Miracle-dz/UniFAR-FTUs huggingface dataset:
Computational Modeling of the Biodegradation Process in a Scaffold for Tissue Engineering

Bone possesses a very efficient repairment and regeneration mechanism. Nevertheless, in situations of infection, large defect size and disease, the healing process may be impaired or unable to occur at all. Common therapies include transplantation of bone and the use of nonbiological implants. However, these solutions have some disadvantages and limitations. Scaffolds combining cells, signaling molecules and porous matrices seem a viable alternative. The factors that conduct to an optimal scaffold performance haven’t been fully determined and for that reason the scaffolds behavior and action inside the human body continue to be extensively analyzed through the execution of experimental studies and the implementation of computational models. In this work, the development of a computational model was proposed. It was intended to simulate the interdependence of scaffold degradation and newly formed tissues invasion. The model is constituted by a degradation module, based on the one suggested in (Chen et al., 2011a), with an adaptation to the scaffold problem of a fracture healing model, based on cell differentiation and growth theories created by (Gómez-Benito et al., 2005). Scaffolds performance was analyzed taking into account tissue formation and effective properties of the system, acquired through the asymptotic homogenization method developed by (Guedes and Kikuchi, 1990). The model was implemented with success, providing predictions that were in a good agreement with other computational models results and with experimental data.
Comprehensive Characterization of Human Mesenchymal Stem/Stromal Cells From Healthy Donors and Acute Myocardial Infarction Patients

Cardiovascular Diseases (CVDs), namely Acute Myocardial Infarction (AMI), are the leading cause of death worldwide. Current treatments of AMI are incapable of regenerating the necrotic tissue resultant from ischemia. Regarding this issue, Mesenchymal Stem/Stromal Cells (MSCs) have shown some promising results in early clinical trials. Autologous MSCs may not represent the best option for a MSC-based therapy since their performance can be impaired by the associated comorbidities, allied to the age, of AMI patients. The use of Bone Marrow (BM)-derived cells can also be potentially limiting since it involves an invasive procedure for cell collection; Adipose Tissue (AT) may represent an alternative promising source of MSCs for AMI settings. The aim of this work was to establish a comparative study between BM-derived MSCs from AMI patients, and BM-, AT-derived MSCs from healthy donors in terms of proliferative, angiogenic, secretory, and oxidative stress resistance potential, in order to identify the most adequate cell type for the clinical setting. Additionally, two different methods for the isolation of Adipose-derived Stem/Stromal Cells (ADSCs) were studied. Healthy BM MSCs presented a higher secretory and angiogenic potential, while AMI BM MSCs had the highest proliferative capacity. The incubation of MSCs under hypoxia (2% O2) boosted the aforementioned results. Further studies should be conducted to investigate the mechanisms underlying the improved in vitro proliferative capacity of AMI BM MSCs demonstrated herein. Concerning ADSCs isolation, these cells might be isolated from AT through a non-enzymatic method, albeit the slight lower yield when compared to the standard enzymatic-based protocol.
Classification and Clustering of Stocks, using Genetic Algorithms and Fundamental Analysis

Since the last two decades the ease of access to information has grown exponentially, making it easier to analyze and use this data in every field of science, including computational finance. This work presents an architecture made from scratch of a trading system that classifies stocks using two techniques, in order to conclude which one is superior: one with user input parameters, and an unsupervised one using a genetic algorithm to optimize clustering position, with a constant number of clusters. A genetic algorithm is also applied to optimize fundamental indicators to give buy and sell signals in each of the groups obtained, in order to conclude if stocks in the same group behave in similar fashion. Results have shown that the group with best results obtained with user input parameters is superior to the group with best returns obtained with the clustering algorithm. However the clustering algorithm classified stocks better, having increased performance over the user input method when used with the genetic algorithm using fundamental indicators. The proposed system was implemented from scratch, and was contains optimization modules, processing modules and a trading simulator.
Hybrid Localization in Underwater Environment

Countless systems require localization methods to accomplish their proposed tasks. While GNSS solutions offer great performance, for large networks this may not be affordable, as it depends on expensive instrumentation. Besides, environmental constraints can even prevent such approaches, as it is the case in indoor or underwater settings. In this work, the latter is used as a testing case for the development of a localization algorithm, suitable for generic mobile networks of agents with range and bearing measurements between them. Existing approaches often depend on parameter tuning, on the accuracy of initialization or their performance may depend on the patterns of motion, thus limiting freedom during missions. Our algorithm deals with these drawbacks, standing on the Maximum Likelihood position estimator for a generic network. The resulting optimization problem is non-convex, so that a convex relaxation is proposed. To serve positioning to mobile agents, two additional horizon-based versions are developed accounting for velocity measurements at each agent. To solve the convex optimization problem, a distributed gradient-based method is provided. This constitutes an advantage over other centralized approaches, which usually exhibit high latency for large networks and present a single point of failure. Additionally, the algorithm estimates all required parameters, and effectively becomes parameter-free. We obtain a parameter-free, outlier-robust and trajectory-agnostic algorithm, with nearly constant positioning error regardless of the trajectories of agents and anchors, achieving better or comparable performance to state-of-the-art methods. Furthermore, the method is distributed, convex and does not require any particular anchor configuration.
Research on Trust Transfer of Heterogeneous Information Sharing Based on Infomediary

