1,744 research outputs found
Ethical and Social Aspects of Self-Driving Cars
As an envisaged future of transportation, self-driving cars are being
discussed from various perspectives, including social, economical, engineering,
computer science, design, and ethics. On the one hand, self-driving cars
present new engineering problems that are being gradually successfully solved.
On the other hand, social and ethical problems are typically being presented in
the form of an idealized unsolvable decision-making problem, the so-called
trolley problem, which is grossly misleading. We argue that an applied
engineering ethical approach for the development of new technology is what is
needed; the approach should be applied, meaning that it should focus on the
analysis of complex real-world engineering problems. Software plays a crucial
role for the control of self-driving cars; therefore, software engineering
solutions should seriously handle ethical and social considerations. In this
paper we take a closer look at the regulative instruments, standards, design,
and implementations of components, systems, and services and we present
practical social and ethical challenges that have to be met, as well as novel
expectations for software engineering.Comment: 11 pages, 3 figures, 2 table
Design concept towards a human-centered learning factory
Learning factories play an important role when studying multi-disciplinary problems. Such a problem is to support operators in multi-variant assembly. Multi-variants cause problems with product quality, production time as well as cognitive load and therefore it is important to find ways to support operators in this context. To assess the effects of multi variants, a design concept were developed in a learning factory environment (SIILab, CPPS-testbed). The concept was constructed at a conveyer belt with three assembly stations using Casat software for instruction presentations. The following aspects were included in the human-centered learning factory: studying the introduction of advanced automation, managing product variety, supporting operators in finding information and supporting existing human-automation interactions
Multisensor Poisson Multi-Bernoulli Filter for Joint Target-Sensor State Tracking
In a typical multitarget tracking (MTT) scenario, the sensor state is either
assumed known, or tracking is performed in the sensor's (relative) coordinate
frame. This assumption does not hold when the sensor, e.g., an automotive
radar, is mounted on a vehicle, and the target state should be represented in a
global (absolute) coordinate frame. Then it is important to consider the
uncertain location of the vehicle on which the sensor is mounted for MTT. In
this paper, we present a multisensor low complexity Poisson multi-Bernoulli MTT
filter, which jointly tracks the uncertain vehicle state and target states.
Measurements collected by different sensors mounted on multiple vehicles with
varying location uncertainty are incorporated sequentially based on the arrival
of new sensor measurements. In doing so, targets observed from a sensor mounted
on a well-localized vehicle reduce the state uncertainty of other poorly
localized vehicles, provided that a common non-empty subset of targets is
observed. A low complexity filter is obtained by approximations of the joint
sensor-feature state density minimizing the Kullback-Leibler divergence (KLD).
Results from synthetic as well as experimental measurement data, collected in a
vehicle driving scenario, demonstrate the performance benefits of joint
vehicle-target state tracking.Comment: 13 pages, 7 figure
Application of design principles for assembly instructions – evaluation of practitioner use
Production complexity causes assembly errors due to that the demands on the operators are high and there is a need to improve assembly instructions. Design principles for Information Presentation (DFIP) is a method developed to support such improvement and its application was evaluated in three case studies, 152 practitioners. Results indicate that DFIP use help simplifying the information presentation so that complexity can be reduced, and that step 4 is easiest to understand. In addition, the implementation of assembly instructions gave positive results
COST Action IC 1402 ArVI: Runtime Verification Beyond Monitoring -- Activity Report of Working Group 1
This report presents the activities of the first working group of the COST
Action ArVI, Runtime Verification beyond Monitoring. The report aims to provide
an overview of some of the major core aspects involved in Runtime Verification.
Runtime Verification is the field of research dedicated to the analysis of
system executions. It is often seen as a discipline that studies how a system
run satisfies or violates correctness properties. The report exposes a taxonomy
of Runtime Verification (RV) presenting the terminology involved with the main
concepts of the field. The report also develops the concept of instrumentation,
the various ways to instrument systems, and the fundamental role of
instrumentation in designing an RV framework. We also discuss how RV interplays
with other verification techniques such as model-checking, deductive
verification, model learning, testing, and runtime assertion checking. Finally,
we propose challenges in monitoring quantitative and statistical data beyond
detecting property violation
Large-Scale Analysis of Framework-Specific Exceptions in Android Apps
Mobile apps have become ubiquitous. For app developers, it is a key priority
to ensure their apps' correctness and reliability. However, many apps still
suffer from occasional to frequent crashes, weakening their competitive edge.
Large-scale, deep analyses of the characteristics of real-world app crashes can
provide useful insights to guide developers, or help improve testing and
analysis tools. However, such studies do not exist -- this paper fills this
gap. Over a four-month long effort, we have collected 16,245 unique exception
traces from 2,486 open-source Android apps, and observed that
framework-specific exceptions account for the majority of these crashes. We
then extensively investigated the 8,243 framework-specific exceptions (which
took six person-months): (1) identifying their characteristics (e.g.,
manifestation locations, common fault categories), (2) evaluating their
manifestation via state-of-the-art bug detection techniques, and (3) reviewing
their fixes. Besides the insights they provide, these findings motivate and
enable follow-up research on mobile apps, such as bug detection, fault
localization and patch generation. In addition, to demonstrate the utility of
our findings, we have optimized Stoat, a dynamic testing tool, and implemented
ExLocator, an exception localization tool, for Android apps. Stoat is able to
quickly uncover three previously-unknown, confirmed/fixed crashes in Gmail and
Google+; ExLocator is capable of precisely locating the root causes of
identified exceptions in real-world apps. Our substantial dataset is made
publicly available to share with and benefit the community.Comment: ICSE'18: the 40th International Conference on Software Engineerin
Development of an Industry 4.0 Demonstrator Using Sequence Planner and ROS2
In many modern automation solutions, manual off-line programming is being replaced by online algorithms that dynamically perform tasks based on the state of the environment. Complexities of such systems are pushed even further with collaboration among robots and humans, where intelligent machines and learning algorithms are replacing more traditional automation solutions. This chapter describes the development of an industrial demonstrator using a control infrastructure called Sequence Planner (SP), and presents some lessons learned during development. SP is based on ROS2 and it is designed to aid in handling the increased complexity of these new systems using formal models and online planning algorithms to coordinate the actions of robots and other devices. During development, SP can auto generate ROS nodes and message types as well as support continuous validation and testing. SP is also designed with the aim to handle traditional challenges of automation software development such as safety, reliability and efficiency. In this chapter, it is argued that ROS2 together with SP could be an enabler of intelligent automation for the next industrial revolution
Using Machine Learning to Generate Test Oracles: A Systematic Literature Review
Machine learning may enable the automated generation of test oracles. We have characterized emerging research in this area through a systematic literature review examining oracle types, researcher goals, the ML techniques applied, how the generation process was assessed, and the open research challenges in this emerging field.Based on a sample of 22 relevant studies, we observed that ML algorithms generated test verdict, metamorphic relation, and - most commonly - expected output oracles. Almost all studies employ a supervised or semi-supervised approach, trained on labeled system executions or code metadata - including neural networks, support vector machines, adaptive boosting, and decision trees. Oracles are evaluated using the mutation score, correct classifications, accuracy, and ROC. Work-to-date show great promise, but there are significant open challenges regarding the requirements imposed on training data, the complexity of modeled functions, the ML algorithms employed - and how they are applied - the benchmarks used by researchers, and replicability of the studies. We hope that our findings will serve as a roadmap and inspiration for researchers in this field
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