Fully- or semi-automatic image enhancement software helps users to increase the visual appeal of photos and does not require in-depth knowledge of manual image editing. However, fully-automatic approaches usually enhance the image in a black-box manner that does not give the user any control over the optimization process, possibly leading to edited images that do not subjectively appeal to the user. Semi-automatic methods mostly allow for controlling which pre-defined editing step is taken, which restricts the users in their creativity and ability to make detailed adjustments, such as brightness or contrast. We argue that incorporating user preferences by guiding an automated enhancement method simplifies image editing and increases the enhancement's focus on the user. This work thus proposes the Neural Image Correction & Enhancement Routine (NICER), a neural network based approach to no-reference image enhancement in a fully-, semi-automatic or fully manual process that is interactive and user-centered. NICER iteratively adjusts image editing parameters in order to maximize an aesthetic score based on image style and content. Users can modify these parameters at any time and guide the optimization process towards a desired direction. This interactive workflow is a novelty in the field of human-computer interaction for image enhancement tasks. In a user study, we show that NICER can improve image aesthetics without user interaction and that allowing user interaction leads to diverse enhancement outcomes that are strongly preferred over the unedited image. We make our code publicly available to facilitate further research in this direction.

As Uber fitfully tries to reset its bad fortunes--see the recent intrigue in its search for a new CEO--Lyft is trying its hardest to catch up with its rival. Beyond positioning itself as the convenient ride-hailing company that isn't afflicted by a toxic tech-bro culture and also keeps an eye out for its drivers (it's allowed riders to tip drivers for years), Lyft wants to show that it can beat Uber at some of its other endeavors, including the most important one of all: innovating at the bleeding edge of transportation technology. While Uber has faced scandals, internal squabbles, and even a messy legal battle with Google's parent Alphabet over whether it stole aspects of its self-driving cars technology, Lyft has launched ambitious partnerships in that field with General Motors and even with Alphabet.

A Samsung Simband displays real-time results on conversational narrative and tone. In the past few years, wearables have offered to track many things, to predict illness, and even to give us rudimentary advice on staying healthy. The makers of a new device hope to expand the role that wearables can play in helping with day-to-day life, however, by adding'conversational wing-person' and'social coach' to their list of skills. Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Institute of Medical Engineering and Science (IMES) have developed programming to help those for whom conversation is difficult to navigate it with ease, and they've put it all in a wearable for real-time assistance. According to the team, the results of their study, "Predicting Latent Narrative Mood using Audio and Physiologic Data" [PDF], suggest that using such technology to pin down the tone of conversation as it happens is nearly within our reach--a potential boon for persons who experience anxiety, aspects of autism spectrum disorder, or other conditions that can make chewing the fat an intimidating prospect.