Data-enabled design for social change: Two case studies

Smartness in contemporary society implies the use of massive data to improve the experience of people with connected services and products. The use of big data to collect information about people's behaviours opens a new concept of "user-centred design" where users are remotely monitored, observed and profiled. In this paradigm, users are considered as sources of information and their participation in the design process is limited to a role of data generators. There is a need to identify methodologies that actively involve people and communities at the core of ecosystems of interconnected products and services. Our contribution to designing for social innovation in ecosystems relies on developing new methods and approaches to transform data-driven design using a participatory and co-creative data-enabled design approach. To this end, we present one of the methods we have developed to design "smart" systems called Experiential Design Landscapes (EDL), and two sample projects, Social Stairs and [Y]our Perspective. Social Stairs faces the topic of behaviour change mediated by sensing technologies. [Y]our Perspective is a social platform to sustain processes of deliberative democracy. Both projects exemplify our approach to data-enabled design as a social proactive participatory design approach

A novel plasticity rule can explain the development of sensorimotor intelligence

Grounding autonomous behavior in the nervous system is a fundamental challenge for neuroscience. In particular, the self-organized behavioral development provides more questions than answers. Are there special functional units for curiosity, motivation, and creativity? This paper argues that these features can be grounded in synaptic plasticity itself, without requiring any higher level constructs. We propose differential extrinsic plasticity (DEP) as a new synaptic rule for self-learning systems and apply it to a number of complex robotic systems as a test case. Without specifying any purpose or goal, seemingly purposeful and adaptive behavior is developed, displaying a certain level of sensorimotor intelligence. These surprising results require no system specific modifications of the DEP rule but arise rather from the underlying mechanism of spontaneous symmetry breaking due to the tight brain-body-environment coupling. The new synaptic rule is biologically plausible and it would be an interesting target for a neurobiolocal investigation. We also argue that this neuronal mechanism may have been a catalyst in natural evolution.Comment: 18 pages, 5 figures, 7 video

Data Quality of Digital Process Data: A Generalized Framework and Simulation/Post-Hoc Identification Strategy

Digital process data are becoming increasingly important for social science research, but their quality has been gravely neglected so far. In this article, we adopt a process perspective and argue that data extracted from socio-technical systems are, in principle, subject to the same error-inducing mechanisms as traditional forms of social science data, namely biases that arise before their acquisition (observational design), during their acquisition (data generation), and after their acquisition (data processing). As the lack of access and insight into the actual processes of data production renders key traditional mechanisms of quality assurance largely impossible, it is essential to identify data quality problems in the data available—that is, to focus on the possibilities post-hoc quality assessment offers to us. We advance a post-hoc strategy of data quality assurance, integrating simulation and explorative identification techniques. As a use case, we illustrate this approach with the example of bot activity and the effects this phenomenon can have on digital process data. First, we employ agent-based modelling to simulate datasets containing these data problems. Subsequently, we demonstrate the possibilities and challenges of post-hoc control by mobilizing geometric data analysis, an exemplary technique for identifying data quality issues.Digitale Prozessdaten werden für die sozialwissenschaftliche Forschung immer wichtiger, doch ihre Qualität wurde in der Diskussion bisher stark vernachlässigt. In diesem Beitrag nehmen wir eine Prozessperspektive ein und argumentieren, dass Daten, die aus soziotechnischen Systemen extrahiert werden, im Prinzip denselben fehlerverursachenden Mechanismen unterliegen wie traditionelle Formen sozialwissenschaftlicher Daten, nämlich Verzerrungen, die vor ihrer Erfassung (Beobachtungsdesign), während ihrer Erfassung (Datengenerierung) und nach ihrer Erfassung (Datenverarbeitung) entstehen. Da der fehlende Zugang und Einblick in die eigentlichen Prozesse der Datenproduktion wichtige Mechanismen der traditionellen Qualitätssicherung weitgehend unmöglich machen, ist es unerlässlich, Datenqualitätsprobleme in den zur Verfügung stehenden Daten zu identifizieren – das heißt, sich auf die Möglichkeiten zu konzentrieren, die uns die post-hoc Qualitätsprüfung bietet. Wir entwickeln eine Post-hoc-Strategie der Datenqualitätssicherung, die Simulation und explorative Identifizierungstechniken integriert. Als Anwendungsfall illustrieren wir diesen Ansatz am Beispiel von Bot-Aktivitäten und den Auswirkungen, die dieses Phänomen auf digitale Prozessdaten haben kann. Dazu setzen wir zunächst eine agentenbasierte Modellierung ein, um Datensätze mit derartigen Datenproblemen zu simulieren. Anschließend demonstrieren wir die Möglichkeiten und Herausforderungen der Post-hoc-Kontrolle, indem wir die geometrische Datenanalyse einsetzen, eine exemplarische Technik zur Identifizierung von Datenqualitätsproblemen

