Object proposal with kernelized partial ranking

Evaluation of online teaching quality has become an important issue because many universities are turning to online classes due to the Corona Virus Disease 2019 (COVID-19) pandemic. In this paper, online teaching quality evaluation is considered as a linguistic multi-attribute group decision-making (MAGDM) problem. Generally, the evaluation sematic information can be symmetrically or asymmetrically distributed in linguistic term sets. Thus, an extended linguistic MAGDM framework is proposed for evaluating online teaching quality. As the main contribution, the proposed method takes into account the risk preferences of assessment experts (AEs) and unknown weight information of attributes and sub-attributes. To be specific, the Delphi method is employed to establish a multi-level evaluation indicator system (EIS) of online teaching quality. Then, by introducing the group generalized linguistic term set (GLTS) with two risk preference parameters, a two-stage optimization model is developed to calculate the weights of attributes and sub-attributes objectively. Subsequently, the linguistic MAGDM framework was divided into two stages. The first stage maximizes the group comprehensive rating values of teachers on different attributes to obtain partial ranking results for teachers on each attribute. The latter stage maximizes the group comprehensive rating values of teachers to evaluate the overall quality. Finally, a case study is provided to illustrate how to apply the framework to evaluate online teaching quality.

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Zero-Shot Detection with Transferable Object Proposal Mechanism

2019 IEEE International Conference on Image Processing (ICIP) ◽

10.1109/icip.2019.8803655 ◽

2019 ◽

Author(s):

Yilan Shao ◽

Yanan Li ◽

Donghui Wang

Keyword(s):

Shot Detection ◽

Object Proposal

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A Genetic Algorithm Approach for Group Recommender System Based on Partial Rankings

Journal of Intelligent Systems ◽

10.1515/jisys-2017-0561 ◽

2018 ◽

Vol 29 (1) ◽

pp. 653-663 ◽

Cited By ~ 1

Author(s):

Ritu Meena ◽

Kamal K. Bharadwaj

Keyword(s):

Genetic Algorithm ◽

Recommender Systems ◽

Rank Aggregation ◽

Partial Ranking ◽

Novel Approach ◽

Kendall Tau Distance ◽

Ultimate Objective ◽

Aggregation Technique ◽

The Individual ◽

Group Recommender

Abstract Many recommender systems frequently make suggestions for group consumable items to the individual users. There has been much work done in group recommender systems (GRSs) with full ranking, but partial ranking (PR) where items are partially ranked still remains a challenge. The ultimate objective of this work is to propose rank aggregation technique for effectively handling the PR problem. Additionally, in real applications, most of the studies have focused on PR without ties (PRWOT). However, the rankings may have ties where some items are placed in the same position, but where some items are partially ranked to be aggregated may not be permutations. In this work, in order to handle problem of PR in GRS for PRWOT and PR with ties (PRWT), we propose a novel approach to GRS based on genetic algorithm (GA) where for PRWOT Spearman foot rule distance and for PRWT Kendall tau distance with bucket order are used as fitness functions. Experimental results are presented that clearly demonstrate that our proposed GRS based on GA for PRWOT (GRS-GA-PRWOT) and PRWT (GRS-GA-PRWT) outperforms well-known baseline GRS techniques.

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