Miracle-dz/UniFAR-FTUs huggingface dataset:
VALUE MODEL OF THE WATER DOMICILIARY DISTRIBUTION TELEMANAGEMENT

1 Abstract. The Internet of Things (IoT) represents a technical innovation that is already starting to play an important role in smarter water management, when a wide variety of sensors are incorporated into intelligent metering equipment and connected through wireless networks throughout the domiciliary water distribution network, being able to measure volume, flow, temperature, pressure, levels of chlorine, salinity and more. In telemanagement, these infrastructure sensors can send information back to the central systems for real-time monitoring, analysis and control of water systems, or they can even act autonomously, for example, by automatically shutting off the water flow and sending an alert if a leak is detected. Water scarcity, aging or inadequate water distribution infrastructure, population variation, pollution, more intense and frequent droughts and floods, generate pressures that converge on the need to increase global investment in water infrastructures and to develop solutions for the conservation and management of water. The main stakeholders in the water distribution sector are the ones that can benefit most from the use of telemanagement. However, the results of adopting this innovation are contrary to expectations, with a slow change in traditional business models. The objective of this research is the construction of a value model that allows the identification of actors and value markets and the exchange of value related to the adoption of telemanagement in Portugal, having a solid theoretical basis and a real practical validation.
Grid-based localization and online mapping with moving objects detection and tracking: new results

In this paper, we present a real-time algorithm for local simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) in dynamic outdoor environments from a moving vehicle equipped with a laser scanner. To correct vehicle location from odometry we introduce a new fast implementation of incremental scan matching method that can work reliably in dynamic outdoor environments. After a good vehicle location is estimated, the surrounding map is updated incrementally and moving objects are detected without a priori knowledge of the targets. Detected moving objects are finally tracked by a multiple hypothesis tracker (MHT) coupled with an adaptive IMM (interacting multiple models) filter. The experimental results on datasets collected from different scenarios such as: urban streets, country roads and highways demonstrate the efficiency of the proposed algorithm on a Daimler Mercedes demonstrator in the framework of the European Project PReVENT-ProFusion2.
C ONTINUOUS –D EPTH V ALUE N ETWORKS FOR P ARAMETRIZED A CTIONS

Action spaces equipped with parameter sets are a common occurrence in reinforcement learning applications. Solutions to problems of this class have been developed under different frameworks, such as parametrized action Markov decision processes (PAMDP) or hierarchical reinforcement learning (HRL). These approaches often require extensions or modifications to standard existing algorithms developed on standard MDPs. For this reason they can be unwieldy and, particularly in the case of HRL, computationally inefficient. We propose adopting a different parametrization scheme for state–action value networks based on neural ordinary differential equations (NODEs) as a scalable, plug–and–play approach for parametrized action spaces. NODEs value networks do not require extensive modification to existing algorithms nor the adoption of HRL methods. Our solution can directly be integrated into existing training algorithms and opens up new opportunities in single–agent and multi–agent settings with tight precision constraints on the action parameters such as robotics.
Comparison of COBIT 5 and ITIL V3 using Semantic Analysis

Enterprise Governance of Information Technology (EGIT) can be defined as a means to achieve business/IT alignment. Organizations can benefit from this alignment by implementing processes defined by EGIT frameworks to become more competitive and produce high-quality products and services. COBIT and ITIL are two well-known Process Reference Models (PRMs) that are widely adopted and adapted by organizations. PRMs are always related to a process assessment model (PAM) which holds all details to determine the capability of the processes of the reference model. However, in multi-framework environments, organizations struggle in assessing these frameworks processes implementation since each framework defines its own scope, structure, definitions, and terminology. To overcome this issue, the authors propose an approach that through semantic similarity compares COBIT PAM and TIPA for ITIL – the PAM for COBIT 5 and ITIL core concepts to highlight their overlapping.
Multiscale line segment detector for robust and accurate SfM

We propose a multiscale extension of a well-known line segment detector, LSD. We show that its multiscale nature makes it much less prone to over-segmentation, more robust to low contrast and less sensitive to noise, while keeping the parameterless advantage of LSD and still being fast. Moreover, we show that in scenes with little or no feature points, but where it is however possible to perform structure from motion from matched line segments, the accuracy is significantly improved. This provides an objective and automatic quantitative assessment of our detector that goes much beyond the usual qualitative visual inspection found in the literature.
Online Master Degrees - the MISE Case Study

