Motivation Interoperability has been a challenging unsolved problem that relies on manual, error-prone solutions and costs bill ions of dollars annually [2, 3]. Semi-automated verification of interoperability can be achieved by a set of limited tools. However, there does not exist any automated tools for the verification and the validation of interoperability soluti ons. This work may enable the next generation of automatically composable and reconfigurable systems, and support formal verification of the currently used standards. In this articl e, we focus on the theoretical framework we built in [1], and construct an algorithmic framework that can be used to apply the theory presented in [1]. W e also provide practical applicat ions using the automated system we built based on the algorithmic framework we present here. To our knowledge, there does not exist any work in the literature which has developed an algorithmic framework or an automated system that is capable of testing for the interope r-ability of CAD systems based on the interchangeability of th eir models with respect to their shape properties. By construct ing such a framework and a system, we aim to show that it is possible to discover the interoperability between CAD syst ems with a predetermined tolerance without translating forma ts or converting representations.

Taxonomies consist of machine-interpretable semantics and provide valuable knowledge for many web applications. For example, online retailers (e.g., Amazon and eBay) use taxonomies for product recommendation, and web search engines (e.g., Google and Bing) leverage taxonomies to enhance query understanding. Enormous efforts have been made on constructing taxonomies either manually or semi-automatically. However, with the fast-growing volume of web content, existing taxonomies will become outdated and fail to capture emerging knowledge. Therefore, in many applications, dynamic expansions of an existing taxonomy are in great demand. In this paper, we study how to expand an existing taxonomy by adding a set of new concepts. We propose a novel self-supervised framework, named TaxoExpan, which automatically generates a set of pairs from the existing taxonomy as training data. Using such self-supervision data, TaxoExpan learns a model to predict whether a query concept is the direct hyponym of an anchor concept. We develop two innovative techniques in TaxoExpan: (1) a position-enhanced graph neural network that encodes the local structure of an anchor concept in the existing taxonomy, and (2) a noise-robust training objective that enables the learned model to be insensitive to the label noise in the self-supervision data. Extensive experiments on three large-scale datasets from different domains demonstrate both the effectiveness and the efficiency of TaxoExpan for taxonomy expansion.

Signal temporal logic (STL) is an expressive language to specify time-bound real-world robotic tasks and safety specifications. Recently, there has been an interest in learning optimal policies to satisfy STL specifications via reinforcement learning (RL). Learning to satisfy STL specifications often needs a sufficient length of state history to compute reward and the next action. The need for history results in exponential state-space growth for the learning problem. Thus the learning problem becomes computationally intractable for most real-world applications. In this paper, we propose a compact means to capture state history in a new augmented state-space representation. An approximation to the objective (maximizing probability of satisfaction) is proposed and solved for in the new augmented state-space. We show the performance bound of the approximate solution and compare it with the solution of an existing technique via simulations.

Explainable Artificial Intelligence and Machine Learning: A reality rooted perspective Frank Emmert-Streib 1,2, Olli Yli-Harja 2, and Matthias Dehmer 3 1 Predictive Society and Data Analytics Lab, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland 2 Institute of Biosciences and Medical Technology, Tampere University of Technology, Tampere, Finland 3 Institute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, Steyr Campus, 4040 Steyr, Austria January 26, 2020 Abstract We are used to the availability of big data generated in nearly all fields of science as a consequence of technological progress. However, the analysis of such data possess vast challenges. One of these relates to the explainability of artificial intelligence (AI) or machine learning methods. Currently, many of such methods are non-transparent with respect to their working mechanism and for this reason are called black box models, most notably deep learning methods. However, it has been realized that this constitutes severe problems for a number of fields including the health sciences and criminal justice and arguments have been brought forward in favor of an explainable AI. In this paper, we do not assume the usual perspective presenting explainable AI as it should be, but rather we provide a discussion what explainable AI can be . The difference is that we do not present wishful thinking but reality grounded properties in relation to a scientific theory beyond physics. 1 Introduction Artificial intelligence (AI) and machine learning (ML) have achieved great successes in a number of different learning tasks including image recognition and speech processing [1-3].

