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Natural, biocompatible, and biodegradable composite nanofibers made of Aloe vera extract, pullulan, chitosan, and citric acid were successfully produced via Forcespinning® technology. The addition of Aloe vera extract at different weight percent loadings was investigated. The morphology, thermal properties, physical properties, and water absorption of the nanofibers were characterized using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and thermogravimetric analysis (TGA). The developed nanofiber membranes exhibited good water absorption capabilities, synergistic antibacterial activity against Escherichia coli, and promoted cell attachment and growth. Its porous and high surface area structure make it a potential candidate for wound dressing applications due to its ability to absorb excessive blood and exudates, as well as provide protection from infection while maintaining good thermal stability.

Many organizations are moving their systems to the cloud, where providers consolidate multiple clients using virtualization, which creates challenges to business-critical applications. Research has shown that hypervisors fail, often causing common-mode failures that may abruptly disrupt dozens of virtual machines simultaneously. We hypothesize and empirically show that a significant percentage of virtual machines affected by a hypervisor failure are capable of continuing execution on a new hypervisor. Supported by this observation, we design a technique for recovering from hypervisor failures through efficient virtual machine migration to a co-located hypervisor, which allows virtual machines to continue executing with minimal downtime and which can be transparently applied to existing applications. We evaluate a proof-of-concept implementation using fault injection of hardware and software faults and show that it can recover, on average, 41-46% of all virtual machines, as well as having a mean virtual machine downtime of 3 seconds.

RESTful frameworks provide a platform for easy deployment, and management of enterprise-level microservices in a scalable and maintainable manner. Like any computer system and its components, RESTful frameworks are susceptible to soft errors, a subset of transient hardware faults that are caused by cosmic rays and package impurities, which can lead to unexpected behaviour from the services that use the frameworks. Failures in these platforms can cause unavailability and unreliability which can lead to major damages including financial or reputation losses to the service providers, and frustration to users who rely on the service provided. Despite soft errors and their impact being a well-studied problem in some fields, such as aeronautics and safety-critical systems, their effect on service frameworks is still uncharacterized. This paper employs fault injection and fuzzing to evaluate how 5 different frameworks behave when affected by soft errors. The obtained results show that using a framework increases the probability of experiencing a failure by an amount that varies from framework to framework and suggest that most failures pose an issue for service availability, which can be relatively easily handled by standard fault tolerance techniques.

In the present research project, a novel portable battery-powered handheld device able to produce micron and submicron fibers using centrifugal forces is proposed. The design includes spinnerets with a clamshell configuration with multiple chambers or reservoirs (2, 4, and 8) and different exit orifice diameters (400, 500, 600, and 800 µm). The rotational speed is controlled via an Arduino microcontroller. To validate the design, a series of experiments were conducted and the effect of the orifice diameter, number of chambers, and velocity on the resulting fibers’ diameter and yield was studied. For the experiments, a polymeric solution of Polyvinyl Alcohol (PVA) was prepared. The fiber yield was gravimetrically quantified, and the fiber morphology and diameter were analyzed by means of scanning electron microscopy (SEM). The experimental results showed that spinnerets with an orifice diameter of 500 microns yielded the greatest amount of fibers (0.0777 g). In addition, the number of chambers also affected the amount of fibers produced, and it was determined that the fiber diameter size is dependent on the spinneret speed. Fibers 80 nm in diameter were observed at 6500 rpm.

Manufacturers of automated systems and their components have been allocating an enormous amount of time and effort in R&D activities. This effort translates into an overhead on the V&V (verification and validation) process making it time-consuming and costly. In this paper, we present an ECSEL JU project (VALU3S) that aims to evaluate the state-of-the-art V&V methods and tools, and design a multi-domain framework to create a clear structure around the components and elements needed to conduct the V&V process. The main expected benefit of the framework is to reduce time and cost needed to verify and validate automated systems with respect to safety, cyber-security, and privacy requirements. This is done through identification and classification of evaluation methods, tools, environments and concepts for V&V of automated systems with respect to the mentioned requirements. To this end, VALU3S brings together a consortium with partners from 10 different countries, amounting to a mix of 25 industrial partners, 6 leading research institutes, and 10 universities to reach the project goal.

