Is blood still thicker than water? A life-course perspective on the transformation of family and friends' roles in personal networks

This thesis addresses the issue of the moving boundaries between family and friends' roles in personal networks, adopting a life-course perspective and using Switzerland as a case study. In a period of major changes in personal life happening in contemporary Western societies, understanding the organization of personal networks intertwined with the unfolding of individual life courses is of prime importance in facing new challenges with regard to social integration. The data stem from a representative national survey carried out in 2011 named Family tiMes, including 803 individuals born either in 1950-1955 or in 1970-1975. An innovative research design was adopted, combing cross-sectional ego-centered network data and retrospective longitudinal life-course data. The results show continuing boundaries between family and friends' roles and that family keeps a prominent role in personal networks despite the notable importance of friendship ties. One relationship stands out above all, that with the partner, followed quite a few steps behind by those with children. Regarding life courses, de-standardization tendencies were found in family formation and also a persistent gendering of occupational trajectories. Two kinds of life trajectories are particularly intertwined with personal networks, co-residence and partnership trajectories, both related to the unfolding of family life. In particular, transition to parenthood functions as a turning point in individuals' lives, deeply transforming their sociability. Finally, a twofold pluralization process was identified, affecting simultaneously the organization of personal networks and the unfolding of individual life courses. This thesis contributes to the literature on the sociology of family and personal life, and to fruitful interlinkage between the network approach and the life-course perspective.

jxSensor: a sensor network integration layer for JXTA

Nowadays, Wireless Sensor Networks (WSN) arealready a very important data source to obtain data about the environment. Thus, they are key to the creation of Cyber-Physical Systems (CPS). Given the popularity of P2P middlewares as ameans to efficiently process information and distribute services, being able to integrate them to WSN¿s is an interesting proposal. JXTA is a widely used P2P middleware that allows peers to easily exchange information, heavily relying on its main architectural highlight, the capability to organize peers with common interests into peer groups. However, right now, approaches to integrate WSNs to a JXTA network seldom take advantage of peer groups. For this reason, in this paper we present jxSensor, an integrationlayer for sensor motes which facilitates the deployment of CPS¿s under this architecture. This integration has been done taking into account JXTA¿s idiosyncrasies and proposing novel ideas,such as the Virtual Peer, a group of sensors that acts as a single entity within the peer group context.

Experimental evaluation of the performance of a wireless sensor network in agricultural environments

The aim of this study was to perform an experimental study to evaluate the proper operation distance between the nodes of a wireless sensor network available on the market for different agricultural crops (maize, physic nut, eucalyptus). The experimental data of the network performance offers to farmers and researchers information that might be useful to the sizing and project of the wireless sensor networks in similar situations to those studied. The evaluation showed that the separation of the nodes depends on the type of culture and it is a critical factor to ensure the feasibility of using WSN. In the configuration used, sending packets every 2 seconds, the battery life was about four days. Therefore, the autonomy may be increased with a longer interval of time between sending packets.

Music digitalization and its effects on the Finnish music industry stakeholders

For the past two decades the music digitalization has been considered the most significant phenomenon in the music industry as the physical sales have been decreasing rapidly. The advancement of the digital technology and the internet have facilitated the digitalization in the music industry and affected all stages of the music value chain, namely music creation, distribution and consumption. The newly created consumer culture has led to the establishment of novel business models such as music subscriptions and à-la-carte downloads websites and live streaming. The dynamic digital environment has presented the music industry stakeholders with the challenge to adapt to the requirements of the constantly changing modern consumers’ needs and demands. The purpose of this study was to identify how music digitalization can influence change in the Finnish music industry value chain; i.e. how digitalization affects the music industry stakeholders, their functions and inter-relatedness and how the stakeholders are able to react to the changes in the industry. The study was conducted as a qualitative research based entirely on primary data in the form of semi-structured interviews with experts from different units of the Finnish music industry value chain. Since the study offers assessment of diverse viewpoints on the value chain, it further provides an integrated picture of the Finnish music industry current situation and its competitive environment. The results suggest that the music industry is currently in a turbulent stage of experimentation with new business models and digital innovations. However, at this point it is impossible to determine which business model will be approved by the consumers in the longer run. Nevertheless, the study confirmed the claim that consumption of music in its digital form is to become dominant over the traditional physical copies sales in the nearest future. As a result the music industry is becoming more user-oriented; that is the focus is shifting from music production towards artist branding and management and visibility to the audience. Furthermore, the music industry is undergoing the process of integration with other industries such as media, social networks, internet services providers and mobile phone manufacturers in order to better fulfill the consumers’ needs. The previously underrated live music and merchandising are also increasing their significance for the revenues in the stagnant music markets. Therefore, the music industry is developing at present towards becoming an integrated entertainment industry deeply penetrating every point of modern people’s leisure activities.

