TY - THES A1 - Opris, Andre T1 - Holomorphic Extensions in the Structure R_{an,exp} N2 - In this thesis we consider real analytic functions, i.e. functions which can be described locally as convergent power series and ask the following: Which real analytic functions definable in R_{an,exp} have a holomorphic extension which is again definable in R_{an,exp}? Finding a holomorphic extension is of course not difficult simply by power series expansion. The difficulty is to construct it in a definably way. We will not answer the question above completely, but introduce a large non trivial class of definable functions in R_{an,exp} where for example functions which are iterated compositions from either side of globally subanalytic functions and the global logarithm are contained. We call them restricted log-exp-analytic. After giving some preliminary results like preparation theorems and Tamm's Theorem for this class of functions we are able to show that real analytic restricted log-exp-analytic functions have a holomorphic extension which is again restricted log-exp-analytic. KW - O-Minimality KW - Preparation Theorems KW - Restricted Log-Exp-Analytic Functions KW - Complexification KW - Tamm's Theorem KW - O-Minimalität Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-10691 ER - TY - THES A1 - Norberto Sales, Juliano Efson T1 - An Explainable Semantic Parser for End-User Development N2 - Programming is a key skill in a world where businesses are driven by digital transformations. Although many of the programming demand can be addressed by a simple set of instructions composing libraries and services available in the web, non-technical professionals, such as domain experts and analysts, are still unable to construct their own programs due to the intrinsic complexity of coding. Among other types of end-user development, natural language programming has emerged to allow users to program without the formalism of traditional programming languages, where a tailored semantic parser can translate a natural language utterance to a formal command representation able to be processed by a computational machine. Currently, semantic parsers are typically built on the top of a learning method that defines its behaviours based on the patterns behind a large training data, whose production frequently are costly and time-consuming. Our research is devoted to study and propose a semantic parser for natural language commands targeting a scenario with low availability of training data. Our proposed semantic parser follows a multi-component architecture, composed of a specialised shallow parser that associates natural language commands to predicate-argument structures, integrated to a distributional ranking model that matches the command to a function signature available from an API knowledge base. Systems developed with statistical learning models and complex linguistics resources, as the proposed semantic parser, do not provide natively an easy way to associate a single feature from the input data to the impact in system behaviour. In this scenario, end-user explanations for intelligent systems has become a strong requirement to increase user confidence and system literacy. Thus, our research designed an explanation model for the proposed semantic parser that fits the heterogeneity of its multi-component architecture. The explanation model explores a hierarchical representation in an increasing degree of technical depth, providing higher-level explanations in the initial layers, going gradually to those that demand technical knowledge, applying different explanation strategies to better express the approach behind each component. With the support of a user-centred experiment, we compared the utility of different types of explanations and the impact of background knowledge in their preferences. Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-10718 ER - TY - CHAP A1 - Parra Rodriguez, Juan D. A1 - Posegga, Joachim T1 - Local Storage on Steroids: Abusing Web Browsers for Hidden Content Storage and Distribution T2 - International Conference on Security and Privacy in Communication Systems N2 - Analysing security assumptions taken for the WebRTC and postMessage APIs led us to find a novel attack abusing the browsers' persistent storage capabilities. The presented attack can be executed without the website's visitor knowledge, and it requires neither browser vulnerabilities nor additional software on the browser's side. To exemplify this, we study how can an attacker use browsers to create a network for persistent storage and distribution of arbitrary data. In our proof of concept, the total storage of the network, and therefore the space used within each browser, grows linearly with the number of origins delivering the malicious JavaScript code. Further, data transfers between browsers are not restricted by the Same Origin Policy, which allows for a unified cross-origin browser network, regardless of the origin from