Connected Hearing Devices and Audiologists: The User-Centered Development of Digital Service Innovations

Today, medical technology manufacturers enter the service market through the development of digital service innovations. In the field of audiology, these developments increasingly shift the service capacities from audiologists to manufacturers and technical systems. However, the technology-driven developments of manufacturers lack acceptance of hearing device users and undermine the important role of audiologists within the service provision. By following a user-centered design approach in order to deal with the technological and social challenges of disruptive services, we aim to develop service innovations on an integrated service platform in the field of tele-audiology. To ensure the acceptance of technology-driven service innovations among hearing device users and audiologists, we systematically integrated these actors in a participatory innovation process. With qualitative and quantitative data we identified several requirements and preferences for different service innovations in the field of tele-audiology. According to the preferences of the different actors, we proposed a service platform approach based on a connected hearing device in three pillars of application: 1) one-to-one (1:1) service innovations based on a remote fitting concept directly improve the availability of services offered by audiologists without being physically present. Based on this, 2) one-to-many (1:N) service innovations allow the use of the connected hearing device as an indirect data source for training a machine learning algorithm that empowers users through the automation of service processes. A centralized server system collects the data and performs the training of this algorithm. As a future outlook, we show potentials to use the connected hearing device for 3) cross-industry (N:M) service innovations in contexts outside the healthcare domain and give practical implications for the market launch of successful service innovations in the field of tele-audiology

To Use or Not to Use Artificial Intelligence? A Framework for the Ideation and Evaluation of Problems to Be Solved with Artificial Intelligence

The recent advent of artificial intelligence (AI) solutions that surpass humans’ problem-solving capabilities has uncovered AIs’ great potential to act as new type of problem solvers. Despite decades of analysis, research on organizational problem solving has commonly assumed that the problem solver is essentially human. Yet, it remains unclear how existing knowledge on human problem solving translates to a context with problem-solving machines. To take a first step to better understand this novel context, we conducted a qualitative study with 24 experts to explore the process of problem finding that forms the essential first step in problem-solving activities and aims at uncovering reasonable problems to be solved. With our study, we synthesize emerged procedural artifacts and key factors to propose a framework for problem finding in AI solver contexts. Our findings enable future research on human-machine problem solving and offer practitioners helpful guidance on identifying and managing reasonable AI initiatives

Multiprocessor System-on-Chips based Wireless Sensor Network Energy Optimization

Wireless Sensor Network (WSN) is an integrated part of the Internet-of-Things (IoT) used to monitor the physical or environmental conditions without human intervention. In WSN one of the major challenges is energy consumption reduction both at the sensor nodes and network levels. High energy consumption not only causes an increased carbon footprint but also limits the lifetime (LT) of the network. Network-on-Chip (NoC) based Multiprocessor System-on-Chips (MPSoCs) are becoming the de-facto computing platform for computationally extensive real-time applications in IoT due to their high performance and exceptional quality-of-service. In this thesis a task scheduling problem is investigated using MPSoCs architecture for tasks with precedence and deadline constraints in order to minimize the processing energy consumption while guaranteeing the timing constraints. Moreover, energy-aware nodes clustering is also performed to reduce the transmission energy consumption of the sensor nodes. Three distinct problems for energy optimization are investigated given as follows: First, a contention-aware energy-efficient static scheduling using NoC based heterogeneous MPSoC is performed for real-time tasks with an individual deadline and precedence constraints. An offline meta-heuristic based contention-aware energy-efficient task scheduling is developed that performs task ordering, mapping, and voltage assignment in an integrated manner. Compared to state-of-the-art scheduling our proposed algorithm significantly improves the energy-efficiency. Second, an energy-aware scheduling is investigated for a set of tasks with precedence constraints deploying Voltage Frequency Island (VFI) based heterogeneous NoC-MPSoCs. A novel population based algorithm called ARSH-FATI is developed that can dynamically switch between explorative and exploitative search modes at run-time. ARSH-FATI performance is superior to the existing task schedulers developed for homogeneous VFI-NoC-MPSoCs. Third, the transmission energy consumption of the sensor nodes in WSN is reduced by developing ARSH-FATI based Cluster Head Selection (ARSH-FATI-CHS) algorithm integrated with a heuristic called Novel Ranked Based Clustering (NRC). In cluster formation parameters such as residual energy, distance parameters, and workload on CHs are considered to improve LT of the network. The results prove that ARSH-FATI-CHS outperforms other state-of-the-art clustering algorithms in terms of LT.University of Derby, Derby, U

Attitude towards the Beyond Budgeting concept in Russia

Masteroppgave i Energy Management - Universitetet i Nordland, 201