Multidimensional Filtering Approach Based on Contextual Information

2006 ◽  
Author(s):  
Sunghoon Cho ◽  
Moohun Lee ◽  
Changbok Jang ◽  
Euiin Choi
Keyword(s):  
Contextual Information ◽  
Multidimensional Filtering ◽  
Filtering Approach

scholarly journals Contextualized Filtering for Shared Cyber Threat Information

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4890
Author(s):  
Athanasios Dimitriadis ◽  
Christos Prassas ◽  
Jose Luis Flores ◽  
Boonserm Kulvatunyou ◽  
Nenad Ivezic ◽  
...  
Keyword(s):  
Information Sharing ◽  
Business Processes ◽  
State Of The Art ◽  
Contextual Information ◽  
Coarse Grained ◽  
Business Information ◽  
Cyber Threat ◽  
Domain Expertise ◽  
Multi Level ◽  
Filtering Approach

Cyber threat information sharing is an imperative process towards achieving collaborative security, but it poses several challenges. One crucial challenge is the plethora of shared threat information. Therefore, there is a need to advance filtering of such information. While the state-of-the-art in filtering relies primarily on keyword- and domain-based searching, these approaches require sizable human involvement and rarely available domain expertise. Recent research revealed the need for harvesting of business information to fill the gap in filtering, albeit it resulted in providing coarse-grained filtering based on the utilization of such information. This paper presents a novel contextualized filtering approach that exploits standardized and multi-level contextual information of business processes. The contextual information describes the conditions under which a given threat information is actionable from an organization perspective. Therefore, it can automate filtering by measuring the equivalence between the context of the shared threat information and the context of the consuming organization. The paper directly contributes to filtering challenge and indirectly to automated customized threat information sharing. Moreover, the paper proposes the architecture of a cyber threat information sharing ecosystem that operates according to the proposed filtering approach and defines the characteristics that are advantageous to filtering approaches. Implementation of the proposed approach can support compliance with the Special Publication 800-150 of the National Institute of Standards and Technology.

scholarly journals Integrating contextual sentiment analysis in collaborative recommender systems

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248695
Author(s):  
Nurul Aida Osman ◽  
Shahrul Azman Mohd Noah ◽  
Mohammad Darwich ◽  
Masnizah Mohd
Keyword(s):  
Sentiment Analysis ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Contextual Information ◽  
User Preferences ◽  
Analysis Technique ◽  
Rating Model ◽  
Multiple Domains ◽  
Filtering Approach

Recently. recommender systems have become a very crucial application in the online market and e-commerce as users are often astounded by choices and preferences and they need help finding what the best they are looking for. Recommender systems have proven to overcome information overload issues in the retrieval of information, but still suffer from persistent problems related to cold-start and data sparsity. On the flip side, sentiment analysis technique has been known in translating text and expressing user preferences. It is often used to help online businesses to observe customers’ feedbacks on their products as well as try to understand customer needs and preferences. However, the current solution for embedding traditional sentiment analysis in recommender solutions seems to have limitations when involving multiple domains. Therefore, an issue called domain sensitivity should be addressed. In this paper, a sentiment-based model with contextual information for recommender system was proposed. A novel solution for domain sensitivity was proposed by applying a contextual information sentiment-based model for recommender systems. In evaluating the contributions of contextual information in sentiment-based recommendations, experiments were divided into standard rating model, standard sentiment model and contextual information model. Results showed that the proposed contextual information sentiment-based model illustrates better performance as compared to the traditional collaborative filtering approach.

scholarly journals A Pre-Filtering Approach for Incorporating Contextual Information Into Deep Learning Based Recommender Systems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 40485-40498 ◽  
Author(s):  
Isam Mashhour Al Jawarneh ◽  
Paolo Bellavista ◽  
Antonio Corradi ◽  
Luca Foschini ◽  
Rebecca Montanari ◽  
...  
Keyword(s):  
Deep Learning ◽  
Recommender Systems ◽  
Contextual Information ◽  
Filtering Approach

A Medieval Burial from Graham Street, Leith, Edinburgh

2003 ◽  
Vol 25 (2) ◽  
pp. 165-169
Author(s):  
Paul R. J. Duffy ◽  
Olivia Lelong
Keyword(s):  
Contextual Information ◽  
Human Remains ◽  
Housing Development ◽  
Archaeological Research ◽  
Archaeological Excavation ◽  
Radiocarbon Dates ◽  
Physical Evidence ◽  
Research Division ◽  
The North ◽  
The Individual

