Miracle-dz/UniFAR-FTUs huggingface dataset:
A cloud-based GiScience learning approach to spanish national parks

GIScience learning is the main aim of the GI Learner project, which can be easily achieved using the cloud: open data online from Spatial Data Infrastructures (SDI of Spain). School on the Cloud network has promoted a cloud-based learning approach by dealing with access, sharing and reusability of cloud resources. Thus, based on the results of two European projects, the objective of the article is to focus on improving awareness of sustainability by building itineraries based on places of interest within the best protected areas in Spain, the National Parks, using SDI geoinformation. The methodology of (re)using open data online from SDI by linking cloud knowledge with real Earth and the three selected Parks for a better spatial understanding has been shown to be a very useful way to understand territory. Thus, to improve the learning of sustainable management of National Parks by using cloud computing and SDI it may well prove to be an essential part of environmental education and social responsibility.
Impact of Atmospheric Parameters on the Propagated Signal Power of Millimeter-Wave Bands Based on Real Measurement Data

Millimeter waves have been recognized as carriers for 5G cellular networks. We carried out a series of millimeter-wave measurements in Beijing, China, and studied the atmospheric impacts on millimeter-wave transmission. Our measurement design is an exemplary LOS transmission link. We also studied a 3 km long commercial E-band millimeter-wave backhaul link in Göteborg, Sweden. We monitored the variation of received signal in the rain, and compared the theoretical rain-induced signal attenuation with the practically monitored signal attenuation. Our results show that there is 1.5–4.5 dB uncertainty between the practical and theoretical rain-induced signal attenuation. Assuming the prediction of rain intensity is available, we have proposed a novel rain-aware radio resource management strategy which adapts the modulation and coding schemes of an OFDM system to rainfall events. We have applied the proposed algorithm to an OFDM - based 5G system, and the throughput result is improved. The result shows that the throughput of a fixed modulation and coding scheme system is between 12%–95% of the system employing the proposed algorithm during rainfall events.
Videojuego con sistema inteligente de aprendizaje por refuerzo profundo

Este trabajo de fin de grado describe el diseno y desarrollo de un videojuego basado en mazmorras, en el que el jugador debe conseguir todos los premios sin que sea atrapado por dos enemigos para pasar de nivel. Los enemigos estan implementados utilizando tecnicas de inteligencia artificial para encontrar el camino mas corto hasta el jugador, atraparlo y ser capaces de huir cuando el jugador consigue un premio especial. Asimismo, se presenta el desarrollo de un sistema inteligente basado en redes de neuronas y aprendizaje por refuerzo profundo para suplantar al jugador del videojuego. ---ABSTRACT---This final degree project describes the design and development of a dungeonbased video game, in which the player must get all the prizes without being caught by two enemies to pass the level. The enemies are implemented using artificial intelligence techniques to find the shortest path to the player, catch him, and be able to escape when the player gets a special prize. Furthermore, this work presents the development of an intelligent system based on deep neural networks and reinforcement learning to impersonate the player in the videogame.
Creating Effective Automation to Maintain Explicit User Engagement

ABSTRACT Loss of user engagement with automation in increasingly complex environments, such as driving a car, can have increasingly unexpected and consequential impacts. This paper presents a three-step process for designing cooperative automation into existing systems to maximize system performance with the constraint of maintaining user engagement. The first step identifies beneficial areas for automation through user testing, focusing on areas where human capabilities constrain performance. Next, an automation is created to perform a portion of the task leading to the constraint. User tests at this point compare different automation types to choose an automation that improves on human-only performance. Lastly, the automation trigger is adjusted to allow the human to maintain engagement with the system at a level desired to avoid the risks associated with loss of user engagement. This process is demonstrated through three human-in-the-loop experiments which illustrate the trade-space between maintaining human engagement and improving system performance.
Estimation de surface de Bézier à partir d'images de lumière structurée dans une optique de compression

Compression of 3D datas has become a topic of increasing importance over the evolution of scanners. With evolution of technologies, the commercial scanners give more and more data points, which can lead to difficulties in transmission or storage. In most cases, the acquisition does not take into account the final use of the data. While in many cases, this would limit the number of points. In this paper, we propose an approach which simplifies the scatterplot for industrial purposes using Bezier surfaces.
Path visualization: a method for objective measurement of spatial visualization

