scholarly journals How does working memory store more information at larger set sizes? A composite code model.

2021 ◽  
Author(s):  
Robert Udale ◽  
Katerina Gramm ◽  
Masud Husain ◽  
Sanjay G Manohar
Keyword(s):  
Working Memory ◽  
Open Data ◽  
Data Sets ◽  
Central Feature ◽  
Limited Capacity ◽  
Reproduction Task ◽  
Set Size ◽  
Code Model ◽  
Delayed Reproduction ◽  
Initial Support

A central feature of working memory is its limited capacity in terms of the amount of information that can be simultaneously maintained. Despite this, many studies observe an increase in the total amount when more items are maintained (set size), as measured by Shannon information. We propose the composite code model which maintains this fixed capacity assumption but demonstrates increasing observed information across set sizes. This relies on the hierarchical organisation of the visual system, in which higher-order information is abstracted about simple study displays. Using Bayesian inference, target responses can be inferred from knowledge about non-targets. We tested this model against our own data from a delayed reproduction task and those of published open data sets. We found initial support for the model, with its predictions matching those of the observed effects.

scholarly journals How does working memory store more information at larger set sizes? A composite code model.

2021 ◽  
Author(s):  
Robert Udale ◽  
Masud Husain ◽  
Sanjay G Manohar
Keyword(s):  
Working Memory ◽  
Open Data ◽  
Data Sets ◽  
Central Feature ◽  
Limited Capacity ◽  
Reproduction Task ◽  
Set Size ◽  
Code Model ◽  
Delayed Reproduction ◽  
Initial Support

A central feature of working memory is its limited capacity in terms of the amount of information that can be simultaneously maintained. Despite this, many studies observe an increase in the total amount when more items are maintained (set size), as measured by Shannon information. We propose the composite code model which maintains this fixed capacity assumption but demonstrates increasing observed information across set sizes. This relies on the hierarchical organisation of the visual system, in which higher-order information is abstracted about simple study displays. Using Bayesian inference, target responses can be inferred from knowledge about non-targets. We tested this model against our own data from a delayed reproduction task and those of published open data sets. We found initial support for the model, with its predictions matching those of the observed effects.

The visual arrays task…visual storage capacity or attention control?

2019 ◽  
Author(s):  
Jessie Martin ◽  
Jason S. Tsukahara ◽  
Christopher Draheim ◽  
Zach Shipstead ◽  
Cody Mashburn ◽  
...  
Keyword(s):  
Working Memory ◽  
Storage Capacity ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Attention Control ◽  
Data Sets ◽  
Independent Data ◽  
Visual Storage ◽  
Design Selection ◽  
The Relationship

**The uploaded manuscript is still in preparation** In this study, we tested the relationship between visual arrays tasks and working memory capacity and attention control. Specifically, we tested whether task design (selection or non-selection demands) impacted the relationship between visual arrays measures and constructs of working memory capacity and attention control. Using analyses from 4 independent data sets we showed that the degree to which visual arrays measures rely on selection influences the degree to which they reflect domain-general attention control.

scholarly journals Smarter Open Government Data for Society 5.0: Are Your Open Data Smart Enough?

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5204
Author(s):  
Anastasija Nikiforova
Keyword(s):  
Industry 4.0 ◽  
Economic Value ◽  
Open Data ◽  
Digital Data ◽  
Open Government ◽  
Data Sets ◽  
Time Data ◽  
Open Government Data ◽  
Information And Communication ◽  
Government Data

