Miracle-dz/UniFAR-FTUs huggingface dataset:
The campfire storytelling system – automatic creation and modification of a narrative

ABSTRACT This paper presents and evaluates a method for automatically generating a narrative that includes initial input from the listener, without forcing him/her to become an actual character in the story. More importantly, the story can be modified by the listener during the course of the story being narrated. This latter feature works without pre-defining alternative paths, thereby permitting the changes to take place at any time throughout the course of the story. This system, called the Campfire Storytelling System, employs a contextual approach to narrative generation called the Cooperating Context Method (CCM). Campfire STS was assessed for its functionality and its user acceptance. Thirty-eight human test subjects evaluated stories generated by Campfire STS. They responded to a survey stating their opinions about the generated stories, their entertainment value for adults, their creativity, and other similar issues. Furthermore, 20 of these subjects who self-reported non-trivial experience with children were additionally asked to evaluate the generated stories with respect to how much children would enjoy them. The results of these evaluations suggest that Campfire STS is able to create interesting and novel stories, as well as change them seamlessly in mid-stream while still maintaining interesting and entertaining stories.
A Steganographic Method Using Tabu Search Approach

The steganography objective consists to hide confidential information within a cover-media in such a way that others cannot detect the presence of the hidden message. In this work we investigate to improve the embedding capacity and provide an imperceptible visual quality, using an adapted tabu search approach to locate the optimal positions of the pixels in the cover image. Experimental results showed that the proposed approach hide more large information and improve the quality of the stego-image.
DBChEx: Interactive Exploration of Data and Schema Change

Data exploration is a visually-driven process that is often used as a ﬁrst step to decide which aspects of a dataset are worth further investigation and analysis. It serves as an important tool to gain a ﬁrst understanding of a dataset and to generate hypotheses. While there are many tools for exploring static datasets, dynamic datasets that change over time still lack eﬀective exploration support. To address this shortcoming, we present our innovative tool Database Change Explorer (DBChEx) that enables exploration of data and schema change through a set of exploration primitives. Users gain valuable insights into data generation processes and data or schema evolution over time by a mix of serendipity and guided investigation. The tool is a server-client application with a web front-end and an underlying database that stores the history of changes in the data and schema in a data model called the change-cube. Our demonstration of DBChEx shows how users can inter-actively explore data and schema change in two real-world datasets, IMDB and Wikipedia infoboxes.
Radiometric Microwave Indices for Remote Sensing of Land Surfaces

This work presents an overview of the potential of microwave indices obtained from multi-frequency/polarization radiometry in detecting the characteristics of land surfaces, in particular soil covered by vegetation or snow and agricultural bare soils. Experimental results obtained with ground-based radiometers on different types of natural surfaces by the Microwave Remote Sensing Group of IFAC-CNR starting from ‘80s, are summarized and interpreted by means of theoretical models. It has been pointed out that, with respect to single frequency/polarization observations, microwave indices revealed a higher sensitivity to some significant parameters, which characterize the hydrological cycle, namely: soil moisture, vegetation biomass and snow depth or snow water equivalent. Electromagnetic models have then been used for simulating brightness temperature and microwave indices from land surfaces. As per vegetation covered soils, the well-known tau-omega (τ-ω) model based on the radiative transfer theory has been used, whereas terrestrial snow cover has been simulated using a multi-layer dense-medium radiative transfer model (DMRT). On the basis of these results, operational inversion algorithms for the retrieval of those hydrological quantities have been successfully implemented using multi-channel data from the microwave radiometric sensors operating from satellite.
ARQuery: Hallucinating Analytics over Real-World Data using Augmented Reality

In addition to the virtual, there is a vast amount of data present in the real world. Given recent advances in computer vision, augmented reality, and cloud services, we are faced with a tremendous opportunity to augment the structured data around end-users with insights. Coinciding with these trends, the number of data-rich end-user activities is also rapidly increasing. Thus, it is useful to investigate the process of data exploration and analysis in augmented and mixed reality settings. In this paper, we describe ARQuery , a query platform that utilizes augmented reality to enable querying over real-world data. We provide an interaction and visualization grammar that is designed to augment real-world data, and a performant framework that enables query exploration in real-time. Our studies show that ARQuery provides a ﬂuid, low-latency query experience for the end-user that is signiﬁcantly faster than traditional approaches.
Vision techniques for on‐board detection, following, and mapping of moving targets