ABSTRACT The ability to mentally represent spatial information is a fundamental cognitive process. To many people, this process feels a bit like visual perception, hence the term ‘spatial visualization’. In this paper, we describe a method for measuring the accuracy of spatial visualization, specifically visualization of a complex path in imaginary space. A critical feature of this method (called Path Visualization) is that it relies on the detection of intersections in a visualized path. Intersection detection is an inherently spatial task that requires a spatial representation. In this paper, we show how the Path Visualization method works, and how it can be customized to address several key research issues in human spatial cognition.
Cryptography Based Security for Cloud Computing System

In this age of cloud computing, we tend to store data which we need frequently in web based cloud storage services so that it can be accessed whenever we need them. This not only provides an immense amount of flexibility to users but also makes our content accessible to us wherever we are and whenever we need them. We have many options in terms choosing web based cloud storage services for backing and archiving our data. There are many web based cloud storage services available out of which Amazon S3 and Google Drive are immensely popular among users. Backing up files so that they are not lost is an all-important step to ensure that nothing is ever lost. But, moving to the cloud is itself a big change and there are real concerns that make people pause before they sign up for any such service. This paper proposes and implements an algorithm which would encrypt the files uploaded on such web based cloud storage services and would decrypt the file once it has been downloaded using the keys that were generated during encryption. This would prevent unwanted intrusion into personal data and lack of standardization, i.e. one service provider may have end-to-end encryption while others do not.
VERSION AND CHANGE CONTROL IN SOFTWARE CONFIGURATION MANAGEMENT USING AGILE METHODOLOGIES: APPLICATION FRAMEWORK IN DEVELOPMENT OF GRAIN TRACEABILITY

In the development of a software, documents are generated and may vary throughout the project. Maintaining control of inclusions, alterations and exclusions of the content, helps maintain the software’s quality during its development. The Software Configuration Management is the area responsible for component version control involved in the development of the software through the Version Control stage, and to indicate why the component was modified. In this sense, it is necessary to adopt methodologies to assist in the version control process. This article presents the version control using the Agile Methodologies Scrum and XP, applied in the Development of Application Framework of Grain Trace-ability - RastroGrao. As a result, the approach brought benefits such as the ability to trace documents and have access to all versions of it.
Low Delay Technology Research of Transmission Line Tower Monitoring Network Integrating WSN and RFID

The tower is a key component of the transmission line system and its stability and reliability is the guarantee for the safety of entire transmission line system. This paper proposes a transmission tower monitoring network integrating wireless sensor network (WSN) and radio frequency identification (RFID). Compared to current designs, the proposed monitoring network shows the advantages of low cost, fast fault locating, tracking and management of grid equipment, and long-time monitoring. Low delay design is critical for the proposed monitoring network. In order to achieve balance between transmission delay and network cost, this paper establishes the transmission delay model for sink node optimization. For the redundant data of RFID, this paper presents a novel data cleaning scheme specially for the transmission tower monitoring, which can effectively reduce the redundant data at the reader end. The simulation results show that the optimal group number is 10 according to the proposed delay model.
AI-Based Learning Approach with Consideration of Safety Criteria on Example of a Depalletization Robot

Robotic systems need to achieve a certain level of process safety during the performance of the task and at the same time ensure compliance with safety criteria for the expected behaviour. To achieve this, the system must be aware of the risks related to the performance of the task in order to be able to take these into account accordingly. Once the safety aspects have been learned from the system, the task performance must no longer influence them. To achieve this, we present a concept for the design of a neural network that combines these characteristics. This enables the learning of safe behaviour and the fixation of it. The subsequent training of the task execution no longer influences safety and achieves targeted results in comparison to a conventional neural network.
A Distributed Approach for Optimal Scheduling of Power in Smart Grid Network Using ADMM

: This paper proposes a new method for Nano grids to operate efficiently. The cost and power are major issues in the existing power distribution technique. To optimize the cost and power, this paper proposes a Nobel method in combination with Hadoop MapReduce and ADMM algorithm. The proposed method effectively balances the power and optimizes the load requirement of grid systems in order to maintain power need. The balancing of power demand and supply are considered in this paper and implemented using distributed environment.
Semantic vector model on the Indonesian prefixes PE- and PEN-

Indonesian has two prefixes which express a range of semantic functions (e.g. agent, instrument, patient). One prefix, PEN-, has six allomorphs (peng-, peny-, pe-, pen, pem-, penge-) . A second prefix, PE, is described as having similar form and meaning as pe-. In this study, we used computational models of distributional semantics to clarify whether PE-and PEN-have discriminable semantics. The cosine similarity measure was used to evaluate to what extent the semantic vectors of pairs of words are similar in meaning. We found that the semantic similarities within the PEN-words are higher than between PE-and PEN-words. Additionally, nouns with PE-are more similar to their base words compared to nouns with PEN-. Furthermore, semantics similarity rating results, on a 5-point Likert scale, gathered from native speakers of Indonesian are in agreement with model predictions.
Sharing Non-Processor Resources in Multiprocessor Real-Time Systems

