000 Informatik, Informationswissenschaft, allgemeine Werke
Refine
Has Fulltext
- yes (98) (remove)
Document Type
- Article (38)
- Doctoral Thesis (23)
- Conference Proceeding (12)
- Master's Thesis (6)
- Preprint (6)
- Bachelor Thesis (5)
- Report (4)
- Book (2)
- Sound (1)
- Study Thesis (1)
Institute
- Junior-Professur Computational Architecture (25)
- Institut für Strukturmechanik (ISM) (19)
- Professur Informatik in der Architektur (10)
- Professur Bauphysik (5)
- Professur Content Management und Webtechnologien (5)
- Professur Systeme der Virtuellen Realität (5)
- Professur Informatik im Bauwesen (4)
- Professur Modellierung und Simulation - Konstruktion (4)
- Bauhaus-Institut für zukunftsweisende Infrastruktursysteme (b.is) (3)
- Professur Mediensicherheit (3)
Keywords
- Maschinelles Lernen (10)
- Architektur (9)
- Machine learning (7)
- machine learning (6)
- CAD (5)
- Städtebau (5)
- BIM (4)
- Deep learning (4)
- OA-Publikationsfonds2020 (4)
- Simulation (4)
Polylactic acid (PLA) is a highly applicable material that is used in 3D printers due to some significant features such as its deformation property and affordable cost. For improvement of the end-use quality, it is of significant importance to enhance the quality of fused filament fabrication (FFF)-printed objects in PLA. The purpose of this investigation was to boost toughness and to reduce the production cost of the FFF-printed tensile test samples with the desired part thickness. To remove the need for numerous and idle printing samples, the response surface method (RSM) was used. Statistical analysis was performed to deal with this concern by considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The artificial intelligence method of artificial neural network (ANN) and ANN-genetic algorithm (ANN-GA) were further developed to estimate the toughness, part thickness, and production-cost-dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could enhance the accuracy of modeling by about 7.5, 11.5, and 4.5% for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other hand, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects.
Cultural Heritage on Mobile Devices: Building Guidelines for UNESCO World Heritage Sites' Apps
(2021)
Technological improvements and access provide a fertile scenario for creating and developing mobile applications (apps). This scenario results in a myriad of Apps providing information regarding touristic destinations, including those with a cultural profile, such as those dedicated to UNESCO World Heritage Sites (WHS). However, not all of the Apps have the same efficiency. In order to have a successful app, its development must consider usability aspects and features aligned with reliable content. Despite the guidelines for mobile usability being broadly available, they are generic, and none of them concentrates specifically into cultural heritage places, especially on those placed in an open-air scenario. This research aims to fulfil this literature gap and discusses how to adequate and develop specific guidelines for a better outdoor WHS experience. It uses an empirical approach applied to an open-air WHS city: Weimar and its Bauhaus and Classical Weimar sites. In order to build a new set of guidelines applied for open-air WHS, this research used a systematic approach to compare literature-based guidelines to industry-based ones (based on affordances), extracted from the available Apps dedicated to WHS set in Germany. The instructions compiled from both sources have been comparatively tested by using two built prototypes from the distinctive guidelines, creating a set of recommendations collecting the best approach from both sources, plus suggesting new ones the evaluation.
Modern cryptography has become an often ubiquitous but essential part of our daily lives. Protocols for secure authentication and encryption protect our communication with various digital services, from private messaging, online shopping, to bank transactions or exchanging sensitive information. Those high-level protocols can naturally be only as secure as the authentication or encryption schemes underneath. Moreover, on a more detailed level, those schemes can also at best inherit the security of their underlying primitives. While widespread standards in modern symmetric-key cryptography, such as the Advanced Encryption Standard (AES), have shown to resist analysis until now, closer analysis and design of related primitives can deepen our understanding.
The present thesis consists of two parts that portray six contributions: The first part considers block-cipher cryptanalysis of the round-reduced AES, the AES-based tweakable block cipher Kiasu-BC, and TNT. The second part studies the design, analysis, and implementation of provably secure authenticated encryption schemes.
