Lanes. A lightweigth overlay for service discovery in mobile ad hoc networks

The ability to discover services offered in a mobile ad hoc network is the major prerequisite for effective usability of these networks. Unfortunately, existing approaches to service trading are not well suited for these highly dynamic topologies since they either rely on centralized servers or on resource-consuming query flooding. Application layer overlays seem to be a more promising approach. However, existing solutions like the Content-Addressable Network (CAN) are especially designed for internet based peer-to-peer networks yielding structural conditions that are far too complex for ad hoc networks. Therefore, in this paper, we propose a more lightweight overlay structure: lanes. We present algorithms to correct and optimize its structure in case of topology changes and show how it enables the trading of services specified by arbitrary descriptions

A strategy for the modularization of courseware

In order to enable courseware reuse, learning platforms nowadays require the materials to be decomposed into small independent learning units. When trying to fulfill this need, authors face the problem of not knowing how to determine suitable learning objects in their content. What is the appropriate size of one such object? The rather general and abstract definitions for learning objects found in the literature are not very helpful for answering this question. What authors need is an operational definition, which can be directly applied to the learning materials. This paper proposes such a set of formal yet practical definitions by describing learning objects along their contents and resource type and shows how these definitions are used by our platform, SCORE

Stimulating cooperative behavior of autonomous devices - an analysis of requirements and existing approaches

In the context of mobile and wireless devices, an information system is no longer a centralized component storing all the relevant data nor is it a decentralized component governed by a common authority. Rather, the information spread across huge numbers of autonomous mobile and wireless devices owned by independent organizations and individuals can be regarded as a highly dynamic, virtual information system. For this vision to become reality, the autonomous devices involved need to be motivated to cooperate. This cooperation needs to occur not only on the application layer, but, depending on the network architecture, also on the lower layers from the link layer on upwards. In this report, we investigate on which protocol layers cooperation is needed and what constitutes uncooperative behavior. We then identify necessary properties of incentive schemes that encourage cooperation and discourage uncooperative behavior. In this context, we examine remuneration types that are a major constituent of incentive schemes. Finally, using the example of ad hoc networks, the most challenging technical basis of a wireless information system, we compare existing incentive schemes to these characteristics

How to Search for Biological Data? A Comparison of User Interfaces in a Semantic Search

Data discovery is a frequent task in a scholar's daily work. In biodiversity, data search is a particular challenge. Here, scholars have complex information needs such as the rich interplay of organisms and their environments that cannot be unambiguously expressed with a traditional keyword search, e.g., Does tree diversity reduce competition in a subtropical forest? Therefore, data repositories usually offer interfaces that enable users to browse datasets by a pre-determined set of categories or facets. Faceted search is a good compromise between cumbersome user interfaces for structured queries (e.g., using SPARQL) and natural language queries that are hard to interpret for machines. Thus, developers can specify relevant relationships between entities explicitly and users can filter search results by selecting proper categories. For the given query, appropriate categories could be Organism and Habitat. However, there are two crucial design issues that have an impact on the effectiveness of category-based query interfaces: The choice of proper categories and the visual presentation of these categories in the query interface. In our work, we focus on the second aspect. We aim to develop two query interfaces: (a) a common one-box keyword search interface that analyzes the entered terms with respect to their categories automatically (b) a form-based query interface where users can enter their search keywords into a form with a query field per category. In both interfaces, the query keywords are matched against concepts in a knowledge base to make their semantics explicit. In case of a successful match the URI is used to obtain the labels of all sub-concepts to expand the query before sending it to the search engine. Retrieved results are displayed in a list. The aim of our system is not to answer the question completely but to support users in retrieving relevant datasets that give hints to answer a research question. In our talk, we will introduce the two interfaces and invite the conference participants to give feedback. We are particularly interested in a discussion on the appropriateness of the suggested user interfaces. Do scholars prefer a form-based user interface or only a one-field search? What other functions might be helpful, e.g., providing more information about other relations and properties from the concept in the ontology? What kind of explanations might be helpful to understand why a certain result was returned

How Reproducible are the Results Gained with the Help of Deep Learning Methods in Biodiversity Research?