Software systems based on Artificial Intelligence (AI) and Machine Learning (ML) are being widely adopted in various scenarios, from online shopping to medical applications. When developing these systems, one needs to take into account that they should be verifiable to make sure that they are in accordance with their requirements. In this work we propose a framework to perform online verification of ML models, through the use of model checking. In order to validate the proposal, we apply it to the medical domain to help qualify medical risk. The results reveal that we can efficiently use the framework to determine if a patient is close to the multidimensional decision boundary of a risk score model. This is particularly relevant since patients in these circumstances are the ones more likely to be misclassified. As such, our framework can be used to help medical teams make better informed decisions.

Improving the energy-efficiency of computing systems while ensuring reliability is a challenge in all domains, ranging from low-power embedded devices to large-scale servers. In this context, a key issue is that many techniques aiming to reduce power consumption negatively affect reliability, while fault tolerance techniques require computation or state redundancy that increases power consumption, thereby leading to systematic tradeoffs. Managing these tradeoffs requires a combination of techniques involving both the hardware and the software, as it is impractical to focus on a single component or level of the system to reach adequate power consumption and reliability. In this paper, we adopt a language-based approach to express reliability and parallelism, in which programs remain adaptable after compilation and may be executed with different strategies concerning reliability and energy consumption. We implement the proposed programming model, which is named $\mathtt {MISO}$ , and perform an experimental analysis aiming to improve the reliability of programs, through fault injection experiments conducted at compile-time, as well as an experimental measurement of power consumption. The results obtained indicate that it is feasible to write programs that remain adaptable after compilation in order to improve the ability to balance reliability, power, and performance.

Recent studies have shown that programmers' cognitive load during typical code development activities can be assessed using wearable and low intrusive devices that capture peripheral physiological responses driven by the autonomic nervous system. In particular, measures such as heart rate variability (HRV) and pupillography can be acquired by nonintrusive devices and provide accurate indication of programmers' cognitive load and attention level in code related tasks, which are known elements of human error that potentially lead to software faults. This paper presents an experimental study designed to evaluate the possibility of using HRV and pupillography together with eye tracking to identify and annotate specific code lines (or even finer grain lexical tokens) of the program under development (or under inspection) with information on the cognitive load of the programmer while dealing with such lines of code. The experimental data is discussed in the paper to assess different alternatives for using code annotations representing programmers' cognitive load while producing or reading code. In particular, we propose the use of biofeedback code highlighting techniques to provide online programmer's warnings for potentially problematic code lines that may need a second look at (to remove possible bugs), and biofeedback-driven software testing to optimize testing effort, focusing the tests on code areas with higher bug probability

Virtual machine migration is an established technique for tolerating hardware faults affecting the virtualization infrastructure. Normally migration is performed between different physical hosts and hypervisors, which requires the memory state to be eventually sent over the network, thereby causing performance degradation in the migrated and co-located virtual machines, particularly when the migrated VMs are running IO-and memory-heavy workloads. Since most of the hardware faults are transient and can be recovered from by refreshing the affected component, we propose and evaluate a technique for migrating virtual machines over the same physical host almost instantly and with no overhead, by avoiding memory copy and taking advantage of Intel EPT's inner workings. This technique can be employed for refreshing the VMs' state held by the hypervisor with a lower VM downtime and performance overhead than what would be possible using traditional live migration.

