Optimal Placement of Delay-constrained In-Network Computing Tasks at the Edge with Minimum Data Exchange

2021 ◽  
Author(s):  
Gianmarco Lia ◽  
Marica Amadeo ◽  
Claudia Campolo ◽  
Giuseppe Ruggeri ◽  
Antonella Molinaro
Keyword(s):  
Data Exchange ◽  
Optimal Placement ◽  
Network Computing ◽  
Minimum Data

scholarly journals Optimal Placement of Social Digital Twins in Edge IoT Networks

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6181
Author(s):  
Olga Chukhno ◽  
Nadezhda Chukhno ◽  
Giuseppe Araniti ◽  
Claudia Campolo ◽  
Antonio Iera ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Social Relationships ◽  
Data Exchange ◽  
Service Discovery ◽  
Programming Model ◽  
Optimal Solution ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Digital Twins ◽  
The Social

In next-generation Internet of Things (IoT) deployments, every object such as a wearable device, a smartphone, a vehicle, and even a sensor or an actuator will be provided with a digital counterpart (twin) with the aim of augmenting the physical object’s capabilities and acting on its behalf when interacting with third parties. Moreover, such objects can be able to interact and autonomously establish social relationships according to the Social Internet of Things (SIoT) paradigm. In such a context, the goal of this work is to provide an optimal solution for the social-aware placement of IoT digital twins (DTs) at the network edge, with the twofold aim of reducing the latency (i) between physical devices and corresponding DTs for efficient data exchange, and (ii) among DTs of friend devices to speed-up the service discovery and chaining procedures across the SIoT network. To this aim, we formulate the problem as a mixed-integer linear programming model taking into account limited computing resources in the edge cloud and social relationships among IoT devices.

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

Geoenvironmental and Geotechnical Data Exchange: Setting the Standard

2008 ◽  
Author(s):  
Scot D. Weaver ◽  
Thomas E. Lefchik ◽  
Marc I. Hoit ◽  
Kirk Beach
Keyword(s):  
Data Exchange ◽  
Geotechnical Data

Optimisation of Cardiac Resynchronisation Therapy with MultiPoint™ Pacing

2015 ◽  
Vol 4 (3) ◽  
pp. 3
Author(s):  
Antonio Curnis ◽  
David O’Donnell ◽  
Axel Kloppe ◽  
Žarko Calovic ◽  
◽  
...  
Keyword(s):  
Systolic Function ◽  
Biventricular Pacing ◽  
Heart Rhythm ◽  
Cardiac Resynchronisation Therapy ◽  
Left Ventricular ◽  
Optimal Placement ◽  
Jude Medical ◽  
Number Of Patients ◽  
Cardiac Resynchronisation ◽  
Resynchronisation Therapy

Cardiac resynchronisation therapy (CRT) using biventricular pacing is an established therapy for impairment of left ventricular (LV) systolic function in patients with heart failure (HF). Although technological advances have improved outcomes in patients undergoing biventricular pacing, the optimal placement of pacing leads remains challenging, and approximately one third of patients have no response to CRT. This may be due to patient selection and lead placement. Electrical mapping can greatly improve outcomes in CRT and increase the number of patients who derive benefit from the procedure. MultiPoint™ pacing (St Jude Medical, St Paul, MN, US) using a quadripolar lead increases the possibility of finding the best pacing site. In clinical studies, use of MultiPoint pacing in HF patients undergoing CRT has been associated with haemodynamic and clinical benefits compared with conventional biventricular pacing, and these benefits have been sustained at 12 months. This article describes the proceedings of a satellite symposium held at the European Heart Rhythm Association (EHRA) Europace conference held in Milan, Italy, in June 2015.

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