Automating the moderation process in GEO using trust metrics

Distributed knowledge bases offer novel and fascinating ways to build, integrate and process knowledge. In these distributed knowledge bases, many users collaborate and contribute content. Verification and validation of the user\u27s contributions become imperative for the success of these knowledge bases, especially if it is a scientific knowledge base. However, manually verifying and moderating the contributions become a bottleneck for an up-to-date system. In this thesis, we propose an algorithm to automate the moderation process and we implement the algorithm in an open scientific knowledge base application called Global Energy Observatory (GEO). Using Trust Metrics, user\u27s contributions can be automatically accepted without waiting for the moderators to verify and validate them. This provides users with a smooth, hassle free experience when they contribute data. We also provide empirical analysis to substantiate our algorithm. An open information exchange architecture, that makes use of Semantic Web formats is also presented

The OpenModelica integrated environment for modeling, simulation, and model-based development

OpenModelica is a unique large-scale integrated open-source Modelica- and FMI-based modeling, simulation, optimization, model-based analysis and development environment. Moreover, the OpenModelica environment provides a number of facilities such as debugging; optimization; visualization and 3D animation; web-based model editing and simulation; scripting from Modelica, Python, Julia, and Matlab; efficient simulation and co-simulation of FMI-based models; compilation for embedded systems; Modelica- UML integration; requirement verification; and generation of parallel code for multi-core architectures. The environment is based on the equation-based object-oriented Modelica language and currently uses the MetaModelica extended version of Modelica for its model compiler implementation. This overview paper gives an up-to-date description of the capabilities of the system, short overviews of used open source symbolic and numeric algorithms with pointers to published literature, tool integration aspects, some lessons learned, and the main vision behind its development.Fil: Fritzson, Peter. Linköping University; SueciaFil: Pop, Adrian. Linköping University; SueciaFil: Abdelhak, Karim. Fachhochschule Bielefeld; AlemaniaFil: Asghar, Adeel. Linköping University; SueciaFil: Bachmann, Bernhard. Fachhochschule Bielefeld; AlemaniaFil: Braun, Willi. Fachhochschule Bielefeld; AlemaniaFil: Bouskela, Daniel. Electricité de France; FranciaFil: Braun, Robert. Linköping University; SueciaFil: Buffoni, Lena. Linköping University; SueciaFil: Casella, Francesco. Politecnico di Milano; ItaliaFil: Castro, Rodrigo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Franke, Rüdiger. Abb Group; AlemaniaFil: Fritzson, Dag. Linköping University; SueciaFil: Gebremedhin, Mahder. Linköping University; SueciaFil: Heuermann, Andreas. Linköping University; SueciaFil: Lie, Bernt. University of South-Eastern Norway; NoruegaFil: Mengist, Alachew. Linköping University; SueciaFil: Mikelsons, Lars. Linköping University; SueciaFil: Moudgalya, Kannan. Indian Institute Of Technology Bombay; IndiaFil: Ochel, Lennart. Linköping University; SueciaFil: Palanisamy, Arunkumar. Linköping University; SueciaFil: Ruge, Vitalij. Fachhochschule Bielefeld; AlemaniaFil: Schamai, Wladimir. Danfoss Power Solutions GmbH & Co; AlemaniaFil: Sjolund, Martin. Linköping University; SueciaFil: Thiele, Bernhard. Linköping University; SueciaFil: Tinnerholm, John. Linköping University; SueciaFil: Ostlund, Per. Linköping University; Sueci

Meta-performance evaluation of sustainability indicators

There are many different kinds of frameworks for evaluating environmental and sustainability performance at the organizational level (profit or not-for-profit, private or public), sectoral level (e.g. industry, transport, agriculture and tourism), and local, regional or country levels. Despite the diversity of methods and tools to measure sustainable development, indicators are one of the approaches most used. However, these tools do not usually include evaluation of the performance measurement instrument itself. The main objective of this research is to develop a conceptual framework to design and assess the effectiveness of the sustainability indicators themselves. To put the proposed tool into practice, a set of key good-practice factors and meta-performance evaluation indicators is proposed for adoption in a national case study—the national sustainable development indicators system, SIDS Portugal, and the usefulness of this methodology is demonstrated. This approach aims to evaluate how appropriate a set of sustainability indicators is and allow an evaluation of overall performancemonitoring activities and results. Stakeholder involvement is an essential component of the proposed framework. The tool developed could support continuous improvement in the performance of ongoing sustainability indicator initiatives, allowing greater guidance, objectivity and transparency in sustainability assessment processes.peerreviewe

