This page displays measures for the network Semantic Network.
Network images have nodes colored by group and sized by betweenness centrality.
Concept-Level Measures
These measures were run on the specified network, and produced a vector of values, one value for each Concept node.
Input network: Semantic Network
Measure Min Mean Max Std.Dev Centrality, Authority 0 0 0 0 Centrality, Authority [Unscaled] 0 0 0 0 Centrality, Betweenness 0 0 0 0 Centrality, Betweenness [Unscaled] 0 0 0 0 Centrality, Bonacich Power 0 7.353e-05 0.002 4.224e-04 Centrality, Bonacich Power [Unscaled] 0 0.029 1 0.169 Centrality, Closeness 0.015 0.015 0.015 3.708e-05 Centrality, Closeness [Unscaled] 1.097e-04 1.098e-04 1.114e-04 2.768e-07 Centrality, Contribution 0 8.651e-04 0.029 0.005 Centrality, Contribution [Unscaled] 0 6.117e-04 0.021 0.004 Centrality, Eccentricity 0 0.059 2 0.338 Centrality, Ego Betweenness 0 0 0 0 Centrality, Eigenvector 0 8.651e-04 0.029 0.005 Centrality, Eigenvector [Unscaled] 0 6.117e-04 0.021 0.004 Centrality, Exponential Rank 0.633 0.644 1 0.062 Centrality, Exponential Rank [Unscaled] 0.014 0.015 0.023 0.001 Centrality, Hub 0 0 0 0 Centrality, Hub [Unscaled] 0 0 0 0 Centrality, In-Closeness 0.004 0.004 0.004 2.292e-06 Centrality, In-Closeness [Unscaled] 2.744e-05 2.744e-05 2.754e-05 1.711e-08 Centrality, In-Degree 0 4.390e-04 0.015 0.003 Centrality, In-Degree [Unscaled] 0 0.059 2 0.338 Centrality, In-Inverse Closeness 0 4.390e-04 0.015 0.003 Centrality, In-Inverse Closeness [Unscaled] 0 0.015 0.500 0.084 Centrality, Information 0 0.015 0.500 0.084 Centrality, Information [Unscaled] 0 0.079 2.693 0.455 Centrality, Inverse Closeness 0 4.390e-04 0.015 0.003 Centrality, Inverse Closeness [Unscaled] 0 0.015 0.500 0.084 Centrality, Katz 0 1.478e-04 0.005 8.490e-04 Centrality, Katz [Unscaled] 0 0.029 1 0.169 Centrality, Out-Degree 0 4.390e-04 0.015 0.003 Centrality, Out-Degree [Unscaled] 0 0.059 2 0.338 Centrality, PageRank 0.008 0.015 0.222 0.036 Centrality, Radiality 0 4.390e-04 0.015 0.003 Centrality, Radiality [Unscaled] 0 3.941 134 22.640 Centrality, Total-Degree 0 4.390e-04 0.015 0.003 Centrality, Total-Degree [Unscaled] 0 0.059 2 0.338 Overall Top-Ranked Concept
This chart shows the Concept nodes that are top-ranked overall using the node-level measures listed below. The value shown for a node is the average of its measure values, each of which is first converted into a z-score. The final scores are then normalized to be between zero and one.
There are 68 nodes in the nodeset.
Overall Score Measure List
Centrality, Authority [Semantic Network]
Centrality, Betweenness [Semantic Network]
Centrality, Bonacich Power [Semantic Network]
Centrality, Closeness [Semantic Network]
Centrality, Contribution [Semantic Network]
Centrality, Eccentricity [Semantic Network]
Centrality, Ego Betweenness [Semantic Network]
Centrality, Eigenvector [Semantic Network]
Centrality, Exponential Rank [Semantic Network]
Centrality, Hub [Semantic Network]
Centrality, In-Closeness [Semantic Network]
Centrality, In-Degree [Semantic Network]
Centrality, In-Inverse Closeness [Semantic Network]
Centrality, Information [Semantic Network]
Centrality, Inverse Closeness [Semantic Network]
Centrality, Katz [Semantic Network]
Centrality, Out-Degree [Semantic Network]
Centrality, PageRank [Semantic Network]
Centrality, Authority
A node is authority-central to the extent that its in-links are from nodes that have many out-links. Individuals or organizations that act as authorities are receiving information from a wide range of others each of whom sends information to a large number of others.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network
All nodes have a score of zero.
Centrality, Betweenness
The Betweenness Centrality of node v in a network is defined as: across all node pairs that have a shortest path containing v, the percentage that pass through v. When the data is weighted, the higher the weight the more value the link has. Individuals or organizations that are potentially influential are positioned to broker connections between groups and to bring to bear the influence of one group on another or serve as a gatekeeper between groups. This agent occurs on many of the shortest paths between other agents. The scientific name of this measure is betweenness centrality and it is calculated on agent by agent matrices.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network
All nodes have a score of zero.
Centrality, Bonacich Power
This computes the centrality of each entity based on the centrality of its neighbors. Beta should be chosen such that its absolute value is less than the reciprocal of the largest eigenvalue of N.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network
Rank Concept Value Unscaled 1 hooking 0.002 1 2 rootkit 0.002 1 3 ___level 0 0 4 ___others 0 0 5 _love2 0 0 6 _toc496765173 0 0 7 based 0 0 8 bit 0 0 9 byod 0 0 10 called 0 0 11 centric 0 0 12 cleaver 0 0 13 coded 0 0 14 coder 0 0 15 complete 0 0 16 content 0 0 17 critical 0 0 18 day 0 0 19 defence 0 0 20 defense 0 0 21 defined 0 0 22 digital 0 0 23 driven 0 0 24 enabled 0 0 25 enter 0 0 26 facilitated 0 0 27 factor 0 0 28 feb 0 0 29 global 0 0 30 going 0 0 31 gram 0 0 32 high 0 0 33 interested 0 0 34 large 0 0 35 led 0 0 36 level 0 0 37 making 0 0 38 malicious 0 0 39 management 0 0 40 mandated 0 0 41 mediated 0 0 42 n-phenylacrylamide 0 0 43 nitro 0 0 44 oct 0 0 45 opioid 0 0 46 oriented 0 0 47 owned 0 0 48 powershell 0 0 49 provide 0 0 50 scada 0 0 51 scale 0 0 52 screening 0 0 53 security 0 0 54 separated 0 0 55 set 0 0 56 side 0 0 57 sir 0 0 58 software 0 0 59 solve 0 0 60 strong 0 0 61 style 0 0 62 t3 0 0 63 term 0 0 64 tolerant 0 0 65 type 0 0 66 variant 0 0 67 wanting 0 0 68 year 0 0
Value statistics Min: 0 Mean: 7.353e-05 Lower quartile: 0 Max: 0.002 Std.dev: 4.224e-04 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 0.029 Lower quartile: 0 Max: 1 Std.dev: 0.169 Median: 0 Upper quartile: 0 Centrality, Closeness
The closeness of a node to the other nodes in a network (also called out-closeness). Loosely, Closeness is the inverse of the sum of distances in the network from a node to all other nodes.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network
Rank Concept Value Unscaled 1 hooking 0.015 1.114e-04 2 rootkit 0.015 1.114e-04 3 ___level 0.015 1.097e-04 4 ___others 0.015 1.097e-04 5 _love2 0.015 1.097e-04 6 _toc496765173 0.015 1.097e-04 7 based 0.015 1.097e-04 8 bit 0.015 1.097e-04 9 byod 0.015 1.097e-04 10 called 0.015 1.097e-04 11 centric 0.015 1.097e-04 12 cleaver 0.015 1.097e-04 13 coded 0.015 1.097e-04 14 coder 0.015 1.097e-04 15 complete 0.015 1.097e-04 16 content 0.015 1.097e-04 17 critical 0.015 1.097e-04 18 day 0.015 1.097e-04 19 defence 0.015 1.097e-04 20 defense 0.015 1.097e-04 21 defined 0.015 1.097e-04 22 digital 0.015 1.097e-04 23 driven 0.015 1.097e-04 24 enabled 0.015 1.097e-04 25 enter 0.015 1.097e-04 26 facilitated 0.015 1.097e-04 27 factor 0.015 1.097e-04 28 feb 0.015 1.097e-04 29 global 0.015 1.097e-04 30 going 0.015 1.097e-04 31 gram 0.015 1.097e-04 32 high 0.015 1.097e-04 33 interested 0.015 1.097e-04 34 large 0.015 1.097e-04 35 led 0.015 1.097e-04 36 level 0.015 1.097e-04 37 making 0.015 1.097e-04 38 malicious 0.015 1.097e-04 39 management 0.015 1.097e-04 40 mandated 0.015 1.097e-04 41 mediated 0.015 1.097e-04 42 n-phenylacrylamide 0.015 1.097e-04 43 nitro 0.015 1.097e-04 44 oct 0.015 1.097e-04 45 opioid 0.015 1.097e-04 46 oriented 0.015 1.097e-04 47 owned 0.015 1.097e-04 48 powershell 0.015 1.097e-04 49 provide 0.015 1.097e-04 50 scada 0.015 1.097e-04 51 scale 0.015 1.097e-04 52 screening 0.015 1.097e-04 53 security 0.015 1.097e-04 54 separated 0.015 1.097e-04 55 set 0.015 1.097e-04 56 side 0.015 1.097e-04 57 sir 0.015 1.097e-04 58 software 0.015 1.097e-04 59 solve 0.015 1.097e-04 60 strong 0.015 1.097e-04 61 style 0.015 1.097e-04 62 t3 0.015 1.097e-04 63 term 0.015 1.097e-04 64 tolerant 0.015 1.097e-04 65 type 0.015 1.097e-04 66 variant 0.015 1.097e-04 67 wanting 0.015 1.097e-04 68 year 0.015 1.097e-04
Value statistics Min: 0.015 Mean: 0.015 Lower quartile: 0.015 Max: 0.015 Std.dev: 3.708e-05 Median: 0.015 Upper quartile: 0.015
Unscaled value statistics Min: 1.097e-04 Mean: 1.098e-04 Lower quartile: 1.097e-04 Max: 1.114e-04 Std.dev: 2.768e-07 Median: 1.097e-04 Upper quartile: 1.097e-04 Centrality, Contribution
This computes Eigenvector Centrality on a transformation of the input network. Link values are transformed to be proportional to the dissimilarity of the nodes they connect. The intuition is that a link between two nodes with the same neighbors is not an important link since neither node gains new neighbors by the connection. Specifically, each link is weighted by the inverse of the Jaccard similarity of its nodes.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network
Rank Concept Value Unscaled 1 hooking 0.029 0.021 2 rootkit 0.029 0.021 3 ___level 0 0 4 ___others 0 0 5 _love2 0 0 6 _toc496765173 0 0 7 based 0 0 8 bit 0 0 9 byod 0 0 10 called 0 0 11 centric 0 0 12 cleaver 0 0 13 coded 0 0 14 coder 0 0 15 complete 0 0 16 content 0 0 17 critical 0 0 18 day 0 0 19 defence 0 0 20 defense 0 0 21 defined 0 0 22 digital 0 0 23 driven 0 0 24 enabled 0 0 25 enter 0 0 26 facilitated 0 0 27 factor 0 0 28 feb 0 0 29 global 0 0 30 going 0 0 31 gram 0 0 32 high 0 0 33 interested 0 0 34 large 0 0 35 led 0 0 36 level 0 0 37 making 0 0 38 malicious 0 0 39 management 0 0 40 mandated 0 0 41 mediated 0 0 42 n-phenylacrylamide 0 0 43 nitro 0 0 44 oct 0 0 45 opioid 0 0 46 oriented 0 0 47 owned 0 0 48 powershell 0 0 49 provide 0 0 50 scada 0 0 51 scale 0 0 52 screening 0 0 53 security 0 0 54 separated 0 0 55 set 0 0 56 side 0 0 57 sir 0 0 58 software 0 0 59 solve 0 0 60 strong 0 0 61 style 0 0 62 t3 0 0 63 term 0 0 64 tolerant 0 0 65 type 0 0 66 variant 0 0 67 wanting 0 0 68 year 0 0
Value statistics Min: 0 Mean: 8.651e-04 Lower quartile: 0 Max: 0.029 Std.dev: 0.005 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 6.117e-04 Lower quartile: 0 Max: 0.021 Std.dev: 0.004 Median: 0 Upper quartile: 0 Centrality, Eccentricity
The Eccentricity Centrality of a node is the normalized inverse of its maximum distance to any other node. It is highest when the distance to all other nodes is small.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network
Rank Concept Value 1 hooking 2 2 rootkit 2 3 ___level 0 4 ___others 0 5 _love2 0 6 _toc496765173 0 7 based 0 8 bit 0 9 byod 0 10 called 0 11 centric 0 12 cleaver 0 13 coded 0 14 coder 0 15 complete 0 16 content 0 17 critical 0 18 day 0 19 defence 0 20 defense 0 21 defined 0 22 digital 0 23 driven 0 24 enabled 0 25 enter 0 26 facilitated 0 27 factor 0 28 feb 0 29 global 0 30 going 0 31 gram 0 32 high 0 33 interested 0 34 large 0 35 led 0 36 level 0 37 making 0 38 malicious 0 39 management 0 40 mandated 0 41 mediated 0 42 n-phenylacrylamide 0 43 nitro 0 44 oct 0 45 opioid 0 46 oriented 0 47 owned 0 48 powershell 0 49 provide 0 50 scada 0 51 scale 0 52 screening 0 53 security 0 54 separated 0 55 set 0 56 side 0 57 sir 0 58 software 0 59 solve 0 60 strong 0 61 style 0 62 t3 0 63 term 0 64 tolerant 0 65 type 0 66 variant 0 67 wanting 0 68 year 0
Unscaled value statistics Min: 0 Mean: 0.059 Lower quartile: 0 Max: 2 Std.dev: 0.338 Median: 0 Upper quartile: 0 Centrality, Ego Betweenness
The Ego (Network) Betweenness Centrality of node in a network is its betweenness score within its own ego network. The ego network contains the following: the node itself, its immediate neighbor nodes, and all links between them.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network
All nodes have a score of zero.
Centrality, Eigenvector
Calculates the principal eigenvector of the network. A node is central to the extent that its neighbors are central.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network
Rank Concept Value Unscaled 1 hooking 0.029 0.021 2 rootkit 0.029 0.021 3 ___level 0 0 4 ___others 0 0 5 _love2 0 0 6 _toc496765173 0 0 7 based 0 0 8 bit 0 0 9 byod 0 0 10 called 0 0 11 centric 0 0 12 cleaver 0 0 13 coded 0 0 14 coder 0 0 15 complete 0 0 16 content 0 0 17 critical 0 0 18 day 0 0 19 defence 0 0 20 defense 0 0 21 defined 0 0 22 digital 0 0 23 driven 0 0 24 enabled 0 0 25 enter 0 0 26 facilitated 0 0 27 factor 0 0 28 feb 0 0 29 global 0 0 30 going 0 0 31 gram 0 0 32 high 0 0 33 interested 0 0 34 large 0 0 35 led 0 0 36 level 0 0 37 making 0 0 38 malicious 0 0 39 management 0 0 40 mandated 0 0 41 mediated 0 0 42 n-phenylacrylamide 0 0 43 nitro 0 0 44 oct 0 0 45 opioid 0 0 46 oriented 0 0 47 owned 0 0 48 powershell 0 0 49 provide 0 0 50 scada 0 0 51 scale 0 0 52 screening 0 0 53 security 0 0 54 separated 0 0 55 set 0 0 56 side 0 0 57 sir 0 0 58 software 0 0 59 solve 0 0 60 strong 0 0 61 style 0 0 62 t3 0 0 63 term 0 0 64 tolerant 0 0 65 type 0 0 66 variant 0 0 67 wanting 0 0 68 year 0 0
Value statistics Min: 0 Mean: 8.651e-04 Lower quartile: 0 Max: 0.029 Std.dev: 0.005 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 6.117e-04 Lower quartile: 0 Max: 0.021 Std.dev: 0.004 Median: 0 Upper quartile: 0 Centrality, Exponential Rank
Computes the Exponential Ranking Centrality that defines the centrality of a node as its trustworthiness, which is based on the degree to which other nodes trust it. The link values define initial trust between nodes: positive values indicate trust and negative values distrust (negative link values can be used). This measure is similar to eigenvector centrality.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network
Rank Concept Value Unscaled 1 hooking 1 0.023 2 rootkit 1 0.023 3 ___level 0.633 0.014 4 ___others 0.633 0.014 5 _love2 0.633 0.014 6 _toc496765173 0.633 0.014 7 based 0.633 0.014 8 bit 0.633 0.014 9 byod 0.633 0.014 10 called 0.633 0.014 11 centric 0.633 0.014 12 cleaver 0.633 0.014 13 coded 0.633 0.014 14 coder 0.633 0.014 15 complete 0.633 0.014 16 content 0.633 0.014 17 critical 0.633 0.014 18 day 0.633 0.014 19 defence 0.633 0.014 20 defense 0.633 0.014 21 defined 0.633 0.014 22 digital 0.633 0.014 23 driven 0.633 0.014 24 enabled 0.633 0.014 25 enter 0.633 0.014 26 facilitated 0.633 0.014 27 factor 0.633 0.014 28 feb 0.633 0.014 29 global 0.633 0.014 30 going 0.633 0.014 31 gram 0.633 0.014 32 high 0.633 0.014 33 interested 0.633 0.014 34 large 0.633 0.014 35 led 0.633 0.014 36 level 0.633 0.014 37 making 0.633 0.014 38 malicious 0.633 0.014 39 management 0.633 0.014 40 mandated 0.633 0.014 41 mediated 0.633 0.014 42 n-phenylacrylamide 0.633 0.014 43 nitro 0.633 0.014 44 oct 0.633 0.014 45 opioid 0.633 0.014 46 oriented 0.633 0.014 47 owned 0.633 0.014 48 powershell 0.633 0.014 49 provide 0.633 0.014 50 scada 0.633 0.014 51 scale 0.633 0.014 52 screening 0.633 0.014 53 security 0.633 0.014 54 separated 0.633 0.014 55 set 0.633 0.014 56 side 0.633 0.014 57 sir 0.633 0.014 58 software 0.633 0.014 59 solve 0.633 0.014 60 strong 0.633 0.014 61 style 0.633 0.014 62 t3 0.633 0.014 63 term 0.633 0.014 64 tolerant 0.633 0.014 65 type 0.633 0.014 66 variant 0.633 0.014 67 wanting 0.633 0.014 68 year 0.633 0.014
Value statistics Min: 0.633 Mean: 0.644 Lower quartile: 0.633 Max: 1 Std.dev: 0.062 Median: 0.633 Upper quartile: 0.633
Unscaled value statistics Min: 0.014 Mean: 0.015 Lower quartile: 0.014 Max: 0.023 Std.dev: 0.001 Median: 0.014 Upper quartile: 0.014 Centrality, Hub
A node is hub-central to the extent that its out-links are to nodes that have many in-links. Individuals or organizations that act as hubs are sending information to a wide range of others each of whom has many others reporting to them. Technically, an agent is hub-central if its out-links are to agents that have many other agents sending links to them. The scientific name of this measure is hub centrality and it is calculated on agent by agent matrices.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network
All nodes have a score of zero.
Centrality, In-Closeness