This study presents computer vision modules of a multi‐unmanned aerial vehicle (UAV) system, which scored gold, silver, and bronze medals at the Mohamed Bin Zayed International Robotics Challenge 2017. This autonomous system, which was running completely on board and in real time, had to address two complex tasks in challenging outdoor conditions. In the first task, an autonomous UAV had to find, track, and land on a human‐driven car moving at 15 km/hr on a figure‐eight‐shaped track. During the second task, a group of three UAVs had to find small colored objects in a wide area, pick them up, and deliver them into a specified drop‐off zone. The computer vision modules presented here achieved computationally efficient detection, accurate localization, robust velocity estimation, and reliable future position prediction of both the colored objects and the car. These properties had to be achieved in adverse outdoor environments with changing light conditions. Lighting varied from intense direct sunlight with sharp shadows cast over the objects by the UAV itself, to reduced visibility caused by overcast to dust and sand in the air. The results presented in this paper demonstrate good performance of the modules both during testing, which took place in the harsh desert environment of the central area of United Arab Emirates, as well as during the contest, which took place at a racing complex in the urban, near‐sea location of Abu Dhabi. The stability and reliability of these modules contributed to the overall result of the contest, where our multi‐UAV system outperformed teams from world’s leading robotic laboratories in two challenging scenarios.
SageDB: A Learned Database System

Modern data processing systems are designed to be general purpose, in that they can handle a wide variety of diﬀerent schemas, data types, and data distributions, and aim to provide eﬃcient access to that data via the use of optimizers and cost models. This general purpose nature results in systems that do not take advantage of the characteristics of the particular application and data of the user. With SageDB we present a vision towards a new type of a data processing system, one which highly specializes to an application through code synthesis and machine learning. By modeling the data distribution, workload, and hardware, SageDB learns the structure of the data and optimal access methods and query plans. These learned models are deeply embedded, through code synthesis, in essentially every component of the database. As such, SageDB presents radical departure from the way database systems are currently developed, raising a host of new problems in databases, machine learning and programming systems.
An asset-liability management stochastic program of a leasing company

We build a multi-stage stochastic program of an asset-liability management problem of a leasing company, analyse model results and present a stress-testing methodology suited for financial applications. At the beginning, the business model of such a company is formulated. We introduce three various risk constraints, namely the chance constraint, the Value-at-Risk constraint and the conditional Value-at-Risk constraint along with the second-order stochastic dominance constraint, which are applied to the model to control risk of the optimal strategy. We also present the structure and the generation process of our scenarios. To capture the evolution of interest rates the Hull-White model is used. Thereafter, results of the model and the effect of the risk constraints on the optimal decisions are thoroughly investigated. In the final part, the performance of the optimal solutions of the problems for unconsidered and unfavourable crisis scenarios is inspected. The methodology of a stress test we used was proposed in such a way that it answers typical questions asked by asset-liability managers.
Data-based intervention approach for Complexity-Causality measure

Causality testing methods are being widely used in various disciplines of science. Model-free methods for causality estimation are very useful, as the underlying model generating the data is often unknown. However, existing model-free/data-driven measures assume separability of cause and effect at the level of individual samples of measurements and unlike model-based methods do not perform any intervention to learn causal relationships. These measures can thus only capture causality which is by the associational occurrence of ‘cause’ and ‘effect’ between well separated samples. In real-world processes, often ‘cause’ and ‘effect’ are inherently inseparable or become inseparable in the acquired measurements. We propose a novel measure that uses an adaptive interventional scheme to capture causality which is not merely associational. The scheme is based on characterizing complexities associated with the dynamical evolution of processes on short windows of measurements. The formulated measure, Compression-Complexity Causality is rigorously tested on simulated and real datasets and its performance is compared with that of existing measures such as Granger Causality and Transfer Entropy. The proposed measure is robust to the presence of noise, long-term memory, filtering and decimation, low temporal resolution (including aliasing), non-uniform sampling, finite length signals and presence of common driving variables. Our measure outperforms existing state-of-the-art measures, establishing itself as an effective tool for causality testing in real world applications.
Dataset Relationship Management

