A Critical Look at Cryptogovernance of the Real World: Challenges for Spatial Representation and Uncertainty on the Blockchain (Short Paper)

Innovation in distributed ledger technologies-blockchains and smart contracts-has been lauded as a game-changer for environmental governance and transparency. Here we critically consider how problems related to spatial representation and uncertainty complicate the picture, focusing on two cases. The first regards the impact of uncertainty on the transfer of spatial assets, and the second regards its impact on smart contract code that relies on software oracles that report sensor measurements of the physical world. Cryptogovernance of the environment will require substantial research on both these fronts if it is to become a reality

Multi-island finite automata and their even computation

summary:This paper discusses $n$-island finite automata whose transition graphs can be expressed as $n$-member sequences of islands $i_1, i_2, \dots , i_n$, where there is a bridge leaving $i_j$ and entering $i_{j+1}$ for each $1 \leq j \leq n - 1$. It concentrates its attention on even computation defined as any sequence of moves during which these automata make the same number of moves in each of the islands. Under the assumption that these automata work only in an evenly computational way, the paper proves its main result stating that $n$-island finite automata and Rosebrugh-Wood $n$-parallel right-linear grammars are equivalent. Then, making use of this main result, it demonstrates that under this assumption, the language family defined by $n$-island finite automata is properly contained in that defined by $(n+1)$-island finite automata for all $n \geq 1$. The paper also points out that this infinite hierarchy occurs between the family of regular languages and that of context-sensitive languages. Open questions are formulated in the conclusion

Regulated Grammar Systems

Práce poskytuje přehled základů teorie formálních jazyků, regulovaných gramatik a analýzy LL(1) jazyků. Je zde navržen a analyzován algoritmus pro analýzu programovaných gramatik, inspirován LL(1) analyzátorem. Třída jazyků přijímaná tímto algoritmem je striktní nadtřídou LL(1) jazyků, obsahující některé jazyky, které nejsou bezkontextové. Tato třída se však jeví být neporovnatelná s třídou bezkontextových jazyků.This thesis recaps a basic theory of formal languages, regulated grammars, and the parsing of LL(1) languages. An algorithm for parsing programmed grammars inspired by LL(1) parsing is suggested and analyzed. The class of languages accepted by this algorithm is shown to be a strict superclass of LL(1) languages, containing some non-context-free languages. However, this class appears to be incomparable with the class of context-free languages.

Discovery of topological constraints on spatial object classes using a refined topological model

In a typical data collection process, a surveyed spatial object is annotated upon creation, and is classified based on its attributes. This annotation can also be guided by textual definitions of objects. However, interpretations of such definitions may differ among people, and thus result in subjective and inconsistent classification of objects. This problem becomes even more pronounced if the cultural and linguistic differences are considered. As a solution, this paper investigates the role of topology as the defining characteristic of a class of spatial objects. We propose a data mining approach based on frequent itemset mining to learn patterns in topological relations between objects of a given class and other spatial objects. In order to capture topological relations between more than two (linear) objects, this paper further proposes a refinement of the 9-intersection model for topological relations of line geometries. The discovered topological relations form topological constraints of an object class that can be used for spatial object classification. A case study has been carried out on bridges in the OpenStreetMap dataset for the state of Victoria, Australia. The results show that the proposed approach can successfully learn topological constraints for the class bridge, and that the proposed refined topological model for line geometries outperforms the 9-intersection model in this task

