Application Platforms for the Internet of Things: Theory, Architecture, Protocols, Data Formats, and Privacy

The Internet of Things (IoT) is the next industrial revolution: we will interact naturally with real and virtual devices as a key part of our daily life. This technology shift is expected to be greater than the Web and Mobile combined. As extremely different technologies are needed to build connected devices, the Internet of Things field is a junction between electronics, telecommunications and software engineering. Internet of Things application development happens in silos, often using proprietary and closed communication protocols. There is the common belief that only if we can solve the interoperability problem we can have a real Internet of Things. After a deep analysis of the IoT protocols, we identified a set of primitives for IoT applications. We argue that each IoT protocol can be expressed in term of those primitives, thus solving the interoperability problem at the application protocol level. Moreover, the primitives are network and transport independent and make no assumption in that regard. This dissertation presents our implementation of an IoT platform: the Ponte project. Privacy issues follows the rise of the Internet of Things: it is clear that the IoT must ensure resilience to attacks, data authentication, access control and client privacy. We argue that it is not possible to solve the privacy issue without solving the interoperability problem: enforcing privacy rules implies the need to limit and filter the data delivery process. However, filtering data require knowledge of how the format and the semantics of the data: after an analysis of the possible data formats and representations for the IoT, we identify JSON-LD and the Semantic Web as the best solution for IoT applications. Then, this dissertation present our approach to increase the throughput of filtering semantic data by a factor of ten.

Multidimensional item response theory models with general and specific latent traits for ordinal data

The aim of the thesis is to propose a Bayesian estimation through Markov chain Monte Carlo of multidimensional item response theory models for graded responses with complex structures and correlated traits. In particular, this work focuses on the multiunidimensional and the additive underlying latent structures, considering that the first one is widely used and represents a classical approach in multidimensional item response analysis, while the second one is able to reflect the complexity of real interactions between items and respondents. A simulation study is conducted to evaluate the parameter recovery for the proposed models under different conditions (sample size, test and subtest length, number of response categories, and correlation structure). The results show that the parameter recovery is particularly sensitive to the sample size, due to the model complexity and the high number of parameters to be estimated. For a sufficiently large sample size the parameters of the multiunidimensional and additive graded response models are well reproduced. The results are also affected by the trade-off between the number of items constituting the test and the number of item categories. An application of the proposed models on response data collected to investigate Romagna and San Marino residents' perceptions and attitudes towards the tourism industry is also presented.

Essays on the Axiomatic Theory of Matching

This dissertation mimics the Turkish college admission procedure. It started with the purpose to reduce the inefficiencies in Turkish market. For this purpose, we propose a mechanism under a new market structure; as we prefer to call, semi-centralization. In chapter 1, we give a brief summary of Matching Theory. We present the first examples in Matching history with the most general papers and mechanisms. In chapter 2, we propose our mechanism. In real life application, that is in Turkish university placements, the mechanism reduces the inefficiencies of the current system. The success of the mechanism depends on the preference profile. It is easy to show that under complete information the mechanism implements the full set of stable matchings for a given profile. In chapter 3, we refine our basic mechanism. The modification on the mechanism has a crucial effect on the results. The new mechanism is, as we call, a middle mechanism. In one of the subdomain, this mechanism coincides with the original basic mechanism. But, in the other partition, it gives the same results with Gale and Shapley's algorithm. In chapter 4, we apply our basic mechanism to well known Roommate Problem. Since the roommate problem is in one-sided game patern, firstly we propose an auxiliary function to convert the game semi centralized two-sided game, because our basic mechanism is designed for this framework. We show that this process is succesful in finding a stable matching in the existence of stability. We also show that our mechanism easily and simply tells us if a profile lacks of stability by using purified orderings. Finally, we show a method to find all the stable matching in the existence of multi stability. The method is simply to run the mechanism for all of the top agents in the social preference.