The online Master Degree in Information and Enterprise Systems (MISE) has been offered in Portuguese jointly by the Instituto Superior T´ecnico (the engineering school of the University of Lisbon) and Universidade Aberta (the Portuguese Open University) since 2013. In this paper we present the results of a case study about Master’s Degree in Information and Enterprise Systems (MISE) based on 61 interviews to the students of the ﬁve editions, focusing on their perspective about MISE. The answers from the students were analysed, and three conceptual maps were designed for different student categories. We conclude that, despite the barriers, the students consider MISE a success.
Metadata Extraction from Scholarly Articles

Modern digital libraries of scholarly materials depend on the availability of quality metadata and, when performed manually, meta-data annotation can be one of the most important costs in building a digital library. This paper describes PaperCut, a system based on supervised machine learning for extracting metadata (cid:28)elds from scholarly articles, using a stack of Conditional Random Fields models for labeling token sequences in the text of the articles, as belonging or not to the considered metadata (cid:28)elds. Experimental results show that PaperCut compares well with other previously published methods.
Support for User Interaction in a Data Cleaning Process

Data transformations to clean dirty datasets are difficult to devise and the tuning of these transformations is complicated by the lack of support for tools that detect data quality problems during intermediate stages of the data cleaning process. Moreover, fully automated cleaning solutions are many times not attainable and users have to be involved in the repair of some data quality problems. As proposed in [7], in order to better support the user involvement in data cleaning processes, defined in terms of graphs of data transformations, can be equipped with data quality constraints that help users identifying the points of the transformation and the records that need their attention and manual data repairs for representing the way users can provide the feedback required to manually clean some data items. In this thesis, we propose a realization of these concepts in the context of AJAX – an existing data cleaning tool that works with data cleaning graphs – and report on the implementation of CLEENEX, a prototypical implementation of the proposed solution. CLEENEX provides proof-of-concept for the integration of data quality constraints and manual data repairs in data cleaning programs, demonstrating the feasibility of the approach to user involvement in data cleaning proposed in [7]. Moreover, CLEENEX is instrumental for performing a thorough evaluation of this approach.
Analyzing machine data for predictive maintenance of electro chemical machining electrodes

This Master Thesis Project is an experimental project that applies machine learning approaches to analyze machine data and explore predictive maintenance questions in Philips Shaver Production Plant use case. The use case is in the context of MANTIS Cyber Physical System based Proactive Collaborative Maintenance. As the MANTIS project book (Mondragon Goi Eskola Politeknikoa S. Coop, 2014) mentions, “The overall concept of MANTIS is to provide a predictive maintenance service platform architecture that allows to estimate future performance, to predict imminent failures and to schedule pro-active maintenance and that consists of distributed processing chains that efficiently transform raw data into knowledge while minimizing the need for bandwidth”. In the MANTIS project book, this processing chain can include topics on local data collecting from smart sensors or events logs, distributed models on data validation and decision-making with machine learning methodologies, as well as cloud-based processing and data availability. In our use case, Philips Consumer Lifestyle Drachten (location of the shaver factory) takes in charge of collecting and storing data from one pilot production line and the maintenance workshop; the Master Thesis Project focuses on machine learning for data validation and decision-making. In this project, we create maintenance piece (MP) split methods on the original data source and define MP classes by maintenance type classification tests; we give solutions on the collaboration drift problem of the original data from different machines; we apply different smoothing methods on the noisy data and check the performance improvements on prediction models; our classification models on impending maintenance actions show promising results to forecast some maintenance actions on dataset subsets; our regression models on the tool remaining useful life estimation are limited by the ground truth problem of response variables (the tool status). ii Analyzing Machine Data for Predictive Maintenance of Electro Chemical Machining Electrodes
Air-Sketching for Object Retrieval

With the development of several modeling and capturing tools, the creation of 3D objects has increased. Hence, the retrieval area of 3D models has been widely explored, as an answer to the resulting challenges from storing and searching for objects. However, the latest works on this subject do not use natural interaction nor an immersive environment to search and explore 3D objects. By executing this work, our purpose was to create a query speciﬁcation for 3D object retrieval system, in an immersive environment. Our goal was to explore a new approach to search methods using sketching retrieval, that would allow a natural interaction. The users are immersed in a virtual reality environment and deﬁne a sketch query that retrieves similar objects using search mechanisms. To validate our prototype we performed tests with users, comparing it to an query speciﬁcation system that uses 2D sketches. The results obtained with the performed tests allow us to conclude the validity of our approach, and understand that this route of investigation will allow to discover viable solutions on the 3D object retrieval context.
Security Testing in Continuous Integration Systems

