Miracle-dz/UniFAR-FTUs huggingface dataset:
Video Summarization by Learning From Unpaired Data

We consider the problem of video summarization. Given an input raw video, the goal is to select a small subset of key frames from the input video to create a shorter summary video that best describes the content of the original video. Most of the current state-of-the-art video summarization approaches use supervised learning and require labeled training data. Each training instance consists of a raw input video and its ground truth summary video curated by human annotators. However, it is very expensive and difficult to create such labeled training examples. To address this limitation, we propose a novel formulation to learn video summarization from unpaired data. We present an approach that learns to generate optimal video summaries using a set of raw videos (V) and a set of summary videos (S), where there exists no correspondence between V and S. We argue that this type of data is much easier to collect. Our model aims to learn a mapping function F : V -> S such that the distribution of resultant summary videos from F(V) is similar to the distribution of S with the help of an adversarial objective. In addition, we enforce a diversity constraint on F(V) to ensure that the generated video summaries are visually diverse. Experimental results on two benchmark datasets indicate that our proposed approach significantly outperforms other alternative methods.
Automated Event Extraction Model for Linked Portuguese Documents

In recent times, Machine Learning is booming and researchers are applying it to the most conceivable cases such as the area of linked documents. This article presents a process of automatic event extraction from Portuguese linked document whose accuracy (95.00%) was calculated by manual veriﬁcation. With the help of an ontological structure, extracted events are mapped as a knowledge graph that represents the named entities and the events associated with each document. Such graphs are accessible through SPARQL queries. This way, the information existing in the linked documents can be easily accessed by resorting to a question-answering approach.
Spatially Controllable Image Synthesis with Internal Representation Collaging

We present a novel CNN-based image editing strategy that allows the user to change the semantic information of an image over an arbitrary region by manipulating the feature-space representation of the image in a trained GAN model. We will present two variants of our strategy: (1) spatial conditional batch normalization (sCBN), a type of conditional batch normalization with user-specifiable spatial weight maps, and (2) feature-blending, a method of directly modifying the intermediate features. Our methods can be used to edit both artificial image and real image, and they both can be used together with any GAN with conditional normalization layers. We will demonstrate the power of our method through experiments on various types of GANs trained on different datasets. Code will be available at this https URL.
Meta-F⋆: Proof Automation with SMT, Tactics, and Metaprograms

We introduce Meta-F⋆, a tactics and metaprogramming framework for the F⋆ program verifier. The main novelty of Meta-F⋆ is allowing the use of tactics and metaprogramming to discharge assertions not solvable by SMT, or to just simplify them into well-behaved SMT fragments. Plus, Meta-F⋆ can be used to generate verified code automatically. Meta-F⋆ is implemented as an F⋆ effect, which, given the powerful effect system of F⋆, heavily increases code reuse and even enables the lightweight verification of metaprograms. Metaprograms can be either interpreted, or compiled to efficient native code that can be dynamically loaded into the F⋆ type-checker and can interoperate with interpreted code. Evaluation on realistic case studies shows that Meta-F⋆ provides substantial gains in proof development, efficiency, and robustness.
Evaluating Young People's Area Estimation of Countries and Continents

For decades, cartographers and cognitive scientists have speculated about the influence of map projections on mental representations of the world. The development of Web 2.0 and web mapping services at the beginning of the 21st century—such as Google Maps, OpenStreetMap, and Baidu Map—led to an enormous spread of cartographic data, which is available to every Internet user. Nevertheless, the cartographic properties of these map services, and, in particular, the selected map projection or the Web Mercator projection, are questionable. The goal of this study is to investigate if the global-scale mental map of young people has been influenced by the increasing availability of web maps and the Web Mercator projection. An application was developed that allowed participants of Belgium and the US to scale the land area of certain countries and continents compared to Europe or the conterminous United States. The results show that the participants’ estimation of the actual land area is quite accurate. Moreover, an indication of the existence of a Mercator effect could not be discovered. To conclude, the young people’s mental map of the world does not appear to be influenced by a specific map projection but by personal characteristics. These elements are varied and require further analysis.
Operational Large-Scale Segmentation of Imagery Based on Iterative Elimination

Image classification and interpretation are greatly aided through the use of image segmentation. Within the field of environmental remote sensing, image segmentation aims to identify regions of unique or dominant ground cover from their attributes such as spectral signature, texture and context. However, many approaches are not scalable for national mapping programmes due to limits in the size of images that can be processed. Therefore, we present a scalable segmentation algorithm, which is seeded using k-means and provides support for a minimum mapping unit through an innovative iterative elimination process. The algorithm has also been demonstrated for the segmentation of time series datasets capturing both the intra-image variation and change regions. The quality of the segmentation results was assessed by comparison with reference segments along with statistics on the inter- and intra-segment spectral variation. The technique is computationally scalable and is being actively used within the national land cover mapping programme for New Zealand. Additionally, 30-m continental mosaics of Landsat and ALOS-PALSAR have been segmented for Australia in support of national forest height and cover mapping. The algorithm has also been made freely available within the open source Remote Sensing and GIS software Library (RSGISLib).
Modelling Construction Site Cost Index Based on Neural Network Ensembles

