In education, open-ended quiz questions have become an important tool for assessing the knowledge of students. Yet, manually preparing such questions is a tedious task, and thus automatic question generation has been proposed as a possible alternative. So far, the vast majority of research has focused on generating the question text, relying on question answering datasets with readily picked answers, and the problem of how to come up with answer candidates in the first place has been largely ignored. Here, we aim to bridge this gap. In particular, we propose a model that can generate a specified number of answer candidates for a given passage of text, which can then be used by instructors to write questions manually or can be passed as an input to automatic answer-aware question generators. Our experiments show that our proposed answer candidate generation model outperforms several baselines.

If the realization that Labor Day is just around the corner brings to mind all the fall essentials you've been meaning to get, you're in luck. Earning its nickname of being the Everything Store for the umpteenth time, Amazon is currently stacked with deals on trending items, devices, gadgets and more. The sales and deals team at Good Housekeeping did a deep dive into the best deals on Amazon ahead of the holiday weekend and found, to our delight, that a number of our readers' favorite brands and products loved by our Good Housekeeping Institute experts are already on sale.

Datasets are essential to AI models. They provide the truth by which we train AI models and measure a model's success. Engineers often look to the AI model as the key to delivering highly accurate results, but in reality it is often the data that determines an AI model success. Data flows through every step of the AI workflow, from model training to deployment, and the way it is prepared can be the main driver of accuracy when designing robust AI models. Engineers can use these five tips to improve their data preparation process and drive success when developing a complete AI system.

"Voice skins" have become a very popular feature for AI-based voice assistants, to help personalize some of the more anodyne aspects of helpful, yet also kind of bland and robotic, speaking voices you get on services like Alexa. Now a startup that is building voice skins for different companies to use across their services, and for third parties to create and apply as well, is raising some funding to fuel its growth. LOVO, the Berkeley, California-based artificial intelligence (AI) voice & synthetic speech tool developer, this week closed a $4.5 million pre-Series A round led by South Korean Kakao Entertainment along with Kakao Investment and LG CNS, an IT solution affiliate of LG Group. Its previous investor SkyDeck Fund and a private investor, vice president of finance at DoorDash, Michael Kim, also joined the funding. The proceeds will be used to propel its research and development in artificial intelligence and synthetic speech and grow the team.

It uses Amazon Textract, a machine learning service that automatically extracts text, handwriting, and data from scanned documents to provide highly …

Transfer learning aims to learn classifiers for a target domain by transferring knowledge from a source domain. However, due to two main issues: feature discrepancy and distribution divergence, transfer learning can be a very difficult problem in practice. In this paper, we present a framework called TLF that builds a classifier for the target domain having only few labeled training records by transferring knowledge from the source domain having many labeled records. While existing methods often focus on one issue and leave the other one for the further work, TLF is capable of handling both issues simultaneously. In TLF, we alleviate feature discrepancy by identifying shared label distributions that act as the pivots to bridge the domains. We handle distribution divergence by simultaneously optimizing the structural risk functional, joint distributions between domains, and the manifold consistency underlying marginal distributions. Moreover, for the manifold consistency we exploit its intrinsic properties by identifying k nearest neighbors of a record, where the value of k is determined automatically in TLF. Furthermore, since negative transfer is not desired, we consider only the source records that are belonging to the source pivots during the knowledge transfer. We evaluate TLF on seven publicly available natural datasets and compare the performance of TLF against the performance of eleven state-of-the-art techniques. We also evaluate the effectiveness of TLF in some challenging situations. Our experimental results, including statistical sign test and Nemenyi test analyses, indicate a clear superiority of the proposed framework over the state-of-the-art techniques.

Recommender systems typically operate on high-dimensional sparse user-item matrices. Matrix completion is a very challenging task to predict one's interest based on millions of other users having each seen a small subset of thousands of items. We propose a Global-Local Kernel-based matrix completion framework, named GLocal-K, that aims to generalise and represent a high-dimensional sparse user-item matrix entry into a low dimensional space with a small number of important features. Our GLocal-K can be divided into two major stages. First, we pre-train an auto encoder with the local kernelised weight matrix, which transforms the data from one space into the feature space by using a 2d-RBF kernel. Then, the pre-trained auto encoder is fine-tuned with the rating matrix, produced by a convolution-based global kernel, which captures the characteristics of each item. We apply our GLocal-K model under the extreme low-resource setting, which includes only a user-item rating matrix, with no side information. Our model outperforms the state-of-the-art baselines on three collaborative filtering benchmarks: ML-100K, ML-1M, and Douban.

In this paper we study the personalized book recommender system in a child-robot interactive environment. Firstly, we propose a novel text search algorithm using an inverse filtering mechanism that improves the efficiency. Secondly, we propose a user interest prediction method based on the Bayesian network and a novel feedback mechanism. According to children's fuzzy language input, the proposed method gives the predicted interests. Thirdly, the domain specific synonym association is proposed based on word vectorization, in order to improve the understanding of user intention. Experimental results show that the proposed recommender system has an improved performance and it can operate on embedded consumer devices with limited computational resources.

Now more than ever, we are seeing digital transformation at the most progressive institutions moving beyond mobile banking upgrades and modest changes in back office technologies to becoming organization-wide initiatives. With budgets increasing and new advanced technologies being implemented, ownership of the digital transformation process at leading banks and credit unions is moving up the organizational chart, with the more digitally mature institutions having the process led by the the CEO or even the board of directors. We are also seeing greater cross-functional engagement across market-leading institutions. Understanding how leading organizations are managing their digital transformation process is more important than ever to the future competitiveness of banks and credit unions of all sizes. Unfortunately, as we found in the area of innovation, a surprisingly small number of financial institutions (12%) consider themselves to be digital transformation'leaders', with 34% considering themselves to be'fast followers' and 55% stating they were either'mainstream players' or'laggards'.

Britain will attempt to move away from European data protection regulations as it overhauls its privacy rules after Brexit, the government has announced. The freedom to chart its own course could lead to an end to irritating cookie popups and consent requests online, said the culture secretary, Oliver Dowden, as he called for rules based on "common sense, not box-ticking". But any changes will be constrained by the need to offer a new regime that the EU deems adequate, otherwise data transfers between the UK and EU could be frozen. A new information commissioner will be put in charge of overseeing the transformation. John Edwards, currently the privacy commissioner of New Zealand, has been named as the government's preferred candidate to replace Elizabeth Denham, whose term in office will end on 31 October after a three-month extension.