The large majority of the research performed on stance detection has been focused on developing more or less sophisticated text classification systems, even when many benchmarks are based on social network data such as Twitter. This paper aims to take on the stance detection task by placing the emphasis not so much on the text itself but on the interaction data available on social networks. More specifically, we propose a new method to leverage social information such as friends and retweets by generating relational embeddings, namely, dense vector representations of interaction pairs. Our method can be applied to any language and target without any manual tuning. Our experiments on seven publicly available datasets and four different languages show that combining our relational embeddings with textual methods helps to substantially improve performance, obtaining best results for six out of seven evaluation settings, outperforming strong baselines based on large pre-trained language models.

Verizon Business announced a partnership with Extreme Networks,, a leader in cloud networking, to deploy wireless connectivity solutions at Liverpool FC's Anfield Stadium as part of Extreme's partnership with the Premier League club. The deployment, expected to begin later this year, includes Extreme Wi-Fi 6 access points which will provide the high-quality, low latency Wi-Fi connectivity required to power memorable, engaging fan-centric experiences such as mobile ticketing, cash free concessions, shopping at the team store, video streaming, and new digital immersive experiences. AI and ML News: Why SMBs Shouldn't Be Afraid of Artificial Intelligence (AI) Founded in 1892, Liverpool Football Club is the most successful British club of all time with 19 league titles and six European Cups. Anfield has a current seating capacity of 54,000 and is being redeveloped to host more than 61,000 fans by the start of the 2023/24 season. As part of the partnership, Liverpool FC will also leverage ExtremeAnalytics to get real-time insights into data, including fan foot traffic, app usage across the stadium and popular concessions.

Privacy-sensitive data is stored in autonomous vehicles, smart devices, or sensor nodes that can move around with making opportunistic contact with each other. Federation among such nodes was mainly discussed in the context of federated learning with a centralized mechanism in many works. However, because of multi-vendor issues, those nodes do not want to rely on a specific server operated by a third party for this purpose. In this paper, we propose a wireless ad hoc federated learning (WAFL) -- a fully distributed cooperative machine learning organized by the nodes physically nearby. WAFL can develop generalized models from Non-IID datasets stored in distributed nodes locally by exchanging and aggregating them with each other over opportunistic node-to-node contacts. In our benchmark-based evaluation with various opportunistic networks, WAFL has achieved higher accuracy of 94.8-96.3% than the self-training case of 84.7%. All our evaluation results show that WAFL can train and converge the model parameters from highly-partitioned Non-IID datasets over opportunistic networks without any centralized mechanisms.

Candidates bring a deep understanding of geometric computer vision and machine learning algorithms and technologies that are necessary building blocks of interactive, real-time, and immersive systems, including Simultaneous localization and mapping (SLAM), and sensor fusion, also applying ML/DL techniques to enhance accuracy and robustness of traditional geometric computer vision algorithms, handle 3D data, modeling geometries of 3D objects, computational geometry, and mathematical optimization. They need to exhibit excellent analytical and algorithm development skills. Experience in designing solutions and implementations for real-time embedded and mobile platforms would be a plus.

Verizon is using a fleet of drones over southwest Florida to provide cellular connectivity to first responders who working around the clock in search and rescue missions to find survivors who may be trapped inside one of the more than 400 buildings destroyed by Hurricane Ian. Tethered drones that can fly for up to 1,000 hours are beaming down 4G and 5G coverage for an approximate radius of five to seven miles. Cory Davis, National Director for Verizon Frontline's Response Team and Public Safety Operations, told DailyMail.com He explained that along with the drones, Verizon is using satellites that beam down internet from low Earth orbit, generators hitched to trailers and recently sent a portable cell site on a barge to Sanibel Island, which has been completely cut off by the hurricane. Ian hit Lee County, home to Fort Myers, the hardest and Verizon, which is calling the county'ground zero,' is using the most assets to provide communications for first responders who have rescued hundreds of people since the monster storm made landfall last week.