The usage of web applications has had a massive growth over the last decades. Together with the ever increasing number of resources available, the attacks performed keep up with this tendency and keep getting more and more sophisticated. Knowing this companies strive to provide secure products to their clients and are constantly in search of new and better methodologies to do so. The present work was developed at Nokia – Portugal aiming to design and develop a set of security tests that would facilitate the detection of vulnerabilities while products are still in the development phase. These tests would, then, be part of a continuous integration system where they are executed automatically. In the end, after being fully developed and automated and taking part in the continuous integration system, this work comes together as the SQM Security Pipe.
An interpretable automated detection system for FISH-based HER2 oncogene amplification testing in histo-pathological routine images of breast and gastric cancer diagnostics

Histo-pathological diagnostics are an inherent part of the everyday work but are particularly laborious and associated with time-consuming manual analysis of image data. In order to cope with the increasing diagnostic case numbers due to the current growth and demographic change of the global population and the progress in personalized medicine, pathologists ask for assistance. Profiting from digital pathology and the use of artificial intelligence, individual solutions can be offered (e.g. detect labeled cancer tissue sections). The testing of the human epidermal growth factor receptor 2 (HER2) oncogene amplification status via fluorescence in situ hybridization (FISH) is recommended for breast and gastric cancer diagnostics and is regularly performed at clinics. Here, we develop an interpretable, deep learning (DL)-based pipeline which automates the evaluation of FISH images with respect to HER2 gene amplification testing. It mimics the pathological assessment and relies on the detection and localization of interphase nuclei based on instance segmentation networks. Furthermore, it localizes and classifies fluorescence signals within each nucleus with the help of image classification and object detection convolutional neural networks (CNNs). Finally, the pipeline classifies the whole image regarding its HER2 amplification status. The visualization of pixels on which the networks' decision occurs, complements an essential part to enable interpretability by pathologists.
Defining Haptic Experience: Foundations for Understanding, Communicating, and Evaluating HX

Haptic technology is maturing, with expectations and evidence that it will contribute to user experience (UX). However, we have very little understanding about how haptic technology can influence people's experience. Researchers and designers need a way to understand, communicate, and evaluate haptic technology's effect on UX. From a literature review and two studies - one with haptics novices, the other with expert hapticians - we developed a theoretical model of the factors that constitute a good haptic experience (HX). We define HX and propose its constituent factors: design parameters of Timeliness, Density, Intensity, and Timbre; the cross-cutting concern of Personalization; usability requirements of Utility, Causality, Consistency, and Saliency; and experiential factors of Harmony, Expressivity, Autotelics, Immersion, and Realism as guiding constructs important for haptic experience. This model will help guide design and research of haptic systems, inform language around haptics, and provide the basis for evaluative instruments, such as checklists, heuristics, or questionnaires.
Effects of Home Visits and Planned Education on Mothers’ Postpartum Depression and Quality of Life

Objective: The present study aims at determining the effects of home visits and planned training on mothers’ postpartum depression and quality of life. Materials and Methods: The sample of quasi-experimental study was consisted of 70 women. Study data were collected by means of a personal information questionnaire, the Edinburgh Postpartum Depression Scale and the World Health Organization Quality of Life Scale (WHOQOL-BREF). Results: The average age of the mothers is between 25.50±4.78. In repeated measurements, statistically significant differences were found among quality of life scores of mothers (p<0.05), and their quality of life scores were found to be higher in the third measurement when compared to the first measurement. In all repeated measurements, a statistically significant negative relationship was found between depression and quality of life scores of mothers (p<0.05), and mothers’ depression scores were seen to decrease as their quality of life scores increased. Statistically significant differences (p<0.05) were found among depression scores according to mothers’ education level and among quality of life scores according to their number of pregnancies. University graduate mothers’ depression scores decreased in the second measurement and scores were the same in the third measurement. In the third measurement, primiparous mothers’ quality of life scores were found to be higher than in the first measurement. Conclusions: The study findings demonstrate that mothers are at risk of depression during the postpartum period, postpartum depression negatively affects their quality of life, and home visits and training increase their quality of life and decrease their depression risk.
ESTIMATION OF SEISMIC AZIMUTHAL ANISOTROPY PARAMETERS OF THE UPPER MANTLE OF THE CENTRAL PART OF THE EAST-EUROPEAN CRATON ACCORDING TO THE DATA OF THE “OBNINSK” AND “MIKHNEVO” SEISMIC STATIONS