The closeness of all other nodes to a node in the network. Loosely, In-Closeness Centrality is the inverse of the sum of distances in the network to a node and from all other nodes.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network
Rank Concept Value Unscaled 1 hooking 0.004 2.754e-05 2 rootkit 0.004 2.754e-05 3 ___level 0.004 2.744e-05 4 ___others 0.004 2.744e-05 5 _love2 0.004 2.744e-05 6 _toc496765173 0.004 2.744e-05 7 based 0.004 2.744e-05 8 bit 0.004 2.744e-05 9 byod 0.004 2.744e-05 10 called 0.004 2.744e-05 11 centric 0.004 2.744e-05 12 cleaver 0.004 2.744e-05 13 coded 0.004 2.744e-05 14 coder 0.004 2.744e-05 15 complete 0.004 2.744e-05 16 content 0.004 2.744e-05 17 critical 0.004 2.744e-05 18 day 0.004 2.744e-05 19 defence 0.004 2.744e-05 20 defense 0.004 2.744e-05 21 defined 0.004 2.744e-05 22 digital 0.004 2.744e-05 23 driven 0.004 2.744e-05 24 enabled 0.004 2.744e-05 25 enter 0.004 2.744e-05 26 facilitated 0.004 2.744e-05 27 factor 0.004 2.744e-05 28 feb 0.004 2.744e-05 29 global 0.004 2.744e-05 30 going 0.004 2.744e-05 31 gram 0.004 2.744e-05 32 high 0.004 2.744e-05 33 interested 0.004 2.744e-05 34 large 0.004 2.744e-05 35 led 0.004 2.744e-05 36 level 0.004 2.744e-05 37 making 0.004 2.744e-05 38 malicious 0.004 2.744e-05 39 management 0.004 2.744e-05 40 mandated 0.004 2.744e-05 41 mediated 0.004 2.744e-05 42 n-phenylacrylamide 0.004 2.744e-05 43 nitro 0.004 2.744e-05 44 oct 0.004 2.744e-05 45 opioid 0.004 2.744e-05 46 oriented 0.004 2.744e-05 47 owned 0.004 2.744e-05 48 powershell 0.004 2.744e-05 49 provide 0.004 2.744e-05 50 scada 0.004 2.744e-05 51 scale 0.004 2.744e-05 52 screening 0.004 2.744e-05 53 security 0.004 2.744e-05 54 separated 0.004 2.744e-05 55 set 0.004 2.744e-05 56 side 0.004 2.744e-05 57 sir 0.004 2.744e-05 58 software 0.004 2.744e-05 59 solve 0.004 2.744e-05 60 strong 0.004 2.744e-05 61 style 0.004 2.744e-05 62 t3 0.004 2.744e-05 63 term 0.004 2.744e-05 64 tolerant 0.004 2.744e-05 65 type 0.004 2.744e-05 66 variant 0.004 2.744e-05 67 wanting 0.004 2.744e-05 68 year 0.004 2.744e-05
Value statistics Min: 0.004 Mean: 0.004 Lower quartile: 0.004 Max: 0.004 Std.dev: 2.292e-06 Median: 0.004 Upper quartile: 0.004
Unscaled value statistics Min: 2.744e-05 Mean: 2.744e-05 Lower quartile: 2.744e-05 Max: 2.754e-05 Std.dev: 1.711e-08 Median: 2.744e-05 Upper quartile: 2.744e-05 Centrality, In-Degree
The number of links directed into a node normalized by the maximum number of such links. This measure is also called Column Degree Centrality because it is computed by taking the sum of the column values in the input network.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network
Rank Concept Value Unscaled 1 hooking 0.015 2 2 rootkit 0.015 2 3 ___level 0 0 4 ___others 0 0 5 _love2 0 0 6 _toc496765173 0 0 7 based 0 0 8 bit 0 0 9 byod 0 0 10 called 0 0 11 centric 0 0 12 cleaver 0 0 13 coded 0 0 14 coder 0 0 15 complete 0 0 16 content 0 0 17 critical 0 0 18 day 0 0 19 defence 0 0 20 defense 0 0 21 defined 0 0 22 digital 0 0 23 driven 0 0 24 enabled 0 0 25 enter 0 0 26 facilitated 0 0 27 factor 0 0 28 feb 0 0 29 global 0 0 30 going 0 0 31 gram 0 0 32 high 0 0 33 interested 0 0 34 large 0 0 35 led 0 0 36 level 0 0 37 making 0 0 38 malicious 0 0 39 management 0 0 40 mandated 0 0 41 mediated 0 0 42 n-phenylacrylamide 0 0 43 nitro 0 0 44 oct 0 0 45 opioid 0 0 46 oriented 0 0 47 owned 0 0 48 powershell 0 0 49 provide 0 0 50 scada 0 0 51 scale 0 0 52 screening 0 0 53 security 0 0 54 separated 0 0 55 set 0 0 56 side 0 0 57 sir 0 0 58 software 0 0 59 solve 0 0 60 strong 0 0 61 style 0 0 62 t3 0 0 63 term 0 0 64 tolerant 0 0 65 type 0 0 66 variant 0 0 67 wanting 0 0 68 year 0 0
Value statistics Min: 0 Mean: 4.390e-04 Lower quartile: 0 Max: 0.015 Std.dev: 0.003 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 0.059 Lower quartile: 0 Max: 2 Std.dev: 0.338 Median: 0 Upper quartile: 0 Centrality, In-Inverse Closeness
The average closeness of a node to the other nodes in a network considering only paths emanating out from a node. Inverse Closeness is the sum of the inverse distances between a node and all other nodes.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network
Rank Concept Value Unscaled 1 hooking 0.015 0.500 2 rootkit 0.015 0.500 3 ___level 0 0 4 ___others 0 0 5 _love2 0 0 6 _toc496765173 0 0 7 based 0 0 8 bit 0 0 9 byod 0 0 10 called 0 0 11 centric 0 0 12 cleaver 0 0 13 coded 0 0 14 coder 0 0 15 complete 0 0 16 content 0 0 17 critical 0 0 18 day 0 0 19 defence 0 0 20 defense 0 0 21 defined 0 0 22 digital 0 0 23 driven 0 0 24 enabled 0 0 25 enter 0 0 26 facilitated 0 0 27 factor 0 0 28 feb 0 0 29 global 0 0 30 going 0 0 31 gram 0 0 32 high 0 0 33 interested 0 0 34 large 0 0 35 led 0 0 36 level 0 0 37 making 0 0 38 malicious 0 0 39 management 0 0 40 mandated 0 0 41 mediated 0 0 42 n-phenylacrylamide 0 0 43 nitro 0 0 44 oct 0 0 45 opioid 0 0 46 oriented 0 0 47 owned 0 0 48 powershell 0 0 49 provide 0 0 50 scada 0 0 51 scale 0 0 52 screening 0 0 53 security 0 0 54 separated 0 0 55 set 0 0 56 side 0 0 57 sir 0 0 58 software 0 0 59 solve 0 0 60 strong 0 0 61 style 0 0 62 t3 0 0 63 term 0 0 64 tolerant 0 0 65 type 0 0 66 variant 0 0 67 wanting 0 0 68 year 0 0
Value statistics Min: 0 Mean: 4.390e-04 Lower quartile: 0 Max: 0.015 Std.dev: 0.003 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 0.015 Lower quartile: 0 Max: 0.500 Std.dev: 0.084 Median: 0 Upper quartile: 0 Centrality, Information
Calculates the Stephenson and Zelen information centrality measure for each node.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network
Rank Concept Value Unscaled 1 hooking 0.500 2.693 2 rootkit 0.500 2.693 3 ___level 0 0 4 ___others 0 0 5 _love2 0 0 6 _toc496765173 0 0 7 based 0 0 8 bit 0 0 9 byod 0 0 10 called 0 0 11 centric 0 0 12 cleaver 0 0 13 coded 0 0 14 coder 0 0 15 complete 0 0 16 content 0 0 17 critical 0 0 18 day 0 0 19 defence 0 0 20 defense 0 0 21 defined 0 0 22 digital 0 0 23 driven 0 0 24 enabled 0 0 25 enter 0 0 26 facilitated 0 0 27 factor 0 0 28 feb 0 0 29 global 0 0 30 going 0 0 31 gram 0 0 32 high 0 0 33 interested 0 0 34 large 0 0 35 led 0 0 36 level 0 0 37 making 0 0 38 malicious 0 0 39 management 0 0 40 mandated 0 0 41 mediated 0 0 42 n-phenylacrylamide 0 0 43 nitro 0 0 44 oct 0 0 45 opioid 0 0 46 oriented 0 0 47 owned 0 0 48 powershell 0 0 49 provide 0 0 50 scada 0 0 51 scale 0 0 52 screening 0 0 53 security 0 0 54 separated 0 0 55 set 0 0 56 side 0 0 57 sir 0 0 58 software 0 0 59 solve 0 0 60 strong 0 0 61 style 0 0 62 t3 0 0 63 term 0 0 64 tolerant 0 0 65 type 0 0 66 variant 0 0 67 wanting 0 0 68 year 0 0
Value statistics Min: 0 Mean: 0.015 Lower quartile: 0 Max: 0.500 Std.dev: 0.084 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 0.079 Lower quartile: 0 Max: 2.693 Std.dev: 0.455 Median: 0 Upper quartile: 0 Centrality, Inverse Closeness
The average closeness of a node to the other nodes in a network (also called out-inverse closeness centrality). Inverse Closeness is the average inverse distance from a node and to all other nodes.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network
Rank Concept Value Unscaled 1 hooking 0.015 0.500 2 rootkit 0.015 0.500 3 ___level 0 0 4 ___others 0 0 5 _love2 0 0 6 _toc496765173 0 0 7 based 0 0 8 bit 0 0 9 byod 0 0 10 called 0 0 11 centric 0 0 12 cleaver 0 0 13 coded 0 0 14 coder 0 0 15 complete 0 0 16 content 0 0 17 critical 0 0 18 day 0 0 19 defence 0 0 20 defense 0 0 21 defined 0 0 22 digital 0 0 23 driven 0 0 24 enabled 0 0 25 enter 0 0 26 facilitated 0 0 27 factor 0 0 28 feb 0 0 29 global 0 0 30 going 0 0 31 gram 0 0 32 high 0 0 33 interested 0 0 34 large 0 0 35 led 0 0 36 level 0 0 37 making 0 0 38 malicious 0 0 39 management 0 0 40 mandated 0 0 41 mediated 0 0 42 n-phenylacrylamide 0 0 43 nitro 0 0 44 oct 0 0 45 opioid 0 0 46 oriented 0 0 47 owned 0 0 48 powershell 0 0 49 provide 0 0 50 scada 0 0 51 scale 0 0 52 screening 0 0 53 security 0 0 54 separated 0 0 55 set 0 0 56 side 0 0 57 sir 0 0 58 software 0 0 59 solve 0 0 60 strong 0 0 61 style 0 0 62 t3 0 0 63 term 0 0 64 tolerant 0 0 65 type 0 0 66 variant 0 0 67 wanting 0 0 68 year 0 0
Value statistics Min: 0 Mean: 4.390e-04 Lower quartile: 0 Max: 0.015 Std.dev: 0.003 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 0.015 Lower quartile: 0 Max: 0.500 Std.dev: 0.084 Median: 0 Upper quartile: 0 Centrality, Katz
This computes the centrality of each entity based on the centrality of its neighbors. Alpha should be chosen such that its absolute value is less than the reciprocal of the largest eigenvalue of N.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network
Rank Concept Value Unscaled 1 hooking 0.005 1 2 rootkit 0.005 1 3 ___level 0 0 4 ___others 0 0 5 _love2 0 0 6 _toc496765173 0 0 7 based 0 0 8 bit 0 0 9 byod 0 0 10 called 0 0 11 centric 0 0 12 cleaver 0 0 13 coded 0 0 14 coder 0 0 15 complete 0 0 16 content 0 0 17 critical 0 0 18 day 0 0 19 defence 0 0 20 defense 0 0 21 defined 0 0 22 digital 0 0 23 driven 0 0 24 enabled 0 0 25 enter 0 0 26 facilitated 0 0 27 factor 0 0 28 feb 0 0 29 global 0 0 30 going 0 0 31 gram 0 0 32 high 0 0 33 interested 0 0 34 large 0 0 35 led 0 0 36 level 0 0 37 making 0 0 38 malicious 0 0 39 management 0 0 40 mandated 0 0 41 mediated 0 0 42 n-phenylacrylamide 0 0 43 nitro 0 0 44 oct 0 0 45 opioid 0 0 46 oriented 0 0 47 owned 0 0 48 powershell 0 0 49 provide 0 0 50 scada 0 0 51 scale 0 0 52 screening 0 0 53 security 0 0 54 separated 0 0 55 set 0 0 56 side 0 0 57 sir 0 0 58 software 0 0 59 solve 0 0 60 strong 0 0 61 style 0 0 62 t3 0 0 63 term 0 0 64 tolerant 0 0 65 type 0 0 66 variant 0 0 67 wanting 0 0 68 year 0 0
Value statistics Min: 0 Mean: 1.478e-04 Lower quartile: 0 Max: 0.005 Std.dev: 8.490e-04 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 0.029 Lower quartile: 0 Max: 1 Std.dev: 0.169 Median: 0 Upper quartile: 0 Centrality, Out-Degree
For any node, e.g. an individual or a resource, the out-links are the connections that the node of interest has to other nodes. For example, imagine an agent by knowledge network where the number of out-links an agent would have is the number of pieces of knowledge it is connected to. The scientific name of this measure is out-degree and it can be calculated on any network.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network
Rank Concept Value Unscaled 1 hooking 0.015 2 2 rootkit 0.015 2 3 ___level 0 0 4 ___others 0 0 5 _love2 0 0 6 _toc496765173 0 0 7 based 0 0 8 bit 0 0 9 byod 0 0 10 called 0 0 11 centric 0 0 12 cleaver 0 0 13 coded 0 0 14 coder 0 0 15 complete 0 0 16 content 0 0 17 critical 0 0 18 day 0 0 19 defence 0 0 20 defense 0 0 21 defined 0 0 22 digital 0 0 23 driven 0 0 24 enabled 0 0 25 enter 0 0 26 facilitated 0 0 27 factor 0 0 28 feb 0 0 29 global 0 0 30 going 0 0 31 gram 0 0 32 high 0 0 33 interested 0 0 34 large 0 0 35 led 0 0 36 level 0 0 37 making 0 0 38 malicious 0 0 39 management 0 0 40 mandated 0 0 41 mediated 0 0 42 n-phenylacrylamide 0 0 43 nitro 0 0 44 oct 0 0 45 opioid 0 0 46 oriented 0 0 47 owned 0 0 48 powershell 0 0 49 provide 0 0 50 scada 0 0 51 scale 0 0 52 screening 0 0 53 security 0 0 54 separated 0 0 55 set 0 0 56 side 0 0 57 sir 0 0 58 software 0 0 59 solve 0 0 60 strong 0 0 61 style 0 0 62 t3 0 0 63 term 0 0 64 tolerant 0 0 65 type 0 0 66 variant 0 0 67 wanting 0 0 68 year 0 0
Value statistics Min: 0 Mean: 4.390e-04 Lower quartile: 0 Max: 0.015 Std.dev: 0.003 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 0.059 Lower quartile: 0 Max: 2 Std.dev: 0.338 Median: 0 Upper quartile: 0 Centrality, PageRank
Calculates the importance of a node based on the importance of its in-coming neighbors. The input network links are normalized and interpreted as the probability of a transition from node i to node j. The PageRank of a node can be interpreted as the fraction of times a node would be visitied when traversing the network according to the network of probabilities.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network
Rank Concept Value 1 hooking 0.222 2 rootkit 0.222 3 ___level 0.008 4 ___others 0.008 5 _love2 0.008 6 _toc496765173 0.008 7 based 0.008 8 bit 0.008 9 byod 0.008 10 called 0.008 11 centric 0.008 12 cleaver 0.008 13 coded 0.008 14 coder 0.008 15 complete 0.008 16 content 0.008 17 critical 0.008 18 day 0.008 19 defence 0.008 20 defense 0.008 21 defined 0.008 22 digital 0.008 23 driven 0.008 24 enabled 0.008 25 enter 0.008 26 facilitated 0.008 27 factor 0.008 28 feb 0.008 29 global 0.008 30 going 0.008 31 gram 0.008 32 high 0.008 33 interested 0.008 34 large 0.008 35 led 0.008 36 level 0.008 37 making 0.008 38 malicious 0.008 39 management 0.008 40 mandated 0.008 41 mediated 0.008 42 n-phenylacrylamide 0.008 43 nitro 0.008 44 oct 0.008 45 opioid 0.008 46 oriented 0.008 47 owned 0.008 48 powershell 0.008 49 provide 0.008 50 scada 0.008 51 scale 0.008 52 screening 0.008 53 security 0.008 54 separated 0.008 55 set 0.008 56 side 0.008 57 sir 0.008 58 software 0.008 59 solve 0.008 60 strong 0.008 61 style 0.008 62 t3 0.008 63 term 0.008 64 tolerant 0.008 65 type 0.008 66 variant 0.008 67 wanting 0.008 68 year 0.008
Unscaled value statistics Min: 0.008 Mean: 0.015 Lower quartile: 0.008 Max: 0.222 Std.dev: 0.036 Median: 0.008 Upper quartile: 0.008 Centrality, Radiality
The Radiality Centrality of a node is the normalized sum of its closeness to all other nodes. The closeness of a node u to another node v is the network diameter minus the shortest path distance from u to v.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network
Rank Concept Value Unscaled 1 hooking 0.015 134 2 rootkit 0.015 134 3 ___level 0 0 4 ___others 0 0 5 _love2 0 0 6 _toc496765173 0 0 7 based 0 0 8 bit 0 0 9 byod 0 0 10 called 0 0 11 centric 0 0 12 cleaver 0 0 13 coded 0 0 14 coder 0 0 15 complete 0 0 16 content 0 0 17 critical 0 0 18 day 0 0 19 defence 0 0 20 defense 0 0 21 defined 0 0 22 digital 0 0 23 driven 0 0 24 enabled 0 0 25 enter 0 0 26 facilitated 0 0 27 factor 0 0 28 feb 0 0 29 global 0 0 30 going 0 0 31 gram 0 0 32 high 0 0 33 interested 0 0 34 large 0 0 35 led 0 0 36 level 0 0 37 making 0 0 38 malicious 0 0 39 management 0 0 40 mandated 0 0 41 mediated 0 0 42 n-phenylacrylamide 0 0 43 nitro 0 0 44 oct 0 0 45 opioid 0 0 46 oriented 0 0 47 owned 0 0 48 powershell 0 0 49 provide 0 0 50 scada 0 0 51 scale 0 0 52 screening 0 0 53 security 0 0 54 separated 0 0 55 set 0 0 56 side 0 0 57 sir 0 0 58 software 0 0 59 solve 0 0 60 strong 0 0 61 style 0 0 62 t3 0 0 63 term 0 0 64 tolerant 0 0 65 type 0 0 66 variant 0 0 67 wanting 0 0 68 year 0 0
Value statistics Min: 0 Mean: 4.390e-04 Lower quartile: 0 Max: 0.015 Std.dev: 0.003 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 3.941 Lower quartile: 0 Max: 134 Std.dev: 22.640 Median: 0 Upper quartile: 0 Centrality, Total-Degree
The Total-Degree Centrality of a node is the normalized sum of its In-Degree and Out-Degree.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network
Rank Concept Value Unscaled 1 hooking 0.015 2 2 rootkit 0.015 2 3 ___level 0 0 4 ___others 0 0 5 _love2 0 0 6 _toc496765173 0 0 7 based 0 0 8 bit 0 0 9 byod 0 0 10 called 0 0 11 centric 0 0 12 cleaver 0 0 13 coded 0 0 14 coder 0 0 15 complete 0 0 16 content 0 0 17 critical 0 0 18 day 0 0 19 defence 0 0 20 defense 0 0 21 defined 0 0 22 digital 0 0 23 driven 0 0 24 enabled 0 0 25 enter 0 0 26 facilitated 0 0 27 factor 0 0 28 feb 0 0 29 global 0 0 30 going 0 0 31 gram 0 0 32 high 0 0 33 interested 0 0 34 large 0 0 35 led 0 0 36 level 0 0 37 making 0 0 38 malicious 0 0 39 management 0 0 40 mandated 0 0 41 mediated 0 0 42 n-phenylacrylamide 0 0 43 nitro 0 0 44 oct 0 0 45 opioid 0 0 46 oriented 0 0 47 owned 0 0 48 powershell 0 0 49 provide 0 0 50 scada 0 0 51 scale 0 0 52 screening 0 0 53 security 0 0 54 separated 0 0 55 set 0 0 56 side 0 0 57 sir 0 0 58 software 0 0 59 solve 0 0 60 strong 0 0 61 style 0 0 62 t3 0 0 63 term 0 0 64 tolerant 0 0 65 type 0 0 66 variant 0 0 67 wanting 0 0 68 year 0 0
Value statistics Min: 0 Mean: 4.390e-04 Lower quartile: 0 Max: 0.015 Std.dev: 0.003 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 0.059 Lower quartile: 0 Max: 2 Std.dev: 0.338 Median: 0 Upper quartile: 0


