This paper presents an investigation of using a co-attention based neural network for source-dependent essay scoring. We use a co-attention mechanism to help the model learn the importance of each part of the essay more accurately. Also, this paper shows that the co-attention based neural network model provides reliable score prediction of source-dependent responses. We evaluate our model on two source-dependent response corpora. Results show that our model outperforms the baseline on both corpora. We also show that the attention of the model is similar to the expert opinions with examples.

Writing a good essay typically involves students revising an initial paper draft after receiving feedback. We present eRevise, a web-based writing and revising environment that uses natural language processing features generated for rubric-based essay scoring to trigger formative feedback messages regarding students' use of evidence in response-to-text writing. By helping students understand the criteria for using text evidence during writing, eRevise empowers students to better revise their paper drafts. In a pilot deployment of eRevise in 7 classrooms spanning grades 5 and 6, the quality of text evidence usage in writing improved after students received formative feedback then engaged in paper revision.

Manually grading the Response to Text Assessment (RTA) is labor intensive. Therefore, an automatic method is being developed for scoring analytical writing when the RTA is administered in large numbers of classrooms. Our long-term goal is to also use this scoring method to provide formative feedback to students and teachers about students' writing quality. As a first step towards this goal, interpretable features for automatically scoring the evidence rubric of the RTA have been developed. In this paper, we present a simple but promising method for improving evidence scoring by employing the word embedding model. We evaluate our method on corpora of responses written by upper elementary students.

Our Innovation Analysts recently looked into emerging technologies and up-and-coming startups in the logistics industry. As there is a large number of startups working on a wide variety of solutions, we decided to share our insights with you. So, let's take a look at promising machine learning solutions disrupting warehousing. For our 4 picks of machine learning startups, we used a data-driven startup scouting approach to identify the most relevant solutions globally. The Global Startup Heat Map below highlights 4 interesting examples out of 157 relevant solutions.

As language learning makes a sea change to online, users will come to expect personalized learning experiences. China's AI-powered online education market alone reached $568 million in 2017 and is expected to surpass $26 billion in 2022. At Sana Labs, we build AI technologies to power these learning experiences through easy to integrate APIs. This means that machine learning models for personalization as well as pronunciation, grammar, and overall fluency feedback can be production ready in days, not months. In this article, I'll highlight why deep learning will power this shift.

At the University of North Carolina Charlotte (UNCC), I recently attended the debut of an exciting new machining-related event where machining had to be explained to some attendees. The inaugural meeting of the Consortium for Self-Aware Machining and Metrology (CSAM) brought together manufacturing experts with mathematicians who had little basic familiarity with machining operations, all with the goal of advancing the development of, in the words of meeting organizer Dr. Tony Schmitz, "production systems with the ability to know their own state and respond." In short, this was a conference entirely focused on uniting machining with artificial intelligence (AI). Of course, the manufacturing people in attendance needed basic instruction also. The hope of applying AI to manufacturing is still in its early stages, and one of the first steps is just to figure out what the one might mean for the other.

At the University of North Carolina Charlotte (UNCC), I recently attended the debut of an exciting new machining-related event where machining had to be explained to some attendees. The inaugural meeting of the Consortium for Self-Aware Machining and Metrology (CSAM) brought together manufacturing experts with mathematicians who had little basic familiarity with machining operations, all with the goal of advancing the development of, in the words of meeting organizer Dr. Tony Schmitz, "production systems with the ability to know their own state and respond." In short, this was a conference entirely focused on uniting machining with artificial intelligence (AI). Of course, the manufacturing people in attendance needed basic instruction also. The hope of applying AI to manufacturing is still in its early stages, and one of the first steps is just to figure out what the one might mean for the other.

With the industry trend of shifting from a traditional hierarchical approach to flatter management structure, crowdsourced performance assessment gained mainstream popularity. One fundamental challenge of crowdsourced performance assessment is the risks that personal interest can introduce distortions of facts, especially when the system is used to determine merit pay or promotion. In this paper, we developed a method to identify bias and strategic behavior in crowdsourced performance assessment, using a rich dataset collected from a professional service firm in China. We find a pattern of "discriminatory generosity" on the part of peer evaluation, where raters downgrade their peer coworkers who have passed objective promotion requirements while overrating their peer coworkers who have not yet passed. This introduces two types of biases: the first aimed against more competent competitors, and the other favoring less eligible peers which can serve as a mask of the first bias. This paper also aims to bring angles of fairness-aware data mining to talent and management computing. Historical decision records, such as performance ratings, often contain subjective judgment which is prone to bias and strategic behavior. For practitioners of predictive talent analytics, it is important to investigate potential bias and strategic behavior underlying historical decision records.

They are ubiquitous in both scientific and business documents. Data visualizations are designed to be effective at conveying trends and comparisons in a glance, while also preserving salient details. Using computer vision to parse these visualizations can enable extraction of information that cannot be gleaned by solely studying a document's text. Despite the high potential payoff and tremendous practical value, this problem has received little attention until recently. In 2018, two datasets for answering questions about data visualizations were introduced along with new algorithms [15, 18]; however, there is considerable room for improvement. Here, we propose a novel algorithm that exceeds the state-of-the-art on both of these datasets by a large margin. Visual question answering (VQA) requires a system to answer questions about images [6, 27, 17].

We compare several approaches to learn an Optimal Map, represented as a neural network, between probability distributions. The approaches fall into two categories: ``Heuristics'' and approaches with a more sound mathematical justification, motivated by the dual of the Kantorovitch problem. Among the algorithms we consider a novel approach involving dynamic flows and reductions of Optimal Transport to supervised learning.