which the script executing the functionality is loaded from. In the course of our work, we assess the feasibility of a real-life deployment of the network by running experiments using Linux containers and browser automation tools. Moreover, we show how security mechanisms against third-party tracking, cross-site scripting and click-jacking can diminish the attack's impact, or even prevent it. KW - Web Security KW - WebRTC KW - postMessage KW - Browser Security Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-6572 SN - 978-3-030-01704-0 PB - Springer CY - Cham ER - TY - JOUR A1 - Hassen, Wiem Fekih A1 - Ben Ahmed, Mariem T1 - Optimization of a Redox-Flow Battery Simulation Model Based on a Deep Reinforcement Learning Approach JF - Batteries N2 - Vanadium redox-flow batteries (VRFBs) have played a significant role in hybrid energy storage systems (HESSs) over the last few decades owing to their unique characteristics and advantages. Hence, the accurate estimation of the VRFB model holds significant importance in large-scale storage applications, as they are indispensable for incorporating the distinctive features of energy storage systems and control algorithms within embedded energy architectures. In this work, we propose a novel approach that combines model-based and data-driven techniques to predict battery state variables, i.e., the state of charge (SoC), voltage, and current. Our proposal leverages enhanced deep reinforcement learning techniques, specifically deep q-learning (DQN), by combining q-learning with neural networks to optimize the VRFB-specific parameters, ensuring a robust fit between the real and simulated data. Our proposed method outperforms the existing approach in voltage prediction. Subsequently, we enhance the proposed approach by incorporating a second deep RL algorithm—dueling DQN—which is an improvement of DQN, resulting in a 10% improvement in the results, especially in terms of voltage prediction. The proposed approach results in an accurate VFRB model that can be generalized to several types of redox-flow batteries. KW - energy storage KW - redox-flow battery KW - battery modeling KW - battery state variables KW - parameter optimization KW - accurate estimation KW - voltage prediction KW - deep reinforcement learning KW - deep q-learning KW - dueling deep q-networks Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-13994 VL - 10 PB - MDPI CY - Basel ER - TY - JOUR A1 - Hassen, Wiem Fekih A1 - Imen Azzouz, Imen Azzouz T1 - Optimization of Electric Vehicles Charging Scheduling Based on Deep Reinforcement Learning: A Decentralized Approach JF - Energies N2 - The worldwide adoption of Electric Vehicles (EVs) has embraced promising advancements toward a sustainable transportation system. However, the effective charging scheduling of EVs is not a trivial task due to the increase in the load demand in the Charging Stations (CSs) and the fluctuation of electricity prices. Moreover, other issues that raise concern among EV drivers are the long waiting time and the inability to charge the battery to the desired State of Charge (SOC). In order to alleviate the range of anxiety of users, we perform a Deep Reinforcement Learning (DRL) approach that provides the optimal charging time slots for EV based on the Photovoltaic power prices, the current EV SOC, the charging connector type, and the history of load demand profiles collected in different locations. Our implemented approach maximizes the EV profit while giving a margin of liberty to the EV drivers to select the preferred CS and the best charging time (i.e., morning, afternoon, evening, or night). The results analysis proves the effectiveness of the DRL model in minimizing the charging costs of the EV up to 60%, providing a full charging experience to the EV with a lower waiting time of less than or equal to 30 min. KW - smart EV charging KW - day-ahead planning KW - deep Q-Network KW - data-driven approach KW - waiting time KW - cost minimization KW - real dataset Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-13985 VL - 16 PB - MDPI CY - Basel ER - TY - THES A1 - Kasinathan, Prabhakaran T1 - Workflow-aware access control for the Internet of Things N2 - IoT is defined as a paradigm where "things" have sensing, actuating, communicating, and self-configuring abilities, and are connected to each other and to the Internet. Recent advancements in the manufacturing industry have helped to produce embedded devices with various sensors and actuators in mass numbers at a reduced cost. As part of the IoT revolution, everyday devices such as television, refrigerator, cars, even industrial machines are now connected IoT devices. Recent studies have predicted that by 2025 there will be over 75 billion of such IoT devices connected to the Internet. The providers of IoT based services want to integrate their services to satisfy customer requirements. For example, in the mobility scenario, different mobility solution providers want