Iker Casillas claims Cristiano Ronaldo should not win 2019 Ballon d’Or
by HarigovindFormer Real Madrid goalkeeper and teammate of Cristiano Ronaldo, Iker Casillas has claimed that the Portuguese legend does not really deserve to win the 2019 Ballon d’Or award.
Ronaldo, who has previously won the prestigious award five times – same as Lionel Messi – is back in contention this year, to add a sixth Ballon d’Or to his collection. As usual, Messi is one of his opponents, while Liverpool’s Virgil van Dijk has also been tipped to win the award.
In every one of the past twelve years or so, Ronaldo had remained a serious contender for the Ballon d’Or – but unfortunately, this time he is said to be below Messi and van Dijk in the pecking order.
And Casillas also feels that the 34-year-old winning the 2019 Ballon d’Or would be illogical, as the others are considered to be more deserving than the Juventus star.
“Van Dijk was the best player for UEFA. Messi was the best for FIFA. Imagine – if Cristiano wins the Ballon d’Or, it would seem to me that the guidelines we follow in football to give prizes do not have much logic, right? Friday reflection. #HappyWeekend,” the 2010 FIFA World Cup winner took to Twitter to express his opinion.
Check out the tweet below:
Messi and Ronaldo first entered the final Ballon d’Or shortlist, in 2007 – when Kaka won the award.
In the ten years that followed, both players lifted five Ballon d’Or‘s each, and their monopoly was finally broken by Real Madrid’s Luka Modric last year.
Will Virgil van Dijk be able to beat them to the award this year? All we can do right now, is to wait and watch.


