Ontology Evolution for Personalized and Adaptive Activity Recognition

Ontology-based knowledge driven Activity Recognition (AR) models play a vital role in realm of Internet of Things (IoTs). However, these models suffer the shortcomings of static nature, inability of self-evolution and lack of adaptivity. Also, AR models cannot be made comprehensive enough to cater all the activities and smart home inhabitants may not be restricted to only those activities contained in AR model. So, AR models may not rightly recognize or infer new activities. In this paper, a framework has been proposed for dynamically capturing the new knowledge from activity patterns to evolve behavioural changes in AR model (i.e. ontology based model). This ontology based framework adapts by learning the specialized and extended activities from existing user-performed activity patterns. Moreover, it can identify new activity patterns previously unknown in AR model, adapt the new properties in existing activity models and enrich ontology model by capturing change representation to enrich ontology model. The proposed framework has been evaluated comprehensively over the metrics of accuracy, statistical heuristics and Kappa Coefficient. A well-known dataset named DAMSH has been used for having an empirical insight to the effectiveness of proposed framework that shows a significant level of accuracy for AR models This paper is a postprint of a paper submitted to and accepted for publication in IET Wireless Sensor Systems and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Librar

Synthesizing design and informing science rationales for driving a decentralized generative knowledge management agenda

CITATION: Schmitt, U. & Gill, T. G. 2019. Synthesizing design and informing science rationales for driving a decentralized generative knowledge management agenda. Informing Science: the International Journal of an Emerging Transdiscipline, 22:1-18, doi:10.28945/4264.The original publication is available at https://www.informingscience.orgAim/Purpose: In a world of rapidly expanding complexity and exponentially increasing data availability, IT-based knowledge management tools will be needed to manage and curate available information. This paper looks at a particular tool architecture that has been previously proposed: The Personal Knowledge Management System (PKMS). The specific focus is on how the proposed architecture conforms to design science principles that relate to how it is likely to evolve. Background: We first introduce some recent informing science and design science research frameworks, then examine how the PKMS architecture would conform to these. Methodology: The approach taken is conceptual analysis. Contribution: The analysis provides a clearer understanding of how the proposed PKMS would serve the diverse-client ambiguous-target (DCAT) informing scenario and how it could be expected to evolve. Findings: We demonstrate how the PKMS informing architecture can be characterized as a “social machine” that appears to conform to a number of principles that would facilitate its long-term evolution. Future Research: The example provided by the paper could serve as a model future research seeking to integrate design science and informing science in the study of IT artefacts.https://www.informingscience.org/Publications/4264?Source=%2FJournals%2FInformingSciJ%2FArticles%3FVolume%3D22-2019Publisher's versio