137Cs monitoring in the meat of wild boar population in Slovakia

Currently, due to the elapsed time and the nature of the Chernobyl accident, the only artificial radionuclide present in the soil is 137Cs, with a physical half-life conversion of 30.17 years. The 137Cs is quickly integrated into a biological cycle, similar to potassium. Generally, radionuclides are characterized by their mobility in soil. Contamination of materials and food by radionuclides represent a serious problem and has a negative impact on human health. The threat of international terrorism and the inability to forestall the impact of natural disasters on nuclear energetic (Fukushima accident), are also reasons for continuous monitoring of food safety. According screening measurement performed in European countries, high radioactivity levels were reported in the wild boars muscles from Sumava (Czech Republic). Seasonal fluctuation of 137Cs activity in the wild boar meat samples was observed in the forests on the southern Rhineland. Monitoring of 137Cs activity in the wild boar meat samples in the hunting grounds in Slovakia was initiated based on the reports on exceeding limits of the content of radiocaesium in the meat of wild boar from the surrounding countries. The aim of this study was to determine the 137Cs post Chernobyl contamination of wild boars population in different hunting districts of Slovakia during 2013 - 2014. A total of 60 thigh muscle samples from wild boars of different age categories (4 months - 2 years) were evaluated. 137Cs activity was measured by gamma spectrometry (Canberra). Despite the fact Slovakia is closer to Chernobyl as Czech Republic and Germany, the 137Cs activity measured was very low and far below the permitted limit. The highest radiocaesium activity level measured in muscle was 37.2 Bq.kg-1 ±4.7%. Wild boar originated from Zlate Moravce district. The measurement results show, that 137Cs contamination levels of game in Slovakia are low. Radiocaesium activity in examined samples was very low and therefore consumption of wild boar meat does not represent a health risk problem

137Cs activity concentration in mushrooms from the Bobrůvka river valley

In the 2017-2018 mushrooming seasons at the (Bohemian-Moravian Highlands near Dolní­ Roží­nka) a total of 505 mushrooms belonging to 9 species were collected, and analyzed by gamma spectrometry for 137Cs activity. The maximum 137Cs activity of 575 Bq.kg-1 was detected in Boletus edulis species, what in native state, is just below the allowed limit. In contrast, in mushroom Imleria badia, which is reported to be associated with the highest cumulative capability from all fungi species, detected activity level was only 316 Bq. kg-1. However, differences in mean contamination values were not significant due to high variability. It was shown, that activity concentration is not dependent on the weight (size) of Imleria badia. Our results also confirmed generally well known lower 137Cs activity in the Russula species representatives belonging to the group of gills or lamella bearing mushrooms

Report from the first workshop on cyber ethics in platial research

The examination of morality has a long-standing history in philosophy, but recent events, including the dramatic rise of computational technologies has expanded the field into a multidisciplinary study of ethics. With the global connectivity provided by the Internet,cyber ethics is unique due to rapid changes in technology and ever-changing ethical considerations pertaining to everything from human subjects to artificial intelligence (AI). Among the deluge of data generated by machines and humans, place-based information is special due to its vague boundaries, subjectivity, and heterogeneity of descriptive data. Compared to spatial data, platial information is a much broader concept as it is more than simply geographic coordinates and often involves human attachments. While we fully support the recent renewed interest in the field of geoethics(Goodchild, 2022), we also feel it is important to discuss ethic beyond the surface of the earth. Here, we propose to extend this discussion to include cyberspace and bring together the concepts of cyber and geo-ethical studies under the umbrella of place-based cyber ethics. We encourage the readers of this workshop report to reflect on these questions and the importance of place-based cyber ethics in their own work

LMSeg: A deep graph message-passing network for efficient and accurate semantic segmentation of large-scale 3D landscape meshes

Semantic segmentation of large-scale 3D landscape meshes is pivotal for various geospatial applications, including spatial analysis, automatic mapping and localization of target objects, and urban planning and development. This requires an efficient and accurate 3D perception system to understand and analyze real-world environments. However, traditional mesh segmentation methods face challenges in accurately segmenting small objects and maintaining computational efficiency due to the complexity and large size of 3D landscape mesh datasets. This paper presents an end-to-end deep graph message-passing network, LMSeg, designed to efficiently and accurately perform semantic segmentation on large-scale 3D landscape meshes. The proposed approach takes the barycentric dual graph of meshes as inputs and applies deep message-passing neural networks to hierarchically capture the geometric and spatial features from the barycentric graph structures and learn intricate semantic information from textured meshes. The hierarchical and local pooling of the barycentric graph, along with the effective geometry aggregation modules of LMSeg, enable fast inference and accurate segmentation of small-sized and irregular mesh objects in various complex landscapes. Extensive experiments on two benchmark datasets (natural and urban landscapes) demonstrate that LMSeg significantly outperforms existing learning-based segmentation methods in terms of object segmentation accuracy and computational efficiency. Furthermore, our method exhibits strong generalization capabilities across diverse landscapes and demonstrates robust resilience against varying mesh densities and landscape topologies