In general, cryptanalysis aims at finding distinguishable properties in the output distribution. Block ciphers are a core primitive of symmetric-key cryptography which are useful for the construction of various higher-level schemes, ranging from authentication, encryption, authenticated encryption up to integrity protection. Therefore, their analysis is crucial to secure cryptographic schemes at their lowest level. With rare exceptions, block-cipher cryptanalysis employs a systematic strategy of investigating known attack techniques. Modern proposals are expected to be evaluated against these techniques. The considerable effort for evaluation, however, demands efforts not only from the designers but also from external sources.
The Advanced Encryption Standard (AES) is one of the most widespread block ciphers nowadays. Therefore, it is naturally an interesting target for further analysis. Tweakable block ciphers augment the usual inputs of a secret key and a public plaintext by an additional public input called tweak. Among various proposals through the previous decade, this thesis identifies Kiasu-BC as a noteworthy attempt to construct a tweakable block cipher that is very close to the AES. Hence, its analysis intertwines closely with that of the AES and illustrates the impact of the tweak on its security best. Moreover, it revisits a generic tweakable block cipher Tweak-and-Tweak (TNT) and its instantiation based on the round-reduced AES.
The first part investigates the security of the AES against several forms of differential cryptanalysis, developing distinguishers on four to six (out of ten) rounds of AES. For Kiasu-BC, it exploits the additional freedom in the tweak to develop two forms of differential-based attacks: rectangles and impossible differentials. The results on Kiasu-BC consider an additional round compared to attacks on the (untweaked) AES. The authors of TNT had provided an initial security analysis that still left a gap between provable guarantees and attacks. Our analysis conducts a considerable step towards closing this gap. For TNT-AES - an instantiation of TNT built upon the AES round function - this thesis further shows how to transform our distinguisher into a key-recovery attack.
Many applications require the simultaneous authentication and encryption of transmitted data. Authenticated encryption (AE) schemes provide both properties. Modern AE schemes usually demand a unique public input called nonce that must not repeat. Though, this requirement cannot always be guaranteed in practice. As part of a remedy, misuse-resistant and robust AE tries to reduce the impact of occasional misuses. However, robust AE considers not only the potential reuse of nonces. Common authenticated encryption also demanded that the entire ciphertext would have to be buffered until the authentication tag has been successfully verified. In practice, this approach is difficult to ensure since the setting may lack the resources for buffering the messages. Moreover, robustness guarantees in the case of misuse are valuable features.
The second part of this thesis proposes three authenticated encryption schemes: RIV, SIV-x, and DCT. RIV is robust against nonce misuse and the release of unverified plaintexts. Both SIV-x and DCT provide high security independent from nonce repetitions. As the core under SIV-x, this thesis revisits the proof of a highly secure parallel MAC, PMAC-x, revises its details, and proposes SIV-x as a highly secure authenticated encryption scheme. Finally, DCT is a generic approach to have n-bit secure deterministic AE but without the need of expanding the ciphertext-tag string by more than n bits more than the plaintext.
From its first part, this thesis aims to extend the understanding of the (1) cryptanalysis of round-reduced AES, as well as the understanding of (2) AES-like tweakable block ciphers. From its second part, it demonstrates how to simply extend known approaches for (3) robust nonce-based as well as (4) highly secure deterministic authenticated encryption.
Mitigating Risks of Corruption in Construction: A theoretical rationale for BIM adoption in Ethiopia
(2021)
This PhD thesis sets out to investigate the potentials of Building Information Modeling (BIM) to mitigate risks of corruption in the Ethiopian public construction sector. The wide-ranging capabilities and promises of BIM have led to the strong perception among researchers and practitioners that it is an indispensable technology. Consequently, it has become the frequent subject of science and research. Meanwhile, many countries, especially the developed ones, have committed themselves to applying the technology extensively. Increasing productivity is the most common and frequently cited reason for that.
However, both technology developers and adopters are oblivious to the potentials of BIM in addressing critical challenges in the construction sector, such as corruption. This particularly would be significant in developing countries like Ethiopia, where its problems and effects are acute. Studies reveal that bribery and corruption have long pervaded the construction industry worldwide. The complex and fragmented nature of the sector provides an environment for corruption. The Ethiopian construction sector is not immune from this epidemic reality. In fact, it is regarded as one of the most vulnerable sectors owing to varying socio-economic and political factors. Since 2015, Ethiopia has started adopting BIM, yet without clear goals and strategies. As a result, the potential of BIM for combating concrete problems of the sector remains untapped. To this end, this dissertation does pioneering work by showing how collaboration and coordination features of the technology contribute to minimizing the opportunities for corruption. Tracing loopholes, otherwise, would remain complex and ineffective in the traditional documentation processes.