Efficient index structures for and applications of the CompleteSearch engine

Traditional search engines, such as Google, offer response times well under one second, even for a corpus with more than a billion documents. They achieve this by making use of a (parallelized) inverted index. However, the inverted index is primarily designed to efficiently process simple key word queries, which is why search engines rarely offer support for queries which cannot be (re-)formulated in this manner, possibly using "special key words';. We have contrived data structures for the CompleteSearch engine, a search engine, developed at the Max-Planck Institute for Computer Science, which supports a far greater set of query types, without sacrificing the efficiency. It is built on top of a context-sensitive prefix search and completion mechanism. This mechanism is, on the one hand, simple enough to be efficiently realized by appropriate algorithms, and, on the other hand, powerful enough to be employed to support additional query types. We present two new data structures, which can be used to solve the underlying prefix search and completion problem. The first one, called AutoTree, has the theoretically desirable property that, for non-degenerate corpora and queries, its running time is proportional to the sum of the sizes of the input and output. The second one, called HYB, focuses on compressibility of the data and is optimized for scenarios, where the index does not fit in main memory but resides on disk. Both beat the baseline algorithm, using an inverted index, by a factor of 4-10 in terms of average processing time. A direct head-to-head comparison shows that, in a general setting, HYB outperforms AutoTree. Thanks to the HYB data structure, the CompleteSearch engine efficiently supports features such as faceted search for categorical information, completion to synonyms, support for basic database style queries on relational tables and the efficient search of ontologies. For each of these features, we demonstrate the viability of our approach through experiments. Finally, we also prove the practical relevance of our work through a small user study with employees of the helpdesk of our institute.Typische Suchmaschinen, wie z.B. Google, erreichen Antwortzeiten deutlich unter einer Sekunde, selbst für einen Korpus mit mehr als einer Milliarde Dokumenten. Sie schaffen dies durch die Nutzung eines (parallelisierten) invertierten Index. Da der invertierte Index jedoch hauptsächlich für die Bearbeitung von einfachen Schlagwortsuchen konzipiert ist, bieten Suchmaschinen nur selten die Möglichkeit, komplexere Anfragen zu beantworten, die sich nicht in solch eine Schlagwortsuche umformulieren lassen, u.U. mit der Zurhilfenahme von speziellen Kunstworten. Wir haben für die CompleteSearch Suchmaschine, konzipiert und implementiert am Max-Planck-Institut für Informatik, spezielle Datenstrukturen entwickelt, die ein deutlich größeres Spektrum an Anfragetypen unterstützen, ohne dabei die Effizienz zu opfern. Die CompleteSearch Suchmaschine baut auf einem kontext-sensitiven Präfixsuch- und Vervollständigungsmechanismus auf. Dieser Mechanismus ist einerseits einfach genug, um eine effiziente Implementierung zu erlauben, andererseits hinreichend mächtig, um die Bearbeitung zusätzlicher Anfragetypen zu erlauben. Wir stellen zwei neue Datenstrukturen vor, die eingesetzt werden können, um das zu Grunde liegende Präfixsuch und Vervollstängigungsproblem zu lösen. Die erste der beiden, AutoTree genannt, hat die theoretisch wünschenswerte Eigenschaft, dass sie für nicht entartete Korpora eine Bearbeitungszeit linear in der aufsummierten Größe der Ein- und Ausgabe zulässt. Die zweite, HYB genannt, ist auf die Komprimierbarkeit der Daten ausgelegt und ist für Szenarien optimiert, in denen der Index nicht in den Hauptspeicher passt, sondern auf der Festplatte ruht. Beide schlagen den Referenzalgorithmus, der den invertierten Index benutzt, um einen Faktor von 4-10 hinsichtlich der durchschnittlichen Bearbeitungszeit. Ein direkter Vergleich zeigt, dass im Allgemeinen HYB schneller ist als AutoTree. Dank der HYB Datenstruktur kann die CompleteSearch Suchmaschine auch anspruchsvollere Anfragetypen, wie Facettensuche für Kategorieninformation, Vervollständigung zu Synonymen, Anfragen im Stile von elementaren, relationalen Datenbankanfragen und die Suche auf Ontologien, effizient bearbeiten. Für jede dieser Fähigkeiten beweisen wir die Realisierbarkeit unseres Ansatzes durch Experimente. Schließlich demonstrieren wir durch eine kleine Nutzerstudie mit Mitarbeitern des Helpdesks unseres Institutes auch den praktischen Nutzen unserer Arbeit

Synthesis and Assessment Product

This Synthesis and Assessment Product (SAP), Uses and Limitations of Observations, Data, Forecasts, and Other Projections in Decision Support for Selected Sectors and Regions. This is part of a series of 21 SAPs produced by the CCSP aimed at providing current assessments of climate change science to inform public debate, policy, and operational decisions. This SAP focuses on the use of climate observations, data, forecasts, and other projections in decision support

Analysis of tropospheric trace gas amounts from satellite and ship-based DOAS-type measurements : NO2 from biomass burning and other sources

