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A Knowledge-based Hybrid Statistical Classifier for Reconstructing the Chronology of the Quran
(2011)
Computationally categorizing Quran’s chapters has been mainly confined to the determination of chapters’ revelation places. However this broad classification is not sufficient to effectively and thoroughly understand and interpret the Quran. The chronology of revelation would not only improve comprehending the philosophy of Islam, but also the easiness of implementing and memorizing its laws and recommendations. This paper attempts estimating possible chapters’ dates of revelation through their lexical frequency profiles. A hybrid statistical classifier consisting of stemming and clustering algorithms for comparing lexical frequency profiles of chapters, and deriving dates of revelation has been developed. The classifier is trained using some chapters with known dates of revelation. Then it classifies chapters with uncertain dates of revelation by computing their proximity to the training ones. The results reported here indicate that the proposed methodology yields usable results in estimating dates of revelation of the Quran’s chapters based on their lexical contents.
Overlapping is a common word used to describe documents whose structural dimensions cannot be adequately represented using tree structure. For instance a quotation that starts in one verse and ends in another verse. The problem of overlapping hierarchies is a recurring one, which has been addressed by a variety of approaches. There are XML based solutions as well as Non-XML ones. The XML-based solutions are: multiple documents, empty elements, fragmentation, out-of-line markup, JITT and BUVH. And the Non-XML approaches comprise CONCUR/XCONCUR, MECS, LMNL ...etc. This paper presents shortly state-of-the-art in overlapping hierarchies, and introduces two variations on the TEI fragmentation markup that have several advantages.
The Visual Editor for XML (Vex)[1] used by TextGrid [2]and other applications has got rendering and layout engines. The layout engine is well documented but the rendering engine is not. This lack of documenting the rendering engine has made refactoring and extending the editor hard and tedious. For instance many CSS2.1 and upcoming CSS3 properties have not been implemented. Software developers in different projects such as TextGrid using Vex would like to update its CSS rendering engine in order to provide advanced user interfaces as well as support different document types. In order to minimize the effort of extending Vex functionality, I found it beneficial to write a basic documentation about Vex software architecture in general and its CSS rendering engine in particular. The documentation is mainly based on the idea of architectural layered diagrams. In fact layered diagrams can help developers understand software’s source code faster and easier in order to alter it, and fix errors. This paper is written for the purpose of providing direct support for exploration in the comprehension process of Vex source code. It discusses Vex software architecture. The organization of packages that make up the software, the architecture of its CSS rendering engine, an algorithm explaining the working principle of its rendering engine are described.
The technique of using Cascading Style Sheets (CSS) to format and present structured data is called CSS processing model. For instance a CSS processing model for XML documents describes steps involved in formatting and presenting XML documents on screens or papers. Many software applications such as browsers and XML editors have their own CSS processing models which are part of their rendering engines. For instance each browser based on its CSS processing model renders CSS layout differently, as a result an inconsistency in the support of CSS features arises. Some browsers support more CSS features than others, and the rendering itself varies. Moreover the W3C standards are not even adhered by some browsers such as Internet Explorer. Test suites and other hacks and filters cannot definitely solve these problems, because these solutions are temporary and fragile. To palliate this inconsistency and browser compatibility issues with respect to CSS, a reference CSS processing model is needed. By extension it could even allow interoperability across CSS rendering engines. A reference architecture would provide common software architecture and interfaces, and facilitate refactoring, reuse, and automated unit testing. In [2] a reference architecture for browsers has been proposed. However this reference architecture is a macro reference model which does not consider separately individual components of rendering and layout engines. In this paper an attempt to develop a reference architecture for CSS processing models is discussed. In addition the Vex editor [3] rendering and layout engines, as well as an extended version of the editor used in TextGrid project [5] are also presented in order to validate the proposed reference architecture.