Statistical mechanics formalism and methods for the analysis of real networks

This thesis provides a thoroughly theoretical background in network theory and shows novel applications to real problems and data. In the first chapter a general introduction to network ensembles is given, and the relations with “standard” equilibrium statistical mechanics are described. Moreover, an entropy measure is considered to analyze statistical properties of the integrated PPI-signalling-mRNA expression networks in different cases. In the second chapter multilayer networks are introduced to evaluate and quantify the correlations between real interdependent networks. Multiplex networks describing citation-collaboration interactions and patterns in colorectal cancer are presented. The last chapter is completely dedicated to control theory and its relation with network theory. We characterise how the structural controllability of a network is affected by the fraction of low in-degree and low out-degree nodes. Finally, we present a novel approach to the controllability of multiplex networks

Perturbation theory for spectral subspaces

In der vorliegenden Arbeit wird die Variation abgeschlossener Unterräume eines Hilbertraumes untersucht, die mit isolierten Komponenten der Spektren von selbstadjungierten Operatoren unter beschränkten additiven Störungen assoziiert sind. Von besonderem Interesse ist hierbei die am wenigsten restriktive Bedingung an die Norm der Störung, die sicherstellt, dass die Differenz der zugehörigen orthogonalen Projektionen eine strikte Normkontraktion darstellt. Es wird ein Überblick über die bisher erzielten Resultate gegeben. Basierend auf einem Iterationsansatz wird eine allgemeine Schranke an die Variation der Unterräume für Störungen erzielt, die glatt von einem reellen Parameter abhängen. Durch Einführung eines Kopplungsparameters wird das Ergebnis auf den Fall additiver Störungen angewendet. Auf diese Weise werden zuvor bekannte Ergebnisse verbessert. Im Falle von additiven Störungen werden die Schranken an die Variation der Unterräume durch ein Optimierungsverfahren für die Stützstellen im Iterationsansatz weiter verschärft. Die zugehörigen Ergebnisse sind die besten, die bis zum jetzigen Zeitpunkt erzielt wurden.

Validation of prognostic factors and survival of patients with multiple myeloma in a real-life autologous stem cell transplantation setting: a Swiss single centre experience

High-dose chemotherapy with subsequent autologous stem cell transplantation (ASCT) is an important treatment option in younger patients with multiple myeloma (MM). We analysed the outcome of patients treated at our institution outside the clinical trials framework and tried to identify risk factors prognostic for survival.

Exercise training in heart failure: from theory to practice. A consensus document of the Heart Failure Association and the European Association for Cardiovascular Prevention and Rehabilitation

The European Society of Cardiology heart failure guidelines firmly recommend regular physical activity and structured exercise training (ET), but this recommendation is still poorly implemented in daily clinical practice outside specialized centres and in the real world of heart failure clinics. In reality, exercise intolerance can be successfully tackled by applying ET. We need to encourage the mindset that breathlessness may be evidence of signalling between the periphery and central haemodynamic performance and regular physical activity may ultimately bring about favourable changes in myocardial function, symptoms, functional capacity, and increased hospitalization-free life span and probably survival. In this position paper, we provide practical advice for the application of exercise in heart failure and how to overcome traditional barriers, based on the current scientific and clinical knowledge supporting the beneficial effect of this intervention.