Submitted by Boris Flegr (boris@uerj.br) on 2021-01-07T18:31:09Z No. of bitstreams: 1 Raul Barbosa de Sousa Dissertacao completa.pdf: 3996839 bytes, checksum: f5f22231d85848c5145bee4a3133e761 (MD5) Made available in DSpace on 2021-01-07T18:31:09Z (GMT). No. of bitstreams: 1 Raul Barbosa de Sousa Dissertacao completa.pdf: 3996839 bytes, checksum: f5f22231d85848c5145bee4a3133e761 (MD5) Previous issue date: 2019-05-10 Acoustic shooting analyzes are a new field of forensic science, police and justice, rely on criminal experts, who apply scientific knowledge to elucidate crimes. And in this condition the expert is often asked to assess subjective issues such as the ability of a listener to identify a particular type of firearm based on their sound, or to discriminate the most different types of firearms in a questioned recording. The present work aims at the creation of a reference database of sounds produced by firearms of various calibers. The formation of this database included samples of impulsive shots of sounds, collected with measuring microphones, spatially distributed at 3º, 30º, 60º, 90º 120º and 180º of the firing line of the weapon, at a height sufficient to minimize the effects of reverberation. Each digital audio file, in .wav format, PCM, 96KHz, 24bit, will be identified, and can be accessed through the internet. Being useful for confrontation with real recordings, of forensic interest, thus contributing, for elucidation of crimes and promotion of justice. They will also be useful as support material for academic acoustics and algorithm evaluation tests that aim to identify impulsive sounds Análises acústicas de tiros são um novo campo das ciências forenses. A polícia e a justiça contam com os peritos criminais que aplicam o conhecimento científico para a elucidação de crimes. E, nesta condição, o perito é muitas vezes solicitado a avaliar questões subjetivas, como a capacidade de um ouvinte identificar um tipo particular de arma de fogo com base em seu som, ou para discriminar os mais diferentes tipos de armas de fogo em uma gravação questionada. O presente trabalho tem como objetivo a criação de um banco de dados de referência de sons produzidos por armas de fogo de diversos calibres. A formação deste banco de dados, contou com amostras de sons impulsivos de tiros, coletados com microfones de medição, distribuídos espacialmente a 3º, 30º, 60º, 90º 120º e 180º da linha de tiro da arma, a uma altura suficiente para minimizar os efeitos da reverberação. Cada arquivo digital dos áudios, em formato .wav, PCM, 96KHz, 24Bits, será identificado e poderá ser acessado por meio da internet. Serão úteis para o confronto com gravações reais, de interesse forense, contribuindo, assim, à elucidação de crimes e à promoção da justiça. Os arquivos também serão úteis como material de apoio para trabalhos acadêmicos em acústica e em testes de avaliação de algoritmos, que objetivam identificação de sons impulsivos

The properties of smart light-emitting textile/fabric with a persistent afterglow can be beneficial for use in anti-counterfeiting, night surveillance, and security trooping among other potential applications. Keeping this in mind, this work is a step forward in designing bright green and red emitting poly vinyl alcohol (PVA) fine fibers. We first synthesized green emitting Zn2GeO4:Mn2+ (ZGOM) and red emitting ZnGa2O4:Cr3+ (ZGOC) nanoparticles using a hydrothermal process. Field Emission Scanning Electron Microscope (FESEM) images depicted the formation of nanorods and sub-10 nm nanoparticles for ZGOM and ZGOC, respectively. These nanoparticles were mixed in a PVA solution and spun into fiber using the Forcespinning® technology. FESEM images of the fiber samples show the presence of long, bead-free, defect-free, smooth surfaces with the diameters ranging from 200 nm to 1.2 μm depending on selected processing parameters; a fiber system was selected considering fiber output and fiber diameter, the selected system had fibers with average diameter of 900 nm for both ZGOM and ZGOC encapsulated PVA fine fibers (MG-PF and CR-PF). Another favorable property was that the thermal stability of the PVA fine fibers was not affected by the nanoparticles. Both MF-PF and CR-PF fine fibers depicted a bright green and red luminescence, respectively, under UV excitation. The fiber can easily accommodate loading up to 1.0% weight without doing any kind of fluorescence quenching. MG-PF fiber displayed a bright and substantial green persistence luminescence up to 500 s. In the case of CR-PF there is a quenching of trap states and therefore intrinsic red persistence luminescence is not retained beyond 100 s. We believe such green fiber if smartly woven in any kind of textile materials/fabric can be an efficient solution to deal with cloth counterfeiting and night surveillance.