In recent years, deep learning methods in the biodiversity domain have gained significant attention due to their ability to handle the complexity of biological data and to make processing of large volumes of data feasible. However, these methods are not easy to interpret, so the opacity of new scientific research and discoveries makes them somewhat untrustworthy. Reproducibility is a fundamental aspect of scientific research, which enables validation and advancement of methods and results. If results obtained with the help of deep learning methods were reproducible, this would increase their trustworthiness. In this study, we investigate the state of reproducibility of deep learning methods in biodiversity research.We propose a pipeline to investigate the reproducibility of deep learning methods in the biodiversity domain. In our preliminary work, we systematically mined the existing literature from Google Scholar to identify publications that employ deep-learning techniques for biodiversity research. By carefully curating a dataset of relevant publications, we extracted reproducibility-related variables for 61 publications using a manual approach, such as the availability of datasets and code that serve as fundamental criteria for reproducibility assessment. Moreover, we extended our analysis to include advanced reproducibility variables, such as the specific deep learning methods, models, hyperparameters, etc., employed in the studies.To facilitate the automatic extraction of information from publications, we plan to leverage the capabilities of large language models (LLMs). By using the latest natural language processing (NLP) techniques, we aim to identify and extract relevant information pertaining to the reproducibility of deep learning methods in the biodiversity domain. This study seeks to contribute to the establishment of robust and reliable research practices. The findings will not only aid in validating existing methods but also guide the development of future approaches, ultimately fostering transparency and trust in the application of deep learning techniques in biodiversity research

ADOnIS: An ontology-based information system providing seamless integration of structured and unstructured data

Scientific information is contained in structured data like spreadsheets as well as in unstructured data like text. For example, scientific results can manifest themselves in one or more data sets containing main characteristics of the scientific process and one or more publications related to the dataset(s). From a scientist's perspective, it is desirable to obtain seamless access to this information regardless of whether it is based on structured or unstructured data. To this end, we developed and continuously extend the AquaDiva Ontology-based Information System, ADOnIS, which endeavors to provide such functionality

ADOnIS - An ontology-based information system providing seamless integration of structured and unstructured data

The CRC AquaDiva is a large collaborative project spanning a variety of domains, such as biology, geology, chemistry, and computer science with the common goal to better understand the Earth's critical zone in particular how environmental conditions and surface properties shape the structure, properties, and functions of the subsurface. This necessitates the collection and integration of large volumes of heterogeneous data. Besides this structured data, knowledge is also encoded in an unstructured form in publications. Ideally, scientists should be able to seamlessly access both types of information. To this end, we are developing the AquaDiva Ontology-based Information System, ADOnIS. This system gives scientists various ways to upload their datasets into a common repository based on the BExIS framework. To enhance the integration process and to resolve conflicts among heterogeneous datasets, we build a conceptual, ontology-based layer on top of the common repository. Finally, the system grants different mechanisms to search and look for a specific piece of information and/or knowledge, including keyword search, semantic search, and interactive search. In all cases, search results will contain structured data as well as publications obtained from PubMed by using SeMedico. The normal search provides the possibility for a scientist to enter a keyword (or a set of keywords) looking for the existence of this keyword either in the set of available metadata or primary data applying an exact match technique. This kind of search completely ignores the semantics of keywords as well as their relationships. Therefore, ADOnIS also provides semantic search exploiting features introduced by the conceptual layer. Finally, interactive search offers a view covering all the geo-related datasets displayed on a map

Interannual variation in land-use intensity enhances grassland multidiversity

Although temporal heterogeneity is a well-accepted driver of biodiversity, effects of interannual variation in land-use intensity (LUI) have not been addressed yet. Additionally, responses to land use can differ greatly among different organisms; therefore, overall effects of land-use on total local biodiversity are hardly known. To test for effects of LUI (quantified as the combined intensity of fertilization, grazing, and mowing) and interannual variation in LUI (SD in LUI across time), we introduce a unique measure of whole-ecosystem biodiversity, multidiversity. This synthesizes individual diversity measures across up to 49 taxonomic groups of plants, animals, fungi, and bacteria from 150 grasslands. Multidiversity declined with increasing LUI among grasslands, particularly for rarer species and aboveground organisms, whereas common species and belowground groups were less sensitive. However, a high level of interannual variation in LUI increased overall multidiversity at low LUI and was even more beneficial for rarer species because it slowed the rate at which the multidiversity of rare species declined with increasing LUI. In more intensively managed grasslands, the diversity of rarer species was, on average, 18% of the maximum diversity across all grasslands when LUI was static over time but increased to 31% of the maximum when LUI changed maximally over time. In addition to decreasing overall LUI, we suggest varying LUI across years as a complementary strategy to promote biodiversity conservation

Global distribution of earthworm diversity

Soil organisms, including earthworms, are a key component of terrestrial ecosystems. However, little is known about their diversity, their distribution, and the threats affecting them. We compiled a global dataset of sampled earthworm communities from 6928 sites in 57 countries as a basis for predicting patterns in earthworm diversity, abundance, and biomass. We found that local species richness and abundance typically peaked at higher latitudes, displaying patterns opposite to those observed in aboveground organisms. However, high species dissimilarity across tropical locations may cause diversity across the entirety of the tropics to be higher than elsewhere. Climate variables were found to be more important in shaping earthworm communities than soil properties or habitat cover. These findings suggest that climate change may have serious implications for earthworm communities and for the functions they provide.status: publishe