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This paper describes the estimation of the body weight of a person in front of an RGB-D camera. A survey of different methods for body weight estimation based on depth sensors is given. First, an estimation of people standing in front of a camera is presented. Second, an approach based on a stream of depth images is used to obtain the body weight of a person walking towards a sensor. The algorithm first extracts features from a point cloud and forwards them to an artificial neural network (ANN) to obtain an estimation of body weight. Besides the algorithm for the estimation, this paper further presents an open-access dataset based on measurements from a trauma room in a hospital as well as data from visitors of a public event. In total, the dataset contains 439 measurements. The article illustrates the efficiency of the approach with experiments with persons lying down in a hospital, standing persons, and walking persons. Applicable scenarios for the presented algorithm are body weight-related dosing of emergency patients.
DLTPulseGenerator: a library for the simulation of lifetime spectra based on detector-output pulses
(2018)
The quantitative analysis of lifetime spectra relevant in both life and materials sciences presents one of the ill-posed inverse problems and, hence, leads to most stringent requirements on the hardware specifications and the analysis algorithms. Here we present DLTPulseGenerator, a library written in native C++ 11, which provides a simulation of lifetime spectra according to the measurement setup. The simulation is based on pairs of non-TTL detector output-pulses. Those pulses require the Constant Fraction Principle (CFD) for the determination of the exact timing signal and, thus, the calculation of the time difference i.e. the lifetime. To verify the functionality, simulation results were compared to experimentally obtained data using Positron Annihilation Lifetime Spectroscopy (PALS) on pure tin.
Lifetime techniques are applied to diverse fields of study including materials sciences, semiconductor physics, biology, molecular biophysics and photochemistry.
Here we present DDRS4PALS, a software for the acquisition and simulation of lifetime spectra using the DRS4 evaluation board (Paul Scherrer Institute, Switzerland) for time resolved measurements and digitization of detector output pulses. Artifact afflicted pulses can be corrected or rejected prior to the lifetime calculation to provide the generation of high-quality lifetime spectra, which are crucial for a profound analysis, i.e. the decomposition of the true information. Moreover, the pulses can be streamed on an (external) hard drive during the measurement and subsequently downloaded in the offline mode without being connected to the hardware. This allows the generation of various lifetime spectra at different configurations from one single measurement and, hence, a meaningful comparison in terms of analyzability and quality. Parallel processing and an integrated JavaScript based language provide convenient options to accelerate and automate time consuming processes such as lifetime spectra simulations.
Maps are the main tool to represent geographical information. Users often zoom in and out to access maps at different scales. Continuous map generalization tries to make the changes between different scales smooth, which is essential to provide users with comfortable zooming experience.
In order to achieve continuous map generalization with high quality, we optimize some important aspects of maps. In this book, we have used optimization in the generalization of land-cover areas, administrative boundaries, buildings, and coastlines. According to our experiments, continuous map generalization indeed benefits from optimization.
The charged aerosol detector (CAD) is the latest representative of aerosol-based detectors that generate a response independent of the analytes' chemical structure. This study was aimed at accurately predicting the CAD response of homologous fatty acids under varying experimental conditions. Fatty acids from C12 to C18 were used as model substances due to semivolatile characterics that caused non-uniform CAD behaviour. Considering both experimental conditions and molecular descriptors, a mixed quantitative structure-property relationship (QSPR) modeling was performed using Gradient Boosted Trees (GBT). The ensemble of 10 decisions trees (learning rate set at 0.55, the maximal depth set at 5, and the sample rate set at 1.0) was able to explain approximately 99% (Q\(^2\): 0.987, RMSE: 0.051) of the observed variance in CAD responses. Validation using an external test compound confirmed the high predictive ability of the model established (R-2: 0.990, RMSEP: 0.050). With respect to the intrinsic attribute selection strategy, GBT used almost all independent variables during model building. Finally, it attributed the highest importance to the power function value, the flow rate of the mobile phase, evaporation temperature, the content of the organic solvent in the mobile phase and the molecular descriptors such as molecular weight (MW), Radial Distribution Function-080/weighted by mass (RDF080m) and average coefficient of the last eigenvector from distance/detour matrix (Ve2_D/Dt). The identification of the factors most relevant to the CAD responsiveness has contributed to a better understanding of the underlying mechanisms of signal generation. An increased CAD response that was obtained for acetone as organic modifier demonstrated its potential to replace the more expensive and environmentally harmful acetonitrile.
