Computing with words (CWW) has emerged as a powerful tool for processing the linguistic information, especially the one generated by human beings. Various CWW approaches have emerged since the inception of CWW, such as perceptual computing, extension principle based CWW approach, symbolic method based CWW approach, and 2-tuple based CWW approach. Furthermore, perceptual computing can use interval approach (IA), enhanced interval approach (EIA), or Hao-Mendel approach (HMA), for data processing. There have been numerous works in which HMA was shown to be better at word modelling than EIA, and EIA better than IA. But, a deeper study of these works reveals that HMA captures lesser fuzziness than the EIA or IA. Thus, we feel that EIA is more suited for word modelling in multi-person systems and HMA for single-person systems (as EIA is an improvement over IA). Furthermore, another set of works, compared the performances perceptual computing to the other above said CWW approaches. In all these works, perceptual computing was shown to be better than other CWW approaches. However, none of the works tried to investigate the reason behind this observed better performance of perceptual computing. Also, no comparison has been performed for scenarios where the inputs are differentially weighted. Thus, the aim of this work is to empirically establish that EIA is suitable for multi-person systems and HMA for single-person systems. Another dimension of this work is also to empirically prove that perceptual computing gives better performance than other CWW approaches based on extension principle, symbolic method and 2-tuple especially in scenarios where inputs are differentially weighted.

In this white paper we provide a vision for 6G Edge Intelligence. Moving towards 5G and beyond the future 6G networks, intelligent solutions utilizing data-driven machine learning and artificial intelligence become crucial for several real-world applications including but not limited to, more efficient manufacturing, novel personal smart device environments and experiences, urban computing and autonomous traffic settings. We present edge computing along with other 6G enablers as a key component to establish the future 2030 intelligent Internet technologies as shown in this series of 6G White Papers. In this white paper, we focus in the domains of edge computing infrastructure and platforms, data and edge network management, software development for edge, and real-time and distributed training of ML/AI algorithms, along with security, privacy, pricing, and end-user aspects. We discuss the key enablers and challenges and identify the key research questions for the development of the Intelligent Edge services. As a main outcome of this white paper, we envision a transition from Internet of Things to Intelligent Internet of Intelligent Things and provide a roadmap for development of 6G Intelligent Edge.

The Ring Stick Up Cam, now in its third generation, can be deployed indoors or out. It operates on battery power, which means you can deploy it just about anywhere your Wi-Fi network can reach, and it can be connected to an optional but inexpensive solar panel. It's virtually identical to the Ring Indoor Cam I reviewed a little more than a week ago, both in form factor and specs, but the Stick Up Cam's battery and weatherized enclosure adds $40 to the price. There are higher-quality indoor/outdoor, battery-powered, Wi-Fi security cameras on the market, but the Ring Stick Up Cam costs half as much as the Arlo Pro 3 that tops our list, and you'll need to buy that camera in a $500 two-pack that includes a smart hub (add-on Arlo cameras cost $200 each). There are major upsides to Arlo's product for sure, but they don't matter if you can't afford the product in the first place.

Semantic segmentation models based on convolutional neural networks (CNNs) have gained much attention in relation to remote sensing and have achieved remarkable performance for the extraction of buildings from high-resolution aerial images. However, the issue of limited generalization for unseen images remains. When there is a domain gap between the training and test datasets, CNN-based segmentation models trained by a training dataset fail to segment buildings for the test dataset. In this paper, we propose segmentation networks based on a domain adaptive transfer attack (DATA) scheme for building extraction from aerial images. The proposed system combines the domain transfer and adversarial attack concepts. Based on the DATA scheme, the distribution of the input images can be shifted to that of the target images while turning images into adversarial examples against a target network. Defending adversarial examples adapted to the target domain can overcome the performance degradation due to the domain gap and increase the robustness of the segmentation model. Cross-dataset experiments and the ablation study are conducted for the three different datasets: the Inria aerial image labeling dataset, the Massachusetts building dataset, and the WHU East Asia dataset. Compared to the performance of the segmentation network without the DATA scheme, the proposed method shows improvements in the overall IoU. Moreover, it is verified that the proposed method outperforms even when compared to feature adaptation (FA) and output space adaptation (OSA).

