Kaiserslautern - Fachbereich Informatik
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Manipulating deformable linear objects - Vision-based recognition of contact state transitions -
(1999)
A new and systematic approach to machine vision-based robot manipulation of deformable (non-rigid) linear objects is introduced. This approach reduces the computational needs by using a simple state-oriented model of the objects. These states describe the relation of the object with respect to an obstacle and are derived from the object image and its features. Therefore, the object is segmented from a standard video frame using a fast segmentation algorithm. Several object features are presented which allow the state recognition of the object while being manipulated by the robot.
A new and systematic basic approach to force- and vision-based robot manipulation of deformable (non-rigid) linear objects is introduced. This approach reduces the computational needs by using a simple state-oriented model of the objects. These states describe the relation between the deformable and rigid obstacles, and are derived from the object image and its features. We give an enumeration of possible contact states and discuss the main characteristics of each state. We investigate the performance of robust transitions between the contact states and derive criteria and conditions for each of the states and for two sensor systems, i.e. a vision sensor and a force/torque sensor. This results in a new and task-independent approach in regarding the handling of deformable objects and in a sensor-based implementation of manipulation primitives for industrial robots. Thus, the usage of sensor processing is an appropriate solution for our problem. Finally, we apply the concept of contact states and state transitions to the description of a typical assembly task. Experimental results show the feasibility of our approach: A robot performs several contact state transitions which can be combined for solving a more complex task.
Self-adaptation allows software systems to autonomously adjust their behavior during run-time by handling all possible
operating states that violate the requirements of the managed system. This requires an adaptation engine that receives adaptation
requests during the monitoring process of the managed system and responds with an automated and appropriate adaptation
response. During the last decade, several engineering methods have been introduced to enable self-adaptation in software systems.
However, these methods lack addressing (1) run-time uncertainty that hinders the adaptation process and (2) the performance
impacts resulted from the complexity and the large number of the adaptation space. This paper presents CRATER, a framework
that builds an external adaptation engine for self-adaptive software systems. The adaptation engine, which is built on Case-based
Reasoning, handles the aforementioned challenges together. This paper is braced with an experiment illustrating the benefits of
this framework. The experimental results shows the potential of CRATER in terms handling run-time uncertainty and adaptation
remembrance that enhances the performance for large number of adaptation space.
Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern Analysis and Prediction
(2018)
Recently, many online social networks, such as MySpace, Orkut, and Friendster, have faced inactivity decay of their members, which contributed to the collapse of these networks. The reasons, mechanics, and prevention mechanisms of such inactivity decay are not fully understood. In this work, we analyze decayed and alive subwebsites from the Stack Exchange platform. The analysis mainly focuses on the inactivity cascades that occur among the members of these communities. We provide measures to understand the decay process and statistical analysis to extract the patterns that accompany the inactivity decay. Additionally, we predict cascade size and cascade virality using machine learning. The results of this work include a statistically significant difference of the decay patterns between the decayed and the alive subwebsites. These patterns are mainly cascade size, cascade virality, cascade duration, and cascade similarity. Additionally, the contributed prediction framework showed satisfactorily prediction results compared to a baseline predictor. Supported by empirical evidence, the main findings of this work are (1) there are significantly different decay patterns in the alive and the decayed subwebsites of the Stack Exchange; (2) the cascade’s node degrees contribute more to the decay process than the cascade’s virality, which indicates that the expert members of the Stack Exchange subwebsites were mainly responsible for the activity or inactivity of the Stack Exchange subwebsites; (3) the Statistics subwebsite is going through decay dynamics that may lead to it becoming fully-decayed; (4) the decay process is not governed by only one network measure, it is better described using multiple measures; (5) decayed subwebsites were originally less resilient to inactivity decay, unlike the alive subwebsites; and (6) network’s structure in the early stages of its evolution dictates the activity/inactivity characteristics of the network.
