The contemporary process-aware information systems possess the capabilities to record the activities generated during the process execution. To leverage these process specific fine-granular data, process mining has recently emerged as a promising research discipline. As an important branch of process mining, predictive business process management, pursues the objective to generate forward-looking, predictive insights to shape business processes. In this study, we propose a conceptual framework sought to establish and promote understanding of decision-making environment, underlying business processes and nature of the user characteristics for developing explainable business process prediction solutions. Consequently, with regard to the theoretical and practical implications of the framework, this study proposes a novel local post-hoc explanation approach for a deep learning classifier that is expected to facilitate the domain experts in justifying the model decisions. In contrary to alternative popular perturbation-based local explanation approaches, this study defines the local regions from the validation dataset by using the intermediate latent space representations learned by the deep neural networks. To validate the applicability of the proposed explanation method, the real-life process log data delivered by the Volvo IT Belgium's incident management system are used. The adopted deep learning classifier achieves a good performance with the Area Under the ROC Curve of 0.94. The generated local explanations are also visualized and presented with relevant evaluation measures that are expected to increase the users' trust in the black-box-model.
Rehm, Georg, Marheinecke, Katrin, Hegele, Stefanie, Piperidis, Stelios, Bontcheva, Kalina, Hajič, Jan, Choukri, Khalid, Vasiļjevs, Andrejs, Backfried, Gerhard, Prinz, Christoph, Pérez, José Manuel Gómez, Meertens, Luc, Lukowicz, Paul, van Genabith, Josef, Lösch, Andrea, Slusallek, Philipp, Irgens, Morten, Gatellier, Patrick, Köhler, Joachim, Bars, Laure Le, Anastasiou, Dimitra, Auksoriūtė, Albina, Bel, Núria, Branco, António, Budin, Gerhard, Daelemans, Walter, De Smedt, Koenraad, Garabík, Radovan, Gavriilidou, Maria, Gromann, Dagmar, Koeva, Svetla, Krek, Simon, Krstev, Cvetana, Lindén, Krister, Magnini, Bernardo, Odijk, Jan, Ogrodniczuk, Maciej, Rögnvaldsson, Eiríkur, Rosner, Mike, Pedersen, Bolette Sandford, Skadiņa, Inguna, Tadić, Marko, Tufiş, Dan, Váradi, Tamás, Vider, Kadri, Way, Andy, Yvon, François
Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality. However, language barriers impacting business, cross-lingual and cross-cultural communication are still omnipresent. Language Technologies (LTs) are a powerful means to break down these barriers. While the last decade has seen various initiatives that created a multitude of approaches and technologies tailored to Europe's specific needs, there is still an immense level of fragmentation. At the same time, AI has become an increasingly important concept in the European Information and Communication Technology area. For a few years now, AI, including many opportunities, synergies but also misconceptions, has been overshadowing every other topic. We present an overview of the European LT landscape, describing funding programmes, activities, actions and challenges in the different countries with regard to LT, including the current state of play in industry and the LT market. We present a brief overview of the main LT-related activities on the EU level in the last ten years and develop strategic guidance with regard to four key dimensions.
Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations.
Adversarial examples are carefully constructed modifications to an input that completely change the output of a classifier but are imperceptible to humans. Despite these successful attacks for continuous data (such as image and audio samples), generating adversarial examples for discrete structures such as text has proven significantly more challenging. In this paper we formulate the attacks with discrete input on a set function as an optimization task. We prove that this set function is submodular for some popular neural network text classifiers under simplifying assumption. This finding guarantees a $1-1/e$ approximation factor for attacks that use the greedy algorithm. Meanwhile, we show how to use the gradient of the attacked classifier to guide the greedy search. Empirical studies with our proposed optimization scheme show significantly improved attack ability and efficiency, on three different text classification tasks over various baselines. We also use a joint sentence and word paraphrasing technique to maintain the original semantics and syntax of the text. This is validated by a human subject evaluation in subjective metrics on the quality and semantic coherence of our generated adversarial text.
Fake news continues to rear its ugly head; in March of this year, half of the U.S. population reported seeing deliberately misleading articles on news websites. A majority of respondents to a recent Edelman survey, meanwhile, said that they couldn't judge the veracity of media reports. And given that fake news has been shown to spread faster than real news, it's no surprise that almost seven in ten people are concerned it might be used as a "weapon." Researchers at the Massachusetts Institute of Technology's Computer Science and Artificial Intelligence Lab (CSAIL) and the Qatar Computing Research Institute believe they've engineered a partial solution. In a study that'll be presented later this month at the 2018 Empirical Methods in Natural Language Processing (EMNLP) conference in Brussels, Belgium, they describe an artificially intelligent (AI) system that can determine whether a source is accurate or politically prejudiced.
A person's university years should be all about expanding your horizons, as well as meeting people with perspectives and backgrounds different from your own. Well, what could be more different than sharing your classroom with a robot? That's what 31 philosophy students at Notre Dame de Namur University in California recently experienced when they were joined in their "Philosophy of Love" program by Bina48, an A.I. animatronic robot. The robot participated via Skype in a series of sessions before appearing "in person" in the final class.
We use text mining a lot in day-to-day data mining operations. In order to share our knowledge on this, to show that R is an extremely mature platform to do business-oriented text analytics and to give you practical experience with text mining, our course on Text Mining with R is scheduled for the 3rd consecutive year at LStat, the Leuven Statistics Research Center (Belgium) as well as at the Data Science Academy in Brussels. Courses are scheduled 2 times in November 2017 and also in March 2018. This course is a hands-on course covering the use of text mining tools for the purpose of data analysis. It covers basic text handling, natural language engineering and statistical modelling on top of textual data.
The text analysis part of the AMiCA project (http://www.amicaproject.be), a cooperation between the University of Antwerp and the University of Ghent, developed methods and software to help moderators detect occurrences of unwanted or dangerous situations in their social networks. More specifically, the project developed prototype systems for the detection of cyberbullying, suicide announcements, and sexually transgressive behavior. In this talk I will focus on the text analysis methods that were used for normalization of social media text, for profiling users, and for detecting dangerous content. I will describe the architectures and results of the three resulting applications.
Machine Learning is the new buzz word and AI is the slang word these days. What does happen in this exiting field in Europe? Is AI common ground for all businesses or the exclusive territory for a few? Who has managed to validate a business model for autonomous vehicles or chatbots?Whatdoesdata-drivenor API-firstbusinessmodelslook like? With this report we want to provide a comprehensive review of investment in startups and high-growth AI and Data Analytics companies across 22 countries in Europe.