Social Network Inspired Approach to Intelligent Monitoring of Intelligence Data

2011 ◽  
pp. 79-100 ◽  
Author(s):  
Qiang Shen ◽  
Tossapon Boongoen
Keyword(s):  
Similarity Measure ◽  
Similarity Measures ◽  
Alternative Methods ◽  
Common Denominator ◽  
Communication Behaviors ◽  
Similarity Estimation ◽  
Order Of Magnitude ◽  
Qualitative Similarity ◽  
The Common ◽  
Link Information

In the wake of recent terrorist atrocities, intelligence experts have commented that failures in detecting terrorist and criminal activities are not so much due to a lack of data, as they are due to difficulties in relating and interpreting the available intelligence. An intelligent tool for monitoring and interpreting intelligence data will provide a helpful means for intelligence analysts to consider emerging scenarios of plausible threats, thereby offering useful assistance in devising and deploying preventive measures against such possibilities. One of the major problems in need of such attention is detecting false identity that has become the common denominator of all serious crime, especially terrorism. Typical approaches to this problem rely on the similarity measure of textual and other content-based characteristics, which are usually not applicable in the case of deceptive and erroneous description. This barrier may be overcome through link information presented in communication behaviors, financial interactions and social networks. Quantitative link-based similarity measures have proven effective for identifying similar problems in the Internet and publication domains. However, these numerical methods only concentrate on link structures, and fail to achieve accurate and coherent interpretation of the information. Inspired by this observation, the chapter presents a novel qualitative similarity measure that makes use of multiple link properties to refine the underlying similarity estimation process and consequently derive semantic-rich similarity descriptors. The approach is based on order-of-magnitude reasoning. Its performance is empirically evaluated over a terrorism-related dataset, and compared against several state-of-the-art link-based algorithms and other alternative methods.

Similarity Measure for Matching Fuzzy Object Shapes

2018 ◽  
pp. 118-136
Keyword(s):  
Similarity Measure ◽  
Similarity Measures ◽  
Experimental Results ◽  
Technical Procedure ◽  
Region Matching ◽  
The Common ◽  
Object Shapes

In this chapter, the Common Bin Similarity Measure (CBSM) is introduced to estimate the degree of overlapping between the query and the database objects. All available similarity measures fail to handle the problem of Integrated Region Matching (IRM). The technical procedure followed for extracting the objects from images is well defined with an example. The performance of CBSM is compared with well-known methods and the results are given. The effect of IRM with CBSM is also proved by the experimental results. In addition, the performance of CBSM in encoded feature is compared with similar approaches. Overall, the CBSM is a novel idea and very much suitable for matching objects and ranking on their similarities.

Hotspots in Psychology – 2020 Edition

2020 ◽  
Vol 228 (1) ◽  
pp. 1-2
Author(s):  
Michael Bošnjak ◽  
Nadine Wedderhoff
Keyword(s):  
Systematic Reviews ◽  
Research Synthesis ◽  
Synthesis Methods ◽  
Common Denominator ◽  
The Common ◽  
Meta Analyses

Abstract. This editorial gives a brief introduction to the six articles included in the fourth “Hotspots in Psychology” of the Zeitschrift für Psychologie. The format is devoted to systematic reviews and meta-analyses in research-active fields that have generated a considerable number of primary studies. The common denominator is the research synthesis nature of the included articles, and not a specific psychological topic or theme that all articles have to address. Moreover, methodological advances in research synthesis methods relevant for any subfield of psychology are being addressed. Comprehensive supplemental material to the articles can be found in PsychArchives ( https://www.psycharchives.org ).

Motives of espionage against ones own country in the light of idiographic studies

2011 ◽  
Vol 42 (1) ◽  
pp. 1-4 ◽  
Author(s):  
Sebastian Michalak
Keyword(s):  
Common Sense ◽  
Control Group ◽  
Common Denominator ◽  
Methodological Problems ◽  
The Common

Motives of espionage against ones own country in the light of idiographic studies The money is perceived as the common denominator among people who have spied against their own country. This assumption is common sense and appears to be self-evident truth. But do we have any hard evidences to prove the validity of such a statement? What method could be applied to determine it? This article is a review of the motives behind one's resorting to spying activity which is a complex and multifarious process. I decided to present only the phenomenon of spying for another country. The studies on the motives behind taking up spying activity are idiographic in character. One of the basic methodological problems to be faced by the researchers of this problem is an inaccessibility of a control group.