Dynamics of Spatial Game Theory Networks with Novel Modifications

Introduction: Advances in biotechnology have shed light on many biological processes. In biological networks, nodes are used to represent the function of individual entities within a system and have historically been studied in isolation. Network structure adds edges that enable communication between nodes. An emerging fieldis to combine node function and network structure to yield network function. One of the most complex networks known in biology is the neural network within the brain. Modeling neural function will require an understanding of networks, dynamics, andneurophysiology. It is with this work that modeling techniques will be developed to work at this complex intersection. Methods: Spatial game theory was developed by Nowak in the context of modeling evolutionary dynamics, or the way in which species evolve over time. Spatial game theory offers a two dimensional view of analyzingthe state of neighbors and updating based on the surroundings. Our work builds upon this foundation by studying evolutionary game theory networks with respect to neural networks. This novel concept is that neurons may adopt a particular strategy that will allow propagation of information. The strategy may therefore act as the mechanism for gating. Furthermore, the strategy of a neuron, as in a real brain, isimpacted by the strategy of its neighbors. The techniques of spatial game theory already established by Nowak are repeated to explain two basic cases and validate the implementation of code. Two novel modifications are introduced in Chapters 3 and 4 that build on this network and may reflect neural networks. Results: The introduction of two novel modifications, mutation and rewiring, in large parametricstudies resulted in dynamics that had an intermediate amount of nodes firing at any given time. Further, even small mutation rates result in different dynamics more representative of the ideal state hypothesized. Conclusions: In both modificationsto Nowak's model, the results demonstrate the network does not become locked into a particular global state of passing all information or blocking all information. It is hypothesized that normal brain function occurs within this intermediate range and that a number of diseases are the result of moving outside of this range.

Applied Graph Theory in Computer Vision and Pattern Recognition

This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to low-level processing of digital images such as a new method for partitioning a given image into a hierarchy of homogeneous areas using graph pyramids, or a study of the relationship between graph theory and digital topology. Part II presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, including a survey of graph based methodologies for pattern recognition and computer vision, a presentation of a series of computationally efficient algorithms for testing graph isomorphism and related graph matching tasks in pattern recognition and a new graph distance measure to be used for solving graph matching problems. Finally, Part III provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks. It includes a critical review of the main graph-based and structural methods for fingerprint classification, a new method to visualize time series of graphs, and potential applications in computer network monitoring and abnormal event detection.

Using a Prediction and Option Generation Paradigm to Understand Decision Making

In many complex and dynamic domains, the ability to generate and then select the appropriate course of action is based on the decision maker's "reading" of the situation--in other words, their ability to assess the situation and predict how it will evolve over the next few seconds. Current theories regarding option generation during the situation assessment and response phases of decision making offer contrasting views on the cognitive mechanisms that support superior performance. The Recognition-Primed Decision-making model (RPD; Klein, 1989) and Take-The-First heuristic (TTF; Johnson & Raab, 2003) suggest that superior decisions are made by generating few options, and then selecting the first option as the final one. Long-Term Working Memory theory (LTWM; Ericsson & Kintsch, 1995), on the other hand, posits that skilled decision makers construct rich, detailed situation models, and that as a result, skilled performers should have the ability to generate more of the available task-relevant options. The main goal of this dissertation was to use these theories about option generation as a way to further the understanding of how police officers anticipate a perpetrator's actions, and make decisions about how to respond, during dynamic law enforcement situations. An additional goal was to gather information that can be used, in the future, to design training based on the anticipation skills, decision strategies, and processes of experienced officers. Two studies were conducted to achieve these goals. Study 1 identified video-based law enforcement scenarios that could be used to discriminate between experienced and less-experienced police officers, in terms of their ability to anticipate the outcome. The discriminating scenarios were used as the stimuli in Study 2; 23 experienced and 26 less-experienced police officers observed temporally-occluded versions of the scenarios, and then completed assessment and response option-generation tasks. The results provided mixed support for the nature of option generation in these situations. Consistent with RPD and TTF, participants typically selected the first-generated option as their final one, and did so during both the assessment and response phases of decision making. Consistent with LTWM theory, participants--regardless of experience level--generated more task-relevant assessment options than task-irrelevant options. However, an expected interaction between experience level and option-relevance was not observed. Collectively, the two studies provide a deeper understanding of how police officers make decisions in dynamic situations. The methods developed and employed in the studies can be used to investigate anticipation and decision making in other critical domains (e.g., nursing, military). The results are discussed in relation to how they can inform future studies of option-generation performance, and how they could be applied to develop training for law enforcement officers.