The database community has largely focused on providing improved transaction management and query capabilities over records (and generalizations thereof). Yet such capabilities address only a small part of today’s data science tasks, which are often much more focused on discovery, linking, comparative analysis, and collaboration across holistic datasets and data products. Data scientists frequently point to a strong need for data management — with respect to their many datasets and data products. We propose the development of the dataset relationship management system to support five main classes of operations on datasets: reuse of schema, data, curation, and work across many datasets; revelation of provenance, context, and assumptions; rapid revision of data and processing steps; system-assisted retargeting of computation to alternative execution environments; and metrics to reward individuals’ contributions to the broader data ecosystem. We argue that the recent adoption of computational notebooks (particularly JupyterLab and Jupyter Notebook), as a unified interface over data tools, provides an ideal way of gathering detailed information about how data is being used, i.e., of transparently capturing dataset provenance and relationships, and thus such notebooks provide an attractive mechanism for integrating dataset relationship management into the data science ecosystem. We briefly outline our experiences in building towards JuNEAU, the first prototype DRMS.
Data Vocalization with CiceroDB

Data vocalization is the process of summarizing data via voice output. We present CiceroDB, a novel database sys-tem, designed from the ground up for vocal output of query results. It is targeted at scenarios in which visual output is either impossible or undesirable. CiceroDB exploits the particularities of vocal output to reduce data processing over-heads. For instance, it uses sampling to converge to high-level voice descriptions and overlaps voice output with background processing tasks. In this paper, we describe prelim-inary results and ongoing research eﬀorts.
Recommending Scientific Collaboration from ResearchGate

Scientific collaboration improves researchers productivity by providing a way to share new ideas, learn new techniques, and find new research applications, increasing the chance to access funding. Beyond ethics and reciprocity, there are other important aspects on achieving scientific collaborations, such as research interests and expected productivity gain, that are paramount to a successful partnership. However, achieve effective collaborations is a hard work and can drain researchers time. In this work, we propose a recommendation approach that uses different strategies to suggest scientific collaboration for researchers based on their research interest. In particular, our approach exploits ResearchGate, a well known research social network from where research interests and researchers production are used to model similarity between them. Experimental results show that the content-based strategy outperforms neighborhood-based collaborative filtering strategies to recommend scientific collaboration with gains of up 16.60% in precision, 37.19% in recall, and 21.16% in F1 for the top-20 recommendation lists.
Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.
Cooperative autonomous search, grasping, and delivering in a treasure hunt scenario by a team of unmanned aerial vehicles

This paper addresses the problem of autonomous cooperative localization, grasping and delivering of colored ferrous objects by a team of unmanned aerial vehicles (UAVs). In the proposed scenario, a team of UAVs is required to maximize the reward by collecting colored objects and delivering them to a predefined location. This task consists of several subtasks such as cooperative coverage path planning, object detection and state estimation, UAV self‐localization, precise motion control, trajectory tracking, aerial grasping and dropping, and decentralized team coordination. The failure recovery and synchronization job manager is used to integrate all the presented subtasks together and also to decrease the vulnerability to individual subtask failures in real‐world conditions. The whole system was developed for the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017, where it achieved the highest score and won Challenge No. 3—Treasure Hunt. This paper does not only contain results from the MBZIRC 2017 competition but it also evaluates the system performance in simulations and field tests that were conducted throughout the year‐long development and preparations for the competition.
Knowledge-oriented task and motion planning for multiple mobile robots

