The Internet of Things (IoT) concept has attracted a lot of attention from the research and innovation community for a number of years already. One of the key drivers for this hype towards the IoT is its applicability to a plethora of different application domains. However, infrastructures enabling experimental assessment of IoT solutions are scarce. Being able to test and assess the behavior and the performance of any piece of technology (i.e., protocol, algorithm, application, service, etc.) under real-world circumstances is of utmost importance to increase the acceptance and reduce the time to market of these innovative developments. This paper describes the federation of eleven IoT deployments from heterogeneous application domains (e.g., smart cities, maritime, smart building, crowd-sensing, smart grid, etc.) with over 10,000 IoT devices overall which produce hundreds of thousands of observations per day. The paper summarizes the resources that are made available through a cloud-based platform. The main contributions from this paper are twofold. In the one hand, the insightful summary of the federated data resources are relevant to the experimenters that might be seeking for an experimental infrastructure to assess their innovations. On the other hand, the identification of the challenges met during the testbed integration process, as well as the mitigation strategies that have been implemented to face them, are of interest for testbed providers that can be considering to join the federation.

Policy decisions and marketing models have a great impact on the specific application of customer relationship management. In various fields of marketing, big data marketing is gradually popularized as a combined product. The so-called big data marketing refers to collecting a large amount of behavior data through the Internet, primarily helping advertisers find out the target audience to analyze the content, time and form of advertising and finally complete the marketing process of advertising. The big data marketing can also be used for effective customer relationship management, which not only effectively enhance customer stickiness and reduce enterprise operating costs, but also has great significance for deeply mining the potential value of existing customers, predicting the future demand trend of customers, discovering new market growth points and developing new customer groups. Taking the large FMCG company A as the research subject, its sales is grim and stagnant: the purchase quantity of products is low; the price is lower than the cost; simple reproduction is difficult to continue. In order to change the current situation of company A, first of all, we cooperate with the marketing team to determine the business needs, using SQL and python to conduct exploratory data analysis (EDA) on 110k + e-commerce transaction data, and generate Data-Driven Insights on purchasing behavior and product sales; secondly, we use Python to establish RFM model (recent degree, frequency, currency), classify customers and calculate. Finally, according to the above customer analysis, a feasible listing strategy is proposed, which is expected to increase the retention rate by 3.5% and the monthly sales by 6%. Through big data marketing calculation, we come to a solution for the current situation of company A, which will have a good influence on many other companies.

This work aims to employ natural language generation (NLG) to rapidly generate items for English language learning applications: this requires both language models capable of generating fluent, high-quality English, and to control the output of the generation to match the requirements of the relevant items. We experiment with deep pretrained models for this task, developing novel methods for controlling items for factors relevant in language learning: diverse sentences for different proficiency levels and argument structure to test grammar. Human evaluation demonstrates high grammatically scores for all models (3.4 and above out of 4), and higher length (24%) and complexity (9%) over the baseline for the advanced proficiency model. Our results show that we can achieve strong performance while adding additional control to ensure diverse, tailored content for individual users.

We describe the AGReE system, which takes user-submitted passages as input and automatically generates grammar practice exercises that can be completed while reading. Multiple-choice practice items are generated for a variety of different grammar constructs: punctuation, articles, conjunctions, pronouns, prepositions, verbs, and nouns. We also conducted a large-scale human evaluation with around 4,500 multiple-choice practice items. We notice for 95% of items, a majority of raters out of five were able to identify the correct answer and for 85% of cases, raters agree that there is only one correct answer among the choices. Finally, the error analysis shows that raters made the most mistakes for punctuation and conjunctions.