Although deep neural models substantially reduce the overhead of feature engineering, the features readily available in the inputs might significantly impact training cost and the performance of the models. In this paper, we explore the impact of an unsuperivsed feature enrichment approach based on variable roles on the performance of neural models of code. The notion of variable roles (as introduced in the works of Sajaniemi et al. [Refs. 1,2]) has been found to help students' abilities in programming. In this paper, we investigate if this notion would improve the performance of neural models of code. To the best of our knowledge, this is the first work to investigate how Sajaniemi et al.'s concept of variable roles can affect neural models of code. In particular, we enrich a source code dataset by adding the role of individual variables in the dataset programs, and thereby conduct a study on the impact of variable role enrichment in training the Code2Seq model. In addition, we shed light on some challenges and opportunities in feature enrichment for neural code intelligence models.

Abstract-- Finger-tip tactile sensors are increasingly used for robotic sensing to establish stable grasps and to infer object properties. Promising performance has been shown in a number of works for inferring adjectives that describe the object, but there remains a question about how each taxel contributes to the performance. This paper explores this question with empirical experiments, leading insights for future finger-tip tactile sensor usage and design. In recent years, with the developments of tactile sensors and machine learning techniques, finger-tip tactile sensors are increasingly used for robotic tasks such as adjective classification [1]. Promising performance has been shown for adjective classification [1], [2], [3] or learning adjective distributions [4].

Computers powered by human brain cells may sound like science fiction, but a team of researchers in the United States believes such machines, part of a new field called "organoid intelligence," could shape the future -- and now they have a plan to get there. Organoids are lab-grown tissues that resemble organs. These three-dimensional structures, usually derived from stem cells, have been used in labs for nearly two decades, where scientists have been able to avoid harmful human or animal testing by experimenting on the stand-ins for kidneys, lungs and other organs. Brain organoids don't actually resemble tiny versions of the human brain, but the pen dot-size cell cultures contain neurons that are capable of brainlike functions, forming a multitude of connections. Scientists call the phenomenon "intelligence in a dish."

Google is using AI to help people facing disease and natural disasters, and to provide new opportunities for underserved communities. The potential of AI to solve big problems is increasing all the time. In the past few years, AI and transformational innovations have become more important in confronting some of society’s biggest challenges. Today, AI is helping countries and communities facing disease and natural disasters, and providing new opportunities for historically underserved groups. Here are seven ways AI is already making the world a better place: 1. Forecasting floods and helping people stay safe through early warning systems Last year…

In this paper, we propose XG-BoT, an explainable deep graph neural network model for botnet node detection. The proposed model comprises a botnet detector and an explainer for automatic forensics. The XG-BoT detector can effectively detect malicious botnet nodes in large-scale networks. Specifically, it utilizes a grouped reversible residual connection with a graph isomorphism network to learn expressive node representations from botnet communication graphs. The explainer, based on the GNNExplainer and saliency map in XG-BoT, can perform automatic network forensics by highlighting suspicious network flows and related botnet nodes. We evaluated XG-BoT using real-world, large-scale botnet network graph datasets. Overall, XG-BoT outperforms state-of-the-art approaches in terms of key evaluation metrics. Additionally, we demonstrate that the XG-BoT explainers can generate useful explanations for automatic network forensics.

In recent years, hate speech has gained great relevance in social networks and other virtual media because of its intensity and its relationship with violent acts against members of protected groups. Due to the great amount of content generated by users, great effort has been made in the research and development of automatic tools to aid the analysis and moderation of this speech, at least in its most threatening forms. One of the limitations of current approaches to automatic hate speech detection is the lack of context. Most studies and resources are performed on data without context; that is, isolated messages without any type of conversational context or the topic being discussed. This restricts the available information to define if a post on a social network is hateful or not. In this work, we provide a novel corpus for contextualized hate speech detection based on user responses to news posts from media outlets on Twitter. This corpus was collected in the Rioplatense dialectal variety of Spanish and focuses on hate speech associated with the COVID-19 pandemic. Classification experiments using state-of-the-art techniques show evidence that adding contextual information improves hate speech detection performance for two proposed tasks (binary and multi-label prediction). We make our code, models, and corpus available for further research.

With the integration of robotic systems in surgery, the adaptability and success rate of surgery has improved noticeably, allowing for surgeons to automate repetitive tasks, reduce the manpower in the OR, as well as reduce the risk posed to the patient by directly alleviating surgeon fatigue (Taylor et al 1995) (Casals 1998) (Michel 2021). Another critical factor that is addressed through the introduction of robotics in surgery is the high level of skill that is demanded from the surgeon; highly delicate surgeries require years of training, in addition to an exceptional understanding of the human anatomy. ESS, characteristically a minimally invasive Endoscopic Sinus Surgery, is one of such surgeries (Fried et al 2005) (Zhao et al 2021) (Lourijsen et al 2022). Given the tight spatial and visual constraints, the increased complexity of the procedure demands the ability to navigate around intraoperative issues such as visual perception, anatomy recognition, and nonhomogeneous 2 Medical and Healthcare Robotics anatomical makeup, not to mention the real-time identification of presence of critical regions like brain tissue, carotid artery, optic nerve, and other intracranial structures (Fried et al 2004). Thus, the importance of extensive practice and training is undoubtedly high for increasing the success rate for such a surgery.

An international team of researchers looking for signs of intelligent life in space have used artificial intelligence (AI) to reveal eight promising radio signals in data collected at a US observatory. The results of their research, published in Nature Astronomy are remarkable. The team hasn't yet carried out an exhaustive analysis, but the paper suggests the signals have many of the characteristics we would expect if they were artificially generated. In other words, they are the kinds of signals we might pick up from an extraterrestrial civilisation broadcasting into space. A cursory review of the new paper suggest these are indeed promising signals. They're much more compelling than what is perhaps the most famous Seti candidate, the "Wow!" signal, radio emission bearing the hallmarks of an extraterrestrial origin that was collected by an Ohio telescope in 1977.

It's a classic exercise in high-school art class: a student sits at her desk, charcoal pencil held in one hand, poised over a sheet of paper, while the other hand lies outstretched in front of her, palm up, fingers relaxed so that they curve inward. Then she uses one hand to draw the other. It's a beginner's assignment, but the task of depicting hands convincingly is one of the most notorious challenges in figurative art. I remember it being incredibly frustrating--getting the angles and proportion of each finger right, determining how the thumb connects to the palm, showing one finger overlapping another just so. Too often, I would end up with a bizarrely long pinky, or a thumb jutting out at an impossible angle like a broken bone.

Twilio powers real-time business communications and data solutions that help companies and developers worldwide build better applications and customer experiences. Although we're headquartered in San Francisco, we have presence throughout South America, Europe, Asia and Australia. We're on a journey to becoming a globally anti-racist, anti-oppressive, anti-bias company that actively opposes racism and all forms of oppression and bias. At Twilio, we support diversity, equity & inclusion wherever we do business. We employ thousands of Twilions worldwide, and we're looking for more builders, creators, and visionaries to help fuel our growth momentum.