Pass your mouse on the markers to see the plugin in action
Left is standard over event
Right is over intent event
Pass your mouse on the markers to see the plugin in action


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Connected Home over IP
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Provides a generic implementation of WiFi-specific ConnectivityManager features for platforms that don't support WiFi. More...
#include <include/platform/internal/GenericConnectivityManagerImpl_NoWiFi.h>
Static Public Member Functions | |
| static const char * | _WiFiStationModeToStr (ConnectivityManager::WiFiStationMode mode) |
| static const char * | _WiFiAPModeToStr (ConnectivityManager::WiFiAPMode mode) |
| static const char * | _WiFiStationStateToStr (ConnectivityManager::WiFiStationState state) |
| static const char * | _WiFiAPStateToStr (ConnectivityManager::WiFiAPState state) |
Provides a generic implementation of WiFi-specific ConnectivityManager features for platforms that don't support WiFi.
This class is intended to be inherited (directly or indirectly) by the ConnectivityManagerImpl class, which also appears as the template's ImplClass parameter.
The members of this class are all inlined methods that do nothing, and return static return values. This allows the compiler to optimize away dead code without the use of #ifdef's. For example:
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Here in Australia, zopiclone is sold as Immovane. My dad does a lot of travelling and was given a prescription by his doctor. I was going through a fairly rough period of my life at this stage, and I was drinking excessively and smoking a lot of weed and taking other stuff whenever I could afford it. Anyway, one day I found dad's Immovane and checked out dosages on the net. They were 7.5 mg tablets and I figured it was (relatively) safe to take all nine tablets that were there (for a total dose of 67.5 mg)
Well, put it this way, it's an experience my parents would rather forget. As for me, it barely matters because I can't remember it anyway. I remember popping the nine tablets, then watching some television. After this, all i have is two memories.  I have had (like most people) memory loss attributed to booze, but nothing like this. My two memories were of snapping a comb in half, apparently because I was really angry, and at talking to my (now ex) girlfriend on the phone, aggressively asking her about her past.
That's the interesting point I want to make about this drug, zopiclone. It's a sleeping tablet, but an excessive dose (67.5mg) did NOT send me to sleep. In fact, I took them about 7:30 pm and didn't go to bed until 2:00 am. During this time I threatened my family, threatened my girlfriend, worried my parents to the point of my mum ringing the police (only to be told that they couldn't do anything unless I actually hurt someone or did some damage) my parents also had to disconnect the phones to stop me making phone calls, hide the car keys cause I was threatening to drive, lock up the bikes because I wanted to go for a ride, and I also confessed everything to my parents about my previous drug use, which included a confession about IV drug use. I don't remember any of this, I was told about it the next day. Apparently, I told my girlfriend I 'felt like my head was gonna explode.'
So, there, that's the tale. Also, the next day was hazy and lethargic, and all food/drink had a metallic tang to it (a common side effect I've since read about).
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