Construction site overhead costs are key components of cost estimation in construction projects. The estimates are expected to be accurate, but there is a growing demand to shorten the time necessary to deliver cost estimates. The balancing (symmetry) between time of calculation and satisfaction of reliable estimation was the reason for developing a new model for cost estimation in construction. This paper reports some results from the authors’ broad research on the modelling processes in engineering related to estimation of construction costs using artificial intelligence tools. The aim of this work was to develop a model capable of predicting a construction site cost index that would benefit from combining several artificial neural networks into an ensemble. Combining selected neural networks and forming the ensemble-based models compromised their strengths and weaknesses. With the use of data including training patterns collected on the basis of studies of completed construction projects, the authors investigated various types of neural networks in order to select the members of the ensemble. Finally, three models that were assessed in terms of performance and prediction quality were proposed. The results revealed that the developed models based on ensemble averaging and stacked generalisation met the expectations of knowledge generalisation and accuracy of prediction of site overhead cost index. The proposed models offer predictions of cost in an accepted error range and prove to deliver better predictions than those based on single neural networks. The developed tools can be used in the decision-making process regarding construction cost estimation.
European Digital Elevation Model Validation against Extensive Global Navigation Satellite Systems Data and Comparison with SRTM DEM and ASTER GDEM in Central Macedonia (Greece)

Digital elevation models (DEMs) are a widely used form of topographic information, with some of the most popular being the Shuttle Radar Topography Mission (SRTM) DEM and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM). These two sources of topographical information are the main constituents of the European Union Digital Elevation Model (EU-DEM), which is a relatively new dataset of the EU’s Copernicus Land Monitoring Service. In this context, the purpose of this study was to validate EU-DEM for its vertical accuracy and to compare it with SRTM DEM and ASTER GDEM data. This was achieved in a Geographic Information System (GIS) environment, using extensive—in the order of tens of thousands of points—geodetic Global Navigation Satellite System (GNSS) measurements and appropriate pre-processing steps. The absolute elevation errors results had a Root Mean Square Error (RMSE) of 2.7 m at a 90% confidence level and characterize the performance of EU-DEM from local to regional scale, generally confirming that it is an enhanced source of elevation information when compared with its predecessors.
Long-Term Impacts of Selective Logging on Amazon Forest Dynamics from Multi-Temporal Airborne LiDAR

Forest degradation is common in tropical landscapes, but estimates of the extent and duration of degradation impacts are highly uncertain. In particular, selective logging is a form of forest degradation that alters canopy structure and function, with persistent ecological impacts following forest harvest. In this study, we employed airborne laser scanning in 2012 and 2014 to estimate three-dimensional changes in the forest canopy and understory structure and aboveground biomass following reduced-impact selective logging in a site in Eastern Amazon. Also, we developed a binary classification model to distinguish intact versus logged forests. We found that canopy gap frequency was significantly higher in logged versus intact forests even after 8 years (the time span of our study). In contrast, the understory of logged areas could not be distinguished from the understory of intact forests after 6–7 years of logging activities. Measuring new gap formation between LiDAR acquisitions in 2012 and 2014, we showed rates 2 to 7 times higher in logged areas compared to intact forests. New gaps were spatially clumped with 76 to 89% of new gaps within 5 m of prior logging damage. The biomass dynamics in areas logged between the two LiDAR acquisitions was clearly detected with an average estimated loss of −4.14 ± 0.76 MgC ha−1 y−1. In areas recovering from logging prior to the first acquisition, we estimated biomass gains close to zero. Together, our findings unravel the magnitude and duration of delayed impacts of selective logging in forest structural attributes, confirm the high potential of airborne LiDAR multitemporal data to characterize forest degradation in the tropics, and present a novel approach to forest classification using LiDAR data.
A Joint Inversion Estimate of Antarctic Ice Sheet Mass Balance Using Multi-Geodetic Data Sets