Modern software is often realized as a modular combination of subsystems for, e. g.,
knowledge management, visualization, verification, or the interaction with users. As
a result, software libraries from possibly different programming languages have to
work together. Even more complex the case is if different programming paradigms
have to be combined. This type of diversification of programming languages and
paradigms in just one software application can only be mastered by mechanisms
for a seamless integration of the involved programming languages. However, the
integration of the common logic programming language Prolog and the popular
object-oriented programming language Java is complicated by various interoperability
problems which stem on the one hand from the paradigmatic gap between the
programming languages, and on the other hand, from the diversity of the available
Prolog systems.
The subject of the thesis is the investigation of novel mechanisms for the integration
of logic programming in Prolog and object–oriented programming in Java. We are
particularly interested in an object–oriented, uniform approach which is not specific
to just one Prolog system. Therefore, we have first identified several important
criteria for the seamless integration of Prolog and Java from the object–oriented
perspective. The main contribution of the thesis is a novel integration framework
called the Connector Architecture for Prolog and Java (CAPJa). The framework is
completely implemented in Java and imposes no modifications to the Java Virtual
Machine or Prolog. CAPJa provides a semi–automated mechanism for the integration
of Prolog predicates into Java. For compact, readable, and object–oriented
queries to Prolog, CAPJa exploits lambda expressions with conditional and relational
operators in Java. The communication between Java and Prolog is based
on a fully automated mapping of Java objects to Prolog terms, and vice versa. In
Java, an extensible system of gateways provides connectivity with various Prolog
system and, moreover, makes any connected Prolog system easily interchangeable,
without major adaption in Java.
Synthetically designed alternative photorespiratory pathways increase the biomass of tobacco and rice plants. Likewise, some in planta–tested synthetic carbon-concentrating cycles (CCCs) hold promise to increase plant biomass while diminishing atmospheric carbon dioxide burden. Taking these individual contributions into account, we hypothesize that the integration of bypasses and CCCs will further increase plant productivity. To test this in silico, we reconstructed a metabolic model by integrating photorespiration and photosynthesis with the synthetically designed alternative pathway 3 (AP3) enzymes and transporters. We calculated fluxes of the native plant system and those of AP3 combined with the inhibition of the glycolate/glycerate transporter by using the YANAsquare package. The activity values corresponding to each enzyme in photosynthesis, photorespiration, and for synthetically designed alternative pathways were estimated. Next, we modeled the effect of the crotonyl-CoA/ethylmalonyl-CoA/hydroxybutyryl-CoA cycle (CETCH), which is a set of natural and synthetically designed enzymes that fix CO₂ manifold more than the native Calvin–Benson–Bassham (CBB) cycle. We compared estimated fluxes across various pathways in the native model and under an introduced CETCH cycle. Moreover, we combined CETCH and AP3-w/plgg1RNAi, and calculated the fluxes. We anticipate higher carbon dioxide–harvesting potential in plants with an AP3 bypass and CETCH–AP3 combination. We discuss the in vivo implementation of these strategies for the improvement of C3 plants and in natural high carbon harvesters.
In today's Internet, building overlay structures to provide a service is becoming more and more common. This approach allows for the utilization of client resources, thus being more scalable than a client-server model in this respect. However, in these architectures the quality of the provided service depends on the clients and is therefore more complex to manage. Resource utilization, both at the clients themselves and in the underlying network, determine the efficiency of the overlay application. Here, a trade-off exists between the resource providers and the end users that can be tuned via overlay mechanisms. Thus, resource management and traffic management is always quality-of-service management as well. In this monograph, the three currently significant and most widely used overlay types in the Internet are considered. These overlays are implemented in popular applications which only recently have gained importance. Thus, these overlay networks still face real-world technical challenges which are of high practical relevance. We identify the specific issues for each of the considered overlays, and show how their optimization affects the trade-offs between resource efficiency and service quality. Thus, we supply new insights and system knowledge that is not provided by previous work.