Nowadays, governments launch open government data (OGD) portals that provide data that can be accessed and used by everyone for their own needs. Although the potential economic value of open (government) data is assessed in millions and billions, not all open data are reused. Moreover, the open (government) data initiative as well as users’ intent for open (government) data are changing continuously and today, in line with IoT and smart city trends, real-time data and sensor-generated data have higher interest for users. These “smarter” open (government) data are also considered to be one of the crucial drivers for the sustainable economy, and might have an impact on information and communication technology (ICT) innovation and become a creativity bridge in developing a new ecosystem in Industry 4.0 and Society 5.0. The paper inspects OGD portals of 60 countries in order to understand the correspondence of their content to the Society 5.0 expectations. The paper provides a report on how much countries provide these data, focusing on some open (government) data success facilitating factors for both the portal in general and data sets of interest in particular. The presence of “smarter” data, their level of accessibility, availability, currency and timeliness, as well as support for users, are analyzed. The list of most competitive countries by data category are provided. This makes it possible to understand which OGD portals react to users’ needs, Industry 4.0 and Society 5.0 request the opening and updating of data for their further potential reuse, which is essential in the digital data-driven world.

scholarly journals Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions

2021 ◽  
Vol 10 (4) ◽  
pp. 251
Author(s):  
Christina Ludwig ◽  
Robert Hecht ◽  
Sven Lautenbach ◽  
Martin Schorcht ◽  
Alexander Zipf
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Open Data ◽  
Green Spaces ◽  
Data Sets ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Urban Quality Of Life ◽  
Shafer Theory ◽  
Sentinel 2

Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets, the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data for urban green space mapping is high but limited due to their respective uncertainties. Sentinel-2 imagery cannot distinguish public from private green spaces and its spatial resolution of 10 m fails to capture fine-grained urban structures, while in OSM green spaces are not mapped consistently and with the same level of completeness everywhere. To address these limitations, we propose to fuse these data sets under explicit consideration of their uncertainties. The Sentinel-2 derived Normalized Difference Vegetation Index was fused with OSM data using the Dempster–Shafer theory to enhance the detection of small vegetated areas. The distinction between public and private green spaces was achieved using a Bayesian hierarchical model and OSM data. The analysis was performed based on land use parcels derived from OSM data and tested for the city of Dresden, Germany. The overall accuracy of the final map of public urban green spaces was 95% and was mainly influenced by the uncertainty of the public accessibility model.

scholarly journals J-CO: A Platform-Independent Framework for Managing Geo-Referenced JSON Data Sets

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 621
Author(s):  
Giuseppe Psaila ◽  
Paolo Fosci
Keyword(s):  
Query Language ◽  
Open Data ◽  
Internet Technology ◽  
Data Sets ◽  
Specific Storage ◽  
Current State ◽  
Execution Engine ◽  
Share Data ◽  
Cloud Servers ◽  
Computational Resources

Internet technology and mobile technology have enabled producing and diffusing massive data sets concerning almost every aspect of day-by-day life. Remarkable examples are social media and apps for volunteered information production, as well as Open Data portals on which public administrations publish authoritative and (often) geo-referenced data sets. In this context, JSON has become the most popular standard for representing and exchanging possibly geo-referenced data sets over the Internet.Analysts, wishing to manage, integrate and cross-analyze such data sets, need a framework that allows them to access possibly remote storage systems for JSON data sets, to retrieve and query data sets by means of a unique query language (independent of the specific storage technology), by exploiting possibly-remote computational resources (such as cloud servers), comfortably working on their PC in their office, more or less unaware of real location of resources. In this paper, we present the current state of the J-CO Framework, a platform-independent and analyst-oriented software framework to manipulate and cross-analyze possibly geo-tagged JSON data sets. The paper presents the general approach behind the J-CO Framework, by illustrating the query language by means of a simple, yet non-trivial, example of geographical cross-analysis. The paper also presents the novel features introduced by the re-engineered version of the execution engine and the most recent components, i.e., the storage service for large single JSON documents and the user interface that allows analysts to comfortably share data sets and computational resources with other analysts possibly working in different places of the Earth globe. Finally, the paper reports the results of an experimental campaign, which show that the execution engine actually performs in a more than satisfactory way, proving that our framework can be actually used by analysts to process JSON data sets.