ABSTRACT Robotic systems composed of several mobile robots moving in human environments pose several problems at perception, planning and control levels. In these environments, there may be obstacles obstructing the paths, which robots can remove by pushing or pulling them. At planning level, therefore, an efficient combination of task and motion planning is required. Even more if we assume a cooperative system in which robots can collaborate with each other by e.g. pushing together a heavy obstacle or by one robot clearing the way to another one. In this paper, we cope with this problem by proposing -TMP, a smart combination of an heuristic task planner based on the Fast Forward method, a physics-based motion planner, and reasoning processes over the ontologies that code the knowledge on the problem. The significance of the proposal relies on how geometric and physics information is used within the computation of the heuristics in order to guide the symbolic search, i.e. how an artificial intelligence planning method is combined with low-level motion planning to achieve a feasible sequence of actions (composed of collision-free motions plus physically-feasible push/pull actions). The proposal has been validated with several simulated scenarios (using up to five robots that need to collaborate with each other to reach the goal state), showing how the method is able to solve challenging situations and also find an efficient solution in terms of power.
Kyrix: Interactive Visual Data Exploration at Scale

Scalable interactive visual data exploration is crucial in many domains due to increasingly large datasets generated at rapid rates. Details-on-demand provides a useful interaction paradigm for exploring large datasets, where users start at an overview, find regions of interest, zoom in to see detailed views, zoom out and then repeat. This paradigm is the primary user interaction mode of widely-used systems such as Google Maps, Aperture Tiles and ForeCache. These earlier systems, however, are highly customized with hardcoded visual representations and optimizations. A more general framework is needed to facilitate the development of visual data exploration systems at scale. In this paper, we present Kyrix, an end-to-end system for developing scalable details-on-demand data exploration applications. Kyrix provides developers with a declarative model for easy specification of general visualizations. Behind the scenes, Kyrix utilizes a suite of performance optimization techniques to achieve a response time within 500ms for various user interactions. We also report results from a performance study which shows that a novel dynamic fetching scheme adopted by Kyrix outperforms tile-based fetching used in earlier systems.
Fully autonomous micro air vehicle flight and landing on a moving target using visual–inertial estimation and model‐predictive control

The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) held in spring 2017 was a very successful competition well attended by teams from all over the world. One of the challenges (Challenge 1) required an aerial robot to detect, follow, and land on a moving target in a fully autonomous fashion. In this paper, we present the hardware components of the micro air vehicle (MAV) we built with off the self components alongside the designed algorithms that were developed for the purposes of the competition. We tackle the challenge of landing on a moving target by adopting a generic approach, rather than following one that is tailored to the MBZIRC Challenge 1 setup, enabling easy adaptation to a wider range of applications and targets, even indoors, since we do not rely on availability of global positioning system. We evaluate our system in an uncontrolled outdoor environment where our MAV successfully and consistently lands on a target moving at a speed of up to 5.0 m/s.
Retrieval of Daily PM2.5 Concentrations Using Nonlinear Methods: A Case Study of the Beijing-Tianjin-Hebei Region, China

Exposure to fine particulate matter (PM2.5) is associated with adverse health impacts on the population. Satellite observations and machine learning algorithms have been applied to improve the accuracy of the prediction of PM2.5 concentrations. In this study, we developed a PM2.5 retrieval approach using machine-learning methods, based on aerosol products from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the NASA Earth Observation System (EOS) Terra and Aqua polar-orbiting satellites, near-ground meteorological variables from the NASA Goddard Earth Observing System (GEOS), and ground-based PM2.5 observation data. Four models, which are orthogonal regression (OR), regression tree (Rpart), random forests (RF), and support vector machine (SVM), were tested and compared in the Beijing–Tianjin–Hebei (BTH) region of China in 2015. Aerosol products derived from the Terra and Aqua satellite sensors were also compared. The 10-repeat 5-fold cross-validation (10 × 5 CV) method was subsequently used to evaluate the performance of the different aerosol products and the four models. The results show that the performance of the Aqua dataset was better than that of the Terra dataset, and that the RF algorithm has the best predictive performance (Terra: R = 0.77, RMSE = 43.51 μg/m3; Aqua: R = 0.85, RMSE = 33.90 μg/m3). This study shows promise for predicting the spatiotemporal distribution of PM2.5 using the RF model and Aqua aerosol product with the assistance of PM2.5 site data.