On the condition of G2B(Government to Business) inter-organizational information sharing, vertical information asymmetry takes up dominant position, especially the credit information asymmetry between banks and government departments. As infomediary between them in the case, credit reporting system is constructed to collect and process government information in China. This research discussed the trust transfer of heterogeneous information within credit reporting system using the method of the multiple mediation path analysis. The result shows trust on information source can transfer to the perceived information quality within credit infomediary directly, and its trust can also be transferred by mediators of relevance, completeness, timeliness and accuracy indirectly. This research will play an active role in academic contribution of inter-organizational heterogeneous information sharing.
Crime at the palace Verbal Interaction

A layer was developed above the existent dialog system EDGAR (Fialho et al., 2013) in such a way that the virtual agent acts as a suspect in a crime and is therefore in a police interview. EDGAR, by itself, is a chat bot with several plugins which picks user input and matches it with the closest question/answer pair in its corpora. Once the question is matched, the new layer calculates which is the most appropriate answer based on emotional factors from the virtual suspect. Which emotional factors and how strongly they impact the answer decision was set according to several rules from the model of the Cognitive Interview used by the Portuguese Escola da Polı́cia Judiciária as well as observations of enacted interviews and other researched psychology theories. The emotional factors of the virtual suspect begin with a static default state which is modified throughout the interactions.
Adaptive system to control the directivity of a Microphone Array controlled with a PC

In this work, a microphone array (MA) with 64 linearly spaced microphones, was built. The next step, was the development of two algorithms: cross correlation between signals (to detect the localization of the speaker), and the delay-and-sum (spatial filtering), both implemented in Matlab, using real data captured by the system. The results have shown an agreement with existing theory. The described algorithms were re-implemented in C language and tested in real time to verify the potential of using a single computer to process the data coming from the array.
Recommender Systems for IT Recruitment

—Recruitment processes have increasingly become dependent on the internet. Companies post job opportunities on their websites or on online job boards and candidates search and apply online. There are inefﬁciencies in the process: candidates are overloaded with opportunities and companies have trouble reaching interesting candidates. The viability of applying Recommender Systems to Online IT Recruitment is the focus of this work. The IT ﬁeld is a particularly good candidate for this research due to a shortage of talent and because most of IT recruitment processes already have an online component. Six different Recommender Systems were tested, using the available data: a binary rating of applications, job descriptions, and candidate proﬁles. The implemented models use a wide range of strategies: Collaborative Filtering, Content Based Filtering, Item-to-Item and User-to-User neighborhood models, Matrix Factorization, and a graph based approach. Based on the results of ﬁve different base models an hybrid model was built to combine their strengths and ﬁght their limitations. The resulting system generates valuable recommendations to 60% of the tested users. While the system is not ready to be used autonomously it is useful as a supporting tool. These results pave the way for further studies in this area, showing that Recommender Systems can bring value to Online IT Recruitment.
Detection of Attacks to Face Recognition Systems

Face recognition systems are increasingly important in today’s society, being mainly employed as a security measure. Everyday items, such as mobile phones and laptops, or more crucial security systems, such as airport access control, are good examples of its usage. Due to its popularity, these biometric systems are vulnerable to a wide range of attacks, which are becoming more and more complex. Therefore the development of eﬀective counter-measures is necessary. The objective of this thesis is to develop a tool which detects intrusions at the sensor level, known as Presentation Attacks (PA). For this, state of the art contributions are reviewed in order to understand their main disadvantages and possibilities of improvement. An approach based on transfer learning using a pre-trained Convolutional Neural Network (CNN) model is presented. This network is then adapted to the problem and several steps are taken to optimise it accordingly. A novel approach is proposed by implementing a layer that performs video analysis for action classiﬁcation, known as Long Short Term Memory (LSTM). The proposed solution achieves a half total error rate (HTER) of 1.09% in the Replay-Attack database. Finally, a conclusion is made about the detection of attacks to facial recognition systems and why is it still an open problem, even though state of the art methods show a high performance in such demanding databases.
Webcam motion detection and tracking interfaces for immersive embodied experiences

