Privacy.tag

Cheng, Bo; Shen, Guobin; Liu, Jie; Li, Xiangyang; Zhang, YongGuang; Zhao, Feng

doi:10.1145/2668332.2668339

Cited by 40 publications

(3 citation statements)

References 20 publications

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“…The allocated tag in such a case was modified by an attacker to minimize its represent ability from the relevance clustering. A similar issue is addressed in [17] where privacy concern about tag forgery was focused. A content-based coding for the enhancement of tag recommendation was proposed in [18].…”

Section: Related Workmentioning

confidence: 99%

Low Dimensional Relevance Coding for Personalized Tag Recommendation in Image Tagging Applications

Anupama Ganesh Phakatkar

2023

IJRITCC

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An approach of image coding for tag recommendation based on feature clustering and weighted coding is presented in this paper. The existing tag recommendation approach develops a decision based on correlation of image features and their tag annotated. The descriptive feature of the image sample defines the content of an image and is correlated with database features for tag recommendation. The feature dimension and its representation have a greater impact on the recommendation performance. The recent method tag recommendation developed CNN based visual features and proposed a tag recommendation based on weight factor. The dimensional feature and the isolated weight allocation limit the performance of presented tag recommendation system. This paper presents a new weight allocation and feature clustering method for tag recommendation. An approach of integral coding for weighted image-tag is presented to improve recommendation accuracy. The proposed recommendation system performance is tested on Flickr dataset for retrieval and recommendation accuracy.

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Section: Related Workmentioning

confidence: 99%

Low Dimensional Relevance Coding for Personalized Tag Recommendation in Image Tagging Applications

Anupama Ganesh Phakatkar

2023

IJRITCC

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“…A bias may be an outcome of many factors, one of them is the privacy aspect. Privacy and security are major increasing concerns in the digital era [22,23]. Privacy is considered as an essential value to liberal society and the individual's autonomy [24,25], and is extensively regulated by governments and intragovernmental organizations, e.g., the EU's General Data Protection Regulation (GDPR) [26,27].…”

Section: Introductionmentioning

confidence: 99%

Introducing the Privacy Aspect to Systems Thinking Assessment Method

Hirschprung¹,

Kordova²

2021

Preprint

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Systems thinking is a valuable skill that may be required for an individual to be promoted in the business arena to managerial or leading positions. Thus, assessing systems thinking skills is an essential transaction for decision-makers in the organization as a preceding step to the promotion decision. One of the well-known and validated tools for this task is a questionnaire. However, because some of the questions invade the employee / candidate’s privacy, the answer may be biased. In this paper, we consider this potential bias, a phenomenon that is becoming more and more significant as privacy concerns and awareness continuously increase in the modern digital world. We propose a formal methodology to optimize the questionnaire based on the privacy sensitivity of each question, thereby providing a more reliable assessment. We conducted an empirical study (n=142), and showed that a systems skills questionnaire can be enhanced. This research has a significant contribution to improving the systems skills assessment process in particular, and also lays the foundations for improving the evaluation of other skills / traits.

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“…A bias may be an outcome of many factors, one of them being the privacy aspect. Privacy and security are major increasing concerns in the digital era [41][42][43]. Privacy is considered as an essential value to liberal society and the individual's autonomy [44,45], and is extensively regulated by governments and intragovernmental organizations, e.g., the EU's General Data Protection Regulation (GDPR) [46][47][48].…”

Section: Introductionmentioning

confidence: 99%

Introducing the Privacy Aspect to Systems Thinking Assessment Method

Hirschprung

Kordova

2021

Systems

View full text Add to dashboard Cite

Systems thinking is a valuable skill that may be required for an individual to be promoted in the business arena to managerial or leading positions. Thus, assessing systems thinking skills is an essential transaction for decision makers in the organization as a preceding step to the promotion decision. One of the well-known and validated tools for this task is a questionnaire. However, because some of the questions invade the employee or candidate’s privacy, the answer may be biased. In this paper, we consider this potential bias, a phenomenon that is becoming more and more significant as privacy concerns and awareness continuously increase in the modern digital world. We propose a formal methodology to optimize the questionnaire based on the privacy sensitivity of each question, thereby providing a more reliable assessment. We conducted an empirical study (n=142) and showed that a systems skills questionnaire can be enhanced. This research makes a significant contribution to improving the systems skills assessment process in particular, and lays the foundations for improving the evaluation of other skills or traits.

show abstract

Privacy.tag

Cited by 40 publications

References 20 publications

Low Dimensional Relevance Coding for Personalized Tag Recommendation in Image Tagging Applications

Low Dimensional Relevance Coding for Personalized Tag Recommendation in Image Tagging Applications

Introducing the Privacy Aspect to Systems Thinking Assessment Method

Introducing the Privacy Aspect to Systems Thinking Assessment Method

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