Startup Cuberg is working on developing lighter, safer, more energy-dense batteries, and they're using a machine learning platform developed by Aionics Technologies to do it faster. "The exciting thing we do is make batteries that are very energy dense. They are much lighter than lithium ion batteries but they have much more energy in them," said Olivia Risset, PhD, senior scientist at Cuberg. The batteries that Cuberg makes are safer than lithium ion batteries because the liquid component, the electrolyte, is nonflammable as opposed to what has been used traditionally in lithium ion batteries. "Because of that," says Risset, "electric aviation is a great place for us because they are very sensitive to weight, but also to safety."

Scientists believe the world will see it's first working thermonuclear fusion reactor by the year 2025. That's a tall order in short form, especially when you consider that fusion has been "almost here" for nearly a century. Fusion reactors – not to be confused with common fission reactors – are the holiest of Grails when it comes to physics achievements. According to most experts, a successful fusion reactor would function as a near-unlimited source of energy. In other words, if there's a working demonstration of an actual fusion reactor by 2025, we could see an end to the global energy crisis within a few decades.

Apple is rolling out a new feature called "battery health management" that will change how laptops charge themselves. The update will mean that laptops may not charge themselves all the way up all of the time, if the computer believes doing so will protect the life of the battery. It aims to avoid a problem that means fully charging a laptop's battery puts a strain on it, because of the chemicals inside. Leaving a computer charged up in this way can therefore reduce its capacity, leading the battery life to fall over time. Instead, if the computer believes that it is not likely to need 100 per cent battery in the future, it will only charge up some of the way.

In the recent application of scientific modeling, machine learning models are largely applied to facilitate computational simulations of fluid systems. Rotation symmetry is a general property for most symmetric fluid systems. However, in general, current machine learning methods have no theoretical way to guarantee rotational symmetry. By observing an important property of contraction and rotation operation on high-order symmetric tensors, we prove that the rotation operation is preserved via tensor contraction. Based on this theoretical justification, in this paper, we introduce Rotation-Equivariant Network (RotEqNet) to guarantee the property of rotation-equivariance for high-order tensors in fluid systems. We implement RotEqNet and evaluate our claims through four case studies on various fluid systems. The property of error reduction and rotation-equivariance is verified in these case studies. Results from the comparative study show that our method outperforms conventional methods, which rely on data augmentation.

What if we could treat the Earth like a computer, a system with an ever-flowing set of data that can be tracked, analyzed, and potentially even predicted. That's the gist of Microsoft's latest environmental initiative, which it's dubbed a "Planetary Computer." The company foresees a world where we can track just about anything happening in the world -- a forest fire in California, the river tides in Uganda -- and have all of that data readily accessible on a single AI-driven platform. If Microsoft succeeds it could reshape our relationship with the Earth entirely. Lucas Joppa, Microsoft's first chief environmental officer, boiled down the concept succinctly in an interview for the Engadget Podcast: "It's a platform that is intended to accelerate our ability to monitor, model and then ultimately manage Earth's natural systems to ask questions like, 'Where are the world's forests? Where are the world's wetlands? How fast are they changing?' And hopefully, what are the sorts of benefits that we are gaining from those ecosystems? What are the services that those ecosystems provision to people?"

With the objective of establishing an ecosystem that will have "zero tolerance" for non-compliance with regulations, the corporate affairs ministry is betting big on AI and data analytics to deal with shell companies. Using these technologies, the ministry is developing an advanced MCA 21 portal. Used for submitting requisite filings under the companies law and managing a repository of data on corporates in India, the portal will enable authorities to weed out entities that do not comply with regulations. Typically, shell companies are floated for illegal activities like money laundering, and a zero tolerance approach to this, enabled by AI, can put a stop to these practices. It would make it "almost impossible for a shell company to survive," points out Corporate Affairs Secretary, Injeti Srinivas.