Video tracking is an important area of Computer Vision research. It has tremendous potential regarding the improvement of human-computer interaction. Thus, it contributes to the most diﬀerent ﬁelds like security and surveillance, video communication and compression, augmented reality, technological control, medical imaging, video editing, among others. In order to track an object in video, it is usual to follow four steps: ﬁrst detect the moving region, then apply object classiﬁcation techniques in that area of the frame to recognize what object has moved and, ﬁnally, proceed with the prediction of the next movement by that object. In this thesis we present the methods that we consider to be more relevant for each step of this quest. The work is undertaken in the context of a collaborative dance-technology project Senses Places, which fosters mixed reality performances that involve participants from all over the world. Meeting physically and/or virtually, in a common environment, they engage kinaesthetically in new shared embodied mediated experiences towards expanding awareness to cross geographical, cultural, disciplinary, artistic and human boundaries. We contribute to the project with a mixed reality interface that recognizes the human movements in front of a webcam in real time and translates them to an online virtual world where an avatar will respond, according to the choreographic approach. In this dissertation we explain the process followed to achieve the best interface, including the tracking techniques used, and the virtual world scripts and animations developed.
SooMPI - Socket over MPI

—In order to mitigate the lack of ability to execute socket based programs directly in HPC clusters, we developed SooMPI , that allows C socket programs to work over MPI, thus opening the HPC environment to Network and Distributed Systems research areas. SooMPI allows the transformation of regular network socket applications into a MPI based one, in order to be executed in HPC cluster. It converts regular socket functions and allows for the assignment of IP addresses to the available MPI processes corresponding to each converted application. SooMPI was evaluated in two different computational environments and with multiple applications. The results show that complex systems can be seamless transformed and executed in multiple MPI based environments. This system’s analysis has been proved to be a successful mechanism for a single base code to be executed in multiple environments effortlessly. SooMPI will be used as a foundation for more efﬁcient network simulators capable to execute in HPC clusters.
Learning from Short Multivariate Time Series

The identification of conditional dependences in multivariate time series (MTS) can be achieved by learning the structure of dynamic Bayesian networks (DBN), a machine learning framework for modelling temporal processes in a compact form. Several methods for DBN learning are concerned with identifying inter time-slice dependences, but disregard the intra-slice connectivity. This thesis proposes a DBN structure learning algorithm that jointly finds the optimal inter and intra time-slice connectivity in a transition network. The search space is constrained to a class of networks called tree-augmented DBN, leading to polynomial time complexity in the number of variables. An assessment of the algorithm is first made on simulated MTS, showing that the proposed method is competitive with state of the art algorithms in recovering the structure underlying the data. Further experimental validation is made on real data, by identifying non-stationary gene regulatory networks of Drosophila melanogaster, and by learning from clinical data describing patients with rheumatoid arthritis, in order to forecast the evolution of the disease.
State-of-the-art report on requirements-based variability modelling and abstract test case generation

test cases from various kinds of models. Some recent approaches are based on machine learning, where supervised and unsupervised learning algorithms are used to build models and extract useful patterns based on training data sets. Test generation from natural language descriptions is still in its infancy; most of the work in this area is focussed on informal use case descriptions, from which keywords and exetion patterns are extracted to generate semi-formal artifacts. Many authors also require that for the description of tests, natural language is only used in a controlled or sytactically restricted way; different approaches, varying by the degree of formalization of the input language have been proposed. Test generation from UML models is the most advanced area, where mostly class diagrams and state machines or sequence diagrams are used to model test cases. Implementation models such as Simulink diagrams or Scade descriptions have also been used to generate test cases; however, if both the implementation and the test suite are automatically generated from the same model, the fault-detection capability of the test execution is very limited. Dedicated test description languages such as TTCN-3 or data models have been suggested
Bifurcation analysis and chaos control of a second-order exponential difference equation

The aim of this article is to study the local stability of equilibria, investigation related to the parametric conditions for transcritical bifurcation, period-doubling bifurcation and Neimark-Sacker bifurcation of the following second-order difference equation xn+1 = ? xn + ? xn-1 exp(-? xn-1) where the initial conditions x-1, x0 are the arbitrary positive real numbers and ? ,? and ? are positive constants. Moreover, chaos control method is implemented for controlling chaotic behavior under the influence of Neimark-Sacker bifurcation and period-doubling bifurcation. Numerical simulations are provided to show effectiveness of theoretical discussion.
MRI-Based Analysis of Brain Atrophy for the Diagnosis of Alzheimers Disease

