We consider the problem of classifying business process instances based on structural features derived from event logs. The main motivation is to provide machine learning based techniques with quick response times for interactive computer assisted root cause analysis. In particular, we create structural features from process mining such as activity and transition occurrence counts, and ordering of activities to be evaluated as potential features for classification. We show that adding such structural features increases the amount of information thus potentially increasing classification accuracy. However, there is an inherent trade-off as using too many features leads to too long run-times for machine learning classification models. One way to improve the machine learning algorithms' run-time is to only select a small number of features by a feature selection algorithm. However, the run-time required by the feature selection algorithm must also be taken into account. Also, the classification accuracy should not suffer too much from the feature selection. The main contributions of this paper are as follows: First, we propose and compare six different feature selection algorithms by means of an experimental setup comparing their classification accuracy and achievable response times. Second, we discuss the potential use of feature selection results for computer assisted root cause analysis as well as the properties of different types of structural features in the context of feature selection.

All this week, two eminent neurologists specialising in Alzheimer's are sharing cutting-edge research with Mail readers and revealing how lifestyle tweaks can help fend off the disease. Today, they show how challenging your mind and increasing your social life can help protect your brain against decay . . . You might be fan of a fiendishly complex crossword puzzle or a demon at sudoku, but even if you regularly rattle off the answers when watching University Challenge on TV or flick through the financial pages of the weekend papers, are you properly exercising your brain? Our work as specialists in Alzheimer's has taught us that simple puzzles are not enough. One fundamental factor in the fight to protect yourself against dementia -- and to slow its march if it has already started -- is the quest to build what neuroscientists call'cognitive reserve'. A healthy brain thrives on challenge, especially challenges that are personally relevant and involve many different parts of the brain at the same time. That's because our brains are designed for complexity and they are sustained by it in old age.

Bottlenose dolphins that work together with humans to catch fish have their own distinctive whistle, one that may help them recognise each other. Off Laguna, Brazil, fishers stand in a line in waist-deep water or wait in canoes while, farther out, bottlenose dolphins chase shoals of mullet to the shore. The fishers can't see the fish in the murky water, so they wait for the dolphins to give a signal -- like an abrupt dive or tail slap -- then cast their nets. Fishers catch larger and more fish when they work with dolphins. "Dolphins likely reap similar benefits," says Mauricio Cantor of the Federal University of Santa Catarina in Brazil – it might be easy for them to gobble up fish disoriented by the nets.

Artifical intelligence is set to transform the marketing and communications world even more than social media has, according to 55% of CMOs surveyed by Weber Shandwick across five markets. The agency's latest study examines current consumer knowledge and attitudes toward AI in the US, UK, Brazil, China and Canada. Of the 150 senior executives surveyed, 68% said their brand is currently selling, using or planning for business in the AI era. Moreover, nearly six in 10 believe that within the next five years, companies will need to compete in the AI space to succeed. Weber Shandwick also polled 2,100 consumers across the five markets, and found that Chinese consumers (31%) report having the strongest knowledge of AI, while UK consumers report the weakest (10%).

We examine the meaning and the complexity of probabilistic logic programs that consist of a set of rules and a set of independent probabilistic facts (that is, programs based on Sato's distribution semantics). We focus on two semantics, respectively based on stable and on well-founded models. We show that the semantics based on stable models (referred to as the "credal semantics") produces sets of probability measures that dominate infinitely monotone Choquet capacities; we describe several useful consequences of this result. We then examine the complexity of inference with probabilistic logic programs. We distinguish between the complexity of inference when a probabilistic program and a query are given (the inferential complexity), and the complexity of inference when the probabilistic program is fixed and the query is given (the query complexity, akin to data complexity as used in database theory). We obtain results on the inferential and query complexity for acyclic, stratified, and normal propositional and relational programs; complexity reaches various levels of the counting hierarchy and even exponential levels.

Switching to efficient artificial intelligence systems has already saved Google a ton of money on its energy bills. And, it seems machine learning may also pose monetary benefits (of a different kind) for electricity providers. With power theft costing the industry roughly $96 billion in losses per year, companies could start looking to AI to help identify pilferers. A team from the University of Luxembourg has developed an algorithm that sifts through electricity meter data to detect abnormal usage. They put the system to work on info compiled from 3.6 million Brazilian households over the course of five years.

We study the quantum synchronization between a pair of two-level systems inside two coupledcavities. Using a digital-analog decomposition of the master equation that rules the system dynamics, we show that this approach leads to quantum synchronization between both two-level systems. Moreover, we can identify in this digital-analog block decomposition the fundamental elements of a quantum machine learning protocol, in which the agent and the environment (learning units) interact through a mediating system, namely, the register. If we can additionally equip this algorithm with a classical feedback mechanism, which consists of projective measurements in the register, reinitialization of the register state and local conditional operations on the agent and register subspace, a powerful and flexible quantum machine learning protocol emerges. Indeed, numerical simulations show that this protocol enhances the synchronization process, even when every subsystem experience different loss/decoherence mechanisms, and give us flexibility to choose the synchronization state. Finally, we propose an implementation based on current technologies in superconducting circuits.

In these spaces of high dimensionality, feature selection is a way to exclude those irrelevant and redundant features, whose presence might complicate the task of knowledge discovery. In classification tasks, a feature is considered irrelevant if it contains no information about the class and therefore it is not necessary at all for the predictive task. Besides, it is widely accepted that two features are redundant if their values are correlated. There are several well known measures that compare features and determine their importance, such as the symmetrical uncertainty (SU)[2]. SU is a measure based on information that uses entropy and conditional entropy values to determine the correlation between pairs of features.

Brazil has a big electricity theft problem. But an AI algorithm tested on several million of the country's households shows promise as a tool for helping cut this out. It could also offer insights for electricity suppliers elsewhere seeking to do a better job of reading your meter. Who is responsible for the theft in Brazil isn't always clear – sending meter readers to check whether meters and overhead cabling have been tampered with is dangerous work, says Adrian Grilli of the Joint Radio Company in London, which does telecommunications for global energy companies. Electricity theft is hardly limited to Brazil: some countries see as much as 40 per cent of their supply siphoned off largely by users who have tampered with meters.

Content-dense news report important factual information about an event in direct, succinct manner. Information seeking applications such as information extraction, question answering and summarization normally assume all text they deal with is content-dense. Here we empirically test this assumption on news articles from the business, U.S. international relations, sports and science journalism domains. Our findings clearly indicate that about half of the news texts in our study are in fact not content-dense and motivate the development of a supervised content-density detector. We heuristically label a large training corpus for the task and train a two-layer classifying model based on lexical and unlexicalized syntactic features. On manually annotated data, we compare the performance of domain-specific classifiers, trained on data only from a given news domain and a general classifier in which data from all four domains is pooled together. Our annotation and prediction experiments demonstrate that the concept of content density varies depending on the domain and that naive annotators provide judgement biased toward the stereotypical domain label. Domain-specific classifiers are more accurate for domains in which content-dense texts are typically fewer. Domain independent classifiers reproduce better naive crowdsourced judgements. Classification prediction is high across all conditions, around 80%.