A Restriction-Centered Theory of Reasoning and Computation to materialize a Web 3.0

Researchers suggest that personalization on the Semantic Web adds up to a Web 3.0 eventually. In this Web, personalized agents process and thus generate the biggest share of information rather than humans. In the sense of emergent semantics, which supplements traditional formal semantics of the Semantic Web, this is well conceivable. An emergent Semantic Web underlying fuzzy grassroots ontology can be accomplished through inducing knowledge from users' common parlance in mutual Web 2.0 interactions [1]. These ontologies can also be matched against existing Semantic Web ontologies, to create comprehensive top-level ontologies. On the Web, if augmented with information in the form of restrictions andassociated reliability (Z-numbers) [2], this collection of fuzzy ontologies constitutes an important basis for an implementation of Zadeh's restriction-centered theory of reasoning and computation (RRC) [3]. By considering real world's fuzziness, RRC differs from traditional approaches because it can handle restrictions described in natural language. A restriction is an answer to a question of the value of a variable such as the duration of an appointment. In addition to mathematically well-defined answers, RRC can likewise deal with unprecisiated answers as "about one hour." Inspired by mental functions, it constitutes an important basis to leverage present-day Web efforts to a natural Web 3.0. Based on natural language information, RRC may be accomplished with Z-number calculation to achieve a personalized Web reasoning and computation. Finally, through Web agents' understanding of natural language, they can react to humans more intuitively and thus generate and process information.

Application of Imagery Theory to Sport Psychology: Some Preliminary Findings

Bioinformational theory has been proposed by Lang (1979a), who suggests that mental images can be understood as products of the brain's information processing capacity. Imagery involves activation of a network of propositionally coded information stored in long-term memory. Propositions concerning physiological and behavioral responses provide a prototype for overt behavior. Processing of response information is associated with somatovisceral arousal. The theory has implications for imagery rehearsal in sport psychology and can account for a variety of findings in the mental practice literature. Hypotheses drawn from bioinformational theory were tested. College athletes imagined four scenes during which their heart rates were recorded. Subjects tended to show increases in heart rate when imagining scenes with which they had personal experience and which would involve cardiovascular activation if experienced in real life. Nonsignificant heart rate changes were found when the scene involved activation but was one with which subjects did not have personal experience.

From Similes to Allegory: The Deconstruction of Chariot Imagery in Early Buddhist Texts, Analyzed with Cognitive Metaphor Theory

The presentation will start by unfolding the various layers of chariot imagery in early Indian sources, namely, chariots as vehicles of gods such as the sun (sūrya), i.e. as symbol of cosmic stability; chariots as symbols of royal power and social prestige e.g. of Brahmins; and, finally, chariots as metaphors for the “person”, the “mind” and the “way to liberation” (e.g., Kaṭ.-Up. III.3; Maitr.-Up. II. 6). In Buddhist and non-Buddhist sources, chariots are in certain aspects used as a metaphor for the (old) human body (e.g., Caraka-S., Vi.3.37-38; D II.100; D II.107); apart from that, there is, of course, mention of the “real” use of chariots in sports, cults, journey, and combat. The most prominent example of the Buddhist use of chariot imagery is its application as a model for the person (S I.134 f.; Milindapañha, ed. Trenckner, 26), i.e., for highlighting the “non-substantial self”. There are, however, other significant examples of the usage of chariot imagery in early Buddhist texts. Of special interest are those cases in which chariot metaphors were applied in order to explain how the ‘self’ may proceed on the way to salvation – with ‘mindfulness’ or the ‘self’ as charioteer, with ‘wisdom’ and ‘confidence’ as horses etc. (e.g. S I. 33; S V.7; Dhp 94; or the Nārada-Jātaka, No. 545, verses 181-190). One might be tempted to say that these instances reaffirm the traditional soteriology of a substantial “progressing soul”. Taking conceptual metaphor analysis as a tool, I will, in contrast, argue that there is a special Buddhist use of this metaphor. Indeed, at first sight, it seems to presuppose a non-Buddhist understanding (the “self” as charioteer; the chariot as vehicle to liberation, etc.). Yet, it will be argued that in these cases the chariot imagery is no longer fully “functional”. The Buddhist usage may, therefore, best be described as a final allegorical phase of the chariot-imagery, which results in a thorough deconstruction of the “chariot” itself.

