Temporal Granularity Enlightened by Knowledge

2001 ◽  
pp. 30-41 ◽  
Author(s):  
Sylviane R. Schwer
Keyword(s):  
Temporal Granularity

Temporal Granularity

2018 ◽  
pp. 3970-3975
Author(s):  
Claudio Bettini ◽  
X. Sean Wang ◽  
Sushil Jajodia
Keyword(s):  
Temporal Granularity

scholarly journals Analyzing the Effect of Time in Migration Measurement Using Georeferenced Digital Trace Data

Demography ◽  
2021 ◽  
Vol 58 (1) ◽  
pp. 51-74
Author(s):  
Lee Fiorio ◽  
Emilio Zagheni ◽  
Guy Abel ◽  
Johnathan Hill ◽  
Gabriel Pestre ◽  
...  
Keyword(s):  
Empirical Data ◽  
Survival Function ◽  
The United States ◽  
Population Characteristics ◽  
Phone Call ◽  
Research Strategies ◽  
Data Set ◽  
Trace Data ◽  
Transition Functions ◽  
Temporal Granularity

Abstract Georeferenced digital trace data offer unprecedented flexibility in migration estimation. Because of their high temporal granularity, many migration estimates can be generated from the same data set by changing the definition parameters. Yet despite the growing application of digital trace data to migration research, strategies for taking advantage of their temporal granularity remain largely underdeveloped. In this paper, we provide a general framework for converting digital trace data into estimates of migration transitions and for systematically analyzing their variation along a quasi-continuous time scale, analogous to a survival function. From migration theory, we develop two simple hypotheses regarding how we expect our estimated migration transition functions to behave. We then test our hypotheses on simulated data and empirical data from three platforms in two internal migration contexts: geotagged Tweets and Gowalla check-ins in the United States, and cell-phone call detail records in Senegal. Our results demonstrate the need for evaluating the internal consistency of migration estimates derived from digital trace data before using them in substantive research. At the same time, however, common patterns across our three empirical data sets point to an emergent research agenda using digital trace data to study the specific functional relationship between estimates of migration and time and how this relationship varies by geography and population characteristics.

A geovisual analytics approach for analyzing event-based geospatial anomalies within movement data

2017 ◽  
Vol 17 (2) ◽  
pp. 91-107 ◽  
Author(s):  
Orland Hoeber ◽  
Monjur Ul Hasan
Keyword(s):  
Data Entry ◽  
Visual Exploration ◽  
Simple Task ◽  
Geographic Ranges ◽  
Movement Data ◽  
Geovisual Analytics ◽  
Monitoring And Enforcement ◽  
Exploratory Data ◽  
Event Based ◽  
Temporal Granularity

Comparing data collected on the movement of an entity to data on the location where the entity was reported to have been can be useful in monitoring and enforcement situations. Anomalies between these datasets may be indicative of illegal activity, systematic reporting errors, data entry errors, or equipment failure. While finding obvious anomalies may be a simple task, the discovery of more subtle inconsistencies can be challenging when there is a mismatch in the temporal granularity between the datasets, or when they cover large temporal and geographic ranges. We have developed a geovisual analytics approach called Visual Exploration of Movement-Event Anomalies (VEMEA) that automatically extracts potential anomalies from the data, visually encodes these on a map, and provides interactive filtering and exploration tools to allow expert analysts to investigate and evaluate the anomalies. Using two case studies from the fisheries enforcement domain, the value of VEMEA is illustrated for both confirmatory and exploratory data analysis tasks. Field trial evaluations conducted with expert fisheries data analysts further support the benefits of the approach.

scholarly journals LDL-AURIS: Error-driven Learning in Modeling Spoken Word Recognition

2020 ◽  
Author(s):  
Elnaz Shafaei-Bajestan ◽  
Masoumeh Moradipour-Tari ◽  
Peter Uhrig ◽  
R. H. Baayen
Keyword(s):  
Word Recognition ◽  
Spoken Word Recognition ◽  
Spoken Word ◽  
High Dimensional ◽  
Excellent Performance ◽  
Auditory Word ◽  
Auditory Word Recognition ◽  
Multivariate Multiple Regression ◽  
Isolated Word ◽  
Temporal Granularity

A computational model for auditory word recognition is presented that enhances the model of Arnold et al. (2017). Real-valued features are extracted from the speech signal instead of discrete features. One-hot encoding for words’ meanings is replaced by real-valued semantic vectors, adding a small amount of noise to safeguard discriminability. Instead of learning with Rescorla-Wagner updating, we use multivariate multiple regression, which captures discrimination learning at the limit of experience. These new design features substantially improve prediction accuracy for words extracted from spontaneous conversations. They also provide enhanced temporal granularity, enabling the modeling of cohort-like effects. Clustering with t-SNE shows that the acoustic form space captures phone-like similarities and differences. Thus, wide learning with high-dimensional vectors and no hidden layers, and no abstract mediating phone-like representations is not only possible but achieves excellent performance that approximates the lower bound of human accuracy on the challenging task of isolated word recognition.

scholarly journals Hierarchical Temporal Multi-Instance Learning for Video-based Student Learning Engagement Assessment

2021 ◽  
Author(s):  
Jiayao Ma ◽  
Xinbo Jiang ◽  
Songhua Xu ◽  
Xueying Qin
Keyword(s):  
Online Education ◽  
State Of The Art ◽  
Multiple Instance Learning ◽  
Automatic Assessment ◽  
Learning Engagement ◽  
Study Session ◽  
Training Stage ◽  
Engagement Assessment ◽  
Instructional Services ◽  
Temporal Granularity

Video-based automatic assessment of a student's learning engagement on the fly can provide immense values for delivering personalized instructional services, a vehicle particularly important for massive online education. To train such an assessor, a major challenge lies in the collection of sufficient labels at the appropriate temporal granularity since a learner's engagement status may continuously change throughout a study session. Supplying labels at either frame or clip level incurs a high annotation cost. To overcome such a challenge, this paper proposes a novel hierarchical multiple instance learning (MIL) solution, which only requires labels anchored on full-length videos to learn to assess student engagement at an arbitrary temporal granularity and for an arbitrary duration in a study session. The hierarchical model mainly comprises a bottom module and a top module, respectively dedicated to learning the latent relationship between a clip and its constituent frames and that between a video and its constituent clips, with the constraints on the training stage that the average engagements of local clips is that of the video label. To verify the effectiveness of our method, we compare the performance of the proposed approach with that of several state-of-the-art peer solutions through extensive experiments.

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