avahi-autoipd
Section: Maintenance Commands (8)Updated: User
Index Return to Main Contents
NAME
avahi-autoipd - IPv4LL network address configuration daemonSYNOPSIS
avahi-autoipd [options] interfaceavahi-autoipd --kill interface
avahi-autoipd --refresh interface
avahi-autoipd --check interface
DESCRIPTION
avahi-autoipd implements IPv4LL, "Dynamic Configuration of IPv4 Link-Local Addresses" (IETF RFC3927), a protocol for automatic IP address configuration from the link-local <IP_ADDRESS>/16 range without the need for a central server. It is primarily intended to be used in ad-hoc networks which lack a DHCP server.IPv4LL is part of the Zeroconf stack.
avahi-autoipd can be used as stand-alone address allocator or as plugin for a DHCP client such as ISC's dhclient, where it can be used as fallback solution if no DHCP server is found.
To allow communication between hosts that have only an IPv4LL address assigned and hosts that only have a routable IP address assigned you may add the following routes to both network configurations:
route add -net <IP_ADDRESS> netmask <IP_ADDRESS> dev eth0 metric 99
route add default dev eth0 metric 99
See http://developer.apple.com/qa/qa2004/qa1357.html for more information.
OPTIONS
- -D | --daemonize
- Daemonize after startup. Implies --syslog.
- -k | --kill
- Kill an already running avahi-autoipd on the specified network interface. (Equivalent to sending a SIGTERM)
- -r | --refresh
- Tell an already running avahi-autoipd to reannounce the acquired IP address on the specified network interface. (Equivalent to sending a SIGHUP)
- -c | --check
- Return 0 as return code if avahi-autoipd is already running for the specified network interface.
- -s | --syslog
- Log to syslog instead of STDERR. Implied by --daemonize.
- --debug
- Enable verbose mode.
- --no-drop-root
- Don't drop root priviliges after startup. We recommend not to use this option.
- --no-chroot
- Don't chroot(2) the daemon. This option is only available when compiled with chroot() support.
- -S | --start=
- Try to acquire the specified IP address, which must be from the IPv4LL range <IP_ADDRESS>/16.
- -w | --wait
- Wait until a IP address has been sucessfully acquired before detaching. Only valid in combination with --daemonize.
- --force-bind
- Acquire an IPv4LL address, even if a routable address has been configured on the interface.
- --no-proc-title
- Don't change the process name while running. Unless this option is specified avahi-autoipd will reflect its current state and the IP address in the process title.
- -h | --help
- Show help.
- -v | --version
- Show version information.
FILES
/etc/avahi/avahi-autoipd.action: the script to run when an IP address as been acquired or is lost.SIGNALS
SIGINT, SIGTERM, SIGQUIT: avahi-autoipd will shutdown. (Same as --kill)SIGHUP: avahi-autoipd will reannounce the acquired IP address. (Same as --refresh)
AUTHORS
The Avahi Developers <avahi (at) lists (dot) freedesktop (dot) org>; Avahi is available from http://avahi.org/SEE ALSO
autoipd.action(8), dhclient(8)http://avahi.org/wiki/AvahiAutoipd documents how avahi-autoipd is best packaged and integrated into distributions.
COMMENTS
This man page was written using xml2man(1) by Oliver Kurth.
Index
This document was created by man2html, using the manual pages.
Time: 05:34:24 GMT, December 24, 2015