The Problem of the Typology of Rationality in Muslim Culture

2019 ◽  
Vol 62 (6) ◽  
pp. 88-99
Author(s):  
Andrey A. Lukashev
Keyword(s):  
Modern Philosophy ◽  
Theoretical Basis ◽  
Theoretical Models ◽  
The Other ◽  
Common Denominator ◽  
Islamic Culture ◽  
The Common ◽  
Per Se ◽  
The One ◽  
Muslim Culture

The typology of rationality is one of major issues of modern philosophy. In an attempt to provide a typology to Oriental materials, a researcher faces additional problems. The diversity of the Orient as such poses a major challenge. When we say “Oriental,” we mean several cultures for which we cannot find a common denominator. The concept of “Orient” involves Arabic, Indian, Chinese, Turkish and other cultures, and the only thing they share is that they are “non-Western.” Moreover, even if we focus just on Islamic culture and look into rationality in this context, we have to deal with a conglomerate of various trends, which does not let us define, with full confidence, a common theoretical basis and treat them as a unity. Nevertheless, we have to go on trying to find common directions in thought development, so as to draw conclusions about types of rationality possible in Islamic culture. A basis for such a typology of rationality in the context of the Islamic world was recently suggested in A.V. Smirnov’s logic of sense theory. However, actual empiric material cannot always fit theoretical models, and the cases that do not fit the common scheme are interesting per se. On the one hand, examination of such cases gives an opportunity to specify certain provisions of the theory and, on the other hand, to define the limits of its applicability.

MATHURA (MBI) - A NOVEL IMPUTATION MEASURE FOR IMPUTATION OF MISSING VALUES IN MEDICAL DATASETS

2019 ◽  
Vol 13 ◽  
Author(s):  
B. Mathura Bai ◽  
N. Mangathayaru ◽  
B. Padmaja Rani ◽  
Shadi Aljawarneh
Keyword(s):  
Similarity Measure ◽  
Medical Records ◽  
Missing Values ◽  
Similarity Measures ◽  
Common Problems ◽  
Experiment Analysis

: Missing attribute values in medical datasets are one of the most common problems faced when mining medical datasets. Estimation of missing values is a major challenging task in pre-processing of datasets. Any wrong estimate of missing attribute values can lead to inefficient and improper classification thus resulting in lower classifier accuracies. Similarity measures play a key role during the imputation process. The use of an appropriate and better similarity measure can help to achieve better imputation and improved classification accuracies. This paper proposes a novel imputation measure for finding similarity between missing and non-missing instances in medical datasets. Experiments are carried by applying both the proposed imputation technique and popular benchmark existing imputation techniques. Classification is carried using KNN, J48, SMO and RBFN classifiers. Experiment analysis proved that after imputation of medical records using proposed imputation technique, the resulting classification accuracies reported by the classifiers KNN, J48 and SMO have improved when compared to other existing benchmark imputation techniques.

scholarly journals A set theory based similarity measure for text clustering and classification

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ali A. Amer ◽  
Hassan I. Abdalla
Keyword(s):  
Set Theory ◽  
Similarity Measure ◽  
Similarity Measures ◽  
Text Clustering ◽  
Plagiarism Detection ◽  
K Nearest Neighbor ◽  
Single Measure ◽  
Highly Effective ◽  
Clustering And Classification ◽  
Effectiveness And Efficiency

