Diversity in Fashion Recommendation using Semantic Parsing

Developing recommendation system for fashion images is challenging due to the inherent ambiguity associated with what criterion a user is looking at. Suggesting multiple images where each output image is similar to the query image on the basis of a different feature or part is one way to mitigate the problem. Existing works for fashion recommendation have used Siamese or Triplet network to learn features between a similar pair and a similar-dissimilar triplet respectively. However, these methods do not provide basic information such as, how two clothing images are similar, or which parts present in the two images make them similar. In this paper, we propose to recommend images by explicitly learning and exploiting part based similarity. We propose a novel approach of learning discriminative features from weakly-supervised data by using visual attention over the parts and a texture encoding network. We show that the learned features surpass the state-of-the-art in retrieval task on DeepFashion dataset. We then use the proposed model to recommend fashion images having an explicit variation with respect to similarity of any of the parts.Comment: 5 pages, ICIP2018, code: https://github.com/sagarverma/fashion_recommendation_stlst

Evaluating pedagogy in educating business majors: an empirical analysis of teaching accounting without debits and credits

An upper-level intermediate accounting course taught at two large mid-west universities in the United States provides a natural experimental setting to examine whether teaching debits/credits in the introductory financial accounting course matters. Students in the upper-level course fall into two groups: those who learned debits/credits in the introductory course and those who weren’t. The performance of both groups is evaluated during the semester while they take the upper level accounting course. Regression results show that the prior knowledge of debits/credits offers only a mild advantage in the first mid-term exam, but not thereafter. Results also indicate that grade point average (standardized tests like ACT scores) are a good (not a good) predictor of the performance in the upper-level accounting class. These results suggest that teaching debits and credits in the introductory accounting course does not provide any advantage in learning the material of upper-level accounting course

The Botany, Chemistry, Pharmacological and Therapeutic Application of Psoralea corylifolia L. – A Review

Psoralea corylifolia Linn. is an endangered and medicinally important plant indigenous to tropical and subtropical regions of the world. Its medicinal usage is reported in Indian pharmaceutical codex, the Chinese, British and the American pharmacopoeias and in different traditional system of medicines such as Ayurveda, Unani and Siddha. The review reveals that wide ranges of phytochemical constituents have been isolated from the plant and it possesses important activities like antibacterial, anti-inflammatory and antitumer. Various other activities like hepatoprotective, antioxidants and antithelminitic have also been reported. These repots are very encouraging and indicate that herb should be studied more expensively for its therapeutic benefits.This article briefly reviews the botany, pharmacology, biochemistry and therapeutic application of the plant. This is an attempt to compile and document information on different aspects of Psoralea corylifolia and highlight the need for research and development.Keywords: - Psoralen, Isopsoralen, Pharmacological activities, Psoralea corylifolia Linn

Minimized Group Delay FIR Low Pass Filter Design Using Modified Differential Search Algorithm, Journal of Telecommunications and Information Technology, 2023, nr 3

Designing a finite impulse response (FIR) filter with minimal group delay has proven to be a difficult task. Many research studies have focused on reducing pass band and stop band ripples in FIR filter design, often overlooking the optimization of group delay. While some works have considered group delay reduction, their approaches were not optimal. Consequently, the achievement of an optimal design for a filter with a low group delay value still remains a challenge. In this work, a modified differential search optimization algorithm has been used for the purpose of designing a minimal group delay FIR filter. The results obtained have been compared with the classical techniques and they turned out to be promising