scholarly journals CDISC SDTM Vaccination Evidence Source Terminology

2020 ◽  
Author(s):  
Keyword(s):  
Evidence Source

scholarly journals ANTI-FORENSICS: COUNTERING SENSOR NOISE CAMERA IDENTIFICATION

2020 ◽  
pp. 45-51
Author(s):  
Nikhith Suvarna
Keyword(s):  
Data Hiding ◽  
Forensic Analysis ◽  
Sensor Noise ◽  
Analysis Techniques ◽  
Camera Identification ◽  
Source Data ◽  
Highly Effective ◽  
Evidence Source ◽  
Noise Signature ◽  
Tools And Techniques

In simple terms, Anti-Forensics can be told as the techniques used to counter forensic analysis done by forensic investigators. This paper mainly focuses on some of the most used anti-forensics techniques along with the challenges the forensics investigator faces. There are many tools and techniques available that when used properly can be highly effective against the forensic analysis techniques. Various tools assist you against various anti-forensics techniques like Elimination of evidence source, Data hiding, and Trail obfuscation. These techniques are used mainly to make the investigation consume more time and money. Sensor Noise Camera Identification is a way to link a photo with the camera the photo was taken from using a noise signature that is unique for every camera. KEYWORDS: Anti-Forensics (AF), Forensic Analysis, Anti-Forensic Techniques, Sensor Noise Camera Identification

Uncertain Confidence Network-Based Collaborative Information Retrieval Relevance Feedback Algorithm

2020 ◽  
pp. 20-39
Author(s):  
Fatiha Naouar ◽  
Lobna Hlaoua ◽  
Mohamed Nazih Omri
Keyword(s):  
Information Retrieval ◽  
Relevance Feedback ◽  
Information Source ◽  
Strong Association ◽  
Relevant Information ◽  
User Query ◽  
Proposed Model ◽  
Evidence Source ◽  
Feedback Algorithm ◽  
Collaborative Information Retrieval

Collaborative retrieval allows increasing the amount of relevant information found and sharing history with others. The collaborative retrieval can reduce the retrieval time performed by the users of the same profile. This chapter proposes a new relevance feedback algorithm to collaborative information retrieval based on a confidence network, which performs propagation relevance between annotations terms. The main contribution in this work is the extraction of relevant terms to reformulate the initial user query considering the annotations as an information source. The proposed model introduces the concept of necessity that allows determining the terms that have strong association relationships estimated to the measure of a confidence. Since the user is overwhelmed by a variety of contradictory annotations, another contribution consists of determining the relevant annotations for a given evidence source. The experimental study gives very encouraging results.

scholarly journals Peer Review and Transparency in Evidence-Source Selection in Value and Health Technology Assessment

2020 ◽  
Vol 23 (6) ◽  
pp. 689-696
Author(s):  
Bansri Desai ◽  
T. Joseph Mattingly ◽  
Remon W.M. van den Broek ◽  
Ngan Pham ◽  
Megan Frailer ◽  
...  
Keyword(s):  
Health Technology Assessment ◽  
Peer Review ◽  
Technology Assessment ◽  
Health Technology ◽  
Source Selection ◽  
Evidence Source

scholarly journals Academic Librarians’ Conception and Use of Evidence Sources in Practice

2012 ◽  
Vol 7 (4) ◽  
pp. 5 ◽  
Author(s):  
Denise Koufogiannakis
Keyword(s):  
Decision Making ◽  
Grounded Theory ◽  
Tacit Knowledge ◽  
Scientific Research ◽  
Complete Picture ◽  
Academic Librarians ◽  
Structured Interviews ◽  
Different Types ◽  
Evidence Source ◽  
Canada Data