Abstract--Industry 5.0 aims at maximizing the collaboration Industry 4.0 and Industry 5.0 both rely heavily on IoT The IIoT's basic premise is that intelligent machines are often more I. HE fourth industrial revolution (Industry 4.0) enables smart manufacturing via the application of various technologies, On the other hand, IIoT data samples are usually nonstationary such as the Internet of Things (IoT), Artificial Intelligence data streams generated in ever-changing IIoT environments (AI), big data analytics, cloud computing, and edge due to their dynamic nature [6]. Industry 4.0 also applications, IIoT data analytics often suffers from concept achieves technological advancements that increase the level of drift issues when IIoT data distributions change over time. Recently, The occurrence of concept drift poses considerable challenges as the fifth industrial revolution, Industry 5.0 has been in developing ML models, since their learning performance proposed as a human-centered design solution for the next evolutionary may progressively degrade owing to data distribution changes state. Thus, advanced online adaptive learning models should and machines work collaboratively with human resources to be developed to detect and react to concept drift that occurs enable customizable autonomous production through business in IIoT data streams. The drift adaptation procedure is also social networks [1]. In Industry 5.0, humans can devote their referred to as automated model updates in the network data creativity to responsible activities, while computers take over analytics automation process, as its main purpose is to improve repetitive and monotonous duties, hence improving production model performance by updating the learning model.

We analyze feature learning in infinite-width neural networks trained with gradient flow through a self-consistent dynamical field theory. We construct a collection of deterministic dynamical order parameters which are inner-product kernels for hidden unit activations and gradients in each layer at pairs of time points, providing a reduced description of network activity through training. These kernel order parameters collectively define the hidden layer activation distribution, the evolution of the neural tangent kernel, and consequently output predictions. We show that the field theory derivation recovers the recursive stochastic process of infinite-width feature learning networks obtained from Yang and Hu (2021) with Tensor Programs . For deep linear networks, these kernels satisfy a set of algebraic matrix equations. For nonlinear networks, we provide an alternating sampling procedure to self-consistently solve for the kernel order parameters. We provide comparisons of the self-consistent solution to various approximation schemes including the static NTK approximation, gradient independence assumption, and leading order perturbation theory, showing that each of these approximations can break down in regimes where general self-consistent solutions still provide an accurate description. Lastly, we provide experiments in more realistic settings which demonstrate that the loss and kernel dynamics of CNNs at fixed feature learning strength is preserved across different widths on a CIFAR classification task.

Anthropomorphic robot avatars present a conceptually novel approach to remote affective communication, allowing people across the world a wider specter of emotional and social exchanges over traditional 2D and 3D image data. However, there are several limitations of current telepresence robots, such as the high weight, complexity of the system that prevents its fast deployment, and the limited workspace of the avatars mounted on either static or wheeled mobile platforms. In this paper, we present a novel concept of telecommunication through a robot avatar based on an anthropomorphic swarm of drones; SwarMan. The developed system consists of nine nanocopters controlled remotely by the operator through a gesture recognition interface. SwarMan allows operators to communicate by directly following their motions and by recognizing one of the prerecorded emotional patterns, thus rendering the captured emotion as illumination on the drones. The LSTM MediaPipe network was trained on a collected dataset of 600 short videos with five emotional gestures. The accuracy of achieved emotion recognition was 97% on the test dataset. As communication through the swarm avatar significantly changes the visual appearance of the operator, we investigated the ability of the users to recognize and respond to emotions performed by the swarm of drones. The experimental results revealed a high consistency between the users in rating emotions. Additionally, users indicated low physical demand (2.25 on the Likert scale) and were satisfied with their performance (1.38 on the Likert scale) when communicating by the SwarMan interface.

Looking for a faster way to understand data issues and patterns, before you dive into the fun part of training your ML model? Wanna learn how to train better ML models, by finding and fixing issues in your data? You've come to the right place. In this article, you will learn how to do data exploration at the speed of light, using the amazing open-source library Sweetviz. Let's go through a hands-on example and code you can find in this GitHub repository.

Deep learning has achieved remarkable success in many machine learning tasks such as image classification, speech recognition, and game playing. However, these breakthroughs are often difficult to translate into real-world engineering systems because deep learning models require a massive number of training samples, which are costly to obtain in practice. To address labeled data scarcity, few-shot meta-learning optimizes learning algorithms that can efficiently adapt to new tasks quickly. While meta-learning is gaining significant interest in the machine learning literature, its working principles and theoretic fundamentals are not as well understood in the engineering community. This review monograph provides an introduction to meta-learning by covering principles, algorithms, theory, and engineering applications. After introducing meta-learning in comparison with conventional and joint learning, we describe the main meta-learning algorithms, as well as a general bilevel optimization framework for the definition of meta-learning techniques. Then, we summarize known results on the generalization capabilities of meta-learning from a statistical learning viewpoint. Applications to communication systems, including decoding and power allocation, are discussed next, followed by an introduction to aspects related to the integration of meta-learning with emerging computing technologies, namely neuromorphic and quantum computing. The monograph is concluded with an overview of open research challenges.