A Type-Theoretic Analysis of Modular Specifications

We study the problem of representing a modular specification language in a type-theory based theorem prover. Our goals are: to provide mechanical support for reasoning about specifications and about the specification language itself; to clarify the semantics of the specification language by formalising them fully; to augment the specification language with a programming language in a setting where they are both part of the same formal environment, allowing us to define a formal implementation relationship between the two. Previous work on similar issues has given rise to a dichotomy between "shallow" and "deep" embedding styles when representing one language within another. We show that the expressiveness of type theory, and the high degree of reflection that it permits, allow us to develop embedding techniques which lie between the "shallow" and "deep" extremes. We consider various possible embedding strategies and then choose one of them to explore more fully. As our object of study we choose a fragment of the Z specification language, which we encode in the type theory UTT, as implemented in the LEGO proof-checker. We use the encoding to study some of the operations on schemas provided by Z. One of our main concerns is whether it is possible to reason about Z specifications at the level of these operations. We prove some theorems about Z showing that, within certain constraints, this kind of reasoning is indeed possible. We then show how these metatheorems can be used to carry out formal reasoning about Z specifications. For this we make use of an example taken from the Z Reference Manual (ZRM). Finally, we exploit the fact that type theory provides a programming language as well as a logic to define a notion of implementation for Z specifications. We illustrate this by encoding some example programs taken from the ZRM

Comparing a Game v. Non-Game approach for plant provenance public education

Plants that are imported bring the risk of non-native threats that can cause environmental and economic harm. Introducing Serious Games is a novel approach to teach the public about the importance of plant provenance. Our study compares the presentation of information in via a game and non-game approach. We assess learning via a quiz completed immediately after the experience, then again three weeks later. We find that enjoyment in Phase 1 is an indicator of better performance in Phase 2

Comparing a Game v. Non-Game approach for plant provenance public education

Plants that are imported bring the risk of non-native threats that can cause environmental and economic harm. Introducing Serious Games is a novel approach to teach the public about the importance of plant provenance. Our study compares the presentation of information in via a game and non-game approach. We assess learning via a quiz completed immediately after the experience, then again three weeks later. We find that enjoyment in Phase 1 is an indicator of better performance in Phase 2

Monitoring water hyacinth in Kuttanad, India using Sentinel-1 SAR data

Water Hyacinth is an aquatic macrophyte and highly invasive species, indigenous to Amazonia, Brazil and tropical South America. It was first introduced to India in 1896 and has now become and environmental and social nuisance throughout the country in community ponds, freshwater lakes, irrigation channels, rivers and most other surface waterbodies. Considering the adverse impact the infesting weed has, a constant monitoring is needed to aid policy makers involved in remedial measures. Due to the synoptic coverage provided by satellite imaging and other remote sensing practices, it is convenient to find a solution using this type of data. This paper looks at the use of Synthetic Aperture Radar (SAR) Sentinel-1 to detect water hyacinth at an early stage of its life-cycle. While SAR has been used prominently to monitor wetlands, the technique is yet to be fully exploited for monitoring water hyacinth and we seek to fill this knowledge gap. We compare different change detection methodologies based on dual po-larimetric data. We also demonstrate how Sentinel-1 can be used to monitor this type of aquatic weeds in our study areas, which is Vembanad Lake in Kuttanad, Kerala

Monitoring Aquatic Weeds In Indian Wetlands Using Multitemporal Remote Sensing Data With Machine Learning Techniques

The main objective of this paper to show the potential of mul-titemporal Sentinel-1 (S-1) and Sentinel-2 (S-2) for detection of water hyacinth in Indian wetlands. Water hyacinth (Pontederia crassipes, also called Eichhornia crassipes) is one of the most destructive invasive weed species in many lakes and river systems worldwide, causing significant adverse economic and ecological impacts. We use the expectation maximization (EM) as a benchmark machine learning algorithm and compare its results with three supervised machine learning classifiers, Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbour (kNN), using both synthetic aperture radar (SAR) and optical data to distinguish between clean and infested waters

Monitoring the spread of water hyacinth (Pontederia crassipes): challenges and future developments

Water hyacinth (Pontederia crassipes, also referred to as Eicchornia crassipes) is one of the most invasive weed species in the world, causing significant adverse economic and ecological impacts, particularly in tropical and sub-tropical regions. Large scale real-time monitoring of areas of chronic infestation is critical to formulate effective control strategies for this fast spreading weed species. Assessment of revenue generation potential of the harvested water hyacinth biomass also requires enhanced understanding to estimate the biomass yield potential for a given water body. Modern remote sensing technologies can greatly enhance our capacity to understand, monitor and estimate water hyacinth infestation within inland as well as coastal freshwater bodies. Readily available satellite imagery with high spectral, temporal and spatial resolution, along with conventional and modern machine learning techniques for automated image analysis, can enable discrimination of water hyacinth infestation from other floating or submerged vegetation. Remote sensing can potentially be complemented with an array of other technology-based methods, including aerial surveys, ground-level sensors, and citizen science, to provide comprehensive, timely and accurate monitoring. This review discusses the latest developments in the use of remote sensing and other technologies to monitor water hyacinth infestation, and proposes a novel, multi-modal approach that combines the strengths of the different methods

Detecting Water Hyacinth Infestation in Kuttanad, India, Using Dual-Pol Sentinel-1 SAR Imagery

Water hyacinth (Pontederia crassipes, also known as Eichhornia crassipes) is a highly invasive aquatic macrophyte species, indigenous to Amazonia, Brazil and tropical South America. It was introduced to India in 1896 and has now become an environmental and social challenge throughout the country in community ponds, freshwater lakes, irrigation channels, rivers and most other surface waterbodies. Considering its large speed of propagation on the water surface under conducive conditions and the adverse impact the infesting weed has, constant monitoring is needed to aid civic bodies, governments and policy makers involved in remedial measures. The synoptic coverage provided by satellite imaging and other remote sensing practices make it convenient to find a solution using this type of data. While there is an established background for the practice of remote sensing in the detection of aquatic plants, the use of Synthetic Aperture Radar (SAR) has yet to be fully exploited in the detection of water hyacinth. This research focusses on detecting water hyacinth within Vembanad Lake, Kuttanad, India. Here, results show that the monitoring of water hyacinth has proven to be possible using Sentinel-1 SAR data. A quantitative analysis of detection performance is presented using traditional and state-of-the-art change detectors. Analysis of these more powerful detectors showed true positive detection ratings of ~95% with 0.1% false alarm, showing significantly greater positive detection ratings when compared to the more traditional detectors. We are therefore confident that water hyacinth can be monitored using SAR data provided the extent of the infestation is significantly larger than the resolution cell (bigger than a quarter of a hectare)