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Sketch Recognition Algorithms for Comparing Complex and Unpredictable Shapes

AAAI Conferences

In an introductory engineering course with an annual enrollment of over 1000 students, a professor has little option but to rely on multiple choice exams for midterms and finals. Furthermore, the teaching assistants are too overloaded to give detailed feedback on submitted homework assignments. We introduce Mechanix, a computer-assisted tutoring system for engineering students. Mechanix uses recognition of freehand sketches to provide instant, detailed, and formative feedback as the student progresses through each homework assignment, quiz, or exam. Free sketch recognition techniques allow students to solve free-body diagram and static truss problems as if they were using a pen and paper. The same recognition algorithms enable professors to add new unique problems simply by sketching out the correct answer. Mechanix is able to ease the burden of grading so that instructors can assign more free response questions, which provide a better measure of student progress than multiple choice questions do.


How well do facial recognition algorithms cope with a million strangers?

@machinelearnbot

This article was written by Jennifer Langston. Co-authors include UW computer science and engineering professor Steve Seitz, undergraduate student and web developer Evan Brossard and former student Daniel Miller. The MegaFace dataset contains 1 million images representing more than 690,000 unique people. It is the first benchmark that tests facial recognition algorithms at a million scale.University of Washington In the last few years, several groups have announced that their facial recognition systems have achieved near-perfect accuracy rates, performing better than humans at picking the same face out of the crowd. But those tests were performed on a datasetwith only 13,000 images -- fewer people than attend an average professional U.S. soccer game.


Amir

AAAI Conferences

This paper presents a plan recognition algorithm for inferring student behavior using virtual science laboratories. The algorithm extends existing plan recognition technology and was integrated with an existing educational application for chemistry. Automatic recognition of students' activities in virtual laboratories can provide important information to teachers as well as serve as the basis for intelligent tutoring. Student use of virtual laboratories presents several challenges: Students may repeat activities indefinitely, interleave between activities, and engage in exploratory behavior using trial-anderror. The plan recognition algorithm uses a recursive grammar that heuristically generates plans on the fly, taking into account chemical reactions and effects to determine students' intended high-level actions. The algorithm was evaluated empirically on data obtained from college students using virtual laboratory software for teaching chemistry. Results show that the algorithm was able to (1) infer the plans used by students to construct their models; (2) recognize such key processes as titration and dilution when they occurred in students' work; (3) identify partial solutions; (4) isolate sequences of actions that were part of a single error.


A List Of Top 10 Free Machine Learning Online Courses and Tutorials

#artificialintelligence

As we all know that machine learning or artificial intelligence has increasingly gained more popularity in the past couple of years and still continues to do so. As at the very moment Big Data is the present trend in the tech industry, machine learning proves to be incredibly powerful when it comes to making predictions or calculated suggestions that are based on large amounts of data. The importance it carries along with the world of amazement it carries is well known and understood.


Deep Learning in Practice: Speech Recognition and Beyond

@machinelearnbot

Andrew Ng is chief scientist of Baidu, chairman and cofounder of Coursera, and a computer science faculty member at Stanford. His AI work focuses on deep learning, which develops learning algorithms by building large-scale simulations of the brain. In 2011, he founded and led the Google Brain project, which built the largest deep-learning (neural network) systems at the time, leading to the celebrated "Google cat" result. His team's technology has also had a huge impact across numerous Google applications, including speech recognition, maps, and more. Ng currently leads Baidu Research in developing the next generation of deep-learning algorithms.