BRYAN C. WARD: Sharing Non-Processor Resources in Multiprocessor Real-Time Systems (Under the direction of James H. Anderson) Computing devices are increasingly being leveraged in cyber-physical systems, in which computing devices sense, control, and interact with the physical world. Associated with many such real-world interactions are strict timing constraints, which if unsatisfied, can lead to catastrophic consequences. Modern examples of such timing constraints are prevalent in automotive systems, such as airbag controllers, anti-lock brakes, and new autonomous features. In all of these examples, a failure to correctly respond to an event in a timely fashion could lead to a crash, damage, injury and even loss of life. Systems with imperative timing constraints are called real-time systems, and are broadly the subject of this dissertation. Much previous work on real-time systems and scheduling theory assumes that computing tasks are independent, i.e., the only resource they share is the platform upon which they are executed. In practice, however, tasks share many resources, ranging from more overt resources such as shared memory objects, to less overt ones, including data buses and other hardware and I/O devices. Accesses to some such resources must be synchronized to ensure safety, i.e., logical correctness, while other resources may exhibit better run-time performance if accesses are explicitly synchronized. The goal of this dissertation was to develop new synchronization algorithms and associated analysis techniques that can be used to synchronize access to many classes of resources, while improving the overall resource utilization, specifically as measured by real-time schedulability. Towards that goal, the Real-Time Nested Locking Protocol (RNLP), the first multiprocessor real-time locking protocol that supports lock nesting or fine-grained locking is proposed and analyzed. Furthermore, the RNLP is extended to support reader/writer locking, as well as k-exclusion locking. All presented RNLP variants are proven optimal. Furthermore, experimental results demonstrate the schedulability-related benefits of the RNLP. Additionally, three new synchronization algorithms are presented, which are specifically motivated by the need to manage shared hardware resources to improve real-time predictability. Furthermore, two new
Modal proof theory through a focused telescope

In this thesis, we use in two ways the concept of synthetic inference rules that can be obtained from a focused proof system; from one side of the “telescope”, focusing allows us to analyse the internal machinery of inference rules; on the other side, it allows us to consider more global behaviours. In the first part, we review existing proof systems for modal logic, concentrating our efforts around the sequent calculus and its extensions. We underline the issues that drive the modal proof theory community, such as the usual distinction between labelled and unlabelled systems that we aim at deconstructing. We present these questions and concepts in parallel for classical and intuitionistic modal logic in chapter 2 and 3 respectively. We in particular go through Fitting’s indexed nested sequents, for which we demonstrated a new completeness result via cut-elimination. The second part recalls first the notion of focusing and of synthetic inference rules in chapter 4, then presents two of our contributions in chapter 5 and 6. Firstly, we show how to emulate Simpson’s labelled sequent calculus for intuitionistic modal logic with Liang and Miller’s focused sequent calculus for first-order logic, therefore extending the result of Miller and Volpe. Secondly, we propose a similar encoding though for ordinary (unlabelled) sequent calculus via an intermediate focused framework based on Negri’s labelled sequent calculus. The third part reports on two other contributions, namely the completeness proofs of two nested sequent calculi for both classical and intuitionistic modal logic, first a focused version in chapter 7, and then a system merely based on synthetic inference rules in chapter 8. These rules only retain the transitions between big steps of reasoning forgetting most of the focused rules, which renders the system presentation clear and elegant while also simplifying the cut-elimination and completeness proofs.
Towards dynamic pharmacophore models through the use of coarse grained molecular dynamic simulations

