1,818 research outputs found

    Configuration Management of Distributed Systems over Unreliable and Hostile Networks

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    Economic incentives of large criminal profits and the threat of legal consequences have pushed criminals to continuously improve their malware, especially command and control channels. This thesis applied concepts from successful malware command and control to explore the survivability and resilience of benign configuration management systems. This work expands on existing stage models of malware life cycle to contribute a new model for identifying malware concepts applicable to benign configuration management. The Hidden Master architecture is a contribution to master-agent network communication. In the Hidden Master architecture, communication between master and agent is asynchronous and can operate trough intermediate nodes. This protects the master secret key, which gives full control of all computers participating in configuration management. Multiple improvements to idempotent configuration were proposed, including the definition of the minimal base resource dependency model, simplified resource revalidation and the use of imperative general purpose language for defining idempotent configuration. Following the constructive research approach, the improvements to configuration management were designed into two prototypes. This allowed validation in laboratory testing, in two case studies and in expert interviews. In laboratory testing, the Hidden Master prototype was more resilient than leading configuration management tools in high load and low memory conditions, and against packet loss and corruption. Only the research prototype was adaptable to a network without stable topology due to the asynchronous nature of the Hidden Master architecture. The main case study used the research prototype in a complex environment to deploy a multi-room, authenticated audiovisual system for a client of an organization deploying the configuration. The case studies indicated that imperative general purpose language can be used for idempotent configuration in real life, for defining new configurations in unexpected situations using the base resources, and abstracting those using standard language features; and that such a system seems easy to learn. Potential business benefits were identified and evaluated using individual semistructured expert interviews. Respondents agreed that the models and the Hidden Master architecture could reduce costs and risks, improve developer productivity and allow faster time-to-market. Protection of master secret keys and the reduced need for incident response were seen as key drivers for improved security. Low-cost geographic scaling and leveraging file serving capabilities of commodity servers were seen to improve scaling and resiliency. Respondents identified jurisdictional legal limitations to encryption and requirements for cloud operator auditing as factors potentially limiting the full use of some concepts

    Current and Future Challenges in Knowledge Representation and Reasoning

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    Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade

    SCALING UP TASK EXECUTION ON RESOURCE-CONSTRAINED SYSTEMS

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    The ubiquity of executing machine learning tasks on embedded systems with constrained resources has made efficient execution of neural networks on these systems under the CPU, memory, and energy constraints increasingly important. Different from high-end computing systems where resources are abundant and reliable, resource-constrained systems only have limited computational capability, limited memory, and limited energy supply. This dissertation focuses on how to take full advantage of the limited resources of these systems in order to improve task execution efficiency from different aspects of the execution pipeline. While the existing literature primarily aims at solving the problem by shrinking the model size according to the resource constraints, this dissertation aims to improve the execution efficiency for a given set of tasks from the following two aspects. Firstly, we propose SmartON, which is the first batteryless active event detection system that considers both the event arrival pattern as well as the harvested energy to determine when the system should wake up and what the duty cycle should be. Secondly, we propose Antler, which exploits the affinity between all pairs of tasks in a multitask inference system to construct a compact graph representation of the task set for a given overall size budget. To achieve the aforementioned algorithmic proposals, we propose the following hardware solutions. One is a controllable capacitor array that can expand the system’s energy storage on-the-fly. The other is a FRAM array that can accommodate multiple neural networks running on one system.Doctor of Philosoph

    Quality of experience and access network traffic management of HTTP adaptive video streaming

