Metadata quality is crucial for digital objects to be discovered through digital library interfaces. However, due to various reasons, the metadata of digital objects often exhibits incomplete, inconsistent, and incorrect values. We investigate methods to automatically detect, correct, and canonicalize scholarly metadata, using seven key fields of electronic theses and dissertations (ETDs) as a case study. We propose MetaEnhance, a framework that utilizes state-of-the-art artificial intelligence methods to improve the quality of these fields. To evaluate MetaEnhance, we compiled a metadata quality evaluation benchmark containing 500 ETDs, by combining subsets sampled using multiple criteria. We tested MetaEnhance on this benchmark and found that the proposed methods achieved nearly perfect F1-scores in detecting errors and F1-scores in correcting errors ranging from 0.85 to 1.00 for five of seven fields.

The Carnegie Mellon University Libraries latest exhibition highlights the history of robotics at CMU and the ongoing work of The Robotics Project to preserve the legacy of the field. The exhibition, "Looking Back To Move Forward / A Re:collection of Robotics at Carnegie Mellon," opened Jan. 19 and runs through Friday, March 18, in the Hunt Library gallery. A virtual tour is available for visitors to explore the exhibition remotely. Curated by archivist and oral historian Katherine Barbera and Kathleen Donahoe, the Robot Archive processing archivist, "Looking Back To Move Forward" invites viewers to explore the history and the wide variety of research areas that CMU is known for, including field robotics, artificial intelligence and human-robot interaction, among others. Visitors will see more than 40 robots and archival artifacts -- such as soccer robots, snake robots, a nurse robot called "Pearl," a "Snackbot" autonomous food-delivery robot, and "Terregator," one of the first outdoor autonomous vehicles -- along with personal recollections from the people who made it all happen.

Since 2019, experts at the University of North Carolina at Chapel Hill's University Libraries have investigated the use of machine learning to identify racist laws from North Carolina's past. Now a grant of $400,000 from The Andrew W. Mellon Foundation will allow them to extend that work to two more states. The grant will also fund research and teaching fellowships for scholars interested in using the project's outputs and techniques. On the Books: Jim Crow and Algorithms of Resistance began with a question from a North Carolina social studies teacher: Was there a comprehensive list of all the Jim Crow laws that had ever been passed in the state? Finding little beyond scholar and activist Pauli Murray's 1951 book "States' laws on race and color," a team of librarians, technologists and data experts set out to fill the gap.

Designing optimal reward functions has been desired but extremely difficult in reinforcement learning (RL). When it comes to modern complex tasks, sophisticated reward functions are widely used to simplify policy learning yet even a tiny adjustment on them is expensive to evaluate due to the drastically increasing cost of training. To this end, we propose a hindsight reward tweaking approach by designing a novel paradigm for deep reinforcement learning to model the influences of reward functions within a near-optimal space. We simply extend the input observation with a condition vector linearly correlated with the effective environment reward parameters and train the model in a conventional manner except for randomizing reward configurations, obtaining a hyper-policy whose characteristics are sensitively regulated over the condition space. We demonstrate the feasibility of this approach and study one of its potential application in policy performance boosting with multiple MuJoCo tasks.

In a grainy video shot in the early 1980s on Carnegie Mellon University's campus, Ivan Sutherland rides on top of the Trojan Cockroach, a six-legged machine considered the first controlled by a computer and capable of carrying a person. Sutherland puts the machine through its paces, slowly walking forward, backward and sideways and turning 180 degrees in the video. At one point, he attempts to balance the massive machine on only two legs. "We believe that a mastery of balance will be important to future walking machines," Sutherland narrates over the footage. That Trojan Cockroach video, complete with Sutherland's prophetic comments on the importance of balance to the future of legged robots, is part of a new interactive, virtual exhibit from University Libraries and the School of Computer Science at CMU that explores the beginnings of and contributions to the field of robotics.

Gary Price (gprice@mediasourceinc.com) is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area. Before launching INFOdocket, Price and Shirl Kennedy were the founders and senior editors at ResourceShelf and DocuTicker for 10 years. From 2006-2009 he was Director of Online Information Services at Ask.com, and is currently a contributing editor at Search Engine Land.

KINGSTON, R.I., Dec. 20, 2017--Students across the University of Rhode Island will soon have access to a new laboratory where they can explore research involving robotics, wearable technology, smart cities and public policy. The Artificial Intelligence Lab, or AI Lab, is scheduled to open in fall 2018 at the Robert L. Carothers Library and Learning Commons on the Kingston campus. The lab is the first of its kind in a college library nationwide, says the lab's team. The lab will support two complementary goals. On the one hand, it will enable students to explore projects on robotics, natural language processing, smart cities, smart homes, the Internet of Things and big data, with tutorials at beginner through advanced levels.

We report on a series of new platforms and events dealing with AI evaluation that may change the way in which AI systems are compared and their progress is measured. The introduction of a more diverse and challenging set of tasks in these platforms can feed AI research in the years to come, shaping the notion of success and the directions of the field. However, the playground of tasks and challenges presented there may misdirect the field without some meaningful structure and systematic guidelines for its organization and use. Anticipating this issue, we also report on several initiatives and workshops that are putting the focus on analyzing the similarity and dependencies between tasks, their difficulty, what capabilities they really measure and – ultimately – on elaborating new concepts and tools that can arrange tasks and benchmarks into a meaningful taxonomy.