As major progress is made in open-ended text generation, measuring how close machine-generated text is to human language remains a critical open problem.

As major progress is made in open-ended text generation, measuring how close machine-generated text is to human language remains a critical open problem.

A smart home is realized by setting up various services. Several methods have been proposed to create smart home services, which can be divided into knowledge-based and data-driven approaches. However, knowledge-based approaches usually require manual input from the inhabitant, which can be complicated if the physical phenomena of the concerned environment states are complex, and the inhabitant does not know how to adjust related actuators to achieve the target values of the states monitored by services. Moreover, machine learning-based data-driven approaches that we are interested in are like black boxes and cannot show the inhabitant in which situations certain services proposed certain actuators' states. To solve these problems, we propose a hybrid system called HKD-SHO (Hybrid Knowledge-based and Data-driven services based Smart HOme system), where knowledge-based and machine learning-based data-driven services are profitably integrated. The principal advantage is that it inherits the explicability of knowledge-based services and the dynamism of data-driven services. We compare HKD-SHO with several systems for creating dynamic smart home services, and the results show the better performance of HKD-SHO.

Vision-language pretraining models have achieved great success in supporting multimedia applications by understanding the alignments between images and text. While existing vision-language pretraining models primarily focus on understanding single image associated with a single piece of text, they often ignore the alignment at the intra-document level, consisting of multiple sentences with multiple images. In this work, we propose DocumentCLIP, a salience-aware contrastive learning framework to enforce vision-language pretraining models to comprehend the interaction between images and longer text within documents. Our model is beneficial for the real-world multimodal document understanding like news article, magazines, product descriptions, which contain linguistically and visually richer content. To the best of our knowledge, we are the first to explore multimodal intra-document links by contrastive learning. In addition, we collect a large Wikipedia dataset for pretraining, which provides various topics and structures. Experiments show DocumentCLIP not only outperforms the state-of-the-art baselines in the supervised setting, but also achieves the best zero-shot performance in the wild after human evaluation. Our code is available at https://github.com/FuxiaoLiu/DocumentCLIP.

Sky-image-based solar forecasting using deep learning has been recognized as a promising approach in reducing the uncertainty in solar power generation. However, one of the biggest challenges is the lack of massive and diversified sky image samples. In this study, we present a comprehensive survey of open-source ground-based sky image datasets for very short-term solar forecasting (i.e., forecasting horizon less than 30 minutes), as well as related research areas which can potentially help improve solar forecasting methods, including cloud segmentation, cloud classification and cloud motion prediction. We first identify 72 open-source sky image datasets that satisfy the needs of machine/deep learning. Then a database of information about various aspects of the identified datasets is constructed. To evaluate each surveyed datasets, we further develop a multi-criteria ranking system based on 8 dimensions of the datasets which could have important impacts on usage of the data. Finally, we provide insights on the usage of these datasets for different applications. We hope this paper can provide an overview for researchers who are looking for datasets for very short-term solar forecasting and related areas.

Seaborne robots have made a startling discovery beneath a 20-mile glacier in Alaska. The technology found the massive rivers of ice may be melting under the LeConte Glacier much faster than previously thought. Scientists programmed autonomous kayaks to swim near the icy cliffs of the glacier to measure the'ambient meltwater intrusions', which shows how much fresh water is flowing into the ocean from underneath the glacier. The study found ambient melting was 100 times higher than models had estimated. This is the first time experts have been able to analyze plumes of meltwater - the water released when snow or ice melts, where glaciers meet the ocean- because the feat is far too dangerous for ships due to falling ice of slabs from the glacier.

Although numerous ethics courses are available, with many focusing specifically on technology and computer ethics, pedagogical approaches employed in these courses rely exclusively on texts rather than on software development or data analysis. Technical students often consider these courses unimportant and a distraction from the "real" material. To develop instructional materials and methodologies that are thoughtful and engaging, we must strive for balance: between texts and coding, between critique and solution, and between cutting-edge research and practical applicability. Finding such balance is particularly difficult in the nascent field of responsible data science (RDS), where we are only starting to understand how to interface between the intrinsically different methodologies of engineering and social sciences. In this paper we recount a recent experience in developing and teaching an RDS course to graduate and advanced undergraduate students in data science. We then dive into an area that is critically important to RDS -- transparency and interpretability of machine-assisted decision-making, and tie this area to the needs of emerging RDS curricula. Recounting our own experience, and leveraging literature on pedagogical methods in data science and beyond, we propose the notion of an "object-to-interpret-with". We link this notion to "nutritional labels" -- a family of interpretability tools that are gaining popularity in RDS research and practice. With this work we aim to contribute to the nascent area of RDS education, and to inspire others in the community to come together to develop a deeper theoretical understanding of the pedagogical needs of RDS, and contribute concrete educational materials and methodologies that others can use. All course materials are publicly available at https://dataresponsibly.github.io/courses.

Fox News Flash top headlines for Nov. 21 are here. Check out what's clicking on Foxnews.com NASA recently showed off its new underwater rover that it hopes one day could help in exploring alien ocean worlds in the search for life. The robot, known as Buyant Rover for Under-Ice Exploration (BRUIE), is designed to crawl under an ice cap. Right now, it is being tested in Antarctica, in hopes one day it could go to ocean worlds such as Saturn's moon, Enceladus, or Jupiter's moon, Europa.

Before they got down to business for the day, students in Devin Tatro's social studies class were offered a quiet moment of self-reflection: On this golden fall afternoon at Nome-Beltz Junior/Senior High School, were they feeling chipper, distressed, or somewhere in between? One by one, they selected the picture of the facial expression that best matched their mood, and with a swift click sent an answer to the teacher. She scanned the responses and made a few mental notes. Then, without missing a beat, she switched the smartboard display and launched into a multiple-choice quiz using a game-based online learning platform called Kahoot! "Tell me one thing you remember about yesterday's lesson on expansions and tax on Native Americans," Tatro said, pacing the front of the classroom. She rattled off students' responses as they popped up on the smartboard in a colorful word cloud: "Forced relocation, reduced population, disease, warfare, cultural destruction ... wow, that's a powerful term."

NASA has released a mesmerizing new video of the moon set to Claude Debussy's famous piano composition, Clair de Lune. The stunning nature documentary-style footage uses high-resolution images from the Lunar Reconnaissance Orbiter to reveal an unprecedented look at the surface of Earth's only natural satellite. It follows the sun over the course of one lunar day, slowly panning over an endless landscape of craters and other surface features brought to life by elevation maps and image mosaics. The new visualization was created to accompany a performance of Claude Debussy's Clair de Lune by the National Symphony Orchestra Pops. It was performed at the Kennedy Center for the Performing Arts in Washington, DC, on June 1 and 2, 2018, led by conductor Emil de Cou, as part of a celebration of NASA's 60th anniversary.