It's Monday, and your coworkers are digging into a long, grueling database project. If you're nice, you'll bring them coffee and bagels. But if you're feeling less charitable, there's always an animated Clippy sticker to help get their week started off on the wrong foot. Microsoft recently confirmed that, yes, you can pull a number of animated Clippy images from within Microsoft Teams. In case you're too young to remember Clippy, the animated paperclip was introduced to Microsoft Word in 1996 as an "office assistant," and is unfondly remembered as a precursor to virtual assistants like Siri and the Google Assistant.

Smart homes are no longer a luxury. They are becoming a natural choice for people who want to enjoy more comfortable, convenient, and safe living spaces. In recent years, artificial intelligence has become a constant and welcomed presence in houses around the world. AI technology has added an extra level of safety and security while allowing people to enjoy a more pleasant way of living. Due to the increased demand for smart homes, home automation tools are now more affordable, and smart household appliances are a must for technology aficionados.

Due to a large amount of information, it is difficult for users to find what they are interested in among the many choices. In order to improve users' experience, recommendation systems have been widely used in music recommendations, movie recommendations, online shopping, and other scenarios. Recently, Knowledge Graph (KG) has been proven to be an effective tool to improve the performance of recommendation systems. However, a huge challenge in applying knowledge graphs for recommendation is how to use knowledge graphs to obtain better user codes and item codes. In response to this problem, this research proposes a user Recurrent Neural Network (RNN) encoder and item encoder recommendation algorithm based on Knowledge Graph (URIR). This study encodes items by capturing high-level neighbor information to generate items' representation vectors and applies an RNN and items' representation vectors to encode users to generate users' representation vectors, and then perform inner product operation on users' representation vectors and items' representation vectors to get probabilities of users interaction with items. Numerical experiments on three real-world datasets demonstrate that URIR is superior performance to state-of-the-art algorithms in indicators such as AUC, Precision, Recall, and MRR. This implies that URIR can effectively use knowledge graph to obtain better user codes and item codes, thereby obtaining better recommendation results.

The invention of artificial intelligence has had the biggest effect on the world since electricity. And just like electricity, it will have a profound impact on virtually every sphere of human endeavor, from warfare to medicine to music. We love to use it in our technology, but artificial intelligence is everywhere. Most of these examples are things we take for granted because they have become so integrated into the fabric of our everyday lives. Artificial intelligence is all around us. We can find it in our homes, in our cars, and even in our relationships. We may not always think about the artificial intelligence that's buzzing around us because it requires an explicit action to activate it. With just a few clicks of the mouse or taps on your phone, artificial intelligence can do everything from sorting your laundry to directing your car. Artificial intelligence is both helpful and scary. From self-driving cars to analyzing medical data, artificial intelligence (AI) is already present in many aspects of our daily lives. While AI may conjure up images of humanoid robots and the "Terminator" movies, the reality is far more mundane. The idea of artificial intelligence can intimidate, but it doesn't need to be scary. AI is simply technology that can make our lives easier. For instance, AI systems handle over half of the United States stock market trades.

Machines make use of intelligence, this intelligence is known as artificial intelligence. Think of artificial intelligence and think of life made easy and less stressful. Artificial intelligence is the simulation of intelligence into machines that are programmed to act and perform human-like actions. Artificial Intelligence has made our lives better ranging from music recommendations, mobile banking to mapping direction. In most cases, artificial intelligence can perform tasks better than humans.

Vyopta, the leader in digital collaboration and experience optimization, today announced it has achieved U.S. Federal Risk and Authorization Management Program (FedRAMP) Authority to Operate (ATO). Vyopta earned this authorization in partnership with the U.S. General Services Administration (GSA), its sponsoring agency. Vyopta is the only multi-vendor collaboration solution available in the FedRAMP Marketplace. FedRAMP is one of the most extensive security authorizations cloud services providers can achieve. It provides a standardized approach to security assessment, authorization and continuous monitoring for cloud services to ensure all federal data is secure in cloud environments.

This may be one of your best chances of scoring a smart speaker or e-reader before the holiday rush. Amazon is selling multiple recent Echo devices (plus a Kindle reader) at very low prices. To start, the fourth-generation Echo speaker is on sale for just $60 (usually $100), or the same as its Prime Day pricing. You can also buy the new Echo Show 5 for an all-time low of $55 (normally $85), while its larger Echo Show 8 counterpart has dropped to $100 (typically $130). You might also appreciate the Kindle deal if you're a book lover.

As recommendation is essentially a comparative (or ranking) process, a good explanation should illustrate to users why an item is believed to be better than another, i.e., comparative explanations about the recommended items. Ideally, after reading the explanations, a user should reach the same ranking of items as the system's. Unfortunately, little research attention has yet been paid on such comparative explanations. In this work, we develop an extract-and-refine architecture to explain the relative comparisons among a set of ranked items from a recommender system. For each recommended item, we first extract one sentence from its associated reviews that best suits the desired comparison against a set of reference items. Then this extracted sentence is further articulated with respect to the target user through a generative model to better explain why the item is recommended. We design a new explanation quality metric based on BLEU to guide the end-to-end training of the extraction and refinement components, which avoids generation of generic content. Extensive offline evaluations on two large recommendation benchmark datasets and serious user studies against an array of state-of-the-art explainable recommendation algorithms demonstrate the necessity of comparative explanations and the effectiveness of our solution.

By enabling computers to perform each task intelligently, machine learning systems can carry out complex processes by learning from data. Recent years have seen exciting advances in machine learning, which have raised its capabilities across a suite of applications. For years, Google has been using machine learning for several tasks, including Autocorrecting misspelled words or showing useful results. Whats's more, they also created an individual's virtual assistant called Google Assistant. Just Say'OK Google,' and your assistant is ready to help you perform various tasks.

Traditional high-utility itemset mining (HUIM) aims to determine all high-utility itemsets (HUIs) that satisfy the minimum utility threshold (\textit{minUtil}) in transaction databases. However, in most applications, not all HUIs are interesting because only specific parts are required. Thus, targeted mining based on user preferences is more important than traditional mining tasks. This paper is the first to propose a target-based HUIM problem and to provide a clear formulation of the targeted utility mining task in a quantitative transaction database. A tree-based algorithm known as Target-based high-Utility iteMset querying using (TargetUM) is proposed. The algorithm uses a lexicographic querying tree and three effective pruning strategies to improve the mining efficiency. We implemented experimental validation on several real and synthetic databases, and the results demonstrate that the performance of \textbf{TargetUM} is satisfactory, complete, and correct. Finally, owing to the lexicographic querying tree, the database no longer needs to be scanned repeatedly for multiple queries.