Miracle-dz/UniFAR-FTUs huggingface dataset:
RedParkMiner A tool for data analysis in car parking business

1. Abstract Nowadays companies are increasingly generating large amounts of data, which in turn are mostly connected to their operational / financial business. Within these companies, there is an increasing necessity to explore this data in order to better understand what type of actions they can take to improve their business growth, in a way that is not exclusively empirical by being supported by a valid scientific model. In this work the focus, in terms of business, has been directed to the car parking business, with special attention to the car parking sessions, that consist on a car entering a park followed by a payment and a park exit. To address the data exploration concern in this business, we’ve developed a data analysis tool whose goal is to receive a dataset, transforming the received dataset into more convenient and interesting datasets, and for each of these datasets return the best set of methodologies to perform predictive analysis, according to a predefined set of metrics. Regarding the development of the data analysis tool, central in this work, we will describe the necessary analysis performed, as well as all the set of methodologies used in the tool. We will also present a case study, within the business scope of which the tool has been developed and in which the tool is used, in order to better understand its purpose. In this case study, we will also understand which methodologies should apply in the current discussed business scope.
SAR ADC for Stochastic Self-Calibration Algorithms

An Analog-to-Digital Converter (ADC) is a device whose function is to convert an analog signal (continuous in time and amplitude) into a digital signal (discrete in time and amplitude). In the past, the signal path in most systems was implemented in the analog domain. Nowadays more of the signal path is implemented in the digital domain, creating a demand for data converters, which bridge the analog and digital domains. The Successive-Approximation-Register (SAR) ADC is becoming a popular architecture for highaccuracy and high-speed applications. Being a switching intensive and free of precision amplification architecture allows it to benefit greatly from faster transistor speed of scaled CMOS technologies. The key linearity limiting factor in SAR ADCs is capacitor mismatch of the DAC caused by production process non-idealities. Laser trimming and precision layout techniques can be used to reduce these mismatches. In this thesis, a sub-radix-2 SAR ADC was implemented that tackles this problem using redundancy and digital calibration. Furthermore, the usage of multiple comparators instead of only one comparator as in a typical SAR ADC was studied. Since the differential voltage at the comparator input (residue voltage) is reduced from the most significant bit (MSB) to the least significant bit (LSB), a higher noise can be tolerated when resolving the MSB while a lower noise is desired when resolving the LSB. As the comparator input-referred noise variance is inversely proportional to its power consumption, using different comparator noise specifications in resolving each bit improves the overall power consumption of the ADC. In a typical implementation, the comparator offset only introduces an offset in the transfer function of the converter. However, when multiple comparators are used, their different offset values generate non-linearity, reducing the dynamic performance of the ADC. To solve this problem, the offset of each comparator is calibrated. The ADC was implemented in a 130 nm process, fitting in an area of 260 × 155 μm2. Simulation results show that the ADC consumes 380 μW while sampling at 17.5 MS/s, having an estimated SNDR of 71.5 dB, which corresponds to an ENOB of 11.6 bit.
A Reference Application Architecture for Citizen Relationship Management of the Portuguese Public Administration

Public sector organizations aspire to provide a new experience, more personalized and where the interaction between clients and companies are easier and agile, starting to adopt Citizen Relationship Management - the Customer Relationship Management processes that ﬁts government needs. With the birth of this new concept arose the need to build a design that is expected to help its implementation and, reference enterprise architectures achieve this objective. This is this research goal: to create a reference application architecture for the Portuguese public administration. This research follows the action research methodology, a ﬁve phases methodology, that allies theory with practice. This holds true in the evaluation of the reference application architecture reached with a practical example, the Cˆamara Municipal de Cascais, a Portuguese municipality. With this work we conclude that the reference application architecture reached, that holds six modules – client/citizen module, case module, human resources module, security module, legal module, administrative module, reporting and analysis module and knowledge module – and the functionalities: client/citizen access; case management; administrative management; licensing, permitting and code enforcement; knowledge database; human resources management and business intelligence management. We assess the ﬁt to the Portuguese public administration by evaluating, improves CRM implementation in Portuguese public administration organizations.
CONTRIBUTION TO THE HOLLOW CYLINDER TEST ON THE STUDY OF ROCK FRACTURING THROUGH ACOUSTIC EMISSION MEASUREMENT