Many recent mass balance estimates using the Gravity Recovery and Climate Experiment (GRACE) and satellite altimetry (including two kinds of sensors of radar and laser) show that the ice mass of the Antarctic ice sheet (AIS) is in overall decline. However, there are still large differences among previously published estimates of the total mass change, even in the same observed periods. The considerable error sources mainly arise from the forward models (e.g., glacial isostatic adjustment [GIA] and firn compaction) that may be uncertain but indispensable to simulate some processes not directly measured or obtained by these observations. To minimize the use of these forward models, we estimate the mass change of ice sheet and present-day GIA using multi-geodetic observations, including GRACE and Ice, Cloud and land Elevation Satellite (ICESat), as well as Global Positioning System (GPS), by an improved method of joint inversion estimate (JIE), which enables us to solve simultaneously for the Antarctic GIA and ice mass trends. The GIA uplift rates generated from our JIE method show a good agreement with the elastic-corrected GPS uplift rates, and the total GIA-induced mass change estimate for the AIS is 54 ± 27 Gt/yr, which is in line with many recent GPS calibrated GIA estimates. Our GIA result displays the presence of significant uplift rates in the Amundsen Sea Embayment of West Antarctica, where strong uplift has been observed by GPS. Over the period February 2003 to October 2009, the entire AIS changed in mass by −84 ± 31 Gt/yr (West Antarctica: −69 ± 24, East Antarctica: 12 ± 16 and the Antarctic Peninsula: −27 ± 8), greater than the GRACE-only estimates obtained from three Mascon solutions (CSR: −50 ± 30, JPL: −71 ± 30, and GSFC: −51 ± 33 Gt/yr) for the same period. This may imply that single GRACE data tend to underestimate ice mass loss due to the signal leakage and attenuation errors of ice discharge are often worse than that of surface mass balance over the AIS.
Weibull multi-state models with misclassification

ABSTRACT It is often important to allow multi-state models (MSMs) to accommodate misclassification of states. We introduce Bayesian parametric MSMs with unknown misclassification of states and Weibull distributed waiting times between states. This allows transitions between states to depend on the time spent in the current state, a feature lacking in commonly used exponential waiting times model. To fit the proposed model, a MCMC algorithm was employed. An example on the progression of bipolar disorder is presented along with simulation results. There was evidence that Weibull waiting times are an improvement over exponential in the study of bipolar disorder.
A Novel Tri-Training Technique for the Semi-Supervised Classification of Hyperspectral Images Based on Regularized Local Discriminant Embedding Feature Extraction

This paper introduces a novel semi-supervised tri-training classification algorithm based on regularized local discriminant embedding (RLDE) for hyperspectral imagery. In this algorithm, the RLDE method is used for optimal feature information extraction, to solve the problems of singular values and over-fitting, which are the main problems in the local discriminant embedding (LDE) and local Fisher discriminant analysis (LFDA) methods. An active learning method is then used to select the most useful and informative samples from the candidate set. In the experiments undertaken in this study, the three base classifiers were multinomial logistic regression (MLR), k-nearest neighbor (KNN), and random forest (RF). To confirm the effectiveness of the proposed RLDE method, experiments were conducted on two real hyperspectral datasets (Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Reflective Optics System Imaging Spectrometer (ROSIS)), and the proposed RLDE tri-training algorithm was compared with its counterparts of tri-training alone, LDE, and LFDA. The experiments confirmed that the proposed approach can effectively improve the classification accuracy for hyperspectral imagery.
Estimating Rice Agronomic Traits Using Drone-Collected Multispectral Imagery

The knowledge of rice nitrogen (N) requirements and uptake capacity are fundamental for the development of improved N management. This paper presents empirical models for predicting agronomic traits that are relevant to yield and N requirements of rice (Oryza sativa L.) through remotely sensed data. Multiple linear regression models were constructed at key growth stages (at tillering and at booting), using as input reflectance values and vegetation indices obtained from a compact multispectral sensor (green, red, red-edge, and near-infrared channels) onboard an unmanned aerial vehicle (UAV). The models were constructed using field data and images from two consecutive years in a number of experimental rice plots in Greece (Thessaloniki Regional Unit), by applying four different N treatments (C0: 0 N kg∙ha−1, C1: 80 N kg∙ha−1, C2: 160 N kg∙ha−1, and C4: 320 N kg∙ha−1). Models for estimating the current crop status (e.g., N uptake at the time of image acquisition) and predicting the future one (e.g., N uptake of grains at maturity) were developed and evaluated. At the tillering stage, high accuracies (R2 ≥ 0.8) were achieved for N uptake and biomass. At the booting stage, similarly high accuracies were achieved for yield, N concentration, N uptake, biomass, and plant height, using inputs from either two or three images. The results of the present study can be useful for providing N recommendations for the two top-dressing fertilizations in rice cultivation, through a cost-efficient workflow.
Determination of Primary and Secondary Lahar Flow Paths of the Fuego Volcano (Guatemala) Using Morphometric Parameters