The choice of programming language depends on a number of factors, such as efficiency and maintainability. With the predicted increase of the soft error rate in coming years due to technological improvements and the popularization of energy-saving techniques, the responsibility of tolerating transient hardware faults is no longer solely on the hardware-side, but is becoming more and more a software problem. Therefore choices taken throughout the software development and compilation process, such as the used programming language, are now a factor that should be taken into account. This paper evaluates the impact of the programming language in the sensitivity and vulnerability to soft errors of an application, through extensive fault injection campaigns that cover 5 benchmarks over 7 programming languages, considering the most common implementation. The results show that both the programming language and workload have a significant correlation with the occurrence of failures due to soft errors. Furthermore, since execution time varied noticeably across programming languages, applications developed in less-efficient languages showed a higher vulnerability time window in which they may be affected by soft errors.

Fault injection (FI) is an experimental methodology used in a wide range of scenarios for validating the fault resilience of applications, especially safety-critical ones. A sufficiently thoroughgoing evaluation produces a significant amount of data regarding the behavior of software components or entire systems in the presence of faults. The core questions that practitioners using fault injection face are 1) how to extract and represent information, 2) how to effectively analyze that data and how to utilize the gained knowledge to improve the FI process. Previous works addressing these questions relied mainly on ad hoc approaches. The current paper presents a modern view of these problems, preparing and executing the knowledge extraction by exploratory (big) data analysis, methods, and tools. A real use-case based on FI campaigns composed of thousands of fault injections into a virtualized system indicates the huge potential of the approach. The outcome is the discovery of an opportunity for a drastic speed-up of the FI process unrevealed by the traditional methodology.

Cloud computing is revolutionizing how organizations treat computing resources. The privileged virtual machine is a key component in systems that use virtualization, but poses a dependability risk for several reasons. The activation of residual software faults that exist in every software project is a real threat and can impact the correct operation of the entire virtualized system. To study this question, we begin by performing a detailed analysis of the privileged virtual machine and its components, followed by software fault injection campaigns that target two of those important components – toolstack and a device driver. The obstacles faced during this experimental phase and how they were overcome is herein described with practitioners in mind. The results show that software faults in those components can have either no impact or lead to drastic failures, showing that the privileged virtual machine is a single point of failure that must be protected (for 4-9% of the faults). Most of the failures are detectable by monitoring basic functionalities, but some faults caused inconsistent states that manifest later on. No silent data failures (SDF) have been observed, but the number of faults injected so far only allows to conclude that SDF are not very frequent.

Software fault injection is a fundamental technique to evaluate the behavior of systems in presence of software defects. However, in spite of the considerable number of software fault injection proposals, the lack of readily available and practical tools persists as the most serious limitation to the generalized use of software fault injection. This paper describes a fault injection tool designed to simplify the software fault injection process. The tool modifies the source code of target programs by altering the abstract syntax tree and producing software patches automatically. Several key challenges were addressed in the development of the tool. Firstly, we formally describe the software fault injection operators, which has been neglected in previous proposals. Secondly, we propose a comprehensive test suite to verify the correctness of software fault injectors. Thirdly, we improve the performance of the fault injection process by compiling only the file in which a defect is injected and linking/installing that file. Finally, we examine the implications of dealing with macros, and the limitations that source code macros impose on software fault injection. The results of injecting software faults into the Apache web server, as an example of possible target, show how these challenges have been addressed.

Reliability prediction methods support early decision on software development by offering means for architects to reason and test their own designs. However, most of today's reliability prediction methods model the failure behavior as two states: functioning properly or failed, neglecting other failure modes. In addition, the lack of realistic case-studies and actual failure data make testing and validation difficult tasks. With this in mind, we propose a reliability modeling approach describing the behavior of a software component taking into account error occurrences and their associated impacts in terms of error masking, error propagation, error recovery and failures modes. To validate our approach we present a real case-study based on Xen where we injected faults at different virtualization layers. The experiments show close results between our modeling approach and the realistic values obtained from the experiment.

