An Implementation of Secure Data Exchange System with Multi-hop Routing in Wireless Delay Tolerant Network Using Attribute-Based Encryption

2015 ◽  
Author(s):  
Amang Sudarsono ◽  
Toru Nakanishi
Keyword(s):  
Data Exchange ◽  
Delay Tolerant Network ◽  
Exchange System ◽  
Attribute Based Encryption ◽  
Secure Data ◽  
Delay Tolerant

A digital envelope approach using attribute-based encryption for secure data exchange in IoT scenarios

2017 ◽  
Author(s):  
Salvador Perez ◽  
Jose L. Hernandez-Ramos ◽  
Diego Pedone ◽  
Domenico Rotondi ◽  
Leonardo Straniero ◽  
...  
Keyword(s):  
Data Exchange ◽  
Attribute Based Encryption ◽  
Secure Data

Secure data exchange system in software-defined networks of energy complex facilities

2021 ◽  
Author(s):  
Rinat Khayretdinov ◽  
Damir Dautov ◽  
Alexey Vulfin ◽  
Konstantin Mironov ◽  
Arkadij Frid
Keyword(s):  
Data Exchange ◽  
Software Defined Networks ◽  
Exchange System ◽  
Energy Complex ◽  
Secure Data

Machine-Scored Syntax: Comparison of the CLAN Automatic Scoring Program to Manual Scoring

2020 ◽  
Vol 51 (2) ◽  
pp. 479-493
Author(s):  
Jenny A. Roberts ◽  
Evelyn P. Altenberg ◽  
Madison Hunter
Keyword(s):  
Data Exchange ◽  
Child Language ◽  
Exchange System ◽  
Absolute Point ◽  
Language Analysis ◽  
Search Patterns ◽  
Language Data ◽  
Report Accuracy ◽  
Point To Point ◽  
Automatic Scoring

Purpose The results of automatic machine scoring of the Index of Productive Syntax from the Computerized Language ANalysis (CLAN) tools of the Child Language Data Exchange System of TalkBank (MacWhinney, 2000) were compared to manual scoring to determine the accuracy of the machine-scored method. Method Twenty transcripts of 10 children from archival data of the Weismer Corpus from the Child Language Data Exchange System at 30 and 42 months were examined. Measures of absolute point difference and point-to-point accuracy were compared, as well as points erroneously given and missed. Two new measures for evaluating automatic scoring of the Index of Productive Syntax were introduced: Machine Item Accuracy (MIA) and Cascade Failure Rate— these measures further analyze points erroneously given and missed. Differences in total scores, subscale scores, and individual structures were also reported. Results Mean absolute point difference between machine and hand scoring was 3.65, point-to-point agreement was 72.6%, and MIA was 74.9%. There were large differences in subscales, with Noun Phrase and Verb Phrase subscales generally providing greater accuracy and agreement than Question/Negation and Sentence Structures subscales. There were significantly more erroneous than missed items in machine scoring, attributed to problems of mistagging of elements, imprecise search patterns, and other errors. Cascade failure resulted in an average of 4.65 points lost per transcript. Conclusions The CLAN program showed relatively inaccurate outcomes in comparison to manual scoring on both traditional and new measures of accuracy. Recommendations for improvement of the program include accounting for second exemplar violations and applying cascaded credit, among other suggestions. It was proposed that research on machine-scored syntax routinely report accuracy measures detailing erroneous and missed scores, including MIA, so that researchers and clinicians are aware of the limitations of a machine-scoring program. Supplemental Material https://doi.org/10.23641/asha.11984364

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