The impact of continually evolving digital technologies and the proliferation of communications and content has now been widely acknowledged to be central to understanding our world. What is less acknowledged is that this is based on the successful arousing of curiosity both at the collective and individual levels. Advertisers, communication professionals and news editors are in constant competition to capture attention of the digital population perennially shifty and distracted. This paper, tries to understand how curiosity works in the digital world by attempting the first ever work done on quantifying human curiosity, basing itself on various theories drawn from humanities and social sciences. Curious communication pushes people to spot, read and click the message from their social feed or any other form of online presentation. Our approach focuses on measuring the strength of the stimulus to generate reader curiosity by using unsupervised and supervised machine learning algorithms, but is also informed by philosophical, psychological, neural and cognitive studies on this topic. Manually annotated news headlines - clickbaits - have been selected for the study, which are known to have drawn huge reader response. A binary classifier was developed based on human curiosity (unlike the work done so far using words and other linguistic features). Our classifier shows an accuracy of 97% . This work is part of the research in computational humanities on digital politics quantifying the emotions of curiosity and outrage on digital media.

Most Reading Comprehension methods limit themselves to queries which can be answered using a single sentence, paragraph, or document. Enabling models to combine disjoint pieces of textual evidence would extend the scope of machine comprehension methods, but currently there exist no resources to train and test this capability. We propose a novel task to encourage the development of models for text understanding across multiple documents and to investigate the limits of existing methods. In our task, a model learns to seek and combine evidence - effectively performing multi-hop (alias multi-step) inference. We devise a methodology to produce datasets for this task, given a collection of query-answer pairs and thematically linked documents. Two datasets from different domains are induced, and we identify potential pitfalls and devise circumvention strategies. We evaluate two previously proposed competitive models and find that one can integrate information across documents. However, both models struggle to select relevant information, as providing documents guaranteed to be relevant greatly improves their performance. While the models outperform several strong baselines, their best accuracy reaches 42.9% compared to human performance at 74.0% - leaving ample room for improvement.

Artificial neural networks tend to learn only what they need for a task. A manipulation of the training data can counter this phenomenon. In this paper, we study the effect of different alterations of the training data, which limit the amount and position of information that is available for the decision making. We analyze the accuracy and robustness against semantic and black box attacks on the networks that were trained on different training data modifications for the particular example of morphing attacks. A morphing attack is an attack on a biometric facial recognition system where the system is fooled to match two different individuals with the same synthetic face image. Such a synthetic image can be created by aligning and blending images of the two individuals that should be matched with this image.

In order for our computer systems to be more human-like, with a higher emotional quotient, they need to be able to process and understand intrinsic human language phenomena like humour. In this paper, we consider a subtype of humour - puns, which are a common type of wordplay-based jokes. In particular, we consider code-mixed puns which have become increasingly mainstream on social media, in informal conversations and advertisements and aim to build a system which can automatically identify the pun location and recover the target of such puns. We first study and classify code-mixed puns into two categories namely intra-sentential and intra-word, and then propose a four-step algorithm to recover the pun targets for puns belonging to the intra-sentential category. Our algorithm uses language models, and phonetic similarity-based features to get the desired results. We test our approach on a small set of code-mixed punning advertisements, and observe that our system is successfully able to recover the targets for 67% of the puns.

A paraphrase is a restatement of the meaning of a text in other words. Paraphrases have been studied to enhance the performance of many natural language processing tasks. In this paper, we propose a novel task iParaphrasing to extract visually grounded paraphrases (VGPs), which are different phrasal expressions describing the same visual concept in an image. These extracted VGPs have the potential to improve language and image multimodal tasks such as visual question answering and image captioning. How to model the similarity between VGPs is the key of iParaphrasing. We apply various existing methods as well as propose a novel neural network-based method with image attention, and report the results of the first attempt toward iParaphrasing.

We present KBLRN, a framework for end-to-end learning of knowledge base representations from latent, relational, and numerical features. KBLRN integrates feature types with a novel combination of neural representation learning and probabilistic product of experts models. To the best of our knowledge, KBLRN is the first approach that learns representations of knowledge bases by integrating latent, relational, and numerical features. We show that instances of KBLRN outperform existing methods on a range of knowledge base completion tasks. We contribute a novel data sets enriching commonly used knowledge base completion benchmarks with numerical features. The data sets are available under a permissive BSD-3 license. We also investigate the impact numerical features have on the KB completion performance of KBLRN.

The first batch of flamethrowers sold by Elon Musk's tunnel construction business The Boring Company have been handed out to customers - with some people traveling thousands of miles to pick one up. The Tesla entrepreneur had suggested the idea of selling a flamethrower at the end of 2017, with the project aiming to raise $10m for The Boring Company, which was founded with the intention of building a network of tunnels to help reduce traffic congestion across the US. Mr Musk claimed that the company had sold 20,000 of the $500 in four days in during January this year, with the first flamethrowers handed out at Boring's Hawthorne, California offices over the weekend. The event took place in a car park adjacent to another of Mr Musk's companies - SpaceX - with the tech billionaire announcing on Twitter that the first 1,000 flamethrowers were bring picked up. Mr Musk has called the item "Not-a-flamethrower" to get around any legal issues of shipping items called flamethrowers, but some customers could not wait to get it into their hands.

The moment work gets too stressful, all you need is a good spa to relax, refresh and rejuvenate yourself. Identifying an opportunity of opening a tech-enabled spa business, engineer-turned-entrepreneur, Ritesh Reddy embarked on an entrepreneurial journey in 2008 to give India, its largest spa stopover ever. The company which started a decade ago has now expanded its business in more than 27 cities in India and 8 cities in Middle East. In an interaction with Entrepreneur India, Reddy talked about how the brand has been able to become a tech disruptor in the wellness industry and what has built the company's reputation over the years. O2 Spas gained its momentum in the market in less than a year.

When Amazon launched in 1995, only 3% of Americans had ever been on the Internet, let alone purchased anything online. Both the concept of the web and e-commerce were startlingly new. Just a year earlier in fact, the New York Times had run a story with the headline: "Attention Shoppers: Internet Is Open", breathlessly reporting the first online transaction, the sale of a Sting CD. Also in 1995, Auction Web went live, which was to become eBay. A broken laser pointer – to a collector of broken laser pointers no less – for $14.83.

The portable robot's name is Mabu, and at the recommendation of his health-care provider, she now lives with Mr. Byrd to help monitor his irregular heartbeat. She checks in on him two or three times a day to make sure he weighs himself, takes his medication and exercises regularly--and relays information back to his health team. "She's my little blue-eyed girlfriend," he says. "She keeps me on my toes." With the senior-citizen population expected to nearly double to 88 million by 2050 and some nursing programs stretched thin, researchers and elder-care centers are exploring the potential of digital companions, in robot or chatbot form, to help the elderly.