The number of scientific publications continues to rise exponentially, especially in Computer Science (CS). However, current solutions to analyze those publications restrict access behind a paywall, offer no features for visual analysis, limit access to their data, only focus on niches or sub-fields, and/or are not flexible and modular enough to be transferred to other datasets. In this thesis, we conduct a scientometric analysis to uncover the implicit patterns hidden in CS metadata and to determine the state of CS research. Specifically, we investigate trends of the quantity, impact, and topics for authors, venues, document types (conferences vs. journals), and fields of study (compared to, e.g., medicine). To achieve this we introduce the CS-Insights system, an interactive web application to analyze CS publications with various dashboards, filters, and visualizations. The data underlying this system is the DBLP Discovery Dataset (D3), which contains metadata from 5 million CS publications. Both D3 and CS-Insights are open-access, and CS-Insights can be easily adapted to other datasets in the future. The most interesting findings of our scientometric analysis include that i) there has been a stark increase in publications, authors, and venues in the last two decades, ii) many authors only recently joined the field, iii) the most cited authors and venues focus on computer vision and pattern recognition, while the most productive prefer engineering-related topics, iv) the preference of researchers to publish in conferences over journals dwindles, v) on average, journal articles receive twice as many citations compared to conference papers, but the contrast is much smaller for the most cited conferences and journals, and vi) journals also get more citations in all other investigated fields of study, while only CS and engineering publish more in conferences than journals.

Math word problem (MWP) solving is an important task in question answering which requires human-like reasoning ability. Analogical reasoning has long been used in mathematical education, as it enables students to apply common relational structures of mathematical situations to solve new problems. In this paper, we propose to build a novel MWP solver by leveraging analogical MWPs, which advance the solver's generalization ability across different kinds of MWPs. The key idea, named analogy identification, is to associate the analogical MWP pairs in a latent space, i.e., encoding an MWP close to another analogical MWP, while moving away from the non-analogical ones. Moreover, a solution discriminator is integrated into the MWP solver to enhance the association between the representations of MWPs and their true solutions. The evaluation results verify that our proposed analogical learning strategy promotes the performance of MWP-BERT on Math23k over the state-of-the-art model Generate2Rank, with 5 times fewer parameters in the encoder. We also find that our model has a stronger generalization ability in solving difficult MWPs due to the analogical learning from easy MWPs.

The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists. The relevant topics of interest for the workshop include, but are not limited to: Music reading systems; Optical music recognition; Datasets and performance evaluation; Image processing on music scores; Writer identification; Authoring, editing, storing and presentation systems for music scores; Multi-modal systems; Novel input-methods for music to produce written music; Web-based Music Information Retrieval services; Applications and projects; Use-cases related to written music. These are the proceedings of the 3rd International Workshop on Reading Music Systems, held in Alicante on the 23rd of July 2021.

Semantic navigation is necessary to deploy mobile robots in uncontrolled environments like our homes, schools, and hospitals. Many learning-based approaches have been proposed in response to the lack of semantic understanding of the classical pipeline for spatial navigation, which builds a geometric map using depth sensors and plans to reach point goals. Broadly, end-to-end learning approaches reactively map sensor inputs to actions with deep neural networks, while modular learning approaches enrich the classical pipeline with learning-based semantic sensing and exploration. But learned visual navigation policies have predominantly been evaluated in simulation. How well do different classes of methods work on a robot? We present a large-scale empirical study of semantic visual navigation methods comparing representative methods from classical, modular, and end-to-end learning approaches across six homes with no prior experience, maps, or instrumentation. We find that modular learning works well in the real world, attaining a 90% success rate. In contrast, end-to-end learning does not, dropping from 77% simulation to 23% real-world success rate due to a large image domain gap between simulation and reality. For practitioners, we show that modular learning is a reliable approach to navigate to objects: modularity and abstraction in policy design enable Sim-to-Real transfer. For researchers, we identify two key issues that prevent today's simulators from being reliable evaluation benchmarks - (A) a large Sim-to-Real gap in images and (B) a disconnect between simulation and real-world error modes - and propose concrete steps forward.

This paper describes important considerations and challenges associated with online reinforcement-learning based waveform selection for target identification in frequency modulated continuous wave (FMCW) automotive radar systems. We present a novel learning approach based on satisficing Thompson sampling, which quickly identifies a waveform expected to yield satisfactory classification performance. We demonstrate through measurement-level simulations that effective waveform selection strategies can be quickly learned, even in cases where the radar must select from a large catalog of candidate waveforms. The radar learns to adaptively select a bandwidth for appropriate resolution and a slow-time unimodular code for interference mitigation in the scene of interest by optimizing an expected classification metric.

As a graduate student doing his master's thesis on speech recognition at the MIT AI Lab (now the MIT Computer Science and Artificial Intelligence Laboratory), Dan Huttenlocher worked closely with Professor Victor Zue. Well known for pioneering the development of systems that enable an user to interact with computers using spoken language, Zue traveled frequently to Asia -- where much of the early research in speech recognition happened during the 1980s. Huttenlocher occasionally accompanied his professor on these trips, many of which involved interactions with members of MIT Industrial Liaison Program, as he recalls. "It was a tremendous opportunity," according to Huttenlocher, "and it was a large part of what built my interest in engaging with companies and industry in addition to the academic side of research." Huttenlocher went on to earn his PhD in computer vision at the Institute and has since embarked on a career that encompasses academia, industry, and the philanthropic sector.

Three years ago, the San Francisco Board of Supervisors made history by becoming the first city in the nation to ban use of facial recognition technology by local government. Last night, the board went in a different direction, giving police the right to kill a criminal suspect with a teleoperated robot if they believe there is an imminent threat of death to police or members of the public. Assistant police chief David Lazar said ahead of the vote that killer robots might be needed in scenarios involving mass shootings or suicide bombers, citing the Mandalay Bay shooting in Las Vegas in 2017 and the killing of five police officers in Dallas, Texas, in 2016. Dallas police ultimately used explosives strapped to a Remotec F5A bomb disposal robot--a model also possessed by the San Francisco Police Department--to kill that suspect. The new administrative code requires a police chief to authorize use of deadly force involving a robot and to first consider de-escalation or an alternative use of force.

FastAPI is a new Python-based web framework used to create Web APIs. FastAPI is fast when serving your application, also enhances the performance of our application. Note: for you to follow along easily, use Google Colab. It's an easy-to-use platform to get started quickly while building models. We will build a machine learning model that will predict the nationality of individuals using their names. This is a simple model that will explain the key concepts used in machine learning modeling. The dataset used will contains common names of people and their nationalities. Pandas is a software library written for the Python programming language for data manipulation and analysis.

Alex Castrounis is the founder and CEO of Why of AI and the author of AI for People and Business. He is also an adjunct professor for Northwestern University's Kellogg / McCormick MBAi program, where he created and teaches the program's core artificial intelligence and machine learning course. Alex has spent the last two decades advising businesses of all sizes, from startups to Fortune 100s, on how to use data, analytics, and technology to drive business and customer success. A significant part of his work has been helping companies embed artificial intelligence and machine learning into every facet of their decision-making, operations, and products. Alex also understands how analytics and data-driven decisions can help businesses gain and maintain a competitive advantage.

Who can take this course? Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. Who can take this course?