다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI

State representation learning aims at learning compact representations from raw observations in robotics and control applications. Approaches used for this objective are auto-encoders, learning forward models, inverse dynamics or learning using generic priors on the state characteristics. However, the diversity in applications and methods makes the field lack standard evaluation datasets, metrics and tasks. This paper provides a set of environments, data generators, robotic control tasks, metrics and tools to facilitate iterative state representation learning and evaluation in reinforcement learning settings.

S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning

Smart Spaces are composed of heterogeneous sensors and devices that collect and share information. This information may contain personal information of the users. Thus, securing the data and preserving the privacy are of paramount importance. In this paper, we propose techniques for information security and privacy protection for Smart Spaces based on the Smart-M3 platform. We propose a) a security framework, and b) a context-aware role-based access control scheme. We model our access control scheme using ontological techniques and Web Ontology Language (OWL), and implement it via CLIPS rules. To evaluate the efficiency of our access control scheme, we measure the time it takes to check the access rights of the access requests. The results demonstrate that the highest response time is approximately 0.2 seconds in a set of 100000 triples. We conclude that the proposed access control scheme produces low overhead and is therefore, an efficient approach for Smart Spaces.

A semantic security framework and context-aware role-based access control ontology for smart spaces

This paper reviews the literature concerning technology used for creating and delivering accessible museum and cultural heritage sites experiences. It highlights the importance of the delivery suited for everyone from different areas of expertise, namely interaction design, pedagogical and participatory design, and it presents how recent and future artificial intelligence (AI) developments can be used for this aim, i.e.,improving and widening online and in situ accessibility. From the literature review analysis, we articulate a conceptual framework that incorporates key elements that constitute museum and cultural heritage online experiences and how these elements are related to each other. Concrete opportunities for future directions empirical research for accessibility of cultural heritage contents are suggested and further discussed.

Human-Centered Artificial Intelligence for Designing Accessible Cultural Heritage

Many high-performance models suffer from a lack of interpretability. There has been an increasing influx of work on explainable artificial intelligence (XAI) in order to disentangle what is meant and expected by XAI. Nevertheless, there is no general consensus on how to produce and judge explanations. In this paper, we discuss why techniques integrating connectionist and symbolic paradigms are the most efficient solutions to produce explanations for non-technical users and we propose a reasoning model, based on definitions by Doran et al. [2017] (arXiv:1710.00794) to explain a neural network's decision. We use this explanation in order to correct bias in the network's decision rationale. We accompany this model with an example of its potential use, based on the image captioning method in Burns et al. [2018] (arXiv:1803.09797).

Towards Explainable Neural-Symbolic Visual Reasoning

Continual learning (CL) is a particular machine learning paradigm where the
data distribution and learning objective changes through time, or where all the
training data and objective criteria are never available at once. The evolution
of the learning process is modeled by a sequence of learning experiences where
the goal is to be able to learn new skills all along the sequence without
forgetting what has been previously learned. Continual learning also aims at
the same time at optimizing the memory, the computation power and the speed
during the learning process. An important challenge for machine learning is not necessarily finding
solutions that work in the real world but rather finding stable algorithms that
can learn in real world. Hence, the ideal approach would be tackling the real
world in a embodied platform: an autonomous agent. Continual learning would
then be effective in an autonomous agent or robot, which would learn
autonomously through time about the external world, and incrementally develop a
set of complex skills and knowledge. Robotic agents have to learn to adapt and interact with their environment
using a continuous stream of observations. Some recent approaches aim at
tackling continual learning for robotics, but most recent papers on continual
learning only experiment approaches in simulation or with static datasets.
Unfortunately, the evaluation of those algorithms does not provide insights on
whether their solutions may help continual learning in the context of robotics.
This paper aims at reviewing the existing state of the art of continual
learning, summarizing existing benchmarks and metrics, and proposing a
framework for presenting and evaluating both robotics and non robotics
approaches in a way that makes transfer between both fields easier.

Natalia Díaz-Rodríguez

Papers

S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning

A semantic security framework and context-aware role-based access control ontology for smart spaces

Human-Centered Artificial Intelligence for Designing Accessible Cultural Heritage

Towards Explainable Neural-Symbolic Visual Reasoning

Continual Learning for Robotics