Advancements under the moniker of the Internet of Things (IoT) allow things to network and become the primary producers of data in the Internet.14 IoT makes the state and interactions of real-world available to Web applications and information systems with minimal latency and complexity.25 By enabling massive telemetry and individual addressing of "things," the IoT offers three prominent benefits: spatial and temporal traceability of individual real-world objects for thief prevention, counterfeit product detection and food safety via accessing their pedigree; enabling ambient data collection and analytics for optimizing crop planning, enabling telemedicine and assisted living; and supporting real-time reactive systems such as smart building, automatic logistics and self-driving, networked cars.11 Realizing these benefits requires the ability to discover and resolve queries for contents in the IoT. Offering these abilities is the responsibility of a class of software system called the Internet of Things search engine (IoTSE).
Algorithms, computation and visual data are the three pillars of computer vision (CV). Researchers, institutions and open source communities have produced sophisticated algorithms and open-sourced code; while global tech giants' supercharged cloud platforms provide all the computational power CV researchers require. However, efficiently sourcing visual data -- particularly images with high-quality annotations -- remains a challenge. Building large datasets is a time-consuming and labor-intensive task which challenges entities with limited budgets. There are hundreds of open visual datasets out there, but searching across them and their millions of entries is not a simple task.
When you're working on ad campaigns at your company or digital advertising agency, do you ever wish the process was less complex, and more, well… creative? Those who produce the most creative this year--digital ad agencies--are made up of a wide variety of different types of staff members. To mention a few, there's: Yes, companies that run their campaigns independently might just have a sprinkling of these professionals in their department. And on occasion, marketing managers are lumped with the entire responsibility a fully-staffed department would normally spread between them. Ad campaigns didn't used to need such a complex skill set.
Artificial intelligence is not just about science-fiction and robots anymore. In today's digital landscape, artificial intelligence technology is pervading and reshaping various industries in the form of self-driving cars, chatbots, & smart devices and digital marketing is no exception. The Search engine optimization medium has faced innumerable fundamental strategic changes over the years, due to revolutionary new intelligent algorithms and ever-adapting process of improving user experience. Marketers agree that any chances of using tactics and tricks to outsmart Google's algorithms are now over. In fact, the SEO is now subjected to the concept-based content, powerful content strategy links the building, and optimization of meta tags.
In Search Engine Optimization All-in-One For Dummies, 3rd Edition, Bruce Clay--whose search engine consultancy predates Google--shares everything you need to know about SEO. In minibooks that cover the entire topic, you'll discover how search engines work, how to apply effective keyword strategies, ways to use SEO to position yourself competitively, the latest on international SEO practices, and more. If SEO makes your head spin, this no-nonsense guide makes it easier. You'll get the lowdown on how to use search engine optimization to improve the quality and volume of traffic on your website via search engine results. Cutting through technical jargon, it gets you up to speed quickly on how to use SEO to get your website in the top of the rankings, target different kinds of searches, and win more industry-specific vertical search engine results!
The research surrounding methods of information retrieval is an entire field of science whose specialists aim to provide us with even better search results – a necessity as the amount of data constantly keeps growing. To succeed in their quest, researchers are focusing on the interaction between humans and computers, connecting methods of machine learning to this interaction. One of these researchers is Dorota Głowacka, who assumed an assistant professorship in machine learning and data science at the Helsinki Centre for Data Science HiDATA at the beginning of 2019. Głowacka is studying what people search for and how they interact with search engines, with a particular focus on exploratory search. This is a search method that helps find matters relevant to the person looking for information, even if they are not entirely certain about what they are looking for to begin with.
In this paper, we introduce the notion of motif closure and describe higher-order ranking and link prediction methods based on the notion of closing higher-order network motifs. The methods are fast and efficient for real-time ranking and link prediction-based applications such as web search, online advertising, and recommendation. In such applications, real-time performance is critical. The proposed methods do not require any explicit training data, nor do they derive an embedding from the graph data, or perform any explicit learning. Existing methods with the above desired properties are all based on closing triangles (common neighbors, Jaccard similarity, and the ilk). In this work, we investigate higher-order network motifs and develop techniques based on the notion of closing higher-order motifs that move beyond closing simple triangles. All methods described in this work are fast with a runtime that is sublinear in the number of nodes. The experimental results indicate the importance of closing higher-order motifs for ranking and link prediction applications. Finally, the proposed notion of higher-order motif closure can serve as a basis for studying and developing better ranking and link prediction methods.
Only a few years ago, web search was simple. Users typed a few words and waded through pages of results. Today, those same users may instead snap a picture on a phone and drop it into a search box or use an intelligent assistant to ask a question without physically touching a device at all. They may also type a question and expect an actual reply, not a list of pages with likely answers. These tasks challenge traditional search engines, which are based around an inverted index system that relies on keyword matches to produce results.
By using artificial intelligence technology, search engines such as Google and Bing allow companies to serve display advertising or standard text ads to previous visitors of their websites. This form of digital advertising is in a way personalized to each user and is more targeted than even a display ad that targets certain search terms. The key word in digital remarketing advertising is relevance. Ads for a company's products are extremely relevant to a person who has visited that company's website or browsed that company's products in the past. Remarketing is more akin to an ad-filtering program that people may use when they browse the web.