hoc network represented by one victim, gateways and mobile nodes.<br> <br> There are 900 different scenarios. For each one, we ran 100 simulations.<br> <br> For more information about this dataset, see the paper: "<em>Framework for gateways specification in an ad hoc network for natural disaster situations</em>"<br> <br> 1st column: l - length of the side of the square area (in meters).<br> 2nd column: n - number of nodes.<br> 3rd column: g - number of gateways.<br> 4th column: p - number of position of victim (see the paper).<br> 5th column to 104th column: t0 to t99 - time of package delivery (for each of 100 simulations).<br> 105th column: m - mean.<br> 106th column: s - standard deviation.<br> 107th column: f - number of failures on package delivery (counted on the 100 simulations).</p>
Reverse Beacon Network Download for Eclipse Data at Corvallis, OR (massaged)
<p>The upload contains an Eclipse Excel spreadsheet that is enhanced from a Reverse Beacon Network (RBN) export, and sorted by receiving callsign and Zulu PM time during the eclipse and 15 minutes before and for a period from 8:45 to 12:30, local Corvallis time. The experiment looked at the RBN sites who received CW signals from the callsign WG0R, which was transmitting test messages at 100 Watts using the below station resources.</p> <p>The transmission resources are: Elecraft KX3, KXPA100, and PX3 transceiver, 100 Watt amplifier, and Panadapter, driving a Carolina Windom Antenna at 10 meterrs above ground level, strung at 150 to 330 degrees in two Oak trees.</p> <p> </p>
STARS4ALL European Photometer Network Nov 2017
<p>Monthly dataset with the measurements taken by the TESS Network (Nov 2017)</p>
Figure 2. Workflow in Collaborative Portals-Questions Regarding Alterity in Social Collaborative Networks
<p>Despite the divergence of each members individual interests and structure, the media as well<br> as the Internet become new forms of social binding, and we believe they do not exclude the<br> traditional ways of communication and interaction, but help people in their duties, in addition to the<br> traditional, formal ways. In Figure 2 [7]. there is a clear image about how each of the benefficiaries<br> interact with the educational portal.</p>
Figure 4. Portal Logical Architecture-A Proposed Data Driven Architecture for Cardiology Network Application
<p>The applications that are based on a SOA environment can be configured to work both<br> as services exchanging data in point to point protocol and as a communication channel<br> between two entities that call a service provided by an entity that has the role of mediator or<br> broker. The letter can be seen as intermediary level that represents an “enterprise service bus”<br> (ESB) between service producers and consumers, offering messaging exchange services.</p>
Figure 2. System General View-A Proposed Data Driven Architecture for Cardiology Network Application
<p>Figure 2 presents a general schematic overview of the medical informational system,<br> underlying the main roles in the system together with their interactions. From an architectural<br> point of view one can identify the following main components:<br> • Host systems, named local (medical data) production systems<br> • General Practitioner system<br> • Analysis Laboratory system<br> • Hospital system<br> • Client system (interface to remote devices)<br> • Local portal (regional, national) for medical assistance, and long term data storage<br> Host systems consist of database servers connected through access points with<br> fixed/mobile medical devices. The portal will supply the functionality necessary to collect<br> information from the local interconnected systems into a centralized repository. Medical<br> services will be provided at portal level using specialized web-services that will allow access<br> to data stored in the repository.</p>
Figure 1. Schema for a data-integration solution-A Proposed Data Driven Architecture for Cardiology Network Application
<p>Data integration has favored loosening the coupling between data. This may involve<br> providing a uniform query interface over a mediated schema (see figure 1), thus transforming<br> a query into specialized queries over the original databases. One can also term this process<br> "view-based query-answering" because each of the data sources functions as a view over the<br> (nonexistent) mediated schema.</p>
Figure 3. Data Integration Sample-A Proposed Data Driven Architecture for Cardiology Network Application
<p>Pentaho Data Integration has implemented a metadata-driven approach where you<br> only specify the data you want integrated, but you do not specify the way you want it done.<br> One of the most important advantages of Pentaho is that one can create complex<br> transformations and jobs in a graphical, drag-and-drop environment without having to create<br> proprietary custom code that will work only with some proprietary application.</p>
Figure 1. 2nd year section in the Educational Portal - Questions Regarding Alterity in Social Collaborative Networks
<p>Portlets proved to be the most intelligent way of presenting information on an educational<br> portal, due to the richness of the user interface tools. The portal may be found at http://cursfs.ub.ro<br> URL. In Figure 1 there is a screenshot of the 2nd year students section in educational portal, which is<br> very easy to use and helpful.</p>
Figure 3: AntCo2 algorithm for graph clustering: on the left the output of the computation on a communication network; on the right the output on a regular grid
<p>Social and human developments are typical complex systems. Urban development<br> and dynamics are the perfect illustration of systems where spatial<br> emergence, self-organization and structural interaction between the system<br> and its components occur [3, 4, 5, 6]. In figure 4, we concentrate on the emergence<br> of organizational systems from geographical systems.</p>
Figure 3. Neural network representation for HMM given in Figure 2.-Neuroevolution Mechanism for Hidden Markov Model
<p>In our proposed structure, we injected a hidden layer to have a multilayer perceptron which<br> is more efficient than single layer perceptron.<br> To make this process clear, Figure 3 shows the neural networks for the HMM presented in<br> Figure 2.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 9. Modular Hierarchical Organization of Perceptual Neuro-Symbolic Networks
<p>In analogy to how it is reported for the brain by A. Luria, connections of the lowest levels of the architecture of Figure 9 are predefined. Higher-level connections are set via a learning process, concretely via a supervised learning process that was described in detail in . More recent research findings indicate that learning could also already take place at lower levels of<br> perception and that unsupervised learning could be crucial for setting these connections. In, first attempts have been made to develop an unsupervised learning strategy for the model.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 14. Number of artificial creatures that could survive in the external environment
<p>Artificial creatures and the virtual environment are designed and implemented in a C++ platform in which simulations are performed. In the GA algorithm, at First step a population of 100 complex artificial creatures that each of them had 150 neurons were tested and evaluated by GA; each neuron connected to 15 post-synaptic neurons with different axonal conduction delays between every two neurons. In every generation fitness function has been calculated for all population. The initial energy level for each creature is considered as 50. Figure 14 the number of survived chromosome increases with<br> generation progressing.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 11. Crossover with two cut points
<p>Then next generation are produced by combination of the elites (15%), crossover (55%) and mutation (30%) of the initial population. Elites are the best chromosomes which are directly transferred to the next generation. Because of long chromosome length, for crossover, five points are randomly chosen in each parent as cut points. Figure 11 shows a typical crossover with two cut points and Figure 12 illustrates a flowchart for the proposed evolutionary model. Selections are based on Roulette Wheel selection, more detailed information can be found in.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.