Supervised Hashing with End-to-End Binary Deep Neural Network

Image hashing is a popular technique applied to large scale content-based visual retrieval due to its compact and efficient binary codes. Our work proposes a new end-to-end deep network architecture for supervised hashing which directly learns binary codes from input images and maintains good properties over binary codes such as similarity preservation, independence, and balancing. Furthermore, we also propose a new learning scheme that can cope with the binary constrained loss function. The proposed algorithm not only is scalable for learning over large-scale datasets but also outperforms state-of-the-art supervised hashing methods, which are illustrated throughout extensive experiments from various image retrieval benchmarks.Comment: Accepted to IEEE ICIP 201

Selective Deep Convolutional Features for Image Retrieval

Convolutional Neural Network (CNN) is a very powerful approach to extract discriminative local descriptors for effective image search. Recent work adopts fine-tuned strategies to further improve the discriminative power of the descriptors. Taking a different approach, in this paper, we propose a novel framework to achieve competitive retrieval performance. Firstly, we propose various masking schemes, namely SIFT-mask, SUM-mask, and MAX-mask, to select a representative subset of local convolutional features and remove a large number of redundant features. We demonstrate that this can effectively address the burstiness issue and improve retrieval accuracy. Secondly, we propose to employ recent embedding and aggregating methods to further enhance feature discriminability. Extensive experiments demonstrate that our proposed framework achieves state-of-the-art retrieval accuracy.Comment: Accepted to ACM MM 201

EFFICACY OF Ageratum LEAF EXTRACT ON POSTHARVEST ROT CAUSED BY Aspergillus niger AND Colletotrichum sp. ON CHILLI FRUITS

Abstract: The damage of chilli, an important spice fruit, comes from fungal diseases caused mainly by Aspergillus niger and Colletotrichum sp. The fungi on chilli fruits would directly harm consumers’ health. Plant extracts containing bio-active compounds with antimicrobial properties could be a good possible solution to deal with the fungi. This study aims to evaluate the in vitro and in vivo efficacy of aqueous extracts from the leaves of Ageratum plants against A. niger and Colletotrichum sp. The results show that the optimal efficacy of the treatment of Ageratum leaf extract is at a concentration of 6%, with the efficacy of Ageratum leaf extract on colonial diameter at approximately 43–44% for the two fungi at 96 hours after inoculation. The 6% Ageratum leaf extract has a high efficacy (~54.23%) on limiting the development of Aspergillus rot lesions on chilli fruits 4 days after inoculation. Meanwhile, the efficacy of the extract on Colletotrichum lesions is 11.34%, lower than that of Aspergillus rot.Keywords: Ageratum leaf extract, chilli fruit, rot lesio

CHARACTERIZATION AND ADSORPTION CAPACITY OF AMINE-SIO2 MATERIAL FOR NITRATE AND PHOSPHATE REMOVAL

Amine-SiO2 material was synthesized and applied as a novel adsorbent for nitrate and phosphate removal from aqueous solution. The characterization of Amine-SiO2 were done by using TGA, FTIR, BET, and SEM analyses. Results showed that Amine-SiO2 had higher nitrate and phosphate adsorption capacity of 1.14 and 4.16 times, respectively, than commercial anion exchange resin (Akualite A420). In addition, Amine-SiO2 also had good durability with stable performance after at least 10 regeneration times, indicating that this material is very promising for commercialization in the future as an adsorbent for water treatment

FACTORS AFFECTING THE ACADEMIC RESULTS OF MASTER STUDENTS IN MATHEMATICS EDUCATION AT CAN THO UNIVERSITY, VIETNAM: A SURVEY STUDY

The study results were based on the survey data of 24 students  studying the master program in math education at Can Tho University, Vietnam. We used the questionnaire to find out the factors affecting students' learning outcomes: Learning time, learning conditions, learning environment, personal level, learning methods, collaborative learning, learning attitudes. The results show factors such as learning conditions, learning environment, time for leaning, qualifications, teaching methods, learning methods, cooperation in learning, attitude in learning are factors that significantly affect the learning of master students in Mathematics education. Therefore, universities with high-level training programs should have adequate facilities for students' learning; lecturers know how to use teaching methods to promote self-study and self-study for students, improve their ability to work independently, the ability to cooperate in the learning and research process of students. In other words, universities must uphold  their responsibilities when implementing intensive training programs, helping learners with necessary competencies as expected of the community and society.  Article visualizations