Forecasting of wind speeds and directions with artificial neural networks

In this master’s thesis, wind speeds and directions were modeled with the aim of developing suitable models for hourly, daily, weekly and monthly forecasting. Artificial Neural Networks implemented in MATLAB software were used to perform the forecasts. Three main types of artificial neural network were built, namely: Feed forward neural networks, Jordan Elman neural networks and Cascade forward neural networks. Four sub models of each of these neural networks were also built, corresponding to the four forecast horizons, for both wind speeds and directions. A single neural network topology was used for each of the forecast horizons, regardless of the model type. All the models were then trained with real data of wind speeds and directions collected over a period of two years in the municipal region of Puumala in Finland. Only 70% of the data was used for training, validation and testing of the models, while the second last 15% of the data was presented to the trained models for verification. The model outputs were then compared to the last 15% of the original data, by measuring the mean square errors and sum square errors between them. Based on the results, the feed forward networks returned the lowest generalization errors for hourly, weekly and monthly forecasts of wind speeds; Jordan Elman networks returned the lowest errors when used for forecasting of daily wind speeds. Cascade forward networks gave the lowest errors when used for forecasting daily, weekly and monthly wind directions; Jordan Elman networks returned the lowest errors when used for hourly forecasting. The errors were relatively low during training of the models, but shot up upon simulation with new inputs. In addition, a combination of hyperbolic tangent transfer functions for both hidden and output layers returned better results compared to other combinations of transfer functions. In general, wind speeds were more predictable as compared to wind directions, opening up opportunities for further research into building better models for wind direction forecasting.

Strategic management of value networks: how to create value in cross-sector collaboration and partnerships

Cross-sector collaboration and partnerships have become an emerging and desired strategy in addressing huge social and environmental challenges. Despite its popularity, cross-sector collaboration management has proven to be very challenging. Even though cross-sector collaboration and partnership management have been widely studied and discussed in recent years, their effectiveness as well as their ability to create value with respect to the problems they address has remained very challenging. There is little or no evidence of their ability to create value. Regarding all these challenges, this study aims to explore how to manage cross-sector collaborations and partnerships to be able to improve their effectiveness and to create more value for all partners involved in collaboration as well as for customers. The thesis is divided into two parts. The first part comprises an overview of relevant literature (including strategic management, value networks and value creation theories), followed by presenting the results of the whole thesis and the contribution made by the study. The second part consists of six research publications, including both quantitative and qualitative studies. The chosen research strategy is triangulation, as the study includes four types of triangulation: (1) theoretical triangulation, (2) methodological triangulation, (3) data triangulation and (4) researcher triangulation. Two publications represent conceptual development, which are based on secondary data research. One publication is a quantitative study, carried out through a survey. The other three publications represent qualitative studies, based on case studies, where data was collected through interviews and workshops, with participation of managers from all three sectors: public, private and the third (nonprofit). The study consolidates the field of “strategic management of value networks,” which is proposed to be applied in the context of cross-sector collaboration and partnerships, with the aim of increasing their effectiveness and the process of value creation. Furthermore, the study proposes a first definition for the strategic management of value networks. The study also proposes and develops two strategy tools that are recommended to be used for the strategic management of value networks in cross-sector collaboration and partnerships. Taking a step forward, the study implements the strategy tools in practice, aiming to show and to demonstrate how new value can be created by using the developed strategy tools for the strategic management of value networks. This study makes four main contributions. (1) First, it brings a theoretical contribution by providing new insights and consolidating the field of strategic management of value networks, also proposing a first definition for the strategic management of value networks. (2) Second, the study makes a methodical contribution by proposing and developing two strategy tools for value networks of cross-sector collaboration: (a) value network mapping, a method that allows us to assess the current and the potential value network and (b) the Value Network Scorecard, a method of performance measurement and performance prediction in cross-sector collaboration. (3) Third, the study has managerial implications, offering new solutions and empirical evidence on how to increase the effectiveness of cross-sector collaboration and also allow managers to understand how new value can be created in cross-sector partnerships and how to get the full potential of collaboration. (4) And fourth, the study also has practical implications, allowing managers to understand how to use in practice the strategy tools developed in this study, providing discussions on the limitations regarding the proposed tools as well as general limitations involved in the study.

