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Faculty / Organisational entity
We present a convenient notation for positive/negativeADconditional equations. Theidea is to merge rules specifying the same function by using caseAD, ifAD, matchAD, and letADexpressions.Based on the presented macroADruleADconstruct, positive/negativeADconditional equational specifiADcations can be written on a higher level. A rewrite system translates the macroADruleADconstructsinto positive/negativeADconditional equations.
Distributed systems are omnipresent nowadays and networking them is fundamental for the continuous dissemination and thus availability of data. Provision of data in real-time is one of the most important non-functional aspects that safety-critical networks must guarantee. Formal verification of data communication against worst-case deadline requirements is key to certification of emerging x-by-wire systems. Verification allows aircraft to take off, cars to steer by wire, and safety-critical industrial facilities to operate. Therefore, different methodologies for worst-case modeling and analysis of real-time systems have been established. Among them is deterministic Network Calculus (NC), a versatile technique that is applicable across multiple domains such as packet switching, task scheduling, system on chip, software-defined networking, data center networking and network virtualization. NC is a methodology to derive deterministic bounds on two crucial performance metrics of communication systems:
(a) the end-to-end delay data flows experience and
(b) the buffer space required by a server to queue all incoming data.
NC has already seen application in the industry, for instance, basic results have been used to certify the backbone network of the Airbus A380 aircraft.
The NC methodology for worst-case performance analysis of distributed real-time systems consists of two branches. Both share the NC network model but diverge regarding their respective derivation of performance bounds, i.e., their analysis principle. NC was created as a deterministic system theory for queueing analysis and its operations were later cast in a (min,+)-algebraic framework. This branch is known as algebraic Network Calculus (algNC). While algNC can efficiently compute bounds on delay and backlog, the algebraic manipulations do not allow NC to attain the most accurate bounds achievable for the given network model. These tight performance bounds can only be attained with the other, newly established branch of NC, the optimization-based analysis (optNC). However, the only optNC analysis that can currently derive tight bounds was proven to be computationally infeasible even for the analysis of moderately sized networks other than simple sequences of servers.
This thesis makes various contributions in the area of algNC: accuracy within the existing framework is improved, distributivity of the sensor network calculus analysis is established, and most significantly the algNC is extended with optimization principles. They allow algNC to derive performance bounds that are competitive with optNC. Moreover, the computational efficiency of the new NC approach is improved such that this thesis presents the first NC analysis that is both accurate and computationally feasible at the same time. It allows NC to scale to larger, more complex systems that require formal verification of their real-time capabilities.
The Internet has fallen prey to its most successful service, the World-Wide Web. The networksdo not keep up with the demands incurred by the huge amount of Web surfers. Thus, it takeslonger and longer to obtain the information one wants to access via the World-Wide Web.Many solutions to the problem of network congestion have been developed in distributed sys-tems research in general and distributed file and database systems in particular. The introduc-tion of caching and replication strategies has proven to help in many situations and thereforethese techniques are also applied to the WWW. Although most problems and associated solu-tions are known, some circumstances are different with the Web, forcing the adaptation ofknown strategies. This paper gives an overview about these differences and about currentlydeployed, developed, and evaluated solutions.
We have developed a middleware framework for workgroup environments that can support distributed software development and a variety of other application domains requiring document management and change management for distributed projects. The framework enables hypermedia-based integration of arbitrary legacy and new information resources available via a range of protocols, not necessarily known in advance to us as the general framework developers nor even to the environment instance designers. The repositories in which such information resides may be dispersed across the Internet and/or an organizational intranet. The framework also permits a range of client models for user and tool interaction, and applies an extensible suite of collaboration services, including but not limited to multi-participant workflow and coordination, to their information retrievals and updates. That is, the framework is interposed between clients, services and repositories - thus "middleware". We explain how our framework makes it easy to realize a comprehensive collection of workgroup and workflow features we culled from a requirements survey conducted by NASA.
Crowd condition monitoring concerns the crowd safety and concerns business performance metrics. The research problem to be solved is a crowd condition estimation approach to enable and support the supervision of mass events by first-responders and marketing experts, but is also targeted towards supporting social scientists, journalists, historians, public relations experts, community leaders, and political researchers. Real-time insights of the crowd condition is desired for quick reactions and historic crowd conditions measurements are desired for profound post-event crowd condition analysis.
This thesis aims to provide a systematic understanding of different approaches for crowd condition estimation by relying on 2.4 GHz signals and its variation in crowds of people, proposes and categorizes possible sensing approaches, applies supervised machine learning algorithms, and demonstrates experimental evaluation results. I categorize four sensing approaches. Firstly, stationary sensors which are sensing crowd centric signals sources. Secondly, stationary sensors which are sensing other stationary signals sources (either opportunistic or special purpose signal sources). Thirdly, a few volunteers within the crowd equipped with sensors which are sensing other surrounding crowd centric device signals (either individually, in a single group or collaboratively) within a small region. Fourthly, a small subset of participants within the crowd equipped with sensors and roaming throughout a whole city to sense wireless crowd centric signals.