Computer aided drug design (CADD) plays a crucial role in the drug discovery pipeline e.g. in virtual screening of chemical databases, de novo drug design, and lead optimization. Due to the increased numbers of protein structures elucidated, structure-based methods for developing pharmacophore models have started gaining in popularity and are becoming of particular importance. There have been a number of studies combining such methods with the use of molecular dynamics (MD) simulations to model protein exibility. In this project, the development and application of a new methodology, based on coarse grained (CG) MD, through the use of the MARTINI forcefield, and employed to explore protein ligand interactions, will be presented. An overview of the history of CADD is presented, along with current computational fragment based methods available for exploring protein-ligand interactions. An overview of the theory and methods behind MD simulations both all atom and CG is also provided. In the first results chapter, the parametrization of MARTINI beads as pharmacophoric probes, the analysis protocol and the application of this method to a data set of water soluble targets of pharmacological interest is described. The results suggest that the pharmacophoric probes have the ability to identify protein-ligand interactions on the targets of interest. The probes are also able to identify the residues involved in forming ligand binding interactions, showing a particular accuracy in identifying "hotspot" interactions. In the second results chapter, the extension of the initial data set to a range of GPCRs is described. The results suggest that the pharmacophoric probes have the ability to accurately explore both the orthosteric and allosteric binding sites of the GPCR targets and accurately identify the interactions and residues involved in ligand binding. This is done without the need to embed the protein in a lipid bilayer. In the final results chapter, the application of the dynamic pharmacophoric probes to identifying PIP2 and cholesterol binding sites, on membrane proteins, is presented. The results suggest that the probes can indeed identify these binding sites, along with identifying the residues that are involved in binding the PIP2 head groups. This was also done without embedding the protein in a lipid bilayer, which is the usual practice for identifying lipid binding interactions, reducing the computational resource requirements.
A matching game for tasks offloading in integrated edge‐fog computing systems

Edge and fog computing paradigms have recently emerged as promising approaches to overcome latency and network congestion drawbacks of the cloud network architecture alternative. In this direction, the paper deals with an integrated edge‐fog computing system to provide computational offloading capabilities to end‐devices by assuming that each task can be alternatively run locally at the end‐device site, offloaded to a nearby device through a direct communication link (ie, device‐to‐device) or to a more far and computational powerful node, ie, a fog node. In particular, the goal of this paper is to identify a suitable tasks allocation strategy in order to minimize both the system energy consumption and the worst overall task completion time. The related optimization problem is formulated here as a matching game with externalities with incomplete preferences lists between tasks and computation sites due to the fact that each end‐device can reach only a subset of the fog nodes of the integrated computing system. Furthermore, this paper pursuits the stability analysis of the outcome matching, and provides a post matching procedure to reach a stable final matching configuration. Finally, the good behavior of the proposed approach is validated by providing performance comparisons with different alternatives, namely a typical cloud approach architecture, potential game and other matching theory based approaches recently proposed in literature.
Improving the Accuracy of M-distance Based Nearest Neighbor Recommendation System by Using Ratings Variance

Received: 10 January 2019 Accepted: 3 April 2019 M-distance based recommendation system (MBR) is a nearest neighbor based recommendation method which uses the average of ratings given to an item as the attribute of that item. This attribute is used to determine similar items. Then, the average of the rating given to the similar items to an item of the active user determines the rating of that item. In this paper, to decrease the error of MBR, by combining the following ideas, eight MBR-based recommendation systems are proposed: (a) Using the variance of item ratings in addition to the average of item ratings, as two attributes of an item, for determining similar items in an item-based nearest neighbor method; (b) Using the variance of user ratings in addition to the average of user ratings, as two attributes of a user, for determining similar users in a user-based nearest neighbor method; (c) Using a weighted average method for combining the ratings of similar items or similar users; (d) Using ensemble learning. Experimental results on real datasets show that our proposed EVMBR and EWVMBR which use ensemble learning have the least error. The error of the suggested EWVMBR is at-least 20% lower than that of MBR, Slope-One, P-kNN, and C-kNN.
Evaluation of interior-point method in Scilab

The interior-point method is one of the best methods for solving linear programming problems. The interior-point method has a polynomial time complexity for solving linear programming problems. Next, Scilab as a free source software provides a function to solve linear programming based on interior-point method, which is called Karmarkar. In this paper, we evaluate some results of Scilab Karmarkar function in solving linear programming problems. We observe that at some cases the results of the Karmarkar function are not as expected.
Acoustic Focusing Imaging Characteristics Based on Double Negative Locally Resonant Phononic Crystal

To achieve high-efficiency and high-resolution acoustic focusing, we used artificial periodic acoustic structures composed of cell arrays to manipulate the acoustic transmission wave front, and used finite element software to simulate the acoustic field characteristics. We found that when focuses generated by arc-shaped acoustic excitation sources are incident on one side of the model, the energy will be localized within the structure to reduce its transmission loss, and then the high-resolution emission focus will be generated on the other side of the model. The magnitude of the acoustic pressure at the focus point will change with the frequency of the incident acoustic wave and will reach an extreme value at the resonant frequency. In addition, we explored the acoustic transmission characteristics of structures at different widths and found that it is only necessary to increase the width of the model by an integer multiple of half a wavelength to achieve low loss focusing effects at various distances. The acoustic phenomena studied in this paper may provide new methods for the application of photoacoustic signal detection, ultrasound medical imaging and ultrasonic nondestructive testing.