Hardware manufacturing advances along with the popularisation of energy saving techniques are predicted to cause an increase in the soft error rate, which in turn will transfer part of the responsibility for tolerating these errors to the software layer. Since the programming language and its supporting implementation have a determinant impact in the produced application, fault injection was used in this paper to evaluate the effect that different programming languages and different implementations (compilers or interpreters) have on the sensitivity and vulnerability to soft errors. The results show that programming language and workload strongly impact these metrics, that programming languages of the same type share similar failure mode distributions, and that the implementation can impact sensitivity and vulnerability in certain scenarios. For these reasons, we suggest that a change in the implementation of the language at a late stage in the project development cycle can be enough to improve the vulnerability and sensitivity without changing the programming language.

Cloud infrastructures provide elastic computing resources to client organizations, enabling them to build online applications while avoiding the fixed costs associated to a complete IT infrastructure. However, such organizations are unlikely to fully trust the cloud for the most critical applications. Among other threats, soft errors are expected to increase with the shrinking geometries of transistors, and many errors are left for the software layers to correct and mask. This paper characterizes the behavior of a virtualized environment, using Xen with CentOS as the hypervisor, in presence of soft errors. One of the main threats arises from soft errors directly affecting the hypervisor, as these faults have the potential to disrupt several virtual machines at once. With this in mind, we develop a fault tolerant architecture for cloud applications, which relies on experimental data collected using fault injection to guide its design. This architecture recovers from bit-flip errors with the help of a watchdog timer, to securely reboot the hypervisor. Nevertheless, errors might still propagate outside the system, for example to a client in a client-server interaction. Despite this, our results suggest that our architecture and a few simple techniques, like timers on the client, can recover a very large fraction of errors in client-server applications with small hardware and performance overhead. Conversely, the fraction of errors requiring Byzantine fault-tolerant techniques is quite small, thus restricting those expensive approaches to highly critical applications.

Muitos problemas da Engenharia, Fisica e Biologia sao modelados por equacoes diferenciais. Atualmente muitos metodos sao utilizados para se obter aproximacoes de equacoes diferenciais, como por exemplo, Elementos Finitos, Diferencas Finitas, Metodo dos Momentos, dentre outros. Alguns desses metodos usam funcoes bases para representar a solucao e, posteriormente, transformam o problema em um sistema linear. Outros aproximam as derivadas em alguns pontos selecionados, que constituem uma malha do dominio. Neste trabalho sao resolvidas, numericamente, equacoes diferenciais atraves de redes neurais artificiais do tipo feedforward [1,2]. Tal solucao e apresentada atraves da soma de duas partes: a primeira e para satisfazer as condicoes iniciais e de contorno e a outra, e oriunda da rede neural com parâmetros ajustaveis (pesos e bias) de tal forma que minimiza uma funcao custo. No treinamento da rede neural e empregado a tecnica de otimizacao de Levenberg- Maquardt. Foi considerada uma rede neural com tres camadas: a camada de entrada; uma camada oculta e a camada de saida. Na camada intermediaria foram adotados dois neuronios e um neuronio na camada de saida.

Esta pesquisa tem como foco descrever e comparar os traços de personalidade de gerentes de loja de centros comerciais e empreendedores, utilizando como instrumento de pesquisa a escala do inventário dos cinco grandes fatores de personalidade da teoria Big Five e avaliar quais traços são mais evidentes em empreendedores e gestores. Também foi avaliado o papel do sexo dentro do contexto empreendedor e gerencial. Foram utilizadas as técnicas exploratória e confirmatória de análise fatorial. Para se aferir a validade das hipóteses foi realizada uma análise de variância multivariada (MANOVA). Os resultados apontam evidências de diferenças entre gerentes e empreendedores em relação aos cinco traços de personalidade. Além disso, ressalta o caráter masculino do ato de empreender e o caráter feminino do aspecto gerencial. Este trabalho contribui para a teoria sobre traços de personalidade quando aplicada em diferentes ambientes organizacionais, evidenciando características que pertencem aos dois grupos estudados na pesquisa. A principal contribuição teórica é a aceitação das hipóteses que foram levantadas referente ao comportamento dos gerentes e empreendedores. No âmbito prático, pode-se utilizar o IGFP-5 reduzido para testes vocacionais e como forma de critério para contratação.