The present work aims to contribute to the development of the hollow cylinder test as a means to study the fracturing in rocks. For this purpose it was developed a methodology to record and locate the source of the acoustic emissions during the rock failure. The laboratory tests were also developed for this work, consisting on internally loading, leading to frank rupture, a triaxialy confined thick walled hollow cylinder. In order to locate the source of AE it has been developed an artificial neural network which was trained with artificial acoustic events using Hsu-Nielsen’s Source. The trained neural network simulates the response time to the inverse location problem and is used as input in the resolution of the location of detected events. The AE location using this methodology shows good agreement with the visual analysis of specimens tested, and the error of the achieved location is near to that of the sensors. This hollow cylinder test was also simulated with RFPA and the results are compared with the laboratory. Then both laboratory tests and RFPA’s energy profiles were analyzed which allowed to draw conclusions on the importance of boundary conditions of the tests for effectiveness on the acurate location of detected events. It was also identified a characteristic energy signature for this hollow cylinder test.
Detection of Individual Trees in Urban Areas Using the Point Cloud Produced by Dense Image Matching Algorithms

Urban environment is the principal habitat for the human population. Trees in urban areas play an important role in modern urban spatial data management, local micro-climatic conditions, the ecosystem and air quality. The distribution and number of trees impact the health and life quality of urban residents considerably. Timely and accurate acquisition of individual tree information is very crucial for the decision makers to supply better living conditions for the residents. Today, LiDAR point cloud with high point density and accuracy is widely available in particular for urban areas for different studies such as 3D city modelling, chance detection, object extraction as well as the delineation of individual trees. Although LiDAR systems have various advantages in recent years, they are still expensive for data acquisition especially in small areas. The cost-effective alternative of the LiDAR systems is the 3D dense point clouds generation with aerial photogrammetry using image matching algorithms such as semi-global matching. This study concentrates on the detection of individual trees in urban environments, which is very difficult task because of structural complexity of urban environment includes various objects such as buildings, roads, trees, temporary objects, power lines and cables. The high density point cloud data obtained with semi-global matching using aerial photographs was used to acquire detailed and accurate information about urban trees instead of LiDAR point cloud. First, a point-based classification has been performed to classify the dense point cloud data automatically which based on hierarchical rules. In this step, the detailed analysis in different pilot areas has been performed for the determination of most suitable parameters used in hierarchical rules. The ground, low vegetation, medium vegetation, high vegetation, building, low point, air point and default classes were obtained with proposed point-based classification approach. After the classification step, a point-based segmentation with advanced clustering techniques carried out using the points in high vegetation class to detect individual urban trees. The dense point cloud data of the test side, which is situated in Zekeriyakoy, Istanbul, were generated with dense image matching algorithms within the scope of High Resolution Digital Surface Model (DSM) and Real Orthophoto Production Project by the Directorate General of Geographic Information Systems of the Republic of Turkey Ministry of Environment and Urbanization. The obtained detection rate of individual trees in the study area verified that individual trees in urban environment can be obtained successfully using the dense point cloud data produced by dense image matching algorithms using aerial photographs.
Drone Vision for Mapping and Detection of Defects of Critical Structures

The study of large infrastructures always implies a high cost and/or poor safety conditions for humans. With this in mind, the project where this work is included developed an aircraft to capture images remotely to be processed in order to evaluate the state and damage of the structure being studied. The objective of this Thesis is to propose a solution for the problem of image mosaicing (stitching), in the scope of structural analysis for detection of failures in civil engineering infrastructures. A solution based on homography transformations was presented. In order to implement the solution proposed, a software tool based on the OpenCV’s stitching module is used, since it is an easy to use open source software. A qualitative evaluation of the tool used is presented, showing the positive results that can be achieved with this method, with some known constraints. Furthermore, a comparison against the National Laboratory for Civil Engineering (LNEC) previously used method (Multi Image Correspondances par Methodes Automatiques de Correlation (MICMAC)) is made, where the proposed solution achieves higher qualitative performance, being an overall improvement over that previous
GeoCake: Expeditious Modeling of Terrains and Subsurface Layers with Hand Gestures

