SAVOIAS: A Diverse, Multi-Category Visual Complexity Dataset

Visual complexity identifies the level of intricacy and details in an image or the level of difficulty to describe the image. It is an important concept in a variety of areas such as cognitive psychology, computer vision and visualization, and advertisement. Yet, efforts to create large, downloadable image datasets with diverse content and unbiased groundtruthing are lacking. In this work, we introduce Savoias, a visual complexity dataset that compromises of more than 1,400 images from seven image categories relevant to the above research areas, namely Scenes, Advertisements, Visualization and infographics, Objects, Interior design, Art, and Suprematism. The images in each category portray diverse characteristics including various low-level and high-level features, objects, backgrounds, textures and patterns, text, and graphics. The ground truth for Savoias is obtained by crowdsourcing more than 37,000 pairwise comparisons of images using the forced-choice methodology and with more than 1,600 contributors. The resulting relative scores are then converted to absolute visual complexity scores using the Bradley-Terry method and matrix completion. When applying five state-of-the-art algorithms to analyze the visual complexity of the images in the Savoias dataset, we found that the scores obtained from these baseline tools only correlate well with crowdsourced labels for abstract patterns in the Suprematism category (Pearson correlation r=0.84). For the other categories, in particular, the objects and advertisement categories, low correlation coefficients were revealed (r=0.3 and 0.56, respectively). These findings suggest that (1) state-of-the-art approaches are mostly insufficient and (2) Savoias enables category-specific method development, which is likely to improve the impact of visual complexity analysis on specific application areas, including computer vision.Comment: 10 pages, 4 figures, 4 table

Scraping social media photos posted in Kenya and elsewhere to detect and analyze food types

Monitoring population-level changes in diet could be useful for education and for implementing interventions to improve health. Research has shown that data from social media sources can be used for monitoring dietary behavior. We propose a scrape-by-location methodology to create food image datasets from Instagram posts. We used it to collect 3.56 million images over a period of 20 days in March 2019. We also propose a scrape-by-keywords methodology and used it to scrape ∼30,000 images and their captions of 38 Kenyan food types. We publish two datasets of 104,000 and 8,174 image/caption pairs, respectively. With the first dataset, Kenya104K, we train a Kenyan Food Classifier, called KenyanFC, to distinguish Kenyan food from non-food images posted in Kenya. We used the second dataset, KenyanFood13, to train a classifier KenyanFTR, short for Kenyan Food Type Recognizer, to recognize 13 popular food types in Kenya. The KenyanFTR is a multimodal deep neural network that can identify 13 types of Kenyan foods using both images and their corresponding captions. Experiments show that the average top-1 accuracy of KenyanFC is 99% over 10,400 tested Instagram images and of KenyanFTR is 81% over 8,174 tested data points. Ablation studies show that three of the 13 food types are particularly difficult to categorize based on image content only and that adding analysis of captions to the image analysis yields a classifier that is 9 percent points more accurate than a classifier that relies only on images. Our food trend analysis revealed that cakes and roasted meats were the most popular foods in photographs on Instagram in Kenya in March 2019.Accepted manuscrip

SIDOD: a synthetic image dataset for 3D object pose recognition with distractors

We present a new, publicly-available image dataset generated by the NVIDIA Deep Learning Data Synthesizer intended for use in object detection, pose estimation, and tracking applications. This dataset contains 144k stereo image pairs that synthetically combine 18 camera viewpoints of three photorealistic virtual environments with up to 10 objects (chosen randomly from the 21 object models of the YCB dataset ) and flying distractors. Object and camera pose, scene lighting, and quantity of objects and distractors were randomized. Each provided view includes RGB, depth, segmentation, and surface normal images, all pixel level. We describe our approach for domain randomization and provide insight into the decisions that produced the dataset.Published versio

Immune and Opioid system interaction in pain modulation

Background: Inflammatory pain is caused by direct stimulation of nociceptors with the release of inflammatory mediators. Several studies about the roles of immune and opioid systems in the pain process have suggested that their crosstalk may have effective in pain modulation. Accordingly, the purpose of this study was review the effect of immune and opioid systems on pain modulation.Evidence acquisition: The increasing demand for mitigating inflammatory pain has led to the introduction of the effect of immune and opioid system interaction in pain modulation. Our literature reviewed 61 articles from 1991 to 2016.Results: In this study, we reviewed most of the existing papers on the role of opioid system in pain modulation especially with a focus of the immune system efficacy. Our review suggested that there is a close correlation between the expression of cytokines and opioid receptors and in the process of inflammatory pain where immune cells have a notable effect on the expression of cytokines and opioid receptors. In the process of inflammation, different types of immune cells constitute a major source of opioid peptides. The endogenous opioids could modulate either their own secretion or secretion of other cytokines. They have also anti-nociceptive and anti-inflammatory effects.Conclusion: Exacerbation of immune and opioid system reactions via correlation between cytokines and opioid peptides in the context of inflammatory pain arises the possibility of the role of interaction of these two important systems in the pain process. Keywords: inflammatory pain, opioids, immune system, cytokines, hyperalgesi

Profile and Predictors of Voluntary Civic Engagement at a Private University in Egypt

This study explored the characteristics and predictors of university student voluntary civic engagement. It was conducted at a private university in Egypt, a developing country where student volunteerism has the potential to significantly impact community development efforts. A total of 518 students responded to the study. Consistent with previous literature, students who chose to participate in community service clubs were more likely to be female and religious. They moreover reported greater commitment to civic service as well as pride and commitment to the university. Results suggested that volunteers fit an “Egyptianized” profile with characteristics including: Egyptian nationality, Muslim religion, attending a high-school located in a less privileged rural governorate, graduating from an Egyptian public school system, being more religious, and speaking more Arabic than English socially. Levels of depression did not differ between volunteers and non-volunteers; however, volunteers reported higher anxiety. Suggestions for future research are offered and findings are discussed in terms of their significance for community practice nationally, regionally and globally

Profile and Predictors of Voluntary Civic Engagement at a Private University in Egypt

This study explored the characteristics and predictors of university student voluntary civic engagement. It was conducted at a private university in Egypt, a developing country where student volunteerism has the potential to significantly impact community development efforts. A total of 518 students responded to the study. Consistent with previous literature, students who chose to participate in community service clubs were more likely to be female and religious. They moreover reported greater commitment to civic service as well as pride and commitment to the university. Results suggested that volunteers fit an “Egyptianized” profile with characteristics including: Egyptian nationality, Muslim religion, attending a high-school located in a less privileged rural governorate, graduating from an Egyptian public school system, being more religious, and speaking more Arabic than English socially. Levels of depression did not differ between volunteers and non-volunteers; however, volunteers reported higher anxiety. Suggestions for future research are offered and findings are discussed in terms of their significance for community practice nationally, regionally and globally