Evidence from the real world: N-15 natural abundances reveal enhanced nitrogen use at high plant diversity in Central European grasslands

Complementarity that leads to more efficient resource use is presumed to be a key mechanism explaining positive biodiversity–productivity relationships but has been described solely for experimental set-ups with controlled environmental settings or for very short gradients of abiotic conditions, land-use intensity and biodiversity. Therefore, we analysed plant diversity effects on nitrogen dynamics across a broad range of Central European grasslands. The 15N natural abundance in soil and plant biomass reflects the net effect of processes affecting ecosystem N dynamics. This includes the mechanism of complementary resource utilization that causes a decrease in the 15N isotopic signal. We measured plant species richness, natural abundance of 15N in soil and plants, above-ground biomass of the community and three single species (an herb, grass and legume) and a variety of additional environmental variables in 150 grassland plots in three regions of Germany. To explore the drivers of the nitrogen dynamics, we performed several analyses of covariance treating the 15N isotopic signals as a function of plant diversity and a large set of covariates. Increasing plant diversity was consistently linked to decreased δ15N isotopic signals in soil, above-ground community biomass and the three single species. Even after accounting for multiple covariates, plant diversity remained the strongest predictor of δ15N isotopic signals suggesting that higher plant diversity leads to a more closed nitrogen cycle due to more efficient nitrogen use. Factors linked to increased δ15N values included the amount of nitrogen taken up, soil moisture and land-use intensity (particularly fertilization), all indicators of the openness of the nitrogen cycle due to enhanced N-turnover and subsequent losses. Study region was significantly related to the δ15N isotopic signals indicating that regional peculiarities such as former intensive land use could strongly affect nitrogen dynamics. Synthesis. Our results provide strong evidence that the mechanism of complementary resource utilization operates in real-world grasslands where multiple external factors affect nitrogen dynamics. Although single species may differ in effect size, actively increasing total plant diversity in grasslands could be an option to more effectively use nitrogen resources and to reduce the negative environmental impacts of nitrogen losses.

Outcome study of real-time MR-guided cervical periradicular injection therapy in an open 1.0 Tesla MRI system

PURPOSE To evaluate the accuracy, safety, and efficacy of cervical nerve root injection therapy using magnetic resonance guidance in an open 1.0 T MRI system. METHODS Between September 2009 and April 2012, a total of 21 patients (9 men, 12 women; mean age 47.1 ± 11.1 years) underwent MR-guided cervical periradicular injection for cervical radicular pain in an open 1.0 T system. An interactive proton density-weighted turbo spin echo (PDw TSE) sequence was used for real-time guidance of the MR-compatible 20-gauge injection needle. Clinical outcome was evaluated on a verbal numeric rating scale (VNRS) before injection therapy (baseline) and at 1 week and 1, 3, and 6 months during follow-up. RESULTS All procedures were technically successful and there were no major complications. The mean preinterventional VNRS score was 7.42 and exhibited a statistically significant decrease (P < 0.001) at all follow-up time points: 3.86 ± 1.53 at 1 week, 3.21 ± 2.19 at 1 month, 2.58 ± 2.54 at 3 months, and 2.76 ± 2.63 at 6 months. At 6 months, 14.3 % of the patients reported complete resolution of radicular pain and 38.1 % each had either significant (4-8 VNRS score points) or mild (1-3 VNRS score points) relief of pain; 9.5 % experienced no pain relief. CONCLUSION Magnetic resonance fluoroscopy-guided periradicular cervical spine injection is an accurate, safe, and efficacious treatment option for patients with cervical radicular pain. The technique may be a promising alternative to fluoroscopy- or CT-guided injections of the cervical spine, especially in young patients and in patients requiring repeat injections.