Illustrating and visualizing of three dimensional (3D) geological models are common tasks in many areas of geology. In most cases, these tasks consist of sketching on bi-dimensional media to transmit a 3D perception of geological concepts. In this thesis, we present a spatial gesture based interaction system for rapid construction of 3D geological models and illustrations. The models are composed by uniform stratigraphic layers (i.e. without geological folds and faults) stacked upon each other. Starting from no-data scenarios, the system enables expeditious modeling, edition and exploration of terrains and subsoil layers within an immersive 3D environment. The modeling principle is based on the fact that the geometry of a terrain or stratigraphic subsurface can be modeled with spatial gestures. The developed system enables the modeling of finer details by interacting with the multi-touch surface with the user’s fingertips. Interaction with the system is acquired by a motion capture technology and a multi-touch table. Visualization is granted by a stereoscopic technology that, using 3D glasses, allows a tridimensional perception of the created 3D scenarios. We performed an experimental user evaluation where we compared the developed prototype with a conventional windows-icon-menu-pointer geologic modeling application. The results show that the proposed system allows the rapid modeling of layered geologic structures and that multi-touch and 3D spatial modeling is a valid alternative to traditional modeling methods.
A Low-Pass Filtered Panel Model For Estimating Production Function Parameters: The Substitution Elasticity And Growth Theory

This paper presents a new estimation strategy that combines low-pass filters with standard panel data techniques to obtain estimates of production function parameters. We are particularly concerned with the value of the elasticity of substitution between labor and capital because of its key role in models of long-run growth. A host of important issues, including the possibility of perpetual growth or decline, depend on the precise positive value of σ. This paper examines the role of σ in the neoclassical growth model and estimates σ by combining a low-pass filter with standard panel data techniques to better measure the theoretical constructs appropriate to production function estimation. Our approach is in the spirit of Friedman's permanent income theory of consumption and Eisner's related permanent income theory of investment. While their approaches and ours are similar in relying on permanent components, we extract these components with spectral methods that are more powerful and general for identifying these unobservables. We transform the data with the Baxter-King low-pass filter that depends on two parameters, the critical periodicity defining the long-run frequencies and a window for the number of lags approximating the ideal low-pass filter. Based on an analysis of the spectrum of the transformed series, we confirm that our choices of the critical periodicity and window emphasize long-run variation. The empirical results are based on the comprehensive panel industry data constructed by Dale Jorgenson and his research associates. Our estimate of σ is 0.288 for the baseline values of the critical periodicity of eight years and window of three years. This result is robust to variations in the window. As the periodicity declines from eight to the minimum value of two, the elasticity declines owing to the distorting effects of transitory variation.
Value of BI projects with Delphi Method

Management and evaluation of benefits in Business Intelligence projects is a recurrent and growing concern in competitive companies, although it is still a poorly explored subject. Given this shortcoming, the focus of this work is to develop a methodical and procedural approach to do it, as well as to make available and assess the main indicators (KPIs) that can contribute to this management and evaluation processes. This research used techniques of the Delphi method to collect and analyses data obtained from an inquiry. Questions were about indicators observed in a literature review in the area, analyzing the main methods used and known KPIs. According to the collected answers, the KPIs collected during the study were indeed essential in the evaluation of BI projects and in particular in analyzing 4 specific factors: Innovation Characteristics, Characteristics of the Organization, Characteristics of the Environment, and Satisfaction / Effectiveness of the project with the users involved we are able to measure the success and benefits of a project with due care. Our conclusions can be influenced by some external factors difficult to measure considering the environment of the case study we explored.
Creating an Agent-Based Framework for Don’t Starve Together

Today’s video games are striving to maintain high levels of fidelity. The realistic graphical representation of virtual worlds is only betrayed by the lack of believability that ingame characters present. To maintain the immersion created by exquisite graphics, characters must be able to create the illusion of life, which requires them to possess basic human traits like social awareness, reactivity, and active goal pursuit. Some game genres, like role playing games, have seen this problem being addressed by using agency based characters. However, survival games have not been subject to the same attention. In this work, we address this issue by proposing a framework that allows developers to create characters based on agency models for survival games. By making use of FAtiMA Toolkit, a fully fledged model for agency, and Don’t Starve Together, a popular survival game, we’ve implemented and published such a framework with an example character, Walter. Walter has been run and tested against a behaviour tree based agent.
Nano-Structure Roughening on Poly(Lactic Acid)PLA Substrates: Scanning Electron Microscopy (SEM) Surface Morphology Characterization

Scanningelectron microscopy (SEM) has been utilized to examine the morphology and topography alterations in the surface of Poly(Lactic Acid)(PLA) fabrics due to UV/Ozoneirradiation. In the past decade, a growing attention in the usage of “Green Techniques” in industrial applications has been observed owing to many benefits such as low impurities and their relatively low cost to substitute the conventional processes.The effects of UV/Ozone irradiation along with the pretreatments with distilled water, hydrogen peroxide, and hydrogen peroxide/sodium silicate solutions on the surface morphology of the PLA fibers by means of SEM were investigated and the images were compared with that of virgin untreated samples.The observations presented dramatically increase in insurface roughness andsurface area of the samples after the treatment. Nano-size roughening (827 nm) has been clearly observed on the samples. The changes in morphology mainly surface roughness and surface area, on the PLA fabrics surface due to UV/Ozone irradiation seem to be due mainly to the intensified etching effect of the UV/Ozone process and these alterations maximized by the pretreatment of the fabrics with the hydrogen peroxide/sodium silicate solution.
Visualizing Usability

