The graph represents a network of 1,031 Twitter users whose tweets in the requested range contained "iiot machinelearning", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 08 October 2021 at 11:03 UTC. The requested start date was Friday, 08 October 2021 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 3-day, 8-hour, 27-minute period from Monday, 04 October 2021 at 15:32 UTC to Friday, 08 October 2021 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

An army of more than 4,000 marching doglike robots is a vaguely menacing sight, even in a simulation. But it may point the way for machines to learn new tricks. The virtual robot army was developed by researchers from ETH Zurich in Switzerland and chipmaker Nvidia. They used the wandering bots to train an algorithm that was then used to control the legs of a real-world robot. In the simulation, the machines--called ANYmals--confront challenges like slopes, steps, and steep drops in a virtual landscape.

Digital Humanities is an emerging research area. Wikipedia says that "Digital Humanities (DH) is an area of scholarly activity at the intersection of computing or digital technologies and the disciplines of the humanities." This statement doesn't fully reveal anything clear or concrete. Whatever the definition, it is important to develop more services where humans and machines work together better. In most cases, AI is not an independent machine that handles all tasks but a tool to help people. That's why we need research and development to get this interaction working better.

As we know the world is getting bigger day by day and we start depending on technology too much. Alternatively, the technologies grow day by day for the fulfillment of customer demand and companies even start presenting people's more options, gadgets, and machines that help people to live easily. These days everything is just one click away from the user like if we want to send a message we just have to say Siri, Google Assistance, or Alexa. These are real examples of artificial intelligence Course. There are other examples as well, like self-driven cars and robots in restaurants, etc. The capacity of a virtual laptop or laptop-managed robot to carry out responsibilities is generally related to smart beings.

For our latest expert interview on our blog, we've welcomed Dhonam Pemba to share his thoughts on the topic of artificial intelligence (AI) and his journey behind founding KidX AI. Dhonam is a neural engineer by PhD, a former rocket scientist and a serial AI entrepreneur with one exit. He was CTO of the exited company, Kadho which was acquired by Roybi for its Voice AI technology. At Kadho Sports he was their Chief Scientist which had clients in MLB, USA Volleyball, NFL, NHL, NBA, and NCAA. His latest company, KidX, is in the AI edtech space, where he has built NLP and Voice assessment to serve China's leading robotics company with 4M users.

A fundamental question in the field of Learning and Games is Nash convergence of online learning dynamics: if the players in a repeated game employ some online learning algorithms to adjust strategies, will their strategies converge to the Nash equilibrium of the game? Although the answer to this question is "no" in general (see Related Works for details), positive results do exist for some special cases of online learning algorithms and games: for example, no-regret learning algorithms provably converge to Nash equilibria in zero-sum games, 2 2 games, and routing games (see e.g., Fudenberg and Levine, 1998; Cesa-Bianchi and Lugosi, 2006; Nisan et al., 2007). In this work, we analyze Nash convergence of online learning dynamics in repeated auctions, where bidders learn to bid using online learning algorithms. Although auctions are of both theoretical and practical importance, little is known about their Nash convergence properties, even for the perhaps simplest and most popular auction, the single-item first-price sealed-bid auction (or first price auction for short). One of the obstacles to the theoretical analysis of Nash convergence in the first price auction is the lack of explicit characterization of its Nash equilibrium.

Recent research on fair regression focused on developing new fairness notions and approximation methods as target variables and even the sensitive attribute are continuous in the regression setting. However, all previous fair regression research assumed the training data and testing data are drawn from the same distributions. This assumption is often violated in real world due to the sample selection bias between the training and testing data. In this paper, we develop a framework for fair regression under sample selection bias when dependent variable values of a set of samples from the training data are missing as a result of another hidden process. Our framework adopts the classic Heckman model for bias correction and the Lagrange duality to achieve fairness in regression based on a variety of fairness notions. Heckman model describes the sample selection process and uses a derived variable called the Inverse Mills Ratio (IMR) to correct sample selection bias. We use fairness inequality and equality constraints to describe a variety of fairness notions and apply the Lagrange duality theory to transform the primal problem into the dual convex optimization. For the two popular fairness notions, mean difference and mean squared error difference, we derive explicit formulas without iterative optimization, and for Pearson correlation, we derive its conditions of achieving strong duality. We conduct experiments on three real-world datasets and the experimental results demonstrate the approach's effectiveness in terms of both utility and fairness metrics.

Peer assessment has been widely applied across diverse academic fields over the last few decades and has demonstrated its effectiveness. However, the advantages of peer assessment can only be achieved with high-quality peer reviews. Previous studies have found that high-quality review comments usually comprise several features (e.g., contain suggestions, mention problems, use a positive tone). Thus, researchers have attempted to evaluate peer-review comments by detecting different features using various machine learning and deep learning models. However, there is no single study that investigates using a multi-task learning (MTL) model to detect multiple features simultaneously. This paper presents two MTL models for evaluating peer-review comments by leveraging the state-of-the-art pre-trained language representation models BERT and DistilBERT. Our results demonstrate that BERT-based models significantly outperform previous GloVe-based methods by around 6% in F1-score on tasks of detecting a single feature, and MTL further improves performance while reducing model size.

In this study, we propose a novel adversarial reprogramming (AR) approach for low-resource spoken command recognition (SCR), and build an AR-SCR system. The AR procedure aims to modify the acoustic signals (from the target domain) to repurpose a pretrained SCR model (from the source domain). To solve the label mismatches between source and target domains, and further improve the stability of AR, we propose a novel similarity-based label mapping technique to align classes. In addition, the transfer learning (TL) technique is combined with the original AR process to improve the model adaptation capability. We evaluate the proposed AR-SCR system on three low-resource SCR datasets, including Arabic, Lithuanian, and dysarthric Mandarin speech. Experimental results show that with a pretrained AM trained on a large-scale English dataset, the proposed AR-SCR system outperforms the current state-of-the-art results on Arabic and Lithuanian speech commands datasets, with only a limited amount of training data.

Learning new skills by observing humans' behaviors is an essential capability of AI. In this work, we leverage instructional videos to study humans' decision-making processes, focusing on learning a model to plan goal-directed actions in real-life videos. In contrast to conventional action recognition, goal-directed actions are based on expectations of their outcomes requiring causal knowledge of potential consequences of actions. Thus, integrating the environment structure with goals is critical for solving this task. Previous works learn a single world model will fail to distinguish various tasks, resulting in an ambiguous latent space; planning through it will gradually neglect the desired outcomes since the global information of the future goal degrades quickly as the procedure evolves. We address these limitations with a new formulation of procedure planning and propose novel algorithms to model human behaviors through Bayesian Inference and model-based Imitation Learning. Experiments conducted on real-world instructional videos show that our method can achieve state-of-the-art performance in reaching the indicated goals. Furthermore, the learned contextual information presents interesting features for planning in a latent space.