Linear integer constraints are one of the most important constraints in combinatorial problems since they are commonly found in many practical applications. Typically, encodings to Boolean satisfiability (SAT) format of conjunctive normal form perform poorly in problems with these constraints in comparison with SAT modulo theories (SMT), lazy clause generation (LCG) or mixed integer programming (MIP) solvers. In this paper we explore and categorize SAT encodings for linear integer constraints. We define new SAT encodings based on multi-valued decision diagrams, and sorting networks. We compare different SAT encodings of linear constraints and demonstrate where one may be preferable to another. We also compare SAT encodings against other solving methods and show they can be better than linear integer (MIP) solvers and sometimes better than LCG or SMT solvers on appropriate problems. Combining the new encoding with lazy decomposition, which during runtime only encodes constraints that are important to the solving process that occurs, gives the best option for many highly combinatorial problems involving linear constraints.

Since COVID-19 was first identified in December 2019, various public health interventions have been implemented across the world. As different measures are implemented at different countries at different times, we conduct an assessment of the relative effectiveness of the measures implemented in 18 countries and regions using data from 22/01/2020 to 02/04/2020. We compute the top one and two measures that are most effective for the countries and regions studied during the period. Two Explainable AI techniques, SHAP and ECPI, are used in our study; such that we construct (machine learning) models for predicting the instantaneous reproduction number ($R_t$) and use the models as surrogates to the real world and inputs that the greatest influence to our models are seen as measures that are most effective. Across-the-board, city lockdown and contact tracing are the two most effective measures. For ensuring $R_t<1$, public wearing face masks is also important. Mass testing alone is not the most effective measure although when paired with other measures, it can be effective. Warm temperature helps for reducing the transmission.

Artificial intelligence (AI) can revolutionise business, but it should not be deployed unless there is a solid business case for it. This is according to Johan Steyn, chair of the Institute of Information Technology Professionals South Africa's (IITPSA) AI and robotics special interest group (SIG), who was addressing the Institute's first Tabling Tech Webinar this week. Steyn said AI could improve business in areas ranging from HR, sales and finance through to R&D and customer service. In fields such as human capital management, AI could enhance recruitment by scanning candidates' social media feeds and monitoring their micro-expressions during interviews; it could help personalise training and development programmes and pre-empt the loss of key skills. In sales, AI could ensure products and services met customer needs and wants, and predict market changes.

In this special guest feature, Sam O'Brien from RingCentral, outlines how AI-driven tools and advancements are changing the face of UC for businesses. Sam is the senior website optimization and user experience manager for Europe, the Middle East and Africa at RingCentral. He has a passion for innovation and loves exploring ways to collaborate more with dispersed teams. The potential applications of AI are stunningly wide-ranging. There's hardly an industry, niche, or sector where the tech isn't proving to be a game changer.

This paper proposes a novel method for learning highly nonlinear, multivariate functions from examples. Our method takes advantage of the property that continuous functions can be approximated by polynomials, which in turn are representable by tensors. Hence the function learning problem is transformed into a tensor reconstruction problem, an inverse problem of the tensor decomposition. Our method incrementally builds up the unknown tensor from rank-one terms, which lets us control the complexity of the learned model and reduce the chance of overfitting. For learning the models, we present an efficient gradient-based algorithm that can be implemented in linear time in the sample size, order, rank of the tensor and the dimension of the input. In addition to regression, we present extensions to classification, multi-view learning and vector-valued output as well as a multi-layered formulation. The method can work in an online fashion via processing mini-batches of the data with constant memory complexity. Consequently, it can fit into systems equipped only with limited resources such as embedded systems or mobile phones. Our experiments demonstrate a favorable accuracy and running time compared to competing methods.

Recent neural models have shown significant progress in dialogue generation. Most generation models are based on language models. However, due to the Long Tail Phenomenon in linguistics, the trained models tend to generate words that appear frequently in training datasets, leading to a monotonous issue. To address this issue, we analyze a large corpus from Wikipedia and propose three frequency-based data normalization methods. We conduct extensive experiments based on transformers and three datasets respectively collected from social media, subtitles, and the industrial application. Experimental results demonstrate significant improvements in diversity and informativeness (defined as the numbers of nouns and verbs) of generated responses. More specifically, the unigram and bigram diversity are increased by 2.6%-12.6% and 2.2%-18.9% on the three datasets, respectively. Moreover, the informativeness, i.e. the numbers of nouns and verbs, are increased by 4.0%-7.0% and 1.4%-12.1%, respectively. Additionally, the simplicity and effectiveness enable our methods to be adapted to different generation models without much extra computational cost.

The problem of achieving common understanding between agents that use different vocabularies has been mainly addressed by techniques that assume the existence of shared external elements, such as a meta-language or a physical environment. In this article, we consider agents that use different vocabularies and only share knowledge of how to perform a task, given by the specification of an interaction protocol. We present a framework that lets agents learn a vocabulary alignment from the experience of interacting. Unlike previous work in this direction, we use open protocols that constrain possible actions instead of defining procedures, making our approach more general. We present two techniques that can be used either to learn an alignment from scratch or to repair an existent one, and we evaluate their performance experimentally.

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The report published on Artificial Intelligence (AI) is a invaluable foundation of insightful data helpful for the decision-makers to form the business strategies related R&D investment, sales and growth, key trends, technological advancement, emerging market and more. The global Artificial Intelligence (AI) market report includes key facts and figures data which helps its users to understand current scenario of the global market along with anticipated growth. The Artificial Intelligence (AI) market report contains quantitative data such as global sales and revenue (USD Million) market size of different categories and sub categories such as regions, CAGR, market shares, revenue insights of market players, and others. The report also gives qualitative insights on the global Artificial Intelligence (AI) market, that gives the exact outlook of the global as well as country level Artificial Intelligence (AI) market. Major Companies Profiled in the Global Artificial Intelligence (AI) Market are: Intel Corporation, MicroStrategy, Amazon, NVIDIA, Baidu, Inc., Atomwise, Inc., Google, Alibaba, H2O ai, Microsoft Corporation, Samsung, IBM, Zebra Medical Vision, Inc., Facebook The focus of the global Artificial Intelligence (AI) market report is to define, categorized, identify the Artificial Intelligence (AI) market in terms of its parameter and specifications/ segments for example by product, by types, by applications, and by end-users.

The Middle East oil and gas industry faces pressure on several fronts. Enterprises around the world are deploying sustainable-energy technology designed to reduce reliance on oil in an effort to curb greenhouse gas emissions, while geopolitical turmoil has caused oil prices to plunge, putting pressure on energy company IT budgets. Shumon Zaman is a UAE-based technology executive and consultant who most recently worked as technology vice president at Lamprell PLC, a company specializing in the oil rig construction business; in this edited Q&A he highlights the power of digital twins and other emerging tech to accelerate digitalization in oil and gas industry -- with the aim of curbing costs, optimising revenue streams and supporting sustainability initiatives. Which emerging technologies will shape the future for the oil and gas industry? Shumon Zaman is a UAE-based technology executive.