Empirical Study on Screen Scraping Web Service Creation: Case of Rhein-Main-Verkehrsverbund (RMV)
(2010)
Internet is the biggest database that science and technology have ever produced. The world wide web is a large repository of information that cannot be used for automation by many applications due to its limited target audience. One of the solutions to the automation problem is to develop wrappers. Wrapping is a process whereby unstructured extracted information is transformed into a more structured one such as XML, which could be provided as webservice to other applications. A web service is a web page whose content is well structured so that a computer program can consume it automatically. This paper describes steps involved in constructing wrappers manually in order to automatically generate web services.
This article discusses web frameworks that are available to a software developer in Java language. It introduces MVC paradigm and some frameworks that implement it. The article presents an overview of Struts, Spring MVC, JSF Frameworks, as well as guidelines for selecting one of them as development environment.
Webservices composition is traditionally carried out using composition technologies such as Business Process Execution Language (BPEL) [1] and Web Service Choreography Interface (WSCI) [2]. The composition technology involves the process of web service discovery, invocation, and composition. However these technologies are not easy and flexible enough because they are mainly developer-centric. Moreover majority of websites have not yet embarked into the world of web service, although they have very important and useful information to offer. Is it because they have not understood the usefulness of web services or is it because of the costs? Whatever might be the answers to these questions, time and money are definitely required in order to create and offer web services. To avoid these expenditures, wrappers [7] to automatically generate webservices from websites would be a cheaper and easier solution. Mashups offer a different way of doing webservices composition. In web environment a Mashup is a web application that brings together data from several sources using webservices, APIs, wrappers and so on, in order to create entirely a new application that was not provided before. This paper presents first an overview of Mashups and the process of web service invocation and composition based on Mashup, then describes an example of a web-based simulator for navigation system in Germany.
This paper discusses the categorization of Quranic chapters by major phases of Prophet Mohammad’s messengership using machine learning algorithms. First, the chapters were categorized by places of revelation using Support Vector Machine and naïve Bayesian classifiers separately, and their results were compared to each other, as well as to the existing traditional Islamic and western orientalists classifications. The chapters were categorized into Meccan (revealed in Mecca) and Medinan (revealed in Medina). After that, chapters of each category were clustered using a kind of fuzzy-single linkage clustering approach, in order to correspond to the major phases of Prophet Mohammad’s life. The major phases of the Prophet’s life were manually derived from the Quranic text, as well as from the secondary Islamic literature e.g hadiths, exegesis. Previous studies on computing the places of revelation of Quranic chapters relied heavily on features extracted from existing background knowledge of the chapters. For instance, it is known that Meccan chapters contain mostly verses about faith and related problems, while Medinan ones encompass verses dealing with social issues, battles…etc. These features are by themselves insufficient as a basis for assigning the chapters to their respective places of revelation. In fact, there are exceptions, since some chapters do contain both Meccan and Medinan features. In this study, features of each category were automatically created from very few chapters, whose places of revelation have been determined through identification of historical facts and events such as battles, migration to Medina…etc. Chapters having unanimously agreed places of revelation were used as the initial training set, while the remaining chapters formed the testing set. The classification process was made recursive by regularly augmenting the training set with correctly classified chapters, in order to classify the whole testing set. Each chapter was preprocessed by removing unimportant words, stemming, and representation with vector space model. The result of this study shows that, the two classifiers have produced useable results, with an outperformance of the support vector machine classifier. This study indicates that, the proposed methodology yields encouraging results for arranging Quranic chapters by phases of Prophet Mohammad’s messengership.
In this research, an attempt to create a knowledge-based learning system for the Quranic text has been performed. The knowledge base is made up of the Quranic text along with detailed information about each chapter and verse, and some rules. The system offers the possibility to study the Quran through web-based interfaces, implementing novel visualization techniques for browsing, querying, consulting, and testing the acquired knowledge. Additionally the system possesses knowledge acquisition facilities for maintaining the knowledge base.
