We consider many-to-one matching problems, where one side consists of students and the other side of schools with capacity constraints. We study how to optimally increase the capacities of the schools so as to obtain a stable and perfect matching (i.e., every student is matched) or a matching that is stable and Pareto-efficient for the students. We consider two common optimality criteria, one aiming to minimize the sum of capacity increases of all schools (abbrv. as MinSum) and the other aiming to minimize the maximum capacity increase of any school (abbrv. as MinMax). We obtain a complete picture in terms of computational complexity: Except for stable and perfect matchings using the MinMax criteria which is polynomial-time solvable, all three remaining problems are NP-hard. We further investigate the parameterized complexity and approximability and find that achieving stable and Pareto-efficient matchings via minimal capacity increases is much harder than achieving stable and perfect matchings.

One important aspect of language is how speakers generate utterances and texts to convey their intended meanings. In this paper, we bring various aspects of the Construction Grammar (CxG) and the Systemic Functional Grammar (SFG) theories in a deep learning computational framework to model empathic language. Our corpus consists of 440 essays written by premed students as narrated simulated patient-doctor interactions. We start with baseline classifiers (state-of-the-art recurrent neural networks and transformer models). Then, we enrich these models with a set of linguistic constructions proving the importance of this novel approach to the task of empathy classification for this dataset. Our results indicate the potential of such constructions to contribute to the overall empathy profile of first-person narrative essays.

Increasingly, researchers have suggested the benefits of temporal analysis to improve our understanding of the learning process. Sequential pattern mining (SPM), as a pattern recognition technique, has the potential to reveal the temporal aspects of learning and can be a valuable tool in educational data science. However, its potential is not well understood and exploited. This chapter addresses this gap by reviewing work that utilizes sequential pattern mining in educational contexts. We identify that SPM is suitable for mining learning behaviors, analyzing and enriching educational theories, evaluating the efficacy of instructional interventions, generating features for prediction models, and building educational recommender systems. SPM can contribute to these purposes by discovering similarities and differences in learners' activities and revealing the temporal change in learning behaviors. As a sequential analysis method, SPM can reveal unique insights about learning processes and be powerful for self-regulated learning research. It is more flexible in capturing the relative arrangement of learning events than the other sequential analysis methods. Future research may improve its utility in educational data science by developing tools for counting pattern occurrences as well as identifying and removing unreliable patterns. Future work needs to establish a systematic guideline for data preprocessing, parameter setting, and interpreting sequential patterns.

AI, technology, innovation, and automation are? This is why having a "?? Getting a deep knowledge of and mastering the fields of the world's new revolutions will help you to become a? This can be done in? So don't let your knowledge and skills become outdated.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Connecticut needs safeguards on state government's use of artificial intelligence including algorithms at child welfare and other agencies to prevent discrimination and increase transparency, an advisory panel to the U.S. Commission on Civil Rights said Thursday. The Connecticut Advisory Committee to the federal commission called on state lawmakers to pass laws regulating such systems, which have sparked concerns in other parts of the country. The problem, critics say, is algorithms can use flawed data that can disproportionately identify minorities, low-income families, disabled people and other groups when agencies make decisions on removing children from homes, approving health, housing and other benefits, where to concentrate law enforcement and assigning children to schools, among other uses.

Machine learning is a rapidly growing field that is revolutionizing many industries, including healthcare, finance, and technology. With its ability to analyze large amounts of data and make predictions and decisions, machine learning is an essential skill for anyone interested in a career in data science or artificial intelligence. If you're looking to learn more about machine learning, you're in luck! There are many high-quality courses available online, offered by some of the top universities in the world. In this article, we'll introduce you to 10 free machine learning courses from top universities.

My journey into the world of AI-generated art began two weeks before Thanksgiving. I sat down in front of my computer to experiment with Midjourney, an artificial intelligence program that creates images from text descriptions. I entered the command "/imagine" and a message from the Midjourney bot appeared: "There are endless possibilities …" A young african american man wearing a white t-shirt, jeans and sneakers, is flying through the clouds and space as if in a dream. Midjourney generated four versions that the beloved painter and art instructor Bob Ross might have called "happy little accidents." In each, the clouds look like unruly cotton balls. The man has no discernible face.

Again, these are broad generalizations and there is a lot of diversity within each generation. It's important to remember that individuals are more than just their generation, and that people of all ages can have a wide range of characteristics and experiences. The format of the above response is typical for queries that require factual information. ChatGPT begins with a generic introduction, then offers several facts, and ends with a suggestion to consider additional information for a more comprehensive understanding.

Purpose: The purpose of this study is to predict dropouts in two runs of the same MOOC using an explainable machine learning approach. With the explainable approach, we aim to enable the interpretation of the black-box predictive models from a pedagogical perspective and to produce actionable insights for related educational interventions. The similarity and the differences in feature importance between the predictive models were also examined. Design/Methodology/Approach: This is a quantitative study performed on a large public dataset containing activity logs in a MOOC. In total, 21 features were generated and standardized before the analysis. Multi-layer perceptron neural network was used as the black-box machine learning algorithm to build the predictive models.

Building and maintaining production-grade ML-enabled components is a complex endeavor that goes beyond the current approach of academic education, focused on the optimization of ML model performance in the lab. In this paper, we present a project-based learning approach to teaching MLOps, focused on the demonstration and experience with emerging practices and tools to automatize the construction of ML-enabled components. We examine the design of a course based on this approach, including laboratory sessions that cover the end-to-end ML component life cycle, from model building to production deployment. Moreover, we report on preliminary results from the first edition of the course. During the present year, an updated version of the same course is being delivered in two independent universities; the related learning outcomes will be evaluated to analyze the effectiveness of project-based learning for this specific subject.