Proceeding from this anticipation, this thesis brings up two primary questions: what are areas and risks of corruption in case of the Ethiopian public construction projects; and how could BIM be leveraged to mitigate these risks? To tackle these and other secondary questions, the research employs a mixed-method approach. The selected main research strategies are Survey, Grounded Theory (GT) and Archival Study. First, the author disseminates an online questionnaire among Ethiopian construction engineering professionals to pinpoint areas of vulnerability to corruption. 155 responses are compiled and scrutinized quantitatively. Then, a semi-structured in-depth interview is conducted with 20 senior professionals, primarily to comprehend opportunities for and risks of corruption in those identified highly vulnerable project stages and decision points. At the same time, open interviews (consultations) are held with 14 informants to be aware of state of the construction documentation, BIM and loopholes for corruption in the country. Consequently, these qualitative data are analyzed utilizing the principles of GT, heat/risk mapping and Social Network Analysis (SNA). The risk mapping assists the researcher in the course of prioritizing corruption risks; whilst through SNA, methodically, it is feasible to identify key actors/stakeholders in the corruption venture. Based on the generated research data, the author constructs a [substantive] grounded theory around the elements of corruption in the Ethiopian public construction sector. This theory, later, guides the subsequent strategic proposition of BIM. Finally, 85 public construction related cases are also analyzed systematically to substantiate and confirm previous findings.
By ways of these multiple research endeavors that is based, first and foremost, on the triangulation of qualitative and quantitative data analysis, the author conveys a number of key findings. First, estimations, tender document preparation and evaluation, construction material as well as quality control and additional work orders are found to be the most vulnerable stages in the design, tendering and construction phases respectively. Second, middle management personnel of contractors and clients, aided by brokers, play most critical roles in corrupt transactions within the prevalent corruption network. Third, grand corruption persists in the sector, attributed to the fact that top management and higher officials entertain their overriding power, supported by the lack of project audits and accountability. Contrarily, individuals at operation level utilize intentional and unintentional 'errors’ as an opportunity for corruption.
In light of these findings, two conceptual BIM-based risk mitigation strategies are prescribed: active and passive automation of project audits; and the monitoring of project information throughout projects’ value chain. These propositions are made in reliance on BIM’s present dimensional capabilities and the promises of Integrated Project Delivery (IPD). Moreover, BIM’s synchronous potentials with other technologies such as Information and Communication Technology (ICT), and Radio Frequency technologies are topics which received a treatment. All these arguments form the basis for the main thesis of this dissertation, that BIM is able to mitigate corruption risks in the Ethiopian public construction sector. The discourse on the skepticisms about BIM that would stem from the complex nature of corruption and strategic as well as technological limitations of BIM is also illuminated and complemented by this work. Thus, the thesis uncovers possible research gaps and lays the foundation for further studies.
The computational analysis of argumentation strategies is substantial for many downstream applications. It is required for nearly all kinds of text synthesis, writing assistance, and dialogue-management tools. While various tasks have been tackled in the area of computational argumentation, such as argumentation mining and quality assessment, the task of the computational analysis of argumentation strategies in texts has so far been overlooked.
This thesis principally approaches the analysis of the strategies manifested in the persuasive argumentative discourses that aim for persuasion as well as in the deliberative argumentative discourses that aim for consensus. To this end, the thesis presents a novel view of argumentation strategies for the above two goals. Based on this view, new models for pragmatic and stylistic argument attributes are proposed, new methods for the identification of the modelled attributes have been developed, and a new set of strategy principles in texts according to the identified attributes is presented and explored.
Overall, the thesis contributes to the theory, data, method, and evaluation aspects of the analysis of argumentation strategies. The models, methods, and principles developed and explored in this thesis can be regarded as essential for promoting the applications mentioned above, among others.