A Comparison Study of Mahalanobis-Taguchi System and Neural Network for Multivariate Pattern Recognition

2005 ◽  
Author(s):  
Jungeui Hong ◽  
Elizabeth A. Cudney ◽  
Genichi Taguchi ◽  
Rajesh Jugulum ◽  
Kioumars Paryani ◽  
...  
Keyword(s):  
Neural Network ◽  
Small Data ◽  
Data Sets ◽  
Comparison Study ◽  
Data Set ◽  
Set Size ◽  
Breast Cancer Study ◽  
Discriminant Ability ◽  
Small Data Sets ◽  
Multivariate Pattern

The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is to compare the ability of the Mahalanobis-Taguchi System and a neural network to discriminate using small data sets. We examine the discriminant ability as a function of data set size using an application area where reliable data is publicly available. The study uses the Wisconsin Breast Cancer study with nine attributes and one class.

Functional Delineation of Prefrontal Networks Underlying Working Memory in Schizophrenia: A Cross-data-set Examination

2021 ◽  
pp. 1-29
Author(s):  
Nicole Sanford ◽  
Todd S. Woodward
Keyword(s):  
Working Memory ◽  
Mixed Model ◽  
Data Sets ◽  
Generation Task ◽  
Data Set ◽  
Set Switching ◽  
Dorsolateral Prefrontal ◽  
Task Conditions ◽  
Experiment Analysis ◽  
Internal Attention

Abstract Background: Working memory (WM) impairment in schizophrenia substantially impacts functional outcome. Although the dorsolateral pFC has been implicated in such impairment, a more comprehensive examination of brain networks comprising pFC is warranted. The present research used a whole-brain, multi-experiment analysis to delineate task-related networks comprising pFC. Activity was examined in schizophrenia patients across a variety of cognitive demands. Methods: One hundred schizophrenia patients and 102 healthy controls completed one of four fMRI tasks: a Sternberg verbal WM task, a visuospatial WM task, a Stroop set-switching task, and a thought generation task (TGT). Task-related networks were identified using multi-experiment constrained PCA for fMRI. Effects of task conditions and group differences were examined using mixed-model ANOVA on the task-related time series. Correlations between task performance and network engagement were also performed. Results: Four spatially and temporally distinct networks with pFC activation emerged and were postulated to subserve (1) internal attention, (2) auditory–motor attention, (3) motor responses, and (4) task energizing. The “energizing” network—engaged during WM encoding and diminished in patients—exhibited consistent trend relationships with WM capacity across different data sets. The dorsolateral-prefrontal-cortex-dominated “internal attention” network exhibited some evidence of hypoactivity in patients, but was not correlated with WM performance. Conclusions: Multi-experiment analysis allowed delineation of task-related, pFC-anchored networks across different cognitive constructs. Given the results with respect to the early-responding “energizing” network, WM deficits in schizophrenia may arise from disruption in the “energization” process described by Donald Stuss' model of pFC functions.

Enhancing transparency through open government data: the case of data portals and their features and capabilities

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Martin Lněnička ◽  
Renata Machova ◽  
Jolana Volejníková ◽  
Veronika Linhartová ◽  
Radka Knezackova ◽  
...  
Keyword(s):  
Open Data ◽  
Open Government ◽  
Data Sets ◽  
Web Content ◽  
Content Type ◽  
Domain Experts ◽  
Computer Mediated ◽  
Open Government Data ◽  
Decision Making Processes ◽  
Government Data

PurposeThe purpose of this paper was to draw on evidence from computer-mediated transparency and examine the argument that open government data and national data infrastructures represented by open data portals can help in enhancing transparency by providing various relevant features and capabilities for stakeholders' interactions.Design/methodology/approachThe developed methodology consisted of a two-step strategy to investigate research questions. First, a web content analysis was conducted to identify the most common features and capabilities provided by existing national open data portals. The second step involved performing the Delphi process by surveying domain experts to measure the diversity of their opinions on this topic.FindingsIdentified features and capabilities were classified into categories and ranked according to their importance. By formalizing these feature-related transparency mechanisms through which stakeholders work with data sets we provided recommendations on how to incorporate them into designing and developing open data portals.Social implicationsThe creation of appropriate open data portals aims to fulfil the principles of open government and enables stakeholders to effectively engage in the policy and decision-making processes.Originality/valueBy analyzing existing national open data portals and validating the feature-related transparency mechanisms, this paper fills this gap in existing literature on designing and developing open data portals for transparency efforts.