Abstract Similarity measures have long been utilized in information retrieval and machine learning domains for multi-purposes including text retrieval, text clustering, text summarization, plagiarism detection, and several other text-processing applications. However, the problem with these measures is that, until recently, there has never been one single measure recorded to be highly effective and efficient at the same time. Thus, the quest for an efficient and effective similarity measure is still an open-ended challenge. This study, in consequence, introduces a new highly-effective and time-efficient similarity measure for text clustering and classification. Furthermore, the study aims to provide a comprehensive scrutinization for seven of the most widely used similarity measures, mainly concerning their effectiveness and efficiency. Using the K-nearest neighbor algorithm (KNN) for classification, the K-means algorithm for clustering, and the bag of word (BoW) model for feature selection, all similarity measures are carefully examined in detail. The experimental evaluation has been made on two of the most popular datasets, namely, Reuters-21 and Web-KB. The obtained results confirm that the proposed set theory-based similarity measure (STB-SM), as a pre-eminent measure, outweighs all state-of-art measures significantly with regards to both effectiveness and efficiency.

The Common Denominator of Land Classification in Canada: an Ecological Approach to Mapping

1980 ◽  
Vol 56 (1) ◽  
pp. 19-20 ◽  
Author(s):  
J. S. Rowe
Keyword(s):  
Ecological Approach ◽  
Common Denominator ◽  
Land Classification ◽  
Ecological Land Classification ◽  
Common Basis ◽  
The Common

The cores and boundaries of land units are located by reference to relationships between climate, landform and biota in ecological land classification. This appeal to relationships, rather than to climate, or to geomorphology, or to soils, or to vegetation alone, provides the common basis for land classification.

Getting beyond the common denominator

2011 ◽  
Vol 26 (2) ◽  
pp. 217-231 ◽  
Author(s):  
L. Hunt ◽  
M. Lundberg ◽  
B. Zuckerman
Keyword(s):  
Common Denominator ◽  
The Common

scholarly journals A New Approach to Measuring the Similarity of Indoor Semantic Trajectories

2021 ◽  
Vol 10 (2) ◽  
pp. 90
Author(s):  
Jin Zhu ◽  
Dayu Cheng ◽  
Weiwei Zhang ◽  
Ci Song ◽  
Jie Chen ◽  
...  
Keyword(s):  
Similarity Measure ◽  
Semantic Information ◽  
Edit Distance ◽  
Similarity Measures ◽  
Indoor Positioning ◽  
Synthetic Dataset ◽  
Shopping Mall ◽  
Indoor Space ◽  
Trajectory Similarity ◽  
Indoor Spaces

People spend more than 80% of their time in indoor spaces, such as shopping malls and office buildings. Indoor trajectories collected by indoor positioning devices, such as WiFi and Bluetooth devices, can reflect human movement behaviors in indoor spaces. Insightful indoor movement patterns can be discovered from indoor trajectories using various clustering methods. These methods are based on a measure that reflects the degree of similarity between indoor trajectories. Researchers have proposed many trajectory similarity measures. However, existing trajectory similarity measures ignore the indoor movement constraints imposed by the indoor space and the characteristics of indoor positioning sensors, which leads to an inaccurate measure of indoor trajectory similarity. Additionally, most of these works focus on the spatial and temporal dimensions of trajectories and pay less attention to indoor semantic information. Integrating indoor semantic information such as the indoor point of interest into the indoor trajectory similarity measurement is beneficial to discovering pedestrians having similar intentions. In this paper, we propose an accurate and reasonable indoor trajectory similarity measure called the indoor semantic trajectory similarity measure (ISTSM), which considers the features of indoor trajectories and indoor semantic information simultaneously. The ISTSM is modified from the edit distance that is a measure of the distance between string sequences. The key component of the ISTSM is an indoor navigation graph that is transformed from an indoor floor plan representing the indoor space for computing accurate indoor walking distances. The indoor walking distances and indoor semantic information are fused into the edit distance seamlessly. The ISTSM is evaluated using a synthetic dataset and real dataset for a shopping mall. The experiment with the synthetic dataset reveals that the ISTSM is more accurate and reasonable than three other popular trajectory similarities, namely the longest common subsequence (LCSS), edit distance on real sequence (EDR), and the multidimensional similarity measure (MSM). The case study of a shopping mall shows that the ISTSM effectively reveals customer movement patterns of indoor customers.

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