In this paper we address the challenge of creating an electronic ticketing system for transportation systems that can partially or completely run on the cloud. This challenge is defined within the scope of an industrial project. The resulting system should be able to reach a large spectrum of customers and should provide two key advantages: lower operational costs, especially for small clients without IT departments, and faster execution of queries for monthly or other sorts of analysis, using the elasticity of cloud-based resources. To fulfill the goals of the project, we propose very standard technologies and procedures: a three-tiered architecture; a separation of the online and analysis databases; and an Enterprise Service Bus to get the input from very diverse hardware and software stacks. In this paper we discuss several options regarding the location of these facilities on the cloud and we also evaluate the costs involved. While this work already defines many features of the system, it must be considered as preliminary, as some open details remain for future work.

The quantitative assessment of quality attributes on software architectures allow to support early decisions in the design phase, certify quality requirements established by stakeholders and improve software quality in future architectural changes. In literature, only few of these quality requirements are verified and most often they are manually checked, which is time-consuming and error-prone due to the overwhelmingly complex designs. The goal of this thesis is to provide means for architects predict and analyze availability constraints on software architectures. We plan to generate a stochastic model from an architectural description specified by an Architecture Description Language (ADL) properly annotated to be solved by a probabilistic model-checking tool. This model will allow to quantitatively predict availability and identify bottlenecks that are negatively influencing the overall system availability. Hence, our approach will help architects to avoid undesired or infeasible architectural designs and prevent extra costs in fixing late life-cycle detected problems.

In this paper we are concerned with the fundamental problem of reaching agreement among a set of distributed processes in presence of an unbounded number of communication failures. We present a probabilistic analysis of a family of synchronous consensus algorithms that aim to solve the 1-ofn selection problem. In this problem, a set of n nodes are to select one common value among a set of n proposed values. There are two possible outcomes of each node's selection process: it can decide either to select a value, or to abort. Agreement implies that all nodes select the same value, or all nodes decide to abort. We know from previous research that it is impossible to guarantee agreement if there is no upper bound on the number of communication failures that can occur. Our aim is to study how the probability of disagreement varies for different decision criteria. The decision criterion consists of the logical expressions that determine whether a process will select a value or decide to abort based on its view of the system state. In this paper we propose and analyse a moderately pessimistic decision criterion. We compared this decision criterion with an optimistic and a pessimistic decision criterion, which we have investigated in our previous work. Our results show that the moderately pessimistic decision criterion for most configurations has a lower maximum probability of disagreement compared with the two other decision criteria. Furthermore, it provides a compromise between the optimistic and the pessimistic approaches since it reduces the probability of disagreement without increasing excessively the probability of agreeing to abort.

This paper addresses the problem of leader election in virtual traffic lights. A virtual traffic light (VTL) is a self-organizing traffic control system that allows road vehicles equipped with vehicle-to-vehicle communication facilities to implement the function of a traffic light without the support of a roadside installation. Previous research has shown that it is impossible to construct a leader election protocol that guarantees agreement among the participating vehicles in the presence of massive communication failures. The paper addresses the problem of calculating the probability of disagreement in situations where a large number of protocol messages are lost due to communication interference, so-called communication grey-outs. To this end, we present a probabilistic analysis of a family of simple round-based consensus algorithms that solve the 1-of-n selection problem. We propose to use these algorithms for the core logic of a VTL leader election protocol (LEP). Our analysis shows that the probability of disagreement depends on: i) the number of vehicles involved in the leader election, ii) the number of rounds of message exchange, iii) the probability of message loss, and iv) the decision criterion used by the LEP. We propose an optimistic and a pessimistic decision criteria for the proposed 1-of-n selection algorithms. The analysis encompass two probabilistic failure models, one for symmetric communication failures and one for asymmetric communication failures.

