In federated learning (FL), it is commonly assumed that all data are placed at clients in the beginning of machine learning (ML) optimization (i.e., offline learning). However, in many real-world applications, it is expected to proceed in an online fashion. To this end, online FL (OFL) has been introduced, which aims at learning a sequence of global models from decentralized streaming data such that the so-called cumulative regret is minimized. Combining online gradient descent and model averaging, in this framework, FedOGD is constructed as the counterpart of FedSGD in FL. While it can enjoy an optimal sublinear regret, FedOGD suffers from heavy communication costs. In this paper, we present a communication-efficient method (named OFedIQ) by means of intermittent transmission (enabled by client subsampling and periodic transmission) and quantization. For the first time, we derive the regret bound that captures the impact of data-heterogeneity and the communication-efficient techniques. Through this, we efficiently optimize the parameters of OFedIQ such as sampling rate, transmission period, and quantization levels. Also, it is proved that the optimized OFedIQ can asymptotically achieve the performance of FedOGD while reducing the communication costs by 99%. Via experiments with real datasets, we demonstrate the effectiveness of the optimized OFedIQ.

Code repository for this post is here: you can see the MLFlow Recipes template in the main branch and the filled-in template on the fill-in-steps branch. The announcement of MLFlow 2.0 included a new framework called MLFlow Recipes. For a Data Scientists, using MLFlow Recipes means cloning a git repository, or "template", that comes with a ready-to-go folder structure for any regression or binary classification problem. This folder structure includes everything, from library requirements, configuration, notebooks and tests, that's needed to make a data science project reproducible and production-ready. It's easy to start a new project with MLFlow Recipes -- git clone a template from the MLFlow repository, and you are good to go.

The intended audience for the workshop includes practitioners, researchers, and developers working to employ technology to improve access to justice. The workshop is intended be accessible to attorneys, computer scientists, legal aid workers, and social scientists. The workshop will address technological innovations intended improve access to justice and delivery of services and benefits to citizens and reduction of risks created by such technology, including the due-process, bias, and privacy concerns that can arise from automated support of self-represented litigants. It will also examine human-computer interface issues concerning how and when self-represented litigants (SRLs) should use AI systems.

Abstract: Multilingual spoken language understanding (SLU) consists of two sub-tasks, namely intent detection and slot filling. To improve the performance of these two sub-tasks, we propose to use consistency regularization based on a hybrid data augmentation strategy. The consistency regularization enforces the predicted distributions for an example and its semantically equivalent augmentation to be consistent. We conduct experiments on the MASSIVE dataset under both full-dataset and zero-shot settings. Experimental results demonstrate that our proposed method improves the performance on both intent detection and slot filling tasks.

In a new podcast series, Everyday AI, host Jon Whittle (CSIRO) explores the AI that is already shaping our lives. With the help of expert guests, he explores how AI is used in creative industries, health, conservation, sports and space. Episode 4: AI and citizen science – AI in ecology This episode features Jessie Barry from Cornell University's Macaulay Library and Merlin Bird ID, ichthyologist Mark McGrouther, and Google's Megha Malpani. Episode 6: The final frontier – AI in space This episode features Astrophysicist Kirsten Banks, NASA researcher Dr Raymond Francis, and Research Astronomer Dr Ivy Wong.

ChatGPT has multiple uses, from writing or fixing a code to getting suggestions, getting explanations, writing non-plagiarised essays, creating summaries of long write-ups, getting solutions to problems etc. The possibilities are still open to explorations as the beta version is available for users to try out. It especially gained popularity when students started realising that they can use ChatGPT to get through their homework assignments and projects by just letting this "assistant" do it for them. Students around the world have been using it to complete their work and teachers have been reporting about how they are doubting the credibility of the work being submitted to them. These developments are catching a lot of traction on the internet.

Many solutions tailored for intuitive visualization or teleoperation of virtual, augmented and mixed (VAM) reality systems are not robust to robot failures, such as the inability to detect and recognize objects in the environment or planning unsafe trajectories. In this paper, we present a novel virtual reality (VR) framework where users can (i) recognize when the robot has failed to detect a real-world object, (ii) correct the error in VR, (iii) modify proposed object trajectories and, (iv) implement behaviors on a real-world robot. Finally, we propose a user study aimed at testing the efficacy of our framework. Project materials can be found in the OSF repository.

Automatic measurement of student engagement provides helpful information for instructors to meet learning program objectives and individualize program delivery. Students' behavioral and emotional states need to be analyzed at fine-grained time scales in order to measure their level of engagement. Many existing approaches have developed sequential and spatiotemporal models, such as recurrent neural networks, temporal convolutional networks, and three-dimensional convolutional neural networks, for measuring student engagement from videos. These models are trained to incorporate the order of behavioral and emotional states of students into video analysis and output their level of engagement. In this paper, backed by educational psychology, we question the necessity of modeling the order of behavioral and emotional states of students in measuring their engagement. We develop bag-of-words-based models in which only the occurrence of behavioral and emotional states of students is modeled and analyzed and not the order in which they occur. Behavioral and affective features are extracted from videos and analyzed by the proposed models to determine the level of engagement in an ordinal-output classification setting. Compared to the existing sequential and spatiotemporal approaches for engagement measurement, the proposed non-sequential approach improves the state-of-the-art results. According to experimental results, our method significantly improved engagement level classification accuracy on the IIITB Online SE dataset by 26% compared to sequential models and achieved engagement level classification accuracy as high as 66.58% on the DAiSEE student engagement dataset.

Vocal fatigue refers to the feeling of tiredness and weakness of voice due to extended utilization. This paper investigates the effectiveness of neural embeddings for the detection of vocal fatigue. We compare x-vectors, ECAPA-TDNN, and wav2vec 2.0 embeddings on a corpus of academic spoken English. Low-dimensional mappings of the data reveal that neural embeddings capture information about the change in vocal characteristics of a speaker during prolonged voice usage. We show that vocal fatigue can be reliably predicted using all three kinds of neural embeddings after only 50 minutes of continuous speaking when temporal smoothing and normalization are applied to the extracted embeddings. We employ support vector machines for classification and achieve accuracy scores of 81% using x-vectors, 85% using ECAPA-TDNN embeddings, and 82% using wav2vec 2.0 embeddings as input features. We obtain an accuracy score of 76%, when the trained system is applied to a different speaker and recording environment without any adaptation.

An artificial intelligence (AI) course is a type of academic program that focuses on teaching students about the principles and techniques of AI. These courses typically cover machine learning, natural language processing, robotics, and computer vision and may also include elements of mathematics, computer science, and engineering. Students who take an artificial intelligence course may learn how to design, implement, and evaluate AI systems and may also have the opportunity to work on projects that involve building or programming AI systems. The specific content of an AI course will vary depending on the course level (e.g., introductory, intermediate, or advanced) and the institution offering the course. Artificial intelligence (AI) is a rapidly growing field that is transforming many aspects of our society and economy.