With no known cure, Alzheimer’s Disease (AD) is the most common form of dementia, causing progressive and irreversible cerebral atrophy. Since treatments exist that enable the deceleration of disease progression, tools that allow an accurate early diagnosis of AD are crucial. Neuroimaging techniques have a critical role in the Computer-Aided Diagnosis (CAD) of the disease. This work looks into the potential use of short term longitudinal data in the field of the CAD of AD. Concretely, voxel-wise feature extraction methods based on flow fields calculated using diffeomorphic fluid registration algorithms – the Christensen, and Diffeomorphic Anatomical Registration using Exponentiated Lie algebra (DARTEL) methods – are employed. From these, the norms of deformation vectors and the determinants of the Jacobian matrices are derived, and extracted as features. Longitudinal and cross-sectional features are then used by a linear Support Vector Machine (SVM) classifier to perform binary classification tasks involving subjects suffering from AD, Mild Cognitive Impairment (MCI), as well as Cognitively Normal (CN) controls, in a dichotomous fashion. The combination of cross-sectional and longitudinal features led to an overall improvement in the accuracy of classifications, compared to when only one type of feature was used. Furthermore, a novel feature selection method, based on the Mutual Information (MI) Ranking of the Voxel Intensities (VI), was shown to improve the accuracy of classification when using longitudinal features, when compared to traditional MI Ranking. Finally, the results obtained in this study are comparable to those of the state of the art literature in this field.
2 Abundance of wild warm-blooded animals ( B )

The management plan of Parque Estadual da Ilha do Cardoso [1] was utilized to estimate population size in The Guarani Mbya village and Marujá. Using Aerophotogrametric images (Figure S3), it was possible to identify the geographical localities where inhabitants logged to build houses or to cultivate crops. The Guarani Mbya village is in the northwest, where approximately 150 people live today [2]. They can log natural vegetation on lowlands for small-scale agriculture and to hunt animals in slopes of the primary tropical forest (Figure S3). Marujá is located on the margins of the southern coastal plain (Figure S3), where 165 inhabitants subsist by fishing and exploring tourism [1].
ASSIMILATION OF INSAT-3D SOUNDER RETRIEVED THERMODYNAMIC PROFILES USING WRF MODEL FOR EXTREME RAINFALL EVENT OVER NORTH CENTRAL PART OF INDIA

The impact of Indian National Satellite-3D (INSAT-3D) retrieved thermodynamic profiles (temperature and humidity) on Weather Research Forecasting (WRF) model forecast is examined in this study. The extreme rainfall event which occurred during July 25-26, 2015 over the North central part of India is taken as the case study. The analysis obtained after assimilation is compared with the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis. Obtained results show quite good improvement in humidity and temperature analysis when compared with ECMWF analysis. Positive improvements are observed in 24 h WRF model predicted rainfall on assimilation of INSAT-3D temperature and humidity profiles.
High through-put identification of miR-145 targets in human articular chondrocytes

Objective MicroRNAs play a key role in biological processes, including cartilage development and homeostasis and are dysregulated in many diseases, including osteoarthritis. MiR-145 modulation induces profound changes in the human articular chondrocyte (HAC) phenotype, partially through direct repression of SOX9. Since miRNAs can simultaneously silence multiple targets, we aimed to identify the whole targetome of miR-145 in HACs. This information is critical if miR-145 is to be considered a target for cartilage repair. Methods RIP-seq (RNA-immunoprecipitation plus HT-sequencing) of miRISC (miRNA-induced silencing complex) was performed in HACs overexpressing miR-145 to identify miR-145 direct targets. The motif discovery method cWords was used to assess enrichment on miR-145 seed matches in the identified targets. RT-qPCR, Western (immuno-)blot and luciferase assays were used to validate miRNA-target interactions. Results MiR-145 overexpression affects the expression of over 350 genes and directly targets more than 50 mRNAs through the 3’UTR or, more commonly, the coding region. We also demonstrate that miR-145 targets DUSP6, involved in cartilage organization and development, at the translational level. DUSP6 depletion using specific siRNAs lead to MMP13 up-regulation, suggesting that miR-145-mediated DUSP6 depletion contributes to the effect of miR-145 on MMP13 expression. Conclusion We demonstrate that miR-145 directly targets several genes in primary chondrocytes including those involved in the expression of the extracellular matrix and inflammation. Thus, we propose miR-145 as an important regulator of chondrocyte function and a new target for cartilage repair.
GPS Ocean Reflection Experiment on Spartan 251 JamesL

It has recently been demonstrated that the GPS signal which has reflected from the ocean surface contains useful geophysical data from which the sea surface wind speed and other parameters can be extracted. This can be used for remote sensing, similar to present day use of radar altimeters or scatterometers, but with significantly smaller instrumentation because of the utilization of the existing GPS broadcast signal for illumination. Several campaigns of aircraft experimentation have been completed 1 https://ntrs.nasa. gov/search.jsp? R=1999011032