Declarative Framework for Semantical Interpretations of Structured Information — An Applicative Approach

We study the applicability of declarative models to encode and describe structured information by means of semantics. Specifically, we introduce D-SPACES, an implementation of constraint systems with space and extrusion operators. Constraint systems are algebraic models that allow for a semantic language-like representation of information in systems where the concept of space is a primary structural feature. We mainly give this information an epistemic or temporal interpretation and consider various agents as entities acting upon it. D-SPACES is coded as a c++ library providing implementations of constraint systems, space functions and extrusion functions. The interfaces to access each implementation are minimal and thoroughly documented. D-SPACES also provides property-checking methods as well as an implementation of a specific type of constraint systems (a boolean algebra). This last implementation serves as an entry point for quick access and proof of concept when using these models. Finally, we show the applicability of this framework with two examples; a scenario in the form of a social network where users post their beliefs and utter their opinions, and a semantical interpretation of a logical language to express time behaviors and properties.

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Sentiment Predictions Using Deep Belief Networks Model for Odd-Even Policy in Delhi

International Journal of Synthetic Emotions ◽

10.4018/ijse.2016070101 ◽

2016 ◽

Vol 7 (2) ◽

pp. 1-22 ◽

Cited By ~ 6

Author(s):

Sudhir Kumar Sharma ◽

Ximi Hoque ◽

Pravin Chandra

Keyword(s):

Social Network ◽

Prediction Models ◽

Academic Research ◽

The Other ◽

Belief Networks ◽

Deep Belief Networks ◽

Preference Model ◽

Structured Information ◽

Application Programming ◽

Programming Interfaces

This paper analyzes the odd-even policy in Delhi using tweets posted on Twitter from December 2015 to August 2016. Twitter is a social network where users post their feelings, opinions and sentiments for any event. This paper transforms the unstructured tweets into structured information using open source libraries. Further objective is to build a model using Deep Belief Networks classification (DBN) to classify unseen tweets on the same context. This paper collects tweets on this event under six hashtags. This study explores three freely available resources / Application Programming Interfaces (APIs) for labeling of tweets for academic research. This paper proposes three sentiment prediction models using the sentiment predictions provided by three APIs. DBN classifier is used to build six models. The performances of these six models are evaluated through standard evaluation metrics. The experimental results reveal that the TextBlob API and proposed Preference Model outperformed than the other four sentiment prediction models.

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Sentiment Predictions Using Deep Belief Networks Model for Odd-Even Policy in Delhi

Cognitive Analytics ◽

10.4018/978-1-7998-2460-2.ch073 ◽

2020 ◽

pp. 1440-1463

Author(s):

Sudhir Kumar Sharma ◽

Ximi Hoque ◽

Pravin Chandra

Keyword(s):

Social Network ◽

Prediction Models ◽

Academic Research ◽

The Other ◽

Belief Networks ◽

Deep Belief Networks ◽

Preference Model ◽

Structured Information ◽

Application Programming ◽

Programming Interfaces

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Dynamic elicited priors for updating covert networks

Network Science ◽

10.1017/nws.2012.6 ◽

2013 ◽

Vol 1 (1) ◽

pp. 68-94 ◽

Cited By ~ 2

Author(s):

JEFF GILL ◽

JOHN R. FREEMAN

Keyword(s):

Social Network ◽

Hierarchical Models ◽

Human Subjects ◽

Probability Distributions ◽

Network Data ◽

Proof Of Concept ◽

Subject Matter Experts ◽

Edge Information ◽

Bayesian Hierarchical ◽

Covert Networks

AbstractThe study of covert networks is plagued by the fact that individuals conceal their attributes and associations. To address this problem, we develop a technology for eliciting this information from qualitative subject-matter experts to inform statistical social network analysis. We show how the information from the subjective probability distributions can be used as input to Bayesian hierarchical models for network data. In the spirit of “proof of concept,” the results of a test of the technology are reported. Our findings show that human subjects can use the elicitation tool effectively, supplying attribute and edge information to update a network indicative of a covert one.