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    The thesis focuses on Quality of Experience (QoE) of HTTP adaptive video streaming (HAS) and traffic management in access networks to improve the QoE of HAS. First, the QoE impact of adaptation parameters and time on layer was investigated with subjective crowdsourcing studies. The results were used to compute a QoE-optimal adaptation strategy for given video and network conditions. This allows video service providers to develop and benchmark improved adaptation logics for HAS. Furthermore, the thesis investigated concepts to monitor video QoE on application and network layer, which can be used by network providers in the QoE-aware traffic management cycle. Moreover, an analytic and simulative performance evaluation of QoE-aware traffic management on a bottleneck link was conducted. Finally, the thesis investigated socially-aware traffic management for HAS via Wi-Fi offloading of mobile HAS flows. A model for the distribution of public Wi-Fi hotspots and a platform for socially-aware traffic management on private home routers was presented. A simulative performance evaluation investigated the impact of Wi-Fi offloading on the QoE and energy consumption of mobile HAS.Die Doktorarbeit beschĂ€ftigt sich mit Quality of Experience (QoE) – der subjektiv empfundenen DienstgĂŒte – von adaptivem HTTP Videostreaming (HAS) und mit Verkehrsmanagement, das in Zugangsnetzwerken eingesetzt werden kann, um die QoE des adaptiven Videostreamings zu verbessern. Zuerst wurde der Einfluss von Adaptionsparameters und der Zeit pro QualitĂ€tsstufe auf die QoE von adaptivem Videostreaming mittels subjektiver Crowdsourcingstudien untersucht. Die Ergebnisse wurden benutzt, um die QoE-optimale Adaptionsstrategie fĂŒr gegebene Videos und Netzwerkbedingungen zu berechnen. Dies ermöglicht Dienstanbietern von Videostreaming verbesserte Adaptionsstrategien fĂŒr adaptives Videostreaming zu entwerfen und zu benchmarken. Weiterhin untersuchte die Arbeit Konzepte zum Überwachen von QoE von Videostreaming in der Applikation und im Netzwerk, die von Netzwerkbetreibern im Kreislauf des QoE-bewussten Verkehrsmanagements eingesetzt werden können. Außerdem wurde eine analytische und simulative Leistungsbewertung von QoE-bewusstem Verkehrsmanagement auf einer Engpassverbindung durchgefĂŒhrt. Schließlich untersuchte diese Arbeit sozialbewusstes Verkehrsmanagement fĂŒr adaptives Videostreaming mittels WLAN Offloading, also dem Auslagern von mobilen VideoflĂŒssen ĂŒber WLAN Netzwerke. Es wurde ein Modell fĂŒr die Verteilung von öffentlichen WLAN Zugangspunkte und eine Plattform fĂŒr sozialbewusstes Verkehrsmanagement auf privaten, hĂ€uslichen WLAN Routern vorgestellt. Abschließend untersuchte eine simulative Leistungsbewertung den Einfluss von WLAN Offloading auf die QoE und den Energieverbrauch von mobilem adaptivem Videostreaming

    RF Wireless Power and Data Transfer : Experiment-driven Analysis and Waveform Design

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    The brisk deployment of the fifth generation (5G) mobile technology across the globe has accelerated the adoption of Internet of Things (IoT) networks. While 5G provides the necessary bandwidth and latency to connect the trillions of IoT sensors to the internet, the challenge of powering such a multitude of sensors with a replenishable energy source remains. Far-field radio frequency (RF) wireless power transfer (WPT) is a promising technology to address this issue. Conventionally, the RF WPT concepts have been deemed inadequate to deliver wireless power due to the undeniably huge over-the-air propagation losses. Nonetheless, the radical decline in the energy requirement of simple sensing and computing devices over the last few decades has rekindled the interest in RF WPT as a feasible solution for wireless power delivery to IoT sensors. The primary goal in any RF WPT system is to maximize the harvested direct current (DC) power from the minuscule incident RF power. As a result, optimizing the receiver power efficiency is pivotal for an RF WPT system. On similar lines, it is essential to minimize the power losses at the transmitter in order to achieve a sustainable and economically viable RF WPT system. In this regard, this thesis explores the system-level study of an RF WPT system using a digital radio transmitter for applications where alternative analog transmit circuits are impractical. A prototype test-bed comprising low-cost software-defined radio (SDR) transmitter and an off-the-shelf RF energy-harvesting (EH) receiver is developed to experimentally analyze the impact of clipping and nonlinear amplification at the digital radio transmitter on digital baseband waveform. The use of an SDR allows leveraging the test-bed for the research on RF simultaneous wireless information and power transfer (SWIPT); the true potential of this technology can be realized by utilizing the RF spectrum to transport data and power together. The experimental results indicate that a digital radio severely distorts high peak-to-average power ratio (PAPR) signals, thereby reducing their average output power and rendering them futile for RF WPT. On similar lines, another test-bed is developed to assess the impact of different waveforms, input impedance mismatch, incident RF power, and load on the receiver power efficiency of an RF WPT system. The experimental results provide the foundation and notion to develop a novel mathematical model for an RF EH receiver. The parametric model relates the harvested DC power to the power distribution of the envelope signal of the incident waveform, which is characterized by the amplitude, phase and frequency of the baseband waveform. The novel receiver model is independent of the receiver circuit’s matching network, rectifier configuration, number of diodes, load as well as input frequency. The efficacy of the model in accurately predicting the output DC power for any given power-level distribution is verified experimentally. Since the novel receiver model associates the output DC power to the parameters of the incident waveform, it is further leveraged to design optimal transmit wave-forms for RF WPT and SWIPT. The optimization problem reveals that a constant envelope signal with varying duty cycle is optimal for maximizing the harvested DC power. Consequently, a pulsed RF waveform is optimal for RF WPT, whereas a continuous phase modulated pulsed RF signal is suitable for RF SWIPT. The superior WPT performance of pulsed RF waveforms over multisine signals is demonstrated experimentally. Similarly, the pulsed phase-shift keying (PSK) signals exhibit superior receiver power efficiency than other communication signals. Nonetheless, varying the duty-cycle of pulsed PSK waveform leads to an efficiency—throughput trade-off in RF SWIPT. Finally, the SDR test-bed is used to evaluate the overall end-to-end power efficiency of different digital baseband waveforms through wireless measurements. The results indicate a 4-PSK modulated signal to be suitable for RF WPT considering the overall power efficiency of the system. The corresponding transmitter, channel and receiver power efficiencies are evaluated as well. The results demonstrate the transmitter power efficiency to be lower than the receiver power efficiency