Objective – The objective of this study was to explore and understand how academic librarians use evidence in their professional decision making. The researcher aimed to gain insights on the relevance of the current EBLIP model to practice, and to understand the possible connections between scientific research and tacit knowledge within the practice of LIS. Methods – A grounded theory methodology was used, following the approach of Charmaz (2006). Participants were 19 academic librarians in Canada. Data was gathered via online diaries and semi-structured interviews over a six-month period in 2011. Results – Two broad types of evidence were identified (hard and soft), and are generally used in conjunction with one another. Librarians examine all evidence sources with a critical eye, and try to determine a complete picture before reaching a conclusion. As well, librarians use a variety of proactive and passive approaches to find evidence. Conclusions – These results provide a strong message that no single evidence source is perfect. Consequently, librarians bring different types of evidence together in order to be as informed as possible before making a decision. Using a combination of evidence sources, depending upon the problem, is the way academic librarians approach decision making.

scholarly journals PDG76 IN VITRO DATA AS COMPLEMENTARY EVIDENCE SOURCE FOR THE ASSESSMENT OF ANTIBIOTICS IN THE GERMAN BENEFIT ASSESSMENT?

2019 ◽  
Vol 22 ◽  
pp. S609
Author(s):  
M. Mechelke ◽  
K. Worf ◽  
D. Eheberg ◽  
T. Roxlau ◽  
D. Bonduelle
Keyword(s):  
Benefit Assessment ◽  
Evidence Source ◽  
Vitro Data ◽  
In Vitro Data

Know the Evidence Source

2018 ◽  
pp. 125-132
Author(s):  
Peter J. Burke ◽  
Sara Jimenez Soffa
Keyword(s):  
Evidence Source

scholarly journals 'Half-cut' science: a qualitative examination of alcohol industry actors' use of peer-reviewed evidence in policy submissions on Minimum Unit Pricing

2019 ◽  
Vol 15 (1) ◽  
pp. 49-66
Author(s):  
Dana Cullen ◽  
Katherine Smith ◽  
Jeff Collin
Keyword(s):  
Reference Lists ◽  
Alcohol Industry ◽  
Scottish Parliament ◽  
Evidence Source ◽  
Industry Policy ◽  
Unit Pricing ◽  
Number Of Citations

Aim: To assess the extent to which alcohol industry actors cited evidence in submissions to the Scottish Parliament Health and Sport Committee's 2011 call for written evidence on the Alcohol (Minimum Pricing) (Scotland) Bill and to compare any citations of peer-reviewed evidence to original sources. Methods: All submissions to the consultation (n=128) were manually searched to identify those written by alcohol industry actors (n=25). The reference lists of all the alcohol industry submissions were reviewed and peer-reviewed sources were retrieved, read and assessed against their in-text citation within the alcohol industry submissions. Results: Although most industry submissions cited evidence of some sort, only 7 (28%) cited peer-reviewed evidence. Comparing the total number of citations to peer-reviewed evidence (n=17) to original sources demonstrates that 82% were questionably cited. Conclusion: Our findings demonstrate that the majority of references to peer-reviewed evidence in this sample of alcohol industry policy submissions were misleading with examples of citations being presented as supportive of arguments that the original evidence source specifically argued against. This suggests that even the depiction of peer-reviewed evidence within alcohol industry policy submissions needs to be treated with caution.

Information Retrieval Model Using Uncertain Confidence's Network

2018 ◽  
pp. 434-452
Author(s):  
Fatiha Naouar ◽  
Lobna Hlaoua ◽  
Mohamed Nazih Omri
Keyword(s):  
Experimental Study ◽  
Information Retrieval ◽  
Relevance Feedback ◽  
Information Source ◽  
Strong Association ◽  
Improvement Rate ◽  
User Query ◽  
Proposed Model ◽  
Evidence Source ◽  
Collaborative Information Retrieval

This paper proposes a new relevance feedback approach to collaborative information retrieval based on a confidence's network, which performs propagation relevance between annotations terms. The main contribution of our approach is to extract relevant terms to reformulate the initial user query considering the annotations as an information source. The proposed model introduces the concept of necessity that allows determining the terms that have strong association relationships. The authors estimated the association relationship to a measure of a confidence. Another contribution consists on determining the relevant annotations for a given evidence source. Since the user is over whelmed by a variety of contradictory annotations on even one which are far from the original subject, the authors' model proceed filtering these annotations to determine the relevant one and then it classify them by grouping those related semantically. The experimental study conducted on different queries gives promoters results. They show very encouraging results that could reach an improvement rate.

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