While developing a product and more specifically an interface, measuring User Experience and how users interact with it is imperative, since a poorly usable interface can affect users’ willingness to interact with it and cause a decline in the overall user interest and satisfaction. The measurement and analysis of Usability related metrics can sometimes be hindered due to the nature and complexity of this data, resulting in a poor extraction of such information. Therefore, Information Visualization can be used as a mechanism to better study and detect product deficiencies at an early stage and represent a powerful tool to improve the analysis of usability and complement user testing, providing insight for changes and, ultimately, an interface with a suitable and pleasurable experience for users. This work addresses ways of exploring and correlating usability metrics and possibly identifying patterns through the usage of Visualizations, which can potentially help draw ”high level” conclusions based on both derived and statistical measures, and thus contributing to better results while designing interfaces. Furthermore, the presented work includes a validation approach through both Usability Testing and Case Studies on the developed solution.
Adaptation of Gaming Features for Motivating Learners

Background. Many learning environments are quickly deserted by learners, even if they are efficient. Gamification of learning environments is a recent approach used to enhance learners’ motivation and participation. Aim. People have various expectations and react differently faced with specific game mechanics. An important goal lies in automatically adapting game mechanics according to player types. In this article, we study the gaming features that can be adapted in learning environments and the player model that can be used for the adaptation process. We propose an approach that aims to predict to which game mechanics a user is responsive, and to adapt the gaming features of the system according to this information. Methodology. An implementation was released, and evaluated through an quasi-experimental study with 59 middle school students, each one using the learning environment during three 45-minutes sessions. Results. The results validate the implementation of the system and show that the users’ activity can help to predict their profile. The adaptation process did not improve learners’ engagement as expected, but it shows a path for future research toward an adaptive approach for learning environment gamification.
Transport mode detection on smartphone

The European project MoTiV aims to study the value of time that people spend on travel and what are the factors that deﬁne this value. The objective of this work is to develop a data collection component that will be used to collect the data needed for the MoTiV project. The data is supposed to be collected automatically from the sensors available in modern smartphones in near real time. One of the data that must be collected is the mode of transport used. The Transport Mode Detection Module (TMDM) was created in order to identify the transport mode used on both Android and iOS operating systems. During the trip this module evaluates the information obtained from the sensors of a smartphone using a machine learning algorithm in order to identify all the modes of transport used. The developed solution allows to identify 6 different modes of transport (walking, bicycle, car, bus, train and still) with an accuracy of 90% using the Random Forest algorithm. In addition, the number of transport modes used during a trip is identiﬁed correctly with an accuracy of 95%.
Visualizing Business Process Based Costs

Enterprises have a pressing need to know their costs. Having a cost model can get them so far, they need to visualize the data in a fast, accessible and eﬃcient way. Using a research methodology we studied the eﬃciency of some forms of data communication in this context. Since it has already been proved that the human brain more eﬃciently processes visual information than text we proposed the application of a diverse range of visual representations speciﬁcally to visualize business process based cost data. The process of evaluation involved collecting feedback and testing with users as to ascertain the degree of usability and eﬃciency and to compare these results with those obtained using a tool familiar to them.
From Embodiment to Metaphor: A Study on Social Cognitive Development and Conceptual Metaphor in Persian-Speaking Children

This study explores the metaphoric comprehension of normal Persian-speaking children, as well as theories of cognitive development and cultural and social impacts. The researchers discuss the improvement of the understanding of ontological conceptual metaphors through age growth and cognitive development, and how it helps to expand children’s thoughts and knowledge of the world. In this study, 121 normal native Persian-speaking children from the age of 5 to 13 with no language and cognitive disorders participated. Pearson correlation and one-way ANOVA were used to examine the relationships between pairs of variables. The results showed that children start to comprehend abstract concepts and primary ontological metaphors at about 5 years of age, which is in contrast with what Piaget has implied. Children’s metaphorical comprehension improved progressively with age, social, and cognitive development as other studies have also implied, and they understood more complex types of metaphors by age growth.