Research Fellow or Senior Research Fellow (Deep Learning for Health Trajectory Perdiction) The full-time fixed term post is available until 31st March 2023 (approximately 3 years). You will work with the Principal Investigator (Dr Leandro Pecchia), the project partners and the Warwick GATEKEEPER team for the successful execution of the project. Further information on the project can be read here https://www.gatekeeper-project.eu/ You will have a PhD in Biomedical Engineering or in a relevant discipline (e.g., Computer Science, Information Engineering, Applied Math or similar disciplines). The level of appointment (Research or Senior Research Fellow) will be determined by the successful candidate--s skills and experience, including a proven ability and achievement in research and the ability to generate external funding to support research projects.

A Russian search engine is being accused of providing an unregulated facial recognition system to members of the public -- violating personal privacy. Experts have slammed the feature as'poor' and'creepy' while dubbing it a'definite privacy concern'. Yandex, much like Google, Bing and other search engines, allows users to input an image and see similar results. But only Yandex, which claims to conduct more than 50 per cent of Russian searches on Android, produces images of the exact same person. MailOnline tested the image search facilities of Yandex, Bing, Google and specialist site TinEye by submitting a photo that was not available online.

There is a new internet watchdog in town and it is powered by Verizon. The tech giant released a'privacy-focused' search engine, called OneSearch, which encrypts searches, leaves results unfiltered and claims to not store or transfer user information. The platform is also Advanced Privacy Mode enabled, meaning all search result links expire within an hour. However, some users are suspicions about the platform, as Verizon has come under fire in the past for its tracking customers as on the internet without permission. Verizon launched a'privacy-focused' search engine, called OneSearch.

With the rapid growth of e-Commerce, online product search has emerged as a popular and effective paradigm for customers to find desired products and engage in online shopping. However, there is still a big gap between the products that customers really desire to purchase and relevance of products that are suggested in response to a query from the customer. In this paper, we propose a robust way of predicting relevance scores given a search query and a product, using techniques involving machine learning, natural language processing and information retrieval. We compare conventional information retrieval models such as BM25 and Indri with deep learning models such as word2vec, sentence2vec and paragraph2vec. We share some of our insights and findings from our experiments.

Word embeddings, i.e., low-dimensional vector representations such as GloVe and SGNS, encode word "meaning" in the sense that distances between words' vectors correspond to their semantic proximity. This enables transfer learning of semantics for a variety of natural language processing tasks. Word embeddings are typically trained on large public corpora such as Wikipedia or Twitter. We demonstrate that an attacker who can modify the corpus on which the embedding is trained can control the "meaning" of new and existing words by changing their locations in the embedding space. We develop an explicit expression over corpus features that serves as a proxy for distance between words and establish a causative relationship between its values and embedding distances. We then show how to use this relationship for two adversarial objectives: (1) make a word a top-ranked neighbor of another word, and (2) move a word from one semantic cluster to another. An attack on the embedding can affect diverse downstream tasks, demonstrating for the first time the power of data poisoning in transfer learning scenarios. We use this attack to manipulate query expansion in information retrieval systems such as resume search, make certain names more or less visible to named entity recognition models, and cause new words to be translated to a particular target word regardless of the language. Finally, we show how the attacker can generate linguistically likely corpus modifications, thus fooling defenses that attempt to filter implausible sentences from the corpus using a language model.

HappyHotel, a Japanese search engine for finding and booking rooms in "love hotels," disclosed a security breach at the end of last year. Love hotels are hotels built and operated primarily for allowing guests privacy for sexual activities. Love hotels, also known as sex hotels, are used by both married couples and cheating spouses, alike, and are found all over the world, but they are particularly popular in East Asia, and especially Japan. HappyHotel.jp is a website that operates similarly to Booking.com, but lets registered users search and book rooms in love hotels across Japan. In a message posted on its website, Almex, the company behind the service, said it detected unauthorized access to its servers on December 22, last year.

Document network embedding aims at learning representations for a structured text corpus i.e. when documents are linked to each other. Recent algorithms extend network embedding approaches by incorporating the text content associated with the nodes in their formulations. In most cases, it is hard to interpret the learned representations. Moreover, little importance is given to the generalization to new documents that are not observed within the network. In this paper, we propose an interpretable and inductive document network embedding method. We introduce a novel mechanism, the Topic-Word Attention (TW A), that generates document representations based on the interplay between word and topic representations. We train these word and topic vectors through our general model, Inductive Document Network Embedding (IDNE), by leveraging the connections in the document network. Quantitative evaluations show that our approach achieves state-of-the-art performance on various networks and we qualitatively show that our model produces meaningful and interpretable representations of the words, topics and documents.

Arguably, the most important scenario for search technology is lifelong learning and education, both for students and all citizens. Human learning is a complex multidimensional activity, which includes procedural learning (e.g., activity patterns associated with cooking, sports) and knowledge-based learning (e.g., mathematics, genetics). It also includes different levels of learning, such as the ability to solve an individual math problem correctly. It also includes the development of meta-cognitive self-regulatory abilities, such as recognizing the type of problem being solved and whether one is in an error state. These latter types of awareness enable correctly regulating ones approach to solving a problem, and recognizing when one is off track by repairing momentary errors as needed. Later stages of learning enable the generalization of learned skills or information from one context or domain to others such as applying math problem solving to calculations in the wild (e.g., calculation of garden space, engineering calculations required for a structurally sound building).

Contributor and SMX speaker, Duane Brown, explains in this video why 2020 is the year to get a handle on your mobile experience as well as find the platforms your customers are on and experiment if they're new to you. I run an agency up in Montreal, Canada. We focus on kind of two areas, paid ads, PPC, Google, Facebook, stuff like that. We also do CRO for clients, we'll often have to figure out how do their websites convert more. And a lot of our clients are in e-commerce.

Did you know that a wide range of businesses and professionals are using facial recognition search engines? Lawyers, photographers, bloggers, celebrities, models, recruiters and marketing agencies are just some of the people who are embracing this new technology. In this post, we showcase 8 facial recognition search engines you can use to find similar pictures and videos online. Online facial recognition search is only a subset of what a full-set solution might be able to accomplish. As technology advances, software vendors have begun offering Face Recognition Online APIs that you can easily integrate with your Apps and information systems.