Computing Generic Causes of Revelation of the Quranic Verses Using Machine Learning Techniques
(2011)
Because many verses of the holy Quran are similar, there is high probability that, similar verses addressing same issues share same generic causes of revelation. In this study, machine learning techniques have been employed in order to automatically derive causes of revelation of Quranic verses. The derivation of the causes of revelation is viewed as a classification problem. Initially the categories are based on the verses with known causes of revelation, and the testing set consists of the remaining verses. Based on a computed threshold value, a naïve Bayesian classifier is used to categorize some verses. After that, using a decision tree classifier the remaining uncategorized verses are separated into verses that contain indicators (resultative connectors, causative expressions…), and those that do not. As for those verses having indicators, each one is segmented into its constituent clauses by identification of the linking indicators. Then a dominant clause is extracted and considered either as the cause of revelation, or post-processed by adding or subtracting some terms to form a causal clause that constitutes the cause of revelation. Concerning remaining unclassified verses without indicators, a naive Bayesian classifier is again used to assign each one of them to one of the existing classes based on features and topics similarity. As for verses that could not be classified so far, manual classification was made by considering each verse as a category on its own. The result obtained in this study is encouraging, and shows that automatic derivation of Quranic verses’ generic causes of revelation is achievable, and reasonably reliable for understanding and implementing the teachings of the Quran.
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. This paper discusses about modeling religious texts using semantic XML markup based on frame-based knowledge representation, with the purpose of assisting understanding, retention, and sharing of knowledge they contain. In this study, books organized in terms of chapters made up of verses are considered as the source of knowledge to model. Some metadata representing the multiple perspectives of knowledge modeling are assigned to each chapter and verse. Chapters and verses with their metadata form a meta-model, which is represented using frames, and published on a web mashup. An XML-based annotation and visualization system equipped with user interfaces for creating static and dynamic metadata, annotating chapters’ contents according to user selected semantics, and templates for publishing generated knowledge on the Internet, has been developed. 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, in order to support analysis, understanding, and retention of the texts.
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 bar diagram. Additionally this study includes a recursive algorithm for automatically reconstructing the original text from the identified base text and ranked witnesses.
The question of why the Quran structure does not follow its chronology of revelation is a recurring one. Some Islamic scholars such as [1] have answered the question using hadiths, as well as other philosophical reasons based on internal evidences of the Quran itself. Unfortunately till today many are still wondering about this issue. Muslims believe that the Quran is a summary and a copy of the content of a preserved tablet called Lawhul-Mahfuz located in the heaven. Logically speaking, this suggests that the arrangement of the verses and chapters is expected to be similar to that of the Lawhul-Mahfuz. As for the arrangement of the verses in each chapter, there is unanimity that it was carried out by the Prophet himself under the guidance of Angel Gabriel with the recommendation of God. But concerning the ordering of the chapters, there are reports about some divergences [3] among the Prophet’s companions as to which chapter should precede which one. This paper argues that Quranic chapters might have been arranged according to months and seasons of revelation. In fact, based on some verses of the Quran, it is defendable that the Lawhul-Mahfuz itself is understood to have been structured in terms of the months of the year. In this study, philosophical and mathematical arguments for computing chapters’ months of revelation are discussed, and the result is displayed on an interactive scatter plot.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Interactive system for similarity-based inspection and assessment of the well-being of mHealth users
(2021)
Recent digitization technologies empower mHealth users to conveniently record their Ecological Momentary Assessments (EMA) through web applications, smartphones, and wearable devices. These recordings can help clinicians understand how the users' condition changes, but appropriate learning and visualization mechanisms are required for this purpose. We propose a web-based visual analytics tool, which processes clinical data as well as EMAs that were recorded through a mHealth application. The goals we pursue are (1) to predict the condition of the user in the near and the far future, while also identifying the clinical data that mostly contribute to EMA predictions, (2) to identify users with outlier EMA, and (3) to show to what extent the EMAs of a user are in line with or diverge from those users similar to him/her. We report our findings based on a pilot study on patient empowerment, involving tinnitus patients who recorded EMAs with the mHealth app TinnitusTips. To validate our method, we also derived synthetic data from the same pilot study. Based on this setting, results for different use cases are reported.
