Aalto University

About: Aalto University is a education organization based out in Espoo, Finland. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 9969 authors who have published 32648 publications receiving 829626 citations. The organization is also known as: TKK & Aalto-korkeakoulu.

On the force potential of strategy texts: a critical discourse analysis of a strategic plan and its power effects in a city organization

Abstract: Despite increasing interest in the discursive aspects of strategy, few studies have examined strategy texts and their power effects. We draw from Critical Discourse Analysis to better understand the power of strategic plans as a directive genre. In our empirical analysis, we examined the creation of the official strategic plan of the City of Lahti in Finland. As a result of our inductive analysis, we identified five central discursive features of this plan: self-authorization, special terminology, discursive innovation, forced consensus and deonticity. We argue that these features can, with due caution, be generalized and conceived as distinctive features of the strategy genre. We maintain that these discursive features are not trivial characteristics; they have important implications for the textual agency of strategic plans, their performative effects, impact on power relations and ideological implications.

State-of-the-art and good practice in the field of living labs

Abstract: Living Labs are an emerging Public Private Partnership (PPP) concept in which firms, public authorities and citizens work together to create, prototype, validate and test new services, businesses, markets and technologies in real-life contexts, such as cities, city regions, rural areas and collaborative virtual networks between public and private players. The real-life and everyday life contexts will both stimulate and challenge research and development as public authorities and citizens will not only participate in, but also contribute to the whole innovation process. This paper examines the state-of-the art in involving the user and stakeholder organisations into the innovation process in various ongoing, embryonic Living Labs initiatives, examines the key practices that need to be in place for the maturation of the concept and gives examples on how those are currently being deployed. The paper concludes with a section dedicated to identifying areas in which future research is required.

DÏoT: A Federated Self-learning Anomaly Detection System for IoT

Abstract: IoT devices are increasingly deployed in daily life. Many of these devices are, however, vulnerable due to insecure design, implementation, and configuration. As a result, many networks already have vulnerable IoT devices that are easy to compromise. This has led to a new category of malware specifically targeting IoT devices. However, existing intrusion detection techniques are not effective in detecting compromised IoT devices given the massive scale of the problem in terms of the number of different types of devices and manufacturers involved. In this paper, we present DIoT, an autonomous self-learning distributed system for detecting compromised IoT devices. DIoT builds effectively on device-type-specific communication profiles without human intervention nor labeled data that are subsequently used to detect anomalous deviations in devices' communication behavior, potentially caused by malicious adversaries. DIoT utilizes a federated learning approach for aggregating behavior profiles efficiently. To the best of our knowledge, it is the first system to employ a federated learning approach to anomaly-detection-based intrusion detection. Consequently, DIoT can cope with emerging new and unknown attacks. We systematically and extensively evaluated more than 30 off-the-shelf IoT devices over a long term and show that DIoT is highly effective (95.6% detection rate) and fast (257 ms) at detecting devices compromised by, for instance, the infamous Mirai malware. DIoT reported no false alarms when evaluated in a real-world smart home deployment setting.

Individual tree biomass estimation using terrestrial laser scanning

Abstract: Determination of stem and crown biomass requires accurate measurements of individual tree stem, bark, branch and needles. These measurements are time-consuming especially for mature trees. Accurate field measurements can be done only in a destructive manner. Terrestrial laser scanning (TLS) measurements are a viable option for measuring the reference information needed. TLS measurements provide dense point clouds in which features describing biomass can be extracted for stem form and canopy dimensions. Existing biomass models do not utilise canopy size information and therefore TLS-based estimation methods should improve the accuracy of biomass estimation. The main objective of this study was to estimate single-tree-level aboveground biomass (AGB), based on models developed using TLS data. The modelling dataset included 64 laboratory-measured trees. Models were developed for total AGB, tree stem-, living branch- and dead branch biomass. Modelling results were also compared with existing individual tree-level biomass models and showed that AGB estimation accuracies were improved, compared with those of existing models. However, current biomass models based on diameter-at-breast height (DBH), tree height and species worked rather well for stem- and total biomass. TLS-based models improved estimation accuracies, especially estimation of branch biomass. We suggest the use of stem curve and crown size geometric measurements from TLS data as a basis for allometric biomass models rather than statistical three-dimensional point metrics, since TLS statistical metrics are dependent on various scanning parameters and tree neighbourhood characteristics.