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This ïŹfth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ïŹelds of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modiïŹed Proportional ConïŹ‚ict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classiïŹers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identiïŹcation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiïŹcation. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classiïŹcation, and hybrid techniques mixing deep learning with belief functions as well

    Comparing the Performance of Initial Coin Offerings to Crowdfunded Equity Ventures

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    Uncertainty in markets increases the likelihood of market failure due to volatility and suboptimal functioning. While initial coin offerings (ICOs) and crowdfunded equity (CFE) offerings may improve functioning in growing markets, there is a lack of knowledge and understanding pertaining to the relative efficiency and behavior of ICO markets compared to CFE markets, potentially perpetuating and thwarting the various communities they are intended to serve. The purpose of this correlational study was to compare a group of ICOs with a group of CFE offerings to identify predictive factors of funding outcomes related to both capital offering types. Efficient market hypothesis was the study’s theoretical foundation, and analysis of variance was used to answer the research question, which examined whether capital offering type predicted the amount of funds raised while controlling for access to the offering companies’ secondary control factors: historical financial data, pro forma financial projections, detailed product descriptions, video of product demonstrations, company website, company history, company leadership, and company investors. Relying on a random sample of 115 campaigns (84 ICOs and 31 CFE) from websites ICOdrops.com, localstake.com, fundable.com, and mainvest.com, results showed differences in mean funds raised between CFEs and ICOs (346,075comparedto346,075 compared to 4,756,464, respectively). ANOVA results showed no single secondary control factors and only one two-factor interaction (company leadership and company investors) influenced mean funds raised. This study may contribute to positive social change by informing best practices among market participants including entrepreneurs, regulators, scholars, and investors

    Artificial Intelligence and International Conflict in Cyberspace

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    This edited volume explores how artificial intelligence (AI) is transforming international conflict in cyberspace. Over the past three decades, cyberspace developed into a crucial frontier and issue of international conflict. However, scholarly work on the relationship between AI and conflict in cyberspace has been produced along somewhat rigid disciplinary boundaries and an even more rigid sociotechnical divide – wherein technical and social scholarship are seldomly brought into a conversation. This is the first volume to address these themes through a comprehensive and cross-disciplinary approach. With the intent of exploring the question ‘what is at stake with the use of automation in international conflict in cyberspace through AI?’, the chapters in the volume focus on three broad themes, namely: (1) technical and operational, (2) strategic and geopolitical and (3) normative and legal. These also constitute the three parts in which the chapters of this volume are organised, although these thematic sections should not be considered as an analytical or a disciplinary demarcation
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