Pseudorandom Permutation (PRP)¶
Pseudorandom permutations are bijective pseudorandom functions that are efficiently invertible. As such, they are of the pseudorandom function type and their input length always equals their output length. In addition (and unlike general pseudorandom functions), they are efficiently invertible.
The PseudorandomPermutation
abstract class¶
The PseudorandomPermutation
class derives the PseudorandomFunction
abstract class, and adds the following functionality.
-
void
PseudorandomPermutation::
invertBlock
(const vector<byte> &inBytes, int inOff, vector<byte> &outBytes, int outOff)¶ Inverts the permutation using the given key.
This function is a part of the PseudorandomPermutation class since any PseudorandomPermutation must be efficiently invertible (given the key). For block ciphers, for example, the length is known in advance and so there is no need to specify the length.
Parameters: - inBytes – input bytes to invert.
- inOff – input offset in the inBytes vector
- outBytes – output bytes. The resulted bytes of invert
- outOff – output offset in the outBytes vector to put the result from
-
void
PseudorandomPermutation::
invertBlock
(const vector<byte> &inBytes, int inOff, vector<byte> &outBytes, int outOff, int len)¶ Inverts the permutation using the given key.
Since PseudorandomPermutation can also have varying input and output length (although the input and the output should be the same length), the common parameter
len
of the input and the output is needed.Parameters: - inBytes – input bytes to invert.
- inOff – input offset in the inBytes vector
- outBytes – output bytes. The resulted bytes of invert
- outOff – output offset in the outBytes vector to put the result from
- len – the length of the input and the output
Basic Usage¶
//Create secretKey and in, out, inv vectors
...
//create the prp object
PseudorandomPermutation* prp = new OpenSSLAES();
//set the key
prp->setKey(secretKey);
//run the permutation on a block-size prefix of in
prp->computeBlock(in, 0, out, 0);
//invert the permutation
prp->invertBlock(out, 0, inv, 0);
Pseudorandom Permutation with Varying Input-Output Lengths¶
A pseudorandom permutation with varying input/output lengths does not have pre-defined input/output lengths. The input and output length (that must be equal) may be different for each function call. The length of the input/output is determined upon user request.
We implement the Luby-Rackoff algorithm as an example of PRP with varying I/O lengths. The class that implements the algorithm is LubyRackoffPrpFromPrfVarying
.
How to use the Varying Input-Output Length PRP¶
//Create secretKey and in, out vectors
...
//create the prp object
PseudorandomPermutation* prp = new LubyRackoffPrpFromPrfVarying();
//set the key
prp->setKey(secretKey);
//invert the permutation with input in and output out of common size 20.
prp->invertBlock(in, 0, out, 0, 20);
Supported Prp Types¶
In this section we present the prp functions provided by libscapi.
The OpenSSL implementation:
| Class Name | Class Location |
|---|---|
| OpenSSLAES | libscapi/include/primitives/PrfOpenSSL.hpp |
| OpenSSLTripleDes | libscapi/include/primitives/PrfOpenSSL.hpp |


Coffee House Chains
A ranking of selected leading coffee house chains worldwide
Group By
Stores worldwide
Revenue in billion U.S. dollars
Sort


Design Rule Verification Report
Date:
3/31/2018
Time:
11:30:27 PM
Elapsed Time:
00:00:01
Filename:
C:\Users\Public\Documents\Altium\AD18\ESP8266-01-Breakout\ESP8266-01-Breakout.PcbDoc
Warnings:
0
Rule Violations:
15
Summary
Warnings
Count
Total
0
Rule Violations
Count
Clearance Constraint (Gap=6mil) (All),(All)
0
Short-Circuit Constraint (Allowed=No) (All),(All)
0
Un-Routed Net Constraint ( (All) )
0
Modified Polygon (Allow modified: No), (Allow shelved: No)
0
Width Constraint (Min=6mil) (Max=100mil) (Preferred=10mil) (All)
0
Power Plane Connect Rule(Relief Connect )(Expansion=20mil) (Conductor Width=10mil) (Air Gap=10mil) (Entries=4) (All)
0
Minimum Annular Ring (Minimum=5mil) (All)
0
Hole Size Constraint (Min=10mil) (Max=260mil) (All)
0
Hole To Hole Clearance (Gap=10mil) (All),(All)
0
Minimum Solder Mask Sliver (Gap=10mil) (All),(All)
0
Silk To Solder Mask (Clearance=10mil) (IsPad),(All)
15
Silk to Silk (Clearance=10mil) (Disabled)(All),(All)
0
Net Antennae (Tolerance=0mil) (All)
0
Height Constraint (Min=0mil) (Max=1000mil) (Prefered=500mil) (All)
0
Total
15
Silk To Solder Mask (Clearance=10mil) (IsPad),(All)
Silk To Solder Mask Clearance Constraint: (Collision < 10mil) Between Pad D1--(208mil,795mil) on Top Layer And Track (113.504mil,765.472mil)(177mil,765.472mil) on Top Overlay [Top Overlay] to [Top Solder] clearance [0mil]
Silk To Solder Mask Clearance Constraint: (2.5mil < 10mil) Between Pad D1--(208mil,795mil) on Top Layer And Track (113.504mil,825mil)(173mil,825mil) on Top Overlay [Top Overlay] to [Top Solder] clearance [2.5mil]
Silk To Solder Mask Clearance Constraint: (Collision < 10mil) Between Pad D1-+(82.008mil,795mil) on Top Layer And Track (113.504mil,765.472mil)(177mil,765.472mil) on Top Overlay [Top Overlay] to [Top Solder] clearance [0mil]
Silk To Solder Mask Clearance Constraint: (Collision < 10mil) Between Pad D1-+(82.008mil,795mil) on Top Layer And Track (113.504mil,825mil)(173mil,825mil) on Top Overlay [Top Overlay] to [Top Solder] clearance [0mil]
Silk To Solder Mask Clearance Constraint: (4.485mil < 10mil) Between Pad P1-1(100mil,100mil) on Multi-Layer And Track (50mil,130mil)(70mil,150mil) on Top Overlay [Top Overlay] to [Top Solder] clearance [4.485mil]
Silk To Solder Mask Clearance Constraint: (0.95mil < 10mil) Between Pad P1-1(100mil,100mil) on Multi-Layer And Track (50mil,75mil)(75mil,50mil) on Top Overlay [Top Overlay] to [Top Solder] clearance [0.95mil]
Silk To Solder Mask Clearance Constraint: (6.89mil < 10mil) Between Pad R1-1(464.567mil,850mil) on Top Layer And Track (498.032mil,825.394mil)(501.969mil,825.394mil) on Top Overlay [Top Overlay] to [Top Solder] clearance [6.89mil]
Silk To Solder Mask Clearance Constraint: (6.89mil < 10mil) Between Pad R1-1(464.567mil,850mil) on Top Layer And Track (498.032mil,874.606mil)(501.969mil,874.606mil) on Top Overlay [Top Overlay] to [Top Solder] clearance [6.89mil]
Silk To Solder Mask Clearance Constraint: (6.89mil < 10mil) Between Pad R1-2(535.433mil,850mil) on Top Layer And Track (498.032mil,825.394mil)(501.969mil,825.394mil) on Top Overlay [Top Overlay] to [Top Solder] clearance [6.89mil]
Silk To Solder Mask Clearance Constraint: (7.583mil < 10mil) Between Pad R1-2(535.433mil,850mil) on Top Layer And Track (498.032mil,874.606mil)(501.969mil,874.606mil) on Top Overlay [Top Overlay] to [Top Solder] clearance [7.583mil]
Silk To Solder Mask Clearance Constraint: (6.89mil < 10mil) Between Pad R2-1(535mil,765mil) on Top Layer And Track (497.598mil,740.394mil)(501.535mil,740.394mil) on Top Overlay [Top Overlay] to [Top Solder] clearance [6.89mil]
Silk To Solder Mask Clearance Constraint: (7.583mil < 10mil) Between Pad R2-1(535mil,765mil) on Top Layer And Track (497.598mil,789.606mil)(501.535mil,789.606mil) on Top Overlay [Top Overlay] to [Top Solder] clearance [7.583mil]
Silk To Solder Mask Clearance Constraint: (6.349mil < 10mil) Between Pad R2-2(464.134mil,765mil) on Top Layer And Text "R2" (370mil,745mil) on Top Overlay [Top Overlay] to [Top Solder] clearance [6.349mil]
Silk To Solder Mask Clearance Constraint: (6.89mil < 10mil) Between Pad R2-2(464.134mil,765mil) on Top Layer And Track (497.598mil,740.394mil)(501.535mil,740.394mil) on Top Overlay [Top Overlay] to [Top Solder] clearance [6.89mil]
Silk To Solder Mask Clearance Constraint: (6.89mil < 10mil) Between Pad R2-2(464.134mil,765mil) on Top Layer And Track (497.598mil,789.606mil)(501.535mil,789.606mil) on Top Overlay [Top Overlay] to [Top Solder] clearance [6.89mil]
Back to top