to offer a multi-modal ticket to their customers jointly. In such a distributed and loosely coupled environment, each owner and stakeholder wants to secure his/her own integrity, confidentiality, and functionality goals. This means that distributed rules and conditions defined by the individual owners must be enforced on the participating entities (e.g., customers or partners using their services). The owners and stakeholders may not necessarily trust each other's actions. Therefore, a mechanism is required that guarantees the rules and conditions specified by the different owners. Attacks on IoT devices and similar computing systems are increasing and getting more advanced. IoT devices are often constrained, i.e., they have limited processing power, memory, and energy. Security mechanisms designed for traditional computing systems, e.g., computers, servers, or mobile computing devices such as smartphones, may not fit in those constrained IoT devices. Weak security mechanisms and unenforced security measures were one of the main reasons for recent successful attacks on IoT devices and services. As IoT is now used in many sensitive places, including critical infrastructures, securing them becomes more critical than ever. This thesis focuses on developing mechanisms that secure IoT devices and services and enforcing the rules and conditions specified by the owners on entities that want to access owners' resources. In classical computer systems, security automata are used for specifying security policies and monitoring mechanisms are used for enforcing such policies. For instance, a reference monitor observes and stops the execution when the security policies are about to be violated, thus, the security policies are enforced. To restrict the adversary from using protected IoT devices or services for malicious purposes, it is required to ensure that a workflow must be followed to access the protected resource. In distributed IoT systems where the policies are governed by different owners, each owner would like to specify their rules and conditions in their workflows. The workflows contain tasks that must be performed in a particular order. The goal of this thesis is to develop mechanisms to specify and enforce these workflows in the distributed IoT environment. This thesis introduces a distributed WFAC framework that restricts the entities to do only what they are allowed to do in a collaborative environment. To gain access to a service protected by the WFAC framework, every workflow participant must prove that he/she is in a particular state of an authorized workflow. Authorized means two things: (a) the owner has authorized the workflow to be executed; (b) the workflow participant is authorized to execute it. This restricts the adversary's access to the devices and its services. The security policies defined by different owners are modeled as workflows and specified using Petri Nets. The policies are then enforced with the help of the WFAC framework which supports error-handling, accountability, integration of practitioner-friendly tools, and interoperability with existing security mechanisms such as OAuth. Thus, the WFAC guarantees the integrity of workflows in a distributed environment. KW - Workflow-Aware Access Control for the Internet of Things KW - Petri Nets, Blockchain, Security Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-8915 ER - TY - JOUR A1 - Kronawitter, Stefan A1 - Lengauer, Christian T1 - Polyhedral Search Space Exploration in the ExaStencils Code Generator JF - ACM Transactions on Architecture and Code Optimization N2 - Performance optimization of stencil codes requires data locality improvements. The polyhedron model for loop transformation is well suited for such optimizations with established techniques, such as the PLuTo algorithm and diamond tiling. However, in the domain of our project ExaStencils, stencil codes, it fails to yield optimal results. As an alternative, we propose a new, optimized, multi-dimensional polyhedral search space exploration and demonstrate its effectiveness: we obtain better results than existing approaches in several cases. We also propose how to specialize the search for the domain of stencil codes, which dramatically reduces the exploration effort without significantly impairing performance. KW - Software performance KW - Source code generation KW - Discrete space search Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-5778 SN - 1544-3973 VL - 15 IS - 4 ER - TY - JOUR A1 - Basmadjian, Robert T1 - Flexibility-Based Energy and Demand Management in Data Centers BT - a Case Study for Cloud Computing JF - Energies N2 - The power demand (kW) and energy consumption (kWh) of data centers were augmenteddrastically due to the increased communication and computation needs of IT services. Leveragingdemand and energy management within data centers is a necessity. Thanks to the automated ICTinfrastructure empowered by the IoT technology, such types of