Urban area function zoning based on user relationships in location-based social networks

This is the final version. Available on open access from IEEE via the DOI in this recordWith advanced development of Internet communication and ubiquitous computing, Social Networks are providing an important information channel for smart city construction. Therefore, analyzing Location-based Social Network is a very valuable work in achieving reasonable urban zoning. In Social Networks, a main purpose of prestige assessment is to extract influential users who are regarded as the key nodes for community detection from Onine Social Networks (OSNs). However, social relationships of users are rarely used to evaluate the popularity of physical locations and zone physical locations. In order to achieve urban area function zoning by evaluating the prestige of geographic regions based on user relationships in Location based Social Networks (LBSNs), this paper proposes a Prestige Density-Based Spatial Clustering of Applications with Noise algorithm (P-DBSCAN) by improving the existing DBSCAN algorithm. Specifically, the algorithm first calculates the centrality of users in the social network, and then converts the centrality of users into the location-centrality through the users' check-in data. After the centrality of each location is obtained, the discrete locations are clustered according to four constraints of the given radius. After clustering, the result of urban area function zoning can be achieved. Extensive experiments are conducted for demonstrating the effectiveness of our proposed algorithm in this paper. In addition, the visualization results reveal the correctness of our proposed approach.National Natural Science Foundation of ChinaEuropean Union Horizon 2020Natural Science Basic Research Plan in Shaanxi Province of ChinaFund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shaanxi ProvinceMinistry of Science and ICT (MSIT), South KoreaNational Research Foundation of Kore

Dynamic Topography Information Landscapes – An Incremental Approach to Visual Knowledge Discovery

Incrementally computed information landscapes are an effective means to visualize longitudinal changes in large document repositories. Resembling tectonic processes in the natural world, dynamic rendering reflects both long-term trends and short-term fluctuations in such repositories. To visualize the rise and decay of topics, the mapping algorithm elevates and lowers related sets of concentric contour lines. Addressing the growing number of documents to be processed by state-of-the-art knowledge discovery applications, we introduce an incremental, scalable approach for generating such landscapes. The processing pipeline includes a number of sequential tasks, from crawling, filtering and pre-processing Web content to projecting, labeling and rendering the aggregated information. Incremental processing steps are localized in the projection stage consisting of document clustering, cluster force-directed placement and fast document positioning. We evaluate the proposed framework by contrasting layout qualities of incremental versus non-incremental versions. Documents for the experiments stem from the blog sample of the Media Watch on Climate Change (www.ecoresearch.net/climate). Experimental results indicate that our incremental computation approach is capable of accurately generating dynamic information landscapes

A policy compliance detection architecture for data exchange infrastructures

Data sharing and federation can significantly increase efficiency and lower the cost of digital collaborations. It is important to convince the data owners that their outsourced data will be used in a secure and controlled manner. To achieve this goal, constructing a policy governing concrete data usage rule among all parties is essential. More importantly, we need to establish digital infrastructures that can enforce the policy. In this thesis, we investigate how to select optimal application-tailored infrastructures and enhance policy compliance capabilities. First, we introduce a component linking the policy to the infrastructure patterns. The mechanism selects digital infrastructure patterns that satisfy the collaboration request to a maximal degree by modelling and closeness identification. Second, we present a threat-analysis driven risk assessment framework. The framework quantitatively assesses the remaining risk of an application delegated to digital infrastructure. The optimal digital infrastructure for a specific data federation application is the one which can support the requested collaboration model and provides the best security guarantee. Finally, we present a distributed architecture that detects policy compliance when an algorithm executes on the data. A profile and an IDS model are built for each containerized algorithm and are distributed to endpoint execution platforms via a secure channel. Syscall traces are monitored and analysed in endpoint points platforms. The machine learning based IDS is retrained periodically to increase generalization. A sanitization algorithm is implemented to filter out malicious samples to further defend the architecture against adversarial machine learning attacks

Incremental and Scalable Computation of Dynamic Topography Information Landscapes

Dynamic topography information landscapes are capable of visualizing longitudinal changes in large document repositories. Resembling tectonic processes in the natural world, dynamic rendering reflects both long-term trends and short-term fluctuations in such repositories. To visualize the rise and decay of topics, the mapping algorithm elevates and lowers related sets of concentric contour lines. Acknowledging the growing number of documents to be processed by state-of-the-art Web intelligence applications, we present a scalable, incremental approach for generating such landscapes. The processing pipeline includes a number of sequential tasks, from crawling, filtering and pre-processing Web content to projecting, labeling and rendering the aggregated information. Processing steps central to incremental processing are found in the projection stage which consists of document clustering, cluster force-directed placement, and fast document positioning. We introduce two different positioning methods and compare them in an incremental setting using two different quality measures. The evaluation is performed on a set of approximately 5000 documents taken from the environmental blog sample of the Media Watch on Climate Change (www.ecoresearch.net/climate), a Web content aggregator about climate change and related environmental issues that serves static versions of the information landscapes presented in this paper as part of a multiple coordinated view representation

