The rising wave of the pandemic and the record number of cases has made it imperative for governments worldwide to identify innovative ways to track, detect, and diagnose COVID-19 cases and prepared for such a crisis in the future. Since the beginning of the outbreak, it has become challenging to track spikes in the coronavirus cases and predict their impact on the community. The contagious virus resulted in uncertainty in every aspect of human life. And it has become soon evident that to tackle the gravity of the situation and be ready for such a future crisis, an extraordinary effort is required. In the last twelve to fourteen months, much research and analysis have been done on discovering the best ways to curb the coronavirus.

Considering processes of a business in a recommender system is highly advantageous. Although most studies in the business process analysis domain are of descriptive and predictive nature, the feasibility of constructing a process-aware recommender system is assessed in a few works. One reason can be the lack of knowledge on process mining potential for recommendation problems. Therefore, this paper aims to identify and analyze the published studies on process-aware recommender system techniques in business process management and process mining domain. A systematic review was conducted on 33 academic articles published between 2008 and 2020 according to several aspects. In this regard, we provide a state-of-the-art review with critical details and researchers with a better perception of which path to pursue in this field. Moreover, based on a knowledge base and holistic perspective, we discuss some research gaps and open challenges in this field.

In addition to their undisputed success in solving classical optimization problems, neuroevolutionary and population-based algorithms have become an alternative to standard reinforcement learning methods. However, evolutionary methods often lack the sample efficiency of standard value-based methods that leverage gathered state and value experience. If reinforcement learning for real-world problems with significant resource cost is considered, sample efficiency is essential. The enhancement of evolutionary algorithms with experience exploiting methods is thus desired and promises valuable insights. This work presents a hybrid algorithm that combines topology-changing neuroevolutionary optimization with value-based reinforcement learning. We illustrate how the behavior of policies can be used to create distance and loss functions, which benefit from stored experiences and calculated state values. They allow us to model behavior and perform a directed search in the behavior space by gradient-free evolutionary algorithms and surrogate-based optimization. For this purpose, we consolidate different methods to generate and optimize agent policies, creating a diverse population. We exemplify the performance of our algorithm on standard benchmarks and a purpose-built real-world problem. Our results indicate that combining methods can enhance the sample efficiency and learning speed for evolutionary approaches.

User-generated data on social media contain rich information about who we are, what we like and how we make decisions. In this paper, we survey representative work on learning a concise latent user representation (a.k.a. user embedding) that can capture the main characteristics of a social media user. The learned user embeddings can later be used to support different downstream user analysis tasks such as personality modeling, suicidal risk assessment and purchase decision prediction. The temporal nature of user-generated data on social media has largely been overlooked in much of the existing user embedding literature. In this survey, we focus on research that bridges the gap by incorporating temporal/sequential information in user representation learning. We categorize relevant papers along several key dimensions, identify limitations in the current work and suggest future research directions.

Transparency is an essential requirement of machine learning based decision making systems that are deployed in real world. Often, transparency of a given system is achieved by providing explanations of the behaviour and predictions of the given system. Counterfactual explanations are a prominent instance of particular intuitive explanations of decision making systems. While a lot of different methods for computing counterfactual explanations exist, only very few work (apart from work from the causality domain) considers feature dependencies as well as plausibility which might limit the set of possible counterfactual explanations. In this work we enhance our previous work on convex modeling for computing counterfactual explanations by a mechanism for ensuring actionability and plausibility of the resulting counterfactual explanations.

Artificial Intelligence is becoming part of any technology we use nowadays. If the AI informs people's decisions, the explanation about AI's outcomes, results, and behavior becomes a necessary capability. However, the discussion of XAI features with various stakeholders is not a trivial task. Most of the available frameworks and methods for XAI focus on data scientists and ML developers as users. Our research is about XAI for end-users of AI systems. We argue that we need to discuss XAI early in the AI-system design process and with all stakeholders. In this work, we aimed at investigating how to operationalize the discussion about XAI scenarios and opportunities among designers and developers of AI and its end-users. We took the Signifying Message as our conceptual tool to structure and discuss XAI scenarios. We experiment with its use for the discussion of a healthcare AI-System.

A comprehensive analysis of the situation of Artificial Intelligence in Digital Labour market is been done in this intelligence report. It includes the investigations done of the past progress, ongoing market scenarios and future prospects. An accurate data of the products, strategies and market shares of leading companies in this particular market is mentioned. It elucidates a detailed outline of Artificial Intelligence in Digital Labour market depending on the important parameters. End users, products, regions and many other segments are studied and explained.

The advent of Transformers in 2017 completely changed the world of neural networks. Ever since, the core concept of Transformers has been remixed, repackaged, and rebundled in several models. The results have surpassed the state of the art in several machine learning benchmarks. In fact, currently all top benchmarks in the field of natural language processing are dominated by Transformer-based models. Some of the Transformer-family models are BERT, ALBERT, and the GPT series of models.

Faire is an online wholesale marketplace built on the belief that the future of retail is local. There are over 1M independent retailers in the U.S. and Canada doing more than twice the revenue of Walmart and Amazon combined. At Faire, we're using the power of technology and data to connect a growing community of over 150,000 brands and independent retailers around the world. Picture your favorite boutique in town -- we help them discover the best products to sell in their stores. By empowering entrepreneurs with the right tools and insights, we believe that we can level the playing field so that small businesses can compete with big box and e-commerce giants.

The belief that AI dominance is imperative for economic development, military control, and strategic competitiveness has accelerated AI development initiatives across countries. The release of national strategic plans has been accompanied by billions of dollars in investment as well as concrete policies to attract relevant talent and technology. In our previous post "How different countries view artificial intelligence", we presented a snapshot of governments' planning for AI, based on our analysis of 34 national strategic AI plans. Our post covered the description of AI plans and categorized countries based on their coverage of various related concepts. In this post, we extend details about what accounts for the variation in countries' AI plans.