State modelling of the land mobilepropagation channel for dual-satellite systems

The quality of service of mobile satellite reception can be improved by using multi-satellite diversity (angle diversity). The recently finalised MiLADY project targeted therefore on the evaluation and modelling of the multi-satellite propagation channel for land mobile users with focus on broadcasting applications. The narrowband model combines the parameters from two measurement campaigns: In the U.S. the power levels of the Satellite Digital Audio Radio Services were recorded with a high sample rate to analyse fast and slow fading effects in great detail. In a complementary campaign signals of Global Navigation Satellite Systems (GNSS) were analysed to obtain information about the slow fading correlation for almost any satellite constellation. The new channel model can be used to generate time series for various satellite constellations in different environments. This article focuses on realistic state sequence modelling for angle diversity, confining on two satellites. For this purpose, different state modelling methods providing a joint generation of the states ‘good good’, ‘good bad’, ‘bad good’ and ‘bad bad’ are compared. Measurements and re-simulated data are analysed for various elevation combinations and azimuth separations in terms of the state probabilities, state duration statistics, and correlation coefficients. The finally proposed state model is based on semi-Markov chains assuming a log-normal state duration distribution

Iterchanging Discrete Event Simulationprocess Interaction Modelsusing The Web Ontology Language - Owl

Discrete event simulation development requires significant investments in time and resources. Descriptions of discrete event simulation models are associated with world views, including the process interaction orientation. Historically, these models have been encoded using high-level programming languages or special purpose, typically vendor-specific, simulation languages. These approaches complicate simulation model reuse and interchange. The current document-centric World Wide Web is evolving into a Semantic Web that communicates information using ontologies. The Web Ontology Language OWL, was used to encode a Process Interaction Modeling Ontology for Discrete Event Simulations (PIMODES). The PIMODES ontology was developed using ontology engineering processes. Software was developed to demonstrate the feasibility of interchanging models from commercial simulation packages using PIMODES as an intermediate representation. The purpose of PIMODES is to provide a vendor-neutral open representation to support model interchange. Model interchange enables reuse and provides an opportunity to improve simulation quality, reduce development costs, and reduce development times

Energy-Aware System-Level Design of Cyber-Physical Systems

Cyber-Physical Systems (CPSs) are heterogeneous systems in which one or several computational cores interact with the physical environment. This interaction is typically performed through electromechanical elements such as sensors and actuators. Many CPSs operate as part of a network and some of them present a constrained energy budget (for example, they are battery powered). Examples of energy constrained CPSs could be a mobile robot, the nodes that compose a Body Area Network or a pacemaker. The heterogeneity present in the composition of CPSs together with the constrained energy availability makes these systems challenging to design. A way to tackle both complexity and costs is the application of abstract modelling and simulation. This thesis proposed the application of modelling at the system level, taking energy consumption in the different kinds of subsystems into consideration. By adopting this cross disciplinary approach to energy consumption it is possible to decrease it effectively. The results of this thesis are a number of modelling guidelines and tool improvements to support this kind of holistic analysis, covering energy consumption in electromechanical, computation and communication subsystems. From a methodological point of view these have been framed within a V-lifecycle. Finally, this approach has been demonstrated on two case studies from the medical domain enabling the exploration of alternative systems architectures and producing energy consumption estimates to conduct trade-off analysis

Modeling toolkit for comparing AC vs. DC electrical distribution efficiency in buildings, A

2021 Summer.Includes bibliographical references.An increasing proportion of electrical devices in residential and commercial buildings operate from direct current (DC) power sources. In addition, distributed power generation systems such as solar photovoltaic (PV) and energy storage natively produce DC power. However, traditional power distribution is based on an alternating current (AC) model. Performing the necessary conversions between AC and DC power to make DC devices compatible with AC distribution results in energy losses. For these reasons, DC distribution may offer energy efficiency advantages in comparison to AC distribution. However, reasonably fast computation and comparison of electrical efficiencies of AC-only, DC-only, and hybrid AC/DC distributions systems is challenging because DC devices are typically (nonlinear) power-electronic converters that produce harmonic content. While detailed time-domain modeling can be used to simulate these harmonics, it is not computationally efficient or practical for many building designers. To address this need, this research describes a toolkit for computation of harmonic spectra and energy efficiency in mixed AC and DC electrical distribution systems, using a Harmonic Power Flow (HPF) methodology. The toolkit includes a library of two-port linear and nonlinear device models which can be used to construct and simulate an electrical distribution system. This dissertation includes a description of the mathematical theory and framework underlying the toolkit, development and fitting of linear and nonlinear device models, software implementation in Modelica, verification of the toolkit with laboratory measurements, and discussion of ongoing and future work to employ the toolkit to a variety of building designs