A key feature for Internet of Things (IoT) is to control what content is available to each user. To handle this access management, encryption schemes can be used. Due to the diverse usage of encryption schemes, there are various realizations of 1-to-1, 1-to-n, and n-to-n schemes in the literature. This multitude of encryption methods with a wide variety of properties presents developers with the challenge of selecting the optimal method for a particular use case, which is further complicated by the fact that there is no overview of existing encryption schemes. To fill this gap, we envision a cryptography encyclopedia providing such an overview of existing encryption schemes. In this survey paper, we take a first step towards such an encyclopedia by creating a sub-encyclopedia for secure group communication (SGC) schemes, which belong to the n-to-n category. We extensively surveyed the state-of-the-art and classified 47 different schemes. More precisely, we provide (i) a comprehensive overview of the relevant security features, (ii) a set of relevant performance metrics, (iii) a classification for secure group communication schemes, and (iv) workflow descriptions of the 47 schemes. Moreover, we perform a detailed performance and security evaluation of the 47 secure group communication schemes. Based on this evaluation, we create a guideline for the selection of secure group communication schemes.
Future broadband wireless networks should be able to support not only best effort traffic but also real-time traffic with strict Quality of Service (QoS) constraints. In addition, their available resources are scare and limit the number of users. To facilitate QoS guarantees and increase the maximum number of concurrent users, wireless networks require careful planning and optimization. In this monograph, we studied three aspects of performance optimization in wireless networks: resource optimization in WLAN infrastructure networks, quality of experience control in wireless mesh networks, and planning and optimization of wireless mesh networks. An adaptive resource management system is required to effectively utilize the limited resources on the air interface and to guarantee QoS for real-time applications. Thereby, both WLAN infrastructure and WLAN mesh networks have to be considered. An a-priori setting of the access parameters is not meaningful due to the contention-based medium access and the high dynamics of the system. Thus, a management system is required which dynamically adjusts the channel access parameters based on the network load. While this is sufficient for wireless infrastructure networks, interferences on neighboring paths and self-interferences have to be considered for wireless mesh networks. In addition, a careful channel allocation and route assignment is needed. Due to the large parameter space, standard optimization techniques fail for optimizing large wireless mesh networks. In this monograph, we reveal that biology-inspired optimization techniques, namely genetic algorithms, are well-suitable for the planning and optimization of wireless mesh networks. Although genetic algorithms generally do not always find the optimal solution, we show that with a good parameter set for the genetic algorithm, the overall throughput of the wireless mesh network can be significantly improved while still sharing the resources fairly among the users.
Die künstliche Intelligenz (KI) entwickelt sich rasant und hat bereits eindrucksvolle Erfolge zu verzeichnen, darunter übermenschliche Kompetenz in den meisten Spielen und vielen Quizshows, intelligente Suchmaschinen, individualisierte Werbung, Spracherkennung, -ausgabe und -übersetzung auf sehr hohem Niveau und hervorragende Leistungen bei der Bildverarbeitung, u. a. in der Medizin, der optischen Zeichenerkennung, beim autonomen Fahren, aber auch beim Erkennen von Menschen auf Bildern und Videos oder bei Deep Fakes für Fotos und Videos. Es ist zu erwarten, dass die KI auch in der Entscheidungsfindung Menschen übertreffen wird; ein alter Traum der Expertensysteme, der durch Lernverfahren, Big Data und Zugang zu dem gesammelten Wissen im Web in greifbare Nähe rückt. Gegenstand dieses Beitrags sind aber weniger die technischen Entwicklungen, sondern mögliche gesellschaftliche Auswirkungen einer spezialisierten, kompetenten KI für verschiedene Bereiche der autonomen, d. h. nicht nur unterstützenden Entscheidungsfindung: als Fußballschiedsrichter, in der Medizin, für richterliche Entscheidungen und sehr spekulativ auch im politischen Bereich. Dabei werden Vor- und Nachteile dieser Szenarien aus gesellschaftlicher Sicht diskutiert.