Primary obligations include obtaining explicit consent from the data subject for the processing of personal data and providing full transparency with respect to processing and sharing. With the coming into effect of the GDPR in May 2018, several tools [11, 16, 19] have recently been developed that can be used to assist companies to assess the compliance of their systems and processes with respect to obligations set forth in the GDPR. However, such tools are targeted at self assessment (i.e. companies complete standard questionnaires in the form of a privacy impact assessment) and cannot be used to automatically check compliance with usage constraints. Such, automated transparency and compliance mechanisms would require not only machine-readable representations of the users consent, but also machine-readable representations of data processing and sharing. SPECIAL 1 is an EU H2020 research and innovation action, which addresses these challenges by demonstrating how Semantic Web technologies can be used for both consent and personal data processing representation and compliance checking. In particular we devise a suite of ontologies and vocabularies that can be used to: (i) model data usage policies, conforming the SPECIAL's Usage Policy Language, (ii) represent data processing and sharing events in a semantic log. Both of which have been developed in close collaboration with legal experts, thus ensuring that our automated compliance checking is tightly coupled with the legal assessment process.1 https://www.specialprivacy.eu/ 1 arXiv:2001.09461v1

This paper aims at demonstrating how a first-order logic reasoning system in combination with a large knowledge base can be understood as an artificial consciousness system. For this we review some aspects from the area of philosophy of mind and in particular Baars' Global Workspace Theory. This will be applied to the reasoning system Hyper with ConceptNet as a knowledge base. Finally we demonstrate that such a system is very well able to do conscious mind wandering.

Today, the prominence of data science within organizations has given rise to teams of data science workers collaborating on extracting insights from data, as opposed to individual data scientists working alone. However, we still lack a deep understanding of how data science workers collaborate in practice. In this work, we conducted an online survey with 183 participants who work in various aspects of data science. We focused on their reported interactions with each other (e.g., managers with engineers) and with different tools (e.g., Jupyter Notebook). We found that data science teams are extremely collaborative and work with a variety of stakeholders and tools during the six common steps of a data science workflow (e.g., clean data and train model). We also found that the collaborative practices workers employ, such as documentation, vary according to the kinds of tools they use. Based on these findings, we discuss design implications for supporting data science team collaborations and future research directions.

In other words, the research from the University of Jyväskylä, indicates that the way you dance is unique, and from the subtle differences between dance patterns, algorithms can tell it's you rather than someone else. The objective of the research was to apply machine learning to understand how and why music affects people the way that it does. To explore this question, the Finnish scientists used motion capture technology (much like the technology now common movies with a CGI element) to gain an insight about the uniqueness of dance moves and to also extrapolate what the dance move might say about the person. From studying different patterns of dancing, the researchers are of the view that they can determine how extroverted or neurotic a person is and also draw insights in the particular mood a person is experiencing. The recent study used seventy-three people, who were motion captured dancing to eight different forms of music: Blues, Country, Dance/Electronica, Jazz, Metal, Pop, Reggae and Rap.

In this special guest feature, Bob Fletcher from Verne Global reflects on how liquid cooling technologies on display at SC19 represent more than just a wave. Bob Fletcher is VP of Artificial Intelligence at Verne Global. Perhaps it is because I returned from my last business trip of 2019 to a flooded house, but more likely it's all the wicked cool water-cooled equipment that I encountered at SC19 that I'm in a watery mood! Many of the hardware vendors at SC19 were pushing their exascale-ready devices and about 15% of the devices on a typical computer manufacturer's booth were water-cooled. Adding rack-level water cooling is theoretically straight forward, so I spent a few minutes checking out the various options. The first thing I noticed is that the chilled water from outside terminates in a Cooling Distribution Unit (CDU) which then has its own water-cooling loop which is connected to the computing equipment.

As well as playing a key role in cracking the Enigma code at Bletchley Park during the Second World War, and conceiving of the modern computer, the British mathematician Alan Turing owes his public reputation to the test he devised in 1950. Crudely speaking, it asks whether a human judge can distinguish between a human and an artificial intelligence based only on their responses to conversation or questions. This test, which he called the "imitation game," was popularised 15 years later in Philip K Dick's science-fiction novel Do Androids Dream of Electric Sheep? But Turing is also widely remembered as having committed suicide in 1954, quite probably driven to it by the hormone treatment he was instructed to take as an alternative to imprisonment for homosexuality (deemed to make him a security risk), and it is only comparatively recently that his genius has been afforded its full due. In 2009, Gordon Brown apologised on behalf of the British government for his treatment; in 2014, his posthumous star rose further again when Benedict Cumberbatch played him in The Imitation Game; and in 2021, he will be the face on the new £50 note.