| No. | Role | Responsibility |
|---|---|---|
| 1 | Developer |
|
| 2 | Manager |
|
| Current, mA | 15 | 16 | 15 | 16 |
| Voltage, V | 3,0 | 3,1 | 3,1 | 3,0 |
| Name | John Doe |
| Age | 32 |
| Company | Acme Inc. |
| Position | Senior Software Developer |
| Name | Jane Doe |
| Age | 29 |
| Company | Acme Inc. |
| Position | Project Manager |
pandoc-crossref
.:first-child
.


amber$ cd main/CSS/\[CSS\]\ how\ to\ make\ border\ of\ the\ element\ collapse\?
[CSS] how to make border of the element collapse?
We all know that border-collapse is used in the table to make the table
or rows' or elements' border collapse. But what if I want the border of
my elements collapse? This could simply be achieved by setting the
margin to negative value while the value is just the thickness of the
border.
Here is the demo:


Task 1: Basic Navigation
Part 1: Activating TalkBack
This tutorial aims to teach users how to use TalkBack to navigate common applications on their phone. Thus, it is recommended for you to activate TalkBack first before proceeding. To do this, go to Settings, then go to Accessibility, and finally TalkBack. Then, turn on TalkBack and return to this application
Part 2: Explore by touch
When enabled, Explore by touch lets you hear what's under your finger. Move your finger over the items in the center of your screen. After you have explored five items, the lesson will continue.
Part 3: Double-tap to activate
To activate the last item you heard, double-tap anywhere. Find Item 4 and activate it now. After that, activate Next to continue.
Part 4: Navigating Lessons
Tutorial lessons are arranged as follows. The top area has the lesson, which you can explore and read at any time. It also has a Tutorial Home button in the top left corner. The middle has a practice area. The bottom has a progress indicator, and lesson navigation buttons. Activate Next now to continue.
Part 5: Swipe to explore
You can also expore by swiping the screen with your finger. Swiping left generally explores up and to the left, while swiping right explores down and to the right. Try it now.
Part 6: Practice
To hear what's under your finger, move it across the screen. Double-tap anywhere to activate what you hear. Swipe left or right to explore.
| Item 1 |
Item 2 |
Item 3 |
Item 4 |
| Item 5 |
Item 6 |
Item 7 |
Item 8 |
| Item 9 |
Item 10 |
Item 11 |
Item 12 |


--- layout: default title: MAYDAY.US - Make your voice heard. Get involved in your district. permalink: /letters-to-the-editor/ meta_content: |
Let's send a message to Congress by placing a letter to the editor in a local paper. With a clear ask of a specific representative or Senator, a letter to the editor immediately gets attention. Write a letter urging your member of Congress to fight political corruption, and we’ll take care of submitting it to your local newspapers.


Tyson revenue, profit, miss as slaughterhouse fire hits beef business
Tyson Foods Inc fell short of Wall Street estimates for quarterly sales on Tuesday, as the No.1 meat processor in the United States was hit by weakness in its beef business, its biggest segment, due to a drop in cattle processing capacity.
REUTERS: Tyson Foods Inc fell short of Wall Street estimates for quarterly sales and profit on Tuesday as a fire at one of its slaughterhouses hit volumes in its beef business, the company's biggest segment.
Shares of the maker of Ball Park hotdogs and Jimmy Dean sausages fell about 4per cent before the bell. They have gained nearly 55per cent this year.
The fire at Tyson's Holcomb, Kansas, slaughterhouse in August left meat buyers for restaurants, food service companies and grocery chains scrambling for beef.
As a result, volumes in the business fell 4.2per cent in the fourth quarter ended Sept. 28, with sales down 1.3per cent.
The fire also resulted in US$31 million of net incremental costs, the company said.
Excluding items, the company earned US$1.21 per share, compared with the average analyst estimate of US$1.29, according to IBES data from Refinitiv.
Total sales rose nearly 9per cent to US$10.88 billion on strength in its pork and chicken segments, but missed the estimate of US$11 billion.
Net income attributable to Tyson fell to US$369 million, or US$1.01 per share, in the fourth quarter ended Sept. 28, from US$537 million, or US$1.47 per share, a year earlier.
(Reporting by Nivedita Balu in Bengaluru; Editing by Sriraj Kalluvila)


@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import config.ApplicationConfig @this( govuk_wrapper: views.html.govuk_wrapper, govukBackLink: GovukBackLink, govukButton: GovukButton ) @()(implicit request: Request[_], messages: Messages, appConfig: ApplicationConfig) @govuk_wrapper(pageTitle = messages("problem.uploading.file.title")) { @govukBackLink(BackLink( href = routes.FileUploadController.get.url, content = Text(messages("back")), attributes = Map("data-journey-click" -> "navigation - link:There has been a problem uploading your file:Back") ))
@messages("upload.file.again").capitalize
@messages("check.file")


{% extends "layout.html" %} {% block body %}
Here you can add campaigns to start tracking, add a few goals and let's see how you go against them.
Value:


Steve_Buscemi, Please reset your password below. You will then be returned to the login page.
Reset Password


CONTACT US Email our team<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>Address: Padre Faura Street, UP Manila


What is it ?
PACKETMAP is a Windows application that generates a realtime animation of internet activity on a map of the earth.
Why is it ?
Good question.
Where is it?
Up on sourceforge. You can start at the project page, but you probably just want to jump straight in and download the thing:
What does it look like ?
Well, it's still a work-in-progress, so it's still pretty rusty. I'll throw some screenshots up here once I'm confident that it's shiny enough.


The website is an open data repository and exploration platform that has been designed to be easy to access, visualize and download key data. The core dataset is the State of Cities Open Data Almanac which contains 102 indicators of city performance. In addition, this platform allows users to explore a prototypical demographics modelling system for Johannesburg and EThekwini (currently).
SCODA offers an interactive user platform comprising:
The Explorer
The Explorer allows users to download full datasets for the SoCR Almanac as well as other key data sets. Data can be downloaded as a csv file or visualized and downloaded as a PNG graphic file. Over time, all data sets generated by the SACN will be made available to download here. The demographic modelling data can also be downloaded in CSV format.
Data visualisations (infographics)
The Indicators button allows users to explore 12 key city indicators as interactive infographics. Each infographic contains an explanation of the indicator as well as reference to where the data was sourced. All of the data for these Indicators is contained within the Almanac
Map
The Map highlights the Cities whose data is contained within SCODA as well as key data. The SACN, through a partnership with the CSIR, has also supported the development of more detailed spatial analyses and visualisations of city development which are available at: http://stepsa.org/sacn_socr.html.
The South African Cities Open Data Almanac or SCODA is a city-centric data portal that aims to support the planning, management, monitoring, and reporting needs of cities. With the fast-changing and increasingly complex dynamics of contemporary South Africa where the majority of people now live in cities, monitoring how cities are planned and managed is important. Indicators are critical to effective planning and management in urban contexts, organising information sets into simpler, and easier to understand and compare ‘measures’ of what is happening. Good urban indicator sets allow for a longitudinal perspective (i.e. change over time) and comparisons between cities locally and internationally, enabling cities to see whether or not they are adequately achieving their objectives. SCODA contains data and indicators organised around key thematic areas:
Each theme has a set of core and secondary indicators.
SCODA is run by the South African Cities Network (SACN), an established network of South African cities and partners that encourages the exchange of information, experience and best practices on urban development and city management. The SACN has published State of South African Cities Reports (SoCR) since 2004, and these reports present a 5-year perspective on the performance and conditions of South Africa’s largest cities, with a focus on the SACN member cities. The SoCR process has always included a data almanac section, and long-envisaged having an online data portal that allows access to up-to-date data and indicators that are important to cities.
The SCODA site now provides a framework to enable an ongoing process of making city data accessible. Through a partnership with the World Council for City Data, SCODA also seeks to align to the ISO 37120 Standard on City Indicators so that South African cities can learn from global practice, and also benchmark themselves against international peers.
Further information about the development of SCODA is available at www.sacities.net.
Photo Acknowledgements
Photos used on this site are from the 2015 SACN Instagram competition and we acknowledge the photographer, by the indicator and their instagram handle:
People and Households: @amzbooysen; Cities over time: @moography; Population pyramids: @greg_alexander; Economic growth: @gavman18; Unemployment: @justjozi; City finance: @raeharrison1984; Non-revenue water: @pandanicoleium; Access to electricity: @ishotimagesza; Access to sanitation: @kojobaffoe; Education: @dane_forman; Urban Crime index: @iamsoweto; Sources of energy 2011: @drae_savvides.
Funding Support
Financial support of this project is provided by the Japan International Cooperation Agency as well as the Indigo Trust


