This is a sample on the methodology for solving exercises on the IP fragmentation algorithm. This methodology is simply a 3 steps process on the basis of:
-Identification of the initial IP datagram to be transmitted
-Identification of the physical network's, through which the IP fragment will be transmitted, limitations
-Creation of the products-fragments table consisting of all relevant data (No of fragment, Header size, Data size, Total size, MF, DF and Fragment's Index Tracker), which will be used for checking the correctness of the data and thus resulting to proper fragmentation of the initial IP datagram
Note: Following the data presented by the school book
Salavasidis Petros (2013) - Methodology for solving exercises on the IP fragm...劉凌 裡留了留硫留溜隆侶
油
This methodology for solving exercises on the IP fragmentation algorithm, is simply a 3 step process on the basis of:
-Identification of the initial IP datagram to be transmitted
-Identification of the physical network's, through which the IP fragment will be transmitted, limitations
-Creation of the products-fragments table consisting of all relevant data (No of fragment, Header size, Data size, Total size, MF, DF and Fragment's Index Tracker), which will be used for checking the correctness of the data and thus resulting to proper fragmentation of the initial IP datagram
Note: Following the data presented by the school book
Theofilos Georgiadis: Library recommendation system for the reuse of software...Manos Tsardoulias
油
This system helps developers when searching for python libraries. The developer constructs the query in natural language and the system returns the 10 most relevant libraries. It is based on a graph, its nodes are constructed by keywords and libraries that was extracted from a set of open source projects. For every keyword that is present with a library we connect the two nodes with an edge. For every time that a keyword is present with a library, the weight of the edge is increased by one. Using this graph we extract representations of the graph's nodes. Lastly using these representations and a method for calculating the similarity, we calculate the similarity of each library with each keyword and we extract a recommendation for the 10 libraries with the highest value of similarity.
Theofilos Georgiadis: Library recommendation system for the reuse of software...Manos Tsardoulias
油
This system helps developers when searching for python libraries. The developer constructs the query in natural language and the system returns the 10 most relevant libraries. It is based on a graph, its nodes are constructed by keywords and libraries that was extracted from a set of open source projects. For every keyword that is present with a library we connect the two nodes with an edge. For every time that a keyword is present with a library, the weight of the edge is increased by one. Using this graph we extract representations of the graph's nodes. Lastly using these representations and a method for calculating the similarity, we calculate the similarity of each library with each keyword and we extract a recommendation for the 10 libraries with the highest value of similarity.