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Event Detection (ED) is an important task in natural language processing. In the past few years, many datasets have been introduced for advancing ED machine learning models. However, most of these datasets are under-explored because not many tools are ...

This paper presents a new Vietnamese spelling correction system that allows users to correct spelling errors in their text. Our system is an interactive writing assistant that integrates advanced technologies in natural language processing to (i) ...

Introductory chemistry courses teach the process of drawing basic chemical molecules with the use of Lewis dot diagrams. However, many beginner students struggle in mastering these diagrams. While there exists educational applications that focus on ...

Explainable Artificial Intelligence (XAI) is an emerging subdiscipline of Machine Learning (ML) and human-computer interaction. Discriminative models need to be understood. An explanation of such ML models is vital when an AI system makes decisions ...

It is essential to understand research trends for researchers, decision-makers, and investors. One way to analyze research trends is to collect and analyze author-defined keywords in scientific papers. Unfortunately, while author-defined keywords are ...

This work aims to explore how a user’s understanding of a creative AI’s decision-making affects their experience when collaborating with it, through the inclusion of Explainable AI features in an interactive generative music system. We have created ...

The volume of information that can be used in the development of knowledge bases that can be used in education is constantly increasing. Also, this amount of data is very difficult to process and store. When designing a knowledge base to optimize the ...

Recent developments, guidelines, and acts by the European Commission have started to frame policy for AI and related areas such as ML and data, not only for the broader community, but in the context of education specifically. This poster presents a ...

The demand is growing for a populace that is literate in Artificial Intelligence (AI); such literacy centers on enabling individuals to evaluate, collaborate with, and effectively use AI. Because the middle school years are a critical time for ...

As one part of an NSF-sponsored Data Science Fellowship at Cal Poly, San Luis Obispo, a group of faculty offered a unique one-unit quarter-long seminar on the history of ideas behind the core principles of Data Science. We present an overview of this ...

We describe "Discover AI in Daily Life", a lesson in Google's Applied Digital Skills curriculum. The lesson introduces elements of AI literacy and is freely available online at g.co/DiscoverAI. It is designed for middle school students while also ...

Machine Learning (ML)-powered apps are used in pervasive devices such as phones, tablets, smartwatches and IoT devices. Recent advances in collaborative, distributed ML such as Federated Learning (FL) attempt to solve privacy concerns of users and data ...

As the size of deep learning models gets larger and larger, training takes longer time and more resources, making fault tolerance critical. Existing state-of-the-art methods like Check-Freq and Elastic Horovod need to back up a copy of the model state ...

Bayesian networks (BNs) are attractive, because they are graphical and interpretable machine learning models. However, exact inference on BNs is time-consuming, especially for complex problems. To improve the efficiency, we propose a fast BN exact ...

All-reduce is the crucial communication primitive to reduce model parameters in distributed Deep Neural Networks (DNN) training. Most existing all-reduce algorithms are designed for traditional electrical interconnect systems, which cannot meet the ...

Algorithms for mobile networking are increasingly being moved from centralized servers towards the edge in order to decrease latency and improve the user experience. While much of this work is traditionally done using ASICs, 6G emphasizes the ...

In this poster, we present a Machine Learning (ML) technique to predict the number of iterations needed for a Pathfinder-based FPGA router to complete a routing problem. Given a placed circuit, our technique uses features gathered on each routing ...

Convolution is one of the most computationally intensive machine learning model operations, usually solved by the known Im2Col + BLAS method. This work proposes a novel convolution-algorithm to improve upon Im2Col + BLAS by introducing (a) CSA: a ...

Radio frequency (RF) fingerprinting is a hardware feature used in Internet of Things (IoT) applications to identify wireless devices. In this paper, we propose few-shot learning (FSL) and prototypical networks (PTNs) to create a new model that can adapt ...

With the increasing population, events with large crowds also increased. It often leads to uncontrolled stampede situations, causing several deaths. Deployment of intelligent systems with the quick alert feature may reduce the impact of stampedes. ...