Editing the utils.js file¶
continuous-rater
has been configured to require minimal coding to repurpose. To customize the app for your needs, simply open src/utils.js
in the continuous-rater
folder, and edit it in the text editor of your choosing. This is what it looks like in VSCode:
There are several sections that need attention. When filling in these variables, make sure to pay attention to the language provided in the following examples, and format your information in the same way.
Lab variables¶
studyLocation
– physical location of your lab/group (e.g., the Scranton, PA branch of Dunder Mifflin)labName
– the name of your lab/group (e.g., Dunder Mifflin)email
– the best email address for participants to use to contact you with questions/concerns (e.g. jim@dundermifflin.com)studyAim
– the main goal of the mturk study (e.g., to learn how to sell more paper)studyTasks
– brief summary of task for inclusion in mturk advertisement and consent form… NOT full instructions (e.g., annoying Dwight and fielding calls with David Wallace)experiment
– the name of your experiment (which should match the name of your outermost collection in Firebase and will be used to access this collection)
If you populate the src/utils.js
file with the above examples, the file should look like this:
and will result in a consent page like this:
HIT variables¶
HITPay
– compensation for completion of task. Format as X.XX (e.g., 2.50), and do not include a $ symbolestHITTime
– estimated time it should take workers to complete task (in minutes; e.g., 5)userGroup
– name of Firebase collection of participants for current task. Can be whatever you want (e.g., MTurk Group)
Note
The app will not work without the ‘userGroup’ variable populated! When just trying things out, I recommend a name like “Demo”. The app will automatically create this group in Firebase, and you can easily remove it later when deploying the app for real data collection. This helps keep things organized.
Stimuli variables¶
ratingTypes
– a list of the types of ratings you are interested in receiving. Each rating should be enclosed in single quotes (e.g., [‘amused’, ‘afraid’] ) and can be as long or short as you like
Firebase variables¶
const firebaseConfig = {
apiKey: "",
authDomain: "",
databaseURL: "",
projectId: "",
storageBucket: "",
messagingSenderId: "",
appId: "",
measurementId: ""
};
The above configuration information can be accessed via the Firebase console (you already saved this code snippet locally in step 9 of Initializing Cloud Firestore section) inside a Firebase project you have started. Copy and paste your unique config information over this blank one in the file.
Save this file with the newly populated variables!


We promise that all our restaurants are 100% Vegan. No need to worry how your food was prepared anymore.
Order from your favorite Vegan restaurants in your area. We are constantly working to get more selection added for our customers.
Going Vegan is good for your health, but we like to take things a step further. Your order directly contributes to giving back to the community.
Our first milestone is spreading the word about our upcoming project. Search engines, social networks, publishers and blog. Please share with your friends, family and fellow Vegans.
We are planning a beta launch, testing the app, fixing bugs and making sure we delivery high quality service for our memebers.
We plan on using our initial feedback to keep improving our application and add additional features to help our members and restaurants use our service.
Our goal is to made vegan food more accessible, we will be reaching out to leaders in the community to find ways we can continue to provide a quality service at an affordable price.
Product Owner
Founder
Mascot
Our team is growing and looking for talent! Please reach out if you have questions.


§302A-1141 Punishment of pupils limited. No physical punishment of any kind may be inflicted upon any pupil, except as provided for under sections 302A-1141.4 and 703-309(2). [L 1996, c 89, pt of §2; am L 2014, c 206, §3]
Note
Report to 2018 legislature on department's progress in implementing use of restraint statutes and expenditure of funds. L 2016, c 151, §1.


This post describes how the Metagraph is used to store the schema in the Neo4j database.
Introduction
InterMine, as a data warehousing system, stores biological data which is loaded from various data sources. It is imperative to ensure that the loaded data conforms to the existing schema/model so as to maintain the data integrity.
Presently the data model in InterMine is stored in an external XML file. For each entity, its attributes, references and collections are stored in this XML file. For example, a part of the file which has the BioEntity model looks like this.
While developing Neo4j prototype of InterMine, to reduce the dependency on external files, it was decided to store the schema in the database itself. Neo4j being a graph database stores data in the form of Nodes and directed Relationships. This brought up two issues:
-
The data model must to be in the form of a graph itself so that it can be stored in the Neo4j database.
-
The data model should represent all the existing Nodes in the database and the Relationships among them.
Since the data model is a graph and it stores information about the InterMine graph, it can be called a metagraph.
Metagraph Structure
Each node in the metagraph is assigned :Metagraph
label. All the metagraph nodes are further classified into two types, NodeType and RelType. As the name suggests, each NodeType node represents a specific type of nodes and each RelType node represents a specific type of relationship in the IM graph. So each node has either :NodeType
or :RelType
label depending on which entity it represents.
NodeType
A metagraph node labelled :NodeType
contains following two properties.
-
labels - A list containing the labels of the nodes that are represented by the
:NodeType
node. For example, [“Gene”,”SequenceFeature”,”BioEntity”]. -
keys - A list containing the keys of all the properties exist amongst the nodes that are represented by the
:NodeType
node. For example, [“primaryIdentifier”, “secondaryIdentifier”, “symbol”].
Each :NodeType
node is uniquely identified by its labels property. So, for all the nodes in the IM graph which are labelled :Gene
, :SequenceFeature
, :BioEntity
, there exists one :NodeType
node in the metagraph which has its labels property set as [“Gene”,”SequenceFeature”,”BioEntity”].
RelType
A metagraph node labelled :RelType
contains following two properties.
-
type - A string denoting the
type
of the relationships that are represented by the:RelType
node. For example, “HOMOLOGUE_OF”. -
keys - A list containing the keys of all the properties exist amongst the relationships that are represented by the
:RelType
node. For example, [“DataSet”].
Each :RelType
node is uniquely identified by its type property. So, for all the relationships of type HOMOLOGUE_OF in the IM graph, there exists one :RelType
node in the metagraph which has its type property set as “HOMOLOGUE_OF”.
Relationships in MetaGraph
Metagraph should not only contain information about the properties of various entities in the IM graph but it should also store how different types of nodes are connected to each other. To represent this information, we make use of Neo4j relationships.
We know that each :RelType
node represents a type of relationships that exist in the IM graph. Now, we create two outgoing relationships/edges from each :RelType
node - :StartNodeType
and :EndNodeType
. These edges end on a :NodeType
node.
Thus the metagraph path (a:RelType)-[:StartNodeType]->(b:NodeType)
shows that the relationships represented by node a
starts from the nodes represented by the node b
. Same case follows for :EndNodeType
.
Generating MetaGraph
Representing Nodes
The following Cypher query, creates :NodeType
nodes for all the nodes that exist in the IM graph.
Representing Relationships
The following Cypher query, creates :RelType
nodes for all the relationships that exist in the IM graph. It also connects them to the :NodeType
nodes with :StartNodeType
and :EndNodeType
relationships.
Once schema is generated and stored in the database, it can be accessed by the data loader at runtime. By enforing the rules of the schema, the loader maintains data integrity while loading new data to the database. The code for generating and querying metadata in Neo4j is developed in the org.intermine.neo4j.metadata package in InterMine/Neo4j repository.
Perhaps in a later post, I will discuss the org.intermine.neo4j.metadata package and will write a small tutorial on how you can use it generate & query your own Neo4j data model. So, stay tuned!


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--- layout: post title: Find Mouse Position Whenever You Need It author: josh tags: - .NET - C# - cursor - cursor object - cursor position - development - mouse - mouse coordinates - mouse position - mouseclick - position - position cursor - system windows ---
When responding to an event from a control, some events provide back the mouse coordinates, but not all of them do. So how do you get the mouse coordinates when your in an event that doesn't provide them in the event args?
Well, the answer is simple: Cursor.Position. Cursor.Position provides a convenient way to get the cursor's position relative to the screen. However, in some cases you want it relative to a control that's on the form, or even the form itself. Here's how you can do that:
{% highlight csharp linenos %} Point point = myControlOrForm.PointToClient(Cursor.Position); // Now fire an appropriate event with MouseEventArgs or whatever // you may need to do with the point. myControlOrForm_MouseClick(myControlOrForm, new MouseEventArgs(MouseButtons.Left, 1, point.X, point.Y, 0)); {% endhighlight %}Note: The Cursor object is found in the System.Windows.Forms
namespace.


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