management are becoming more feasiblethan ever. In this paper, we look at management from two different perspectives: (1) minimization of theoverall energy consumption and (2) reduction of peak power demand during demand-response periods.Both perspectives have a positive impact on total cost of ownership for data centers. We exhaustivelyreviewed the potential mechanisms in data centers that provided flexibilities together with flexiblecontracts such as green service level and supply-demand agreements. We extended state-of-the-artby introducing the methodological building blocks and foundations of management systems for theabove mentioned two perspectives. We validated our results by conducting experiments on a lab-gradescale cloud computing data center at the premises of HPE in Milano. The obtained results support thetheoretical model, by highlighting the excellent potential of flexible service level agreements in Green IT:33% of overall energy savings and 50% of power demand reduction during demand-response periods inthe case of data center federation. Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-9251 VL - 2019 IS - 12 SP - 1 EP - 22 PB - MDPI CY - Basel ER - TY - THES A1 - Lachat, Paul T1 - Detecting Inference Attacks Involving Sensor Data N2 - The collection of personal information by organizations has become increasingly essential for social interactions. Nevertheless, according to the GDPR (General Data Protection Regulation), the organizations have to protect collected data. Access Control (AC) mechanisms are traditionally used to secure information systems against unauthorized access to sensitive data. The increased availability of personal sensor data, thanks to IoT-oriented applications, motivates new services to offer insights about individuals. Consequently, data mining algorithms have been proposed to infer personal insights from collected sensor data. Although they can be used for genuine purposes, attackers can leverage those outcomes, combining them with other type of data, and further breaching individuals’ privacy. Thus, bypassing AC mechanisms thanks to such insights is a concrete problem. We propose an inference detection system based on the analysis of queries issued on a sensor database. The knowledge obtained through these queries, and the inference channels corresponding to the use of data mining algorithms on sensor data to infer individual information, are described using Raw sensor data based Inference ChannEl Model (RICE-M). The detection is carried out by RICE-M based inference detection System (RICE-Sy). RICE-Sy considers at the time of the query, the knowledge that a user obtains via a new query and has obtained via his query history, and determines whether this is sufficient to allow that user to operate a channel. Thus, privacy protection systems can take advantage of the inferences detected by RICE-Sy, taking into account individuals’ information obtained by the attackers via a database of sensors, to further protect these individuals. Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-14149 ER - TY - JOUR A1 - Anagnostopoulos, Nikolaos Athanasios A1 - Teymuri, Benyamin A1 - Serati, Reza A1 - Rasti, Mehdi ED - Xie, Bin ED - Wang, Ning ED - Gu, Yi ED - Stefanidis, Angelos T1 - LP-MAB: Improving the Energy Efficiency of LoRaWAN Using a Reinforcement-Learning-Based Adaptive Configuration Algorithm JF - Sensors N2 - In the Internet of Things (IoT), Low-Power Wide-Area Networks (LPWANs) are designed to provide low energy consumption while maintaining a long communications’ range for End Devices (EDs). LoRa is a communication protocol that can cover a wide range with low energy consumption. To evaluate the efficiency of the LoRa Wide-Area Network (LoRaWAN), three criteria can be considered, namely, the Packet Delivery Rate (PDR), Energy Consumption (EC), and coverage area. A set of transmission parameters have to be configured to establish a communication link. These parameters can affect the data rate, noise resistance, receiver sensitivity, and EC. The Adaptive Data Rate (ADR) algorithm is a mechanism to configure the transmission parameters of EDs aiming to improve the PDR. Therefore, we introduce a new algorithm using the Multi-Armed Bandit (MAB) technique, to configure the EDs’ transmission parameters in a centralized manner on the Network Server (NS) side, while improving the EC, too. The performance of the proposed algorithm, the Low-Power Multi-Armed Bandit (LP-MAB), is evaluated through simulation results and is compared with other approaches in different scenarios. The simulation results indicate that the LP-MAB’s EC outperforms other algorithms while maintaining a relatively high PDR in various circumstances. KW - Internet of Things (IoT) KW - LoRaWAN KW - adaptive configuration KW - machine learning KW - reinforcement learning Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-11853 SN - 1424-8220 VL - 23 IS - 4 PB - MDPI CY - Basel, Switzerland ER -