This research aims to model soil temperature (ST) using machine learning models of multilayer perceptron (MLP) algorithm and support vector machine (SVM) in hybrid form with the Firefly optimization algorithm, i.e. MLP-FFA and SVM-FFA. In the current study, measured ST and meteorological parameters of Tabriz and Ahar weather stations in a period of 2013–2015 are used for training and testing of the studied models with one and two days as a delay. To ascertain conclusive results for validation of the proposed hybrid models, the error metrics are benchmarked in an independent testing period. Moreover, Taylor diagrams utilized for that purpose. Obtained results showed that, in a case of one day delay, except in predicting ST at 5 cm below the soil surface (ST5cm) at Tabriz station, MLP-FFA produced superior results compared with MLP, SVM, and SVM-FFA models. However, for two days delay, MLP-FFA indicated increased accuracy in predicting ST5cm and ST 20cm of Tabriz station and ST10cm of Ahar station in comparison with SVM-FFA. Additionally, for all of the prescribed models, the performance of the MLP-FFA and SVM-FFA hybrid models in the testing phase was found to be meaningfully superior to the classical MLP and SVM models.
Image Analysis Using Human Body Geometry and Size Proportion Science for Action Classification
(2020)
Gestures are one of the basic modes of human communication and are usually used to represent different actions. Automatic recognition of these actions forms the basis for solving more complex problems like human behavior analysis, video surveillance, event detection, and sign language recognition, etc. Action recognition from images is a challenging task as the key information like temporal data, object trajectory, and optical flow are not available in still images. While measuring the size of different regions of the human body i.e., step size, arms span, length of the arm, forearm, and hand, etc., provides valuable clues for identification of the human actions. In this article, a framework for classification of the human actions is presented where humans are detected and localized through faster region-convolutional neural networks followed by morphological image processing techniques. Furthermore, geometric features from human blob are extracted and incorporated into the classification rules for the six human actions i.e., standing, walking, single-hand side wave, single-hand top wave, both hands side wave, and both hands top wave. The performance of the proposed technique has been evaluated using precision, recall, omission error, and commission error. The proposed technique has been comparatively analyzed in terms of overall accuracy with existing approaches showing that it performs well in contrast to its counterparts.
The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 Düzce Earthquake in Turkey, where the building’s data consists of 22 performance modifiers that have been implemented with supervised machine learning.
Wind effects can be critical for the design of lifelines such as long-span bridges. The existence of a significant number of aerodynamic force models, used to assess the performance of bridges, poses an important question regarding their comparison and validation. This study utilizes a unified set of metrics for a quantitative comparison of time-histories in bridge aerodynamics with a host of characteristics. Accordingly, nine comparison metrics are included to quantify the discrepancies in local and global signal features such as phase, time-varying frequency and magnitude content, probability density, nonstationarity and nonlinearity. Among these, seven metrics available in the literature are introduced after recasting them for time-histories associated with bridge aerodynamics. Two additional metrics are established to overcome the shortcomings of the existing metrics. The performance of the comparison metrics is first assessed using generic signals with prescribed signal features. Subsequently, the metrics are applied to a practical example from bridge aerodynamics to quantify the discrepancies in the aerodynamic forces and response based on numerical and semi-analytical aerodynamic models. In this context, it is demonstrated how a discussion based on the set of comparison metrics presented here can aid a model evaluation by offering deeper insight. The outcome of the study is intended to provide a framework for quantitative comparison and validation of aerodynamic models based on the underlying physics of fluid-structure interaction. Immediate further applications are expected for the comparison of time-histories that are simulated by data-driven approaches.
In conjunction with the improved methods of monitoring damage and degradation processes, the interest in reliability assessment of reinforced concrete bridges is increasing in recent years. Automated imagebased inspections of the structural surface provide valuable data to extract quantitative information about deteriorations, such as crack patterns. However, the knowledge gain results from processing this information in a structural context, i.e. relating the damage artifacts to building components. This way, transformation to structural analysis is enabled. This approach sets two further requirements: availability of structural bridge information and a standardized storage for interoperability with subsequent analysis tools. Since the involved large datasets are only efficiently processed in an automated manner, the implementation of the complete workflow from damage and building data to structural analysis is targeted in this work. First, domain concepts are derived from the back-end tasks: structural analysis, damage modeling, and life-cycle assessment. The common interoperability format, the Industry Foundation Class (IFC), and processes in these domains are further assessed. The need for usercontrolled interpretation steps is identified and the developed prototype thus allows interaction at subsequent model stages. The latter has the advantage that interpretation steps can be individually separated into either a structural analysis or a damage information model or a combination of both. This approach to damage information processing from the perspective of structural analysis is then validated in different case studies.