On 3 June 2018, a strong eruption of the Fuego volcano in Guatemala produced a dense cloud of 10-km-high volcanic ash and destructive pyroclastic flows that caused nearly 200 deaths and huge economic losses in the region. Subsequently, due to heavy rains, destructive secondary lahars were produced, which were not plotted on the hazard maps using the LAHAR Z software. In this work we propose to complement the mapping of this type of lahars using remote-sensing (Differential Interferometry, DINSAR) in Sentinel images 1A and 2A, to locate areas of deformation of the relief on the flanks of the volcano, areas that are possibly origin of these lahars. To determine the trajectory of the lahars, parameters and morphological indices were analyzed with the software System for Automated Geoscientific Analysis (SAGA). The parameters and morphological indices used were the accumulation of flow (FCC), the topographic wetness index (TWI), the length-magnitude factor of the slope (LS). Finally, a slope stability analysis was performed using the Shallow Landslide Susceptibility software (SHALSTAB) based on the Mohr–Coulomb theory and its parameters: internal soil saturation degree and effective precipitation, parameters required to destabilize a hillside. In this case, the application of this complementary methodology provided a more accurate response of the areas destroyed by primary and secondary lahars in the vicinity of the volcano.
Identifying Mangrove Deforestation Hotspots in South Asia, Southeast Asia and Asia-Pacific

Mangroves inhabit highly productive inter-tidal ecosystems in >120 countries in the tropics and subtropics providing critical goods and services to coastal communities and contributing to global climate change mitigation owing to substantial carbon stocks. Despite their importance, global mangrove distribution continues to decline primarily due to anthropogenic drivers which vary by region/country. South Asia, Southeast Asia and Asia-Pacific contain approximately 46% of the world’s mangrove ecosystems, including the most biodiverse mangrove forests. This region also exhibits the highest global rates of mangrove loss. Remotely sensed data provides timely and accurate information on mangrove distribution and dynamics critical for targeting loss hotspots and guiding intervention. This report inventories, describes and compares all known single- and multi-date remotely sensed datasets with regional coverage and provides areal mangrove extents by country. Multi-date datasets were used to estimate dynamics and identify loss hotspots (i.e., countries that exhibit greatest proportional loss). Results indicate Myanmar is the primary mangrove loss hotspot, exhibiting 35% loss from 1975–2005 and 28% between 2000–2014. Rates of loss in Myanmar were four times the global average from 2000–2012. The Philippines is additionally identified as a loss hotspot, with secondary hotspots including Malaysia, Cambodia and Indonesia. This information helps inform and guide mangrove conservation, restoration and managed-use within the region.
Efficient algorithms for online learning over graphs

In this thesis we consider the problem of online learning with labelled graphs, in particular designing algorithms that can perform this problem quickly and with low memory requirements. We consider the tasks of Classification (in which we are asked to predict the labels of vertices) and Similarity Prediction (in which we are asked to predict whether two given vertices have the same label). The first half of the thesis considers non- probabilistic online learning, where there is no probability distribution on the labelling and we bound the number of mistakes of an algorithm by a function of the labelling’s complexity (i.e. its “naturalness"), often the cut- size. The second half of the thesis considers probabilistic machine learning in which we have a known probability distribution on the labelling. Before considering probabilistic online learning we first analyse the junction tree algorithm, on which we base our online algorithms, and design a new ver- sion of it, superior to the otherwise current state of the art. Explicitly, the novel contributions of this thesis are as follows: • A new algorithm for online prediction of the labelling of a graph which has better performance than previous algorithms on certain graph and labelling families. • Two algorithms for online similarity prediction on a graph (a novel problem solved in this thesis). One performs very well whilst the other not so well but which runs exponentially faster. • A new (better than before, in terms of time and space complexity) state of the art junction tree algorithm, as well as an application of it to the problem of online learning in an Ising model. • An algorithm that, in linear time, finds the optimal junction tree for online inference in tree-structured Ising models, the resulting online junction tree algorithm being far superior to the previous state of the art. All claims in this thesis are supported by mathematical proofs.
Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns

The most commonly used model for analyzing satellite imagery is the Support Vector Machine (SVM). Since there are a large number of possible variables for use in SVM, this paper will provide a combination of parameters that fit best for extracting green urban areas from Copernicus mission satellite images. This paper aims to provide a combination of parameters to extract green urban areas with the highest degree of accuracy, in order to speed up urban planning and ultimately improve town environments. Two different towns in Croatia were investigated, and the results provide an optimal combination of parameters for green urban areas extraction with an overall kappa index of 0.87 and 0.89, which demonstrates a very high classification accuracy.
Mutual illumination photometric stereo

Many techniques have been developed in computer vision to recover three-dimensional shape from two-dimensional images. These techniques impose various combinations of assumptions/restrictions of conditions to produce a representation of shape (e.g. surface normals or a height map). Although great progress has been made it is a problem which remains far from solved. In this thesis we propose a new approach to shape recovery - namely `mutual illumination photometric stereo'. We exploit the presence of colourful mutual illumination in an environment to recover the shape of objects from a single image.