With Twitter's growth and popularity, a huge number of views are shared by users on various topics, making this platform a valuable information source on various political, social, and economic issues. This paper investigates English tweets on the Russia-Ukraine war to analyze trends reflecting users' opinions and sentiments regarding the conflict. The tweets' positive and negative sentiments are analyzed using a BERT-based model, and the time series associated with the frequency of positive and negative tweets for various countries is calculated. Then, we propose a method based on the neighborhood average for modeling and clustering the time series of countries. The clustering results provide valuable insight into public opinion regarding this conflict. Among other things, we can mention the similar thoughts of users from the United States, Canada, the United Kingdom, and most Western European countries versus the shared views of Eastern European, Scandinavian, Asian, and South American nations toward the conflict.

Real-world applications often require learning continuously from a stream of data under ever-changing conditions. When trying to learn from such non-stationary data, deep neural networks (DNNs) undergo catastrophic forgetting of previously learned information. Among the common approaches to avoid catastrophic forgetting, rehearsal-based methods have proven effective. However, they are still prone to forgetting due to task-interference as all parameters respond to all tasks. To counter this, we take inspiration from sparse coding in the brain and introduce dynamic modularity and sparsity (Dynamos) for rehearsal-based general continual learning. In this setup, the DNN learns to respond to stimuli by activating relevant subsets of neurons. We demonstrate the effectiveness of Dynamos on multiple datasets under challenging continual learning evaluation protocols. Finally, we show that our method learns representations that are modular and specialized, while maintaining reusability by activating subsets of neurons with overlaps corresponding to the similarity of stimuli.

Robotic teleoperation is a key technology for a wide variety of applications. It allows sending robots instead of humans in remote, possibly dangerous locations while still using the human brain with its enormous knowledge and creativity, especially for solving unexpected problems. A main challenge in teleoperation consists of providing enough feedback to the human operator for situation awareness and thus create full immersion, as well as offering the operator suitable control interfaces to achieve efficient and robust task fulfillment. We present a bimanual telemanipulation system consisting of an anthropomorphic avatar robot and an operator station providing force and haptic feedback to the human operator. The avatar arms are controlled in Cartesian space with a direct mapping of the operator movements. The measured forces and torques on the avatar side are haptically displayed to the operator. We developed a predictive avatar model for limit avoidance which runs on the operator side, ensuring low latency. The system was successfully evaluated during the ANA Avatar XPRIZE competition semifinals. In addition, we performed in lab experiments and carried out a small user study with mostly untrained operators.

Abstract: The rapid development of technology has brought unmanned aerial vehicles (UAVs) to become widely known in the current era. The market of UAVs is also predicted to continue growing with related technologies in the future. UAVs have been used in various sectors, including livestock, forestry, and agriculture. In agricultural applications, UAVs are highly capable of increasing the productivity of the farm and reducing farmers' workload. This study examines the urgency of UAV implementation in the agriculture sector. A short history of UAVs is provided in this paper to portray the development of UAVs from time to time. The classification of UAVs is also discussed to differentiate various types of UAVs. The application of UAVs in spraying and crop monitoring is based on the previous studies that have been done by many scientific groups and researchers who are working closely to propose solutions for agriculture-related issues. Furthermore, the limitations of UAV applications are also identified. The challenges in implementing agricultural UAVs in Indonesia are also presented. Keywords: Unmanned aerial vehicle, agricultural UAV, spraying, crop monitoring. 1. Introduction According to the United Nations (UN), the world population is projected to reach 9.7 billion people in 2050 (UN, 2015). This vast population would potentially double the food demand in the future (Hunter et al., 2017). Consequently, the ever-growing population that would emerge could cause food shortages in the future. This issue has become a severe problem since the Food and Agriculture Organization (FAO) announced similar speculation in which the current agricultural production must be increased by 70 percent by 2050 to meet the increasing demand for highquality food (Mundial, 2021). Many people suffering from hunger become a signal of how severe the food shortage is, and it was reported that more than 820 million people in 2018 were considered undernutrition (WHO, 2019). Surprisingly, the earlier data mentioned shows the increasing tendency towards people suffering from hunger since only around 690 million people were considered suffering from hunger in 2015.

The stylistic properties of text have intrigued computational linguistics researchers in recent years. Specifically, researchers have investigated the Text Style Transfer (TST) task, which aims to change the stylistic properties of the text while retaining its style independent content. Over the last few years, many novel TST algorithms have been developed, while the industry has leveraged these algorithms to enable exciting TST applications. The field of TST research has burgeoned because of this symbiosis. This article aims to provide a comprehensive review of recent research efforts on text style transfer. More concretely, we create a taxonomy to organize the TST models and provide a comprehensive summary of the state of the art. We review the existing evaluation methodologies for TST tasks and conduct a large-scale reproducibility study where we experimentally benchmark 19 state-of-the-art TST algorithms on two publicly available datasets. Finally, we expand on current trends and provide new perspectives on the new and exciting developments in the TST field.

The rise of artificial intelligence and machine learning is examined in Mike Hodgkinson's story.

Rep. Rob Wittman, R-Va., joins'Fox News Live' to react to the United States Air Force's unveiling of its new B-21 raider stealth bomber, named'The Raider' for Jimmy Doolittle's famous bombing raid on Japan in WW2. With the launch of the Air Force's hypersonic missile off the coast of California earlier this month, the Navy's development of water-based drones over the summer and the recent unveiling of the B-21 Raiders, the U.S. military has made major technological advancements over the past year. The military unveiled the U.S. Air Force B-21 Raider in Palmdale, California. The B-21 Raider is the first new American bomber aircraft in more than three decades. In an email to Fox News Digital, a spokesperson confirmed the Air Force would transition its three-bomber fleet to a two-bomber fleet of B-21s and modernized B-52s.

In this article, we discuss the future of artificial intelligence, including 10 predictions about 10 companies. The future of artificial intelligence (AI) is a topic of much speculation and debate. Some experts believe that AI has the potential to revolutionize many aspects of society and industry, while others are more cautious about its potential impact. The artificial intelligence sector is thriving across the globe. AI-enabled tools, like smarter chat-bots for customer service and robots for self-service at banks, are gaining traction in the mainstream.

Artificial intelligence, or AI, has the potential to revolutionize many aspects of our society, but it also has the potential to be harmful. As AI becomes more advanced and widespread, it raises ethical concerns about how it will impact jobs, privacy and inequality. Overall, it is important to carefully consider the potential drawbacks of AI and take steps to mitigate them. Would it surprise you if I said that the above paragraph was written entirely by AI? It's true -- using the newish AI called ChatGPT, I typed in the following query: "Write me an opening paragraph about the dangers of artificial intelligence." ChatGPT was developed by OpenAI, a startup founded in part by Big Tech mogul Elon Musk.

As the final year of college approached, my friends and I, all electronics engineering students at Sudan University of Science and Technology, found ourselves struggling to come up with ideas for our graduation project. One day, while sitting in what we called "Jabnah" a version of a local cafe here is a picture of what it looks like I suggested using deep learning for malaria detection. My friends were skeptical, as we knew nothing about either deep learning or malaria. But I was determined to learn and proposed that we take an online course on deep learning. The course was challenging, but I was determined to succeed.