Instagram appears to be in the process of bringing direct messaging to its desktop website. As Wong states in one of the tweets shown above, Instagram Direct on desktop closely resembles the Facebook Messenger experience on desktop. Perhaps motivated by the popularity of Facebook Messenger, Instagram is making efforts to have its platform seen as more of a destination for direct messaging. Just last week, Instagram introduced Threads, which is a standalone messaging app for communicating with your closest Instagram friends. According to another leak from Jane Manchun Wong, Instagram is adopting another Messenger-like feature in the form of a "Shared" section which is a collection of all media shared in a particular chat.

Google announced it will soon be using machine learning to manage ad frequency when third-party cookies are missing. This change will first roll out in the coming weeks to Display & Video 360, though Google has plans to bring this capability to its display offerings in Google Ads as well. Google is rolling out this change as part of a larger effort to improve user privacy while still being able to serve ads in a way that's effective for publishers and marketers. Usually, when third-party cookies are blocked or restricted, advertisers no longer have the ability to limit the number of times someone sees an ad. That means someone who's blocking cookies may end up seeing the same ad over and over again.

Regular web search engines like Google may be great for finding a restaurant, but they are lousy for locating a snippet of code. In a bid to help software developers and foster innovative code search research, GitHub last week announced the CodeSearchNet Challenge in a joint effort with California-based machine learning development tools startup Weights & Biases. A large dataset and several baseline models showing the current state of the art in code search have been released to help scientists build models for the challenge. Faced with unsatisfactory code search results from natural language processing engines, researchers have in recent years been applying machine learning techniques to improve their code searches. They quickly realized however that, unlike natural language with GLUE benchmarks, there are currently no standard datasets suitable for evaluating code search processes.

The rise of Amazon has also seen an explosion in eCommerce websites hoping to capitalize on online eCommerce traffic. It's pretty easy to get online and set up your eCommerce store by using one of the many free solutions available like WordPress Woocommerce, Magento or even Xcart. However, getting sales is a different matter. Getting to the top of the search results is the biggest win a company can make and perfect your SEO strategy can make this a game-changer. According to statistics, 95% of all purchases begin with an online search and of those 75% of users never go past the first page of Google.

The unevenness importance of criminal activities in the onion domains of the Tor Darknet and the different levels of their appeal to the end-user make them tangled to measure their influence. To this end, this paper presents a novel content-based ranking framework to detect the most influential onion domains. Our approach comprises a modeling unit that represents an onion domain using forty features extracted from five different resources: user-visible text, HTML markup, Named Entities, network topology, and visual content. And also, a ranking unit that, using the Learning-to-Rank (LtR) approach, automatically learns a ranking function by integrating the previously obtained features. Using a case-study based on drugs-related onion domains, we obtained the following results. (1) Among the explored LtR schemes, the listwise approach outperforms the benchmarked methods with an NDCG of 0.95 for the top-10 ranked domains. (2) We proved quantitatively that our framework surpasses the link-based ranking techniques. Also, (3) with the selected feature, we observed that the textual content, composed by text, NER, and HTML features, is the most balanced approach, in terms of efficiency and score obtained. The proposed framework might support Law Enforcement Agencies in detecting the most influential domains related to possible suspicious activities.

Despite efforts to curb online piracy, some estimates suggest that pirate websites secured around 190 billion visits in 2018, affecting industries spanning TV, movies, music, publishing, and software. According to a recent report by the U.S. Chamber of Commerce, digital video piracy alone results in nearly $30 billion in lost revenue annually in the U.S. Against this backdrop, Smart Protection is working to help brands and rightsholders identify online hubs that host illegal streams, downloads, and other types of content infringement. Founded out of Madrid, Spain in 2015, Smart Protection uses machine learning and big data processing -- in concert with natural language processing (NLP), computer vision, keyword searches, and more -- to find the hubs hosting piracy and counterfeit content. By scanning the web, Smart Protection effectively builds a huge database of URLs, and it then applies its various machine learning algorithms that have been tailored to each content type and classifies the URLs based on the likelihood that they are hosting illicit content. "Typically, we eliminate approximately 98% [of the URLs], and it is from the remaining 2% that counterfeits, piracy, or brand abuse are hosted," Javier Perea, CEO and cofounder of Smart Protection, told VentureBeat.

This paper presents our system details and results of participation in the RDoC Tasks of BioNLP-OST 2019. Research Domain Criteria (RDoC) construct is a multi-dimensional and broad framework to describe mental health disorders by combining knowledge from genomics to behaviour. Non-availability of RDoC labelled dataset and tedious labelling process hinders the use of RDoC framework to reach its full potential in Biomedical research community and Healthcare industry. Therefore, Task-1 aims at retrieval and ranking of PubMed abstracts relevant to a given RDoC construct and Task-2 aims at extraction of the most relevant sentence from a given PubMed abstract. We investigate (1) attention based supervised neural topic model and SVM for retrieval and ranking of PubMed abstracts and, further utilize BM25 and other relevance measures for re-ranking, (2) supervised and unsupervised sentence ranking models utilizing multi-view representations comprising of query-aware attention-based sentence representation (QAR), bag-of-words (BoW) and TF-IDF. Our best systems achieved 1st rank and scored 0.86 mean average precision (mAP) and 0.58 macro average accuracy (MAA) in Task-1 and Task-2 respectively.

Regular web search engines like Google may be great for finding a restaurant, but they are lousy for locating a snippet of code. In a bid to help software developers and foster innovative code search research, GitHub last week announced the CodeSearchNet Challenge in a joint effort with California-based machine learning development tools startup Weights & Biases. A large dataset and several baseline models showing the current state of the art in code search have been released to help scientists build models for the challenge. Faced with unsatisfactory code search results from natural language processing engines, researchers have in recent years been applying machine learning techniques to improve their code searches. They quickly realized however that, unlike natural language with GLUE benchmarks, there are currently no standard datasets suitable for evaluating code search processes.

Artificial Intelligence is the latest technology that as introduced in the market. The technology has outgrown all other existing technologies that make many small and large scale organizations to introduce Artificial Intelligence to their business process. Artificial Intelligence is used to make search more human and to make the use of human interactions up to minimal. Artificial Intelligence in SEO is introduced with the help of one of its fields that is known as'Machine Learning', MI helps in inferring intent from complex and ambiguous search queries and it uses feedback data to improve the accuracy of its results. The SEO process can be automated with the help of Artificial Intelligence with the help of various SEO tools.

Natural Language Processing is one of the fields of computational linguistics and artificial intelligence that is concerned with human-computer interaction. It provides a seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning. This book will give you expertise on how to employ various NLP tasks in Python, giving you an insight into the best practices when designing and building NLP-based applications using Python. It will help you become an expert in no time and assist you in creating your own NLP projects using NLTK. You will sequentially be guided through applying machine learning tools to develop various models. We'll give you clarity on how to create training data and how to implement major NLP applications such as Named Entity Recognition, Question Answering System, Discourse Analysis, Transliteration, Word Sense disambiguation, Information Retrieval, Sentiment Analysis, Text Summarization, and Anaphora Resolution.