Kiteiden välinen vuorovaikutus hitsatun rakenneteräksen paikallisessa deformaatiossa

Polycrystalline body-centred cubic (BCC) steel is the most commonly used structural material in the transportation industry. In order to optimize sustainable use of steel materials in welded structures such as ships, better fundamental understanding of the factors affecting material behaviour is required in different length scales. This thesis studies the length scale interface between microstructural features and continuum scale deformation in BCC steel materials. Special attention is paid on characterization of microstructure and local plastic deformation in heterogeneous steel weld metals.  In this work, microstructural characteristics are correlated with plastic deformation response, and the fundamental deformation mechanisms are resolved using electron backscatter diffraction. The local deformation process is characterized for welded steel using instrumented indentation testing in different length scales, ranging from a fraction of one grain to tens of grains. A serial sectioning procedure is developed in order to consider the stochastics of the plastic deformation process in heterogeneous microstructures. Furthermore, advanced orientation data post-processing and misorientation analysis are utilized to identify the plastic deformation zone and formation of dislocation cells beneath hardness indentations.  The results of the thesis reveal the importance of grain size dispersion to mechanical properties, and the significance of grain interactions for the plastic deformation process in polycrystalline BCC steel. Comparison between base and weld metal revealed that the size of the plastic deformation zone is proportional to the average grain size. A transition region was defined between continuum and single crystal material behaviour, where the interaction of grains of different size controls the local plastic deformation of polycrystalline steel. The strongest influence grain interaction on hardness variation was found to take place at indentation diagonal lengths 0.1 – 2dv, when slip transmission primarily occurs between two grains.  When the size of the plastic deformation zone is considerably larger than the average grain size, spatial hardness variation decreases significantly. In this regime, hardness and strength are affected by average grain size and grain size dispersion. To consider these aspects, a modified Hall-Petch relationship is introduced utilizing the volume-weighted average grain size based on the rule of mixtures. The modified Hall-Petch relationship is validated with literature data, showing its applicability to a wide range of materials that show grain size dependent mechanical properties.Rakenneteräkset ovat yleisimmin käytetty materiaali kuljetusvälineteollisuudessa. Jotta perusmateriaalin ominaisuuksia voidaan hyödyntää parhaalla mahdollisella tavalla hitsatuissa rakenteissa kuten laivoissa, tarvitaan syvällistä tietoa materiaalin käyttäytymisestä. Tämä väitöskirja tutkii monikiteisiä teräksiä, joissa on tilakeskeinen kuutiollinen kiderakenne. Olennaista teräksen tehokkaassa hyödyntämisessä on mikrorakenteen ja lujuuden välisten tekijöiden määrittäminen.  Tässä työssä keskitytään hitsatun rakenneteräksen mikrorakenteen ja plastisen deformaation karakterisointiin. Mikrorakenne ja plastinen deformaatio mitataan elektronisuihkumikroskopian (SEM) avulla, sekä mikrorakenteen geometrisia mittoja verrataan paikallisiin materiaalin lujuusarvoihin. Lujuutta mitataan instrumentoidulla kovuuskokeella eri mittakaavoissa, kattaen deformaatioalueen koon yksittäisen kiteen murto-osasta kymmeniin kiteisiin. Kovuusmittauksista tehdään mikrorakenneanalyysiin soveltuvat poikkileikkaukset, jotta deformaatioprosessin tilastollinen vaihtelu pystytään määrittämään. Kideorientaatioanalyysiä varten kehitetään jälkikäsittely- ja analysointimenetelmät, joilla paikalliset deformaatioalueet voidaan määrittää kiteeseen muodostuvien alirakenteiden perusteella.  Työn tulokset osoittavat, että kidekokojakauma vaikuttaa huomattavasti hitsatun teräksen lujuuteen ja plastisen deformaatioalueen laajuus osoitetaan riippuvaiseksi teräksen keskimääräisestä kidekoosta. Lisäksi eri kokoisten kiteiden vuorovaikutus vaikuttaa oleellisesti deformaatioprosessiin. Kovuusmittausarvojen hajonta on suurinta kidevuorovaikutuksen takia, kun kovuusmittausjäljen diagonaalimitta on 0.1 – 2 kertaa keskimääräinen kidekoko (dv). Tämä mittakaava sijaitsee yksittäiskiteen deformaation ja kontinuumimekaniikan rajapinnassa.  Mitattu kovuuden hajonta pienenee huomattavasti, kun kovuusmittausjäljen koko on moninkertainen keskimääräiseen kidekokoon nähden. Tässä mittakaavassa lujuuteen vaikuttaa eniten keskimääräinen kidekoko ja kidekoon hajonta. Nämä tekijät voidaan ottaa huomioon lujuuden ennustamisessa käyttämällä tilavuuspainotettua keskimääräistä kidekokoa (dv). Tätä parametria käytetään modifioidussa Hall-Petch yhtälössä lujuuden ennustamiseen, ja suoritettujen validointivertailujen perusteella sen todetaan soveltuvan laaja-alaisesti kidekokoriippuvaisten mekaanisten ominaisuuksien ennustamiseen