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Paul Lukowicz

Publications and source records attributed to Paul Lukowicz.

115 records · Page 7Linked to original sources
arXiv2019

Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields

The field of collaborative interactive learning (CIL) aims at developing and investigating the technological foundations for a new generation of smart systems that support humans in their everyday life. While the concept of CIL has already been carved out in detail (including the fields of dedicated CIL and opportunistic CIL) and many research objectives have been stated, there is still the need to clarify some terms such as information, knowledge, and experience in the context of CIL and to differentiate CIL from recent and ongoing research in related fields such as active learning, collaborative learning, and others. Both aspects are addressed in this paper.

cs.LG↗
arXiv2017

Transforming Sensor Data to the Image Domain for Deep Learning - an Application to Footstep Detection

Convolutional Neural Networks (CNNs) have become the state-of-the-art in various computer vision tasks, but they are still premature for most sensor data, especially in pervasive and wearable computing. A major reason for this is the limited amount of annotated training data. In this paper, we propose the idea of leveraging the discriminative power of pre-trained deep CNNs on 2-dimensional sensor data by transforming the sensor modality to the visual domain. By three proposed strategies, 2D sensor output is converted into pressure distribution imageries. Then we utilize a pre-trained CNN for transfer learning on the converted imagery data. We evaluate our method on a gait dataset of floor surface pressure mapping. We obtain a classification accuracy of 87.66%, which outperforms the conventional machine learning methods by over 10%.

cs.CV↗
arXiv2017

Self-Adaptation of Activity Recognition Systems to New Sensors

Traditional activity recognition systems work on the basis of training, taking a fixed set of sensors into account. In this article, we focus on the question how pattern recognition can leverage new information sources without any, or with minimal user input. Thus, we present an approach for opportunistic activity recognition, where ubiquitous sensors lead to dynamically changing input spaces. Our method is a variation of well-established principles of machine learning, relying on unsupervised clustering to discover structure in data and inferring cluster labels from a small number of labeled dates in a semi-supervised manner. Elaborating the challenges, evaluations of over 3000 sensor combinations from three multi-user experiments are presented in detail and show the potential benefit of our approach.

cs.CV↗
arXiv2015

A New Vision of Collaborative Active Learning

Active learning (AL) is a learning paradigm where an active learner has to train a model (e.g., a classifier) which is in principal trained in a supervised way, but in AL it has to be done by means of a data set with initially unlabeled samples. To get labels for these samples, the active learner has to ask an oracle (e.g., a human expert) for labels. The goal is to maximize the performance of the model and to minimize the number of queries at the same time. In this article, we first briefly discuss the state of the art and own, preliminary work in the field of AL. Then, we propose the concept of collaborative active learning (CAL). With CAL, we will overcome some of the harsh limitations of current AL. In particular, we envision scenarios where an expert may be wrong for various reasons, there might be several or even many experts with different expertise, the experts may label not only samples but also knowledge at a higher level such as rules, and we consider that the labeling costs depend on many conditions. Moreover, in a CAL process human experts will profit by improving their own knowledge, too.

cs.LG↗
arXiv2013

A planetary nervous system for social mining and collective awareness

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining, and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level.Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network.

cs.CY↗
arXiv2012

FuturICT

FuturlCT is a FET Flagship project using collective, participatory research, integrated across ICT, the social sciences and complexity science, to design socio-inspired technology and develop a science of global, socially interactive systems. The project will bring together, on a global level, Big Data, new modelling techniques and new forms of interaction, leading to a new understanding of society and its coevolution with technology. It aims to understand, explore and manage our complex, connected world in a more sustainable and resilient way. FuturICT is motivated by the fact that ubiquitous communication and sensing blur the boundaries between the physical and digital worlds, creating unparalleled opportunities for understanding the socio-economic fabric of our world, and for empowering humanity to make informed, responsible decisions for its future. The intimate, complex and dynamic relationship between global, networked ICT systems and human society directly influences the complexity and manageability of both. This also opens up the possibility to fundamentally change the way ICT will be designed, built and operated, to reflect the need for socially interactive, ethically sensitive, trustworthy, self-organised and reliable systems. FuturICT will create a new public resource - value-oriented tools and models to aggregate, access, query and understand vast amounts of data. Information from open sources, real-time devices and mobile sensors will be integrated with multi-scale models of the behaviour of social, technological, environmental and economic systems, which can be interrogated by policy-makers, business people and citizens alike. Together, these will build an eco-system that will lead to new business models, scientific paradigm shifts and more rapid and effective ways to create and disseminate new knowledge and social benefits - thereby forming an innovation accelerator.

nlin.AO↗