Processing History: Potentials of Transformers for 3D Reconstruction of Historical Objects with the Help of Artifcial Intelligence

The digital preservation of cultural heritage is an important and challenging task for the research community. Reconstructing historical objects, which do not exist anymore, in the form of digital 3D models makes it possible to visualize them and present them to the public. The reconstruction process as well as the visualization lead to a deeper understanding of the lost historical objects. But the process of the digitalreconstruction is complex and time consuming as diverse sources have to be consulted and interpreted. Therefore, in this paper the latest technology in the feld of artifcial intelligence (AI) is used to support researchers in the feld of Digital Humanities: A Transformer deep learning model based on questions answering methods is introduced to assist to digitally reconstruct historical objects in 3D. It implies a new dimension of data availability, which supports the knowledge process by making large amounts of data qualitatively accessible. [Aus: Einleitung

Detecting Treasures in Museums with Artificial Intelligence

Museums around the world possess hundreds of thousands of priceless objects, which have stories to tell about human history. While students and scholars study them, even the general public is interested in these stories. If there is a way to automate the information delivery system about these objects it will be of immense value, e.g. it will support students to study these objects and speed up research. Adaptive blended learning options are conceivable, which can perfectly merge digital analysis and onsite viewing. Thus, the preparation and post-processing of studied objects is just as conceivable as the adequate acquisition of information for on-site studies. Examples of such solutions would be mobile apps and computer software that can be used for history and archaeology education as well. However, it is important to identify these objects correctly in order to build such solutions. Computer vision technologies in artificial intelligence (AI) can be used for this. Therefore, this paper will show how AI-algorithms can be used for digital humanities in novel ways, such as for detecting museum treasures

Supporting Learning in Art History – Artificial Intelligence in Digital Humanities Education

In recent years and especially in the context of the coronavirus pandemic, digital distance learning increases. But for academic students, the selection of adequate learning materials for educational purposes is becoming more and more complex. This marks only one starting point where the use of artificial intelligence (AI) offers additional value. AI has a great potential to enhance and support research and education in the field of digital humanities (DH). As international organisations have just expressed their thoughts on the subject, AI is the topic par excellence and will decisively shape the future development of educational processes

Introducing an Automated Pipeline for a Browser-based, City-scale Mobile 4D VR Application Based on Historical Images

The process for automatically creating 3D city models from contemporary photographs and visualizing them on mobile devices is now well established, but historical 4D city models are more challenging. The fourth dimension here is time. This contribution describes an automated VR pipeline based on historical photographs and resulting in an interactive browser-based device-rendered 4D visualization and information system for mobile devices. Since the pipeline shown is currently still under development, initial results for stages of the process will be shown and assessed for feasibility

Mahasen : distributed storage resource broker

Modern day systems are facing an avalanche of data, and they are being forced to handle more and more data intensive use cases. These data comes in many forms and shapes: Sensors (RFID, Near Field Communication, Weather Sensors), transaction logs, Web, social networks etc. As an example, weather sensors across the world generate a large amount of data throughout the year. Handling these and similar data require scalable, efficient, reliable and very large storages with support for efficient metadata based searching. This paper present Mahasen, a highly scalable storage for high volume data intensive applications built on top of a peer-to-peer layer. In addition to scalable storage, Mahasen also supports efficient searching, built on top of the Distributed Hash table (DHT)

Processing History: Potentials of Transformers for 3D Reconstruction of Historical Objects with the Help of Artifcial Intelligence

The digital preservation of cultural heritage is an important and challenging task for the research community. Reconstructing historical objects, which do not exist anymore, in the form of digital 3D models makes it possible to visualize them and present them to the public. The reconstruction process as well as the visualization lead to a deeper understanding of the lost historical objects. But the process of the digitalreconstruction is complex and time consuming as diverse sources have to be consulted and interpreted. Therefore, in this paper the latest technology in the feld of artifcial intelligence (AI) is used to support researchers in the feld of Digital Humanities: A Transformer deep learning model based on questions answering methods is introduced to assist to digitally reconstruct historical objects in 3D. It implies a new dimension of data availability, which supports the knowledge process by making large amounts of data qualitatively accessible. [Aus: Einleitung

Processing History: Potentials of Transformers for 3D Reconstruction of Historical Objects with the Help of Artifcial Intelligence

The digital preservation of cultural heritage is an important and challenging task for the research community. Reconstructing historical objects, which do not exist anymore, in the form of digital 3D models makes it possible to visualize them and present them to the public. The reconstruction process as well as the visualization lead to a deeper understanding of the lost historical objects. But the process of the digitalreconstruction is complex and time consuming as diverse sources have to be consulted and interpreted. Therefore, in this paper the latest technology in the feld of artifcial intelligence (AI) is used to support researchers in the feld of Digital Humanities: A Transformer deep learning model based on questions answering methods is introduced to assist to digitally reconstruct historical objects in 3D. It implies a new dimension of data availability, which supports the knowledge process by making large amounts of data qualitatively accessible. [Aus: Einleitung

Gajaba : dynamic rule based load balancing framework

Cloud computing has become the norm of today’s heavily used computer science applications. Load balancing is the key to efficient cloud based deployment architectures. It is an essential component in the deployment architecture when it comes to cloud native attributes of multi-tenancy, elasticity, distributed and dynamic wiring, and incremental deployment and testability. A load balancer that can base its traffic routing decisions on multiple cloud services is called a service-aware load balancer. We are introducing a novel implementation of a flexible load balancing framework which can be customized using a domain specific scripting language. Using this approach the user can customize the framework to take into account the different services running on each cluster (service-awareness) as well as the dynamically changing tenants in each cluster (tenant-awareness) before making the load balancing decisions. This scripting language lets users to define rules and configure message routing decisions. This methodology is more light weight and expressive than products already available, making the cluster based load balancing more efficient and productive.

Detecting Treasures in Museums with Artificial Intelligence

Museums around the world possess hundreds of thousands of priceless objects, which have stories to tell about human history. While students and scholars study them, even the general public is interested in these stories. If there is a way to automate the information delivery system about these objects it will be of immense value, e.g. it will support students to study these objects and speed up research. Adaptive blended learning options are conceivable, which can perfectly merge digital analysis and onsite viewing. Thus, the preparation and post-processing of studied objects is just as conceivable as the adequate acquisition of information for on-site studies. Examples of such solutions would be mobile apps and computer software that can be used for history and archaeology education as well. However, it is important to identify these objects correctly in order to build such solutions. Computer vision technologies in artificial intelligence (AI) can be used for this. Therefore, this paper will show how AI-algorithms can be used for digital humanities in novel ways, such as for detecting museum treasures

Detecting Treasures in Museums with Artificial Intelligence

Museums around the world possess hundreds of thousands of priceless objects, which have stories to tell about human history. While students and scholars study them, even the general public is interested in these stories. If there is a way to automate the information delivery system about these objects it will be of immense value, e.g. it will support students to study these objects and speed up research. Adaptive blended learning options are conceivable, which can perfectly merge digital analysis and onsite viewing. Thus, the preparation and post-processing of studied objects is just as conceivable as the adequate acquisition of information for on-site studies. Examples of such solutions would be mobile apps and computer software that can be used for history and archaeology education as well. However, it is important to identify these objects correctly in order to build such solutions. Computer vision technologies in artificial intelligence (AI) can be used for this. Therefore, this paper will show how AI-algorithms can be used for digital humanities in novel ways, such as for detecting museum treasures