If you make a purchase by clicking one of our links, we may earn a small share of the revenue. Our picks and opinions are independent from any business incentives. Every now and then someone (usually a random person on the internet) turns me onto a product that is so useful it turns my whole world upside-down. When these tiny treasures end up being cheap, the feeling's even better; it's like you've been let into a secret club. Here are five products that have two things in common: they're ten dollars or less and I can't imagine living without them.
Images can convey rich semantics and evoke strong emotions in viewers. The research of my PhD thesis focuses on image emotion computing (IEC), which aims to predict the emotion perceptions of given images. The development of IEC is greatly constrained by two main challenges: affective gap and subjective evaluation. Previous works mainly focused on finding features that can express emotions better to bridge the affective gap, such as elements-of-art based features and shape features. According to the emotion representation models, including categorical emotion states (CES) and dimensional emotion space (DES), three different tasks are traditionally performed on IEC: affective image classification, regression and retrieval. The state-of-the-art methods on the three above tasks are image-centric, focusing on the dominant emotions for the majority of viewers. For my PhD thesis, I plan to answer the following questions: (1) Compared to the low-level elements-of-art based features, can we find some higher level features that are more interpretable and have stronger link to emotions? (2) Are the emotions that are evoked in viewers by an image subjective and different? If they are, how can we tackle the user-centric emotion prediction? (3) For image-centric emotion computing, can we predict the emotion distribution instead of the dominant emotion category?
Also, you can always find new data sets by searching for data sets on DSC. They have a scraper robot which crawls Indiegogo projects and collects data about them. They also have a scraper robot which crawls all Kickstarter projects and collects data in JSON format. From March 2016 they run this data crawl once a month.
We built a series of web scrapers across a number of high-volume e-commerce platforms. Leveraging additional web scrapers and the many Amazon APIs readily available, we automated the process of comparing products, re-pricing, and quantity change detection in a near real-time data warehouse. This information was passed in an automated process to human evaluators who made the final decision to sell the products on the Amazon platform. Auto re-pricing and re-scraping algorithms were also built into the machine to avoid the catastrophic scenario of selling a product that was out-of-stock with the supplier.