Heuristic Scheduler for Proximity Data Exchange

There are several contact tracing solutions, some are for closed user groups, some are for the residents of a country, and some are open solutions with no clear boundary defined. These independent solutions are not adequate to support pre-pandemic global mobility. Ideally, what is needed is a global solution that can support decentralized control over data and at the same time support proximity data exchange among Apps developed by different vendors and for different countries. This paper proposes a family of contact tracing designs with low-risk anonymity that includes a centralized design, a distributed design, and a federated design for global solution.

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Novel Distributed and Federated Contact Tracing Systems – A Global Solution

10.36227/techrxiv.14661138.v1 ◽

2021 ◽

Author(s):

Hari T S Narayanan

Keyword(s):

Global Solution ◽

Decentralized Control ◽

Data Exchange ◽

Low Risk ◽

Contact Tracing ◽

Distributed Design ◽

Proximity Data ◽

Global Mobility ◽

User Groups

There are several contact tracing solutions, some are for closed user groups, some are for the residents of a country, and some are open solutions with no clear boundary defined. These independent solutions are not adequate to support pre-pandemic global mobility. Ideally, what is needed is a global solution that can support decentralized control over data and at the same time support proximity data exchange among Apps developed by different vendors and for different countries. This paper proposes a family of contact tracing designs with low-risk anonymity that includes a centralized design, a distributed design, and a federated design for global solution.

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A Novel Distributed and Federated Digital Contact Tracing Systems

10.36227/techrxiv.14661138.v2 ◽

2021 ◽

Author(s):

Hari T S Narayanan

Keyword(s):

Global Solution ◽

Decentralized Control ◽

Data Exchange ◽

Low Risk ◽

Contact Tracing ◽

Distributed Design ◽

Proximity Data ◽

Global Mobility ◽

User Groups

There are several contact tracing solutions, some are for closed user groups, some are for the residents of a country, and some are open solutions with no clear boundary defined. These independent solutions are not adequate to support pre-pandemic global mobility. Ideally, what is needed is a global solution that can support decentralized control over data and at the same time support proximity data exchange among Apps developed by different vendors and for different countries. This paper proposes a family of contact tracing designs with low-risk anonymity that includes a centralized design, a distributed design, and a federated design for global solution.

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

Language Speech and Hearing Services in Schools ◽

10.1044/2019_lshss-19-00056 ◽

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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Proposed work group: Community participation in community mental health centers: A data exchange and search for action implications

PsycEXTRA Dataset ◽

10.1037/e611312012-413 ◽

1972 ◽

Author(s):

Charles Windle ◽

Fred Spaner ◽

Gottlieb Simon ◽

William Meyers ◽

Susan Salasin ◽

...

Keyword(s):

Mental Health ◽

Community Mental Health ◽

Community Participation ◽

Data Exchange ◽

Work Group ◽

Health Centers ◽

Community Mental Health Centers ◽

Mental Health Centers

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