Sensor communication in Smart cities and regions: An efficient IoT-based remote health monitoring system

Recent advances in Information and Communication Technology (ICT), especially those related to the Internet of Things (IoT), are facilitating smart regions. Among many services that a smart region can offer, remote health monitoring is a typical application of IoT paradigm. It offers the ability to continuously monitor and collect health-related data from a person, and transmit the data to a remote entity (for example, a healthcare service provider) for further processing and knowledge extraction. An IoT-based remote health monitoring system can be beneficial in rural areas belonging to the smart region where people have limited access to regular healthcare services. The same system can be beneficial in urban areas where hospitals can be overcrowded and where it may take substantial time to avail healthcare. However, this system may generate a large amount of data. In order to realize an efficient IoT-based remote health monitoring system, it is imperative to study the network communication needs of such a system; in particular the bandwidth requirements and the volume of generated data. The thesis studies a commercial product for remote health monitoring in Skellefteå, Sweden. Based on the results obtained via the commercial product, the thesis identified the key network-related requirements of a typical remote health monitoring system in terms of real-time event update, bandwidth requirements and data generation. Furthermore, the thesis has proposed an architecture called IReHMo - an IoT-based remote health monitoring architecture. This architecture allows users to incorporate several types of IoT devices to extend the sensing capabilities of the system. Using IReHMo, several IoT communication protocols such as HTTP, MQTT and CoAP has been evaluated and compared against each other. Results showed that CoAP is the most efficient protocol to transmit small size healthcare data to the remote servers. The combination of IReHMo and CoAP significantly reduced the required bandwidth as well as the volume of generated data (up to 56 percent) compared to the commercial product. Finally, the thesis conducted a scalability analysis, to determine the feasibility of deploying the combination of IReHMo and CoAP in large numbers in regions in north Sweden.

Adaptive Real-time Anomaly-based Intrusion Detection using Data Mining and Machine Learning Techniques