Summary An archaeological excavation was carried out at Graham Street, Leith, Edinburgh by Glasgow University Archaeological Research Division (GUARD) as part of the Historic Scotland Human Remains Call-off Contract following the discovery of human remains during machine excavation of a foundation trench for a new housing development. Excavation demonstrated that the burial was that of a young adult male who had been interred in a supine position with his head orientated towards the north. Radiocarbon dates obtained from a right tibia suggest the individual died between the 15th and 17th centuries AD. Little contextual information exists in documentary or cartographic sources to supplement this scant physical evidence. Accordingly, it is difficult to further refine the context of burial, although a possible link with a historically attested siege or a plague cannot be discounted.

Towards Gene Recognition from Rare and Ambiguous Abbreviations using a Filtering Approach

2014 ◽  
Author(s):  
Matthias Hartung ◽  
Roman Klinger ◽  
Matthias Zwick ◽  
Philipp Cimiano
Keyword(s):  
Gene Recognition ◽  
Filtering Approach

"He seems odd": The effects of risk-irrelevant information and actuarial risk estimates on mock jurors' perceptions of sexual recidivism risk

2020 ◽  
Author(s):  
Jennifer Kamorowski ◽  
Karl Ask ◽  
Maartje Schreuder ◽  
Marko Jelicic ◽  
Corine de Ruiter
Keyword(s):  
Contextual Information ◽  
Individual Characteristics ◽  
Risk Estimate ◽  
Case Vignette ◽  
Future Research ◽  
Sexual Recidivism ◽  
Mock Jurors ◽  
Related Factors ◽  
Risk Estimates ◽  
Actuarial Risk

Previous research has shown that mock and actual jurors give little weight to actuarial sexual offending recidivism risk estimates when making decisions regarding civil commitment for so-called sexually violent predators (SVPs). We hypothesized that non-risk related factors, such as irrelevant contextual information and jurors’ information-processing style, would influence mock jurors’ perceptions of sexual recidivism risk. This preregistered experimental study examined the effects of mock jurors’ (N = 427) need for cognition (NFC), irrelevant contextual information in the form of the offender’s social attractiveness, and an actuarial risk estimate on mock jurors’ estimates of sexual recidivism risk related to a simulated SVP case vignette. Mock jurors exposed to negative risk-irrelevant characteristics of the offender estimated sexual recidivism risk as higher than mock jurors exposed to positive information about the offender. However, this effect was no longer significant after mock jurors had reviewed Static-99R actuarial risk estimate information. We found no support for the hypothesis that the level of NFC moderates the relationship between risk-irrelevant contextual information and risk estimates. Future research could explore additional individual characteristics or attitudes among mock jurors that may influence perceptions of sexual recidivism risk and insensitivity to actuarial risk estimates.

Minimizing Bias in Forensic Handwriting Examinations

2015 ◽  
Vol 25 ◽  
pp. 17-26 ◽  
Author(s):  
L. C. Alewijnse ◽  
E.J.A.T. Mattijssen ◽  
R.D. Stoel
Keyword(s):  
Decision Making ◽  
Information Management ◽  
Contextual Information ◽  
Context Information ◽  
Decision Making Process ◽  
Potential Bias ◽  
Potential Influence ◽  
Management Procedures

The purpose of this paper is to contribute to the increasing awareness about the potential bias on the interpretation and conclusions of forensic handwriting examiners (FHEs) by contextual information. We briefly provide the reader with an overview of relevant types of bias, the difficulties associated with studying bias, the sources of bias and their potential influence on the decision making process in casework, and solutions to minimize bias in casework. We propose that the limitations of published studies on bias need to be recognized and that their conclusions must be interpreted with care. Instead of discussing whether bias is an issue in casework, the forensic handwriting community should actually focus on how bias can be minimized in practice. As some authors have already shown (e.g., Found & Ganas, 2014), it is relatively easy to implement context information management procedures in practice. By introducing appropriate procedures to minimize bias, not only forensic handwriting examination will be improved, it will also increase the acceptability of the provided evidence during court hearings. Purchase Article - $10

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