Shannon channel capacity estimation, based on large packet length is used in traditional Radio Resource Management (RRM) optimization. This is good for the normal transmission of data in a wired or wireless system. For industrial automation and control, rather short packages are used due to the short-latency requirements. Using Shannon’s formula leads in this case to inaccurate RRM solutions, thus another formula should be used to optimize radio resources in short block-length packet transmission, which is the basic of Ultra-Reliable Low-Latency Communications (URLLCs). The stringent requirement of delay Quality of Service (QoS) for URLLCs requires a link-level channel model rather than a physical level channel model. After finding the basic and accurate formula of the achievable rate of short block-length packet transmission, the RRM optimization problem can be accurately formulated and solved under the new constraints of URLLCs. In this short paper, the current mathematical models, which are used in formulating the effective transmission rate of URLLCs, will be briefly explained. Then, using this rate in RRM for URLLC will be discussed.
Having a mixed-cultural membership becomes increasingly common in our modern society. It is thus beneficial in several ways to create Intelligent Virtual Agents (IVAs) that reflect a mixed-cultural background as well, e.g., for educational settings. For research with such IVAs, it is essential that they are classified as non-native by members of a target culture. In this paper, we focus on variations of IVAs’ speech to create the impression of non-native speakers that are identified as such by speakers of two different mother tongues. In particular, we investigate grammatical mistakes and identify thresholds beyond which the agents is clearly categorised as a non-native speaker. Therefore, we conducted two experiments: one for native speakers of German, and one for native speakers of English. Results of the German study indicate that beyond 10% of word order mistakes and 25% of infinitive mistakes German-speaking IVAs are perceived as non-native speakers. Results of the English study indicate that beyond 50% of omission mistakes and 50% of infinitive mistakes English-speaking IVAs are perceived as non-native speakers. We believe these thresholds constitute helpful guidelines for computational approaches of non-native speaker generation, simplifying research with IVAs in mixed-cultural settings.
Despite the fact that mixed-cultural backgrounds become of increasing importance in our daily life, the representation of multiple cultural backgrounds in one entity is still rare in socially interactive agents (SIAs). This paper’s contribution is twofold. First, it provides a survey of research on mixed-cultured SIAs. Second, it presents a study investigating how mixed-cultural speech (in this case, non-native accent) influences how a virtual robot is perceived in terms of personality, warmth, competence and credibility. Participants with English or German respectively as their first language watched a video of a virtual robot speaking in either standard English or German-accented English. It was expected that the German-accented speech would be rated more positively by native German participants as well as elicit the German stereotypes credibility and conscientiousness for both German and English participants. Contrary to the expectations, German participants rated the virtual robot lower in terms of competence and credibility when it spoke with a German accent, whereas English participants perceived the virtual robot with a German accent as more credible compared to the version without an accent. Both the native English and native German listeners classified the virtual robot with a German accent as significantly more neurotic than the virtual robot speaking standard English. This work shows that by solely implementing a non-native accent in a virtual robot, stereotypes are partly transferred. It also shows that the implementation of a non-native accent leads to differences in the perception of the virtual robot.
Knowledge encoding in game mechanics: transfer-oriented knowledge learning in desktop-3D and VR
(2019)
Affine Transformations (ATs) are a complex and abstract learning content. Encoding the AT knowledge in Game Mechanics (GMs) achieves a repetitive knowledge application and audiovisual demonstration. Playing a serious game providing these GMs leads to motivating and effective knowledge learning. Using immersive Virtual Reality (VR) has the potential to even further increase the serious game’s learning outcome and learning quality. This paper compares the effectiveness and efficiency of desktop-3D and VR in respect to the achieved learning outcome. Also, the present study analyzes the effectiveness of an enhanced audiovisual knowledge encoding and the provision of a debriefing system. The results validate the effectiveness of the knowledge encoding in GMs to achieve knowledge learning. The study also indicates that VR is beneficial for the overall learning quality and that an enhanced audiovisual encoding has only a limited effect on the learning outcome.
Impaired decision-making leads to the inability to distinguish between advantageous and disadvantageous choices. The impairment of a person’s decision-making is a common goal of gambling games. Given the recent trend of gambling using immersive Virtual Reality it is crucial to investigate the effects of both immersion and the virtual environment (VE) on decision-making. In a novel user study, we measured decision-making using three virtual versions of the Iowa Gambling Task (IGT). The versions differed with regard to the degree of immersion and design of the virtual environment. While emotions affect decision-making, we further measured the positive and negative affect of participants. A higher visual angle on a stimulus leads to an increased emotional response. Thus, we kept the visual angle on the Iowa Gambling Task the same between our conditions. Our results revealed no significant impact of immersion or the VE on the IGT. We further found no significant difference between the conditions with regard to positive and negative affect. This suggests that neither the medium used nor the design of the VE causes an impairment of decision-making. However, in combination with a recent study, we provide first evidence that a higher visual angle on the IGT leads to an effect of impairment.