Die zunehmende Vernetzung der Informations- und Kommunikationssysteme führt zu einer weiteren Erhöhung der Komplexität und damit auch zu einer weiteren Zunahme von Sicherheitslücken. Klassische Schutzmechanismen wie Firewall-Systeme und Anti-Malware-Lösungen bieten schon lange keinen Schutz mehr vor Eindringversuchen in IT-Infrastrukturen. Als ein sehr wirkungsvolles Instrument zum Schutz gegenüber Cyber-Attacken haben sich hierbei die Intrusion Detection Systeme (IDS) etabliert. Solche Systeme sammeln und analysieren Informationen von Netzwerkkomponenten und Rechnern, um ungewöhnliches Verhalten und Sicherheitsverletzungen automatisiert festzustellen. Während signatur-basierte Ansätze nur bereits bekannte Angriffsmuster detektieren können, sind anomalie-basierte IDS auch in der Lage, neue bisher unbekannte Angriffe (Zero-Day-Attacks) frühzeitig zu erkennen. Das Kernproblem von Intrusion Detection Systeme besteht jedoch in der optimalen Verarbeitung der gewaltigen Netzdaten und der Entwicklung eines in Echtzeit arbeitenden adaptiven Erkennungsmodells. Um diese Herausforderungen lösen zu können, stellt diese Dissertation ein Framework bereit, das aus zwei Hauptteilen besteht. Der erste Teil, OptiFilter genannt, verwendet ein dynamisches "Queuing Concept", um die zahlreich anfallenden Netzdaten weiter zu verarbeiten, baut fortlaufend Netzverbindungen auf, und exportiert strukturierte Input-Daten für das IDS. Den zweiten Teil stellt ein adaptiver Klassifikator dar, der ein Klassifikator-Modell basierend auf "Enhanced Growing Hierarchical Self Organizing Map" (EGHSOM), ein Modell für Netzwerk Normalzustand (NNB) und ein "Update Model" umfasst. In dem OptiFilter werden Tcpdump und SNMP traps benutzt, um die Netzwerkpakete und Hostereignisse fortlaufend zu aggregieren. Diese aggregierten Netzwerkpackete und Hostereignisse werden weiter analysiert und in Verbindungsvektoren umgewandelt. Zur Verbesserung der Erkennungsrate des adaptiven Klassifikators wird das künstliche neuronale Netz GHSOM intensiv untersucht und wesentlich weiterentwickelt. In dieser Dissertation werden unterschiedliche Ansätze vorgeschlagen und diskutiert. So wird eine classification-confidence margin threshold definiert, um die unbekannten bösartigen Verbindungen aufzudecken, die Stabilität der Wachstumstopologie durch neuartige Ansätze für die Initialisierung der Gewichtvektoren und durch die Stärkung der Winner Neuronen erhöht, und ein selbst-adaptives Verfahren eingeführt, um das Modell ständig aktualisieren zu können. Darüber hinaus besteht die Hauptaufgabe des NNB-Modells in der weiteren Untersuchung der erkannten unbekannten Verbindungen von der EGHSOM und der Überprüfung, ob sie normal sind. Jedoch, ändern sich die Netzverkehrsdaten wegen des Concept drif Phänomens ständig, was in Echtzeit zur Erzeugung nicht stationärer Netzdaten führt. Dieses Phänomen wird von dem Update-Modell besser kontrolliert. Das EGHSOM-Modell kann die neuen Anomalien effektiv erkennen und das NNB-Model passt die Änderungen in Netzdaten optimal an. Bei den experimentellen Untersuchungen hat das Framework erfolgversprechende Ergebnisse gezeigt. Im ersten Experiment wurde das Framework in Offline-Betriebsmodus evaluiert. Der OptiFilter wurde mit offline-, synthetischen- und realistischen Daten ausgewertet. Der adaptive Klassifikator wurde mit dem 10-Fold Cross Validation Verfahren evaluiert, um dessen Genauigkeit abzuschätzen. Im zweiten Experiment wurde das Framework auf einer 1 bis 10 GB Netzwerkstrecke installiert und im Online-Betriebsmodus in Echtzeit ausgewertet. Der OptiFilter hat erfolgreich die gewaltige Menge von Netzdaten in die strukturierten Verbindungsvektoren umgewandelt und der adaptive Klassifikator hat sie präzise klassifiziert. Die Vergleichsstudie zwischen dem entwickelten Framework und anderen bekannten IDS-Ansätzen zeigt, dass der vorgeschlagene IDSFramework alle anderen Ansätze übertrifft. Dies lässt sich auf folgende Kernpunkte zurückführen: Bearbeitung der gesammelten Netzdaten, Erreichung der besten Performanz (wie die Gesamtgenauigkeit), Detektieren unbekannter Verbindungen und Entwicklung des in Echtzeit arbeitenden Erkennungsmodells von Eindringversuchen.

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 5: Affiliation Networks

How can we analyze and understand affiliation networks? In this class, we will discuss properties of affiliation networks and we will investigate the use of Galois lattices for the exploration of structural patterns in bi-partite graphs. Optional : L.C. Freeman and D.R. White. Using Galois Lattices to Represent Network Data. Sociological Methodology, (23):127--146, (1993)

Chess Endgames and Neural Networks

The existence of endgame databases challenges us to extract higher-grade information and knowledge from their basic data content. Chess players, for example, would like simple and usable endgame theories if such holy grail exists: endgame experts would like to provide such insights and be inspired by computers to do so. Here, we investigate the use of artificial neural networks (NNs) to mine these databases and we report on a first use of NNs on KPK. The results encourage us to suggest further work on chess applications of neural networks and other data-mining techniques.