Context-aware Security for Vehicles and Fleets: A Survey

Vehicles are becoming increasingly intelligent and connected. Interfaces for communication with the vehicle, such as WiFi and 5G, enable seamless integration into the user’s life, but also cyber attacks on the vehicle. Therefore, research is working on in-vehicle countermeasures such as authentication, access controls, or intrusion detection. Recently, legal regulations have also become effective that require automobile manufacturers to set up a monitoring system for fleet-wide security analysis. The growing amount of software, networking, and the automation of driving create new challenges for security. Context-awareness, situational understanding, adaptive security, and threat intelligence are necessary to cope with these ever-increasing risks. In-vehicle security should be adaptive to secure the car in an infinite number of (driving) situations. For fleet-wide analysis and alert triage, knowledge and understanding of the circumstances are required. Context-awareness, nonetheless, has been sparsely considered in the field of vehicle security. This work aims to be a precursor to context-aware, adaptive and intelligent security for vehicles and fleets. To this end, we provide a comprehensive literature review that analyzes the vehicular as well as related domains. Our survey is mainly characterized by the detailed analysis of the context information that is relevant for vehicle security in the future

Evolutionary Model Discovery: Automating Causal Inference for Generative Models of Human Social Behavior

The desire to understand the causes of complex societal phenomena is fundamental to the social sciences. Society, at a macro-scale has many measurable characteristics in the form of statistical distributions and aggregate measures; data which is increasingly abundant with the proliferation of online social media, mobile devices, and the internet of things. However, the decision-making processes and limits of the individuals who interact to generate these statistical patterns are often difficult to unravel. Furthermore, multiple causal factors often interact to determine the outcome of a particular behavior. Quantifying the importance of these causal factors and their interactions, which make up a particular decision-making process, towards a societal outcome of interest helps extract explanations that provide a deeper understanding of social behavior. Holistic, generative modeling techniques, in particular agent-based modeling, are able to \u27grow\u27 artificial societies that replicate emergent patterns seen in the real world. Driving the autonomous agents of these models are rules, generalized hypotheses of human behavior, which upon validation against real-world data, help assemble theories of human behavior. Yet often, multiple hypothetical causal factors can be suggested for the construction of these rules. With traditional agent-based modeling, it is often up to the modeler\u27s discretion to decide which combination of factors best represent the rule at hand. Yet, due to the aforementioned lack of insight, the modeled agent rule is often one out of a vast space of possible rules. In this dissertation, I introduce Evolutionary Model Discovery, a novel framework for automated causal inference, which treats such artificial societies as sandboxes for rule discovery and causal factor importance evaluation. Evolutionary Model Discovery consists of two major phases. Firstly, a rule of interest of a given agent-based model is genetically programmed with combinations of hypothesized factors, attempting to find rules which enable the agent-based model to more closely mimic real-world phenomena. Secondly, the data produced through genetic programming, regarding the correspondence of factor presence in the rule to fitness, is used to train a random forest regressor for importance evaluation. Besides its scientific contributions, this work has also led to the contribution of two Python open-source software libraries for high performance computing with NetLogo, Evolutionary Model Discovery and NL4Py. The results of applying Evolutionary Model Discovery for the causal inference of three very different cases of human social behavior are discussed, revisiting the rules underlying two widely studied models in the literature, the Artificial Anasazi and Schelling\u27s Segregation, and an ensemble model of diffusion of information and information overload. First, previously unconsidered factors driving the socio-agricultural behavior of an ancient Pueblo society are discovered, assisting in the construction of a more robust and accurate version of the Artificial Anasazi model. Second, factors that contribute to the coexistence of mixed patterns of segregation and integration are discovered on a recent extension of Schelling\u27s Segregation model. Finally, causal factors important to the prioritization of social media notifications under loss of attention due to information overload are discovered on an ensemble of a model of Extended Working Memory and the Multi-Action Cascade Model of conversation

Engineering Model-Based Adaptive Software Systems

Adaptive software systems are able to cope with changes in the environment by self-adjusting their structure and behavior. Robustness refers to the ability of the systems to deal with uncertainty, i.e. perturbations (e.g., Denial of Service attacks) or not-modeled system dynamics (e.g., independent cloud applications hosted on the same physical machine) that can affect the quality of the adaptation. To build robust adaptive systems we need models that accurately describe the managed system and methods for how to react to different types of change. In this thesis we introduce techniques that will help an engineer design adaptive systems for web applications. We describe methods to accurately model web applications deployed in cloud in such a way that it accounts for cloud variability and to keep the model synchronized with the actual system at runtime. Using the model, we present methods to optimize the deployed architecture at design- and run-time, uncover bottlenecks and the workloads that saturate them, maintain the service level objective by changing the quantity of available resources (for regular operating conditions or during a Denial of Service attack). We validate the proposed contributions on experiments performed on Amazon EC2 and simulators. The types of applications that benefit the most from our contributions are web-based information systems deployed in cloud