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A social network for supporting end users in the composition of services: definition and proof of concept

Computing ◽

10.1007/s00607-020-00796-8 ◽

2020 ◽

Vol 102 (8) ◽

pp. 1909-1940

Author(s):

Pedro Valderas ◽

Victoria Torres ◽

Vicente Pelechano

Keyword(s):

Social Network ◽

End Users ◽

Proof Of Concept

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Graph Analysis Using Fast Fourier Transform Applied on Grayscale Bitmap Images

Information ◽

10.3390/info12110454 ◽

2021 ◽

Vol 12 (11) ◽

pp. 454

Author(s):

Pawel Baszuro ◽

Jakub Swacha

Keyword(s):

Fourier Transform ◽

Social Network ◽

Fast Fourier Transform ◽

Graph Analysis ◽

Primary Concern ◽

Proof Of Concept ◽

Reference Dataset ◽

Large Size ◽

Overall Performance ◽

Original Approach

There is spiking interest in graph analysis, mainly sparked by social network analysis done for various purposes. With social network graphs often achieving very large size, there is a need for capable tools to perform such an analysis. In this article, we contribute to this area by presenting an original approach to calculating various graph morphisms, designed with overall performance and scalability as the primary concern. The proposed method generates a list of candidates for further analysis by first decomposing a complex network into a set of sub-graphs, transforming sub-graphs into intermediary structures, which are then used to generate grey-scaled bitmap images, and, eventually, performing image comparison using Fast Fourier Transform. The paper discusses the proof-of-concept implementation of the method and provides experimental results achieved on sub-graphs in different sizes randomly chosen from a reference dataset. Planned future developments and key considered areas of application are also described.

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Social Network Analysis and the Law: A Proof of Concept for Argentina

SSRN Electronic Journal ◽

10.2139/ssrn.3711556 ◽

2020 ◽

Author(s):

Ramiro Álvarez-Ugarte

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Proof Of Concept ◽

The Law

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Logical Constraints: The Limitations of QCA in Social Science Research

Political Analysis ◽

10.1017/pan.2020.7 ◽

2020 ◽

Vol 28 (4) ◽

pp. 552-568

Author(s):

Kevin A. Clarke

Keyword(s):

Comparative Analysis ◽

Social Science ◽

Boolean Algebra ◽

Qualitative Comparative Analysis ◽

Social Science Research ◽

Science Research ◽

Social World ◽

Logical Language ◽

The Social ◽

Logical Constraints

Researchers employing qualitative comparative analysis (QCA) and its variants use two-element Boolean algebra to compare cases and identify putative causal conditions. I show that the two-element Boolean algebra constrains research in three important ways: it restricts what we can say about sets and the interactions between sets, it embodies a logical language that is too weak to capture modern social science theories, and it restricts our analysis of causation to necessity and sufficiency accounts and does not allow for counterfactuals. Modern quantitative analysis suffers none of these restrictions and provides a much richer way to understand the social world.

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Direct measurement of electron-diffraction-pattern intensities using an energy loss spectrometer

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100155311 ◽

1989 ◽

Vol 47 ◽

pp. 668-669

Author(s):

A. G. Jackson ◽

M. Rowe

Keyword(s):

Thermal Effects ◽

Dynamic Range ◽

Scattering Amplitude ◽

Dynamical Theory ◽

Site Symmetry ◽

Proof Of Concept ◽

Parallel Acquisition ◽

Kinematic Approximation ◽

Speed Up ◽

Structure Calculations

Diffraction intensities from intermetallic compounds are, in the kinematic approximation, proportional to the scattering amplitude from the element doing the scattering. More detailed calculations have shown that site symmetry and occupation by various atom species also affects the intensity in a diffracted beam. [1] Hence, by measuring the intensities of beams, or their ratios, the occupancy can be estimated. Measurement of the intensity values also allows structure calculations to be made to determine the spatial distribution of the potentials doing the scattering. Thermal effects are also present as a background contribution. Inelastic effects such as loss or absorption/excitation complicate the intensity behavior, and dynamical theory is required to estimate the intensity value.The dynamic range of currents in diffracted beams can be 104or 105:1. Hence, detection of such information requires a means for collecting the intensity over a signal-to-noise range beyond that obtainable with a single film plate, which has a S/N of about 103:1. Although such a collection system is not available currently, a simple system consisting of instrumentation on an existing STEM can be used as a proof of concept which has a S/N of about 255:1, limited by the 8 bit pixel attributes used in the electronics. Use of 24 bit pixel attributes would easily allowthe desired noise range to be attained in the processing instrumentation. The S/N of the scintillator used by the photoelectron sensor is about 106 to 1, well beyond the S/N goal. The trade-off that must be made is the time for acquiring the signal, since the pattern can be obtained in seconds using film plates, compared to 10 to 20 minutes for a pattern to be acquired using the digital scan. Parallel acquisition would, of course, speed up this process immensely.

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