A data collection case study supporting requirements oriented prediction and management in software developments

Consider the statement "this project should cost X and has risk of Y". Such statements are used daily in industry as the basis for making decisions. The work reported here is part of a study aimed at providing a rational and pragmatic basis for such statements. Of particular interest are predictions made in the requirements and early phases of projects. A preliminary model has been constructed using Bayesian Belief Networks and in support of this, a programme to collect and study data during the execution of various software development projects commenced in May 2002. The data collection programme is undertaken under the constraints of a commercial industrial regime of multiple concurrent small to medium scale software development projects. Guided by pragmatism, the work is predicated on the use of data that can be collected readily by project managers; including expert judgements, effort, elapsed times and metrics collected within each project.

Adaptive modelling, estimation and fusion from data: a neurofuzzy approach

In a world of almost permanent and rapidly increasing electronic data availability, techniques of filtering, compressing, and interpreting this data to transform it into valuable and easily comprehensible information is of utmost importance. One key topic in this area is the capability to deduce future system behavior from a given data input. This book brings together for the first time the complete theory of data-based neurofuzzy modelling and the linguistic attributes of fuzzy logic in a single cohesive mathematical framework. After introducing the basic theory of data-based modelling, new concepts including extended additive and multiplicative submodels are developed and their extensions to state estimation and data fusion are derived. All these algorithms are illustrated with benchmark and real-life examples to demonstrate their efficiency. Chris Harris and his group have carried out pioneering work which has tied together the fields of neural networks and linguistic rule-based algortihms. This book is aimed at researchers and scientists in time series modeling, empirical data modeling, knowledge discovery, data mining, and data fusion.

Image fusion based on median filters and SOFM neural networks: a three-step scheme

This paper presents a new image data fusion scheme by combining median filtering with self-organizing feature map (SOFM) neural networks. The scheme consists of three steps: (1) pre-processing of the images, where weighted median filtering removes part of the noise components corrupting the image, (2) pixel clustering for each image using self-organizing feature map neural networks, and (3) fusion of the images obtained in Step (2), which suppresses the residual noise components and thus further improves the image quality. It proves that such a three-step combination offers an impressive effectiveness and performance improvement, which is confirmed by simulations involving three image sensors (each of which has a different noise structure).

Fault tolerant decentralised K-Means clustering for asynchronous large-scale networks

The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks, such as massively parallel processors and clusters of workstations. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered useless by a single communication failure or high latency in communication paths. The lack of scalable and fault tolerant global communication and synchronisation methods in large-scale systems has hindered the adoption of the K-Means algorithm for applications in large networked systems such as wireless sensor networks, peer-to-peer systems and mobile ad hoc networks. This work proposes a fully distributed K-Means algorithm (EpidemicK-Means) which does not require global communication and is intrinsically fault tolerant. The proposed distributed K-Means algorithm provides a clustering solution which can approximate the solution of an ideal centralised algorithm over the aggregated data as closely as desired. A comparative performance analysis is carried out against the state of the art sampling methods and shows that the proposed method overcomes the limitations of the sampling-based approaches for skewed clusters distributions. The experimental analysis confirms that the proposed algorithm is very accurate and fault tolerant under unreliable network conditions (message loss and node failures) and is suitable for asynchronous networks of very large and extreme scale.

Complex-valued b-spline neural networks for modelling and inverse of wiener systems

Communication signal processing applications often involve complex-valued (CV) functional representations for signals and systems. CV artificial neural networks have been studied theoretically and applied widely in nonlinear signal and data processing [1–11]. Note that most artificial neural networks cannot be automatically extended from the real-valued (RV) domain to the CV domain because the resulting model would in general violate Cauchy-Riemann conditions, and this means that the training algorithms become unusable. A number of analytic functions were introduced for the fully CV multilayer perceptrons (MLP) [4]. A fully CV radial basis function (RBF) nework was introduced in [8] for regression and classification applications. Alternatively, the problem can be avoided by using two RV artificial neural networks, one processing the real part and the other processing the imaginary part of the CV signal/system. A even more challenging problem is the inverse of a CV