The Fifth Generation (5G) communication technology, its infrastructure and architecture, though already deployed in campus and small scale networks, is still undergoing continuous changes and research. Especially, in the light of future large scale deployments and industrial use cases, a detailed analysis of the performance and utilization with regard to latency and service times constraints is crucial. To this end, a fine granular investigation of the Network Function (NF) based core system and the duration for all the tasks performed by these services is necessary. This work presents the first steps towards analyzing the signaling traffic in 5G core networks, and introduces a tool to automatically extract sequence diagrams and service times for NF tasks from traffic traces.
This work proposes a novel approach to disperse dense transmission intervals and reduce bursty traffic patterns without the need for centralized control. Furthermore, by keeping the mechanism as close to the Long Range Wide Area Network (LoRaWAN) standard as possible the suggested mechanism can be deployed within existing networks and can even be co-deployed with other devices.
The introduction of new types of frequency spectrum in 6G technology facilitates the convergence of conventional mobile communications and radar functions. Thus, the mobile network itself becomes a versatile sensor system. This enables mobile network operators to offer a sensing service in addition to conventional data and telephony services. The potential benefits are expected to accrue to various stakeholders, including individuals, the environment, and society in general. The paper discusses technological development, possible integration, and use cases, as well as future development areas.
Background
Localization-based super-resolution microscopy resolves macromolecular structures down to a few nanometers by computationally reconstructing fluorescent emitter coordinates from diffraction-limited spots. The most commonly used algorithms are based on fitting parametric models of the point spread function (PSF) to a measured photon distribution. These algorithms make assumptions about the symmetry of the PSF and thus, do not work well with irregular, non-linear PSFs that occur for example in confocal lifetime imaging, where a laser is scanned across the sample. An alternative method for reconstructing sparse emitter sets from noisy, diffraction-limited images is compressed sensing, but due to its high computational cost it has not yet been widely adopted. Deep neural network fitters have recently emerged as a new competitive method for localization microscopy. They can learn to fit arbitrary PSFs, but require extensive simulated training data and do not generalize well. A method to efficiently fit the irregular PSFs from confocal lifetime localization microscopy combining the advantages of deep learning and compressed sensing would greatly improve the acquisition speed and throughput of this method.
Results
Here we introduce ReCSAI, a compressed sensing neural network to reconstruct localizations for confocal dSTORM, together with a simulation tool to generate training data. We implemented and compared different artificial network architectures, aiming to combine the advantages of compressed sensing and deep learning. We found that a U-Net with a recursive structure inspired by iterative compressed sensing showed the best results on realistic simulated datasets with noise, as well as on real experimentally measured confocal lifetime scanning data. Adding a trainable wavelet denoising layer as prior step further improved the reconstruction quality.
Conclusions
Our deep learning approach can reach a similar reconstruction accuracy for confocal dSTORM as frame binning with traditional fitting without requiring the acquisition of multiple frames. In addition, our work offers generic insights on the reconstruction of sparse measurements from noisy experimental data by combining compressed sensing and deep learning. We provide the trained networks, the code for network training and inference as well as the simulation tool as python code and Jupyter notebooks for easy reproducibility.
In the last 40 years, complexity theory has grown to a rich and powerful field in theoretical computer science. The main task of complexity theory is the classification of problems with respect to their consumption of resources (e.g., running time or required memory). To study the computational complexity (i.e., consumption of resources) of problems, similar problems are grouped into so called complexity classes. During the systematic study of numerous problems of practical relevance, no efficient algorithm for a great number of studied problems was found. Moreover, it was unclear whether such algorithms exist. A major breakthrough in this situation was the introduction of the complexity classes P and NP and the identification of hardest problems in NP. These hardest problems of NP are nowadays known as NP-complete problems. One prominent example of an NP-complete problem is the satisfiability problem of propositional formulas (SAT). Here we get a propositional formula as an input and it must be decided whether an assignment for the propositional variables exists, such that this assignment satisfies the given formula. The intensive study of NP led to numerous related classes, e.g., the classes of the polynomial-time hierarchy PH, P, #P, PP, NL, L and #L. During the study of these classes, problems related to propositional formulas were often identified to be complete problems for these classes. Hence some questions arise: Why is SAT so hard to solve? Are there modifications of SAT which are complete for other well-known complexity classes? In the context of these questions a result by E. Post is extremely useful. He identified and characterized all classes of Boolean functions being closed under superposition. It is possible to study problems which are connected to generalized propositional logic by using this result, which was done in this thesis. Hence, many different problems connected to propositional logic were studied and classified with respect to their computational complexity, clearing the borderline between easy and hard problems.