Learning From Networked-data: Methods and Models for Understanding Online Social Networks Dynamics
(2020)
Abstract
Nowadays, people and systems created by people are generating an unprecedented amount of
data. This data has brought us data-driven services with a variety of applications that affect
people’s behavior. One of these applications is the emergent online social networks as a method
for communicating with each other, getting and sharing information, looking for jobs, and many
other things. However, the tremendous growth of these online social networks has also led to many
new challenges that need to be addressed. In this context, the goal of this thesis is to better understand
the dynamics between the members of online social networks from two perspectives. The
first perspective is to better understand the process and the motives underlying link formation in
online social networks. We utilize external information to predict whether two members of an online
social network are friends or not. Also, we contribute a framework for assessing the strength of
friendship ties. The second perspective is to better understand the decay dynamics of online social
networks resulting from the inactivity of their members. Hence, we contribute a model, methods,
and frameworks for understanding the decay mechanics among the members, for predicting members’
inactivity, and for understanding and analyzing inactivity cascades occurring during the decay.
The results of this thesis are: (1) The link formation process is at least partly driven by interactions
among members that take place outside the social network itself; (2) external interactions might
help reduce the noise in social networks and for ranking the strength of the ties in these networks;
(3) inactivity dynamics can be modeled, predicted, and controlled using the models contributed in
this thesis, which are based on network measures. The contributions and the results of this thesis
can be beneficial in many respects. For example, improving the quality of a social network by introducing
new meaningful links and removing noisy ones help to improve the quality of the services
provided by the social network, which, e.g., enables better friend recommendations and helps to
eliminate fake accounts. Moreover, understanding the decay processes involved in the interaction
among the members of a social network can help to prolong the engagement of these members. This
is useful in designing more resilient social networks and can assist in finding influential members
whose inactivity may trigger an inactivity cascade resulting in a potential decay of a network.
Building interoperation among separately developed software units requires checking their conceptual assumptions and constraints. However, eliciting such assumptions and constraints is time consuming and is a challenging task as it requires analyzing each of the interoperating software units. To address this issue we proposed a new conceptual interoperability analysis approach which aims at decreasing the analysis cost and the conceptual mismatches between the interoperating software units. In this report we present the design of a planned controlled experiment for evaluating the effectiveness, efficiency, and acceptance of our proposed conceptual interoperability analysis approach. The design includes the study objectives, research questions, statistical hypotheses, and experimental design. It also provides the materials that will be used in the execution phase of the planned experiment.
Typically software engineers implement their software according to the design of the software
structure. Relations between classes and interfaces such as method-call relations and inheritance
relations are essential parts of a software structure. Accordingly, analyzing several types of
relations will benefit the static analysis process of the software structure. The tasks of this
analysis include but not limited to: understanding of (legacy) software, checking guidelines,
improving product lines, finding structure, or re-engineering of existing software. Graphs with
multi-type edges are possible representation for these relations considering them as edges, while
nodes represent classes and interfaces of software. Then, this multiple type edges graph can
be mapped to visualizations. However, the visualizations should deal with the multiplicity of
relations types and scalability, and they should enable the software engineers to recognize visual
patterns at the same time.
To advance the usage of visualizations for analyzing the static structure of software systems,
I tracked difierent development phases of the interactive multi-matrix visualization (IMMV)
showing an extended user study at the end. Visual structures were determined and classified
systematically using IMMV compared to PNLV in the extended user study as four categories:
High degree, Within-package edges, Cross-package edges, No edges. In addition to these structures
that were found in these handy tools, other structures that look interesting for software
engineers such as cycles and hierarchical structures need additional visualizations to display
them and to investigate them. Therefore, an extended approach for graph layout was presented
that improves the quality of the decomposition and the drawing of directed graphs
according to their topology based on rigorous definitions. The extension involves describing
and analyzing the algorithms for decomposition and drawing in detail giving polynomial time
complexity and space complexity. Finally, I handled visualizing graphs with multi-type edges
using small-multiples, where each tile is dedicated to one edge-type utilizing the topological
graph layout to highlight non-trivial cycles, trees, and DAGs for showing and analyzing the
static structure of software. Finally, I applied this approach to four software systems to show
its usefulness.
This paper deals with the handling of deformable linear objects (DLOs), such as hoses, wires, or leaf springs. It investigates usable features for the vision-based detection of a changing contact situation between a DLO and a rigid polyhedral obstacle and a classification of such contact state transitions. The result is a complete classification of contact state transitions and of the most significant features for each class. This knowledge enables reliable detection of changes in the DLO contact situation, facilitating implementation of sensor-based manipulation skills for all possible contact changes.