Nitrogen oxides (NOx) play key roles in atmospheric chemistry, air pollution, and climate. While the largest fraction of these reactive gases is released by anthropogenic emission sources, a significant amount can be attributed to vegetation fires. Tropospheric nitrogen dioxide (NO2) amounts can be retrieved from ground-based, ship-based, aircraft-based, and satellite-based remote sensing measurements. The focus of this thesis is to analyze such NO2 measurements for the characterization of NOx from open biomass burning and other sources. In the first part of this thesis, satellite measurements of tropospheric NO2 from GOME-2 and OMI as well as fire radiative power (FRP) from the MODIS instruments are used to derive seasonally averaged fire emission rates (FERs) of NOx for different types of vegetation using a simple statistical approach. Monthly means of tropospheric NO2 vertical columns (TVC NO2) are analyzed for their temporal correlation with the monthly means of FRP for a multi-year period. The strongest correlation is found to be largely confined to tropical and subtropical regions, which account for more than 80% of yearly burned area, on average, globally. As atmospheric models typically require values for the amount of NOx being released as a function of time, the retrieved TVC NO2 is converted into production rates of NOx from fire (Pf). By separating the monthly means of Pf and FRP according to land cover type, FERs of NOx could be derived for different biomes and regions. The estimated FERs for the dominating types of vegetation burned are lowest for boreal forest, open shrublands, and savannas (0.25-1.03 g NOx s^-1 MW^-1) and highest for croplands and woody savannas (0.82-1.56 g NOx s^-1 MW^-1). This analysis demonstrates that the strong empirical relationship between TVC NO2 and FRP and the following simplified assumptions are a useful tool for the characterization of NOx emission rates from vegetation fires in the tropics, subtropics, and in boreal regions. As current fire emission inventories apply emission factors (EFs) of NOx for the translation of biomass burned into trace gas emissions, the satellite-derived FERs of NOx are converted into EFs of NOx. A comparison with NOx EFs found in the literature shows good agreement for some biomes (e.g. boreal forest, tropical forest, and crop residue). However, the EFs for savanna and grassland obtained from satellite measurements are lower by a factor of 2.5. This has possible implications for future work in this field, in particular because savanna and grassland is the most frequently burned biome on Earth. As recent satellite-based studies have indicated substantial spatio-temporal variations in NOx EFs for several biomes, a modified approach is used for the computation of monthly resolved FERs of NOx. In order to evaluate the impact when such seasonal changes are not included, a case study for the African continent to estimate total NOx emissions from open biomass burning is performed by applying both seasonally averaged and monthly resolved FERs of NOx. The results indicate differences between the two tested approaches of up to 90%, in particular on a monthly basis. In the second part of this thesis, ship-based MAX-DOAS measurements performed within the SHIVA campaign in November 2011 on board RV Sonne in the South China and Sulu Sea are analyzed. Spectral measurements for a total of eleven days are used to retrieve tropospheric slant column densities (SCDs) of NO2 and sulfur dioxide (SO2) in the marine environment. An improved NO2 fit including a cross section for liquid water and an empirical correction spectrum accounting for the effects of liquid water and vibrational Raman scattering and a novel SO2 fit are applied to the ship-based measurements. The conversion of SCDs into TVC NO2 is achieved using both a simple geometric approach and the Bremian advanced MAX-DOAS Retrieval Algorithm (BREAM), which is based on the optimal estimation method and accounts for atmospheric radiative transfer. The results show that the geometric approach using the 15 deg measurements is in good agreement with BREAM, revealing that measurements at 15 deg elevation angle can be used for retrieving TVC NO2 in tropical marine environments. As expected, the values of TVC NO2 are generally low ( 2 x 10^15 molec cm^-2) are observed in the morning when the RV Sonne was heading along the coast of Borneo. This is in good agreement with satellite measurements. Interestingly, elevated tropospheric SO2 amounts for measurements taken in a busy shipping lane are consistent with the time series of tropospheric NO2

Forests, carbon cycle and climate change

The results presented in this book summarize the main findings of the CARBOFOR project, which brought together 52 scientists from 14 research units to investigate the effects of future climate on the carbon cycle, the productivity and vulnerability of French forests. This book explains the current forest carbon cycle in temperate and Mediterranean climates, including the dynamics of soil carbon and the total carbon stock of French forests, based on forest inventories. It reviews and illustrates the main ground-based methods for estimating carbon stocks in tree biomass. Spatial variations in projected climate change over metropolitan France throughout the 21st century are described. The book then goes on to consider the impacts of climate change on tree phenology and forest carbon balance, evapotranspiration and production as well as their first order interaction with forest management alternatives. The impact of climate change on forest vulnerability is analysed. A similar simulation study was carried out for a range of pathogenic fungi, emphasizing the importance of both warming and precipitation changes. The consequences of climate change on the occurrence of forest fires and the forest carbon cycle in the Mediterranean zone are also considered.A valuable reference for researchers and academics, forest engineers and managers, and graduate level students in forest ecology, ecological modelling and forestry