Practical optimization problems often comprise several incomparable and conflicting objectives. When booking a trip using several means of transport, for instance, it should be fast and at the same time not too expensive. The first part of this thesis is concerned with the algorithmic solvability of such multiobjective optimization problems. Several solution notions are discussed and compared with respect to their difficulty. Interestingly, these solution notions are always equally difficulty for a single-objective problem and they differ considerably already for two objectives (unless P = NP). In this context, the difference between search and decision problems is also investigated in general. Furthermore, new and improved approximation algorithms for several variants of the traveling salesperson problem are presented. Using tools from discrepancy theory, a general technique is developed that helps to avoid an obstacle that is often hindering in multiobjective approximation: The problem of combining two solutions such that the new solution is balanced in all objectives and also mostly retains the structure of the original solutions. The second part of this thesis is dedicated to several aspects of systems of equations for (formal) languages. Firstly, conjunctive and Boolean grammars are studied, which are extensions of context-free grammars by explicit intersection and complementation operations, respectively. Among other results, it is shown that one can considerably restrict the union operation on conjunctive grammars without changing the generated language. Secondly, certain circuits are investigated whose gates do not compute Boolean values but sets of natural numbers. For these circuits, the equivalence problem is studied, i.\,e.\ the problem of deciding whether two given circuits compute the same set or not. It is shown that, depending on the allowed types of gates, this problem is complete for several different complexity classes and can thus be seen as a parametrized) representative for all those classes.
Learning is a central component of human life and essential for personal development. Therefore, utilizing new technologies in the learning context and exploring their combined potential are considered essential to support self-directed learning in a digital age. A learning environment can be expanded by various technical and content-related aspects. Gamification in the form of elements from video games offers a potential concept to support the learning process. This can be supplemented by technology-supported learning. While the use of tablets is already widespread in the learning context, the integration of a social robot can provide new perspectives on the learning process. However, simply adding new technologies such as social robots or gamification to existing systems may not automatically result in a better learning environment. In the present study, game elements as well as a social robot were integrated separately and conjointly into a learning environment for basic Spanish skills, with a follow-up on retained knowledge. This allowed us to investigate the respective and combined effects of both expansions on motivation, engagement and learning effect. This approach should provide insights into the integration of both additions in an adult learning context. We found that the additions of game elements and the robot did not significantly improve learning, engagement or motivation. Based on these results and a literature review, we outline relevant factors for meaningful integration of gamification and social robots in learning environments in adult learning.
Lightning has fascinated humanity since the beginning of our existence. Different types of lightning like sprites and blue jets were discovered, and many more are theorized. However, it is very likely that these phenomena are not exclusive to our home planet. Venus’s dense and active atmosphere is a place where lightning is to be expected. Missions like Venera, Pioneer, and Galileo have carried instruments to measure electromagnetic activity. These measurements have indeed delivered results. However, these results are not clear. They could be explained by other effects like cosmic rays, plasma noise, or spacecraft noise. Furthermore, these lightning seem different from those we know from our home planet. In order to tackle these issues, a different approach to measurement is proposed. When multiple devices in different spacecraft or locations can measure the same atmospheric discharge, most other explanations become increasingly less likely. Thus, the suggested instrument and method of VELEX incorporates multiple spacecraft. With this approach, the question about the existence of lightning on Venus could be settled.
The first step towards aerial planetary exploration has been made. Ingenuity shows extremely promising results, and new missions are already underway. Rotorcraft are capable of flight. This capability could be utilized to support the last stages of Entry, Descent, and Landing. Thus, mass and complexity could be scaled down.