Highly Automated Driving (HAD) vehicles represent complex and safety critical systems. They are deployed in an open context i.e., an intricate environment which undergoes continual changes. The complexity of these systems and insufficiencies in sensing and understanding the open context may result in unsafe and uncertain behaviour. The safety critical nature of the HAD vehicles requires modelling of root causes for unsafe behaviour and their mitigation to argue sufficient reduction of residual risk.
Standardization activities such as ISO 21448 provide guidelines on the Safety Of The Intended Functionality (SOTIF) and focus on the analysis of performance limitations under the influence of triggering conditions that can lead to hazardous behaviour. SOTIF references traditional safety analyses methods e.g., Failure Mode and Effect Analysis (FMEA) and Fault Tree Analysis (FTA) to perform safety analysis. These analyses methods are based on certain assumptions e.g., single point failure in FMEA and independence of basic events in FTA. Moreover, these analyses are generally based on expert knowledge i.e., data-based models or hybrid approaches (expert and data) are seldom practised. The resulting safety model is fixed i.e., it is generally seen as a one-time artefact. Open context environment may contain triggering conditions which may not be evident to the expert. Open context also evolves over time and new phenomena may emerge.
This thesis explores the applicability of the traditional safety analyses techniques to provide safety models for HAD vehicles operating in the open context, under the light of modelling assumptions taken by traditional safety analyses techniques. Moreover, incorporating uncertainties into safety analyses models is also explored. An explicit distinction between the inherent uncertainty of a probabilistic event (aleatory) and uncertainty due to lack of knowledge (epistemic) is made to formalize models to perform SOTIF analysis. A further distinction is made for conditions of complete ignorance and termed as ontological uncertainty. The distinction is important as for HAD vehicles operating in open context the ontological uncertainty can never be completely disregarded.
This thesis proposes a novel framework of SOTIF to model, estimate and dis cover triggering conditions relevant to performance limitations. The framework provides the ability to model uncertainties while also providing a hybrid approach i.e., supporting inclusion of expert knowledge as well as data driven engineering processes. Two representative algorithms are provided to support the framework. Bayesian Network (BN) and p-value hypothesis testing are utilised in this regard. The framework is implemented on a real-world case study in which LIDARs based perception systems are used as vehicle detection system.
Dealing with information in modern times involves users to cope with hundreds of thousands of documents, such as articles, emails, Web pages, or News feeds.
Above all information sources, the World Wide Web presents information seekers with great challenges.
It offers more text in natural language than one is capable to read.
The key idea for this research intends to provide users with adaptable filtering techniques, supporting them in filtering out the specific information items they need.
Its realization focuses on developing an Information Extraction system,
which adapts to a domain of concern, by interpreting the contained formalized knowledge.
Utilizing the Resource Description Framework (RDF), which is the Semantic Web's formal language for exchanging information,
allows extending information extractors to incorporate the given domain knowledge.
Because of this, formal information items from the RDF source can be recognized in the text.
The application of RDF allows a further investigation of operations on recognized information items, such as disambiguating and rating the relevance of these.
Switching between different RDF sources allows changing the application scope of the Information Extraction system from one domain of concern to another.
An RDF-based Information Extraction system can be triggered to extract specific kinds of information entities by providing it with formal RDF queries in terms of the SPARQL query language.
Representing extracted information in RDF extends the coverage of the Semantic Web's information degree and provides a formal view on a text from the perspective of the RDF source.
In detail, this work presents the extension of existing Information Extraction approaches by incorporating the graph-based nature of RDF.
Hereby, the pre-processing of RDF sources allows extracting statistical information models dedicated to support specific information extractors.
These information extractors refine standard extraction tasks, such as the Named Entity Recognition, by using the information provided by the pre-processed models.
The post-processing of extracted information items enables representing these results in RDF format or lists, which can now be ranked or filtered by relevance.
Post-processing also comprises the enrichment of originating natural language text sources with extracted information items by using annotations in RDFa format.
The results of this research extend the state-of-the-art of the Semantic Web.
This work contributes approaches for computing customizable and adaptable RDF views on the natural language content of Web pages.
Finally, due to the formal nature of RDF, machines can interpret these views allowing developers to process the contained information in a variety of applications.