SLIC Based Digital Image Enlargement

Low resolution image enhancement is a classical computer vision problem. Selecting the best method to reconstruct an image to a higher resolution with the limited data available in the low-resolution image is quite a challenge. A major drawback from the existing enlargement techniques is the introduction of color bleeding while interpolating pixels over the edges that separate distinct colors in an image. The color bleeding causes to accentuate the edges with new colors as a result of blending multiple colors over adjacent regions. This paper proposes a novel approach to mitigate the color bleeding by segmenting the homogeneous color regions of the image using Simple Linear Iterative Clustering (SLIC) and applying a higher order interpolation technique separately on the isolated segments. The interpolation at the boundaries of each of the isolated segments is handled by using a morphological operation. The approach is evaluated by comparing against several frequently used image enlargement methods such as bilinear and bicubic interpolation by means of Peak Signal-to-Noise-Ratio (PSNR) value. The results obtained exhibit that the proposed method outperforms the baseline methods by means of PSNR and also mitigates the color bleeding at the edges which improves the overall appearance.Comment: 6 page

MicroConceptBERT: concept-relation based document information extraction framework.

Extracting information from documents is a crucial task in natural language processing research. Existing information extraction methodologies often focus on specific domains, such as medicine, education or finance, and are limited by language constraints. However, more comprehensive approaches that transcend document types, languages, contexts, and structures would significantly advance the field proposed in recent research. This study addresses this challenge by introducing microConceptBERT: a concept-relations-based framework for document information extraction, which offers flexibility for various document processing tasks while accounting for hierarchical, semantic, and heuristic features. The proposed framework has been applied to a question-answering task on benchmark datasets: SQUAD 2.0 and DOCVQA. Notably, the F1 evaluation metric attains an outperforming 87.01 performance rate on the SQUAD 2.0 dataset compared to baseline models: BERT-base and BERT-large models

Can Deep Learning Approach Be Virtually Cultivated Via Social Learning Network

With the development of information technology especially kinds of social interaction techniques, social learning networks as a new platform have changed students’ learning behaviors and improve their learning performance. However, how this change happens especially how social learning networks change students’ learning approaches were not very clear. To address this gap, in this research, we try to investigate the impacts of social learning network on students’ learning approaches by conducting an experiment. In the experiment, students were randomly divided into two groups: control group and experimental group. We try to investigate the differences of students’ leaning behavior in terms of learning approaches in the two groups. We also present the theoretical, practical implications and future research

Perceived Importance of Portfolios in a Smart CV after an Education Reform: An Empirical Analysis

Recent developments in recruitment processes have demonstrated that job applicants are increasingly using online Smart CVs instead of traditional approaches like hardcopy or emailing CVs. This study aims at examining perceived importance university undergraduate students of Hong Kong place or put on portfolios of Smart CVs, such as internship experience, exchange experience, scholarships & awards, participation in competitions, academic performance, and extra-curricular activities when building a Smart CV, and on investigating potential effects of the 3+3+4 academic reform in Hong Kong and admission mode. Participants were 256 undergraduate students in BBA majoring either in Information Management or in Electronic Commerce. A survey consisting of 44 items, which measured perceptions on the importance of the 6 proposed portfolios of Smart CVs, was used to collect data. Principal component analysis was used to analyze the items and 34 items were included in the final factor structure out of which 27 items got retained after subsequent reliability analysis. The 6 portfolios were positively inter-correlated. Students who were admitted under the new 4-year undergraduate curriculum using examination results of the new Hong Kong Diploma of Secondary Education (HKDSE) perceived internship experience and participation in competitions as more important in their Smart CVs, which was not the case with those who were admitted under the 3-year undergraduate curriculum using the results of the Hong Kong Advanced Level Examination (HKALE), which is no longer in use since 2012. The admission routes of students did not affect perceived importance in a Smart CV of the 6 proposed portfolios

A WEIGHTED TOPIC MODEL ENHANCED APPROACH FOR COMPLEMENTARY COLLABORATOR RECOMMENDATION

Collaborations among interdisciplinary scientists are playing an increasingly important role in science innovations. As it is very difficult for a researcher to master the full knowledge of his/her targeted research areas, how to find suitable collaborators of complementary expertise has turned to be a key factor for researchers to succeed. With the expansion of the Web, the availability of sheer volume of information has resulted in information overload issue and posed significant challenges on determining appropriate scientists to collaborate with effectively for research opportunities. However, current studies on collaborator recommendation ignored this phenomenon and particularly overlooked the complementarity of their expertise within a restrictive context, i.e. for a given funding proposal or a research manuscript draft. In this study we propose a complementary expertise analysis enhanced approach to retrieval experts for research collaboration. It produces recommendation list using a heuristic greedy algorithm based on probabilistic topic model, and generates experts who ought to be complemented in expertise as well as to have good ability. The proposed method has been implemented in ScholarMate research community (www.scholarmate.com). We have conducted a user study to verify the effectiveness of the proposed approach and the preliminary results show its good performance comparing to the benchmarks

Depuration reduces microplastic content in wild and farmed mussels

Plastic pollution is a pervasive problem to marine life. This study aimed (1) to investigate levels of microplastic in wild and farmed mussels (Perna perna), and (2) to assess the effectiveness of depuration in reducing micro plastics. Wild and farmed mussels were sampled from Guanabara Bay (Southwestern Atlantic). Four treatments were compared (N = 10 mussels/treatment): wild non-depurated mussels, wild depurated mussels, farmed non depurated mussels, and farmed depurated mussels. Up to 31.2 +/- 17.8 microplastics/mussel (>= 0.45 pm) were detected (means +/- SD), and microplastics were present in all 40 individuals analyzed. Nylon fibers were more abundant than polymethyl methacrylate (PMMA) fragments. Blue, transparent, and red nylon fibers were more abundant in both wild and farmed mussels. Although 93 h-depuration significantly reduced microplastics (ANOVA, p = 0.02) in both wild (46.79%) and farmed mussels (28.95%), differences between farmed and wild mussels were not significant (p > 0.05). Depuration was more effective in removing blue fibers. Our results highlight the importance of depuration in reducing microplastic pollution in seafood.Brazilian National Research Council (CNPq)National Council for Scientific and Technological Development (CNPq