Implementing Dampster-Shafer Theory for Property Similarity in Conceptual Spaces Modeling

Jodi Magness’ proposal that an altar existed at Qumran leaves some unanswered questions; nevertheless, her conclusions are worthy of consideration. This study examines her claim that the residents at Qumran had an altar, modeled off of the Wilderness Tabernacle, through the lens of critical spatial theory. The conceptual spaces of some of the Dead Sea Scrolls, such as The Damascus Document and The Community Rule, as well as the spatial practices of the site of Qumran do not rule out – and even support – the idea that Qumran itself was highly delimited and therefore its spaces hierarchized in such a way that it could have supported a central cultic site.

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A hybrid approach to model the dykes in Sungun porphyry copper deposit using Dempster–Shafer theory

Arabian Journal of Geosciences ◽

10.1007/s12517-020-06241-6 ◽

2020 ◽

Vol 13 (24) ◽

Author(s):

Sajjad Talesh Hosseini ◽

Omid Asghari ◽

Parham Pahlavani

Keyword(s):

Porphyry Copper ◽

Hybrid Approach ◽

Copper Deposit ◽

Porphyry Copper Deposit ◽

Dempster Shafer Theory ◽

Shafer Theory

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

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10040251 ◽

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.

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Potential advantages and limitations of using information fusion in media forensics—a discussion on the example of detecting face morphing attacks

EURASIP Journal on Information Security ◽

10.1186/s13635-021-00123-4 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Christian Kraetzer ◽

Andrey Makrushin ◽

Jana Dittmann ◽

Mario Hildebrandt

Keyword(s):

Information Fusion ◽

Classification Accuracy ◽

Negative Impact ◽

Field Application ◽

Attack Detection ◽

Majority Voting ◽

Forensic Sciences ◽

List Type ◽

Media Forensics ◽

Shafer Theory

AbstractInformation fusion, i.e., the combination of expert systems, has a huge potential to improve the accuracy of pattern recognition systems. During the last decades, various application fields started to use different fusion concepts extensively. The forensic sciences are still hesitant if it comes to blindly applying information fusion. Here, a potentially negative impact on the classification accuracy, if wrongly used or parameterized, as well as the increased complexity (and the inherently higher costs for plausibility validation) of fusion is in conflict with the fundamental requirements for forensics.The goals of this paper are to explain the reasons for this reluctance to accept such a potentially very beneficial technique and to illustrate the practical issues arising when applying fusion. For those practical discussions the exemplary application scenario of morphing attack detection (MAD) is selected with the goal to facilitate the understanding between the media forensics community and forensic practitioners.As general contributions, it is illustrated why the naive assumption that fusion would make the detection more reliable can fail in practice, i.e., why fusion behaves in a field application sometimes differently than in the lab. As a result, the constraints and limitations of the application of fusion are discussed and its impact to (media) forensics is reflected upon.As technical contributions, the current state of the art of MAD is expanded by: The introduction of the likelihood-based fusion and an fusion ensemble composition experiment to extend the set of methods (majority voting, sum-rule, and Dempster-Shafer Theory of evidence) used previously The direct comparison of the two evaluation scenarios “MAD in document issuing” and “MAD in identity verification” using a realistic and some less restrictive evaluation setups A thorough analysis and discussion of the detection performance issues and the reasons why fusion in a majority of the test cases discussed here leads to worse classification accuracy than the best individual classifier

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Decision Support System for Advanced Traffic Management Through Data Fusion

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1804-23 ◽

2002 ◽

Vol 1804 (1) ◽

pp. 173-178 ◽

Cited By ~ 14

Author(s):

Lawrence A. Klein ◽

Ping Yi ◽

Hualiang Teng

Keyword(s):