The results of determining the parameters of seismic anisotropy of the upper mantle of the central part of the East-European craton are given. According to the tectonic concepts actively developing in recent decades, the East-European craton consists of megablocks (microplates) of different age: there are three main megablocks – Fennoscandia, Volga-Uralia and Sarmatia. The area of their triple junction is the subject of this article. The SKS/SKKS anisotropy method was applied. Its essence is to use SKS phases in order to obtain delay times for the arrival of two quasi-shear waves that are formed during the trace of a shear wave through an anisotropic medium and gets through it with different velocities. The result is to simulate the azimuth of the maximum velocity axis along which “fast” quasi-shear wave trace and to get the delay time between “fast” and “slow” waves. The technique does not allow to obtain accurate estimates of the depth of the anisotropic layer, however, taking into account the long periods of the SKS wave and the pronounced anisotropic properties of olivine, as the main material composing the upper mantle, it is assumed that these effects are related to the mantle processes. Authors used the data from “Obninsk” (OBN) and “Mikhnevo” (MHV) stations that are located in the zone of the triple junction of blocks. For Obninsk station – 1266 events were used, for Mikhnevo station – 472. For each station, we obtained estimates of the azimuth of the maximum speed axis and the delay time of arrival of quasi-shear waves. As a result of the study, the mantle anisotropy of the region is assessed as weak, which is an expected result for tectonically stable platform regions (delay time 0.4 s for the OBN station and 0.2 s for the MHV station). The direction of the maximum speed axis is strictly sub-latitude (90 for the OBN and 100 for MHV station), which is in accordance with the known the East-European lithospheric plate motion. For the OBN station, two groups of events were identified, with significantly different results, structured by azimuth. For events mainly with western azimuths of arrival of seismic waves, the solution is 90 and 0.4 s, while processing events from the eastern direction of arrival, along with a coordinated solution, a second pronounced local extremum is detected, corresponding to angles and delay time about 0.5–1.0 s. According to the results of MHV data processing, a second local extremum was also found, corresponding to similar angles of about 160, however, with a shorter delay time (about 0.1–0.2 s). It can be assumed that the presence of two extrema is associated with the anisotropic parameters of two different megablocks, near the junction boundary of which are located the OBN and MHV stations.
Multi-Agent Dynamic Coupling for Cooperative Vehicles Modeling

Cooperative Intelligent Transportation Systems (C-ITS) are complex systems well-suited to a multi-agent modeling. We propose a multi-agent based modeling of a C-ITS, that couples 3 dynamics (physical, informational and control dynamics) in order to ensure a smooth cooperation between non cooperative and cooperative vehicles, that communicate with each other (V2V communication) and the infrastructure (I2V and V2I communication). We present our multi-agent model, tested through simulations using real traffic data and integrated into our extension of the Multi-model Open-source Vehicular-traffic SIMulator (MovSim).
Time-Series Restrictions for the Cross-Section of Expected Returns: Evaluating Multifactor CCAPMs

A number of recent papers have developed multifactor extensions of the classic consumption capital asset pricing model (CCAPM), and found that they perform remarkably well in explaining the cross-section of stock returns. While the extant literature has generally concluded that conditioning information improves the empirical performance of the CCAPM, the empirical work to date has primarily employed cross-sectional regressions that ignore theoretical restrictions on the time-series intercepts in regressions of each test asset return on the model’s factors. This paper asks whether the superior empirical performance of the multifactor CCAPMs is maintained once the time-series intercept restrictions have been explicitly tested. The use of mimicking portfolios makes it straightforward to test whether such multifactor CCAPMs satisfy the timeseries intercept restrictions, since in this case the single testable implication of the model is that each intercept should be zero. The empirical findings generally support the conclusion that multifactor CCAPMs can explain the cross-section of expected stock returns better than classic unconditional models such as the CAPM and CCAPM.
Some Slicing Issues in Rapid Prototyping

Rapid prototyping (RP) processes such as fused deposition modeling (FDM) and stereolithography (SL) are being widely used in the industry with great potential for direct digital manufacturing of functional parts. In the RP process, surface finish and build time are important and are affected due to improper selection of slice thickness. It is evident that there is a strong relation between the slicing and tessellation process for which more scientific efforts are necessary to develop methods for creating more efficient STL files. The aim of this study is to show the effect of slicing on the surface finish, layering error and build time of a prototype. Furthermore, this study shows how tolerance and slice thickness are related in creating more efficient STL files. In this regard, three objects were modeled for generation of STL files. Each object’s STL file was sliced with different slice thickness values. Screenshots approach was used to show the slicing effect on layering error. The build times were calculated and documented. The study results show that slice thickness has a great impact on several factors. The results will serve the industry in understanding how the STL generation and slicing process can be carried out effectively for efficient utilization of various resources.