The successful development and classroom integration of Virtual (VR) and Augmented Reality (AR) learning environments requires competencies and content knowledge with respect to media didactics and the respective technologies. The paper discusses a pedagogical concept specifically aiming at the interdisciplinary education of pre-service teachers in collaboration with human-computer interaction students. The students’ overarching goal is the interdisciplinary realization and integration of VR/AR learning environments in teaching and learning concepts. To assist this approach, we developed a specific tutorial guiding the developmental process. We evaluate and validate the effectiveness of the overall pedagogical concept by analyzing the change in attitudes regarding 1) the use of VR/AR for educational purposes and in competencies and content knowledge regarding 2) media didactics and 3) technology. Our results indicate a significant improvement in the knowledge of media didactics and technology. We further report on four STEM learning environments that have been developed during the seminar.
The landscape of today’s programming languages is manifold. With the diversity of applications, the difficulty of adequately addressing and specifying the used programs increases. This often leads to newly designed and implemented domain-specific languages. They enable domain experts to express knowledge in their preferred format, resulting in more readable and concise programs. Due to its flexible and declarative syntax without reserved keywords, the logic programming language Prolog is particularly suitable for defining and embedding domain-specific languages.
This thesis addresses the questions and challenges that arise when integrating domain-specific languages into Prolog. We compare the two approaches to define them either externally or internally, and provide assisting tools for each. The grammar of a formal language is usually defined in the extended Backus–Naur form. In this work, we handle this formalism as a domain-specific language in Prolog, and define term expansions that allow to translate it into equivalent definite clause grammars. We present the package library(dcg4pt) for SWI-Prolog, which enriches them by an additional argument to automatically process the term’s corresponding parse tree. To simplify the work with definite clause grammars, we visualise their application by a web-based tracer.
The external integration of domain-specific languages requires the programmer to keep the grammar, parser, and interpreter in sync. In many cases, domain-specific languages can instead be directly embedded into Prolog by providing appropriate operator definitions. In addition, we propose syntactic extensions for Prolog to expand its expressiveness, for instance to state logic formulas with their connectives verbatim. This allows to use all tools that were originally written for Prolog, for instance code linters and editors with syntax highlighting. We present the package library(plammar), a standard-compliant parser for Prolog source code, written in Prolog. It is able to automatically infer from example sentences the required operator definitions with their classes and precedences as well as the required Prolog language extensions. As a result, we can automatically answer the question: Is it possible to model these example sentences as valid Prolog clauses, and how?
We discuss and apply the two approaches to internal and external integrations for several domain-specific languages, namely the extended Backus–Naur form, GraphQL, XPath, and a controlled natural language to represent expert rules in if-then form. The created toolchain with library(dcg4pt) and library(plammar) yields new application opportunities for static Prolog source code analysis, which we also present.
Making machines understand natural language is a dream of mankind that existed
since a very long time. Early attempts at programming machines to converse with
humans in a supposedly intelligent way with humans relied on phrase lists and simple
keyword matching. However, such approaches cannot provide semantically adequate
answers, as they do not consider the specific meaning of the conversation. Thus, if we
want to enable machines to actually understand language, we need to be able to access
semantically relevant background knowledge. For this, it is possible to query so-called
ontologies, which are large networks containing knowledge about real-world entities
and their semantic relations. However, creating such ontologies is a tedious task, as often
extensive expert knowledge is required. Thus, we need to find ways to automatically
construct and update ontologies that fit human intuition of semantics and semantic
relations. More specifically, we need to determine semantic entities and find relations
between them. While this is usually done on large corpora of unstructured text, previous
work has shown that we can at least facilitate the first issue of extracting entities by
considering special data such as tagging data or human navigational paths. Here, we do
not need to detect the actual semantic entities, as they are already provided because of
the way those data are collected. Thus we can mainly focus on the problem of assessing
the degree of semantic relatedness between tags or web pages. However, there exist
several issues which need to be overcome, if we want to approximate human intuition of
semantic relatedness. For this, it is necessary to represent words and concepts in a way
that allows easy and highly precise semantic characterization. This also largely depends
on the quality of data from which these representations are constructed.
In this thesis, we extract semantic information from both tagging data created by users
of social tagging systems and human navigation data in different semantic-driven social
web systems. Our main goal is to construct high quality and robust vector representations
of words which can the be used to measure the relatedness of semantic concepts.