Autorotation is one method of descent. It describes unpowered descent and landing, typically performed by helicopters in case of an engine failure. MAPLE is suggested to test these procedures and understand autorotation on other planets. In this series of experiments, the Ingenuity helicopter is utilized. Ingenuity would autorotate a ”mid-air-landing” before continuing with normal flight. Ultimately, the collected data shall help to understand autorotation on Mars and its utilization for interplanetary exploration.
Background: Since the replication crisis, standardization has become even more important in psychological science and neuroscience. As a result, many methods are being reconsidered, and researchers’ degrees of freedom in these methods are being discussed as a potential source of inconsistencies across studies.
New Method: With the aim of addressing these subjectivity issues, we have been working on a tutorial-like EEG (pre-)processing pipeline to achieve an automated method based on the semi-automated analysis proposed by Delorme and Makeig.
Results: Two scripts are presented and explained step-by-step to perform basic, informed ERP and frequency-domain analyses, including data export to statistical programs and visual representations of the data. The open-source software EEGlab in MATLAB is used as the data handling platform, but scripts based on code provided by Mike Cohen (2014) are also included.
Comparison with existing methods: This accompanying tutorial-like article explains and shows how the processing of our automated pipeline affects the data and addresses, especially beginners in EEG-analysis, as other (pre)-processing chains are mostly targeting rather informed users in specialized areas or only parts of a complete procedure. In this context, we compared our pipeline with a selection of existing approaches.
Conclusion: The need for standardization and replication is evident, yet it is equally important to control the plausibility of the suggested solution by data exploration. Here, we provide the community with a tool to enhance the understanding and capability of EEG-analysis. We aim to contribute to comprehensive and reliable analyses for neuro-scientific research.
Inside 2003: IT-Sicherheit
(2003)
The DAEDALUS mission concept aims at exploring and characterising the entrance and initial part of Lunar lava tubes within a compact, tightly integrated spherical robotic device, with a complementary payload set and autonomous capabilities.
The mission concept addresses specifically the identification and characterisation of potential resources for future ESA exploration, the local environment of the subsurface and its geologic and compositional structure.
A sphere is ideally suited to protect sensors and scientific equipment in rough, uneven environments.
It will house laser scanners, cameras and ancillary payloads.
The sphere will be lowered into the skylight and will explore the entrance shaft, associated caverns and conduits. Lidar (light detection and ranging) systems produce 3D models with high spatial accuracy independent of lighting conditions and visible features.
Hence this will be the primary exploration toolset within the sphere.
The additional payload that can be accommodated in the robotic sphere consists of camera systems with panoramic lenses and scanners such as multi-wavelength or single-photon scanners.
A moving mass will trigger movements.
The tether for lowering the sphere will be used for data communication and powering the equipment during the descending phase.
Furthermore, the connector tether-sphere will host a WIFI access point, such that data of the conduit can be transferred to the surface relay station. During the exploration phase, the robot will be disconnected from the cable, and will use wireless communication.
Emergency autonomy software will ensure that in case of loss of communication, the robot will continue the nominal mission.
Modern immersive multimodal technologies enable the learners to completely get immersed in various learning situations in a way that feels like experiencing an authentic learning environment. These environments also allow the collection of multimodal data, which can be used with artificial intelligence to further improve the immersion and learning outcomes. The use of artificial intelligence has been widely explored for the interpretation of multimodal data collected from multiple sensors, thus giving insights to support learners’ performance by providing personalised feedback. In this paper, we present a conceptual approach for creating immersive learning environments, integrated with multi-sensor setup to help learners improve their psychomotor skills in a remote setting.
In this thesis, we present novel approaches for formation driving of nonholonomic robots and optimal trajectory planning to reach a target region. The methods consider a static known map of the environment as well as unknown and dynamic obstacles detected by sensors of the formation. The algorithms are based on leader following techniques, where the formation of car-like robots is maintained in a shape determined by curvilinear coordinates. Beyond this, the general methods of formation driving are specialized and extended for an application of airport snow shoveling. Detailed descriptions of the algorithms complemented by relevant stability and convergence studies will be provided in the following chapters. Furthermore, discussions of the applicability will be verified by various simulations in existing robotic environments and also by a hardware experiment.