Asymmetric Open Government Data (OGD) framework in India

2018 ◽  
Vol 20 (5) ◽  
pp. 434-448 ◽  
Author(s):  
Stuti Saxena
Keyword(s):  
Economic Value ◽  
Open Data ◽  
Developed Countries ◽  
Open Government ◽  
Data Sets ◽  
Indian States ◽  
Content Type ◽  
Open Government Data ◽  
Government Data ◽  
The Government

Purpose With the ongoing drives towards Open Government Data (OGD) initiatives across the globe, governments have been keen on pursuing their OGD policies to ensure transparency, collaboration and efficiency in administration. As a developing country, India has recently adopted the OGD policy (www.data.gov.in); however, the percolation of this policy in the States has remained slow. This paper aims to underpin the “asymmetry” in OGD framework as far as the Indian States are concerned. Besides, the study also assesses the contribution of “Open Citizens” in furthering the OGD initiatives of the country. Design/methodology/approach An exploratory qualitative following a case study approach informs the present study using documentary analysis where evidentiary support from five Indian States (Uttar Pradesh, Telangana, West Bengal, Sikkim and Gujarat) is being drawn to assess the nature and scope of the OGD framework. Further, conceptualization for “Open Citizen” framework is provided to emphasize upon the need to have aware, informed and pro-active citizens to spearhead the OGD initiatives in the country. Findings While the National OGD portal has a substantial number of data sets across different sectors, the States are lagging behind in the adoption and implementation of OGD policies, and while Telangana and Sikkim have been the frontrunners in adoption of OGD policies in a rudimentary manner, others are yet to catch up with them. Further, there is “asymmetry” in terms of the individual contribution of the government bodies to the open data sets where some government bodies are more reluctant to share their datasets than the others. Practical implications It is the conclusion of the study that governments need to institutionalize the OGD framework in the country, and all the States should appreciate the requirement of adopting a robust OGD policy for furthering transparency, collaboration and efficiency in administration. Social implications As an “Open Citizen”, it behooves upon the citizens to be pro-active and contribute towards the open data sets which would go a long way in deriving social and economic value out of these data sets. Originality/value While there are many studies on OGD in the West, studies focused upon the developing countries are starkly lacking. This study plugs this gap by attempting a comparative analysis of the OGD frameworks across Indian States. Besides, the study has provided a conceptualization of “Open Citizen” (OGD) which may be tapped for further research in developing and developed countries to ascertain the linkage between OGD and OC.

scholarly journals Conceptual distinctiveness does not improve visual working memory for objects but aids object-location binding

2021 ◽  
Author(s):  
Yuri Markov ◽  
Igor Utochkin
Keyword(s):  
Working Memory ◽  
Visual Working Memory ◽  
Competitive Interactions ◽  
Object Location ◽  
Location Information ◽  
Limited Capacity ◽  
Common Features ◽  
Categorical Information ◽  
Location Binding ◽  
Different Sources

Visual working memory (VWM) is prone to interference from stored items competing for its limited capacity. These competitive interactions can arise from different sources. For example, one such source is poor item distinctiveness causing a failure to discriminate between items sharing common features. Another source of interference is imperfect binding, a problem of determining which of the remembered features belonged to which object or which item was in which location. In two experiments, we studied how the conceptual distinctiveness of real-world objects (i.e., whether the objects belong to the same or different basic categories) affects VWM for objects and object-location binding. In Experiment 1, we found that distinctiveness did not affect memory for object identities or for locations, but low-distinctive objects were more frequently reported at “swapped” locations that originally went with different objects. In Experiment 2 we found evidence that the effect of distinctiveness on the object-location swaps was due to the use of categorical information for binding. In particular, we found that observers swapped the location of a tested object with another object from the same category more frequently than with any of the objects from another category. This suggests that observers can use some coarse category-location information when objects are conceptually distinct. Taken together, our findings suggest that object distinction and object-location binding act upon different components of VWM.

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