Ragas is an open-source framework designed to evaluate and enhance the performance of applications built on Large Language Models (LLMs). It provides developers with tools to assess the robustness and quality of their LLM applications, ensuring they meet desired standards. Key Features and Functionality: - Automatic Metrics: Ragas offers a suite of metrics that automatically evaluate the performance and robustness of LLM applications, providing insights into areas such as context relevance, re
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C-Prot Embedded AppDefense is an SDK solution that enables easy integration into mobile applications and solutions developed by app developers, security providers or mobile service providers to ensure security.
Radar provides cloud & CDN performance benchmarks
Pasadena is a award-winning graphic design studio located in Stockholm. We believe in the power of good design and strong ideas.
PowerRadar is a powerfully simple energy management platform that gives organisations the flexibility to control and manage all their energy data using a single, holistic view of their energy footprint. It provides detailed and up-to-date visibility of energy performance – helping you to use clear, reliable data to confidently turn your energy into a competitive asset.
Extracting actionable information from satellite and aerial imagery.
ComplyRadar helps address AML transaction monitoring requirements by automatically identifying suspicious behaviour in real-time or on a scheduled basis, while minimising false positives
A free web app for characterizing temporal change in ecological communities.
RAMAS Ecotoxicology is used to make population-level ecological risk assessments for environmental contaminants. It imports data from standard laboratory bioassays, incorporates these data into the parameters of a population model, and performs a risk assessment by analyzing population-level differences between control and impacted samples.
RAMAS Ecosystem performs ecological risk assessments for food chain and food web models, linking bioassay results to community dynamics. It imports data from standard laboratory bioassays, incorporates these data into the parameters of a population model, and performs a risk assessment by analyzing population-level differences between control and impacted samples. RAMAS Ecosystem facilitates the management of variability and uncertainty in data, expressing results as ecological risks.
RAMAS Landscape integrates the landscape model LANDIS with our RAMAS/GIS habitat-based metapopulation model. With the integration of a landscape and a metapopulation model, predictions about the viability, recovery, and growth of a species can be based on the predicted changes in the landscapes in which they live.
Patient health records possess a great deal of information that would be useful in medical research, but access to these data is impossible or severely limited because of the private nature of most personal health records. Anonymization strategies, to be effective, must usually go much further than simply omitting explicit identifiers because even statistics computed from groups of records can often be leveraged by hackers to re-identify individuals. Methods of balancing the informativeness of d
RAMAS GIS is designed to link your GIS with a metapopulation model for population viability analysis (PVA) and extinction risk assessment. Habitats used by most species are becoming increasingly fragmented, requiring a metapopulation modeling approach to risk analysis. Recognizing habitat patchiness from an endangered species' point of view requires spatial information on habitat suitability. RAMAS GIS meets both of these requirements by linking metapopulation modeling with landscape data and GI
RAMAS Metapop is an interactive program that allows you to build models for species that live in multiple patches. It incorporates the spatial aspects of metapopulation dynamics, such as the configuration of the populations, dispersal and recolonization among patches and similarity of environmental patterns experienced by the populations. The program can be used to predict extinction risks and explore management options such as reserve design, translocations and reintroductions, and to assess hu
Risk analysis for egineering, human health, and the evironment. Environmental education software. Data analysis for endangered and threatened species. Models of extinction risk assessment linked to geographic information systems.
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