I present and evaluate an approach with meshed stationary sensors which were sensing crowd centric devices. This was demonstrated and empirically evaluated within an industrial project during three of the world-wide largest automotive exhibitions. With over 30 meshed stationary sensors in an optimized setup across 6400m2 I achieved a mean absolute error of the crowd density of just 0.0115
people per square meter which equals to an average of below 6% mean relative error from the ground truth. I validate the contextual crowd condition anomaly detection method during the visit of chancellor Mrs. Merkel and during a large press conference during the exhibition. I present the approach of opportunistically sensing stationary based wireless signal variations and validate this during the Hannover CeBIT exhibition with 80 opportunistic sources with a crowd condition estimation relative error of below 12% relying only on surrounding signals in influenced by humans. Pursuing this approach I present an approach with dedicated signal sources and sensors to estimate the condition of shared office environments. I demonstrate methods being viable to even detect low density static crowds, such as people sitting at their desks, and evaluate this on an eight person office scenario. I present the approach of mobile crowd density estimation by a group of sensors detecting other crowd centric devices in the proximity with a classification accuracy of the crowd density of 66 % (improvement of over 22% over a individual sensor) during the crowded Oktoberfest event. I propose a collaborative mobile sensing approach which makes the system more robust against variations that may result from the background of the people rather than the crowd condition with differential features taking information about the link structure between actively scanning devices, the ratio between values observed by different devices, ratio of discovered crowd devices over time, team-wise diversity of discovered devices, number of semi- continuous device visibility periods, and device visibility durations into account. I validate the approach on multiple experiments including the Kaiserslautern European soccer championship public viewing event and evaluated the collaborative mobile sensing approach with a crowd condition estimation accuracy of 77 % while outperforming previous methods by 21%. I present the feasibility of deploying the wireless crowd condition sensing approach to a citywide scale during an event in Zurich with 971 actively sensing participants and outperformed the reference method by 24% in average.
In recent years several computational systems and techniques fortheorem proving by analogy have been developed. The obvious prac-tical question, however, as to whether and when to use analogy hasbeen neglected badly in these developments. This paper addresses thisquestion, identifies situations where analogy is useful, and discussesthe merits of theorem proving by analogy in these situations. Theresults can be generalized to other domains.
In this paper we present the results of the project “#Datenspende” where during the German election in 2017 more than 4000 people contributed their search results regarding keywords connected to the German election campaign.
Analyzing the donated result lists we prove, that the room for personalization of the search results is very small. Thus the opportunity for the effect mentioned in Eli Pariser’s filter bubble theory to occur in this data is also very small, to a degree that it is negligible. We achieved these results by applying various similarity measures to the result lists that were donated. The first approach using the number of common results as a similarity measure showed that the space for personalization is less than two results out of ten on average when searching for persons and at most four regarding the search for parties. Application of other, more specific measures show that the space is indeed smaller, so that the presence of filter bubbles is not evident.
Moreover this project is also a proof of concept, as it enables society to permanently monitor a search engine’s degree of personalization for any desired search terms. The general design can also be transferred to intermediaries, if appropriate APIs restrict selective access to contents relevant to the study in order to establish a similar degree of trustworthiness.
Wearable activity recognition aims to identify and assess human activities with the help
of computer systems by evaluating signals of sensors which can be attached to the human
body. This provides us with valuable information in several areas: in health care, e.g. fluid
and food intake monitoring; in sports, e.g. training support and monitoring; in entertainment,
e.g. human-computer interface using body movements; in industrial scenarios, e.g.
computer support for detected work tasks. Several challenges exist for wearable activity
recognition: a large number of nonrelevant activities (null class), the evaluation of large
numbers of sensor signals (curse of dimensionality), ambiguity of sensor signals compared
to the activities and finally the high variability of human activity in general.
This thesis develops a new activity recognition strategy, called invariants classification,
which addresses these challenges, especially the variability in human activities. The
core idea is that often even highly variable actions include short, more or less invariant
sub-actions which are due to hard physical constraints. If someone opens a door, the
movement of the hand to the door handle is not fixed. However the door handle has to
be pushed to open the door. The invariants classification algorithm is structured in four
phases: segmentation, invariant identification, classification, and spotting. The segmentation
divides the continuous sensor data stream into meaningful parts, which are related
to sub-activities. Our segmentation strategy uses the zero crossings of the central difference
quotient of the sensor signals, as segment borders. The invariant identification finds
the invariant sub-activities by means of clustering and a selection strategy dependent on
certain features. The classification identifies the segments of a specific activity class, using
models generated from the invariant sub-activities. The models include the invariant
sub-activity signal and features calculated on sensor signals related to the sub-activity. In
the spotting, the classified segments are used to find the entire activity class instances in
the continuous sensor data stream. For this purpose, we use the position of the invariant
sub-activity in the related activity class instance for the estimation of the borders of the
activity instances.
In this thesis, we show that our new activity recognition strategy, built on invariant
sub-activities, is beneficial. We tested it on three human activity datasets with wearable
inertial measurement units (IMU). Compared to previous publications on the same
datasets we got improvement in the activity recognition in several classes, some with a
large margin. Our segmentation achieves a sensible method to separate the sensor data in
relation to the underlying activities. Relying on sub-activities makes us independent from
imprecise labels on the training data. After the identification of invariant sub-activities,
we calculate a value called cluster precision for each sensor signal and each class activity.
This tells us which classes can be easily classified and which sensor channels support
the classification best. Finally, in the training for each activity class, our algorithm selects
suitable signal channels with invariant sub-activities on different points in time and
with different length. This makes our strategy a multi-dimensional asynchronous motif
detection with variable motif length.
We study the global solution of Fredholm integral equations of the second kind by the help of Monte Carlo methods. Global solution means that we seek to approximate the full solution function. This is opposed to the usual applications of Monte Carlo, were one only wants to approximate a functional of the solution. In recent years several researchers developed Monte Carlo methods also for the global problem. In this paper we present a new Monte Carlo algorithm for the global solution of integral equations. We use multiwavelet expansions to approximate the solution. We study the behaviour of variance on increasing levels, and based on this, develop a new variance reduction technique. For classes of smooth kernels and right hand sides we determine the convergence rate of this algorithm and show that it is higher
than those of previously developed algorithms for the global problem. Moreover, an information-based complexity analysis shows that our algorithm is optimal among all stochastic algorithms of the same computational
cost and that no deterministic algorithm of the same cost can reach its convergence rate.