scholarly journals Toward an Understanding of (Actual) Data Structures

1978 ◽  
Vol 21 (2) ◽  
pp. 98-104
Author(s):  
W. L. Honig ◽  
C. R. Carlson
Keyword(s):  
1994 ◽  
Vol 9 (3) ◽  
pp. 127
Author(s):  
X.-B. Lu ◽  
F. Stetter
Keyword(s):  

Disputatio ◽  
2019 ◽  
Vol 11 (55) ◽  
pp. 345-369
Author(s):  
Peter Ludlow

AbstractDavid Chalmers argues that virtual objects exist in the form of data structures that have causal powers. I argue that there is a large class of virtual objects that are social objects and that do not depend upon data structures for their existence. I also argue that data structures are themselves fundamentally social objects. Thus, virtual objects are fundamentally social objects.


2020 ◽  
Vol 4 (1) ◽  
pp. 51-63
Author(s):  
Peter Neuhaus ◽  
Chris Jumonville ◽  
Rachel A. Perry ◽  
Roman Edwards ◽  
Jake L. Martin ◽  
...  

AbstractTo assess the comparative similarity of squat data collected as they wore a robotic exoskeleton, female athletes (n=14) did two exercise bouts spaced 14 days apart. Data from their exoskeleton workout was compared to a session they did with free weights. Each squat workout entailed a four-set, four-repetition paradigm with 60-second rest periods. Sets for each workout involved progressively heavier (22.5, 34, 45.5, 57 kg) loads. The same physiological, perceptual, and exercise performance dependent variables were measured and collected from both workouts. Per dependent variable, Pearson correlation coefficients, t-tests, and Cohen's d effect size compared the degree of similarity between values obtained from the exoskeleton and free weight workouts. Results show peak O2, heart rate, and peak force data produced the least variability. In contrast, far more inter-workout variability was noted for peak velocity, peak power, and electromyography (EMG) values. Overall, an insufficient amount of comparative similarity exists for data collected from both workouts. Due to the limited data similarity, the exoskeleton does not exhibit an acceptable degree of validity. Likely the cause for the limited similarity was due to the brief amount of familiarization subjects had to the exoskeleton prior to actual data collection. A familiarization session that accustomed subjects to squats done with the exoskeleton prior to actual data collection may have considerably improved the validity of data obtained from that device.


2020 ◽  
Vol 2 (2) ◽  
pp. 454
Author(s):  
Julkifli Purnama ◽  
Ahmad Juliana

Investment in the capital market every manager needs to analyze to make decisions so that the right target to produce profits in accordance with what is expected. For that, we need a way to predict the decisions that will be taken in the future. The research objective is to find the best model and forecasting of the composite stock price index (CSPI). Data analysis technique The ARIMA Model time series data from historical data is the basis for forecasting. Secondary data is the closing price of the JCI on July 16 2018 to July 16 2019 to see how accurate the forecasting is done on the actual data at that time. The results of the study that the best Arima model is Arima 2.1.2 with an R-squared value of 0.014500, Schwarz criterion 10.83497 and Akaike info criterion of 10.77973. Results of forecasting actual data are 6394,609, dynamic forecast 6387,551 selisish -7,05799, statistics forecas 6400,653 difference of 6,043909. For investors or the public can use the ARIMA method to be able to predict or predict the capital market that will occur in the next period.


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