Data Fusion ◽

Traffic Management ◽

Information Sources ◽

Partial Information ◽

Weather Conditions ◽

Field Testing ◽

Traffic Operations ◽

Dempster Shafer Theory ◽

Classification Technique ◽

Shafer Theory

The Dempster–Shafer theory for data fusion and mining in support of advanced traffic management is introduced and tested. Dempste–Shafer inference is a statistically based classification technique that can be applied to detect traffic events that affect normal traffic operations. It is useful when data or information sources contribute partial information about a scenario, and no single source provides a high probability of identifying the event responsible for the received information. The technique captures and combines whatever information is available from the data sources. Dempster’s rule is applied to determine the most probable event—as that with the largest probability based on the information obtained from all contributing sources. The Dempster–Shafer theory is explained and its implementation described through numerical examples. Field testing of the data fusion technique demonstrated its effectiveness when the probability masses, which quantify the likelihood of the postulated events for the scenario, reflect current traffic and weather conditions.

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Representing wine concepts: A hybrid approach

Applied Ontology ◽

10.3233/ao-200239 ◽

2020 ◽

Vol 15 (4) ◽

pp. 475-491

Author(s):

M. Cristina Amoretti ◽

Marcello Frixione

Keyword(s):

Hybrid Approach ◽

Sufficient Conditions ◽

Geometric Representation ◽

Conceptual Spaces ◽

Geographical Indication ◽

Knowledge Based ◽

Quality Dimensions ◽

Grape Varieties ◽

Degree Of Similarity ◽

Representation Of Knowledge

Wines with geographical indication can be classified and represented by such features as designations of origin, producers, vintage years, alcoholic strength, and grape varieties; these features allow us to define wines in terms of a set of necessary and/or sufficient conditions. However, wines can also be identified by other characteristics, involving their look, smell, and taste; in this case, it is hard to define wines in terms of necessary and/or sufficient conditions, as wine concepts exhibit typicality effects. This is a setback for the design of computer science ontologies aiming to represent wine concepts, since knowledge representation formalisms commonly adopted in this field do not allow for the representation of concepts in terms of typical traits. To solve this problem, we propose to adopt a hybrid approach in which ontology-oriented formalisms are combined with a geometric representation of knowledge based on conceptual spaces. As in conceptual spaces, concepts are identified in terms of a number of quality dimensions. In order to determine those relevant for wine representation, we use the terminology developed by the Italian Association of Sommeliers to describe wines. This will allow us to understand typicality effects about wines, determine prototypes and better exemplars, and measure the degree of similarity between different wines.

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Automatic Updates of Transition Potential Matrices in Dempster-Shafer Networks Based on Evidence Inputs

Sensors ◽

10.3390/s20133727 ◽

2020 ◽

Vol 20 (13) ◽

pp. 3727

Author(s):

Joel Dunham ◽

Eric Johnson ◽

Eric Feron ◽

Brian German

Keyword(s):

Sensor Fusion ◽

A Priori ◽

Evidential Reasoning ◽

Unmanned Aerial Systems ◽

Sufficient Information ◽

User Input ◽

Transition Potential ◽

Dempster Shafer Theory ◽

Shafer Theory ◽

Aerial Systems

Sensor fusion is a topic central to aerospace engineering and is particularly applicable to unmanned aerial systems (UAS). Evidential Reasoning, also known as Dempster-Shafer theory, is used heavily in sensor fusion for detection classification. High computing requirements typically limit use on small UAS platforms. Valuation networks, the general name given to evidential reasoning networks by Shenoy, provides a means to reduce computing requirements through knowledge structure. However, these networks use conditional probabilities or transition potential matrices to describe the relationships between nodes, which typically require expert information to define and update. This paper proposes and tests a novel method to learn these transition potential matrices based on evidence injected at nodes. Novel refinements to the method are also introduced, demonstrating improvements in capturing the relationships between the node belief distributions. Finally, novel rules are introduced and tested for evidence weighting at nodes during simultaneous evidence injections, correctly balancing the injected evidenced used to learn the transition potential matrices. Together, these methods enable updating a Dempster-Shafer network with significantly less user input, thereby making these networks more useful for scenarios in which sufficient information concerning relationships between nodes is not known a priori.

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