First, we show that navigation in the social media systems Wikipedia and BibSonomy is
driven by a semantic component. After this, we discuss and extend methods to model
the semantic information in tagging data as low-dimensional vectors. Furthermore, we
show that tagging pragmatics influences different facets of tagging semantics. We then
investigate the usefulness of human navigational paths in several different settings on
Wikipedia and BibSonomy for measuring semantic relatedness. Finally, we propose
a metric-learning based algorithm in adapt pre-trained word embeddings to datasets
containing human judgment of semantic relatedness.
This work contributes to the field of studying semantic relatedness between words
by proposing methods to extract semantic relatedness from web navigation, learn highquality
and low-dimensional word representations from tagging data, and to learn
semantic relatedness from any kind of vector representation by exploiting human
feedback. Applications first and foremest lie in ontology learning for the Semantic Web,
but also semantic search or query expansion.
The emerging serverless computing may meet Edge Cloud in a beneficial manner as the two offer flexibility and dynamicity in optimizing finite hardware resources. However, the lack of proper study of a joint platform leaves a gap in literature about consumption and performance of such integration. To this end, this paper identifies the key questions and proposes a methodology to answer them.
This paper discusses the problem of finding multiple shortest disjoint paths in modern communication networks, which is essential for ultra-reliable and time-sensitive applications. Dijkstra’s algorithm has been a popular solution for the shortest path problem, but repetitive use of it to find multiple paths is not scalable. The Multiple Disjoint Path Algorithm (MDPAlg), published in 2021, proposes the use of a single full graph to construct multiple disjoint paths. This paper proposes modifications to the algorithm to include a delay constraint, which is important in time-sensitive applications. Different delay constraint least-cost routing algorithms are compared in a comprehensive manner to evaluate the benefits of the adapted MDPAlg algorithm. Fault tolerance, and thereby reliability, is ensured by generating multiple link-disjoint paths from source to destination.
Given a collection of diverging documents about some lost original text, any person interested in the text would try reconstructing it from the diverging documents. Whether it is eclecticism, stemmatics, or copy-text, one is expected to explicitly or indirectly select one of the documents as a starting point or as a base text, which could be emended through comparison with remaining documents, so that a text that could be designated as the original document is generated. Unfortunately the process of giving priority to one of the documents also known as witnesses is a subjective approach. In fact even Cladistics, which could be considered as a computer-based approach of implementing stemmatics, does not present or recommend users to select a certain witness as a starting point for the process of reconstructing the original document. In this study, a computational method using a rule-based Bayesian classifier is used, to assist text scholars in their attempts of reconstructing a non-existing document from some available witnesses. The method developed in this study consists of selecting a base text successively and collating it with remaining documents. Each completed collation cycle stores the selected base text and its closest witness, along with a weighted score of their similarities and differences. At the end of the collation process, a witness selected more often by majority of base texts is considered as the probable base text of the collection. Witnesses’ scores are weighted using a weighting system, based on effects of types of textual modifications on the process of reconstructing original documents. Users have the possibility to select between baseless and base text collation. If a base text is selected, the task is reduced to ranking the witnesses with respect to the base text, otherwise a base text as well as ranking of the witnesses with respect to the base text are computed and displayed on a histogram.
Learning a book in general involves reading it, underlining important words, adding comments, summarizing some passages, and marking up some text or concepts. Once deeper understanding is achieved, one would like to organize and manage her/his knowledge in such a way that, it could be easily remembered and efficiently transmitted to others. In this paper, books organized in terms of chapters consisting of verses, are considered as the source of knowledge to be modeled. The knowledge model consists of verses with their metadata and semantic annotations. The metadata represent the multiple perspectives of knowledge modeling. Verses with their metadata and annotations form a meta-model, which will be published on a web Mashup. The meta-model with linking between its elements constitute a knowledge base. An XML-based annotation system breaking down the learning process into specific tasks, helps constructing the desired meta-model. The system is made up of user interfaces for creating metadata, annotating chapters’ contents according to user selected semantics, and templates for publishing the generated knowledge on the Internet. The proposed software system improves comprehension and retention of knowledge contained in religious texts through modeling and visualization. The system has been applied to the Quran, and the result obtained shows that multiple perspectives of information modeling can be successfully applied to religious texts. It is expected that this short ongoing study would motivate others to engage in devising and offering software systems for cross-religions learning.