Software engineers and practitioners regard software architecture as an important artifact, providing the means to model the structure and behavior of systems and to support early decisions on dependability and other quality attributes. Since systems are most often subject to evolution, the software architecture can be used as an early indicator on the impact of the planned evolution on quality attributes. We propose an automated approach to evaluate the impact on reliability of architecture evolution. Our approach provides relevant information for architects to predict the impact of component reliabilities, usage profile and system structure on the overall reliability. We translate a system's architectural description written in an Architecture Description Language (ADL) to a stochastic model suitable for performing a thorough analysis on the possible architectural modifications. We applied our method to a case study widely used in research in which we identified the reliability bottlenecks and performed structural modifications to obtain an improved architecture regarding its reliability.

Quantitative assessment of quality attributes (i.e., non-functional requirements, such as performance, safety or reliability) of software architectures during design supports important early decisions and validates the quality requirements established by the stakeholder. In current practice, these quality requirements are most often manually checked, which is time-consuming and error-prone due to the overwhelmingly complex designs. We propose an automated approach to assess the reliability of software architectures. It consists in extracting a Markov model from the system specification written in an Architecture Description Language (ADL). Our approach translates the specified architecture to a high-level probabilistic model-checking language, supporting system validation and quantitative reliability prediction against usage profile, component arrangement and architectural styles. We validate our approach by applying it to different architectural styles and comparing those with two different quantitative reliability assessment methods presented in the literature: the composite and the hierarchical methods.

Experimental dependability studies usually produce an amount of data substantially greater than what can be presented in a research paper or a technical report. For this reason, authors condensate the results into more succinct forms that allow them to convey their message. Since a large amount of the original data is left unexplored, sharing it allows other teams to discover additional facts (as well as to compare the results to other studies). In a previous paper, we investigated sources of uncertainty in measurement results obtained using three different fault injection techniques. The resulting experimental data was shared in the AMBER raw data repository. This paper gives an overview of the study and makes an attempt at further exploring the shared data.

This paper investigates sources of uncertainty in measurement results obtained using three different fault injection techniques. Two software-implemented and one test port-based technique are characterized and compared. The three techniques can be used to inject the same faults, which are defined in a shared database. Due to the uncertainties associated with the techniques, which we identify and discuss, the results of injecting a given fault may differ to some extent. The paper analyzes the results of using the three techniques to inject faults into two experimental targets: a brake-by-wire controller and a partitioning operating system. The objective of the experiments is to determine whether the results of the different techniques are metrologically compatible and, consequently, meaningful when disseminated and compared. Our observations indicate that, even though the outcome of many individual experiments is affected by uncertainties, the three techniques produce similar average results over a large number of experiments.

This paper describes the implementation of a processor- group membership protocol in an experimental real-time network. The protocol is appropriate for fault-tolerant distributed systems using TDMA for scheduling messages. It allows nodes to maintain a consensus on the operational state of all nodes, in the presence of node failures and restarts, as well as network failures. The protocol is based on the principle that, in a system of n nodes, each node must acknowledge the messages from k other nodes in the membership group, where k can assume values between 2 and n - 1. Membership agreement is guaranteed if f les k - 1 failures occur during n consecutive transmission slots. We have implemented the membership protocol in a time-triggered network based on COTS Ethernet hardware, programmed to schedule messages according to the TDMA method.

This paper presents a processor group membership protocol for fault-tolerant distributed real-time systems that utilize periodic, time-triggered scheduling for sending messages over the system's communication network. The protocol allows fault-free nodes to reach agreement on the operational state of all nodes in the presence of fail-silent or fail-reporting node failures as well as network failures (lost or corrupted messages). The protocol is based on the principle that each message sent by a node in the membership is acknowledged by k other nodes in a system of n nodes, where k can be set to any number between 2 and n - 1. Agreement on node failure (membership departure) and agreement on node recovery (membership reintegration) are handled by two different mechanisms. Agreement on departure is guaranteed if no more than f = k - 1 failures occur in the same communication round, while at most one node can be reintegrated into the membership per communication round