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.

Recent research has shown that numerous human-interpretable directions exist in the latent space of GANs. In this paper, we develop an automatic procedure for finding directions that lead to foreground-background image separation, and we use these directions to train an image segmentation model without human supervision. Our method is generator-agnostic, producing strong segmentation results with a wide range of different GAN architectures. Furthermore, by leveraging GANs pretrained on large datasets such as ImageNet, we are able to segment images from a range of domains without further training or finetuning. Evaluating our method on image segmentation benchmarks, we compare favorably to prior work while using neither human supervision nor access to the training data. Broadly, our results demonstrate that automatically extracting foreground-background structure from pretrained deep generative models can serve as a remarkably effective substitute for human supervision.

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.

We propose a novel reinforcement learning based framework PoBRL for solving multi-document summarization. PoBRL jointly optimizes over the following three objectives necessary for a high-quality summary: importance, relevance, and length. Our strategy decouples this multi-objective optimization into different subproblems that can be solved individually by reinforcement learning. Utilizing PoBRL, we then blend each learned policies together to produce a summary that is a concise and complete representation of the original input. Our empirical analysis shows state-of-the-art performance on several multi-document datasets. Human evaluation also shows that our method produces high-quality output.

In many public health settings, it is important for patients to adhere to health programs, such as taking medications and periodic health checks. Unfortunately, beneficiaries may gradually disengage from such programs, which is detrimental to their health. A concrete example of gradual disengagement has been observed by an organization that carries out a free automated call-based program for spreading preventive care information among pregnant women. Many women stop picking up calls after being enrolled for a few months. To avoid such disengagements, it is important to provide timely interventions. Such interventions are often expensive and can be provided to only a small fraction of the beneficiaries. We model this scenario as a restless multi-armed bandit (RMAB) problem, where each beneficiary is assumed to transition from one state to another depending on the intervention. Moreover, since the transition probabilities are unknown a priori, we propose a Whittle index based Q-Learning mechanism and show that it converges to the optimal solution. Our method improves over existing learning-based methods for RMABs on multiple benchmarks from literature and also on the maternal healthcare dataset.

Width-based search algorithms seek plans by prioritizing states according to a suitably defined measure of novelty, that maps states into a set of novelty categories. Space and time complexity to evaluate state novelty is known to be exponential on the cardinality of the set. We present novel methods to obtain polynomial approximations of novelty and width-based search. First, we approximate novelty computation via random sampling and Bloom filters, reducing the runtime and memory footprint. Second, we approximate the best-first search using an adaptive policy that decides whether to forgo the expansion of nodes in the open list. These two techniques are integrated into existing width-based algorithms, resulting in new planners that perform significantly better than other state-of-the-art planners over benchmarks from the International Planning Competitions.

Higher-order methods for dependency parsing can partially but not fully addresses the issue that edges in dependency tree should be constructed at the text span/subtree level rather than word level. % This shortcoming can cause an incorrect span covered the corresponding tree rooted at a certain word though the word is correctly linked to its head. In this paper, we propose a new method for dependency parsing to address this issue. The proposed method constructs dependency trees by directly modeling span-span (in other words, subtree-subtree) relations. It consists of two modules: the {\it text span proposal module} which proposes candidate text spans, each of which represents a subtree in the dependency tree denoted by (root, start, end); and the {\it span linking module}, which constructs links between proposed spans. We use the machine reading comprehension (MRC) framework as the backbone to formalize the span linking module in an MRC setup, where one span is used as a query to extract the text span/subtree it should be linked to. The proposed method comes with the following merits: (1) it addresses the fundamental problem that edges in a dependency tree should be constructed between subtrees; (2) the MRC framework allows the method to retrieve missing spans in the span proposal stage, which leads to higher recall for eligible spans. Extensive experiments on the PTB, CTB and Universal Dependencies (UD) benchmarks demonstrate the effectiveness of the proposed method. We are able to achieve new SOTA performances on PTB and UD benchmarks, and competitive performances to previous SOTA models on the CTB dataset. Code is available at https://github.com/ShannonAI/mrc-for-dependency-parsing.

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This evening at a press event to kickoff MWC Barcelona, I had the pleasure of joining CEO Satya Nadella and Technical Fellow Alex Kipman onstage to talk in depth about Microsoft's worldview for the intelligent cloud and intelligent edge. As part of today's press event, we also introduced the world to HoloLens 2. This is a tremendously exciting time for Microsoft, our partners, our customers, the computing industry and indeed the world. The virtually limitless computing power and capability of the cloud combined with increasingly intelligent and perceptive edge devices embedded throughout the physical world create experiences we could only imagine a few short years ago. When intelligent cloud and intelligent edge experiences are infused with mixed reality, we have a framework for achieving amazing things and empowering even more people. Today represents an important milestone for Microsoft.

This story was originally published by Undark and is reproduced here as part of the Climate Desk collaboration. Early one winter morning in 2020, Kurt VerCauteren discovered a cluster of dead birds in a barren field in northwest Texas. They were small birds, mostly dark-eyed juncos, but also a smattering of white-crowned sparrows. VerCauteren's team had poisoned them, inadvertently. The clues were clear, the death uncomplicated: The birds had flown in before dawn to scavenge deadly morsels of a contaminated peanut paste, left behind after a sounder of wild hogs had torn through the area in a feeding frenzy. The birds likely died within minutes of eating. "I couldn't even see the crumbs," says VerCauteren, a wildlife biologist at the US Department of Agriculture in Fort Collins, Colorado, who has spent years developing and testing pig poisons. The birds were the unintended victims of a field experiment to test a toxicant--one intended for feral pigs, but no other animals--that had been developed in Australia.